Ethics of artificial intelligence

Ethical_challenges AI weapons do present a type of danger different from that of human-controlled weapons.  Many governments have begun to fund programs to develop AI weaponry.  The United States Navy recently announced plans to develop autonomous drone weapons, paralleling similar announcements by Russia and South Korea respectively.  Due to the potential of AI weapons becoming more dangerous than human-operated weapons, Stephen Hawking and Max Tegmark signed a "Future of Life" petition to ban AI weapons.  The message posted by Hawking and Tegmark states that AI weapons pose an immediate danger and that action is required to avoid catastrophic disasters in the near future.  "If any major military power pushes ahead with the AI weapon development, a global arms race is virtually inevitable, and the endpoint of this technological trajectory is obvious: autonomous weapons will become the Kalashnikovs of tomorrow", says the petition, which includes Skype co-founder Jaan Tallinn and MIT professor of linguistics Noam Chomsky as additional supporters against AI weaponry.  Physicist and Astronomer Royal Sir Martin Rees has warned of catastrophic instances like "dumb robots going rogue or a network that develops a mind of its own. " Huw Price, a colleague of Rees at Cambridge, has voiced a similar warning that humans might not survive when intelligence "escapes the constraints of biology".  These two professors created the Centre for the Study of Existential Risk at Cambridge University in the hope of avoiding this threat to human existence.  Regarding the potential for smarter-than-human systems to be employed militarily, the Open Philanthropy Project writes that these scenarios "seem potentially as important as the risks related to loss of control", but research investigating AI's long-run social impact have spent relatively little time on this concern: "this class of scenarios has not been a major focus for the organizations that have been most active in this space, such as the Machine Intelligence Research Institute (MIRI) and the Future of Humanity Institute (FHI), and there seems to have been less analysis and debate regarding them".  Approaches like machine learning with neural networks can result in computers making decisions that they and the humans who programmed them cannot explain.  It is difficult for people to determine if such decisions are fair and trustworthy, leading potentially to bias in AI systems going undetected, or people rejecting the use of such systems.  This has led to advocacy and in some jurisdictions legal requirements for explainable artificial intelligence.  Explainable artificial intelligence encompasses both explainability and interpretability, with explainability relating to summarizing neural network behavior and building user confidence, while interpretability is defined as the comprehension of what a model has done or could do.  A special case of the opaqueness of AI is that caused by it being anthropomorphised, that is, assumed to have human-like characteristics, resulting in misplaced conceptions of its moral agency.  This can cause people to overlook whether either human negligence or deliberate criminal action has led to unethical outcomes produced through an AI system.


Ethics of artificial intelligence

Ethical_challenges Some recent digital governance regulation, such as the EU's AI Act is set out to rectify this, by ensuring that AI systems are treated with at least as much care as one would expect under ordinary product liability.  This includes potentially AI audits.


LAION

LAION LAION (acronym for Large-scale Artificial Intelligence Open Network) is a German non-profit which makes open-sourced artificial intelligence models and datasets. It is best known for releasing a number of large datasets of images and captions scraped from the web which have been used to train a number of high-profile text-to-image models, including Stable Diffusion and Imagen. In February 2023, LAION was named in the Getty Images lawsuit against Stable Diffusion as a non-party. In April 2023, LAION was directly sued by a German photographer who wanted to have his images removed from the training set. On April 15, 2023, LAION and contributors released to public an open source AI assistant chatbot OpenAssistant.


