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AlaaElhilo/Wikipedia_ComputerScience | 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 ... |
AlaaElhilo/Wikipedia_ComputerScience | Typically, machine learning models require a high quantity of reliable data in order for the models 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 c... |
AlaaElhilo/Wikipedia_ComputerScience | Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training ... |
AlaaElhilo/Wikipedia_ComputerScience | There are many applications for machine learning, including: |
AlaaElhilo/Wikipedia_ComputerScience | 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 wit... |
AlaaElhilo/Wikipedia_ComputerScience | 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 data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and eva... |
AlaaElhilo/Wikipedia_ComputerScience | 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 out of the data. The House of Lords Select Commi... |
AlaaElhilo/Wikipedia_ComputerScience | 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... |
AlaaElhilo/Wikipedia_ComputerScience | 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 se... |
AlaaElhilo/Wikipedia_ComputerScience | Machine learning approaches in particular 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 p... |
AlaaElhilo/Wikipedia_ComputerScience | Language models learned from data have been shown to contain human-like biases. In an experiment carried out by ProPublica, an investigative journalism organization, a machine learning algorithm's insight towards the recidivism rates among prisoners falsely flagged “black defendants high risk twice as often as white de... |
AlaaElhilo/Wikipedia_ComputerScience | 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 ... |
AlaaElhilo/Wikipedia_ComputerScience | Explainable AI , or Interpretable AI, or Explainable Machine Learning , is artificial intelligence 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. ... |
AlaaElhilo/Wikipedia_ComputerScience | 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 penalizing the theory in accordance with how complex the theory is. |
AlaaElhilo/Wikipedia_ComputerScience | Learners can also disappoint 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... |
AlaaElhilo/Wikipedia_ComputerScience | 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 and/or evasion via adversarial machine learning. |
AlaaElhilo/Wikipedia_ComputerScience | Researchers have demonstrated how backdoors can be placed undetectably into classifying machine learning models which are often developed and/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 includin... |
AlaaElhilo/Wikipedia_ComputerScience | Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set 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... |
AlaaElhilo/Wikipedia_ComputerScience | In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate and True Negative Rate respectively. Similarly, investigators sometimes report the false positive rate as well as the false negative rate . However, these rates are ratios that fail to reveal their... |
AlaaElhilo/Wikipedia_ComputerScience | Machine learning poses a host of ethical questions. Systems that are trained on datasets collected with biases may exhibit these biases upon use , thus digitizing 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 t... |
AlaaElhilo/Wikipedia_ComputerScience | 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 lack of participation and representation of minority population in the field of AI for machine learning's vulnerability to biases. In fact, according to ... |
AlaaElhilo/Wikipedia_ComputerScience | AI can be well-equipped to make decisions in technical fields, which rely heavily on data and historical information. These decisions rely on objectivity and logical reasoning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases. |
AlaaElhilo/Wikipedia_ComputerScience | Other forms of ethical challenges, not related to personal biases, are seen in health care. 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 ... |
AlaaElhilo/Wikipedia_ComputerScience | Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units. By 2019, graphic processing units , often with AI-specific enhancements, had displaced CPUs as the dominant metho... |
AlaaElhilo/Wikipedia_ComputerScience | A physical neural network or Neuromorphic computer is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-bas... |
AlaaElhilo/Wikipedia_ComputerScience | Embedded Machine Learning is a sub-field of machine learning, where the machine learning model is run on embedded systems with limited computing resources such as wearable computers, edge devices and microcontrollers. Running machine learning model in embedded devices removes the need for transferring and storing data ... |
AlaaElhilo/Wikipedia_ComputerScience | Software suites containing a variety of machine learning algorithms include the following: |
