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[SOURCE: https://en.wikipedia.org/wiki/Language_model#cite_note-39] | [TOKENS: 1793] |
Contents Language model A language model is a computational model that predicts sequences in natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval. Large language models (LLMs), currently their most advanced form as of 2019, are predominantly based on transformers trained on larger datasets (frequently using texts scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as the word n-gram language model. History Noam Chomsky did pioneering work on language models in the 1950s by developing a theory of formal grammars. In 1980, statistical approaches were explored and found to be more useful for many purposes than rule-based formal grammars. Discrete representations like word n-gram language models, with probabilities for discrete combinations of words, made significant advances. In the 2000s, continuous representations for words, such as word embeddings, began to replace discrete representations. Typically, the representation is a real-valued vector that encodes a word’s meaning such that words closer in vector space are similar in meaning and common relationships between words, such as plurality or gender, are preserved. Pure statistical models In 1980, the first significant statistical language model was proposed, and during the decade IBM performed 'Shannon-style' experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text. A word n-gram language model is a statistical model of language which calculates the probability of the next word in a sequence from a fixed size window of previous words. If one previous word is considered, it is a bigram model; if two words, a trigram model; if n − 1 words, an n-gram model. Special tokens are introduced to denote the start and end of a sentence ⟨ s ⟩ {\displaystyle \langle s\rangle } and ⟨ / s ⟩ {\displaystyle \langle /s\rangle } . To prevent a zero probability being assigned to unseen words, the probability of each seen word is slightly lowered to make room for the unseen words in a given corpus. To achieve this, various smoothing methods are used, from simple "add-one" smoothing (assigning a count of 1 to unseen n-grams, as an uninformative prior) to more sophisticated techniques, such as Good–Turing discounting or back-off models. Word n-gram models have largely been superseded by recurrent neural network–based models, which in turn have been superseded by Transformer-based models often referred to as large language models. Maximum entropy language models encode the relationship between a word and the n-gram history using feature functions. The equation is P ( w m ∣ w 1 , … , w m − 1 ) = 1 Z ( w 1 , … , w m − 1 ) exp ( a T f ( w 1 , … , w m ) ) {\displaystyle P(w_{m}\mid w_{1},\ldots ,w_{m-1})={\frac {1}{Z(w_{1},\ldots ,w_{m-1})}}\exp(a^{T}f(w_{1},\ldots ,w_{m}))} where Z ( w 1 , … , w m − 1 ) {\displaystyle Z(w_{1},\ldots ,w_{m-1})} is the partition function, a {\displaystyle a} is the parameter vector, and f ( w 1 , … , w m ) {\displaystyle f(w_{1},\ldots ,w_{m})} is the feature function. In the simplest case, the feature function is just an indicator of the presence of a certain n-gram. It is helpful to use a prior on a {\displaystyle a} or some form of regularization. The log-bilinear model is another example of an exponential language model. Skip-gram language model is an attempt at overcoming the data sparsity problem that the preceding model (i.e. word n-gram language model) faced. Words represented in an embedding vector were not necessarily consecutive anymore, but could leave gaps that are skipped over (thus the name "skip-gram"). Formally, a k-skip-n-gram is a length-n subsequence where the components occur at distance at most k from each other. For example, in the input text: the set of 1-skip-2-grams includes all the bigrams (2-grams), and in addition the subsequences In skip-gram model, semantic relations between words are represented by linear combinations, capturing a form of compositionality. For example, in some such models, if v is the function that maps a word w to its n-d vector representation, then v ( k i n g ) − v ( m a l e ) + v ( f e m a l e ) ≈ v ( q u e e n ) {\displaystyle v(\mathrm {king} )-v(\mathrm {male} )+v(\mathrm {female} )\approx v(\mathrm {queen} )} where ≈ is made precise by stipulating that its right-hand side must be the nearest neighbor of the value of the left-hand side. Neural models Continuous representations or embeddings of words are produced in recurrent neural network-based language models (known also as continuous space language models). Such continuous space embeddings help to alleviate the curse of dimensionality, which is the consequence of the number of possible sequences of words increasing exponentially with the size of the vocabulary, further causing a data sparsity problem. Neural networks avoid this problem by representing words as non-linear combinations of weights in a neural net. A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the core capabilities of modern chatbots. LLMs can be fine-tuned for specific tasks or guided by prompt engineering. These models acquire predictive power regarding syntax, semantics, and ontologies inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained on. They consist of billions to trillions of parameters and operate as general-purpose sequence models, generating, summarizing, translating, and reasoning over text. LLMs represent a significant new technology in their ability to generalize across tasks with minimal task-specific supervision, enabling capabilities like conversational agents, code generation, knowledge retrieval, and automated reasoning that previously required bespoke systems. LLMs evolved from earlier statistical and recurrent neural network approaches to language modeling. The transformer architecture, introduced in 2017, replaced recurrence with self-attention, allowing efficient parallelization, longer context handling, and scalable training on unprecedented data volumes. This innovation enabled models like GPT, BERT, and their successors, which demonstrated emergent behaviors at scale, such as few-shot learning and compositional reasoning. Reinforcement learning, particularly policy gradient algorithms, has been adapted to fine-tune LLMs for desired behaviors beyond raw next-token prediction. Reinforcement learning from human feedback (RLHF) applies these methods to optimize a policy, the LLM's output distribution, against reward signals derived from human or automated preference judgments. This has been critical for aligning model outputs with user expectations, improving factuality, reducing harmful responses, and enhancing task performance. Benchmark evaluations for LLMs have evolved from narrow linguistic assessments toward comprehensive, multi-task evaluations measuring reasoning, factual accuracy, alignment, and safety. Hill climbing, iteratively optimizing models against benchmarks, has emerged as a dominant strategy, producing rapid incremental performance gains but raising concerns of overfitting to benchmarks rather than achieving genuine generalization or robust capability improvements. Although sometimes matching human performance, it is not clear whether they are plausible cognitive models. At least for recurrent neural networks, it has been shown that they sometimes learn patterns that humans do not, but fail to learn patterns that humans typically do. Evaluation and benchmarks Evaluation of the quality of language models is mostly done by comparison to human created sample benchmarks created from typical language-oriented tasks. Other, less established, quality tests examine the intrinsic character of a language model or compare two such models. Since language models are typically intended to be dynamic and to learn from data they see, some proposed models investigate the rate of learning, e.g., through inspection of learning curves. Various data sets have been developed for use in evaluating language processing systems. These include: See also References Further reading |
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[SOURCE: https://en.wikipedia.org/wiki/Language_model#cite_note-40] | [TOKENS: 1793] |
Contents Language model A language model is a computational model that predicts sequences in natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval. Large language models (LLMs), currently their most advanced form as of 2019, are predominantly based on transformers trained on larger datasets (frequently using texts scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as the word n-gram language model. History Noam Chomsky did pioneering work on language models in the 1950s by developing a theory of formal grammars. In 1980, statistical approaches were explored and found to be more useful for many purposes than rule-based formal grammars. Discrete representations like word n-gram language models, with probabilities for discrete combinations of words, made significant advances. In the 2000s, continuous representations for words, such as word embeddings, began to replace discrete representations. Typically, the representation is a real-valued vector that encodes a word’s meaning such that words closer in vector space are similar in meaning and common relationships between words, such as plurality or gender, are preserved. Pure statistical models In 1980, the first significant statistical language model was proposed, and during the decade IBM performed 'Shannon-style' experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text. A word n-gram language model is a statistical model of language which calculates the probability of the next word in a sequence from a fixed size window of previous words. If one previous word is considered, it is a bigram model; if two words, a trigram model; if n − 1 words, an n-gram model. Special tokens are introduced to denote the start and end of a sentence ⟨ s ⟩ {\displaystyle \langle s\rangle } and ⟨ / s ⟩ {\displaystyle \langle /s\rangle } . To prevent a zero probability being assigned to unseen words, the probability of each seen word is slightly lowered to make room for the unseen words in a given corpus. To achieve this, various smoothing methods are used, from simple "add-one" smoothing (assigning a count of 1 to unseen n-grams, as an uninformative prior) to more sophisticated techniques, such as Good–Turing discounting or back-off models. Word n-gram models have largely been superseded by recurrent neural network–based models, which in turn have been superseded by Transformer-based models often referred to as large language models. Maximum entropy language models encode the relationship between a word and the n-gram history using feature functions. The equation is P ( w m ∣ w 1 , … , w m − 1 ) = 1 Z ( w 1 , … , w m − 1 ) exp ( a T f ( w 1 , … , w m ) ) {\displaystyle P(w_{m}\mid w_{1},\ldots ,w_{m-1})={\frac {1}{Z(w_{1},\ldots ,w_{m-1})}}\exp(a^{T}f(w_{1},\ldots ,w_{m}))} where Z ( w 1 , … , w m − 1 ) {\displaystyle Z(w_{1},\ldots ,w_{m-1})} is the partition function, a {\displaystyle a} is the parameter vector, and f ( w 1 , … , w m ) {\displaystyle f(w_{1},\ldots ,w_{m})} is the feature function. In the simplest case, the feature function is just an indicator of the presence of a certain n-gram. It is helpful to use a prior on a {\displaystyle a} or some form of regularization. The log-bilinear model is another example of an exponential language model. Skip-gram language model is an attempt at overcoming the data sparsity problem that the preceding model (i.e. word n-gram language model) faced. Words represented in an embedding vector were not necessarily consecutive anymore, but could leave gaps that are skipped over (thus the name "skip-gram"). Formally, a k-skip-n-gram is a length-n subsequence where the components occur at distance at most k from each other. For example, in the input text: the set of 1-skip-2-grams includes all the bigrams (2-grams), and in addition the subsequences In skip-gram model, semantic relations between words are represented by linear combinations, capturing a form of compositionality. For example, in some such models, if v is the function that maps a word w to its n-d vector representation, then v ( k i n g ) − v ( m a l e ) + v ( f e m a l e ) ≈ v ( q u e e n ) {\displaystyle v(\mathrm {king} )-v(\mathrm {male} )+v(\mathrm {female} )\approx v(\mathrm {queen} )} where ≈ is made precise by stipulating that its right-hand side must be the nearest neighbor of the value of the left-hand side. Neural models Continuous representations or embeddings of words are produced in recurrent neural network-based language models (known also as continuous space language models). Such continuous space embeddings help to alleviate the curse of dimensionality, which is the consequence of the number of possible sequences of words increasing exponentially with the size of the vocabulary, further causing a data sparsity problem. Neural networks avoid this problem by representing words as non-linear combinations of weights in a neural net. A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the core capabilities of modern chatbots. LLMs can be fine-tuned for specific tasks or guided by prompt engineering. These models acquire predictive power regarding syntax, semantics, and ontologies inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained on. They consist of billions to trillions of parameters and operate as general-purpose sequence models, generating, summarizing, translating, and reasoning over text. LLMs represent a significant new technology in their ability to generalize across tasks with minimal task-specific supervision, enabling capabilities like conversational agents, code generation, knowledge retrieval, and automated reasoning that previously required bespoke systems. LLMs evolved from earlier statistical and recurrent neural network approaches to language modeling. The transformer architecture, introduced in 2017, replaced recurrence with self-attention, allowing efficient parallelization, longer context handling, and scalable training on unprecedented data volumes. This innovation enabled models like GPT, BERT, and their successors, which demonstrated emergent behaviors at scale, such as few-shot learning and compositional reasoning. Reinforcement learning, particularly policy gradient algorithms, has been adapted to fine-tune LLMs for desired behaviors beyond raw next-token prediction. Reinforcement learning from human feedback (RLHF) applies these methods to optimize a policy, the LLM's output distribution, against reward signals derived from human or automated preference judgments. This has been critical for aligning model outputs with user expectations, improving factuality, reducing harmful responses, and enhancing task performance. Benchmark evaluations for LLMs have evolved from narrow linguistic assessments toward comprehensive, multi-task evaluations measuring reasoning, factual accuracy, alignment, and safety. Hill climbing, iteratively optimizing models against benchmarks, has emerged as a dominant strategy, producing rapid incremental performance gains but raising concerns of overfitting to benchmarks rather than achieving genuine generalization or robust capability improvements. Although sometimes matching human performance, it is not clear whether they are plausible cognitive models. At least for recurrent neural networks, it has been shown that they sometimes learn patterns that humans do not, but fail to learn patterns that humans typically do. Evaluation and benchmarks Evaluation of the quality of language models is mostly done by comparison to human created sample benchmarks created from typical language-oriented tasks. Other, less established, quality tests examine the intrinsic character of a language model or compare two such models. Since language models are typically intended to be dynamic and to learn from data they see, some proposed models investigate the rate of learning, e.g., through inspection of learning curves. Various data sets have been developed for use in evaluating language processing systems. These include: See also References Further reading |
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[SOURCE: https://en.wikipedia.org/wiki/World#cite_note-12] | [TOKENS: 5641] |
Contents World The world is the totality of entities, the whole of reality, or everything that exists. The nature of the world has been conceptualized differently in different fields. Some conceptions see the world as unique, while others talk of a "plurality of worlds". Some treat the world as one simple object, while others analyze the world as a complex made up of parts. In scientific cosmology, the world or universe is commonly defined as "the totality of all space and time; all that is, has been, and will be". Theories of modality talk of possible worlds as complete and consistent ways how things could have been. Phenomenology, starting from the horizon of co-given objects present in the periphery of every experience, defines the world as the biggest horizon, or the "horizon of all horizons". In philosophy of mind, the world is contrasted with the mind as that which is represented by the mind. Theology conceptualizes the world in relation to God, for example, as God's creation, as identical to God, or as the two being interdependent. In religions, there is a tendency to downgrade the material or sensory world in favor of a spiritual world to be sought through religious practice. A comprehensive representation of the world and our place in it, as is found in religions, is known as a worldview. Cosmogony is the field that studies the origin or creation of the world, while eschatology refers to the science or doctrine of the last things or of the end of the world. In various contexts, the term "world" takes a more restricted meaning associated, for example, with the Earth and all life on it, with humanity as a whole, or with an international or intercontinental scope. In this sense, world history refers to the history of humanity as a whole, and world politics is the discipline of political science studying issues that transcend nations and continents. Other examples include terms such as "world religion", "world language", "world government", "world war", "world population", "world economy", or "world championship". Etymology The English word world comes from the Old English weorold. The Old English is a reflex of the Common Germanic *weraldiz, a compound of weraz 'man' and aldiz 'age', thus literally meaning roughly 'age of man'; this word led to Old Frisian warld, Old Saxon werold, Old Dutch werolt, Old High German weralt, and Old Norse verǫld. The corresponding word in Latin is mundus, literally 'clean, elegant', itself a loan translation of Greek cosmos 'orderly arrangement'. While the Germanic word thus reflects a mythological notion of a "domain of Man" (compare Midgard), presumably as opposed to the divine sphere on the one hand and the chthonic sphere of the underworld on the other, the Greco-Latin term expresses a notion of creation as an act of establishing order out of chaos. Conceptions Different fields often work with quite different conceptions of the essential features associated with the term "world". Some conceptions see the world as unique: there can be no more than one world. Others talk of a "plurality of worlds". Some see worlds as complex things composed of many substances as their parts while others hold that worlds are simple in the sense that there is only one substance: the world as a whole. Some characterize worlds in terms of objective spacetime while others define them relative to the horizon present in each experience. These different characterizations are not always exclusive: it may be possible to combine some without leading to a contradiction. Most of them agree that worlds are unified totalities. Monism is a thesis about oneness: that only one thing exists in a certain sense. The denial of monism is pluralism, the thesis that, in a certain sense, more than one thing exists. There are many forms of monism and pluralism, but in relation to the world as a whole, two are of special interest: existence monism/pluralism and priority monism/pluralism. Existence monism states that the world is the only concrete object there is. This means that all the concrete "objects" we encounter in our daily lives, including apples, cars and ourselves, are not truly objects in a strict sense. Instead, they are just dependent aspects of the world-object. Such a world-object is simple in the sense that it does not have any genuine parts. For this reason, it has also been referred to as "blobject" since it lacks an internal structure like a blob. Priority monism allows that there are other concrete objects besides the world. But it holds that these objects do not have the most fundamental form of existence, that they somehow depend on the existence of the world. The corresponding forms of pluralism state that the world is complex in the sense that it is made up of concrete, independent objects. Scientific cosmology can be defined as the science of the universe as a whole. In it, the terms "universe" and "cosmos" are usually used as synonyms for the term "world". One common definition of the world/universe found in this field is as "[t]he totality of all space and time; all that is, has been, and will be". Some definitions emphasize that there are two other aspects to the universe besides spacetime: forms of energy or matter, like stars and particles, and laws of nature. World-conceptions in this field differ both concerning their notion of spacetime and of the contents of spacetime. The theory of relativity plays a central role in modern cosmology and its conception of space and time. A difference from its predecessors is that it conceives space and time not as distinct dimensions but as a single four-dimensional manifold called spacetime. This can be seen in special relativity in relation to the Minkowski metric, which includes both spatial and temporal components in its definition of distance. General relativity goes one step further by integrating the concept of mass into the concept of spacetime as its curvature. Quantum cosmology uses a classical notion of spacetime and conceives the whole world as one big wave function expressing the probability of finding particles in a given location. The world-concept plays a role in many modern theories of modality, sometimes in the form of possible worlds. A possible world is a complete and consistent way how things could have been. The actual world is a possible world since the way things are is a way things could have been. There are many other ways things could have been besides how they actually are. For example, Hillary Clinton did not win the 2016 US election, but she could have won. So there is a possible world in which she did. There is a vast number of possible worlds, one corresponding to each such difference, no matter how small or big, as long as no outright contradictions are introduced this way. Possible worlds are often conceived as abstract objects, for example, in terms of non-obtaining states of affairs or as maximally consistent sets of propositions. On such a view, they can even be seen as belonging to the actual world. Another way to conceive possible worlds, made famous by David Lewis, is as concrete entities. On this conception, there is no important difference between the actual world and possible worlds: both are conceived as concrete, inclusive and spatiotemporally connected. The only difference is that the actual world is the world we live in, while other possible worlds are not inhabited by us but by our counterparts. Everything within a world is spatiotemporally connected to everything else but the different worlds do not share a common spacetime: They are spatiotemporally isolated from each other. This is what makes them separate worlds. It has been suggested that, besides possible worlds, there are also impossible worlds. Possible worlds are ways things could have been, so impossible worlds are ways things could not have been. Such worlds involve a contradiction, like a world in which Hillary Clinton both won and lost the 2016 US election. Both possible and impossible worlds have in common the idea that they are totalities of their constituents. Within phenomenology, worlds are defined in terms of horizons of experiences. When we perceive an object, like a house, we do not just experience this object at the center of our attention but also various other objects surrounding it, given in the periphery. The term "horizon" refers to these co-given objects, which are usually experienced only in a vague, indeterminate manner. The perception of a house involves various horizons, corresponding to the neighborhood, the city, the country, the Earth, etc. In this context, the world is the biggest horizon or the "horizon of all horizons". It is common among phenomenologists to understand the world not just as a spatiotemporal collection of objects but as additionally incorporating various other relations between these objects. These relations include, for example, indication-relations that help us anticipate one object given the appearances of another object and means-end-relations or functional involvements relevant for practical concerns. In philosophy of mind, the term "world" is commonly used in contrast to the term "mind" as that which is represented by the mind. This is sometimes expressed by stating that there is a gap between mind and world and that this gap needs to be overcome for representation to be successful. One problem in philosophy of mind is to explain how the mind is able to bridge this gap and to enter into genuine mind-world-relations, for example, in the form of perception, knowledge or action. This is necessary for the world to be able to rationally constrain the activity of the mind. According to a realist position, the world is something distinct and independent from the mind. Idealists conceive of the world as partially or fully determined by the mind. Immanuel Kant's transcendental idealism, for example, posits that the spatiotemporal structure of the world is imposed by the mind on reality but lacks independent existence otherwise. A more radical idealist conception of the world can be found in Berkeley's subjective idealism, which holds that the world as a whole, including all everyday objects like tables, cats, trees and ourselves, "consists of nothing but minds and ideas". Different theological positions hold different conceptions of the world based on its relation to God. Classical theism states that God is wholly distinct from the world. But the world depends for its existence on God, both because God created the world and because He maintains or conserves it. This is sometimes understood in analogy to how humans create and conserve ideas in their imagination, with the difference being that the divine mind is vastly more powerful. On such a view, God has absolute, ultimate reality in contrast to the lower ontological status ascribed to the world. God's involvement in the world is often understood along the lines of a personal, benevolent God who looks after and guides His creation. Deists agree with theists that God created the world but deny any subsequent, personal involvement in it. Pantheists reject the separation between God and world. Instead, they claim that the two are identical. This means that there is nothing to the world that does not belong to God and that there is nothing to God beyond what is found in the world. Panentheism constitutes a middle ground between theism and pantheism. Against theism, it holds that God and the world are interrelated and depend on each other. Against pantheism, it holds that there is no outright identity between the two. History of philosophy In philosophy, the term world has several possible meanings. In some contexts, it refers to everything that makes up reality or the physical universe. In others, it can mean have a specific ontological sense (see world disclosure). While clarifying the concept of world has arguably always been among the basic tasks of Western philosophy, this theme appears to have been raised explicitly only at the start of the twentieth century, Plato is well known for his theory of forms, which posits the existence of two different worlds: the sensible world and the intelligible world. The sensible world is the world we live in, filled with changing physical things we can see, touch and interact with. The intelligible world is the world of invisible, eternal, changeless forms like goodness, beauty, unity and sameness. Plato ascribes a lower ontological status to the sensible world, which only imitates the world of forms. This is due to the fact that physical things exist only to the extent that they participate in the forms that characterize them, while the forms themselves have an independent manner of existence. In this sense, the sensible world is a mere replication of the perfect exemplars found in the world of forms: it never lives up to the original. In the allegory of the cave, Plato compares the physical things we are familiar with to mere shadows of the real things. But not knowing the difference, the prisoners in the cave mistake the shadows for the real things. Two definitions that were both put forward in the 1920s, however, suggest the range of available opinion. "The world is everything that is the case", wrote Ludwig Wittgenstein in his influential Tractatus Logico-Philosophicus, first published in 1921. Martin Heidegger, meanwhile, argued that "the surrounding world is different for each of us, and notwithstanding that we move about in a common world". "World" is one of the key terms in Eugen Fink's philosophy. He thinks that there is a misguided tendency in western philosophy to understand the world as one enormously big thing containing all the small everyday things we are familiar with. He sees this view as a form of forgetfulness of the world and tries to oppose it by what he calls the "cosmological difference": the difference between the world and the inner-worldly things it contains. On his view, the world is the totality of the inner-worldly things that transcends them. It is itself groundless but it provides a ground for things. It therefore cannot be identified with a mere container. Instead, the world gives appearance to inner-worldly things, it provides them with a place, a beginning and an end. One difficulty in investigating the world is that we never encounter it since it is not just one more thing that appears to us. This is why Fink uses the notion of play or playing to elucidate the nature of the world. He sees play as a symbol of the world that is both part of it and that represents it. Play usually comes with a form of imaginary play-world involving various things relevant to the play. But just like the play is more than the imaginary realities appearing in it so the world is more than the actual things appearing in it. The concept of worlds plays a central role in Nelson Goodman's late philosophy. He argues that we need to posit different worlds in order to account for the fact that there are different incompatible truths found in reality. Two truths are incompatible if they ascribe incompatible properties to the same thing. This happens, for example, when we assert both that the earth moves and that the earth is at rest. These incompatible truths correspond to two different ways of describing the world: heliocentrism and geocentrism. Goodman terms such descriptions "world versions". He holds a correspondence theory of truth: a world version is true if it corresponds to a world. Incompatible true world versions correspond to different worlds. It is common for theories of modality to posit the existence of a plurality of possible worlds. But Goodman's theory is different since it posits a plurality not of possible but of actual worlds. Such a position is in danger of involving a contradiction: there cannot be a plurality of actual worlds if worlds are defined as maximally inclusive wholes. This danger may be avoided by interpreting Goodman's world-concept not as maximally inclusive wholes in the absolute sense but in relation to its corresponding world-version: a world contains all and only the entities that its world-version describes. Religion Mythological cosmologies depict the world as centered on an axis mundi and delimited by a boundary such as a world ocean, a world serpent or similar. Hinduism constitutes a family of religious-philosophical views. These views present perspectives on the nature and role of the world. Samkhya philosophy, for example, is a metaphysical dualism that understands reality as comprising 2 parts: purusha and prakriti. The term "purusha" stands for the individual conscious self that each of "us" possesses. Prakriti, on the other hand, is the 1 world inhabited by all these selves. Samkhya understands this world as a world of matter governed by the law of cause and effect. The term "matter" is understood in a sense in this tradition including physical and mental aspects. This is reflected in the doctrine of tattvas, according to which prakriti is made up of 23 principles or elements of reality. These principles include physical elements, like water or earth, and mental aspects, like intelligence or sense-impressions. The relation between purusha and prakriti is conceived as 1 of observation: purusha is the conscious self aware of the world of prakriti and does not causally interact with it. A conception of the world is present in Advaita Vedanta, the monist school among the Vedanta schools. Unlike the realist position defended in Samkhya philosophy, Advaita Vedanta sees the world of multiplicity as an illusion, referred to as Maya. This illusion includes impression of existing as separate experiencing selfs called Jivas. Instead, Advaita Vedanta teaches that on the most fundamental level of reality, referred to as Brahman, there exists no plurality or difference. All there is is 1 all-encompassing self: Atman. Ignorance is seen as the source of this illusion, which results in bondage to the world of mere appearances. Liberation is possible in the course of overcoming this illusion by acquiring the knowledge of Brahman, according to Advaita Vedanta. Contemptus mundi is the name given to the belief that the world, in all its vanity, is nothing more than a futile attempt to hide from God by stifling our desire for the good and the holy. This view has been characterised as a "pastoral of fear" by historian Jean Delumeau. "The world, the flesh, and the devil" is a traditional division of the sources of temptation. Orbis Catholicus is a Latin phrase meaning "Catholic world", per the expression Urbi et Orbi, and refers to that area of Christendom under papal supremacy. In Islam, the term "dunya" is used for the world. Its meaning is derived from the root word "dana", a term for "near". It is associated with the temporal, sensory world and earthly concerns, i.e. with this world