LAION

Image_datasets LAION has publicly released a number of large datasets of image-caption pairs which have been widely used by AI researchers. The data is derived from the Common Crawl, a dataset of scraped web pages. The developers searched the crawled html for <img> tags and treated their alt attributes as captions. They used CLIP to identify and discard images whose content did not appear to match their captions. LAION does not host the content of scraped images themselves; rather, the dataset contains URLs pointing to images, which researchers must download themselves. The first such dataset, LAION-400M, was released in August 2021 and consisted of 400 million image-caption pairs. The pairs were extracted from a random subset of webpages scraped by Common Crawl between 2014 and 2021. It was an attempt to recreate the process used by OpenAI to collect the 400 million image-caption pairs they used to train the CLIP model - the company had chosen to open-source the model's code and weights, but not its training dataset. Imagen, a text-to-image model announced by Google Brain in 2022, was trained on LAION-400M in combination with private internal datasets. A successor of more than 5 billion pairs, LAION-5B, was released in March 2022. As of its release, it was the largest freely available dataset of image-caption pairs in existence. Its creation was funded by Doodlebot, Hugging Face and Stability AI, the AI company behind the funding of the Stable Diffusion text-to-image model, which was trained on it.


LAION

OpenAssistant OpenAssistant is an artificial intelligence (AI) open source chat-based assistant that understands tasks, can interact with third-party systems and retrieve information dynamically to do so. The project is developed by a group of volunteers in collaboration with LAION. One of the goals for development includes free access to large language models that can be run locally on consumer hardware. The project is backed by a worldwide crowdsourcing effort involving over 13,500 volunteers who have created 600k human-generated data points.


Question answering

Architecture As of 2001[update], question-answering systems typically included a question classifier module that determined the type of question and the type of answer. Different types of question-answering systems employ different architectures. For example, modern open-domain question answering systems may use a retriever-reader architecture. The retriever is aimed at retrieving relevant documents related to a given question, while the reader is used to infer the answer from the retrieved documents. Systems such as GPT-3, T5, and BART use an end-to-end[jargon] architecture in which a transformer-based[jargon] architecture stores large-scale textual data in the underlying parameters. Such models can answer questions without accessing any external knowledge sources.


Question answering

Progress Question answering systems have been extended in recent[may be outdated as of April 2023] years to encompass additional domains of knowledge For example, systems have been developed to automatically answer temporal and geospatial questions, questions of definition and terminology, biographical questions, multilingual questions, and questions about the content of audio, images, and video. Current question answering research topics include: interactivity���clarification of questions or answers[further explanation needed] answer reuse or caching semantic parsing answer presentation[further explanation needed] knowledge representation and semantic entailment social media analysis[further explanation needed] with question answering systems sentiment analysis utilization of thematic roles Image captioning for visual question answering Embodied question answeringIn 2011, Watson, a question answering computer system developed by IBM, competed in two exhibition matches of Jeopardy! against Brad Rutter and Ken Jennings, winning by a significant margin. Facebook Research made their DrQA system available under an open source license. This system uses Wikipedia as knowledge source. The open source framework Haystack by deepset combines open-domain question answering with generative question answering and supports the domain adaptation[clarification needed] of the underlying[clarification needed] language models for industry use cases[vague].


OpenAI Codex

OpenAI Codex OpenAI Codex is an artificial intelligence model developed by OpenAI. It parses natural language and generates code in response. It powers GitHub Copilot, a programming autocompletion tool for select IDEs, like Visual Studio Code and Neovim. Codex is a descendant of OpenAI's GPT-3 model, fine-tuned for use in programming applications. OpenAI released an API for Codex in closed beta. In March 2023, OpenAI shut down access to Codex. Due to public appeals from researchers OpenAI reversed course. The Codex model can still be used by researchers of the OpenAI Research Access Program.


OpenAI Codex

Capabilities Based on GPT-3, a neural network trained on text, Codex was additionally trained on 159 gigabytes of Python code from 54 million GitHub repositories. A typical use case of Codex is for a user to type a comment, such as "//compute the moving average of an array for a given window size", then use the AI to suggest a block of code that satisfies that comment prompt. OpenAI stated that Codex can complete approximately 37% of requests and is meant to make human programming faster rather than to replace it. According to OpenAI's blog, Codex excels most at "mapping... simple problems to existing code", which they describe as "probably the least fun part of programming". Jeremy Howard, co-founder of Fast.ai, stated that "Codex is a way of getting code written without having to write as much code" and that "it is not always correct, but it is just close enough". According to a paper written by OpenAI researchers, when Codex attempted each test case 100 times, it generated working solutions for 70.2% of prompts. OpenAI claims that Codex can create code in over a dozen programming languages, including Go, JavaScript, Perl, PHP, Ruby, Shell, Swift, and TypeScript, though it is most effective in Python. According to VentureBeat, demonstrations uploaded by OpenAI showed impressive coreference resolution capabilities. The demonstrators were able to create a browser game in JavaScript and generate data science charts using matplotlib. OpenAI showed that Codex can interface with services and apps such as Mailchimp, Microsoft Word, Spotify, and Google Calendar. Microsoft is reportedly interested in exploring[vague] Codex's capabilities.