AlaaElhilo/Wikipedia_ComputerScience | Artificial intelligence , in its broadest sense, is intelligence exhibited by machines, particularly computer systems. It is a field of research in computer science that develops and studies methods and software which enable machines to perceive their environment and uses learning and intelligence to take actions that ... |
AlaaElhilo/Wikipedia_ComputerScience | AI technology is widely used throughout industry, government, and science. Some high-profile applications include advanced web search engines ; recommendation systems ; interacting via human speech ; autonomous vehicles ; generative and creative tools ; and superhuman play and analysis in strategy games . However, many... |
AlaaElhilo/Wikipedia_ComputerScience | Alan Turing was the first person to conduct substantial research in the field that he called machine intelligence. Artificial intelligence was founded as an academic discipline in 1956. The field went through multiple cycles of optimism, followed by periods of disappointment and loss of funding, known as AI winter. Fun... |
AlaaElhilo/Wikipedia_ComputerScience | The growing use of artificial intelligence in the 21st century is influencing a societal and economic shift towards increased automation, data-driven decision-making, and the integration of AI systems into various economic sectors and areas of life, impacting job markets, healthcare, government, industry, and education... |
AlaaElhilo/Wikipedia_ComputerScience | The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and support for robotics. General intelligence—the ability to complet... |
AlaaElhilo/Wikipedia_ComputerScience | To reach these goals, AI researchers have adapted and integrated a wide range of techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics. AI also draws upon psychology, linguistics, philosophy, neuroscience... |
AlaaElhilo/Wikipedia_ComputerScience | The general problem of simulating intelligence has been broken into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research. |
AlaaElhilo/Wikipedia_ComputerScience | Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics. |
AlaaElhilo/Wikipedia_ComputerScience | Many of these algorithms are insufficient for solving large reasoning problems because they experience a "combinatorial explosion": they became exponentially slower as the problems grew larger. Even humans rarely use the step-by-step deduction that early AI research could model. They solve most of their problems using ... |
AlaaElhilo/Wikipedia_ComputerScience | Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery , and other areas. |
AlaaElhilo/Wikipedia_ComputerScience | A knowledge base is a body of knowledge represented in a form that can be used by a program. An ontology is the set of objects, relations, concepts, and properties used by a particular domain of knowledge. Knowledge bases need to represent things such as: objects, properties, categories and relations between objects; s... |
AlaaElhilo/Wikipedia_ComputerScience | Among the most difficult problems in knowledge representation are: the breadth of commonsense knowledge ; and the sub-symbolic form of most commonsense knowledge . There is also the difficulty of knowledge acquisition, the problem of obtaining knowledge for AI applications. |
AlaaElhilo/Wikipedia_ComputerScience | An "agent" is anything that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen. In automated planning, the agent has a specific goal. In automated decision making, the agent has preferences—there are some situations it would prefer to be in, and som... |
AlaaElhilo/Wikipedia_ComputerScience | In classical planning, the agent knows exactly what the effect of any action will be. In most real-world problems, however, the agent may not be certain about the situation they are in and it may not know for certain what will happen after each possible action . It must choose an action by making a probabilistic guess... |
AlaaElhilo/Wikipedia_ComputerScience | In some problems, the agent's preferences may be uncertain, especially if there are other agents or humans involved. These can be learned or the agent can seek information to improve its preferences. Information value theory can be used to weigh the value of exploratory or experimental actions. The space of possible f... |
AlaaElhilo/Wikipedia_ComputerScience | A Markov decision process has a transition model that describes the probability that a particular action will change the state in a particular way, and a reward function that supplies the utility of each state and the cost of each action. A policy associates a decision with each possible state. The policy could be calc... |
AlaaElhilo/Wikipedia_ComputerScience | Game theory describes rational behavior of multiple interacting agents, and is used in AI programs that make decisions that involve other agents. |
AlaaElhilo/Wikipedia_ComputerScience | Machine learning is the study of programs that can improve their performance on a given task automatically. It has been a part of AI from the beginning. |