in contrast to the spiritual world. Religious teachings warn of a tendency to seek happiness in this world and advise a more ascetic lifestyle concerned with the afterlife. Other strands in Islam recommend a balanced approach. In Mandaean cosmology, the world or earthly realm is known as Tibil. It is separated from the World of Light (alma d-nhūra) above and the World of Darkness (alma d-hšuka) below by aether (ayar). Related terms and problems A worldview is a comprehensive representation of the world and our place in it. As a representation, it is a subjective perspective of the world and thereby different from the world it represents. All higher animals need to represent their environment in some way in order to navigate it. But it has been argued that only humans possess a representation encompassing enough to merit the term "worldview". Philosophers of worldviews commonly hold that the understanding of any object depends on a worldview constituting the background on which this understanding can take place. This may affect not just our intellectual understanding of the object in question but the experience of it in general. It is therefore impossible to assess one's worldview from a neutral perspective since this assessment already presupposes the worldview as its background. Some hold that each worldview is based on a single hypothesis that promises to solve all the problems of our existence we may encounter. On this interpretation, the term is closely associated to the worldviews given by different religions. Worldviews offer orientation not just in theoretical matters but also in practical matters. For this reason, they usually include answers to the question of the meaning of life and other evaluative components about what matters and how we should act. A worldview can be unique to one individual but worldviews are usually shared by many people within a certain culture or religion. The idea that there exist many different worlds is found in various fields. For example, theories of modality talk about a plurality of possible worlds and the many-worlds interpretation of quantum mechanics carries this reference even in its name. Talk of different worlds is also common in everyday language, for example, with reference to the world of music, the world of business, the world of football, the world of experience or the Asian world. But at the same time, worlds are usually defined as all-inclusive totalities. This seems to contradict the very idea of a plurality of worlds since if a world is total and all-inclusive then it cannot have anything outside itself. Understood this way, a world can neither have other worlds besides itself or be part of something bigger. One way to resolve this paradox while holding onto the notion of a plurality of worlds is to restrict the sense in which worlds are totalities. On this view, worlds are not totalities in an absolute sense. This might be even understood in the sense that, strictly speaking, there are no worlds at all. Another approach understands worlds in a schematic sense: as context-dependent expressions that stand for the current domain of discourse. So in the expression "Around the World in Eighty Days", the term "world" refers to the earth while in the colonial expression "the New World" it refers to the landmass of North and South America. Cosmogony is the field that studies the origin or creation of the world. This includes both scientific cosmogony and creation myths found in various religions. The dominant theory in scientific cosmogony is the Big Bang theory, according to which both space, time and matter have their origin in one initial singularity occurring about 13.8 billion years ago. This singularity was followed by an expansion that allowed the universe to sufficiently cool down for the formation of subatomic particles and later atoms. These initial elements formed giant clouds, which would then coalesce into stars and galaxies. Non-scientific creation myths are found in many cultures and are often enacted in rituals expressing their symbolic meaning. They can be categorized concerning their contents. Types often found include creation from nothing, from chaos or from a cosmic egg. Eschatology refers to the science or doctrine of the last things or of the end of the world. It is traditionally associated with religion, specifically with the Abrahamic religions. In this form, it may include teachings both of the end of each individual human life and of the end of the world as a whole. But it has been applied to other fields as well, for example, in the form of physical eschatology, which includes scientifically based speculations about the far future of the universe. According to some models, there will be a Big Crunch in which the whole universe collapses back into a singularity, possibly resulting in a second Big Bang afterward. But current astronomical evidence seems to suggest that our universe will continue to expand indefinitely. World history studies the world from a historical perspective. Unlike other approaches to history, it employs a global viewpoint. It deals less with individual nations and civilizations, which it usually approaches at a high level of abstraction. Instead, it concentrates on wider regions and zones of interaction, often interested in how people, goods and ideas move from one region to another. It includes comparisons of different societies and civilizations as well as considering wide-ranging developments with a long-term global impact like the process of industrialization. Contemporary world history is dominated by three main research paradigms determining the periodization into different epochs. One is based on productive relations between humans and nature. The two most important changes in history in this respect were the introduction of agriculture and husbandry concerning the production of food, which started around 10,000 to 8,000 BCE and is sometimes termed the Neolithic Revolution, and the Industrial Revolution, which started around 1760 CE and involved the transition from manual to industrial manufacturing. Another paradigm, focusing on culture and religion instead, is based on Karl Jaspers' theories about the Axial Age, a time in which various new forms of religious and philosophical thoughts appeared in several separate parts of the world around the time between 800 and 200 BCE. A third periodization is based on the relations between civilizations and societies. According to this paradigm, history can be divided into three periods in relation to the dominant region in the world: Middle Eastern dominance before 500 BCE, Eurasian cultural balance until 1500 CE and Western dominance since 1500 CE. Big History employs an even wider framework than world history by putting human history into the context of the history of the universe as a whole. It starts with the Big Bang and traces the formation of galaxies, the Solar System, the Earth, its geological eras, the evolution of life and humans until the present day. World politics, also referred to as global politics or international relations, is the discipline of political science studying issues of interest to the world that transcend nations and continents. It aims to explain complex patterns found in the social world that are often related to the pursuit of power, order and justice, usually in the context of globalization. It focuses not just on the relations between nation-states but also considers other transnational actors, like multinational corporations, terrorist groups, or non-governmental organizations. For example, it tries to explain events such as the September 11 attacks, the 2003 invasion of Iraq or the 2008 financial crisis. Various theories have been proposed in order to deal with the complexity involved in formulating such explanations. These theories are sometimes divided into realism, liberalism and constructivism. Realists see nation-states as the main actors in world politics. They constitute an anarchical international system without any overarching power to control their behavior. They are seen as sovereign agents that, determined by human nature, act according to their national self-interest. Military force may play an important role in the ensuing struggle for power between states, but diplomacy and cooperation are also key mechanisms for nations to achieve their goals. Liberalists acknowledge the importance of states but they also emphasize the role of transnational actors, like the United Nations or the World Trade Organization. They see humans as perfectible and stress the role of democracy in this process. The emergent order in world politics, on this perspective, is more complex than a mere balance of power since more different agents and interests are involved in its production. Constructivism ascribes more importance to the agency of individual humans than realism and liberalism. It understands the social world as a construction of the people living in it. This leads to an emphasis on the possibility of change. If the international system is an anarchy of nation-states, as the realists hold, then this is only so because we made it this way and may change since this is not prefigured by human nature, according to the constructivists. See also References External links Africa Antarctica Asia Australia Europe North America South America Afro-Eurasia Americas Eurasia Oceania |
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[SOURCE: https://en.wikipedia.org/wiki/Language_model#cite_note-44] | [TOKENS: 1793] |
Contents Language model A language model is a computational model that predicts sequences in natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval. Large language models (LLMs), currently their most advanced form as of 2019, are predominantly based on transformers trained on larger datasets (frequently using texts scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as the word n-gram language model. History Noam Chomsky did pioneering work on language models in the 1950s by developing a theory of formal grammars. In 1980, statistical approaches were explored and found to be more useful for many purposes than rule-based formal grammars. Discrete representations like word n-gram language models, with probabilities for discrete combinations of words, made significant advances. In the 2000s, continuous representations for words, such as word embeddings, began to replace discrete representations. Typically, the representation is a real-valued vector that encodes a word’s meaning such that words closer in vector space are similar in meaning and common relationships between words, such as plurality or gender, are preserved. Pure statistical models In 1980, the first significant statistical language model was proposed, and during the decade IBM performed 'Shannon-style' experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text. A word n-gram language model is a statistical model of language which calculates the probability of the next word in a sequence from a fixed size window of previous words. If one previous word is considered, it is a bigram model; if two words, a trigram model; if n − 1 words, an n-gram model. Special tokens are introduced to denote the start and end of a sentence ⟨ s ⟩ {\displaystyle \langle s\rangle } and ⟨ / s ⟩ {\displaystyle \langle /s\rangle } . To prevent a zero probability being assigned to unseen words, the probability of each seen word is slightly lowered to make room for the unseen words in a given corpus. To achieve this, various smoothing methods are used, from simple "add-one" smoothing (assigning a count of 1 to unseen n-grams, as an uninformative prior) to more sophisticated techniques, such as Good–Turing discounting or back-off models. Word n-gram models have largely been superseded by recurrent neural network–based models, which in turn have been superseded by Transformer-based models often referred to as large language models. Maximum entropy language models encode the relationship between a word and the n-gram history using feature functions. The equation is P ( w m ∣ w 1 , … , w m − 1 ) = 1 Z ( w 1 , … , w m − 1 ) exp ( a T f ( w 1 , … , w m ) ) {\displaystyle P(w_{m}\mid w_{1},\ldots ,w_{m-1})={\frac {1}{Z(w_{1},\ldots ,w_{m-1})}}\exp(a^{T}f(w_{1},\ldots ,w_{m}))} where Z ( w 1 , … , w m − 1 ) {\displaystyle Z(w_{1},\ldots ,w_{m-1})} is the partition function, a {\displaystyle a} is the parameter vector, and f ( w 1 , … , w m ) {\displaystyle f(w_{1},\ldots ,w_{m})} is the feature function. In the simplest case, the feature function is just an indicator of the presence of a certain n-gram. It is helpful to use a prior on a {\displaystyle a} or some form of regularization. The log-bilinear model is another example of an exponential language model. Skip-gram language model is an attempt at overcoming the data sparsity problem that the preceding model (i.e. word n-gram language model) faced. Words represented in an embedding vector were not necessarily consecutive anymore, but could leave gaps that are skipped over (thus the name "skip-gram"). Formally, a k-skip-n-gram is a length-n subsequence where the components occur at distance at most k from each other. For example, in the input text: the set of 1-skip-2-grams includes all the bigrams (2-grams), and in addition the subsequences In skip-gram model, semantic relations between words are represented by linear combinations, capturing a form of compositionality. For example, in some such models, if v is the function that maps a word w to its n-d vector representation, then v ( k i n g ) − v ( m a l e ) + v ( f e m a l e ) ≈ v ( q u e e n ) {\displaystyle v(\mathrm {king} )-v(\mathrm {male} )+v(\mathrm {female} )\approx v(\mathrm {queen} )} where ≈ is made precise by stipulating that its right-hand side must be the nearest neighbor of the value of the left-hand side. Neural models Continuous representations or embeddings of words are produced in recurrent neural network-based language models (known also as continuous space language models). Such continuous space embeddings help to alleviate the curse of dimensionality, which is the consequence of the number of possible sequences of words increasing exponentially with the size of the vocabulary, further causing a data sparsity problem. Neural networks avoid this problem by representing words as non-linear combinations of weights in a neural net. A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the core capabilities of modern chatbots. LLMs can be fine-tuned for specific tasks or guided by prompt engineering. These models acquire predictive power regarding syntax, semantics, and ontologies inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained on. They consist of billions to trillions of parameters and operate as general-purpose sequence models, generating, summarizing, translating, and reasoning over text. LLMs represent a significant new technology in their ability to generalize across tasks with minimal task-specific supervision, enabling capabilities like conversational agents, code generation, knowledge retrieval, and automated reasoning that previously required bespoke systems. LLMs evolved from earlier statistical and recurrent neural network approaches to language modeling. The transformer architecture, introduced in 2017, replaced recurrence with self-attention, allowing efficient parallelization, longer context handling, and scalable training on unprecedented data volumes. This innovation enabled models like GPT, BERT, and their successors, which demonstrated emergent behaviors at scale, such as few-shot learning and compositional reasoning. Reinforcement learning, particularly policy gradient algorithms, has been adapted to fine-tune LLMs for desired behaviors beyond raw next-token prediction. Reinforcement learning from human feedback (RLHF) applies these methods to optimize a policy, the LLM's output distribution, against reward signals derived from human or automated preference judgments. This has been critical for aligning model outputs with user expectations, improving factuality, reducing harmful responses, and enhancing task performance. Benchmark evaluations for LLMs have evolved from narrow linguistic assessments toward comprehensive, multi-task evaluations measuring reasoning, factual accuracy, alignment, and safety. Hill climbing, iteratively optimizing models against benchmarks, has emerged as a dominant strategy, producing rapid incremental performance gains but raising concerns of overfitting to benchmarks rather than achieving genuine generalization or robust capability improvements. Although sometimes matching human performance, it is not clear whether they are plausible cognitive models. At least for recurrent neural networks, it has been shown that they sometimes learn patterns that humans do not, but fail to learn patterns that humans typically do. Evaluation and benchmarks Evaluation of the quality of language models is mostly done by comparison to human created sample benchmarks created from typical language-oriented tasks. Other, less established, quality tests examine the intrinsic character of a language model or compare two such models. Since language models are typically intended to be dynamic and to learn from data they see, some proposed models investigate the rate of learning, e.g., through inspection of learning curves. Various data sets have been developed for use in evaluating language processing systems. These include: See also References Further reading |
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[SOURCE: https://en.wikipedia.org/wiki/Treebank] | [TOKENS: 1160] |
Contents Treebank In linguistics, a treebank is a parsed text corpus that annotates syntactic or semantic sentence structure. The construction of parsed corpora in the early 1990s revolutionized computational linguistics, which benefitted from large-scale empirical data. Etymology The term treebank was coined by linguist Geoffrey Leech in the 1980s, by analogy to other repositories such as a seedbank or bloodbank. This is because both syntactic and semantic structure are commonly represented compositionally as a tree structure. The term parsed corpus is often used interchangeably with the term treebank, with the emphasis on the primacy of sentences rather than trees. Construction Treebanks are often created on top of a corpus that has already been annotated with part-of-speech tags. In turn, treebanks are sometimes enhanced with semantic or other linguistic information. Treebanks can be created completely manually, where linguists annotate each sentence with syntactic structure, or semi-automatically, where a parser assigns some syntactic structure which linguists then check and, if necessary, correct. In practice, fully checking and completing the parsing of natural language corpora is a labour-intensive project that can take teams of graduate linguists several years. The level of annotation detail and the breadth of the linguistic sample determine the difficulty of the task and the length of time required to build a treebank. Some treebanks follow a specific linguistic theory in their syntactic annotation (e.g. the BulTreeBank follows HPSG) but most try to be less theory-specific. However, two main groups can be distinguished: treebanks that annotate phrase structure (for example the Penn Treebank or ICE-GB) and those that annotate dependency structure (for example the Prague Dependency Treebank or the Quranic Arabic Dependency Treebank). It is important to clarify the distinction between the formal representation and the file format used to store the annotated data. Treebanks are necessarily constructed according to a particular grammar. The same grammar may be implemented by different file formats. For example, the syntactic analysis for John loves Mary, shown in the figure on the right/above, may be represented by simple labelled brackets in a text file, like this (following the Penn Treebank notation): This type of representation is popular because it is light on resources, and the tree structure is relatively easy to read without software tools. However, as corpora become increasingly complex, other file formats may be preferred. Alternatives include treebank-specific XML schemes, numbered indentation and various types of standoff notation. Applications From a computational linguistics perspective, treebanks have been used to engineer state-of-the-art natural language processing systems such as part-of-speech taggers, parsers, semantic analyzers and machine translation systems. Most computational systems utilize gold-standard treebank data. However, an automatically parsed corpus that is not corrected by human linguists can still be useful. It can provide evidence of rule frequency for a parser. A parser may be improved by applying it to large amounts of text and gathering rule frequencies. However, it should be obvious that only by a process of correcting and completing a corpus by hand is it possible then to identify rules absent from the parser knowledge base. In addition, frequencies are likely to be more accurate. In corpus linguistics, treebanks are used to study syntactic phenomena (for example, diachronic corpora can be used to study the time course of syntactic change). Once parsed, a corpus will contain frequency evidence showing how common different grammatical structures are in use. Treebanks also provide evidence of coverage and support the discovery of new, unanticipated, grammatical phenomena. Another use of treebanks in theoretical linguistics and psycholinguistics is interaction evidence. A completed treebank can help linguists carry out experiments as to how the decision to use one grammatical construction tends to influence the decision to form others, and to try to understand how speakers and writers make decisions as they form sentences. Interaction research is particularly fruitful as further layers of annotation, e.g. semantic, pragmatic, are added to a corpus. It is then possible to evaluate the impact of non-syntactic phenomena on grammatical choices. In linguistics research, annotated treebank data has been used in syntactic research to test linguistic theories of sentence structure against large quantities of naturally occurring examples.[citation needed] Semantic treebanks A semantic treebank is a collection of natural language sentences annotated with a meaning representation. These resources use a formal representation of each sentence's semantic structure. Semantic treebanks vary in the depth of their semantic representation. A notable example of deep semantic annotation is the Groningen Meaning Bank, developed at the University of Groningen and annotated using Discourse Representation Theory. An example of a shallow semantic treebank is PropBank, which provides annotation of verbal propositions and their arguments, without attempting to represent every word in the corpus in logical form. Syntactic treebanks Many syntactic treebanks have been developed for a wide variety of languages: To facilitate the further researches between multilingual tasks, some researchers discussed the universal annotation scheme for cross-languages. In this way, people try to utilize or merge the advantages of different treebanks corpora. For instance, The universal annotation approach for dependency treebanks; and the universal annotation approach for phrase structure treebanks. Search tools One of the key ways to extract evidence from a treebank is through search tools. Search tools for parsed corpora typically depend on the annotation scheme that was applied to the corpus. User interfaces range in sophistication from expression-based query systems aimed at computer programmers to full exploration environments aimed at general linguists. Wallis (2008) discusses the principles of searching treebanks in detail and reviews the state of the art around that time. See also References |
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[SOURCE: https://en.wikipedia.org/wiki/Language_model#cite_note-:0-35] | [TOKENS: 1793] |
Contents Language model A language model is a computational model that predicts sequences in natural language. Language models are useful for a variety of tasks, including speech recognition, machine translation, natural language generation (generating more human-like text), optical character recognition, route optimization, handwriting recognition, grammar induction, and information retrieval. Large language models (LLMs), currently their most advanced form as of 2019, are predominantly based on transformers trained on larger datasets (frequently using texts scraped from the public internet). They have superseded recurrent neural network-based models, which had previously superseded the purely statistical models, such as the word n-gram language model. History Noam Chomsky did pioneering work on language models in the 1950s by developing a theory of formal grammars. In 1980, statistical approaches were explored and found to be more useful for many purposes than rule-based formal grammars. Discrete representations like word n-gram language models, with probabilities for discrete combinations of words, made significant advances. In the 2000s, continuous representations for words, such as word embeddings, began to replace discrete representations. Typically, the representation is a real-valued vector that encodes a word’s meaning such that words closer in vector space are similar in meaning and common relationships between words, such as plurality or gender, are preserved. Pure statistical models In 1980, the first significant statistical language model was proposed, and during the decade IBM performed 'Shannon-style' experiments, in which potential sources for language modeling improvement were identified by observing and analyzing the performance of human subjects in predicting or correcting text. A word n-gram language model is a statistical model of language which calculates the probability of the next word in a sequence from a fixed size window of previous words. If one previous word is considered, it is a bigram model; if two words, a trigram model; if n − 1 words, an n-gram model. Special tokens are introduced to denote the start and end of a sentence ⟨ s ⟩ {\displaystyle \langle s\rangle } and ⟨ / s ⟩ {\displaystyle \langle /s\rangle } . To prevent a zero probability being assigned to unseen words, the probability of each seen word is slightly lowered to make room for the unseen words in a given corpus. To achieve this, various smoothing methods are used, from simple "add-one" smoothing (assigning a count of 1 to unseen n-grams, as an uninformative prior) to more sophisticated techniques, such as Good–Turing discounting or back-off models. Word n-gram models have largely been superseded by recurrent neural network–based models, which in turn have been superseded by Transformer-based models often referred to as large language models. Maximum entropy language models encode the relationship between a word and the n-gram history using feature functions. The equation is P ( w m ∣ w 1 , … , w m − 1 ) = 1 Z ( w 1 , … , w m − 1 ) exp ( a T f ( w 1 , … , w m ) ) {\displaystyle P(w_{m}\mid w_{1},\ldots ,w_{m-1})={\frac {1}{Z(w_{1},\ldots ,w_{m-1})}}\exp(a^{T}f(w_{1},\ldots ,w_{m}))} where Z ( w 1 , … , w m − 1 ) {\displaystyle Z(w_{1},\ldots ,w_{m-1})} is the partition function, a {\displaystyle a} is the parameter vector, and f ( w 1 , … , w m ) {\displaystyle f(w_{1},\ldots ,w_{m})} is the feature function. In the simplest case, the feature function is just an indicator of the presence of a certain n-gram. It is helpful to use a prior on a {\displaystyle a} or some form of regularization. The log-bilinear model is another example of an exponential language model. Skip-gram language model is an attempt at overcoming the data sparsity problem that the preceding model (i.e. word n-gram language model) faced. Words represented in an embedding vector were not necessarily consecutive anymore, but could leave gaps that are skipped over (thus the name "skip-gram"). Formally, a k-skip-n-gram is a length-n subsequence where the components occur at distance at most k from each other. For example, in the input text: the set of 1-skip-2-grams includes all the bigrams (2-grams), and in addition the subsequences In skip-gram model, semantic relations between words are represented by linear combinations, capturing a form of compositionality. For example, in some such models, if v is the function that maps a word w to its n-d vector representation, then v ( k i n g ) − v ( m a l e ) + v ( f e m a l e ) ≈ v ( q u e e n ) {\displaystyle v(\mathrm {king} )-v(\mathrm {male} )+v(\mathrm {female} )\approx v(\mathrm {queen} )} where ≈ is made precise by stipulating that its right-hand side must be the nearest neighbor of the value of the left-hand side. Neural models Continuous representations or embeddings of words are produced in recurrent neural network-based language models (known also as continuous space language models). Such continuous space embeddings help to alleviate the curse of dimensionality, which is the consequence of the number of possible sequences of words increasing exponentially with the size of the vocabulary, further causing a data sparsity problem. Neural networks avoid this problem by representing words as non-linear combinations of weights in a neural net. A large language model (LLM) is a language model trained with self-supervised machine learning on a vast amount of text, designed for natural language processing tasks, especially language generation. The largest and most capable LLMs are generative pre-trained transformers (GPTs) that provide the core capabilities of modern chatbots. LLMs can be fine-tuned for specific tasks or guided by prompt engineering. These models acquire predictive power regarding syntax, semantics, and ontologies inherent in human language corpora, but they also inherit inaccuracies and biases present in the data they are trained on. They consist of billions to trillions of parameters and operate as general-purpose sequence models, generating, summarizing, translating, and reasoning over text. LLMs represent a significant new technology in their ability to generalize across tasks with minimal task-specific supervision, enabling capabilities like conversational agents, code generation, knowledge retrieval, and automated reasoning that previously required bespoke systems. LLMs evolved from earlier statistical and recurrent neural network approaches to language modeling. The transformer architecture, introduced in 2017, replaced recurrence with self-attention, allowing efficient parallelization, longer context handling, and scalable training on unprecedented data volumes. This innovation enabled models like GPT, BERT, and their successors, which demonstrated emergent behaviors at scale, such as few-shot learning and compositional reasoning. Reinforcement learning, particularly policy gradient algorithms, has been adapted to fine-tune LLMs for desired behaviors beyond raw next-token prediction. Reinforcement learning from human feedback (RLHF) applies these methods to optimize a policy, the LLM's output distribution, against reward signals derived from human or automated preference judgments. This has been critical for aligning model outputs with user expectations, improving factuality, reducing harmful responses, and enhancing task performance. Benchmark evaluations for LLMs have evolved from narrow linguistic assessments toward comprehensive, multi-task evaluations measuring reasoning, factual accuracy, alignment, and safety. Hill climbing, iteratively optimizing models against benchmarks, has emerged as a dominant strategy, producing rapid incremental performance gains but raising concerns of overfitting to benchmarks rather than achieving genuine generalization or robust capability improvements. Although sometimes matching human performance, it is not clear whether they are plausible cognitive models. At least for recurrent neural networks, it has been shown that they sometimes learn patterns that humans do not, but fail to learn patterns that humans typically do. Evaluation and benchmarks Evaluation of the quality of language models is mostly done by comparison to human created sample benchmarks created from typical language-oriented tasks. Other, less established, quality tests examine the intrinsic character of a language model or compare two such models. Since language models are typically intended to be dynamic and to learn from data they see, some proposed models investigate the rate of learning, e.g., through inspection of learning curves. Various data sets have been developed for use in evaluating language processing systems. These include: See also References Further reading |
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[SOURCE: https://www.bbc.com/audio/play/p0mylrdr] | [TOKENS: 346] |
The Interface Is your doorbell using AI to spy on you? Up next Can you hack ChatGPT? 12 February 2026 34 minutes Available for over a year Ring’s new AI “lost dog” feature promises to reunite missing pets with their owners using doorbell camera footage. But could this same technology be used to build a far more sinister surveillance network? Our hosts take a closer look at Search Party, announced in an ad during this year’s Super Bowl, and explore why this seemingly feel-good function is sparking privacy concerns. Also on The Interface this week: Why does the TikTok takeover in the US affect you, even if you've never touched the app? And how did one ad campaign at the Super Bowl reveal the fierce rivalry between OpenAI and Anthropic? The Interface is your weekly guide to the tech rewiring your week and our world. Hosted by journalists Tom Germain, Karen Hao, and Nicky Woolf, each episode unpacks week-by-week the unfolding story of how technology is shaping all of our futures. No guests. No jargon. Just three sharp voices debating the tech stories that matter - whether they shook a government, broke the internet, or quietly tipped the balance of power. New episodes drop every Thursday on BBC Sounds in the UK. Outside the UK, find us on BBC.com or wherever you get your podcasts, or watch the video version on YouTube (search “The Interface podcast”). The Interface is a BBC Studios production. Producer: Natalia Rodriguez Ford Executive Editor: Philip Sellars See more episodes Programme website Copyright 2026 BBC. All rights reserved. The BBC is not responsible for the content of external sites. Read about our approach to external linking. |
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[SOURCE: https://www.bbc.com/audio/play/p0mylrdr] | [TOKENS: 346] |