OpenAI Codex

Issues OpenAI demonstrations showcased flaws such as inefficient code and one-off quirks in code samples. In an interview with The Verge, OpenAI chief technology officer Greg Brockman said that "sometimes [Codex] doesn't quite know exactly what you're asking" and that it can require some trial and error. OpenAI researchers found that Codex struggles with multi-step and higher-level[clarification needed] prompts, often failing or yielding counter-intuitive behavior. Additionally, they brought up several safety issues, such as over-reliance by novice programmers, biases based on the training data, and security impacts due to vulnerable code. VentureBeat stated that because Codex is trained on public data, it could be vulnerable to "data poisoning" via intentional uploads of malicious code. According to a study by researchers from New York University, approximately 40% of code generated by GitHub Copilot (which uses Codex) in scenarios relevant to high-risk CWEs included glitches or other exploitable design flaws. The Free Software Foundation expressed concerns that code snippets generated by Copilot and Codex could violate copyright, in particular the condition of the GPL that requires derivative works to be licensed under equivalent terms. Issues they raised include whether training on public repositories falls into fair use or not, how developers could discover infringing generated code, whether trained machine learning models could be considered modifiable source code or a compilation of the training data, and if machine learning models could themselves be copyrighted and by whom. An internal GitHub study found that approximately 0.1% of generated code contained direct copies from the training data. In one example the model outputted the training data code implementing the fast inverse square root algorithm, including comments and an incorrect copyright notice. In response, OpenAI stated that "legal uncertainty on the copyright implications of training AI systems imposes substantial costs on AI developers and so should be authoritatively resolved." The copyright issues with Codex have been compared to the Authors Guild, Inc. v. Google, Inc. court case, in which judges ruled that Google Books's use of text snippets from millions of scanned books constituted fair use.


Cache language model

Cache language model A cache language model is a type of statistical language model.  These occur in the natural language processing subfield of computer science and assign probabilities to given sequences of words by means of a probability distribution.  Statistical language models are key components of speech recognition systems and of many machine translation systems: they tell such systems which possible output word sequences are probable and which are improbable.  The particular characteristic of a cache language model is that it contains a cache component and assigns relatively high probabilities to words or word sequences that occur elsewhere in a given text.  The primary, but by no means sole, use of cache language models is in speech recognition systems. [citation needed] To understand why it is a good idea for a statistical language model to contain a cache component one might consider someone who is dictating a letter about elephants to a speech recognition system.  Standard (non-cache) N-gram language models will assign a very low probability to the word "elephant" because it is a very rare word in English.  If the speech recognition system does not contain a cache component, the person dictating the letter may be annoyed: each time the word "elephant" is spoken another sequence of words with a higher probability according to the N-gram language model may be recognized (e. g. , "tell a plan").  These erroneous sequences will have to be deleted manually and replaced in the text by "elephant" each time "elephant" is spoken.  If the system has a cache language model, "elephant" will still probably be misrecognized the first time it is spoken and will have to be entered into the text manually; however, from this point on the system is aware that "elephant" is likely to occur again ��� the estimated probability of occurrence of "elephant" has been increased, making it more likely that if it is spoken it will be recognized correctly.  Once "elephant" has occurred several times, the system is likely to recognize it correctly every time it is spoken until the letter has been completely dictated.  This increase in the probability assigned to the occurrence of "elephant" is an example of a consequence of machine learning and more specifically of pattern recognition.  There exist variants of the cache language model in which not only single words but also multi-word sequences that have occurred previously are assigned higher probabilities (e. g. , if "San Francisco" occurred near the beginning of the text subsequent instances of it would be assigned a higher probability).  The cache language model was first proposed in a paper published in 1990, after which the IBM speech-recognition group experimented with the concept.  The group found that implementation of a form of cache language model yielded a 24% drop in word-error rates once the first few hundred words of a document had been dictated.