AlaaElhilo/Wikipedia_ComputerScience | There are several kinds of machine learning. Unsupervised learning analyzes a stream of data and finds patterns and makes predictions without any other guidance. Supervised learning requires a human to label the input data first, and comes in two main varieties: classification and regression . |
AlaaElhilo/Wikipedia_ComputerScience | In reinforcement learning the agent is rewarded for good responses and punished for bad ones. The agent learns to choose responses that are classified as "good". Transfer learning is when the knowledge gained from one problem is applied to a new problem. Deep learning is a type of machine learning that runs inputs thro... |
AlaaElhilo/Wikipedia_ComputerScience | Computational learning theory can assess learners by computational complexity, by sample complexity , or by other notions of optimization. |
AlaaElhilo/Wikipedia_ComputerScience | Natural language processing allows programs to read, write and communicate in human languages such as English. Specific problems include speech recognition, speech synthesis, machine translation, information extraction, information retrieval and question answering. |
AlaaElhilo/Wikipedia_ComputerScience | Early work, based on Noam Chomsky's generative grammar and semantic networks, had difficulty with word-sense disambiguation unless restricted to small domains called "micro-worlds" . Margaret Masterman believed that it was meaning, and not grammar that was the key to understanding languages, and that thesauri and not d... |
AlaaElhilo/Wikipedia_ComputerScience | Modern deep learning techniques for NLP include word embedding , transformers , and others. In 2019, generative pre-trained transformer language models began to generate coherent text, and by 2023 these models were able to get human-level scores on the bar exam, SAT test, GRE test, and many other real-world applicatio... |
AlaaElhilo/Wikipedia_ComputerScience | Machine perception is the ability to use input from sensors to deduce aspects of the world. Computer vision is the ability to analyze visual input. |
AlaaElhilo/Wikipedia_ComputerScience | The field includes speech recognition, image classification, facial recognition, object recognition, and robotic perception. |
AlaaElhilo/Wikipedia_ComputerScience | Affective computing is an interdisciplinary umbrella that comprises systems that recognize, interpret, process or simulate human feeling, emotion and mood. For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dyn... |
AlaaElhilo/Wikipedia_ComputerScience | However, this tends to give naïve users an unrealistic conception of the intelligence of existing computer agents. Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal sentiment analysis, wherein AI classifies the affects displayed by a videotaped subject. |
AlaaElhilo/Wikipedia_ComputerScience | A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence. |
AlaaElhilo/Wikipedia_ComputerScience | AI research uses a wide variety of techniques to accomplish the goals above. |
AlaaElhilo/Wikipedia_ComputerScience | AI can solve many problems by intelligently searching through many possible solutions. There are two very different kinds of search used in AI: state space search and local search. |
AlaaElhilo/Wikipedia_ComputerScience | State space search searches through a tree of possible states to try to find a goal state. For example, planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis. |
AlaaElhilo/Wikipedia_ComputerScience | Simple exhaustive searches are rarely sufficient for most real-world problems: the search space quickly grows to astronomical numbers. The result is a search that is too slow or never completes. "Heuristics" or "rules of thumb" can help to prioritize choices that are more likely to reach a goal. |
AlaaElhilo/Wikipedia_ComputerScience | Adversarial search is used for game-playing programs, such as chess or Go. It searches through a tree of possible moves and counter-moves, looking for a winning position. |
AlaaElhilo/Wikipedia_ComputerScience | Local search uses mathematical optimization to find a solution to a problem. It begins with some form of guess and refines it incrementally. |
AlaaElhilo/Wikipedia_ComputerScience | Gradient descent is a type of local search that optimizes a set of numerical parameters by incrementally adjusting them to minimize a loss function. Variants of gradient descent are commonly used to train neural networks. |
AlaaElhilo/Wikipedia_ComputerScience | Another type of local search is evolutionary computation, which aims to iteratively improve a set of candidate solutions by "mutating" and "recombining" them, selecting only the fittest to survive each generation. |
AlaaElhilo/Wikipedia_ComputerScience | Distributed search processes can coordinate via swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization and ant colony optimization . |
AlaaElhilo/Wikipedia_ComputerScience | Formal logic is used for reasoning and knowledge representation.