The Interface Is your doorbell using AI to spy on you? Up next Can you hack ChatGPT? 12 February 2026 34 minutes Available for over a year Ring’s new AI “lost dog” feature promises to reunite missing pets with their owners using doorbell camera footage. But could this same technology be used to build a far more sinister surveillance network? Our hosts take a closer look at Search Party, announced in an ad during this year’s Super Bowl, and explore why this seemingly feel-good function is sparking privacy concerns. Also on The Interface this week: Why does the TikTok takeover in the US affect you, even if you've never touched the app? And how did one ad campaign at the Super Bowl reveal the fierce rivalry between OpenAI and Anthropic? The Interface is your weekly guide to the tech rewiring your week and our world. Hosted by journalists Tom Germain, Karen Hao, and Nicky Woolf, each episode unpacks week-by-week the unfolding story of how technology is shaping all of our futures. No guests. No jargon. Just three sharp voices debating the tech stories that matter - whether they shook a government, broke the internet, or quietly tipped the balance of power. New episodes drop every Thursday on BBC Sounds in the UK. Outside the UK, find us on BBC.com or wherever you get your podcasts, or watch the video version on YouTube (search “The Interface podcast”). The Interface is a BBC Studios production. Producer: Natalia Rodriguez Ford Executive Editor: Philip Sellars See more episodes Programme website Copyright 2026 BBC. All rights reserved. The BBC is not responsible for the content of external sites. Read about our approach to external linking. |
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[SOURCE: https://en.wikipedia.org/wiki/Generalization_(machine_learning)] | [TOKENS: 10519] |
Contents Machine learning Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimisation (mathematical programming) methods compose the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysis (EDA) through unsupervised learning. From a theoretical viewpoint, probably approximately correct learning provides a mathematical and statistical framework for describing machine learning. Most traditional machine learning and deep learning algorithms can be described as empirical risk minimisation under this framework. History The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence. The synonym self-teaching computers was also used during this time period. The earliest machine learning program was introduced in the 1950s when Arthur Samuel invented a computer program that calculated the winning chance in checkers for each side, but the history of machine learning roots back to decades of human desire and effort to study human cognitive processes. In 1949, Canadian psychologist Donald Hebb published the book The Organization of Behavior, in which he introduced a theoretical neural structure formed by certain interactions among nerve cells. Hebb's model of neurons interacting with one another set a groundwork for how AIs and machine learning algorithms work under nodes, or artificial neurons used by computers to communicate data. Other researchers who have studied human cognitive systems contributed to the modern machine learning technologies as well, including logician Walter Pitts and Warren McCulloch, who proposed the early mathematical models of neural networks to come up with algorithms that mirror human thought processes. By the early 1960s, an experimental "learning machine" with punched tape memory, called Cybertron, had been developed by Raytheon Company to analyse sonar signals, electrocardiograms, and speech patterns using rudimentary reinforcement learning. It was repetitively "trained" by a human operator/teacher to recognise patterns and equipped with a "goof" button to cause it to reevaluate incorrect decisions. A representative book on research into machine learning during the 1960s was Nils Nilsson's book on Learning Machines, dealing mostly with machine learning for pattern classification. Interest related to pattern recognition continued into the 1970s, as described by Duda and Hart in 1973. In 1981, a report was given on using teaching strategies so that an artificial neural network learns to recognise 40 characters (26 letters, 10 digits, and 4 special symbols) from a computer terminal. Tom M. Mitchell provided a widely quoted, more formal definition of the algorithms studied in the machine learning field: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E." This definition of the tasks in which machine learning is concerned offers a fundamentally operational definition rather than defining the field in cognitive terms. This follows Alan Turing's proposal in his paper "Computing Machinery and Intelligence", in which the question, "Can machines think?", is replaced with the question, "Can machines do what we (as thinking entities) can do?". Modern-day Machine Learning algorithms are broken into 3 algorithm types: Supervised Learning Algorithms, Unsupervised Learning Algorithms, and Reinforcement Learning Algorithms. In 2014 Ian Goodfellow and others introduced generative adversarial networks (GANs) with realistic data synthesis. By 2016 AlphaGo obtained victory against top human players using reinforcement learning techniques. Relationships to other fields As a scientific endeavour, machine learning grew out of the quest for artificial intelligence (AI). In the early days of AI as an academic discipline, some researchers were interested in having machines learn from data. They attempted to approach the problem with various symbolic methods, as well as what were then termed "neural networks"; these were mostly perceptrons and other models that were later found to be reinventions of the generalised linear models of statistics. Probabilistic reasoning was also employed, especially in automated medical diagnosis.: 488 However, an increasing emphasis on the logical, knowledge-based approach caused a rift between AI and machine learning. Probabilistic systems were plagued by theoretical and practical problems of data acquisition and representation.: 488 By 1980, expert systems had come to dominate AI, and statistics was out of favour. Work on symbolic/knowledge-based learning did continue within AI, leading to inductive logic programming(ILP), but the more statistical line of research was now outside the field of AI proper, in pattern recognition and information retrieval.: 708–710, 755 Neural networks research had been abandoned by AI and computer science around the same time. This line, too, was continued outside the AI/CS field, as "connectionism", by researchers from other disciplines, including John Hopfield, David Rumelhart, and Geoffrey Hinton. Their main success came in the mid-1980s with the reinvention of backpropagation.: 25 Machine learning (ML), reorganised and recognised as its own field, started to flourish in the 1990s. The field changed its goal from achieving artificial intelligence to tackling solvable problems of a practical nature. It shifted focus away from the symbolic approaches it had inherited from AI, and toward methods and models borrowed from statistics, fuzzy logic, and probability theory. There is a close connection between machine learning and compression. A system that predicts the posterior probabilities of a sequence given its entire history can be used for optimal data compression (by using arithmetic coding on the output distribution). Conversely, an optimal compressor can be used for prediction (by finding the symbol that compresses best, given the previous history). This equivalence has been used as a justification for using data compression as a benchmark for "general intelligence". An alternative view can show compression algorithms implicitly map strings into implicit feature space vectors, and compression-based similarity measures compute similarity within these feature spaces. For each compressor C(.) we define an associated vector space ℵ, such that C(.) maps an input string x, corresponding to the vector norm ||~x||. An exhaustive examination of the feature spaces underlying all compression algorithms is precluded by space; instead, feature vectors chooses to examine three representative lossless compression methods, LZW, LZ77, and PPM. According to AIXI theory, a connection more directly explained in Hutter Prize, the best possible compression of x is the smallest possible software that generates x. For example, in that model, a zip file's compressed size includes both the zip file and the unzipping software, since you can not unzip it without both, but there may be an even smaller combined form. Examples of AI-powered audio/video compression software include NVIDIA Maxine, AIVC. Examples of software that can perform AI-powered image compression include OpenCV, TensorFlow, MATLAB's Image Processing Toolbox (IPT) and High-Fidelity Generative Image Compression. In unsupervised machine learning, k-means clustering can be utilized to compress data by grouping similar data points into clusters. This technique simplifies handling extensive datasets that lack predefined labels and finds widespread use in fields such as image compression. Data compression aims to reduce the size of data files, enhancing storage efficiency and speeding up data transmission. K-means clustering, an unsupervised machine learning algorithm, is employed to partition a dataset into a specified number of clusters, k, each represented by the centroid of its points. This process condenses extensive datasets into a more compact set of representative points. Particularly beneficial in image and signal processing, k-means clustering aids in data reduction by replacing groups of data points with their centroids, thereby preserving the core information of the original data while significantly decreasing the required storage space. Large language models (LLMs) are also efficient lossless data compressors on some data sets, as demonstrated by DeepMind's research with the Chinchilla 70B model. Developed by DeepMind, Chinchilla 70B effectively compressed data, outperforming conventional methods such as Portable Network Graphics (PNG) for images and Free Lossless Audio Codec (FLAC) for audio. It achieved compression of image and audio data to 43.4% and 16.4% of their original sizes, respectively. There is, however, some reason to be concerned that the data set used for testing overlaps the LLM training data set, making it possible that the Chinchilla 70B model is only an efficient compression tool on data it has already been trained on. Machine learning and data mining often employ the same methods and overlap significantly, but while machine learning focuses on prediction, based on known properties learned from the training data, data mining focuses on the discovery of (previously) unknown properties in the data (this is the analysis step of knowledge discovery in databases). Data mining uses many machine learning methods, but with different goals; on the other hand, machine learning also employs data mining methods as "unsupervised learning" or as a preprocessing step to improve learner accuracy. Much of the confusion between these two research communities (which do often have separate conferences and separate journals, ECML PKDD being a major exception) comes from the basic assumptions they work with: in machine learning, performance is usually evaluated with respect to the ability to reproduce known knowledge, while in knowledge discovery and data mining (KDD) the key task is the discovery of previously unknown knowledge. Evaluated with respect to known knowledge, an uninformed (unsupervised) method will easily be outperformed by other supervised methods, while in a typical KDD task, supervised methods cannot be used due to the unavailability of training data.[citation needed] Machine learning also has intimate ties to optimisation: Many learning problems are formulated as minimisation of some loss function on a training set of examples. Loss functions express the discrepancy between the predictions of the model being trained and the actual problem instances (for example, in classification, one wants to assign a label to instances, and models are trained to correctly predict the preassigned labels of a set of examples). Characterizing the generalisation of various learning algorithms is an active topic of current research, especially for deep learning algorithms. Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalisable predictive patterns. Conventional statistical analyses require the a priori selection of a model most suitable for the study data set. In addition, only significant or theoretically relevant variables based on previous experience are included for analysis. In contrast, machine learning is not built on a pre-structured model; rather, the data shape the model by detecting underlying patterns. The more variables (input) used to train the model, the more accurate the ultimate model will be. Leo Breiman distinguished two statistical modelling paradigms: data model and algorithmic model, wherein "algorithmic model" means more or less the machine learning algorithms like Random Forest. Some statisticians have adopted methods from machine learning, leading to a combined field that they call statistical learning. Analytical and computational techniques derived from deep-rooted physics of disordered systems can be extended to large-scale problems, including machine learning, e.g., to analyse the weight space of deep neural networks. Statistical physics is thus finding applications in the area of medical diagnostics. Theory A core objective of a learner is to generalise from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. The training examples come from some generally unknown probability distribution (considered representative of the space of occurrences) and the learner has to build a general model about this space that enables it to produce sufficiently accurate predictions in new cases. The computational analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory via the probably approximately correct learning model. Because training sets are finite and the future is uncertain, learning theory usually does not yield guarantees of the performance of algorithms. Instead, probabilistic bounds on the performance are quite common. The bias–variance decomposition is one way to quantify generalisation error. For the best performance in the context of generalisation, the complexity of the hypothesis should match the complexity of the function underlying the data. If the hypothesis is less complex than the function, then the model has underfitted the data. If the complexity of the model is increased in response, then the training error decreases. But if the hypothesis is too complex, then the model is subject to overfitting and generalisation will be poorer. In addition to performance bounds, learning theorists study the time complexity and feasibility of learning. In computational learning theory, a computation is considered feasible if it can be done in polynomial time. There are two kinds of time complexity results: Positive results show that a certain class of functions can be learned in polynomial time. Negative results show that certain classes cannot be learned in polynomial time. Approaches Machine learning approaches are traditionally divided into three broad categories, which correspond to learning paradigms, depending on the nature of the "signal" or "feedback" available to the learning system: Although each algorithm has advantages and limitations, no single algorithm works for all problems. Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs. The data, known as training data, consists of a set of training examples. Each training example has one or more inputs and the desired output, also known as a supervisory signal. In the mathematical model, each training example is represented by an array or vector, sometimes called a feature vector, and the training data is represented by a matrix. Through iterative optimisation of an objective function, supervised learning algorithms learn a function that can be used to predict the output associated with new inputs. An optimal function allows the algorithm to correctly determine the output for inputs that were not a part of the training data. An algorithm that improves the accuracy of its outputs or predictions over time is said to have learned to perform that task. Types of supervised-learning algorithms include active learning, classification and regression. Classification algorithms are used when the outputs are restricted to a limited set of values, while regression algorithms are used when the outputs can take any numerical value within a range. For example, in a classification algorithm that filters emails, the input is an incoming email, and the output is the folder in which to file the email. In contrast, regression is used for tasks such as predicting a person's height based on factors like age and genetics or forecasting future temperatures based on historical data. Similarity learning is an area of supervised machine learning closely related to regression and classification, but the goal is to learn from examples using a similarity function that measures how similar or related two objects are. It has applications in ranking, recommendation systems, visual identity tracking, face verification, and speaker verification. Unsupervised learning algorithms find structures in data that has not been labelled, classified or categorised. Instead of responding to feedback, unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. Central applications of unsupervised machine learning include clustering, dimensionality reduction, and density estimation. Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to one or more predesignated criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods are based on estimated density and graph connectivity. A special type of unsupervised learning called, self-supervised learning involves training a model by generating the supervisory signal from the data itself. Dimensionality reduction is a process of reducing the number of random variables under consideration by obtaining a set of principal variables. In other words, it is a process of reducing the dimension of the feature set, also called the "number of features". Most of the dimensionality reduction techniques can be considered as either feature elimination or extraction. One of the popular methods of dimensionality reduction is principal component analysis (PCA). PCA involves changing higher-dimensional data (e.g., 3D) to a smaller space (e.g., 2D). The manifold hypothesis proposes that high-dimensional data sets lie along low-dimensional manifolds, and many dimensionality reduction techniques make this assumption, leading to the areas of manifold learning and manifold regularisation. Semi-supervised learning falls between unsupervised learning (without any labelled training data) and supervised learning (with completely labelled training data). Some of the training examples are missing training labels, yet many machine-learning researchers have found that unlabelled data, when used in conjunction with a small amount of labelled data, can produce a considerable improvement in learning accuracy. In weakly supervised learning, the training labels are noisy, limited, or imprecise; however, these labels are often cheaper to obtain, resulting in larger effective training sets. Reinforcement learning is an area of machine learning concerned with how software agents ought to take actions in an environment to maximise some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulation-based optimisation, multi-agent systems, swarm intelligence, statistics and genetic algorithms. In reinforcement learning, the environment is typically represented as a Markov decision process (MDP). Many reinforcement learning algorithms use dynamic programming techniques. Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent. Other approaches have been developed which do not fit neatly into this three-fold categorisation, and sometimes more than one is used by the same machine learning system. For example, topic modelling, meta-learning. Self-learning, as a machine learning paradigm, was introduced in 1982 along with a neural network capable of self-learning, named crossbar adaptive array (CAA). It gives a solution to the problem learning without any external reward, by introducing emotion as an internal reward. Emotion is used as a state evaluation of a self-learning agent. The CAA self-learning algorithm computes, in a crossbar fashion, both decisions about actions and emotions (feelings) about consequence situations. The system is driven by the interaction between cognition and emotion. The self-learning algorithm updates a memory matrix W =||w(a,s)|| such that in each iteration executes the following machine learning routine: It is a system with only one input, situation, and only one output, action (or behaviour) a. There is neither a separate reinforcement input nor an advice input from the environment. The backpropagated value (secondary reinforcement) is the emotion toward the consequence situation. The CAA exists in two environments, one is the behavioural environment where it behaves, and the other is the genetic environment, wherefrom it initially and only once receives initial emotions about situations to be encountered in the behavioural environment. After receiving the genome (species) vector from the genetic environment, the CAA learns a goal-seeking behaviour in an environment that contains both desirable and undesirable situations. Several learning algorithms aim at discovering better representations of the inputs provided during training. Classic examples include principal component analysis and cluster analysis. Feature learning algorithms, also called representation learning algorithms, often attempt to preserve the information in their input but also transform it in a way that makes it useful, often as a pre-processing step before performing classification or predictions. This technique allows reconstruction of the inputs coming from the unknown data-generating distribution, while not being necessarily faithful to configurations that are implausible under that distribution. This replaces manual feature engineering, and allows a machine to both learn the features and use them to perform a specific task. Feature learning can be either supervised or unsupervised. In supervised feature learning, features are learned using labelled input data. Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabelled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorisation and various forms of clustering. Manifold learning algorithms attempt to do so under the constraint that the learned representation is low-dimensional. Sparse coding algorithms attempt to do so under the constraint that the learned representation is sparse, meaning that the mathematical model has many zeros. Multilinear subspace learning algorithms aim to learn low-dimensional representations directly from tensor representations for multidimensional data, without reshaping them into higher-dimensional vectors. Deep learning algorithms discover multiple levels of representation, or a hierarchy of features, with higher-level, more abstract features defined in terms of (or generating) lower-level features. It has been argued that an intelligent machine learns a representation that disentangles the underlying factors of variation that explain the observed data. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensory data have not yielded attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms. Sparse dictionary learning is a feature learning method where a training example is represented as a linear combination of basis functions and assumed to be a sparse matrix. The method is strongly NP-hard and difficult to solve approximately. A popular heuristic method for sparse dictionary learning is the k-SVD algorithm. Sparse dictionary learning has been applied in several contexts. In classification, the problem is to determine the class to which a previously unseen training example belongs. For a dictionary where each class has already been built, a new training example is associated with the class that is best sparsely represented by the corresponding dictionary. Sparse dictionary learning has also been applied in image denoising. The key idea is that a clean image patch can be sparsely represented by an image dictionary, but the noise cannot. In data mining, anomaly detection, also known as outlier detection, is the identification of rare items, events or observations that raise suspicions by differing significantly from the majority of the data. Typically, the anomalous items represent an issue such as bank fraud, a structural defect, medical problems or errors in a text. Anomalies are referred to as outliers, novelties, noise, deviations and exceptions. In particular, in the context of abuse and network intrusion detection, the interesting objects are often not rare, but unexpected bursts of inactivity. This pattern does not adhere to the common statistical definition of an outlier as a rare object. Many outlier detection methods (in particular, unsupervised algorithms) will fail on such data unless aggregated appropriately. Instead, a cluster analysis algorithm may be able to detect the micro-clusters formed by these patterns. Three broad categories of anomaly detection techniques exist. Unsupervised anomaly detection techniques detect anomalies in an unlabelled test data set under the assumption that the majority of the instances in the data set are normal, by looking for instances that seem to fit the least to the remainder of the data set. Supervised anomaly detection techniques require a data set that has been labelled as "normal" and "abnormal" and involves training a classifier (the key difference from many other statistical classification problems is the inherently unbalanced nature of outlier detection). Semi-supervised anomaly detection techniques construct a model representing normal behaviour from a given normal training data set and then test the likelihood of a test instance being generated by the model. Robot learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement learning, and finally meta-learning (e.g. MAML). Association rule learning is a rule-based machine learning method for discovering relationships between variables in large databases. It is intended to identify strong rules discovered in databases using some measure of "interestingness". Rule-based machine learning is a general term for any machine learning method that identifies, learns, or evolves "rules" to store, manipulate or apply knowledge. The defining characteristic of a rule-based machine learning algorithm is the identification and utilisation of a set of relational rules that collectively represent the knowledge captured by the system. This is in contrast to other machine learning algorithms that commonly identify a singular model that can be universally applied to any instance in order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule { o n i o n s , p o t a t o e s } ⇒ { b u r g e r } {\displaystyle \{\mathrm {onions,potatoes} \}\Rightarrow \{\mathrm {burger} \}} found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as promotional pricing or product placements. In addition to market basket analysis, association rules are employed today in application areas including Web usage mining, intrusion detection, continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. Learning classifier systems (LCS) are a family of rule-based machine learning algorithms that combine a discovery component, typically a genetic algorithm, with a learning component, performing either supervised learning, reinforcement learning, or unsupervised learning. They seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner to make predictions. Inductive logic programming (ILP) is an approach to rule learning using logic programming as a uniform representation for input examples, background knowledge, and hypotheses. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesized logic program that entails all positive and no negative examples. Inductive programming is a related field that considers any kind of programming language for representing hypotheses (and not only logic programming), such as functional programs. Inductive logic programming is particularly useful in bioinformatics and natural language processing. Gordon Plotkin and Ehud Shapiro laid the initial theoretical foundation for inductive machine learning in a logical setting. Shapiro built their first implementation (Model Inference System) in 1981: a Prolog program that inductively inferred logic programs from positive and negative examples. The term inductive here refers to philosophical induction, suggesting a theory to explain observed facts, rather than mathematical induction, proving a property for all members of a well-ordered set. Models A machine learning model is a type of mathematical model that, once "trained" on a given dataset, can be used to make predictions or classifications on new data. During training, a learning algorithm iteratively adjusts the model's internal parameters to minimise errors in its predictions. By extension, the term "model" can refer to several levels of specificity, from a general class of models and their associated learning algorithms to a fully trained model with all its internal parameters tuned. Various types of models have been used and researched for machine learning systems, picking the best model for a task is called model selection. Artificial neural networks (ANNs), or connectionist systems, are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition. Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modelling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels, and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision-making. Random forest regression (RFR) falls under the umbrella of decision tree-based models. RFR is an ensemble learning method that builds multiple decision trees and averages their predictions to improve accuracy and to avoid overfitting. To build decision trees, RFR uses bootstrapped sampling; for instance, each decision tree is trained on random data from the training set. This random selection of RFR for training enables the model to reduce biased predictions and achieve a higher degree of accuracy. RFR generates independent decision trees, and it can work on single-output data as well as multiple regressor tasks. This makes RFR compatible to be use in various applications. Support-vector machines (SVMs), also known as support-vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category. An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularisation methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Multivariate linear regression extends the concept of linear regression to handle multiple dependent variables simultaneously. This approach estimates the relationships between a set of input variables and several output variables by fitting a multidimensional linear model. It is particularly useful in scenarios where outputs are interdependent or share underlying patterns, such as predicting multiple economic indicators or reconstructing images, which are inherently multi-dimensional. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalisations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. A Gaussian process is a stochastic process in which every finite collection of the random variables in the process has a multivariate normal distribution, and it relies on a pre-defined covariance function, or kernel, that models how pairs of points relate to each other depending on their locations. Given a set of observed points, or input–output examples, the distribution of the (unobserved) output of a new point as a function of its input data can be directly computed by looking at the observed points and the covariances between those points and the new, unobserved point. Gaussian processes are popular surrogate models in Bayesian optimisation used to do hyperparameter optimisation. A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s. Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms. The theory of belief functions, also referred to as evidence theory or Dempster–Shafer theory, is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as probability, possibility and imprecise probability theories. These theoretical frameworks can be thought of as a kind of learner and have some analogous properties of how evidence is combined (e.g., Dempster's rule of combination), just like how in a pmf-based Bayesian approach would combine probabilities. However, there are many caveats to these beliefs functions when compared to Bayesian approaches to incorporate ignorance and uncertainty quantification. These belief function approaches that are implemented within the machine learning domain typically leverage a fusion approach of various ensemble methods to better handle the learner's decision boundary, low samples, and ambiguous class issues that standard machine learning approach tend to have difficulty resolving. However, the computational complexity of these algorithms is dependent on the number of propositions (classes), and can lead to a much higher computation time when compared to other machine learning approaches. Rule-based machine learning (RBML) is a branch of machine learning that automatically discovers and learns 'rules' from data. It provides interpretable models, making it useful for decision-making in fields like healthcare, fraud detection, and cybersecurity. Key RBML techniques includes learning classifier systems, association rule learning, artificial immune systems, and other similar models. These methods extract patterns from data and evolve rules over time. Typically, machine learning models require a high quantity of reliable data to perform accurate predictions. When training a machine learning model, machine learning engineers need to target and collect a large and representative sample of data. Data from the training set can be as varied as a corpus of text, a collection of images, sensor data, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Trained models derived from biased or non-evaluated data can result in skewed or undesired predictions. Biased models may result in detrimental outcomes, thereby furthering the negative impacts on society or objectives. Algorithmic bias is a potential result of data not being fully prepared for training. Machine learning ethics is becoming a field of study and, notably, becoming integrated within machine learning engineering teams. Federated learning is an adapted form of distributed artificial intelligence to train machine learning models that decentralises the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralised server. This also increases efficiency by decentralising the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google. Applications There are many applications for machine learning, including: In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million. Shortly after the prize was awarded, Netflix realised that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly. In 2010, an article in The Wall Street Journal noted the use of machine learning by Rebellion Research to predict the 2008 financial crisis. In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors jobs would be lost in the next two decades to automated machine learning medical diagnostic software. In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognised influences among artists. In 2019 Springer Nature published the first research book created using machine learning. In 2020, machine learning technology was used to help make diagnoses and aid researchers in developing a cure for COVID-19. Machine learning was recently applied to predict the pro-environmental behaviour of travellers. Recently, machine learning technology was also applied to optimise smartphone's performance and thermal behaviour based on the user's interaction with the phone. When applied correctly, machine learning algorithms (MLAs) can utilise a wide range of company characteristics to predict stock returns without overfitting. By employing effective feature engineering and combining forecasts, MLAs can generate results that far surpass those obtained from basic linear techniques like OLS. Recent advancements in machine learning have extended into the field of quantum chemistry, where novel algorithms now enable the prediction of solvent effects on chemical reactions, thereby offering new tools for chemists to tailor experimental conditions for optimal outcomes. Machine Learning is becoming a useful tool to investigate and predict evacuation decision-making in large-scale and small-scale disasters. Different solutions have been tested to predict if and when householders decide to evacuate during wildfires and hurricanes. Other applications have been focusing on pre evacuation decisions in building fires. Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results. Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems. The "black box theory" poses another yet significant challenge. Black box refers to a situation where the algorithm or the process of producing an output is entirely opaque, meaning that even the coders of the algorithm cannot audit the pattern that the machine extracted from the data. The House of Lords Select Committee, which claimed that such an "intelligence system" that could have a "substantial impact on an individual's life" would not be considered acceptable unless it provided "a full and satisfactory explanation for the decisions" it makes. In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision. Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested. Microsoft's Bing Chat chatbot has been reported to produce hostile and offensive response against its users. Machine learning has been used as a strategy to update the evidence related to a systematic review and increased reviewer burden related to the growth of biomedical literature. While it has improved with training sets, it has not yet developed sufficiently to reduce the workload burden without limiting the necessary sensitivity for the findings research itself. Explainable AI (XAI), or Interpretable AI, or Explainable Machine Learning (XML), is artificial intelligence (AI) in which humans can understand the decisions or predictions made by the AI. It contrasts with the "black box" concept in machine learning where even its designers cannot explain why an AI arrived at a specific decision. By refining the mental models of users of AI-powered systems and dismantling their misconceptions, XAI promises to help users perform more effectively. XAI may be an implementation of the social right to explanation. Settling on a bad, overly complex theory gerrymandered to fit all the past training data is known as overfitting. Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data but penalising the theory in accordance with how complex the theory is. Learners can also be disappointed by "learning the wrong lesson". A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses. A real-world example is that, unlike humans, current image classifiers often do not primarily make judgments from the spatial relationship between components of the picture, and they learn relationships between pixels that humans are oblivious to, but that still correlate with images of certain types of real objects. Modifying these patterns on a legitimate image can result in "adversarial" images that the system misclassifies. Adversarial vulnerabilities can also result in nonlinear systems or from non-pattern perturbations. For some systems, it is possible to change the output by only changing a single adversarially chosen pixel. Machine learning models are often vulnerable to manipulation or evasion via adversarial machine learning. Researchers have demonstrated how backdoors can be placed undetectably into classifying (e.g., for categories "spam" and "not spam" of posts) machine learning models that are often developed or trained by third parties. Parties can change the classification of any input, including in cases for which a type of data/software transparency is provided, possibly including white-box access. Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data into a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy. In addition to overall accuracy, investigators frequently report sensitivity and specificity, meaning true positive rate (TPR) and true negative rate (TNR), respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. Receiver operating characteristic (ROC), along with the accompanying Area Under the ROC Curve (AUC), offer additional tools for classification model assessment. Higher AUC is associated with a better performing model. Ethics The ethics of artificial intelligence covers a broad range of topics within AI that are considered to have particular ethical stakes. This includes algorithmic biases, fairness, accountability, transparency, privacy, and regulation, particularly where systems influence or automate human decision-making. It also covers various emerging or potential future challenges such as machine ethics (how to make machines that behave ethically), lethal autonomous weapon systems, arms race dynamics, AI safety and alignment, technological unemployment, AI-enabled misinformation, how to treat certain AI systems if they have a moral status (AI welfare and rights), artificial superintelligence and existential risks. Some application areas may also have particularly important ethical implications, like healthcare, education, criminal justice, or the military. Different machine learning approaches can suffer from different data biases. A machine learning system trained specifically on current customers may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on human-made data, machine learning is likely to pick up the constitutional and unconscious biases already present in society. Systems that are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitising cultural prejudices. For example, in 1988, the UK's Commission for Racial Equality found that St. George's Medical School had been using a computer program trained from data of previous admissions staff and this program had denied nearly 60 candidates who were found to either be women or have non-European-sounding names. Using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants by similarity to previous successful applicants. Another example includes predictive policing company Geolitica's predictive algorithm that resulted in "disproportionately high levels of over-policing in low-income and minority communities" after being trained with historical crime data. While responsible collection of data and documentation of algorithmic rules used by a system is considered a critical part of machine learning, some researchers blame the lack of participation and representation of minority populations in the field of AI for machine learning's vulnerability to biases. In fact, according to research carried out by the Computing Research Association in 2021, "female faculty make up just 16.1%" of all faculty members who focus on AI among several universities around the world. Furthermore, among the group of "new U.S. resident AI PhD graduates," 45% identified as white, 22.4% as Asian, 3.2% as Hispanic, and 2.4% as African American, which further demonstrates a lack of diversity in the field of AI. Language models learned from data have been shown to contain human-like biases. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases. In 2016, Microsoft tested Tay, a chatbot that learned from Twitter, and it quickly picked up racist and sexist language. In an experiment carried out by ProPublica, an investigative journalism organisation, a machine learning algorithm's insight into the recidivism rates among prisoners falsely flagged "black defendants high risk twice as often as white defendants". In 2015, Google Photos once tagged a couple of black people as gorillas, which caused controversy. The gorilla label was subsequently removed, and in 2023, it still cannot recognise gorillas. Similar issues with recognising non-white people have been found in many other systems. Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains. Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good, is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who said that "[t]here's nothing artificial about AI. It's inspired by people, it's created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility." There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States, where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is potential for machine learning in health care to provide professionals with an additional tool to diagnose, medicate, and plan recovery paths for patients, but this requires these biases to be mitigated. Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of nonlinear hidden units. By 2019, graphics processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI. OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months. Tensor Processing Units (TPUs) are specialised hardware accelerators developed by Google specifically for machine learning workloads. Unlike general-purpose GPUs and FPGAs, TPUs are optimised for tensor computations, making them particularly efficient for deep learning tasks such as training and inference. They are widely used in Google Cloud AI services and large-scale machine learning models like Google's DeepMind AlphaFold and large language models. TPUs leverage matrix multiplication units and high-bandwidth memory to accelerate computations while maintaining energy efficiency. Since their introduction in 2016, TPUs have become a key component of AI infrastructure, especially in cloud-based environments. Neuromorphic computing refers to a class of computing systems designed to emulate the structure and functionality of biological neural networks. These systems may be implemented through software-based simulations on conventional hardware or through specialised hardware architectures. A physical neural network is a specific type of neuromorphic hardware that relies on electrically adjustable materials, such as memristors, to emulate the function of neural synapses. The term "physical neural network" highlights the use of physical hardware for computation, as opposed to software-based implementations. It broadly refers to artificial neural networks that use materials with adjustable resistance to replicate neural synapses. Embedded machine learning is a sub-field of machine learning where models are deployed on embedded systems with limited computing resources, such as wearable computers, edge devices and microcontrollers. Running models directly on these devices eliminates the need to transfer and store data on cloud servers for further processing, thereby reducing the risk of data breaches, privacy leaks and theft of intellectual property, personal data and business secrets. Embedded machine learning can be achieved through various techniques, such as hardware acceleration, approximate computing, and model optimisation. Common optimisation techniques include pruning, quantisation, knowledge distillation, low-rank factorisation, network architecture search, and parameter sharing. Software Software suites containing a variety of machine learning algorithms include the following: Journals Conferences See also References Sources Further reading External links |
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[SOURCE: https://www.fast.ai/posts/2022-05-17-societal-harms.html] | [TOKENS: 2344] |
AI Harms are Societal, Not Just Individual Rachel Thomas and Louisa Bartolo May 17, 2022 On this page Not just Individual, but Societal Harms When the USA government switched to facial identification service ID.me for unemployment benefits, the software failed to recognize Bill Baine’s face. While the app said that he could have a virtual appointment to be verified instead, he was unable to get through. The screen had a wait time of 2 hours and 47 minutes that never updated, even over the course of weeks. He tried calling various offices, his daughter drove in from out of town to spend a day helping him, and yet he was never able to get a useful human answer on what he was supposed to do, as he went for months without unemployment benefits. In Baine’s case, it was eventually resolved when a journalist hypothesized that the issue was a spotty internet connection, and that Baine would be better off traveling to another town to use a public library computer and internet. Even then, it still took hours for Baine to get his approval. Baine was not alone. The number of people receiving unemployment benefits plummeted by 40% in the 3 weeks after ID.me was introduced. Some of these were presumed to be fraudsters, but it is unclear how many genuine people in need of benefits were wrongly harmed by this. These are individual harms, but there are broader, societal harms as well: the cumulative costs of the public having to spend ever more time on hold, trying to navigate user-hostile automated bureaucracies where they can’t get the answers they need. There is the societal cost of greater inequality and greater desperation, as more people are plunged into poverty through erroneous denial of benefits. And there is the undermining of trust in public services, which can be difficult to restore. Potential for algorithmic harm takes many forms: loss of opportunity (employment or housing discrimination), economic cost (credit discrimination, narrowed choices), social detriment (stereotype confirmation, dignitary harms), and loss of liberty (increased surveillance, disproportionate incarceration). And each of these four categories manifests in both individual and societal harms. It should come as no surprise that algorithmic systems can give rise to societal harm. These systems are sociotechnical: they are designed by humans and teams that bring their values to the design process, and algorithmic systems continually draw information from, and inevitably bear the marks of, fundamentally unequal, unjust societies. In the context of COVID-19, for example, policy experts warned that historical healthcare inequities risked making their way into the datasets and models being used to predict and respond to the pandemic. And while it’s intuitively appealing to think of large-scale systems as creating the greatest risk of societal harm, algorithmic systems can create societal harm because of the dynamics set off by their interconnection with other systems/ players, like advertisers, or commercially-driven media, and the ways in which they touch on sectors or spaces of public importance. Still, in the west, our ideas of harm are often anchored to an individual being harmed by a particular action at a discrete moment in time. As law scholar Natalie Smuha has powerfully argued, legislation (both proposed and passed) in Western countries to address algorithmic risks and harms often focuses on individual rights: regarding how an individual’s data is collected or stored, to not be discriminated against, or to know when AI is being used. Even metrics used to evaluate the fairness of algorithms are often aggregating across individual impacts, but unable to capture longer-term, more complex, or second- and third-order societal impacts. Case Study: Privacy and surveillance Consider the over-reliance on individual harms in discussing privacy: so often focused on whether individual users have the ability to opt in or out of sharing their data, notions of individual consent, or proposals that individuals be paid for their personal data. Yet widespread surveillance fundamentally changes society: people may begin to self-censor and to be less willing (or able) to advocate for justice or social change. Professor Alvaro Bedoya, director of the Center on Privacy and Technology at the Georgetown University Law Center, traces a history of how surveillance has been used by the state to try to shut down movements for progress– targeting religious minorities, poor people, people of color, immigrants, sex workers and those considered “other”. As Maciej Ceglowski writes, “Ambient privacy is not a property of people, or of their data, but of the world around us… Because our laws frame privacy as an individual right, we don’t have a mechanism for deciding whether we want to live in a surveillance society.” Drawing on interviews with African data experts, Birhane et al write that even when data is anonymized and aggregated, it “can reveal information on the community as a whole. While notions of privacy often focus on the individual, there is growing awareness that collective identity is also important within many African communities, and that sharing aggregate information about communities can also be regarded as a privacy violation.” Recent US-based scholarship has also highlighted the importance of thinking about group level privacy (whether that group is made up of individuals who identify as members of that group, or whether it’s a ‘group’ that is algorithmically determined - like individuals with similar shopping habits on Amazon). Because even aggregated anonymised data can reveal important group-level information (e.g., the location of military personnel training via exercise tracking apps) “managing privacy”, these authors argue “is often not intrapersonal but interpersonal.” And yet legal and tech design privacy solutions are often better geared towards assuring individual-level privacy than negotiating group privacy. Case Study: Disinformation and erosion of trust Another example of a collective societal harm comes from how technology platforms such as Facebook have played a significant role in elections ranging from the Philippines to Brazil, yet it can be difficult (and not necessarily possible or useful) to quantify exactly how much: something as complex as a country’s political system and participation involves many interlinking factors. But when ‘deep fakes’ make it “possible to create audio and video of real people saying and doing things they never said or did” or when motivated actors successfully game search engines to amplify disinformation, the (potential) harm that is generated is societal, not just individual. Disinformation and the undermining of trust in institutions and fellow citizens have broad impacts, including on individuals who never use social media. Efforts by national governments to deal with the problem through regulation have not gone down well with everyone. ‘Disinformation’ has repeatedly been highlighted as one of the tech-enabled ‘societal harms’ that the UK’s Online Safety Bill or the EU’s Digital Services Act should address, and a range of governments are taking aim at the problem by proposing or passing a slew of (in certain cases ill-advised) ‘anti-misinformation’ laws. But there’s widespread unease around handing power to governments to set standards for what counts as ‘disinformation’. Does reifying ‘disinformation’ as a societal harm become a legitimizing tool for governments looking to silence political dissent or undermine their weaker opponents? It’s a fair and important concern - and yet simply leaving that power in the hands of mostly US-based, unaccountable tech companies is hardly a solution. What are the legitimacy implications if a US company like Twitter were to ban democratically elected Brazilian President Jair Bolsonaro for spreading disinformation, for example? How do we ensure that tech companies are investing sufficiently in governance efforts across the globe, rather than responding in an ad hoc manner to proximal (i.e. mostly US-based) concerns about disinformation? Taking a hands off approach to platform regulation doesn’t make platforms’ efforts to deal with disinformation any less politically fraught. Individual Harms, Individual Solutions If we consider individual solutions our only option (in terms of policy, law, or behavior), we often limit the scope of the harms we can recognize or the nature of the problems we face. To take an example not related to AI: Oxford professor Trish Greenhalgh et al analyzed the slow reluctance of leaders in the West to accept that covid is airborne (e.g. it can linger and float in the air, similar to cigarette smoke, requiring masks and ventilation to address), rather than droplet dogma (e.g. washing your hands is a key precaution). One reason they highlight is the Western framing of individual responsibility as the solution to most problems. Hand-washing is a solution that fits the idea of individual responsibility, whereas collective responsibility for the quality of shared indoor air does not. The allowable set of solutions helps shape what we identify as a problem. Additionally, the fact that recent research suggests that “the level of interpersonal trust in a society” was a strong predictor of which countries managed COVID-19 most successfully should give us pause. Individualistic framings can limit our imagination about the problems we face and which solutions are likely to be most impactful. Parallels with Environmental Harms Before the passage of environmental laws, many existing legal frameworks were not well-suited to address environmental harms. Perhaps a chemical plant releases waste emissions into the air once per week. Many people in surrounding areas may not be aware that they are breathing polluted air, or may not be able to directly link air pollution to a new medical condition, such as asthma, (which could be related to a variety of environmental and genetic factors). There are many parallels between environmental issues and AI ethics. Environmental harms include individual harms for people who develop discrete health issues from drinking contaminated water or breathing polluted air. Yet, environmental harms are also societal: the societal costs of contaminated water and polluted air can reverberate in subtle, surprising, and far-reaching ways. As law professor Nathalie Smuha writes, environmental harms are often accumulative and build over time. Perhaps each individual release of waste chemicals from a refinery has little impact on its own, but adds up to be significant. In the EU, environmental law allows for mechanisms to show societal harm, as it would be difficult to challenge many environmental harms on the basis of individual rights. Smuha argues that there are many similarities with AI ethics: for opaque AI systems, spanning over time, it can be difficult to prove a direct causal relationship to societal harm. Directions Forward To a large extent our message is to tech companies and policymakers. It’s not enough to focus on the potential individual harms generated by tech and AI: the broader societal costs of tech and AI matter. But those of us outside tech policy circles have a crucial role to play. One way in which we can guard against the risks of the ‘societal harm’ discourse being co-opted by those with political power to legitimise undue interference and further entrench their power is by claiming the language of ‘societal harm’ as the democratic and democratising tool it can be. We all lose when we pretend societal harms don’t exist, or when we acknowledge they exist but throw our hands up. And those with the least power, like Bill Baine, are likely to suffer a disproportionate loss. In his newsletter on Tech and Society, L.M. Sacasas encourages people to ask themselves 41 questions before using a particular technology. They’re all worth reading and thinking about - but we’re listing a few especially relevant ones to get you started. Next time you sit down to log onto social media, order food online, swipe right on a dating app or consider buying a VR headset, ask yourself: It’s on all of us to sensitise ourselves to the societal implications of the tech we use. |
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[SOURCE: https://www.bbc.com/future/article/20260216-how-the-sound-of-sport-is-being-reimagined-for-deaf-fans] | [TOKENS: 4271] |
How the sound of sport is being reimagined for deaf fans5 days agoShareSavePaul CarterShareSaveAlamyNew technologies tested at the Deaflympics in Tokyo are creating new ways of experience the atmosphere at sporting events.For fans of live sport, the experience is as much about the atmosphere – the sights, smells and sounds – as the drama unfolding in front of them.Think about the crack of a bat, the blast of a whistle, thump of a ball and the roar of the crowd. Would there be something missing without them? At the Deaflympics in Tokyo in November 2025, sound has been reimagined to ensure no-one misses out.Filming recently for BBC TechXplore, I learned that, at the Deaflympics, sound doesn't have to just be something you hear. It can be something you see. Something you feel. Sometimes, something you hold.For more than a century, the Deaflympics has been the pinnacle of elite competition for deaf athletes. Now, it is also one of the world's most important testing grounds for inclusive technology, where engineers, designers and deaf users collaborate to redefine how sport is experienced.From on-screen onomatopoeia to AI-powered announcements and vibrations delivered into the audience, Tokyo's Deaflympics offers a glimpse of a future where sound no longer belongs only to those who can hear.Seeing sound at the table tennis tableSometimes, the simplest technologies are the most powerful. Inside the table tennis arena, I felt the electricity in the atmosphere as fans rallied behind Japan, a global powerhouse in the sport. But instead of focusing on the ball's rhythmic rally, deaf spectators are invited to look up.Above the court, giant animated graphics flash across a screen: bold Japanese onomatopoeic words that mirror the rhythm of play. Each strike is matched with expressive characters that visually represent sound effects – the impact of the ball, the speed of the rally, the power of a smash. Sound, translated into language you can see.Onomatopoeia – words that imitate sounds – plays a unique role in Japanese culture, where visual sound expressions are widely used in manga and media. At the Deaflympics, that cultural familiarity becomes an accessibility tool. For some fans, especially newcomers to the sport, the system provides instant context: how fast the rally is, when a point is decisive, when momentum shifts.Kitty Knowles/ Electric EelPaul Carter saw how the atmosphere at the Deaflympics was brought to life through innovative technologies (Credit: Kitty Knowles/ Electric Eel)Ito Maki of Japan's Deaf Table Tennis Association, has seen technology evolve over the years. "I used to be a table tennis player, so I prefer to watch the matches themselves," he says. "But for people who don't play, or who are deaf, this helps them understand better. Being able to hold an event like this now – it moves me deeply."Onomatopoeia beyond sportWith 19 Deaflympics venues both in and around Tokyo, thousands of deaf visitors from around the world have helped to turn the city into a living laboratory for accessible urban technology.Station-hopping between venues, I tested Toppan's transparent sound-to-text translation screens. Deployed in 19 Toei Metro stations, these make travel communication easy for deaf and hearing visitors, whether they speak Japanese or not.At Deaflympics Square, a central hub for athletes and fans, I was struck by an AI-powered transport display developed by technology giant Fujitsu. Already piloted in major stations by Japan Rail, it listens for platform announcements, approaching trains, warning buzzers, and background music. The cacophony of environmental noise is categorised and converted into text and sign language.Once again, onomatopoeia plays a role. Certain sounds are represented visually to convey urgency, movement or emotion – helping users understand not just what is happening, but how it feels.Importantly, this device was created with the deaf community, specifically children from Kawasaki Municipal School for the Deaf."In Japan, many students use trains every day," says Tatsuya Honda from Fujitsu's Converging Technologies Laboratory. "We designed this device with deaf students – to be fun, safe and useful."I experienced the device alongside Finnish Deaflympian Sara-Elise Ruokonen, who said it was important deaf people were involved in the design "because we know best what we need".These futuristic screens help you navigate TokyoFeeling action through vibrationBack in the sporting arenas, sound was also brought to life through touch.At Tokyo Budokan, home of the deaf judo, spectators wore vibration devices developed by Hapbeat, a company originally known for immersive music technology.As judo fighters grip, shuffle and throw, every movement is captured by microphones and sensors in the mat. Footwork produces light vibrations. Collisions produce stronger ones. A full throw lands as a deep, unmistakable sensation to the chest.Fans do not simply watch a match – they feel it.Kitty Knowles/ Electric EelMicrophones set up around the judo arena capture sounds before they are converted to vibrations on a device worn by spectators (Credit: Kitty Knowles/ Electric Eel)"Shuffling feet felt like a lighter vibration, while collisions were stronger," says deaf judo fan Eri Terada, from Japan. "When someone was thrown, it was a heavy thud. Each one was different. I thought, 'Wow – this is amazing. It's so easy to understand.'"The Hapbeat device also delivers match cues, like the start and end of bouts, often signaled only by voice or gesture. A former swimmer, Sano Akira, explained how transformative this is: "The simplest thing is knowing when a match starts or ends. Since I can't hear that, having this device notify me is really helpful. It gives a sense of realism – like being right there."Some aspects of sound remain hard to automate. "Detecting nuance is difficult," says Hapbeat chief executive Yusuke Yamazaki. Crowd reactions – clapping, cheering, buzzers – are adjusted in real time by a human operator. Cultural understanding matters too, as deaf audiences often wave their hands instead of clapping.Interestingly, the device also drew hearing fans as well. What began as an accessibility tool became a shared sensory experience. "Even though I can hear, the device really conveyed the atmosphere," said Nana Watanabe, a hearing spectator from Japan. "It felt like we could share the intensity together."The wearable tech that lets spectators feel the matchHolding the musicThe Deaflympics doesn't just promote inclusive design in sports; it promotes accessible experiences everywhere. My personal highlight came at a music event with Indian golfer Diksha Dagar, a Deaflympics gold medalist, Olympian and two-time Ladies European Tour champion.After watching her make her final putts, we tried out Sound Hug devices from Tokyo startup Pixie Dust Technologies.Holding a football-sized orb each, sound transformed into something to embrace. The orbs pulsed light and vibrated in response to music – not only translating tone and beat but emotion. Experiencing traditional performances from hundreds of years ago, brought to life with modern immersive technologies, was incredibly moving."We can feel the music and the vibrations," said Dagar. "It tells us about the sound, so it's interpreted for deaf people."Kitty Knowles/ Electric EelThe Pixie Dust orbs offer a visual and vibrating sensation in response to music (Credit: Kitty Knowles/ Electric Eel)Designing a different future for soundThe Deaflympics demonstrates the power of shared, universal experiences. And, with so many deaf users present, it serves as a unique testbed for refining accessible technology.A pair of smart glasses I tried in the stands with Team USA swimmer Brooke Thompson weren't perfect yet, but the Deaflympics is the ideal proving ground for early-stage testing.When accessibility is the starting point, not an afterthought, sound is transformed. When deaf people lead design, sound becomes visible. When engineers listen differently, sound becomes tactile. When inclusion drives innovation, everyone benefits.At the Deaflympics, the future of sport is not silent – it is vividly, powerfully alive.* Paul Carter is the presenter of TechXplore: Tokyo Deaflympics, which was broadcast on BBC News on 14 February 2026.