Cache language model

Cache language model A detailed survey of language modeling techniques concluded that the cache language model was one of the few new language modeling techniques that yielded improvements over the standard N-gram approach: "Our caching results show that caching is by far the most useful technique for perplexity reduction at small and medium training data sizes".  The development of the cache language model has generated considerable interest among those concerned with computational linguistics in general and statistical natural language processing in particular: recently, there has been interest in applying the cache language model in the field of statistical machine translation.  The success of the cache language model in improving word prediction rests on the human tendency to use words in a "bursty" fashion: when one is discussing a certain topic in a certain context, the frequency with which one uses certain words will be quite different from their frequencies when one is discussing other topics in other contexts.  The traditional N-gram language models, which rely entirely on information from a very small number (four, three, or two) of words preceding the word to which a probability is to be assigned, do not adequately model this "burstiness". [citation needed] Recently, the cache language model concept - originally conceived for the N-gram statistical language model paradigm - has been adapted for use in the neural paradigm.  For instance, recent work on continuous cache language models in the recurrent neural network (RNN) setting has applied the cache concept to much larger contexts than before, yielding significant reductions in perplexity .  Another recent line of research involves incorporating a cache component in a feed-forward neural language model (FN-LM) to achieve rapid domain adaptation .


Convolutional neural network

Translation_equivariance_and_aliasing It is commonly assumed that CNNs are invariant to shifts of the input. Convolution or pooling layers within a CNN that do not have a stride greater than one are indeed equivariant to translations of the input. However, layers with a stride greater than one ignore the Nyquist-Shannon sampling theorem and might lead to aliasing of the input signal While, in principle, CNNs are capable of implementing anti-aliasing filters, it has been observed that this does not happen in practice and yield models that are not equivariant to translations. Furthermore, if a CNN makes use of fully connected layers, translation equivariance does not imply translation invariance, as the fully connected layers are not invariant to shifts of the input. One solution for complete translation invariance is avoiding any down-sampling throughout the network and applying global average pooling at the last layer. Additionally, several other partial solutions have been proposed, such as anti-aliasing before downsampling operations, spatial transformer networks, data augmentation, subsampling combined with pooling, and capsule neural networks.


Turing test

Variations Numerous other versions of the Turing test, including those expounded above, have been raised through the years.  A modification of the Turing test wherein the objective of one or more of the roles have been reversed between machines and humans is termed a reverse Turing test.  An example is implied in the work of psychoanalyst Wilfred Bion, who was particularly fascinated by the "storm" that resulted from the encounter of one mind by another.  In his 2000 book, among several other original points with regard to the Turing test, literary scholar Peter Swirski discussed in detail the idea of what he termed the Swirski test���essentially the reverse Turing test.  He pointed out that it overcomes most if not all standard objections levelled at the standard version.  Carrying this idea forward, R.  D.  Hinshelwood described the mind as a "mind recognizing apparatus".  The challenge would be for the computer to be able to determine if it were interacting with a human or another computer.  This is an extension of the original question that Turing attempted to answer but would, perhaps, offer a high enough standard to define a machine that could "think" in a way that we typically define as characteristically human.  CAPTCHA is a form of reverse Turing test.  Before being allowed to perform some action on a website, the user is presented with alphanumerical characters in a distorted graphic image and asked to type them out.  This is intended to prevent automated systems from being used to abuse the site.  The rationale is that software sufficiently sophisticated to read and reproduce the distorted image accurately does not exist (or is not available to the average user), so any system able to do so is likely to be a human.  Software that could reverse CAPTCHA with some accuracy by analysing patterns in the generating engine started being developed soon after the creation of CAPTCHA.  In 2013, researchers at Vicarious announced that they had developed a system to solve CAPTCHA challenges from Google, Yahoo! , and PayPal up to 90% of the time.  In 2014, Google engineers demonstrated a system that could defeat CAPTCHA challenges with 99.8% accuracy.  In 2015, Shuman Ghosemajumder, former click fraud czar of Google, stated that there were cybercriminal sites that would defeat CAPTCHA challenges for a fee, to enable various forms of fraud.  A further variation is motivated by the concern that modern Natural Language Processing prove to be highly successful in generating text on the basis of a huge text corpus and could eventually pass the Turing test simply by manipulating words and sentences that have been used in the initial training of the model.  Since the interrogator has no precise understanding of the training data, the model might simply be returning sentences that exist in similar fashion in the enormous amount of training data.  For this reason, Arthur Schwaninger proposes a variation of the Turing test that can distinguish between systems that are only capable of using language and systems that understand language.


