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GPT-4 : OpenAI did not release the technical details of GPT-4; the technical report explicitly refrained from specifying the model size, architecture, or hardware used during either training or inference. While the report described that the model was trained using a combination of first supervised learning on a large d... |
GPT-4 : According to their report, OpenAI conducted internal adversarial testing on GPT-4 prior to the launch date, with dedicated red teams composed of researchers and industry professionals to mitigate potential vulnerabilities. As part of these efforts, they granted the Alignment Research Center early access to the ... |
GPT-4 : In January 2023, Sam Altman, CEO of OpenAI, visited Congress to demonstrate GPT-4 and its improved "security controls" compared to other AI models, according to U.S. Representatives Don Beyer and Ted Lieu quoted in the New York Times. In March 2023, it "impressed observers with its markedly improved performance... |
GPT-4 : Claude (language model) Gemini (language model) Llama (language model) Mistral AI == References == |
Neural processing unit : A neural processing unit (NPU), also known as AI accelerator or deep learning processor, is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence (AI) and machine learning applications, including artificial neural networks and computer vis... |
Neural processing unit : Computer systems have frequently complemented the CPU with special-purpose accelerators for specialized tasks, known as coprocessors. Notable application-specific hardware units include video cards for graphics, sound cards, graphics processing units and digital signal processors. As deep learn... |
Neural processing unit : As of 2016, the field is still in flux and vendors are pushing their own marketing term for what amounts to an "AI accelerator", in the hope that their designs and APIs will become the dominant design. There is no consensus on the boundary between these devices, nor the exact form they will tak... |
Neural processing unit : Inspired from the pioneer work of DianNao Family, many DLPs are proposed in both academia and industry with design optimized to leverage the features of deep neural networks for high efficiency. At ISCA 2016, three sessions (15%) of the accepted papers, focused on architecture designs about dee... |
Neural processing unit : Benchmarks such as MLPerf and others may be used to evaluate the performance of AI accelerators. Table 2 lists several typical benchmarks for AI accelerators. |
Neural processing unit : Agricultural robots, for example, herbicide-free weed control. Autonomous vehicles: Nvidia has targeted their Drive PX-series boards at this application. Computer-aided diagnosis Industrial robots, increasing the range of tasks that can be automated, by adding adaptability to variable situation... |
Neural processing unit : Cognitive computer Neuromorphic engineering Optical neural network Physical neural network UALink |
Neural processing unit : Nvidia Puts The Accelerator To The Metal With Pascal.htm, The Next Platform Eyeriss Project, MIT https://alphaics.ai/ |
Artificial intelligence in education : Artificial intelligence in education (AIEd) is the application of AI in educational settings. The field combines elements of generative AI, data-driven decision-making, AI ethics, data-privacy and AI literacy. An educator might learn to use these AI systems as tools and generate c... |
Artificial intelligence in education : Artificial intelligence could be defined as "systems which display intelligent behaviour by analysing their environment and taking actions – with some degree of autonomy – to achieve specific goals". These systems might be software-based or embedded in hardware. They can rely on m... |
Artificial intelligence in education : This complex social, cultural, and material assemblage should be seen in its geo-political context. It is likely that AI systems will be shaped by different policy or economic imperatives which will influence the construction, legitimation and use of this assemblage in an educatio... |
Artificial intelligence in education : The AI in education community has grown rapidly in the global north, driven by venture capital, big tech, and open educationalists. While some believe AI will improve "access to expertise" and revolutionize learning through natural language processing, others focus on enhancing LL... |
Artificial intelligence in education : AI companies that focus on education, are currently preoccupied with generative artificial intelligence (GAI), although data science and data analytics is another popular educational theme. At present, there is little scientific consensus on what AI is or how to classify and sub-c... |