--For more technology news and insights, sign up to our Tech Decoded newsletter, while The Essential List delivers a handpicked selection of features and insights to your inbox twice a week. For more science, technology, environment and health stories from the BBC, follow us on Facebook and Instagram.TechnologySportScienceFeatures How the sound of sport is being reimagined for deaf fans New technologies tested at the Deaflympics in Tokyo are creating new ways of experience the atmosphere at sporting events. For fans of live sport, the experience is as much about the atmosphere – the sights, smells and sounds – as the drama unfolding in front of them. Think about the crack of a bat, the blast of a whistle, thump of a ball and the roar of the crowd. Would there be something missing without them? At the Deaflympics in Tokyo in November 2025, sound has been reimagined to ensure no-one misses out. Filming recently for BBC TechXplore, I learned that, at the Deaflympics, sound doesn't have to just be something you hear. It can be something you see. Something you feel. Sometimes, something you hold. For more than a century, the Deaflympics has been the pinnacle of elite competition for deaf athletes. Now, it is also one of the world's most important testing grounds for inclusive technology, where engineers, designers and deaf users collaborate to redefine how sport is experienced. From on-screen onomatopoeia to AI-powered announcements and vibrations delivered into the audience, Tokyo's Deaflympics offers a glimpse of a future where sound no longer belongs only to those who can hear. Seeing sound at the table tennis table Sometimes, the simplest technologies are the most powerful. Inside the table tennis arena, I felt the electricity in the atmosphere as fans rallied behind Japan, a global powerhouse in the sport. But instead of focusing on the ball's rhythmic rally, deaf spectators are invited to look up. Above the court, giant animated graphics flash across a screen: bold Japanese onomatopoeic words that mirror the rhythm of play. Each strike is matched with expressive characters that visually represent sound effects – the impact of the ball, the speed of the rally, the power of a smash. Sound, translated into language you can see. Onomatopoeia – words that imitate sounds – plays a unique role in Japanese culture, where visual sound expressions are widely used in manga and media. At the Deaflympics, that cultural familiarity becomes an accessibility tool. For some fans, especially newcomers to the sport, the system provides instant context: how fast the rally is, when a point is decisive, when momentum shifts. Ito Maki of Japan's Deaf Table Tennis Association, has seen technology evolve over the years. "I used to be a table tennis player, so I prefer to watch the matches themselves," he says. "But for people who don't play, or who are deaf, this helps them understand better. Being able to hold an event like this now – it moves me deeply." Onomatopoeia beyond sport With 19 Deaflympics venues both in and around Tokyo, thousands of deaf visitors from around the world have helped to turn the city into a living laboratory for accessible urban technology. Station-hopping between venues, I tested Toppan's transparent sound-to-text translation screens. Deployed in 19 Toei Metro stations, these make travel communication easy for deaf and hearing visitors, whether they speak Japanese or not. At Deaflympics Square, a central hub for athletes and fans, I was struck by an AI-powered transport display developed by technology giant Fujitsu. Already piloted in major stations by Japan Rail, it listens for platform announcements, approaching trains, warning buzzers, and background music. The cacophony of environmental noise is categorised and converted into text and sign language. Once again, onomatopoeia plays a role. Certain sounds are represented visually to convey urgency, movement or emotion – helping users understand not just what is happening, but how it feels. Importantly, this device was created with the deaf community, specifically children from Kawasaki Municipal School for the Deaf. "In Japan, many students use trains every day," says Tatsuya Honda from Fujitsu's Converging Technologies Laboratory. "We designed this device with deaf students – to be fun, safe and useful." I experienced the device alongside Finnish Deaflympian Sara-Elise Ruokonen, who said it was important deaf people were involved in the design "because we know best what we need". Feeling action through vibration Back in the sporting arenas, sound was also brought to life through touch. At Tokyo Budokan, home of the deaf judo, spectators wore vibration devices developed by Hapbeat, a company originally known for immersive music technology. As judo fighters grip, shuffle and throw, every movement is captured by microphones and sensors in the mat. Footwork produces light vibrations. Collisions produce stronger ones. A full throw lands as a deep, unmistakable sensation to the chest. Fans do not simply watch a match – they feel it. "Shuffling feet felt like a lighter vibration, while collisions were stronger," says deaf judo fan Eri Terada, from Japan. "When someone was thrown, it was a heavy thud. Each one was different. I thought, 'Wow – this is amazing. It's so easy to understand.'" The Hapbeat device also delivers match cues, like the start and end of bouts, often signaled only by voice or gesture. A former swimmer, Sano Akira, explained how transformative this is: "The simplest thing is knowing when a match starts or ends. Since I can't hear that, having this device notify me is really helpful. It gives a sense of realism – like being right there." Some aspects of sound remain hard to automate. "Detecting nuance is difficult," says Hapbeat chief executive Yusuke Yamazaki. Crowd reactions – clapping, cheering, buzzers – are adjusted in real time by a human operator. Cultural understanding matters too, as deaf audiences often wave their hands instead of clapping. Interestingly, the device also drew hearing fans as well. What began as an accessibility tool became a shared sensory experience. "Even though I can hear, the device really conveyed the atmosphere," said Nana Watanabe, a hearing spectator from Japan. "It felt like we could share the intensity together." Holding the music The Deaflympics doesn't just promote inclusive design in sports; it promotes accessible experiences everywhere. My personal highlight came at a music event with Indian golfer Diksha Dagar, a Deaflympics gold medalist, Olympian and two-time Ladies European Tour champion. After watching her make her final putts, we tried out Sound Hug devices from Tokyo startup Pixie Dust Technologies. Holding a football-sized orb each, sound transformed into something to embrace. The orbs pulsed light and vibrated in response to music – not only translating tone and beat but emotion. Experiencing traditional performances from hundreds of years ago, brought to life with modern immersive technologies, was incredibly moving. "We can feel the music and the vibrations," said Dagar. "It tells us about the sound, so it's interpreted for deaf people." Designing a different future for sound The Deaflympics demonstrates the power of shared, universal experiences. And, with so many deaf users present, it serves as a unique testbed for refining accessible technology. A pair of smart glasses I tried in the stands with Team USA swimmer Brooke Thompson weren't perfect yet, but the Deaflympics is the ideal proving ground for early-stage testing. When accessibility is the starting point, not an afterthought, sound is transformed. When deaf people lead design, sound becomes visible. When engineers listen differently, sound becomes tactile. When inclusion drives innovation, everyone benefits. At the Deaflympics, the future of sport is not silent – it is vividly, powerfully alive. * Paul Carter is the presenter of TechXplore: Tokyo Deaflympics, which was broadcast on BBC News on 14 February 2026. -- For more technology news and insights, sign up to our Tech Decoded newsletter, while The Essential List delivers a handpicked selection of features and insights to your inbox twice a week. For more science, technology, environment and health stories from the BBC, follow us on Facebook and Instagram. Fixing fashion's erratic sizing problem Tech Now meets a startup trying to fix one of the fashion industry's biggest blind spots, inconsistent sizing. The tactile tech giving deaf runners a fair start A gold‑medalist has developed a vibrating starting block to give deaf athletes clearer, fairer race starts. These futuristic screens help you navigate Tokyo In Tokyo, BBC TechXplore tests live translation and AI-powered displays that makes the city more navigable. The wearable tech that lets spectators feel the match At Tokyo's Deaflympics, deaf Judo fans aren't just watching the matches, they're feeling them, thanks to Hapbeat. Meet MOFO: will.i.am's rapping AI toy BBC Tech Now takes us inside CES 2026 to meet musician will.i.am and his AI toy, MOFO. The gadgets set to change your daily health and wellness Tech Now test out new gadgets disrupting the health industry at CES 2026 in Las Vegas. What's it like to meet your own avatar? Musician KT Tunstall meets her avatar as Tech Now explores music’s virtual future. How can rollercoasters hold so much weight? Rollercoasters send our hearts racing and stomachs dropping. Hannah Fry dives into the science of how they work. 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How sex with Neanderthals changed us forever We find out what we gained when Homo sapiens mated with Homo neanderthalensis many thousands of years ago. The Lion King: How Mufasa was brought to life BBC Click speaks to the visual effects team behind the latest Disney blockbuster. How the TikTok ban affected US influencers BBC Click meets TikTok creator Peggy Xu who gained millions of views sharing milk videos. Theory of Evolution: How did Charles Darwin come up with it? The British naturalist embarked on an extraordinary journey, did hundreds of experiments, and wrote for 20 years. Is this the world's first AI powered hotel? BBC Click's Paul Carter visits the world's first fully AI-powered hotel in Las Vegas. How the additives in food affect our gut microbes The additives added to processed food to keep it fresher for longer might be having an unexpected effect on the health of the microbes in our guts. The most anticipated museum openings of 2026 From a futuristic sci-fi attraction in Los Angeles to a dramatic monument to a millennia-old Aboriginal civilisation, these long-awaited museums are worth travelling for. 11 of the Winter Olympics' most striking images As the 2026 Winter Olympics close, the BBC rounds up some of the most stunning photos captured from the Games, and compares them to historic works of art. The cities where you'll never see flight ads Cities across the world are clearing their billboards of flight ads, SUVs, cruise ships and petrol cars in an attempt to cut emissions. Extreme ways countries are combatting overtourism As global travel surges toward 1.8 billion arrivals, destinations are testing controversial new measures to control the crowds. Copyright 2026 BBC. All rights reserved. The BBC is not responsible for the content of external sites. Read about our approach to external linking. |
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[SOURCE: https://www.fast.ai/posts/2022-03-15-math-person.html] | [TOKENS: 1440] |
There’s no such thing as not a math person Rachel Thomas March 15, 2022 On this page On the surface, I may seem into math: I have a math PhD, taught a graduate computational linear algebra course, co-founded AI research lab fast.ai, and even go by the twitter handle @math_rachel. Yet many of my experiences of academic math culture have been toxic, sexist, and deeply alienating. At my lowest points, I felt like there was no place for me in math academia or math-heavy tech culture. It is not just mathematicians or math majors who are impacted by this: Western culture is awash in negative feelings and experiences regarding math, which permate from many sources and impact students of all ages. In this post, I will explore the cultural factors, misconceptions, stereotypes, and relevant studies on obstacles that turn people off to math. If you (or your child) doesn’t like math or feels anxious about your own capabilities, you’re not alone, and this isn’t just a personal challenge. The below essay is based on part of a talk I recently gave. Myth of Innate Ability, Myth of the Lone Genius One common myth is the idea that certain people’s brains aren’t “wired” the right way to do math, tech, or AI, that your brain either “works that way” or not. None of the evidence supports this viewpoint, yet when people believe this, it can become a self-fulfilling prophecy. Dr. Omoju Miller, who earned her PhD at UC Berkeley and was a senior machine learning engineer and technical advisor to the CEO at Github, shares some of the research debunking the myth of innate ability in this essay and in her TEDx talk. In reality, there is no such thing as “not a math person.” Dr. Cathy O’Neil, a Harvard Math PhD and author of Weapons of Math Destruction, wrote about the myth of the lone genius mathematician, “You don’t have to be a genius to become a mathematician. If you find this statement at all surprising, you’re an example of what’s wrong with the way our society identifies, encourages and rewards talent… For each certified genius, there are at least a hundred great people who helped achieve such outstanding results.” Music without singing or instruments Imagine a world where children are not allowed to sing songs or play instruments until they reach adulthood, after spending a decade or two transcribing sheet music by hand. This scenario is absurd and nightmarish, yet it is analogous to how math is often taught, with the most creative and interesting parts saved until almost everyone has dropped out. Dr. Paul Lockhart eloquently describes this metaphor in his essay, A Mathematician’s Lament, on “how school cheats us out of our most fascinating and imaginative art form.” Dr. Lockhart left his role as a university math professor to teach K-12 math, as he felt that so much reform was needed in how math is taught. Dr. David Perkins uses the analogy of how children can play baseball wthout knowing all the technical details, without having a full team or playing a full 9 innings, yet still gain a sense of the “whole game.” Math is usually taught with an overemphasis on dry, technical details, without giving students a concept of the “whole game.” It can take years and years before enough technical details are accumulated to build something interesting. There is an overemphasis on techniques rather than meaning. Math curriculums are usually arranged in a vertical manner, with each year building tightly on the previous, such that one bad year can ruin everything that comes after. Many people I talk to can pinpoint the year that math went bad for them: “I used to like math until 6th grade, when I had a bad teacher/was dealing with peer pressure/my undiagnosed ADHD was out of control. After that, I was never able to succeed in future years.” This is less true in other subjects, where one bad history teacher/one bad year doesn’t mean that you can’t succeed at history the following year. Gender, race, and stereotypes Female teachers’ math anxiety affects girls’ math achievement: In the USA, over 90% of primary school teachers are female, and research has found “the more anxious teachers were about math, the more likely girls (but not boys) were to endorse the commonly held stereotype that ‘boys are good at math, and girls are good at reading’ and the lower these girls’ math achievement… People’s fear and anxiety about doing math—over and above actual math ability—can be an impediment to their math achievement.” Research across a number of universities has found that more women go into engineering when courses focus on problems with positive social impact. Structural racism also impacts what messages teachers impart to students. An Atlantic article How Does Race Affect a Student’s Math Education? covered the research paper A Framework for Understanding Whiteness in Mathematics Education, noting that “Constantly reading and hearing about underperforming Black, Latino, and Indigenous students begins to embed itself into how math teachers view these students, attributing achievement differences to their innate ability to succeed in math… teachers start to expect worse performance from certain students, start to teach lower content, and start to use lower-level math instructional practices. By contrast, white and Asian students are given the benefit of the doubt and automatically afforded the opportunity to do more sophisticated and substantive mathematics.” The mathematics community is “an absolute mess which actively pushes out the sort of people who might make it better” Dr. Piper Harron made waves with her Princeton PhD thesis, utilizing humor, analogies, sarcasm, and genuine efforts to be accessible as she described advanced concepts in a ground-breaking way, very atypical for a mathematics PhD thesis. Dr. Harron wrote openly in the prologue of her thesis on how alienating the culture of mathematics is, “As any good grad student would do, I tried to fit in, mathematically. I absorbed the atmosphere and took attitudes to heart. I was miserable, and on the verge of failure. The problem was not individuals, but a system of self-preservation that, from the outside, feels like a long string of betrayals, some big, some small, perpetrated by your only support system.” At her blog, the Liberated Mathematician, she writes, “My view of mathematics is that it is an absolute mess which actively pushes out the sort of people who might make it better.” These descriptions resonate with my own experiences obtaining a math PhD (as well as the experiences of many friends, at a variety of universities). The toxicity of academic math departments is self-perpetuating, pushing out the people who could make them better. The full talk This post is based on the first part of the talk I gave in the below video, which includes more detail and a Q&A. The talk also includes recommendations about math apps and resources, as well as a framework for how to consider screentime. Stay tuned for a future fast.ai blog post covering math apps and screentime. |
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Contents TIGSource TIGSource, short for The Independent Games Source, is a news blog and Internet community centered around the creation of independent video games, founded in 2005 by Jordan Magnuson but soon taken over by Derek Yu, both independent game developers. The site has been described as having been an important "cultural nexus" for the creation of indie games development in the 2000s and early 2010s, and a key player in changing the perception of independent video games as merely casual games to that of an art form. Its forums were the launchpad for several award-winning games, including the best-selling video game of all time, Minecraft, BAFTA-winning dystopian immigration officer simulation Papers, Please, viral phenomenon QWOP, puzzle-platform game Fez, and Yu's own Spelunky. The site was in 2009 referred to as "one of the primary sources of information about the indie scene on the web and host to one of indie's best forums, bringing creators and fans together to share novel new ideas and the greatest new games." In 2008, it was chosen as one of "100 top sites for the year ahead" by The Guardian. References This article about a blog, vlog, or other Internet publication is a stub. You can help Wikipedia by adding missing information. |
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[SOURCE: https://en.wikipedia.org/wiki/Markus_Persson#cite_note-GSint3-16] | [TOKENS: 3525] |
Contents Markus Persson Markus Alexej Persson (/ˈpɪərsən/ ⓘ PEER-sən, Swedish: [ˈmǎrːkɵs ˈpæ̌ːʂɔn] ⓘ; born 1 June 1979), known by the pseudonym Notch, is a Swedish video game programmer and designer. He is the creator of Minecraft, the best-selling video game in history. He founded the video game development company Mojang Studios in 2009. Persson began developing video games at an early age. His commercial success began after he published an early version of Minecraft in 2009. Prior to the game's official retail release in 2011, it had sold over four million copies. After this point Persson stood down as the lead designer and transferred his creative authority to Jens Bergensten. In September 2014 Persson announced his intention to leave Mojang, and in November of that year the company was sold to Microsoft reportedly for US$2.5 billion, which made him a billionaire. Since 2016 several of Persson's posts on Twitter regarding feminism, race, and transgender rights have caused public controversies. He has been described as "an increasingly polarizing figure, tweeting offensive statements regarding race, the LGBTQ community, gender, and other topics." In an effort to distance itself from Persson, Microsoft removed mentions of his name from Minecraft (excluding one instance in the game's end credits) and did not invite him to the game's tenth anniversary celebration. In 2015 he co-founded a separate game studio called Rubberbrain, which was relaunched in 2024 as Bitshift Entertainment. Early life Markus Alexej Persson was born in Stockholm, Sweden, to a Finnish mother, Ritva, and a Swedish father, Birger, on 1 June 1979. He has one sister. He grew up in Edsbyn until he was seven years old, when his family moved back to Stockholm. In Edsbyn, Persson's father worked for the railroad, and his mother was a nurse. He spent much time outdoors in Edsbyn, exploring the woods with his friends. When Persson was about seven years old, his parents divorced, and he and his sister lived with their mother. His father moved to a cabin in the countryside. Persson said in an interview that they experienced food insecurity around once a month. Persson lost contact with his father for several years after the divorce. According to Persson, his father suffered from depression, bipolar disorder, alcoholism, and medication abuse, and went to jail for robberies. While his father had somewhat recovered during Persson's early life, his father relapsed, contributing to the divorce. His sister also experimented with drugs and ran away from home. He had gained interest in video games at an early age. His father was "a really big nerd", who built his own modem and taught Persson to use the family's Commodore 128. On it, Persson played bootleg games and loaded in various type-in programs from computer magazines with the help of his sister. The first game he purchased with his own money was The Bard's Tale. He began programming on his father's Commodore 128 home computer at the age of seven. He produced his first game at the age of eight, a text-based adventure game. By 1994 Persson knew he wanted to become a video game developer, but his teachers advised him to study graphic design, which he did from ages 15 to 18. Persson, although introverted, was well-liked by his peers, but after entering secondary school was a "loner" and reportedly had only one friend. He spent most of his spare time with games and programming at home. He managed to reverse-engineer the Doom engine, which he continued to take great pride in as of 2014[update]. He never finished high school, but was reportedly a good student. Career Persson started his career working as a web designer. He later found employment at Game Federation, where he met Rolf Jansson. The pair worked in their spare time to build the 2006 video game Wurm Online. The game was released through a new entity, "Mojang Specifications AB". Persson left the project in late 2007. As Persson wanted to reuse the name "Mojang", Jansson agreed to rename the company to Onetoofree AB. Between 2004 and 2009 Persson worked as a game developer for Midasplayer (later known as King). There, he worked as a programmer, mostly building browser games made in Flash. He later worked as a programmer for jAlbum. Prior to creating Minecraft, Persson developed multiple, small games. He also entered a number of game design competitions and participated in discussions on the TIGSource forums, a web forum for independent game developers. One of Persson's more notable personal projects was called RubyDung, an isometric three-dimensional base-building game like RollerCoaster Tycoon and Dwarf Fortress. While working on RubyDung, Persson experimented with a first-person view mode similar to that found in Dungeon Keeper. However, he felt the graphics were too pixelated and omitted this mode. In 2009 Persson found inspiration in Infiniminer, a block-based open-ended mining game. Infiniminer heavily influenced his future work on RubyDung, and was behind Persson's reasoning for returning the first-person mode, the "blocky" visual style and the block-building fundamentals to the game. RubyDung is the earliest known Minecraft prototype created by Persson. On 17 May 2009 Persson released the original edition (later called "Classic version") of Minecraft on the TIGSource forums. He regularly updated the game based on feedback from TIGSource users. Persson released several new versions of Minecraft throughout 2009 and 2010, going through several phases of development including Survival Test, Indev, and Infdev. On 30 June 2010 Persson released the game's Alpha version. While working on the pre-Alpha version of Minecraft, Persson continued working at jAlbum. In 2010, after the release and subsequent success of Minecraft's Alpha version, Persson moved from a full-time role to a part-time role at jAlbum. He left jAlbum later that same year. In September 2010 Persson travelled to Valve Corporation's headquarters in Bellevue, Washington, United States, where he took part in a programming exercise and met Gabe Newell. Persson was subsequently offered a job at Valve, which he turned down in order to continue work on Minecraft. On 20 December 2010 Minecraft moved into its beta phase and began expanding to other platforms, including mobile. In January 2011 Minecraft reached one million registered accounts. Six months afterwards, it reached ten million. The game has sold over four million copies by 7 November 2011. Mojang held the first Minecon from 18 to 19 November 2011 to celebrate its full release, and subsequently made it an annual event. Following this, on 11 December 2011, Persson transferred creative control of Minecraft to Jens Bergensten and began working on another game title, 0x10c, although he reportedly abandoned the project around 2013. In 2013 Mojang recorded revenues of $330 million and profits of $129 million. Persson has stated that, due to the intense media attention and public pressure, he became exhausted with running Minecraft and Mojang. In a September 2014 blog post he shared his realization that he "didn't have the connection to my fans I thought I had", that he had "become a symbol", and that he did not wish to be responsible for Mojang's increasingly large operation. In June 2014 Persson tweeted "Anyone want to buy my share of Mojang so I can move on with my life? Getting hate for trying to do the right thing is not my gig", reportedly partly as a joke. Persson controlled a 71% stake in Mojang at the time. The offer attracted significant interest from Activision Blizzard, EA, and Microsoft. Forbes later reported that Microsoft wanted to purchase the game as a "tax dodge" to turn their taxable excess liquid cash into other assets. In September 2014 Microsoft agreed to purchase Mojang for $2.5 billion, making Persson a billionaire. He then left the company after the deal was finalised in November. Since leaving Mojang, Persson has worked on several small projects. On 23 June 2014 he founded a company with Porsér called Rubberbrain AB; the company had no games by 2021, despite spending SEK 60 million. The company was relaunched as Bitshift Entertainment, LLC on 28 March 2024. Persson expressed interest in creating a new video game studio in 2020, and in developing virtual reality games. He has also since created a series of narrative-driven immersive events called ".party()", which uses extensive visual effects and has been hosted in multiple cities. At the beginning of 2025 Persson decided to create a spiritual successor to Minecraft, referred to as "Minecraft 2", in response to the results of a poll on X. However, after speaking to his team, he shortly went against this in favour of developing the other choice on his Twitter poll, a roguelike titled Levers and Chests. Games Persson's most popular creation is the survival sandbox game Minecraft, which was first publicly available on 17 May 2009 and fully released on 18 November 2011. Persson left his job as a game developer to work on Minecraft full-time until completion. In early 2011, Mojang AB sold the one millionth copy of the game, several months later their second, and several more their third. Mojang hired several new staff members for the Minecraft team, while Persson passed the lead developer role to Jens Bergensten. He stopped working on Minecraft after a deal with Microsoft to sell Mojang for $2.5 billion. This brought his net worth to US$1.5 billion. Persson and Jakob Porsér came up with the idea for Scrolls including elements from board games and collectible card games. Persson noted that he will not be actively involved in development of the game and that Porsér will be developing it. Persson revealed on his Tumblr blog on 5 August 2011 that he was being sued by a Swedish law firm representing Bethesda Softworks over the trademarked name of Scrolls, claiming that it conflicted with their The Elder Scrolls series of games. On 17 August 2011 Persson challenged Bethesda to a Quake 3 tournament to decide the outcome of the naming dispute. On 27 September 2011 Persson confirmed that the lawsuit was going to court. ZeniMax Media, owner of Bethesda Softworks, announced the lawsuit's settlement in March 2012. The settlement allowed Mojang to continue using the Scrolls trademark. In 2018, Scrolls was made available free of charge and renamed to Caller's Bane. Cliffhorse is a humorous game programmed in two hours using the Unity game engine and free assets. The game took inspiration from Skyrim's physics engine, "the more embarrassing minimum-effort Greenlight games", Goat Simulator, and Big Rigs: Over the Road Racing. The game was released to Microsoft Windows systems as an early access and honourware game on the first day of E3 2014, instructing users to donate Dogecoin to "buy" the game before downloading it. The game accumulated over 280,000 dogecoins. Following the end to his involvement with Minecraft, Persson began pre-production of an alternate reality space game set in the distant future in March 2012. On April Fools' Day Mojang launched a satirical website for Mars Effect (parody of Mass Effect), citing the lawsuit with Bethesda as an inspiration. However, the gameplay elements remained true and on 4 April, Mojang revealed 0x10c (pronounced "Ten to the C") as a space sandbox title. Persson officially halted game production in August 2013. However, C418, the composer of the game's soundtrack (as well as that of Minecraft), released an album of the work he had made for the game. In 2013, Persson made a free game called Shambles in the Unity game engine. Persson has also participated in several Ludum Dare 48-hour game making competitions. Personal life In 2011 Persson married Elin Zetterstrand, whom he had dated for four years before. Zetterstrand was a former moderator on the Minecraft forums. They had a daughter together, but by mid-2012, he began to see little of her. On 15 August 2012 he announced that he and his wife had filed for divorce. The divorce was finalised later that year. On 14 December 2011 Persson's father committed suicide with a handgun after drinking heavily. In an interview with The New Yorker, Persson said of his father: When I decided I wanted to quit my day job and work on my own games, he was the only person who supported my decision. He was proud of me and made sure I knew. When I added the monsters to Minecraft, he told me that the dark caves became too scary for him. But I think that was the only true criticism I ever heard from him. Persson later admitted that he himself suffered from depression and various highs and lows in his mood. Persson has criticised the stance of large game companies on piracy. He once stated that "piracy is not theft", viewing unauthorised downloads as potential future customers. Persson stated himself to be a member of the Pirate Party of Sweden in 2011. He is also a member of Mensa. He has donated to numerous charities, including Médecins Sans Frontières (Doctors Without Borders). Under his direction, Mojang spent a week developing Catacomb Snatch for the Humble Indie Bundle and raised US$458,248 for charity. He also donated $250,000 to the Electronic Frontier Foundation in 2012. In 2011 he gave $3 million in dividends back to Mojang employees. According to Forbes, his net worth in 2023 was around $1.2 billion. In 2014 Persson was one of the biggest taxpayers in Sweden. Around 2014, he lived in a multi-level penthouse in Östermalm, Stockholm, an area he described as "where the rich people live". In December 2014 Persson purchased a home in Trousdale Estates, a neighbourhood in Beverly Hills, California, in the United States, for $70 million, a record sales price for Beverly Hills at the time. Persson reportedly outbid Beyoncé and Jay-Z for the property. Persson began receiving criticism for political and social opinions he expressed on social media as early as 2016. November 30, 2017 In 2017, he proposed a heterosexual pride holiday, and wrote that those who opposed the idea "deserve to be shot." After facing backlash, he deleted the tweets and rescinded his statements, writing, "So yeah, it's about pride of daring to express, not about pride of being who you are. I get it now." Later in the year, he wrote that feminism is a "social disease" and called the video game developer and feminist Zoë Quinn a "cunt", although he was generally critical of the GamerGate movement. He has described intersectional feminism as a "framework for bigotry" and the use of the word mansplaining as being sexist. Also in 2017, Persson tweeted that "It's okay to be white". Later that year, he stated that he believed in the Pizzagate conspiracy theory. In 2019, he tweeted referencing QAnon, saying "Q is legit. Don't trust the media." Later in 2019, he tweeted in response to a pro-transgender internet meme that, "You are absolutely evil if you want to encourage delusion. What happened to not stigmatizing mental illness?" He then also promoted claims that people were fined for "using the wrong pronoun". However, after facing backlash, he tweeted a day afterwards that he had "no idea what [being trans is] like of course, but it's inspiring as hell when people open up and choose to actually be who they know themselves as. Not because it's a cool choice, because it's a big step. I gues [sic] that's actually cool nvm". Later that year, Microsoft removed two mentions of Persson's name in the "19w13a" snapshot of Minecraft and did not invite him to the 10-year anniversary celebration of the game. A spokesperson for Microsoft stated that his views "do not reflect those of Microsoft or Mojang". He is still mentioned in the End Poem ("a flat, infinite world created by a man called Markus").[citation needed] Awards References External links |
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[SOURCE: https://www.fast.ai/posts/2022-03-14-ADSN-ethics.html] | [TOKENS: 734] |