Formal logic comes in two main forms: propositional logic
and predicate logic . |
AlaaElhilo/Wikipedia_ComputerScience | Deductive reasoning in logic is the process of proving a new statement from other statements that are given and assumed to be true . Proofs can be structured as proof trees, in which nodes are labelled by sentences, and children nodes are connected to parent nodes by inference rules. |
AlaaElhilo/Wikipedia_ComputerScience | Given a problem and a set of premises, problem-solving reduces to searching for a proof tree whose root node is labelled by a solution of the problem and whose leaf nodes are labelled by premises or axioms. In the case of Horn clauses, problem-solving search can be performed by reasoning forwards from the premises or b... |
AlaaElhilo/Wikipedia_ComputerScience | Inference in both Horn clause logic and first-order logic is undecidable, and therefore intractable. However, backward reasoning with Horn clauses, which underpins computation in the logic programming language Prolog, is Turing complete. Moreover, its efficiency is competitive with computation in other symbolic program... |
AlaaElhilo/Wikipedia_ComputerScience | Fuzzy logic assigns a "degree of truth" between 0 and 1. It can therefore handle propositions that are vague and partially true. |
AlaaElhilo/Wikipedia_ComputerScience | Non-monotonic logics, including logic programming with negation as failure, are designed to handle default reasoning.
Other specialized versions of logic have been developed to describe many complex domains. |
AlaaElhilo/Wikipedia_ComputerScience | Many problems in AI require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics. Precise mathematical tools have been developed that analyze how an agent can make choices and plan, using... |
AlaaElhilo/Wikipedia_ComputerScience | Bayesian networks are a tool that can be used for reasoning , learning , planning and perception . |
AlaaElhilo/Wikipedia_ComputerScience | Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time . |
AlaaElhilo/Wikipedia_ComputerScience | The simplest AI applications can be divided into two types: classifiers , on one hand, and controllers , on the other hand. Classifiers
are functions that use pattern matching to determine the closest match. They can be fine-tuned based on chosen examples using supervised learning. Each pattern is labeled with a certa... |
AlaaElhilo/Wikipedia_ComputerScience | There are many kinds of classifiers in use. The decision tree is the simplest and most widely used symbolic machine learning algorithm. K-nearest neighbor algorithm was the most widely used analogical AI until the mid-1990s, and Kernel methods such as the support vector machine displaced k-nearest neighbor in the 1990... |
AlaaElhilo/Wikipedia_ComputerScience | An artificial neural network is based on a collection of nodes also known as artificial neurons, which loosely model the neurons in a biological brain. It is trained to recognise patterns; once trained, it can recognise those patterns in fresh data. There is an input, at least one hidden layer of nodes and an output. E... |
AlaaElhilo/Wikipedia_ComputerScience | Learning algorithms for neural networks use local search to choose the weights that will get the right output for each input during training. The most common training technique is the backpropagation algorithm.
Neural networks learn to model complex relationships between inputs and outputs and find patterns in data. In... |
AlaaElhilo/Wikipedia_ComputerScience | In feedforward neural networks the signal passes in only one direction.
Recurrent neural networks feed the output signal back into the input, which allows short-term memories of previous input events. Long short term memory is the most successful network architecture for recurrent networks.
Perceptrons
use only a singl... |
AlaaElhilo/Wikipedia_ComputerScience | Deep learning
uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digi... |
AlaaElhilo/Wikipedia_ComputerScience | Deep learning has profoundly improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, natural language processing, image classification
and others. The reason that deep learning performs so well in so many applications is not known as of... |
AlaaElhilo/Wikipedia_ComputerScience | Generative pre-trained transformers are large language models that are based on the semantic relationships between words in sentences . Text-based GPT models are pre-trained on a large corpus of text which can be from the internet. The pre-training consists in predicting the next token . Throughout this pre-training, ... |
AlaaElhilo/Wikipedia_ComputerScience | Current models and services include: Gemini , ChatGPT, Grok, Claude, Copilot and LLaMA. Multimodal GPT models can process different types of data such as images, videos, sound and text. |
AlaaElhilo/Wikipedia_ComputerScience | In the late 2010s, graphics processing units that were increasingly designed with AI-specific enhancements and used with specialized TensorFlow software, had replaced previously used central processing unit as the dominant means for large-scale machine learning models' training. Historically, specialized languages, ... |