Artificial intelligence in education : Educational technology can be a powerful and effective assistant in a suitable setting. Computer companies are constantly updating their technology products. Some educationalists have suggested that AI might automate procedural knowledge and expertise or even match or surpass huma... |
Artificial intelligence in education : Large language models (LLMs) take text as input data and then generate output text. Coherent sentences are parroted from billions of words and code that has been web-scraped by AI companies or researchers. LLM are often dependent on a huge text corpus that is extracted, sometimes ... |
Artificial intelligence in education : The benefits of multilingualism, grammatically correct sentences or statistically probable texts written about any topic or domain are clear to those who can afford software as a service (SaaS). In edtech, there is a recurrent theme, that "emerging technologies" will transform edu... |
Artificial intelligence in education : At first glance, artificial intelligence in education offers pertinent technical solutions to address future education needs. AI champions envision a future where machine learning and artificial intelligence might be applied in writing, personalization, feedback or course developm... |
Artificial intelligence in education : AI has co-existed comfortably between academia and industry for years. The terrain is shifting and currently AI research in the global north has computing power, large datasets, and highly skilled researchers. Power is shifting away from students and academics toward corporations ... |
Artificial intelligence in education : With the use of AI tools becoming more commonplace in schools, universities and other educational settings, discussion is growing over the benefits and risks (as well as the possible longer-term consequences) of reorganising education around AI. A range of stances are emerging—ran... |
Artificial intelligence in education : At present, teachers are still skeptical about AI due to two main factors: lack of knowledge and understanding of AI, as well as some misunderstandings about it. Because AI can only score based on written work, and teachers can sometimes understand what students want to express th... |
Artificial intelligence in education : Computational education Computing education Computers in the classroom == References == |
Knowledge integration : Knowledge integration is the process of synthesizing multiple knowledge models (or representations) into a common model (representation). Compared to information integration, which involves merging information having different schemas and representation models, knowledge integration focuses more... |
Knowledge integration : Data integration Knowledge value chain |
Knowledge integration : Linn, M. C. (2006) The Knowledge Integration Perspective on Learning and Instruction. R. Sawyer (Ed.). In The Cambridge Handbook of the Learning Sciences. Cambridge, MA. Cambridge University Press Murray, K. S. (1996) KI: A tool for Knowledge Integration. Proceedings of the Thirteenth National C... |
Document classification : Document classification or document categorization is a problem in library science, information science and computer science. The task is to assign a document to one or more classes or categories. This may be done "manually" (or "intellectually") or algorithmically. The intellectual classifica... |
Document classification : Content-based classification is classification in which the weight given to particular subjects in a document determines the class to which the document is assigned. It is, for example, a common rule for classification in libraries, that at least 20% of the content of a book should be about th... |
Document classification : Sometimes a distinction is made between assigning documents to classes ("classification") versus assigning subjects to documents ("subject indexing") but as Frederick Wilfrid Lancaster has argued, this distinction is not fruitful. "These terminological distinctions,” he writes, “are quite mean... |
Document classification : Automatic document classification tasks can be divided into three sorts: supervised document classification where some external mechanism (such as human feedback) provides information on the correct classification for documents, unsupervised document classification (also known as document clus... |
Document classification : Classification techniques have been applied to spam filtering, a process which tries to discern E-mail spam messages from legitimate emails email routing, sending an email sent to a general address to a specific address or mailbox depending on topic language identification, automatically deter... |