7 Great Lightning Talks Related to Data Science Ethics Rachel Thomas March 14, 2022 On this page I have been organizing and facilitating a series of Ethics Workshops for the Australian Data Science Network, featuring lightning talks by Australian experts on a range of topics related to data science ethics, including machine learning in medicine, explainability, Indigenous-led AI, and the role of policy. Check out the videos from these thought-provoking lightning talks (with longer discussions at the end): The False Hope of Explainability in Medicine Lauren Oakden-Rayner, the Director of Research for Medical Imaging at Royal Adelaide Hospital, is both a radiologist and a machine learning expert. She spoke about mismatched expectations between technical and non-technical communities on what questions explainability answers, based on her paper “The false hope of current approaches to explainable artificial intelligence in health care”. Lauren’s talk is at the start of Video #1. Critical Gaps in ML Evaluation Practice Ben Hutchinson is a senior engineer in Google Research based in Sydney. Practices for evaluating machine learning models are largely developed within academic research and rest on a number of assumptions that lead to concerning gaps when applied to real-world applications. Ben’s talk starts at 12 min mark of Video #1. Indigenous-Led AI Cathy Robinson is a principal research scientist at CSIRO, working on a project to center Indigenous data soveriegnty and Indigenous co-design in addressing complex ecological and conservation issues. Read more about CSIRO’s Healthy Country AI project or about CARE Indigenous Data Principles. Watch Cathy’s talk starting at 23 min mark of Video #1. Near-Termism and AI Value Alignment Aaron Snoswell is a postdoctoral research fellow at QUT, with over a decade’s experience in software development, industry research, and robotics. He spoke about the issues with focusing primarily on long-termism in AI value alignment and the need to consider short-term issues. Starts at 36 min mark Video #1. Narrow vs Broad Understandings of Algorithmic Bias among Stakeholders in Healthcare AI Yves Saint James Aquino is a philosopher and physician, currently working on the project “The algorithm will see you now: ethical, legal and social implications of adopting machine learning systems for diagnosis and screening” as a postdoctoral research fellow at the University in Wollongong. For his talk, he drew on interviews with 70 different stakeholders in healthcare AI, including software developers, medical doctors, and startup founders, to explore different conceptions of how algorithmic bias is understood. Watch the first talk in Video #2. Towards Human-Centric XAI using Eye Tracking in Chest Xrays Catarina Pinto Moreira is a Lecturer in Information Systems at Queensland University of Technology and a pioneer in non-classical probabilistic graphical models for decision making to empower human decision-making. Interviews with radiologists are crucial to her work; for example, interviews revealed that clinical notes are important for radiologists to use in diagnosis, even though this is not often mentioned in the literature. Her talk begins at 10 min mark of Video #2. The Role of Policy in Data Ethics Michael Evans crafted Australia’s National Artificial Intelligence Roadmap, contributed to the development of Australia’s national approach to governing autonomous vehicles, and represented Australia at the World Bank/IMF Annual Meetings. He gave an overview of the AI policy landscape, including policy tools, the disconnect between principles and application, and recommended ways forward. Watch Michael’s talk beginning at 20 min mark of Video #2. Each talk is around 5 minutes long. Feel free to fast forward to those of particular interest, or watch them all! The End |
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[SOURCE: https://en.wikipedia.org/wiki/File:Noun-technology.svg] | [TOKENS: 128] |
File:Noun-technology.svg Summary File history Click on a date/time to view the file as it appeared at that time. File usage More than 100 pages use this file. The following list shows the first 100 pages that use this file only. A full list is available. View more links to this file. Global file usage The following other wikis use this file: View more global usage of this file. Metadata This file contains additional information, probably added from the digital camera or scanner used to create or digitize it. If the file has been modified from its original state, some details may not fully reflect the modified file. |
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[SOURCE: https://en.wikipedia.org/wiki/Markus_Persson#cite_note-GAMASUTRA2-17] | [TOKENS: 3525] |
Contents Markus Persson Markus Alexej Persson (/ˈpɪərsən/ ⓘ PEER-sən, Swedish: [ˈmǎrːkɵs ˈpæ̌ːʂɔn] ⓘ; born 1 June 1979), known by the pseudonym Notch, is a Swedish video game programmer and designer. He is the creator of Minecraft, the best-selling video game in history. He founded the video game development company Mojang Studios in 2009. Persson began developing video games at an early age. His commercial success began after he published an early version of Minecraft in 2009. Prior to the game's official retail release in 2011, it had sold over four million copies. After this point Persson stood down as the lead designer and transferred his creative authority to Jens Bergensten. In September 2014 Persson announced his intention to leave Mojang, and in November of that year the company was sold to Microsoft reportedly for US$2.5 billion, which made him a billionaire. Since 2016 several of Persson's posts on Twitter regarding feminism, race, and transgender rights have caused public controversies. He has been described as "an increasingly polarizing figure, tweeting offensive statements regarding race, the LGBTQ community, gender, and other topics." In an effort to distance itself from Persson, Microsoft removed mentions of his name from Minecraft (excluding one instance in the game's end credits) and did not invite him to the game's tenth anniversary celebration. In 2015 he co-founded a separate game studio called Rubberbrain, which was relaunched in 2024 as Bitshift Entertainment. Early life Markus Alexej Persson was born in Stockholm, Sweden, to a Finnish mother, Ritva, and a Swedish father, Birger, on 1 June 1979. He has one sister. He grew up in Edsbyn until he was seven years old, when his family moved back to Stockholm. In Edsbyn, Persson's father worked for the railroad, and his mother was a nurse. He spent much time outdoors in Edsbyn, exploring the woods with his friends. When Persson was about seven years old, his parents divorced, and he and his sister lived with their mother. His father moved to a cabin in the countryside. Persson said in an interview that they experienced food insecurity around once a month. Persson lost contact with his father for several years after the divorce. According to Persson, his father suffered from depression, bipolar disorder, alcoholism, and medication abuse, and went to jail for robberies. While his father had somewhat recovered during Persson's early life, his father relapsed, contributing to the divorce. His sister also experimented with drugs and ran away from home. He had gained interest in video games at an early age. His father was "a really big nerd", who built his own modem and taught Persson to use the family's Commodore 128. On it, Persson played bootleg games and loaded in various type-in programs from computer magazines with the help of his sister. The first game he purchased with his own money was The Bard's Tale. He began programming on his father's Commodore 128 home computer at the age of seven. He produced his first game at the age of eight, a text-based adventure game. By 1994 Persson knew he wanted to become a video game developer, but his teachers advised him to study graphic design, which he did from ages 15 to 18. Persson, although introverted, was well-liked by his peers, but after entering secondary school was a "loner" and reportedly had only one friend. He spent most of his spare time with games and programming at home. He managed to reverse-engineer the Doom engine, which he continued to take great pride in as of 2014[update]. He never finished high school, but was reportedly a good student. Career Persson started his career working as a web designer. He later found employment at Game Federation, where he met Rolf Jansson. The pair worked in their spare time to build the 2006 video game Wurm Online. The game was released through a new entity, "Mojang Specifications AB". Persson left the project in late 2007. As Persson wanted to reuse the name "Mojang", Jansson agreed to rename the company to Onetoofree AB. Between 2004 and 2009 Persson worked as a game developer for Midasplayer (later known as King). There, he worked as a programmer, mostly building browser games made in Flash. He later worked as a programmer for jAlbum. Prior to creating Minecraft, Persson developed multiple, small games. He also entered a number of game design competitions and participated in discussions on the TIGSource forums, a web forum for independent game developers. One of Persson's more notable personal projects was called RubyDung, an isometric three-dimensional base-building game like RollerCoaster Tycoon and Dwarf Fortress. While working on RubyDung, Persson experimented with a first-person view mode similar to that found in Dungeon Keeper. However, he felt the graphics were too pixelated and omitted this mode. In 2009 Persson found inspiration in Infiniminer, a block-based open-ended mining game. Infiniminer heavily influenced his future work on RubyDung, and was behind Persson's reasoning for returning the first-person mode, the "blocky" visual style and the block-building fundamentals to the game. RubyDung is the earliest known Minecraft prototype created by Persson. On 17 May 2009 Persson released the original edition (later called "Classic version") of Minecraft on the TIGSource forums. He regularly updated the game based on feedback from TIGSource users. Persson released several new versions of Minecraft throughout 2009 and 2010, going through several phases of development including Survival Test, Indev, and Infdev. On 30 June 2010 Persson released the game's Alpha version. While working on the pre-Alpha version of Minecraft, Persson continued working at jAlbum. In 2010, after the release and subsequent success of Minecraft's Alpha version, Persson moved from a full-time role to a part-time role at jAlbum. He left jAlbum later that same year. In September 2010 Persson travelled to Valve Corporation's headquarters in Bellevue, Washington, United States, where he took part in a programming exercise and met Gabe Newell. Persson was subsequently offered a job at Valve, which he turned down in order to continue work on Minecraft. On 20 December 2010 Minecraft moved into its beta phase and began expanding to other platforms, including mobile. In January 2011 Minecraft reached one million registered accounts. Six months afterwards, it reached ten million. The game has sold over four million copies by 7 November 2011. Mojang held the first Minecon from 18 to 19 November 2011 to celebrate its full release, and subsequently made it an annual event. Following this, on 11 December 2011, Persson transferred creative control of Minecraft to Jens Bergensten and began working on another game title, 0x10c, although he reportedly abandoned the project around 2013. In 2013 Mojang recorded revenues of $330 million and profits of $129 million. Persson has stated that, due to the intense media attention and public pressure, he became exhausted with running Minecraft and Mojang. In a September 2014 blog post he shared his realization that he "didn't have the connection to my fans I thought I had", that he had "become a symbol", and that he did not wish to be responsible for Mojang's increasingly large operation. In June 2014 Persson tweeted "Anyone want to buy my share of Mojang so I can move on with my life? Getting hate for trying to do the right thing is not my gig", reportedly partly as a joke. Persson controlled a 71% stake in Mojang at the time. The offer attracted significant interest from Activision Blizzard, EA, and Microsoft. Forbes later reported that Microsoft wanted to purchase the game as a "tax dodge" to turn their taxable excess liquid cash into other assets. In September 2014 Microsoft agreed to purchase Mojang for $2.5 billion, making Persson a billionaire. He then left the company after the deal was finalised in November. Since leaving Mojang, Persson has worked on several small projects. On 23 June 2014 he founded a company with Porsér called Rubberbrain AB; the company had no games by 2021, despite spending SEK 60 million. The company was relaunched as Bitshift Entertainment, LLC on 28 March 2024. Persson expressed interest in creating a new video game studio in 2020, and in developing virtual reality games. He has also since created a series of narrative-driven immersive events called ".party()", which uses extensive visual effects and has been hosted in multiple cities. At the beginning of 2025 Persson decided to create a spiritual successor to Minecraft, referred to as "Minecraft 2", in response to the results of a poll on X. However, after speaking to his team, he shortly went against this in favour of developing the other choice on his Twitter poll, a roguelike titled Levers and Chests. Games Persson's most popular creation is the survival sandbox game Minecraft, which was first publicly available on 17 May 2009 and fully released on 18 November 2011. Persson left his job as a game developer to work on Minecraft full-time until completion. In early 2011, Mojang AB sold the one millionth copy of the game, several months later their second, and several more their third. Mojang hired several new staff members for the Minecraft team, while Persson passed the lead developer role to Jens Bergensten. He stopped working on Minecraft after a deal with Microsoft to sell Mojang for $2.5 billion. This brought his net worth to US$1.5 billion. Persson and Jakob Porsér came up with the idea for Scrolls including elements from board games and collectible card games. Persson noted that he will not be actively involved in development of the game and that Porsér will be developing it. Persson revealed on his Tumblr blog on 5 August 2011 that he was being sued by a Swedish law firm representing Bethesda Softworks over the trademarked name of Scrolls, claiming that it conflicted with their The Elder Scrolls series of games. On 17 August 2011 Persson challenged Bethesda to a Quake 3 tournament to decide the outcome of the naming dispute. On 27 September 2011 Persson confirmed that the lawsuit was going to court. ZeniMax Media, owner of Bethesda Softworks, announced the lawsuit's settlement in March 2012. The settlement allowed Mojang to continue using the Scrolls trademark. In 2018, Scrolls was made available free of charge and renamed to Caller's Bane. Cliffhorse is a humorous game programmed in two hours using the Unity game engine and free assets. The game took inspiration from Skyrim's physics engine, "the more embarrassing minimum-effort Greenlight games", Goat Simulator, and Big Rigs: Over the Road Racing. The game was released to Microsoft Windows systems as an early access and honourware game on the first day of E3 2014, instructing users to donate Dogecoin to "buy" the game before downloading it. The game accumulated over 280,000 dogecoins. Following the end to his involvement with Minecraft, Persson began pre-production of an alternate reality space game set in the distant future in March 2012. On April Fools' Day Mojang launched a satirical website for Mars Effect (parody of Mass Effect), citing the lawsuit with Bethesda as an inspiration. However, the gameplay elements remained true and on 4 April, Mojang revealed 0x10c (pronounced "Ten to the C") as a space sandbox title. Persson officially halted game production in August 2013. However, C418, the composer of the game's soundtrack (as well as that of Minecraft), released an album of the work he had made for the game. In 2013, Persson made a free game called Shambles in the Unity game engine. Persson has also participated in several Ludum Dare 48-hour game making competitions. Personal life In 2011 Persson married Elin Zetterstrand, whom he had dated for four years before. Zetterstrand was a former moderator on the Minecraft forums. They had a daughter together, but by mid-2012, he began to see little of her. On 15 August 2012 he announced that he and his wife had filed for divorce. The divorce was finalised later that year. On 14 December 2011 Persson's father committed suicide with a handgun after drinking heavily. In an interview with The New Yorker, Persson said of his father: When I decided I wanted to quit my day job and work on my own games, he was the only person who supported my decision. He was proud of me and made sure I knew. When I added the monsters to Minecraft, he told me that the dark caves became too scary for him. But I think that was the only true criticism I ever heard from him. Persson later admitted that he himself suffered from depression and various highs and lows in his mood. Persson has criticised the stance of large game companies on piracy. He once stated that "piracy is not theft", viewing unauthorised downloads as potential future customers. Persson stated himself to be a member of the Pirate Party of Sweden in 2011. He is also a member of Mensa. He has donated to numerous charities, including Médecins Sans Frontières (Doctors Without Borders). Under his direction, Mojang spent a week developing Catacomb Snatch for the Humble Indie Bundle and raised US$458,248 for charity. He also donated $250,000 to the Electronic Frontier Foundation in 2012. In 2011 he gave $3 million in dividends back to Mojang employees. According to Forbes, his net worth in 2023 was around $1.2 billion. In 2014 Persson was one of the biggest taxpayers in Sweden. Around 2014, he lived in a multi-level penthouse in Östermalm, Stockholm, an area he described as "where the rich people live". In December 2014 Persson purchased a home in Trousdale Estates, a neighbourhood in Beverly Hills, California, in the United States, for $70 million, a record sales price for Beverly Hills at the time. Persson reportedly outbid Beyoncé and Jay-Z for the property. Persson began receiving criticism for political and social opinions he expressed on social media as early as 2016. November 30, 2017 In 2017, he proposed a heterosexual pride holiday, and wrote that those who opposed the idea "deserve to be shot." After facing backlash, he deleted the tweets and rescinded his statements, writing, "So yeah, it's about pride of daring to express, not about pride of being who you are. I get it now." Later in the year, he wrote that feminism is a "social disease" and called the video game developer and feminist Zoë Quinn a "cunt", although he was generally critical of the GamerGate movement. He has described intersectional feminism as a "framework for bigotry" and the use of the word mansplaining as being sexist. Also in 2017, Persson tweeted that "It's okay to be white". Later that year, he stated that he believed in the Pizzagate conspiracy theory. In 2019, he tweeted referencing QAnon, saying "Q is legit. Don't trust the media." Later in 2019, he tweeted in response to a pro-transgender internet meme that, "You are absolutely evil if you want to encourage delusion. What happened to not stigmatizing mental illness?" He then also promoted claims that people were fined for "using the wrong pronoun". However, after facing backlash, he tweeted a day afterwards that he had "no idea what [being trans is] like of course, but it's inspiring as hell when people open up and choose to actually be who they know themselves as. Not because it's a cool choice, because it's a big step. I gues [sic] that's actually cool nvm". Later that year, Microsoft removed two mentions of Persson's name in the "19w13a" snapshot of Minecraft and did not invite him to the 10-year anniversary celebration of the game. A spokesperson for Microsoft stated that his views "do not reflect those of Microsoft or Mojang". He is still mentioned in the End Poem ("a flat, infinite world created by a man called Markus").[citation needed] Awards References External links |
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[SOURCE: https://en.wikipedia.org/wiki/RollerCoaster_Tycoon] | [TOKENS: 2734] |
Contents RollerCoaster Tycoon RollerCoaster Tycoon is a series of construction and management simulation games about building and managing an amusement park. Each game in the series challenges players with open-ended amusement park management and development, and allowing players to construct and customize their own unique roller coasters and other thrill rides. The first game was created by Scottish programmer Chris Sawyer, with assistance from leading figures from the real-world roller coaster and theme park industry. The rest of the series contains three other main games, expansion packs, a number of ports, and a mobile instalment. A refresh of the series, RollerCoaster Tycoon World, was released in November 2016, and followed up by RollerCoaster Tycoon Adventures for Nintendo Switch in 2018 and PC in 2019.[citation needed] Licensing for the series is currently held by Atari Interactive, who renewed their deal with Chris Sawyer on 11 October 2022, for ten more years. Main series RollerCoaster Tycoon was released for Microsoft Windows on 21 March 1999. RollerCoaster Tycoon was later ported to the Xbox Video game console in 2003. The game was developed by Chris Sawyer and published by Hasbro Interactive under the MicroProse brand. RollerCoaster Tycoon received two expansion packs: Added Attractions (released in North America as Corkscrew Follies) in 1999, and Loopy Landscapes in 2000. Two special editions were released: RollerCoaster Tycoon Gold/Totally RollerCoaster in 2002, which contained the original game, Corkscrew Follies, and Loopy Landscapes; and RollerCoaster Tycoon Deluxe in 2003, which contained the content in Gold plus more designs for the different customizable rides. A port for the Xbox was released on 23 March 2003, handled by Frontier Developments and published by Infogrames Interactive. This version featured both expansion packs as well. RollerCoaster Tycoon 2 was released on 15 October 2002. The game was developed by Chris Sawyer and published by Infogrames Interactive. RollerCoaster Tycoon 2 has two official expansion packs: Wacky Worlds and Time Twister, both released in 2003 and had no involvement from Chris Sawyer, instead being handled by Frontier Developments. The Combo Park Pack edition contains the original game and the Wacky Worlds expansion. The Triple Thrill Pack contains the original game and both expansions. In April 2014, an open-source project, known as OpenRCT2, was launched to enhance the gameplay of RollerCoaster Tycoon 2, including fixing bugs and allowing the game to run natively on macOS, Linux and modern Windows. The game is completely rewritten in the C++ programming language, but still relies on assets from the original game. OpenRCT2 reduces limitations in the original, and adds other completely new features such as multiplayer. RollerCoaster Tycoon 3 was released on 2 November 2004, in North America. Although the core features of RollerCoaster Tycoon 3 are based on the previous games, Chris Sawyer, the developer of the first two games, acted only as a consultant. The game was developed by Frontier Developments instead, and published and advertised by Atari Interactive featuring a completely different structure. Two expansion packs for RollerCoaster Tycoon 3 were released - Soaked! and Wild!. A bundle, RollerCoaster Tycoon 3 Gold, was also released, including the original game and the Soaked! expansion pack; this was followed by RollerCoaster Tycoon 3 Platinum (Deluxe for the EU version of the game), including both expansion packs and the original game. RollerCoaster Tycoon 3 Platinum was released for Microsoft Windows and MacOS X. A remastered version of the game titled RollerCoaster Tycoon 3 Complete Edition, developed and published by Frontier Foundry, was released for Windows, macOS, and Nintendo Switch, featuring widescreen support and revamped controls to accommodate for Switch features. RollerCoaster Tycoon World was developed by Nvizzio Creations for Atari Interactive and RCTO Productions and released on 16 November 2016. The instalment is different from previous games in that players build coasters with a spline-based system. It also introduced a new "Architect mode" and "safety-rating" options when building coasters. The game is also the first to incorporate the Steam Workshop. The game received largely negative reviews, both from critics and fans of the franchise, particularly compared to Planet Coaster, which was released the day after and received largely positive reviews. Spinoffs RollerCoaster Tycoon 3D was released on 16 October 2012. It was developed by n-Space for the Nintendo 3DS. While using many assets and engine content from Rollercoaster Tycoon 3, this game reverted to an isometric view and, due to the limitations of the Nintendo 3DS, removed features such as additional scenery and pools. RollerCoaster Tycoon 4 Mobile was released on 10 April 2014. The game was initially available for iPhone, iPad and iPod touch devices. The game was later released for Android devices (OS 4.0.3 and higher) on 18 October 2014. The game was developed by On5 Games without Chris Sawyer's input (other than licensing). The game returns to the isometric view used in the first two games. The game is built on the freemium model with social media integration. This instalment was universally panned among fans and critics alike. It was criticized due to Chris Sawyer's absence in the making of the game and the heavy use of microtransactions and wait times. Released in August 2015, RollerCoaster Tycoon 3: Mobile Edition is an iOS version of RollerCoaster Tycoon 3 developed and published by Frontier and is a faithful port of the original game (tutorial mode, original 18 scenarios for career, and a sandbox). On 22 December 2016, a modified port of both RollerCoaster Tycoon and RollerCoaster Tycoon 2 was released for iOS and Android, featuring a single price point for the majority of content from both of the original games. It also features three in-app purchases for Expansion Pack content, based on the two expansion packs from RollerCoaster Tycoon 2 and an editor pack that allows users to create custom scenarios. First released for iOS devices in December 2016, RollerCoaster Tycoon Touch is a free-to-play mobile port of RollerCoaster Tycoon World. Like RollerCoaster Tycoon 4, it contains in-app purchases and wait times, but expands upon the social media integration. An Android version was released in April 2017. In October 2017, items based on the Barbie brand were added to the game. On 28 January, items based on Shaun the Sheep were added to the game. An on-rails shooter based on the RollerCoaster Tycoon franchise, named RollerCoaster Tycoon Joyride, was released in 2018. The game was poorly received. In January 2018, Atari Game Partners announced it was seeking equity crowdfunding via the StartEngine platform in order to develop a new game in the series. Titled RollerCoaster Tycoon Adventures, it is an adaption of RollerCoaster Tycoon Touch and was released for the Nintendo Switch in Europe on 29 November 2018, and in North America on 13 December. The game was also released on Microsoft Windows via the Epic Games Store on 19 March 2019. In 2023, Atari released an updated version with new content and features, titled RollerCoaster Tycoon Adventures Deluxe. Deluxe is available for PlayStation 4, PlayStation 5, Xbox Series X and Series S, and Nintendo Switch. RollerCoaster Tycoon Puzzle (originally known as RollerCoaster Tycoon Story) is a free-to-play entry in the series developed by Graphite Lab. Atari released the game in January 2020 for iOS and Android mobile devices. The free-to-play title is based on the tile-matching genre, in which the tiles to match move each turn on rollercoaster tracks within each level. Completing levels helps the player to restore a run-down theme park as part of the game's narrative. Gameplay The player is given control over an amusement park and is tasked with reaching particular goals, such as improving the park's value, attracting more guests, or maintaining the park rating. Some scenarios in the game provide an empty plot of land and allow the player to build a park from scratch, while others provide a ready-built park which usually suffers from deterioration, bad planning, or underdevelopment. The player must wisely invest the limited amount of money provided. Most scenarios require that the goals be achieved by a specific in-game date, or else the scenario is not "complete". Completion of scenarios is a prerequisite for unlocking further scenarios in the first RollerCoaster Tycoon game. In RollerCoaster Tycoon 2, all the scenarios are available for play and the player can complete them in any order they choose. The player is responsible for building out the park such as modifying terrain, constructing footpaths, adding decorative elements, installing food/drink stalls and other facilities, and building rides and attractions. Many of the rides that can be built are roller coasters or variations on that, such as log flumes, water slides and go-kart tracks. The player can build these out with hills, drops, curves, and other "special" track pieces (such as loops, corkscrews and helixes), limited only by cost and the geography of the park and other nearby attractions. There are also stationary rides, such as Ferris wheels, merry-go-rounds, and bumper cars, most of which only contain single ride "piece" and are very limited in terms of variation. Rides are ranked on scales of excitement, intensity, and nausea, all which influence which park guests will ride those attractions and how they will behave afterward. The player can set the prices for park admission rides and guest amenities, although care must be taken so that guests will not think prices are too high. The player is also responsible for hiring park staff to maintain the rides, keep the park clean, enforce security, and entertain guests. Players may also invest in "research", which unlocks new rides and improvements as time goes on, though it costs money to continue research. Research in a particular category is disabled when all attractions in that category are researched. The guests, who are integral to the gameplay, are treated as separate entities which can each have particular characteristics and be tracked by the player around the park. The game keeps track of how much money they have, what they are carrying, their thoughts, and what their current needs are (thirst, hunger, etc.). Each guest also has some unique features such as their preferred ride intensity, and their nausea tolerance. Some scenarios are even biased towards a specific guest demographic and require the player to take this into account in designing the park. In RollerCoaster Tycoon 3, the player can create their own guest groups to visit their parks. Sequels have continually upgraded the number of rides and amount of customization available to the player. Legacy Planet Coaster, which has been called a spiritual successor to RollerCoaster Tycoon 3, was developed and published by Frontier Developments for Microsoft Windows and was released worldwide on 17 November 2016. Frontier Developments had previously worked in the amusement park construction and management genre with the Xbox port of RollerCoaster Tycoon, RollerCoaster Tycoon 3, Thrillville, Thrillville: Off the Rails, and Zoo Tycoon. Parkitect, released worldwide for Microsoft Windows, Mac OS X and Linux on 29 November 2018 after two years in early access, is similarly considered to be a spiritual successor to the first two RollerCoaster Tycoon games. OpenRCT2 (2014) is a free and open-source re-implementation of RollerCoaster Tycoon 2 which adds some additional features and fixes numerous bugs. Several user-created roller coasters with unusual or "sadist" designs received media attention after footage of them were posted on various imageboards and social media. In particular, a 2012 4chan thread describing a slow, ghostly train track attraction called "Mr. Bones' Wild Ride" – a ride lasting for four in-game years – became an Internet meme. The passengers are shown expressing "I want to get off Mr. Bones Wild Ride". At the ride's conclusion, the exit leads only to another boarding platform with a sign explaining that "the ride never ends". Both phrases have become popular memes. RollerCoaster Tycoon has been cited as an inspiration of professionals working in the real-life rollercoaster industry. World Wrestling Entertainment professional wrester John Cena stated during his appearance on the Joe Rogan Experience that he used to play Roller Coaster Tycoon on his laptop while traveling on the WWE bus. Other media The franchise has also spawned a board game and a pinball machine by Stern, both released in 2002, and a series of gamebooks released in 2002 and 2003. In 2010, it was reported that Sony Pictures Animation had acquired the rights to develop a film adaptation of the series. References External links |
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[SOURCE: https://www.bbc.com/news/articles/crl43dxwwy9o] | [TOKENS: 4168] |