Turing test

Variations He proposes a test in which the machine is confronted with philosophical questions that do not depend on any prior knowledge and yet require self-reflection to be answered appropriately.  Another variation is described as the subject-matter expert Turing test, where a machine's response cannot be distinguished from an expert in a given field.  This is also known as a "Feigenbaum test" and was proposed by Edward Feigenbaum in a 2003 paper.  Robert French (1990) makes the case that an interrogator can distinguish human and non-human interlocutors by posing questions that reveal the low-level (i. e. , unconscious) processes of human cognition, as studied by cognitive science.  Such questions reveal the precise details of the human embodiment of thought and can unmask a computer unless it experiences the world as humans do.  The "Total Turing test" variation of the Turing test, proposed by cognitive scientist Stevan Harnad, adds two further requirements to the traditional Turing test.  The interrogator can also test the perceptual abilities of the subject (requiring computer vision) and the subject's ability to manipulate objects (requiring robotics).  A letter published in Communications of the ACM describes the concept of generating a synthetic patient population and proposes a variation of Turing test to assess the difference between synthetic and real patients.  The letter states: "In the EHR context, though a human physician can readily distinguish between synthetically generated and real live human patients, could a machine be given the intelligence to make such a determination on its own? " and further the letter states: "Before synthetic patient identities become a public health problem, the legitimate EHR market might benefit from applying Turing Test-like techniques to ensure greater data reliability and diagnostic value.  Any new techniques must thus consider patients' heterogeneity and are likely to have greater complexity than the Allen eighth-grade-science-test is able to grade. " The minimum intelligent signal test was proposed by Chris McKinstry as "the maximum abstraction of the Turing test", in which only binary responses (true/false or yes/no) are permitted, to focus only on the capacity for thought.  It eliminates text chat problems like anthropomorphism bias, and does not require emulation of unintelligent human behaviour, allowing for systems that exceed human intelligence.  The questions must each stand on their own, however, making it more like an IQ test than an interrogation.  It is typically used to gather statistical data against which the performance of artificial intelligence programs may be measured.  The organisers of the Hutter Prize believe that compressing natural language text is a hard AI problem, equivalent to passing the Turing test.  The data compression test has some advantages over most versions and variations of a Turing test, including: It gives a single number that can be directly used to compare which of two machines is "more intelligent. " It does not require the computer to lie to the judgeThe main disadvantages of using data compression as a test are: It is not possible to test humans this way.