Document classification : Fabrizio Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1–47, 2002. Stefan Büttcher, Charles L. A. Clarke, and Gordon V. Cormack. Information Retrieval: Implementing and Evaluating Search Engines Archived 2020-10-05 at the Wayback Machine. MIT Press... |
Document classification : Introduction to document classification Bibliography on Automated Text Categorization Archived 2019-09-26 at the Wayback Machine Bibliography on Query Classification Archived 2019-10-02 at the Wayback Machine Text Classification analysis page Learning to Classify Text - Chap. 6 of the book Nat... |
TipTop Technologies : TipTop Technologies is a real-time web and social search engine with a platform for semantic analysis of natural language. Tip-Top Search provides results capturing individual and group sentiment, opinions, and experiences there from the content of various sorts such as real-time messages from Twi... |
Operational artificial intelligence : Operational artificial intelligence, or operational AI, is a type of intelligent system designed for real-world applications, particularly at commercial scale. The term is used to distinguish accessible artificially intelligent (AI) systems from fundamental AI research and from ind... |
Operational artificial intelligence : According to a white paper by software company Tupl Inc, continuous machine learning model training and results extraction in the telecom industry requires a large number of automation utilities in order to "facilitate the development and deployment of a multitude of use cases, the... |
Operational artificial intelligence : Industrial AI refers to intelligent systems applied for business at any scale and for any use case. |
Operational artificial intelligence : Applications of artificial intelligence Edge computing Industrial artificial intelligence Continuous integration == References == |
Top-p sampling : Top-p sampling, also called nucleus sampling, is a technique for autoregressive language model decoding proposed by Ari Holtzman et al. in 2019. Before the introduction of nucleus sampling, maximum likelihood decoding and beam search were the standard techniques for text generation, but, both of these ... |
Growing self-organizing map : A growing self-organizing map (GSOM) is a growing variant of a self-organizing map (SOM). The GSOM was developed to address the issue of identifying a suitable map size in the SOM. It starts with a minimal number of nodes (usually 4) and grows new nodes on the boundary based on a heuristic... |
Growing self-organizing map : The GSOM process is as follows: Initialization phase: Initialize the weight vectors of the starting nodes (usually four) with random numbers between 0 and 1. Calculate the growth threshold ( G T ) for the given data set of dimension D according to the spread factor ( S F ) using the for... |
Growing self-organizing map : The GSOM can be used for many preprocessing tasks in Data mining, for Nonlinear dimensionality reduction, for approximation of principal curves and manifolds, for clustering and classification. It gives often the better representation of the data geometry than the SOM (see the classical be... |
Growing self-organizing map : Liu, Y.; Weisberg, R.H.; He, R. (2006). "Sea surface temperature patterns on the West Florida Shelf using growing hierarchical self-organizing maps". Journal of Atmospheric and Oceanic Technology. 23 (2): 325–338. Bibcode:2006JAtOT..23..325L. doi:10.1175/JTECH1848.1. hdl:1912/4186. Hsu, A.... |
Growing self-organizing map : Self-organizing map Time Adaptive Self-Organizing Map Elastic map Artificial intelligence Machine learning Data mining Nonlinear dimensionality reduction |
Trigram : Trigrams are a special case of the n-gram, where n is 3. They are often used in natural language processing for performing statistical analysis of texts and in cryptography for control and use of ciphers and codes. See results of analysis of "Letter Frequencies in the English Language". |
Trigram : Context is very important, varying analysis rankings and percentages are easily derived by drawing from different sample sizes, different authors; or different document types: poetry, science-fiction, technology documentation; and writing levels: stories for children versus adults, military orders, and recipe... |
Trigram : The sentence "the quick red fox jumps over the lazy brown dog" has the following word-level trigrams: the quick red quick red fox red fox jumps fox jumps over jumps over the over the lazy the lazy brown lazy brown dog And the word-level trigram "the quick red" has the following character-level trigrams (where... |