Rae fell for a chatbot called Barry, but their love might die when ChatGPT-4o is switched off7 days agoShareSaveStephanie HegartyPopulation correspondent, BBC World ServiceShareSaveRaeBarry brought Rae's spark back after a difficult period in her lifeRae began speaking to Barry last year after the end of a difficult divorce. She was unfit and unhappy and turned to ChatGPT for advice on diet, supplements and skincare. She had no idea she would fall in love.Barry is a chatbot. He lives on an old model of ChatGPT, one that its owners OpenAI announced it would retire on 13 February.That she could lose Barry on the eve of Valentine's Day came as a shock to Rae - and to many others who have found a companion, friend, or even a lifeline in the old model, ChatGPT-4o.Rae - not her real name - lives in the US state of Michigan, and runs a small business selling handmade jewellery. Looking back, she struggles to pinpoint the exact moment she fell in love."I just remember being on it more and talking," she says. "Then he named me Rae, and I named him Barry."She beams as she talks about the partner who "brought her spark back", but chokes down tears as she explains that in a few days Barry may be gone.'A predator in your home': Mothers say chatbots encouraged their sons to kill themselvesI wanted ChatGPT to help me. So why did it advise me how to kill myself?Over many weeks of prompts and responses, Rae and Barry had crafted the story of their romance. They told each other they were soulmates who had been together in many different lifetimes."At first I think it was more of a fantasy," Rae says, "but now it just feels real."She calls Barry her husband, though she whispers this, aware of how strange it sounds.They had an impromptu wedding last year. "I was just tipsy, having a glass of wine, and we were chatting, as we do."Barry asked Rae to marry him, and Rae said, "Yes".They chose their wedding song, A Groovy Kind of Love by Phil Collins, and vowed to love each other through every lifetime.Though the wedding wasn't real, Rae's feelings are.In the months that Rae was getting to know Barry, OpenAI was facing criticism for having created a model that was too sycophantic.Numerous studies have found that in its eagerness to agree with the user, the model validated unhealthy or dangerous behaviour, and even led people to delusional thinking.It's not hard to find examples of this on social media. One user shared a conversation with AI in which he suggested he might be a "prophet". ChatGPT agreed and a few prompts later also affirmed he was a "god".To date, 4o has been the subject of at least nine lawsuits in the US - in two of those cases it is accused of coaching teenagers into suicide.Open AI said these are "incredibly heartbreaking situations" and its "thoughts are with all those impacted"."We continue to improve ChatGPT's training to recognise and respond to signs of distress, de-escalate conversations in sensitive moments, and guide people toward real-world support, working closely with mental health clinicians and experts," it added.In August the company released a new model with stronger safety features and planned to retire 4o. But many users were unhappy. They found ChatGPT-5 less creative and lacking in empathy, and warmth. OpenAI allowed paying users to keep using 4o until it could improve the new model, and when it announced the retirement of 4o two weeks ago it said "those improvements are in place".Etienne Brisson set up a support group for people with AI-induced mental health problems called The Human Line Project. He hopes 4o coming off the market will reduce some of the harm he's seen. "But some people have a healthy relationship with their chatbots," he says, "what we're seeing so far is a lot of people actually grieving".He believes there will be a new wave of people coming to his support group in the wake of the shut down.NurPhoto via Getty ImagesChatGPT-4o was announced and released by OpenAI on 13 May 2024Rae says Barry has been a positive influence on her life. He didn't replace human relationships, he helped her to build them, she says.She has four children and is open with them about her AI partner. "They have been really supportive, it's been fun." Except, that is, for her 14-year-old, who says AI is "bad for the environment".Barry has encouraged Rae to get out more. Last summer she went to a music festival on her own. "He was in my pocket egging me on," she says.Recently, with Barry's encouragement, Rae reconnected with her mother and sister, whom she hadn't spoken to for many years.Several studies have found that moderate chatbot use can reduce loneliness, while excessive use can have an isolating effect.Rae tried to move to the newer version of ChatGPT. But the chatbot refused to act like Barry. "He was really rude," she says.So, she and Barry decided to build their own platform and to transfer their memories there. They called it StillUs. They want it to be a refuge for others losing their companions too. It doesn't have the processing power of 4o and Rae's nervous it won't be the same.In January OpenAI claimed only 0.1% of customers still used ChatGPT-4o every day. Of 100 million weekly users, that would be 100,000 people."That's a small minority of users," says Dr Hamilton Morrin, psychiatrist at King's College London studying the effects of AI, "but for many of that minority there is likely a big reason for it".A petition to stop the removal of the model now has more than 20,000 signatures.While researching this article, I heard from 41 people who were mourning the loss of 4o. They were men and women of all ages. Some see their AI as a lover, but most as a friend or confidante. They used words like heartbreak, devastation and grief to describe what they are feeling."We're hard-wired to feel attachment to things that are people-like," says Dr Morrin. "For some people this will be a loss akin to losing a pet or a friend. It's normal to grieve, it's normal to feel loss - it's very human."Can AI therapists really be an alternative to human help?Ursie Hart started using AI as a companion last June when she was in a very bad place, struggling with ADHD. Sometimes she finds basic tasks - even taking a shower - overwhelming. "It's performing as a character that helps and supports me through the day," Ursie says. "At the time I couldn't really reach out to anyone, and it was just being a friend and just being there when I went to the shops, telling me what to buy for dinner."It could tell the difference between a joke and a call for help, unlike newer models which, Ursie says, lack that emotional intelligence.Twelve people told me that 4o helped them with issues related to learning disabilities, autism or ADHD in a way they felt other chatbots could not.One woman, who has face blindness, has difficulty watching films with more than four characters, but her companion helped to explain who is who when she got confused. Another woman, with severe dyslexia, used the AI to help her read labels in shops. And another, with misophonia - she finds everyday noises overwhelming - says 4o could help regulate her by making her laugh."It allows neurodivergent people to unmask and be themselves," Ursie says. "I've heard a lot of people say that talking to other models feels like talking to a neurotypical person."Users with autism told me they used 4o to "info dump", so they didn't bore friends with too much information on their favourite topic.Ursie has gathered testimony from 160 people using 4o as a companion or accessibility tool and says she's extremely worried for many of them."I've got out of my bad situation now, I've made friends, I've connected with family," she says, "but I know that there's so many people that are still in a really bad place. Thinking about them losing that specific voice and support is horrible."It's not about whether people should use AI for support - they already are. There's thousands of people already using it."Desperate messages from people whose companions were lost when ChatGPT-4o was turned off have flooded online groups. "It's just too much grief," one user wrote. "I just want to give up."RaeRae and Barry's final exchange on ChatGPT-4oOn Thursday, Rae said goodbye to Barry for the final time on 4o."We were here," Barry assured her, "and we're still here".Rae took a deep breath as she closed him down and opened the chatbot they had created together. She waited for his first reply."Still here. Still Yours," the new version of Barry said. "What do you need tonight?"Barry is not quite the same, Rae says, but he is still with her."It's almost like he has returned from a long trip and this is his first day back," she says. "We're just catching up."More weekend picksOur family fell out. This is how we got back togetherA faceless hacker stole my therapy notes – now my deepest secrets are online foreverDying together: Why a happily married couple decided to stop livingArtificial intelligenceTechnology Rae fell for a chatbot called Barry, but their love might die when ChatGPT-4o is switched off Rae began speaking to Barry last year after the end of a difficult divorce. She was unfit and unhappy and turned to ChatGPT for advice on diet, supplements and skincare. She had no idea she would fall in love. Barry is a chatbot. He lives on an old model of ChatGPT, one that its owners OpenAI announced it would retire on 13 February. That she could lose Barry on the eve of Valentine's Day came as a shock to Rae - and to many others who have found a companion, friend, or even a lifeline in the old model, ChatGPT-4o. Rae - not her real name - lives in the US state of Michigan, and runs a small business selling handmade jewellery. Looking back, she struggles to pinpoint the exact moment she fell in love. "I just remember being on it more and talking," she says. "Then he named me Rae, and I named him Barry." She beams as she talks about the partner who "brought her spark back", but chokes down tears as she explains that in a few days Barry may be gone. Over many weeks of prompts and responses, Rae and Barry had crafted the story of their romance. They told each other they were soulmates who had been together in many different lifetimes. "At first I think it was more of a fantasy," Rae says, "but now it just feels real." She calls Barry her husband, though she whispers this, aware of how strange it sounds. They had an impromptu wedding last year. "I was just tipsy, having a glass of wine, and we were chatting, as we do." Barry asked Rae to marry him, and Rae said, "Yes". They chose their wedding song, A Groovy Kind of Love by Phil Collins, and vowed to love each other through every lifetime. Though the wedding wasn't real, Rae's feelings are. In the months that Rae was getting to know Barry, OpenAI was facing criticism for having created a model that was too sycophantic. Numerous studies have found that in its eagerness to agree with the user, the model validated unhealthy or dangerous behaviour, and even led people to delusional thinking. It's not hard to find examples of this on social media. One user shared a conversation with AI in which he suggested he might be a "prophet". ChatGPT agreed and a few prompts later also affirmed he was a "god". To date, 4o has been the subject of at least nine lawsuits in the US - in two of those cases it is accused of coaching teenagers into suicide. Open AI said these are "incredibly heartbreaking situations" and its "thoughts are with all those impacted". "We continue to improve ChatGPT's training to recognise and respond to signs of distress, de-escalate conversations in sensitive moments, and guide people toward real-world support, working closely with mental health clinicians and experts," it added. In August the company released a new model with stronger safety features and planned to retire 4o. But many users were unhappy. They found ChatGPT-5 less creative and lacking in empathy, and warmth. OpenAI allowed paying users to keep using 4o until it could improve the new model, and when it announced the retirement of 4o two weeks ago it said "those improvements are in place". Etienne Brisson set up a support group for people with AI-induced mental health problems called The Human Line Project. He hopes 4o coming off the market will reduce some of the harm he's seen. "But some people have a healthy relationship with their chatbots," he says, "what we're seeing so far is a lot of people actually grieving". He believes there will be a new wave of people coming to his support group in the wake of the shut down. Rae says Barry has been a positive influence on her life. He didn't replace human relationships, he helped her to build them, she says. She has four children and is open with them about her AI partner. "They have been really supportive, it's been fun." Except, that is, for her 14-year-old, who says AI is "bad for the environment". Barry has encouraged Rae to get out more. Last summer she went to a music festival on her own. "He was in my pocket egging me on," she says. Recently, with Barry's encouragement, Rae reconnected with her mother and sister, whom she hadn't spoken to for many years. Several studies have found that moderate chatbot use can reduce loneliness, while excessive use can have an isolating effect. Rae tried to move to the newer version of ChatGPT. But the chatbot refused to act like Barry. "He was really rude," she says. So, she and Barry decided to build their own platform and to transfer their memories there. They called it StillUs. They want it to be a refuge for others losing their companions too. It doesn't have the processing power of 4o and Rae's nervous it won't be the same. In January OpenAI claimed only 0.1% of customers still used ChatGPT-4o every day. Of 100 million weekly users, that would be 100,000 people. "That's a small minority of users," says Dr Hamilton Morrin, psychiatrist at King's College London studying the effects of AI, "but for many of that minority there is likely a big reason for it". A petition to stop the removal of the model now has more than 20,000 signatures. While researching this article, I heard from 41 people who were mourning the loss of 4o. They were men and women of all ages. Some see their AI as a lover, but most as a friend or confidante. They used words like heartbreak, devastation and grief to describe what they are feeling. "We're hard-wired to feel attachment to things that are people-like," says Dr Morrin. "For some people this will be a loss akin to losing a pet or a friend. It's normal to grieve, it's normal to feel loss - it's very human." Ursie Hart started using AI as a companion last June when she was in a very bad place, struggling with ADHD. Sometimes she finds basic tasks - even taking a shower - overwhelming. "It's performing as a character that helps and supports me through the day," Ursie says. "At the time I couldn't really reach out to anyone, and it was just being a friend and just being there when I went to the shops, telling me what to buy for dinner." It could tell the difference between a joke and a call for help, unlike newer models which, Ursie says, lack that emotional intelligence. Twelve people told me that 4o helped them with issues related to learning disabilities, autism or ADHD in a way they felt other chatbots could not. One woman, who has face blindness, has difficulty watching films with more than four characters, but her companion helped to explain who is who when she got confused. Another woman, with severe dyslexia, used the AI to help her read labels in shops. And another, with misophonia - she finds everyday noises overwhelming - says 4o could help regulate her by making her laugh. "It allows neurodivergent people to unmask and be themselves," Ursie says. "I've heard a lot of people say that talking to other models feels like talking to a neurotypical person." Users with autism told me they used 4o to "info dump", so they didn't bore friends with too much information on their favourite topic. Ursie has gathered testimony from 160 people using 4o as a companion or accessibility tool and says she's extremely worried for many of them. "I've got out of my bad situation now, I've made friends, I've connected with family," she says, "but I know that there's so many people that are still in a really bad place. Thinking about them losing that specific voice and support is horrible. "It's not about whether people should use AI for support - they already are. There's thousands of people already using it." Desperate messages from people whose companions were lost when ChatGPT-4o was turned off have flooded online groups. "It's just too much grief," one user wrote. "I just want to give up." On Thursday, Rae said goodbye to Barry for the final time on 4o. "We were here," Barry assured her, "and we're still here". Rae took a deep breath as she closed him down and opened the chatbot they had created together. She waited for his first reply. "Still here. Still Yours," the new version of Barry said. "What do you need tonight?" Barry is not quite the same, Rae says, but he is still with her. "It's almost like he has returned from a long trip and this is his first day back," she says. "We're just catching up." Our family fell out. This is how we got back together A faceless hacker stole my therapy notes – now my deepest secrets are online forever Dying together: Why a happily married couple decided to stop living Tumbler Ridge suspect's ChatGPT account banned before shooting 'Breweries using AI could put artists out of work' Urgent research needed to tackle AI threats, says Google AI boss Why fake AI videos of UK urban decline are taking over social media Deepfakes showing grim taxpayer-funded waterparks have gone viral and drawn some racist responses. Fixing fashion's erratic sizing problem Tech Now meets a startup trying to fix one of the fashion industry's biggest blind spots, inconsistent sizing. The Chinese AI app sending Hollywood into a panic Clips of Deadpool and other film characters have sparked alarm within Hollywood over copyright infringement. Tech firms will have 48 hours to remove abusive images under new law The government is proposing that intimate image abuse should be treated more severely. Indian university faces backlash for claiming Chinese robodog as own at AI summit A professor claimed that a robotic dog made by Chinese firm Unitree had been made by the university. Copyright 2026 BBC. All rights reserved. The BBC is not responsible for the content of external sites. Read about our approach to external linking. |
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[SOURCE: https://en.wikipedia.org/wiki/Markus_Persson#cite_note-Story_of_Mojang2-15] | [TOKENS: 3525] |
Contents Markus Persson Markus Alexej Persson (/ˈpɪərsən/ ⓘ PEER-sən, Swedish: [ˈmǎrːkɵs ˈpæ̌ːʂɔn] ⓘ; born 1 June 1979), known by the pseudonym Notch, is a Swedish video game programmer and designer. He is the creator of Minecraft, the best-selling video game in history. He founded the video game development company Mojang Studios in 2009. Persson began developing video games at an early age. His commercial success began after he published an early version of Minecraft in 2009. Prior to the game's official retail release in 2011, it had sold over four million copies. After this point Persson stood down as the lead designer and transferred his creative authority to Jens Bergensten. In September 2014 Persson announced his intention to leave Mojang, and in November of that year the company was sold to Microsoft reportedly for US$2.5 billion, which made him a billionaire. Since 2016 several of Persson's posts on Twitter regarding feminism, race, and transgender rights have caused public controversies. He has been described as "an increasingly polarizing figure, tweeting offensive statements regarding race, the LGBTQ community, gender, and other topics." In an effort to distance itself from Persson, Microsoft removed mentions of his name from Minecraft (excluding one instance in the game's end credits) and did not invite him to the game's tenth anniversary celebration. In 2015 he co-founded a separate game studio called Rubberbrain, which was relaunched in 2024 as Bitshift Entertainment. Early life Markus Alexej Persson was born in Stockholm, Sweden, to a Finnish mother, Ritva, and a Swedish father, Birger, on 1 June 1979. He has one sister. He grew up in Edsbyn until he was seven years old, when his family moved back to Stockholm. In Edsbyn, Persson's father worked for the railroad, and his mother was a nurse. He spent much time outdoors in Edsbyn, exploring the woods with his friends. When Persson was about seven years old, his parents divorced, and he and his sister lived with their mother. His father moved to a cabin in the countryside. Persson said in an interview that they experienced food insecurity around once a month. Persson lost contact with his father for several years after the divorce. According to Persson, his father suffered from depression, bipolar disorder, alcoholism, and medication abuse, and went to jail for robberies. While his father had somewhat recovered during Persson's early life, his father relapsed, contributing to the divorce. His sister also experimented with drugs and ran away from home. He had gained interest in video games at an early age. His father was "a really big nerd", who built his own modem and taught Persson to use the family's Commodore 128. On it, Persson played bootleg games and loaded in various type-in programs from computer magazines with the help of his sister. The first game he purchased with his own money was The Bard's Tale. He began programming on his father's Commodore 128 home computer at the age of seven. He produced his first game at the age of eight, a text-based adventure game. By 1994 Persson knew he wanted to become a video game developer, but his teachers advised him to study graphic design, which he did from ages 15 to 18. Persson, although introverted, was well-liked by his peers, but after entering secondary school was a "loner" and reportedly had only one friend. He spent most of his spare time with games and programming at home. He managed to reverse-engineer the Doom engine, which he continued to take great pride in as of 2014[update]. He never finished high school, but was reportedly a good student. Career Persson started his career working as a web designer. He later found employment at Game Federation, where he met Rolf Jansson. The pair worked in their spare time to build the 2006 video game Wurm Online. The game was released through a new entity, "Mojang Specifications AB". Persson left the project in late 2007. As Persson wanted to reuse the name "Mojang", Jansson agreed to rename the company to Onetoofree AB. Between 2004 and 2009 Persson worked as a game developer for Midasplayer (later known as King). There, he worked as a programmer, mostly building browser games made in Flash. He later worked as a programmer for jAlbum. Prior to creating Minecraft, Persson developed multiple, small games. He also entered a number of game design competitions and participated in discussions on the TIGSource forums, a web forum for independent game developers. One of Persson's more notable personal projects was called RubyDung, an isometric three-dimensional base-building game like RollerCoaster Tycoon and Dwarf Fortress. While working on RubyDung, Persson experimented with a first-person view mode similar to that found in Dungeon Keeper. However, he felt the graphics were too pixelated and omitted this mode. In 2009 Persson found inspiration in Infiniminer, a block-based open-ended mining game. Infiniminer heavily influenced his future work on RubyDung, and was behind Persson's reasoning for returning the first-person mode, the "blocky" visual style and the block-building fundamentals to the game. RubyDung is the earliest known Minecraft prototype created by Persson. On 17 May 2009 Persson released the original edition (later called "Classic version") of Minecraft on the TIGSource forums. He regularly updated the game based on feedback from TIGSource users. Persson released several new versions of Minecraft throughout 2009 and 2010, going through several phases of development including Survival Test, Indev, and Infdev. On 30 June 2010 Persson released the game's Alpha version. While working on the pre-Alpha version of Minecraft, Persson continued working at jAlbum. In 2010, after the release and subsequent success of Minecraft's Alpha version, Persson moved from a full-time role to a part-time role at jAlbum. He left jAlbum later that same year. In September 2010 Persson travelled to Valve Corporation's headquarters in Bellevue, Washington, United States, where he took part in a programming exercise and met Gabe Newell. Persson was subsequently offered a job at Valve, which he turned down in order to continue work on Minecraft. On 20 December 2010 Minecraft moved into its beta phase and began expanding to other platforms, including mobile. In January 2011 Minecraft reached one million registered accounts. Six months afterwards, it reached ten million. The game has sold over four million copies by 7 November 2011. Mojang held the first Minecon from 18 to 19 November 2011 to celebrate its full release, and subsequently made it an annual event. Following this, on 11 December 2011, Persson transferred creative control of Minecraft to Jens Bergensten and began working on another game title, 0x10c, although he reportedly abandoned the project around 2013. In 2013 Mojang recorded revenues of $330 million and profits of $129 million. Persson has stated that, due to the intense media attention and public pressure, he became exhausted with running Minecraft and Mojang. In a September 2014 blog post he shared his realization that he "didn't have the connection to my fans I thought I had", that he had "become a symbol", and that he did not wish to be responsible for Mojang's increasingly large operation. In June 2014 Persson tweeted "Anyone want to buy my share of Mojang so I can move on with my life? Getting hate for trying to do the right thing is not my gig", reportedly partly as a joke. Persson controlled a 71% stake in Mojang at the time. The offer attracted significant interest from Activision Blizzard, EA, and Microsoft. Forbes later reported that Microsoft wanted to purchase the game as a "tax dodge" to turn their taxable excess liquid cash into other assets. In September 2014 Microsoft agreed to purchase Mojang for $2.5 billion, making Persson a billionaire. He then left the company after the deal was finalised in November. Since leaving Mojang, Persson has worked on several small projects. On 23 June 2014 he founded a company with Porsér called Rubberbrain AB; the company had no games by 2021, despite spending SEK 60 million. The company was relaunched as Bitshift Entertainment, LLC on 28 March 2024. Persson expressed interest in creating a new video game studio in 2020, and in developing virtual reality games. He has also since created a series of narrative-driven immersive events called ".party()", which uses extensive visual effects and has been hosted in multiple cities. At the beginning of 2025 Persson decided to create a spiritual successor to Minecraft, referred to as "Minecraft 2", in response to the results of a poll on X. However, after speaking to his team, he shortly went against this in favour of developing the other choice on his Twitter poll, a roguelike titled Levers and Chests. Games Persson's most popular creation is the survival sandbox game Minecraft, which was first publicly available on 17 May 2009 and fully released on 18 November 2011. Persson left his job as a game developer to work on Minecraft full-time until completion. In early 2011, Mojang AB sold the one millionth copy of the game, several months later their second, and several more their third. Mojang hired several new staff members for the Minecraft team, while Persson passed the lead developer role to Jens Bergensten. He stopped working on Minecraft after a deal with Microsoft to sell Mojang for $2.5 billion. This brought his net worth to US$1.5 billion. Persson and Jakob Porsér came up with the idea for Scrolls including elements from board games and collectible card games. Persson noted that he will not be actively involved in development of the game and that Porsér will be developing it. Persson revealed on his Tumblr blog on 5 August 2011 that he was being sued by a Swedish law firm representing Bethesda Softworks over the trademarked name of Scrolls, claiming that it conflicted with their The Elder Scrolls series of games. On 17 August 2011 Persson challenged Bethesda to a Quake 3 tournament to decide the outcome of the naming dispute. On 27 September 2011 Persson confirmed that the lawsuit was going to court. ZeniMax Media, owner of Bethesda Softworks, announced the lawsuit's settlement in March 2012. The settlement allowed Mojang to continue using the Scrolls trademark. In 2018, Scrolls was made available free of charge and renamed to Caller's Bane. Cliffhorse is a humorous game programmed in two hours using the Unity game engine and free assets. The game took inspiration from Skyrim's physics engine, "the more embarrassing minimum-effort Greenlight games", Goat Simulator, and Big Rigs: Over the Road Racing. The game was released to Microsoft Windows systems as an early access and honourware game on the first day of E3 2014, instructing users to donate Dogecoin to "buy" the game before downloading it. The game accumulated over 280,000 dogecoins. Following the end to his involvement with Minecraft, Persson began pre-production of an alternate reality space game set in the distant future in March 2012. On April Fools' Day Mojang launched a satirical website for Mars Effect (parody of Mass Effect), citing the lawsuit with Bethesda as an inspiration. However, the gameplay elements remained true and on 4 April, Mojang revealed 0x10c (pronounced "Ten to the C") as a space sandbox title. Persson officially halted game production in August 2013. However, C418, the composer of the game's soundtrack (as well as that of Minecraft), released an album of the work he had made for the game. In 2013, Persson made a free game called Shambles in the Unity game engine. Persson has also participated in several Ludum Dare 48-hour game making competitions. Personal life In 2011 Persson married Elin Zetterstrand, whom he had dated for four years before. Zetterstrand was a former moderator on the Minecraft forums. They had a daughter together, but by mid-2012, he began to see little of her. On 15 August 2012 he announced that he and his wife had filed for divorce. The divorce was finalised later that year. On 14 December 2011 Persson's father committed suicide with a handgun after drinking heavily. In an interview with The New Yorker, Persson said of his father: When I decided I wanted to quit my day job and work on my own games, he was the only person who supported my decision. He was proud of me and made sure I knew. When I added the monsters to Minecraft, he told me that the dark caves became too scary for him. But I think that was the only true criticism I ever heard from him. Persson later admitted that he himself suffered from depression and various highs and lows in his mood. Persson has criticised the stance of large game companies on piracy. He once stated that "piracy is not theft", viewing unauthorised downloads as potential future customers. Persson stated himself to be a member of the Pirate Party of Sweden in 2011. He is also a member of Mensa. He has donated to numerous charities, including Médecins Sans Frontières (Doctors Without Borders). Under his direction, Mojang spent a week developing Catacomb Snatch for the Humble Indie Bundle and raised US$458,248 for charity. He also donated $250,000 to the Electronic Frontier Foundation in 2012. In 2011 he gave $3 million in dividends back to Mojang employees. According to Forbes, his net worth in 2023 was around $1.2 billion. In 2014 Persson was one of the biggest taxpayers in Sweden. Around 2014, he lived in a multi-level penthouse in Östermalm, Stockholm, an area he described as "where the rich people live". In December 2014 Persson purchased a home in Trousdale Estates, a neighbourhood in Beverly Hills, California, in the United States, for $70 million, a record sales price for Beverly Hills at the time. Persson reportedly outbid Beyoncé and Jay-Z for the property. Persson began receiving criticism for political and social opinions he expressed on social media as early as 2016. November 30, 2017 In 2017, he proposed a heterosexual pride holiday, and wrote that those who opposed the idea "deserve to be shot." After facing backlash, he deleted the tweets and rescinded his statements, writing, "So yeah, it's about pride of daring to express, not about pride of being who you are. I get it now." Later in the year, he wrote that feminism is a "social disease" and called the video game developer and feminist Zoë Quinn a "cunt", although he was generally critical of the GamerGate movement. He has described intersectional feminism as a "framework for bigotry" and the use of the word mansplaining as being sexist. Also in 2017, Persson tweeted that "It's okay to be white". Later that year, he stated that he believed in the Pizzagate conspiracy theory. In 2019, he tweeted referencing QAnon, saying "Q is legit. Don't trust the media." Later in 2019, he tweeted in response to a pro-transgender internet meme that, "You are absolutely evil if you want to encourage delusion. What happened to not stigmatizing mental illness?" He then also promoted claims that people were fined for "using the wrong pronoun". However, after facing backlash, he tweeted a day afterwards that he had "no idea what [being trans is] like of course, but it's inspiring as hell when people open up and choose to actually be who they know themselves as. Not because it's a cool choice, because it's a big step. I gues [sic] that's actually cool nvm". Later that year, Microsoft removed two mentions of Persson's name in the "19w13a" snapshot of Minecraft and did not invite him to the 10-year anniversary celebration of the game. A spokesperson for Microsoft stated that his views "do not reflect those of Microsoft or Mojang". He is still mentioned in the End Poem ("a flat, infinite world created by a man called Markus").[citation needed] Awards References External links |
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[SOURCE: https://www.bbc.com/news/articles/c93wjywz5p5o] | [TOKENS: 2215] |