Turing test

Variations It is unknown what particular "score" on this test���if any���is equivalent to passing a human-level Turing test. A related approach to Hutter's prize which appeared much earlier in the late 1990s is the inclusion of compression problems in an extended Turing test.  or by tests which are completely derived from Kolmogorov complexity.  Other related tests in this line are presented by Hernandez-Orallo and Dowe.  Algorithmic IQ, or AIQ for short, is an attempt to convert the theoretical Universal Intelligence Measure from Legg and Hutter (based on Solomonoff's inductive inference) into a working practical test of machine intelligence.  Two major advantages of some of these tests are their applicability to nonhuman intelligences and their absence of a requirement for human testers.  The Turing test inspired the Ebert test proposed in 2011 by film critic Roger Ebert which is a test whether a computer-based synthesised voice has sufficient skill in terms of intonations, inflections, timing and so forth, to make people laugh.  Taking advantage of Large Language Models, in 2023 the research company AI21 Labs created an online social experiment titled "Human or Not? ".  It was played more than 10 million times by more than 2 million people.  It is the biggest Turing-style experiment to that date.  The results showed that 32% of people couldn't distinguish between humans and machines.


Turing test

See_also Natural language processing Artificial intelligence in fiction Blindsight Causality Chatbot ChatGPT Computer game bot Turing Test Dead Internet theory Explanation Explanatory gap Functionalism Graphics Turing Test Ex Machina (film) Hard problem of consciousness List of things named after Alan Turing Mark V. Shaney (Usenet bot) Mind-body problem Mirror neuron Philosophical zombie Problem of other minds Reverse engineering Sentience Simulated reality Social bot Technological singularity Theory of mind Uncanny valley Voight-Kampff machine (fictitious Turing test from Blade Runner) Winograd Schema Challenge SHRDLU


Outline of machine learning

Machine_learning_tools Comparison of deep learning softwareAmazon Machine Learning Microsoft Azure Machine Learning Studio DistBelief ��� replaced by TensorFlowApache Singa Apache MXNet Caffe PyTorch mlpack TensorFlow Torch CNTK Accord.Net Jax MLJ.jl ��� A machine learning framework for JuliaDeeplearning4j Theano scikit-learn KerasAlmeida���Pineda recurrent backpropagation ALOPEX Backpropagation Bootstrap aggregating CN2 algorithm Constructing skill trees Dehaene���Changeux model Diffusion map Dominance-based rough set approach Dynamic time warping Error-driven learning Evolutionary multimodal optimization Expectation���maximization algorithm FastICA Forward���backward algorithm GeneRec Genetic Algorithm for Rule Set Production Growing self-organizing map Hyper basis function network IDistance K-nearest neighbors algorithm Kernel methods for vector output Kernel principal component analysis Leabra Linde���Buzo���Gray algorithm Local outlier factor Logic learning machine LogitBoost Manifold alignment Markov chain Monte Carlo (MCMC) Minimum redundancy feature selection Mixture of experts Multiple kernel learning Non-negative matrix factorization Online machine learning Out-of-bag error Prefrontal cortex basal ganglia working memory PVLV Q-learning Quadratic unconstrained binary optimization Query-level feature Quickprop Radial basis function network Randomized weighted majority algorithm Reinforcement learning Repeated incremental pruning to produce error reduction (RIPPER) Rprop Rule-based machine learning Skill chaining Sparse PCA State���action���reward���state���action Stochastic gradient descent Structured kNN T-distributed stochastic neighbor embedding Temporal difference learning Wake-sleep algorithm Weighted majority algorithm (machine learning)