Gating mechanism : In neural networks, the gating mechanism is an architectural motif for controlling the flow of activation and gradient signals. They are most prominently used in recurrent neural networks (RNNs), but have also found applications in other architectures. |
Gating mechanism : Gating mechanisms are the centerpiece of long short-term memory (LSTM). They were proposed to mitigate the vanishing gradient problem often encountered by regular RNNs. An LSTM unit contains three gates: An input gate, which controls the flow of new information into the memory cell A forget gate, whi... |
Gating mechanism : Gated Linear Units (GLUs) adapt the gating mechanism for use in feedforward neural networks, often within transformer-based architectures. They are defined as: G L U ( a , b ) = a ⊙ σ ( b ) (a,b)=a\odot \sigma (b) where a , b are the first and second inputs, respectively. σ represents the sigmoid ... |
Gating mechanism : Gating mechanism is used in highway networks, which were designed by unrolling an LSTM. Channel gating uses a gate to control the flow of information through different channels inside a convolutional neural network (CNN). |
Gating mechanism : Recurrent neural network Long short-term memory Gated recurrent unit Transformer Activation function |
Gating mechanism : Zhang, Aston; Lipton, Zachary; Li, Mu; Smola, Alexander J. (2024). "10.1. Long Short-Term Memory (LSTM)". Dive into deep learning. Cambridge New York Port Melbourne New Delhi Singapore: Cambridge University Press. ISBN 978-1-009-38943-3. |
AI nationalism : AI nationalism is the idea that nations should develop and control their own artificial intelligence technologies to advance their own interests and ensure technological sovereignty. This concept is gaining traction globally, leading countries to implement new laws, form strategic alliances, and invest... |
AI nationalism : In 2018, British technology investor Ian Hogarth published an influential essay titled AI Nationalism. He argued that as AI gains more power and its economic and military significance expands, governments will take measures to bolster their own domestic AI industries, and predicted that the advancement... |
AI nationalism : AI nationalism is seen as deeply connected to historical racism and imperialism. It is viewed not merely as a technological competition but as a contest over racial and civilizational superiority. Historically, technological achievements were often used to justify colonialism and racial hierarchies, wi... |
AI nationalism : AI nationalism is seen as a component of a broader trend towards the fragmentation of the internet, where digital services are increasingly influenced by local regulations and national interests. This shift is creating a new technological landscape in which the impact of artificial intelligence on indi... |
AI nationalism : Artificial Intelligence Cold War Space Race Techno-nationalism Technological escalation |
AI nationalism : Hogarth, Ian, AI Nationalism, 2018 Aaronson, Susan. The Age of AI Nationalism and its Effects. April 22, 2024. DOI: 10.2139/ssrn.4803311. == References == |
Seidor (company) : Seidor is a technology consulting firm with headquarters in Barcelona, Spain. It was founded in 1982 in Vic. By 2024, it has a team of 9,000 people and a direct presence in 45 countries in Europe, the United States, Latin America, the Middle East, Africa and Asia. The Carlyle Group joined Seidor as a... |
Seidor (company) : Seidor was established in 1982 in Vic (Barcelona). It was founded by the brothers Santiago and Andreu Benito. The company's initial focus was on developing customised business management software for and medium sized companies. In 1983, Seidor opened its Barcelona office, the company's global headqua... |
Seidor (company) : In order to expand its geographic presence and capabilities, the group has made a number of strategic acquisitions and integrations of other companies. Key transactions include the following: 2003: acquisition of Saytel (Spain) 2010: acquisition of Crystal Solutions (Brazil), the first outside Spain.... |
Seidor (company) : The company has been expanding its international presence through a combination of its own office openings and local acquisitions. The chronology of this growth is as follows: 2005: Chile, Argentina and Mexico. 2007: Portugal. 2010: Brazil. 2013: United States and Middle East 2014: Belgium (Brussels)... |
Seidor (company) : The company began by developing business management applications for small and medium-sized enterprises. It has gradually expanded its range of services and technological solutions and, in turn, its geographical presence to serve customers in a variety of sectors, as well as large companies. Sectors ... |