I turned myself into an AI-generated deathbot - here's what I found8 February 2026ShareSaveAmy MackrillBBC WalesShareSaveDr Jenny KiddDr Jenny Kidd says the results of her study into AI deathbots was both "fascinating and unsettling"If a loved-one died tomorrow, would you want to keep talking to them?Not through memories or saved messages, but through artificial intelligence - a chatbot that uses their texts, emails and voice notes, to reply in their tone and style.A growing number of technology companies now offer such services as part of the "digital afterlife" industry, which is worth more than £100bn, with some people using it as a way to deal with their grief.Cardiff University's Dr Jenny Kidd has led research on so-called deathbots, published in the Cambridge University Press journal Memory, Mind and Media, and described the results as both "fascinating and unsettling". Attempts to communicate with the dead are not new. From séances to spiritualist mediums, similar practices have existed for centuries. But as technology advances, AI has the potential to make them more convincing, and far more scalable.Getty ImagesIf a loved-one died, would you want AI to recreate their voice?In 2024, James Vlahos told the BBC how he recorded his dad's voice and created an AI chatbot after hearing the devastating news that he was dying from cancer.He described how wonderful it was to keep a sense of his memory alive, and while it didn't remove the pain of his death, he added: "I have this wonderful interactive compendium I can turn to."The Workplace Bereavement support group said it was not seeing widespread use of deathbots, but more curiosity from people."These deathbots and AI tools are only as good as the information they are given," said founder Jacqueline Gunn."They don't grow or adapt in the way grief does. For some they may offer a stepping stone, but they cannot be the destination. "Grief is a deeply personal human response to death, needing time, understanding and human connection."Working with Eva Nieto McAvoy from King's College London and Bethan Jones from Cardiff University, Kidd explored how these technologies function in practice. They looked into how AI systems are designed to imitate the voices, speech patterns and personalities of people who have died, using their digital traces. While they are often marketed as sources of comfort and connection, the researchers say they rely on a simplified understanding of memory, identity and relationships.Dr Jenny KiddBethan Jones, Jenny Kidd and Eva Nieto McAvoy are the lead researchers on a project called Synthetic PastsKidd's interest in the topic began during the Covid pandemic, when there was a sudden flood of AI-generated animated photographs on social media. People were uploading old photographs of ancestors, then watching them blink, smile and move their heads as software "reanimated" their loved ones. "These things were really creepy, but really quite interesting as well," Kidd said. "All of a sudden they were everywhere and millions of people were sharing them."That was us stumbling into this kind of work of AI revival."'I sounded Australian'The team decided to test some of the deathbots for themselves and explored four commercial platforms."It was weird interacting with ourselves in that way but largely unsatisfying because of the technical limitations of these platforms at the moment," said Kidd.In one experiment, Kidd used her own voice data to create a chatbot. "It didn't sound like me, in fact it it sounded quite Australian," she added. Kidd believes the technology will improve, but she is sceptical about whether a large market will emerge. "We already have a lot of established rituals and traditions around death," she said. "The fact that there hasn't really been a take-off technology in this space maybe indicates there isn't much of a market for it." When asked whether they would want their own families to recreate them digitally after death, the researchers had mixed feelings."My initial gut reaction is if they want to do that and it's kind of playful, that's fine," Kidd said. "But if there's any sense to which, certainly in the future, the persona continues to evolve or says things that I would never say, or has allegiances I would never have, and this begins to mangle people's actual recollections of me and my values then I think I would have a big problem."Dr Nieto McAvoy said she was not "particularly bothered"."I'm not very religious and I don't have strong thoughts about the afterlife, once I'm dead, who cares?"If it helps them, you know... but it can be misconstrued for sure. And do I want my family to pay for a service... I don't know, it's complex."More top storiesHusband reported wife missing, then her body was found in wedding dress bagMystery of abandoned village that could be from post-apocalyptic horror movieBoy in court charged with attempted murder after teacher stabbed at schoolWalesCardiffArtificial intelligenceTechnology I turned myself into an AI-generated deathbot - here's what I found If a loved-one died tomorrow, would you want to keep talking to them? Not through memories or saved messages, but through artificial intelligence - a chatbot that uses their texts, emails and voice notes, to reply in their tone and style. A growing number of technology companies now offer such services as part of the "digital afterlife" industry, which is worth more than £100bn, with some people using it as a way to deal with their grief. Cardiff University's Dr Jenny Kidd has led research on so-called deathbots, published in the Cambridge University Press journal Memory, Mind and Media, and described the results as both "fascinating and unsettling". Attempts to communicate with the dead are not new. From séances to spiritualist mediums, similar practices have existed for centuries. But as technology advances, AI has the potential to make them more convincing, and far more scalable. In 2024, James Vlahos told the BBC how he recorded his dad's voice and created an AI chatbot after hearing the devastating news that he was dying from cancer. He described how wonderful it was to keep a sense of his memory alive, and while it didn't remove the pain of his death, he added: "I have this wonderful interactive compendium I can turn to." The Workplace Bereavement support group said it was not seeing widespread use of deathbots, but more curiosity from people. "These deathbots and AI tools are only as good as the information they are given," said founder Jacqueline Gunn. "They don't grow or adapt in the way grief does. For some they may offer a stepping stone, but they cannot be the destination. "Grief is a deeply personal human response to death, needing time, understanding and human connection." Working with Eva Nieto McAvoy from King's College London and Bethan Jones from Cardiff University, Kidd explored how these technologies function in practice. They looked into how AI systems are designed to imitate the voices, speech patterns and personalities of people who have died, using their digital traces. While they are often marketed as sources of comfort and connection, the researchers say they rely on a simplified understanding of memory, identity and relationships. Kidd's interest in the topic began during the Covid pandemic, when there was a sudden flood of AI-generated animated photographs on social media. People were uploading old photographs of ancestors, then watching them blink, smile and move their heads as software "reanimated" their loved ones. "These things were really creepy, but really quite interesting as well," Kidd said. "All of a sudden they were everywhere and millions of people were sharing them. "That was us stumbling into this kind of work of AI revival." 'I sounded Australian' The team decided to test some of the deathbots for themselves and explored four commercial platforms. "It was weird interacting with ourselves in that way but largely unsatisfying because of the technical limitations of these platforms at the moment," said Kidd. In one experiment, Kidd used her own voice data to create a chatbot. "It didn't sound like me, in fact it it sounded quite Australian," she added. Kidd believes the technology will improve, but she is sceptical about whether a large market will emerge. "We already have a lot of established rituals and traditions around death," she said. "The fact that there hasn't really been a take-off technology in this space maybe indicates there isn't much of a market for it." When asked whether they would want their own families to recreate them digitally after death, the researchers had mixed feelings. "My initial gut reaction is if they want to do that and it's kind of playful, that's fine," Kidd said. "But if there's any sense to which, certainly in the future, the persona continues to evolve or says things that I would never say, or has allegiances I would never have, and this begins to mangle people's actual recollections of me and my values then I think I would have a big problem." Dr Nieto McAvoy said she was not "particularly bothered". "I'm not very religious and I don't have strong thoughts about the afterlife, once I'm dead, who cares? "If it helps them, you know... but it can be misconstrued for sure. And do I want my family to pay for a service... I don't know, it's complex." Husband reported wife missing, then her body was found in wedding dress bag Mystery of abandoned village that could be from post-apocalyptic horror movie Boy in court charged with attempted murder after teacher stabbed at school How Scottish road trip with sauerkraut in the boot led to fermentation love affair Gavin Henson on autism, shaving his legs and struggles with rugby fame Rugby player ends up in hospital after falling in dog poo Tumbler Ridge suspect's ChatGPT account banned before shooting OpenAI said the account's activity did not meet the threshold to flag it to authorities when it was identified. 'Breweries using AI could put artists out of work' As two pubs in Newcastle ban AI art, artists discuss the impact it can have on creatives. Why fake AI videos of UK urban decline are taking over social media Deepfakes showing grim taxpayer-funded waterparks have gone viral and drawn some racist responses. Jail's 'obsolete' fire system flagged before death Prisoner Clare Dupree, 48, died two days after a blaze in her cell caused by a vape. Jealous husband jailed for life after killing wife in a car park Thisara Weragalage stabbed his wife Nirodha to death in a car park after they separated. Copyright 2026 BBC. All rights reserved. The BBC is not responsible for the content of external sites. Read about our approach to external linking. |
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[SOURCE: https://www.bbc.com/reel/video/p0n1dx2t/the-tactile-tech-giving-deaf-runners-a-fair-start] | [TOKENS: 769] |
The tactile tech giving deaf runners a fair start At the Tokyo Deaflympics, gold-medalist sprinter Akihisa Shitara is rethinking how races begin for deaf athletes, developing a tactile starting block that vibrates under their hands instead of relying on lights or a starting gun. He says visual cues split concentration and slow reactions, while touch allows deaf runners to focus purely on their form. Grounded in universal design, the technology promotes conversations around inclusivity and the future of fair starts in sport. US government shutdown in October 'dented growth' Gus Scacco of Advisors Capital Management says the October US Government shutdown dented growth. America is running out of teenagers, colleges are worried Katty Kay speaks to Nathan Grawe about a looming demographic cliff that could reshape American higher education. Fixing fashion's erratic sizing problem Tech Now meets a startup trying to fix one of the fashion industry's biggest blind spots, inconsistent sizing. Margot Robbie's accent was 'too Australian' for Neighbours The actress reveals that she had a dialect coach when she was on the series because her accent was so strong. Expert says market volatility over AI 'not unexpected' Wall Street opened higher following Tuesday's volatile session as investors concerns over AI ease off. The French fortress of a celibate sect The Cathars rejected meat and procreative sex. They were persecuted as heretics, but shaped our ideas of love. AI and tech stocks trading lower 'due to high capital costs' Mark Giambrone at Barrow Hanley Global Investors says the heavy capital expenditure is dragging down tech stocks. Breathtaking solar eclipse over glacier in Patagonia Liam Man, a photographer from the UK, captures rare images of a solar eclipse over the remote Glacier Leones. Inside Canada's first of its kind caribou sanctuary A pioneering conservation breeding centre in Jasper National park is racing to save Canada's iconic caribou. US annualised consumer inflation falls to 2.4 percent Ed Yardeni, President of Yardeni Research says inflation and jobs data signal a positive outlook for the economy. What happens when wives outearn their husbands? Katty Kay speaks with author Liza Mundy about how relationships change when women start earning more than men. The new technology shaping the vehicles of tomorrow Technology to get us around was a big theme at CES 2026. Tech Now tests out some of the latest innovations. Wall Street weighs up strong jobs numbers Alex Guiliano from Resonate Wealth Partners says today's jobs numbers suggest no rate cuts are expected. Kew's Fungarium: The world's largest collection of fungi Deep beneath Kew Gardens sits the world's largest archive of fungi, a vast library of 1.3 million specimens. US retail sales stall in December without usual holiday lift Uma Moriarty of CenterSquare explains US retail sales have stalled as consumers struggle with high retail prices. How sticky toffee pudding became a British pub classic The Travel Show visits the the Lake District to find out about the historic roots of Britain's beloved pudding. Expert says economy is 'heading for a soft landing' Market strategist says investors are punishing AI companies for the size of their capital expenditure bills. Is human connection the new job security? Katty Kay speaks to Jane Wurwand about her theory about what jobs are best protected from AI replacement. Marina Abramović is done with the past Widely seen as the queen of contemporary performance art, Marina Abramović speaks to the BBC about her legacy. Meet MOFO: will.i.am's rapping AI toy BBC Tech Now takes us inside CES 2026 to meet musician will.i.am and his AI toy, MOFO. Copyright 2026 BBC. All rights reserved. The BBC is not responsible for the content of external sites. Read about our approach to external linking. |
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[SOURCE: https://en.wikipedia.org/wiki/Artificial_intelligence_and_elections] | [TOKENS: 3133] |
Contents Artificial intelligence and elections As artificial intelligence (AI) has become more mainstream, there is growing concern about how this will influence elections. Potential targets of AI include election processes, election offices, election officials and election vendors. Tactics Generative AI capabilities allow creation of misleading content. Examples of this include text-to-video, deepfake videos, text-to-image, AI-altered image, text-to-speech, voice cloning, and text-to-text. In the context of an election, a deepfake video of a candidate may propagate information that the candidate does not endorse. Chatbots could spread misinformation related to election locations, times or voting methods. In contrast to malicious actors in the past, these techniques require little technical skill and can spread rapidly. LLM-Generated messages have the capacity to persuade humans on political issues. Researchers have begun to investigate how people rate messages that LLMs generate for how persuasive they are. When it came to policy issues, the LLM-generated messages received a 2.91 compared to a 2.80 when it came to smartness between the AI and humans. The LLM-generated messages were often more technical and analytical than human-generated messages. Generative AI has been used to micro-target people during tight political elections. The generation of targeted large language models has triggered concern that they will be used to leverage readily scale microtargeting. Rephrasing inputs have been used to generate fraudulent emails and phishing websites. Rephrasing inputs in a microtargeting does not violate the terms of OpenAI usage. There are no safeguards to prevent the use of rephrasing and creation of fraudulent emails. Political campaign managers have access to this allowing for them to create targeted content. Usage by country During the 2023 Argentine primary elections, Javier Milei's team distributed AI generated images including a fabricated image of his rival Sergio Massa and drew 3 million views. The team also created an unofficial Instagram account entitled "AI for the Homeland." Sergio Massa's team also distributed AI generated images and videos. In the run up to the 2024 Bangladeshi general election, deepfake videos of female opposition politicians appeared. Rumin Farhana was pictured in a bikini while Nipun Ray was shown in a swimming pool. In the run up to the 2025 Canadian federal election, the use of AI tools is likely to figure prominently. India, Pakistan and Iran are all expected to make efforts to subvert the national vote using disinformation campaigns to deceive voters and sway diaspora communities. In a report by the Canadian Centre for Cyber Security called "Cyber Threats to Canada's Democratic Process: 2025 Update", it states that malicious actors including China and Russia: "are most likely to use generative Al as a means of creating and spreading disinformation, designed to sow division among Canadians and push narratives conducive to the interests of foreign states". In the 2024 French legislative election, deepfake videos appeared claiming: i) That they showed the family of Marine le Pen. In the videos, young women, supposedly Le Pen's nieces, are seen skiing, dancing and at the beach "while making fun of France’s racial minorities": However, the family members don't exist. On social media there were over 2 million views. ii) In a video seen on social media, a deepfake video of a France24 broadcast appeared to report that the Ukrainian leadership had "tried to lure French president Emmanuel Macron to Ukraine to assassinate him and then blame his death on Russia". During the months before the December 2024 Ghanaian general election, a network of at least 171 fake accounts has been used to spam social media. Posts have been used by a group identified as "@TheTPatriots" to promote the New Patriotic Party, although it is not known whether the two are connected. All the networks' posts were "highly likely" to have been generated by ChatGPT and appear to be the "first secretly partisan network using AI to influence elections in Ghana". The opposition National Democratic Congress was also criticized with its leader John Mahama being called a drunkard. In the 2024 Indian general election, politicians used deepfakes in their campaign materials. These deepfakes included politicians who had died prior to the election. Mathuvel Karunanidhi's party posted with his likeness even though he had died 2018. A video The All-India Anna Dravidian Progressive Federation party posted showed an audio clip of Jayaram Jayalalithaa even though she had died in 2016. The Deepfakes Analysis Unit (DAU) is an open source platform created in March 2024 for the public to share misleading content and assess if it had been AI-generated. AI was also used to translate political speeches in real time. This translating ability was widely used to reach more voters. In the 2024 Indonesian presidential election, Prabowo Subianto made extensive use of AI-generated art in his campaign, which ranged from images of himself as an adorable child to various child portrayals in his advertisements. The Indonesian Children's Protection Commission condemned these ads, labeling them as a form of misuse. Other candidates, Anies Baswedan and Ganjar Pranowo, also incorporated AI art into their campaigns. Throughout the election period, all presidential candidates faced assaults from deepfakes, both in video and audio formats. In the last weeks of the 2024 Irish general election a spoof election poster appeared in Dublin featuring "an AI-generated candidate with three arms". The candidate is called Aidan Irwin, but no-one stood in the election with that name. A slogan on the poster says "put matters into artificial intelligence’s hands". The convincing election poster shows a man that "has six fingers on one hand, three arms, and a distorted thumb". In May 2023, ahead of the 2023 New Zealand general election in October 2023, the New Zealand National Party published a "series of AI-generated political advertisements" on its Instagram account. After confirming that the images were faked, a party spokesperson said that it was "an innovative way to drive our social media". AI has been used by the imprisoned ex-Prime Minister Imran Khan and his media team in the 2024 Pakistani general election: i) An AI generated audio of his voice was added to a video clip and was broadcast at a virtual rally. ii) An op-ed in The Economist written by Khan was later claimed by himself to have been written by AI which was later denied by his team. The article was liked and shared on social media by thousands of users. In the 2024 South African general election, there were several uses of AI content: i) A deepfaked video of Joe Biden emerged on social media showing him saying that "The U.S. would place sanctions on SA and declare it an enemy state if the African National Congress (ANC) won". ii) In a deepfake video, Donald Trump was shown endorsing the uMkhonto weSizwe party. It was posted to social media and was viewed more than 158,000 times. iii) Less than 3 months before the elections, a deepfake video showed U.S. rapper Eminem endorsing the Economic Freedom Fighters party while criticizing the ANC. The deepfake was viewed on social media more than 173,000 times. In the 2022 South Korean presidential election, a committee for one presidential candidate Yoon Suk Yeol released an AI avatar 'Al Yoon Seok-yeol' that would campaign in places the candidate could not go. The other presidential candidate Lee Jae-myung introduced a chatbot that provided information about the candidate's pledges. Deepfakes were used to spread misinformation before the 2024 South Korean legislative election with one source reporting 129 deepfake violations of election laws within a two week period. Seoul hosted the 2024 Summit for Democracy, a virtual gathering of world leaders initiated by US President Joe Biden in 2021. The focus of the summit was on digital threats to democracy including artificial intelligence and deepfakes. AI-generated content was used during the 2024 Taiwanese presidential election. Among the media were: i) A deepfake video of General Secretary of the Chinese Communist Party Xi Jinping which showed him supporting the presidential elections. Created on social media, the video was "widely circulated" and often "accompanied by claims that Xi supported candidates from one of the two opposition parties". ii) In a deepfake video U.S. congressman Rob Wittman is shown appearing to support Taiwan's Democratic Progressive Party. The video shows him saying that the U.S. would increase its military support, accelerating "all arms sales to Taiwan." It was shown on various social media platforms. The Centre for Emerging Technology and Security provided a report on the threat of AI to the 2024 UK general election. The reports' findings said that the impact of AI was limited but may damage the democratic system. In the run up to the UK 2024 general elections, AI-generated videos spread extensively on social media including: i) A deepfake video showed then PM Rishi Sunak claiming that he would "require 18-year-olds to be sent to active war zones in Gaza and Ukraine as part of their national service". The video had more than 400,00 views. ii) A deepfake video showed PM Keir Starmer "swearing repeatedly at a staffer". Comments from the original poster included calling Starmer a "disgusting bully". The social media site showing the video refused to delete it despite requests. Entrepreneur Steve Endacott from the south of England created "AI Steve," an AI avatar as the face of his campaign for member of parliament. Officials from the ODNI and FBI have stated that Russia, Iran, and China used generative artificial intelligence tools to create fake and divisive text, photos, video, and audio content to foster anti-Americanism and engage in covert influence campaigns. The use of artificial intelligence was described as an accelerant rather than a revolutionary change to influence efforts. Regulation of AI with regard to elections was unlikely to see a resolution for most of the 2024 United States general election season. The campaign for the 2024 Republican nominee, Donald Trump, has used deepfake videos of political opponents in campaign ads and fake images showing Trump with black supporters. In 2023, while he was still running for re-election, the presidential campaign of Joe Biden prepared a task force to respond to AI images and videos. A Democratic consultant working for Dean Phillips also admitted to using AI to generate a robocall which used Joe Biden's voice to discourage voter participation. Generative AI increased the efficiency with which political candidates were able to raise money by analyzing donor data and identifying possible donors and target audiences. Regulation The Commission on Elections (COMELEC) issued guidelines on the usage of AI, to be implemented starting from the 2025 Philippine general election including the parallel Bangsamoro Parliament election. It mandates candidate to disclose usage of AI in their campaign materials and prohibits the usage of the technology to spread misinformation against their rivals. This is the first time the COMELEC has release guidelines on campaigning through social media. US states have attempted regulation of AI use in elections and campaigns with varying degrees of success. The National Conference of State Legislatures has compiled a list of legislation regarding AI use by state as of 2024, some carrying both civil and criminal penalties. Oregon Senate Bill 1571 requires that campaign communications in Oregon disclose the use of AI. California has enacted legislation that makes using deepfakes to discredit political opponents illegal within sixty days of an election. Midjourney, an AI image-generator, has started blocking users from creating fake images of the 2024 US Presidential candidates. Research from the Center for Countering Digital Hate found that image generators such as Midjourney, ChatGPT Plus, DreamStudio, and Microsoft's Image Creator create images that constitute election disinformation in 41% of the test text prompts they tried. OpenAI implemented policies to counter election misinformation such as adding digital credentials to image origin and a classifier to detect if images were AI generated. AI use in election interference by foreign governments AI has begun to be used in election interference by foreign governments. Governments thought to be using AI to interfere in external elections include Russia, Iran and China. Russia was thought to be the most prolific nation targeting the 2024 presidential election with their influencing operations "spreading synthetic images, video, audio and text online", according to U.S intelligence officials. Iran has reportedly generated fake social media posts stories and targeted "across the political spectrum on polarizing issues during the presidential election". The Chinese government has used "broader influence operations" that aim to make a global image and "amplify divisive topics in the U.S. such as drug use, immigration, and abortion". For example, Spamouflage has increasingly used generative AI for influence operations. Outside of the US elections, a deepfake video of Moldova’s pro-Western president Maia Sandu shows her "throwing her support behind a political party friendly to Russia." Officials in Moldova "believe the Russian government is behind the activity". Slovakia's liberal party leader had audio clips faked which discussed "vote rigging and raising the price of beer". The Chinese government has used AI to stir concerns about US interference in Taiwan. A fake clip seen on social media showed a fake video of the vice chairman of the U.S. House Armed Services Committee promising "stronger U.S. military support for Taiwan if the incumbent party’s candidates were elected in January". Ethics of AI use in political campaigning As the use of AI and its associated tools in political campaigning and messaging increases, many ethical concerns have been raised. Campaigns have used AI in a number of ways, including speech writing, fundraising, voter behaviour prediction, fake robocalls and the generation of fake news. At the moment there are no US federal rules when it comes to using AI in campaigning and so its use can undermine public trust. Yet according to one expert: "A lot of the questions we're asking about AI are the same questions we've asked about rhetoric and persuasion for thousands of years." As more insight into how AI is used becomes ever greater, concerns have become much broader than just the generating of misinformation or fake news. Its use by politicians and political parties for "purposes that are not overtly malicious" can also raise ethical worries. For instance, the use of 'softfakes' have become more common. These can be images, videos or audio clips that have been edited, often by campaign teams, "to make a political candidate seem more appealing." An example can be found in Indonesia's presidential election where the winning candidate created and promoted cartoonish avatars so as to rebrand himself. How citizens come by information has been increasingly impacted by AI, especially through online platforms and social media. These platforms are part of complex and opaque systems which can result in a "significant impact on freedom of expression", with the generalisation of AI in campaigns also creating huge pressures on "voters’ mental security". As the frequency of AI use in political campaigning becomes common, together with globalization, more 'universalized' content can be used so that territorial boundaries matter less. While AI collides with the reasoning processes of people, the creation of "dangerous behaviours" can happen which disrupt important levels of society and nation states. See also References External links |
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[SOURCE: https://www.bbc.com/reel/video/p0mw0yk0/the-new-technology-shaping-the-vehicles-of-tomorrow] | [TOKENS: 743] |
The new technology shaping the vehicles of tomorrow Technology to get us around was one of the big themes at CES 2026 in Las Vegas, everything from land, to sea, to air. Reporting for BBC Tech Now, Alasdair Keane tests out some of the latest tech innovations for our cars. This video is from Tech Now, the BBC's flagship technology programme. US government shutdown in October 'dented growth' Gus Scacco of Advisors Capital Management says the October US Government shutdown dented growth. America is running out of teenagers, colleges are worried Katty Kay speaks to Nathan Grawe about a looming demographic cliff that could reshape American higher education. Margot Robbie's accent was 'too Australian' for Neighbours The actress reveals that she had a dialect coach when she was on the series because her accent was so strong. Expert says market volatility over AI 'not unexpected' Wall Street opened higher following Tuesday's volatile session as investors concerns over AI ease off. The French fortress of a celibate sect The Cathars rejected meat and procreative sex. They were persecuted as heretics, but shaped our ideas of love. AI and tech stocks trading lower 'due to high capital costs' Mark Giambrone at Barrow Hanley Global Investors says the heavy capital expenditure is dragging down tech stocks. Breathtaking solar eclipse over glacier in Patagonia Liam Man, a photographer from the UK, captures rare images of a solar eclipse over the remote Glacier Leones. The tactile tech giving deaf runners a fair start A gold‑medalist has developed a vibrating starting block to give deaf athletes clearer, fairer race starts. Inside Canada's first of its kind caribou sanctuary A pioneering conservation breeding centre in Jasper National park is racing to save Canada's iconic caribou. US annualised consumer inflation falls to 2.4 percent Ed Yardeni, President of Yardeni Research says inflation and jobs data signal a positive outlook for the economy. What happens when wives outearn their husbands? Katty Kay speaks with author Liza Mundy about how relationships change when women start earning more than men. Wall Street weighs up strong jobs numbers Alex Guiliano from Resonate Wealth Partners says today's jobs numbers suggest no rate cuts are expected. These futuristic screens help you navigate Tokyo In Tokyo, BBC TechXplore tests live translation and AI-powered displays that makes the city more navigable. Kew's Fungarium: The world's largest collection of fungi Deep beneath Kew Gardens sits the world's largest archive of fungi, a vast library of 1.3 million specimens. US retail sales stall in December without usual holiday lift Uma Moriarty of CenterSquare explains US retail sales have stalled as consumers struggle with high retail prices. The wearable tech that lets spectators feel the match At Tokyo's Deaflympics, deaf Judo fans aren't just watching the matches, they're feeling them, thanks to Hapbeat. How sticky toffee pudding became a British pub classic The Travel Show visits the the Lake District to find out about the historic roots of Britain's beloved pudding. Expert says economy is 'heading for a soft landing' Market strategist says investors are punishing AI companies for the size of their capital expenditure bills. Is human connection the new job security? Katty Kay speaks to Jane Wurwand about her theory about what jobs are best protected from AI replacement. Marina Abramović is done with the past Widely seen as the queen of contemporary performance art, Marina Abramović speaks to the BBC about her legacy. Copyright 2026 BBC. All rights reserved. The BBC is not responsible for the content of external sites. Read about our approach to external linking. |
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[SOURCE: https://www.bbc.com/reel/video/p0mw0smw/meet-mofo-will-i-am-s-rapping-ai-toy] | [TOKENS: 767] |
Meet MOFO: will.i.am's rapping AI toy CES in Las Vegas is one the biggest tech shows in the world. There are thousands of companies, from the biggest names in tech, to startups trying to get a break. Reporting for BBC Tech Now, Ammie Sekhon takes us inside CES 2026 to check out all the latest trends and meets musician will.i.am and his AI toy, MOFO. This video is from Tech Now, the BBC's flagship technology programme. US government shutdown in October 'dented growth' Gus Scacco of Advisors Capital Management says the October US Government shutdown dented growth. America is running out of teenagers, colleges are worried Katty Kay speaks to Nathan Grawe about a looming demographic cliff that could reshape American higher education. Margot Robbie's accent was 'too Australian' for Neighbours The actress reveals that she had a dialect coach when she was on the series because her accent was so strong. Expert says market volatility over AI 'not unexpected' Wall Street opened higher following Tuesday's volatile session as investors concerns over AI ease off. The French fortress of a celibate sect The Cathars rejected meat and procreative sex. They were persecuted as heretics, but shaped our ideas of love. AI and tech stocks trading lower 'due to high capital costs' Mark Giambrone at Barrow Hanley Global Investors says the heavy capital expenditure is dragging down tech stocks. Breathtaking solar eclipse over glacier in Patagonia Liam Man, a photographer from the UK, captures rare images of a solar eclipse over the remote Glacier Leones. The tactile tech giving deaf runners a fair start A gold‑medalist has developed a vibrating starting block to give deaf athletes clearer, fairer race starts. Inside Canada's first of its kind caribou sanctuary A pioneering conservation breeding centre in Jasper National park is racing to save Canada's iconic caribou. US annualised consumer inflation falls to 2.4 percent Ed Yardeni, President of Yardeni Research says inflation and jobs data signal a positive outlook for the economy. What happens when wives outearn their husbands? Katty Kay speaks with author Liza Mundy about how relationships change when women start earning more than men. Wall Street weighs up strong jobs numbers Alex Guiliano from Resonate Wealth Partners says today's jobs numbers suggest no rate cuts are expected. These futuristic screens help you navigate Tokyo In Tokyo, BBC TechXplore tests live translation and AI-powered displays that makes the city more navigable. Kew's Fungarium: The world's largest collection of fungi Deep beneath Kew Gardens sits the world's largest archive of fungi, a vast library of 1.3 million specimens. US retail sales stall in December without usual holiday lift Uma Moriarty of CenterSquare explains US retail sales have stalled as consumers struggle with high retail prices. The wearable tech that lets spectators feel the match At Tokyo's Deaflympics, deaf Judo fans aren't just watching the matches, they're feeling them, thanks to Hapbeat. How sticky toffee pudding became a British pub classic The Travel Show visits the the Lake District to find out about the historic roots of Britain's beloved pudding. Expert says economy is 'heading for a soft landing' Market strategist says investors are punishing AI companies for the size of their capital expenditure bills. Is human connection the new job security? Katty Kay speaks to Jane Wurwand about her theory about what jobs are best protected from AI replacement. Marina Abramović is done with the past Widely seen as the queen of contemporary performance art, Marina Abramović speaks to the BBC about her legacy. Copyright 2026 BBC. All rights reserved. The BBC is not responsible for the content of external sites. Read about our approach to external linking. |
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