AlaaElhilo/Wikipedia_ComputerScience | AI and machine learning technology is used in most of the essential applications of the 2020s, including: search engines , targeting online advertisements, recommendation systems , driving internet traffic, targeted advertising , virtual assistants , autonomous vehicles , automatic language translation , facial recogni... |
AlaaElhilo/Wikipedia_ComputerScience | The application of AI in medicine and medical research has the potential to increase patient care and quality of life. Through the lens of the Hippocratic Oath, medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients. |
AlaaElhilo/Wikipedia_ComputerScience | For medical research, AI is an important tool for processing and integrating big data. This is particularly important for organoid and tissue engineering development which use microscopy imaging as a key technique in fabrication. It has been suggested that AI can overcome discrepancies in funding allocated to different... |
AlaaElhilo/Wikipedia_ComputerScience | Game playing programs have been used since the 1950s to demonstrate and test AI's most advanced techniques. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997. In 2011, in a Jeopardy! quiz show exhibition match, IBM's question answering syste... |
AlaaElhilo/Wikipedia_ComputerScience | Various countries are deploying AI military applications. The main applications enhance command and control, communications, sensors, integration and interoperability. Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehic... |
AlaaElhilo/Wikipedia_ComputerScience | In November 2023, US Vice President Kamala Harris disclosed a declaration signed by 31 nations to set guardrails for the military use of AI. The commitments include using legal reviews to ensure the compliance of military AI with international laws, and being cautious and transparent in the development of this technolo... |
AlaaElhilo/Wikipedia_ComputerScience | In the early 2020s, generative AI gained widespread prominence. In March 2023, 58% of US adults had heard about ChatGPT and 14% had tried it. The increasing realism and ease-of-use of AI-based text-to-image generators such as Midjourney, DALL-E, and Stable Diffusion sparked a trend of viral AI-generated photos. Widespr... |
AlaaElhilo/Wikipedia_ComputerScience | There are also thousands of successful AI applications used to solve specific problems for specific industries or institutions. In a 2017 survey, one in five companies reported they had incorporated "AI" in some offerings or processes. A few examples are energy storage, medical diagnosis, military logistics, applicatio... |
AlaaElhilo/Wikipedia_ComputerScience | In agriculture, AI has helped farmers identify areas that need irrigation, fertilization, pesticide treatments or increasing yield. Agronomists use AI to conduct research and development. AI has been used to predict the ripening time for crops such as tomatoes, monitor soil moisture, operate agricultural robots, conduc... |
AlaaElhilo/Wikipedia_ComputerScience | Artificial intelligence is used in astronomy to analyze increasing amounts of available data and applications, mainly for "classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights" for example for discovering exoplanets, forecasting solar activity, and d... |
AlaaElhilo/Wikipedia_ComputerScience | AI has potential benefits and potential risks. AI may be able to advance science and find solutions for serious problems: Demis Hassabis of Deep Mind hopes to "solve intelligence, and then use that to solve everything else". However, as the use of AI has become widespread, several unintended consequences and risks have... |
AlaaElhilo/Wikipedia_ComputerScience | Machine-learning algorithms require large amounts of data. The techniques used to acquire this data have raised concerns about privacy, surveillance and copyright. |
AlaaElhilo/Wikipedia_ComputerScience | Technology companies collect a wide range of data from their users, including online activity, geolocation data, video and audio.
For example, in order to build speech recognition algorithms, Amazon has recorded millions of private conversations and allowed temporary workers to listen to and transcribe some of them. Op... |
AlaaElhilo/Wikipedia_ComputerScience | AI developers argue that this is the only way to deliver valuable applications. and have developed several techniques that attempt to preserve privacy while still obtaining the data, such as data aggregation, de-identification and differential privacy. Since 2016, some privacy experts, such as Cynthia Dwork, have begun... |
AlaaElhilo/Wikipedia_ComputerScience | Generative AI is often trained on unlicensed copyrighted works, including in domains such as images or computer code; the output is then used under the rationale of "fair use". Website owners who do not wish to have their copyrighted content AI-indexed or 'scraped' can add code to their site if they do not want their w... |
AlaaElhilo/Wikipedia_ComputerScience | YouTube, Facebook and others use recommender systems to guide users to more content. These AI programs were given the goal of maximizing user engagement . The AI learned that users tended to choose misinformation, conspiracy theories, and extreme partisan content, and, to keep them watching, the AI recommended more of ... |
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