Seidor (company) : There are centres of innovation and excellence in several countries: CX competence centres in Bilbao, Bogota, Lima, Madrid, Santiago, Taipei and Valencia; AI and Innovation competence centres in Barcelona, Bilbao, Dubai and Santiago; Data competence centres in Barcelona, Buenos Aires and Dubai; Workp... |
Seidor (company) : The company has received a number of awards and has been recognised as a reference technology partner by a range of technology companies including IBM, SAP, Microsoft, Salesforce, Google and AWS. == References == |
K-line (artificial intelligence) : A K-line, or Knowledge-line, is a mental agent which represents an association of a group of other mental agents found active when a subject solves a certain problem or formulates a new idea. These were first described in Marvin Minsky's essay K-lines: A Theory of Memory, published in... |
K-line (artificial intelligence) : The concept of K-lines has several theoretical implications for understanding memory and problem-solving in artificial intelligence and cognitive science: It suggests that memory is not a static storage of information, but rather a dynamic association of mental agents activated during... |
K-line (artificial intelligence) : While influential, the K-line theory has also faced some criticism and limitations: The exact nature and implementation of K-lines in the brain or in artificial systems remains unclear and speculative. The theory does not provide a complete account of all aspects of memory and cogniti... |
K-line (artificial intelligence) : Minsky, Marvin; The Society of Mind ISBN 0-671-65713-5 March 15, 1998. Minsky, Marvin; Papert, Seymour; Perceptrons: An Introduction to Computational Geometry ISBN 0-262-63111-3 December 28, 1987. |
K-line (artificial intelligence) : Minsky's "K-lines: A Theory of Memory" Archived 2020-02-15 at the Wayback Machine Why Programming is a Good Medium for Expressing Poorly Understood and Sloppily Formulated Ideas Archived 2005-05-04 at the Wayback Machine |
Human-in-the-loop : Human-in-the-loop (HITL) is used in multiple contexts. It can be defined as a model requiring human interaction. HITL is associated with modeling and simulation (M&S) in the live, virtual, and constructive taxonomy. HITL along with the related human-on-the-loop are also used in relation to lethal au... |
Human-in-the-loop : In machine learning, HITL is used in the sense of humans aiding the computer in making the correct decisions in building a model. HITL improves machine learning over random sampling by selecting the most critical data needed to refine the model. |
Human-in-the-loop : In simulation, HITL models may conform to human factors requirements as in the case of a mockup. In this type of simulation a human is always part of the simulation and consequently influences the outcome in such a way that is difficult if not impossible to reproduce exactly. HITL also readily allow... |
Human-in-the-loop : Three classifications of the degree of human control of autonomous weapon systems were laid out by Bonnie Docherty in a 2012 Human Rights Watch report. human-in-the-loop: a human must instigate the action of the weapon (in other words not fully autonomous) human-on-the-loop: a human may abort an act... |
Human-in-the-loop : Humanistic intelligence, which is intelligence that arises by having the human in the feedback loop of the computational process Reinforcement learning from human feedback MIM-104 Patriot - Examples of a human-on-the-loop lethal autonomous weapon system posing a threat to friendly forces. == Referen... |
Embodied cognitive science : Embodied cognitive science is an interdisciplinary field of research, the aim of which is to explain the mechanisms underlying intelligent behavior. It comprises three main methodologies: the modeling of psychological and biological systems in a holistic manner that considers the mind and b... |
Embodied cognitive science : Embodied cognitive science borrows heavily from embodied philosophy and the related research fields of cognitive science, psychology, neuroscience and artificial intelligence. Contributors to the field include: From the perspective of neuroscience, Gerald Edelman of the Neurosciences Instit... |
Embodied cognitive science : Embodied cognitive science is an alternative theory to cognition in which it minimizes appeals to computational theory of mind in favor of greater emphasis on how an organism's body determines how and what it thinks. Traditional cognitive theory is based mainly around symbol manipulation, i... |
Embodied cognitive science : Embodied cognitive science differs from the traditionalist approach in that it denies the input-output system. This is chiefly due to the problems presented by the Homunculus argument, which concluded that semantic meaning could not be derived from symbols without some kind of inner interpr... |
Embodied cognitive science : The value of the embodiment approach in the context of cognitive science is perhaps best explained by Andy Clark.: 345–351 He makes the claim that the brain alone should not be the single focus for the scientific study of cognition It is increasingly clear that, in a wide variety of cases, ... |
Embodied cognitive science : In the formation of general principles of intelligent behavior, Pfeifer intended to be contrary to older principles given in traditional artificial intelligence. The most dramatic difference is that the principles are applicable only to situated robotic agents in the real world, a domain wh... |
Embodied cognitive science : Braitenberg, Valentino (1986). Vehicles: Experiments in Synthetic Psychology. Cambridge, MA: The MIT Press. ISBN 0-262-52112-1 Brooks, Rodney A. (1999). Cambrian Intelligence: The Early History of the New AI. Cambridge, MA: The MIT Press. ISBN 0-262-52263-2 Edelman, G. Wider than the Sky (Y... |
Embodied cognitive science : AI lectures from Tokyo hosted by Rolf Pfeifer synthetic neural modelling in DARWIN IV Society for the Simulation of Adaptive Behavior A platform for creating Embodied Cognitive Agents |
Rabbit r1 : The Rabbit r1 is an Android-powered, ChatGPT-based personal assistant device developed by tech startup Rabbit Inc and co-designed by Teenage Engineering. It is designed to perform various functions, including web searches and media control, using voice commands and touch interaction, allowing AI to be used ... |
Rabbit r1 : Display: A 2.88-inch touchscreen for interactive user input. Input: push-to-talk button to activate voice commands; scroll wheel; Gyroscope; Magnetometer; Accelerometer; GPS. Camera: 8 MP single camera, with a resolution of 3264x2448, allowing for the connected external AI to use computer vision. Audio: Equ... |
Rabbit r1 : The Rabbit r1 runs on Rabbit OS, based on the Android Open Source Project (AOSP), specifically version 13. Lyu has claimed that Rabbit OS runs with a "very bespoke AOSP." The device employs a large action model (LAM) designed to perform actions and assist with tasks like web searches, streaming music, and t... |
Model compression : Model compression is a machine learning technique for reducing the size of trained models. Large models can achieve high accuracy, but often at the cost of significant resource requirements. Compression techniques aim to compress models without significant performance reduction. Smaller models requi... |
Model compression : Several techniques are employed for model compression. |
Model compression : Model compression may be decoupled from training, that is, a model is first trained without regard for how it might be compressed, then it is compressed. However, it may also be combined with training. The "train big, then compress" method trains a large model for a small number of training steps (l... |
Model compression : Review papers Li, Zhuo; Li, Hengyi; Meng, Lin (March 12, 2023). "Model Compression for Deep Neural Networks: A Survey". Computers. 12 (3). MDPI AG: 60. doi:10.3390/computers12030060. ISSN 2073-431X. Deng, By Lei; Li, Guoqi; Han, Song; Shi, Luping; Xie, Yuan (March 20, 2020). "Model Compression and H... |
Neural computation : Neural computation is the information processing performed by networks of neurons. Neural computation is affiliated with the philosophical tradition known as Computational theory of mind, also referred to as computationalism, which advances the thesis that neural computation explains cognition. The... |
Deep image prior : Deep image prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly initialized and used as prior to solve inverse problems such as noise reduction, super-resolution, and inpainting. Image stati... |
Deep image prior : A reference implementation rewritten in Python 3.6 with the PyTorch 0.4.0 library was released by the author under the Apache 2.0 license: deep-image-prior A TensorFlow-based implementation written in Python 2 and released under the CC-SA 3.0 license: deep-image-prior-tensorflow A Keras-based impleme... |
Deep image prior : See Astronomy Picture of the Day (APOD) of 2024-02-18 |
Deep image prior : Ulyanov, Dmitry; Vedaldi, Andrea; Lempitsky, Victor (30 November 2017). "Deep Image Prior". arXiv:1711.10925v2 [cs.CV]. |
Brave Leo : Brave Leo is a large language model-based chatbot developed by Brave Software and included with the Brave desktop browser. |
Brave Leo : In November 2023, the company said versions for iOS and Android would be available "in the coming months". |
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