Outline of machine learning

Machine_learning_methods K-nearest neighbors algorithm (KNN) Learning vector quantization (LVQ) Self-organizing map (SOM)Logistic regression Ordinary least squares regression (OLSR) Linear regression Stepwise regression Multivariate adaptive regression splines (MARS)Regularization algorithm Ridge regression Least Absolute Shrinkage and Selection Operator (LASSO) Elastic net Least-angle regression (LARS) Classifiers Probabilistic classifier Naive Bayes classifier Binary classifier Linear classifier Hierarchical classifierRidge regression Least Absolute Shrinkage and Selection Operator (LASSO) Elastic net Least-angle regression (LARS)Probabilistic classifier Naive Bayes classifier Binary classifier Linear classifier Hierarchical classifierNaive Bayes classifierDimensionality reduction Canonical correlation analysis (CCA) Factor analysis Feature extraction Feature selection Independent component analysis (ICA) Linear discriminant analysis (LDA) Multidimensional scaling (MDS) Non-negative matrix factorization (NMF) Partial least squares regression (PLSR) Principal component analysis (PCA) Principal component regression (PCR) Projection pursuit Sammon mapping t-distributed stochastic neighbor embedding (t-SNE)Ensemble learning AdaBoost Boosting Bootstrap aggregating (Bagging) Ensemble averaging ��� process of creating multiple models and combining them to produce a desired output, as opposed to creating just one model.  Frequently an ensemble of models performs better than any individual model, because the various errors of the models "average out. " Gradient boosted decision tree (GBDT) Gradient boosting machine (GBM) Random Forest Stacked Generalization (blending)Meta-learning Inductive bias MetadataReinforcement learning Q-learning State���action���reward���state���action (SARSA) Temporal difference learning (TD) Learning AutomataSupervised learning Averaged one-dependence estimators (AODE) Artificial neural network Case-based reasoning Gaussian process regression Gene expression programming Group method of data handling (GMDH) Inductive logic programming Instance-based learning Lazy learning Learning Automata Learning Vector Quantization Logistic Model Tree Minimum message length (decision trees, decision graphs, etc.


Outline of machine learning

Machine_learning_methods ) Nearest Neighbor Algorithm Analogical modeling Probably approximately correct learning (PAC) learning Ripple down rules, a knowledge acquisition methodology Symbolic machine learning algorithms Support vector machines Random Forests Ensembles of classifiers Bootstrap aggregating (bagging) Boosting (meta-algorithm) Ordinal classification Conditional Random Field ANOVA Quadratic classifiers k-nearest neighbor Boosting SPRINT Bayesian networks Naive Bayes Hidden Markov models Hierarchical hidden Markov modelNearest Neighbor Algorithm Analogical modelingBootstrap aggregating (bagging) Boosting (meta-algorithm)SPRINTNaive BayesHierarchical hidden Markov modelBayesian statistics Bayesian knowledge base Naive Bayes Gaussian Naive Bayes Multinomial Naive Bayes Averaged One-Dependence Estimators (AODE) Bayesian Belief Network (BBN) Bayesian Network (BN)Decision tree algorithm Decision tree Classification and regression tree (CART) Iterative Dichotomiser 3 (ID3) C4.5 algorithm C5.0 algorithm Chi-squared Automatic Interaction Detection (CHAID) Decision stump Conditional decision tree ID3 algorithm Random forest SLIQLinear classifier Fisher's linear discriminant Linear regression Logistic regression Multinomial logistic regression Naive Bayes classifier Perceptron Support vector machineUnsupervised learning Expectation-maximization algorithm Vector Quantization Generative topographic map Information bottleneck method Association rule learning algorithms Apriori algorithm Eclat algorithmApriori algorithm Eclat algorithmArtificial neural network Feedforward neural network Extreme learning machine Convolutional neural network Recurrent neural network Long short-term memory (LSTM) Logic learning machine Self-organizing mapExtreme learning machine Convolutional neural networkLong short-term memory (LSTM)Association rule learning Apriori algorithm Eclat algorithm FP-growth algorithmHierarchical clustering Single-linkage clustering Conceptual clusteringCluster analysis BIRCH DBSCAN Expectation-maximization (EM) Fuzzy clustering Hierarchical Clustering K-means clustering K-medians Mean-shift OPTICS algorithmAnomaly detection k-nearest neighbors algorithm (k-NN) Local outlier factorSemi-supervised learning Active learning ��� special case of semi-supervised learning in which a learning algorithm is able to interactively query the user (or some other information source) to obtain the desired outputs at new data points.


Outline of machine learning

Machine_learning_projects Machine learning projects DeepMind Google Brain OpenAI Meta AI


Outline of machine learning


Outline of machine learning

See_also Outline of artificial intelligence Outline of computer vision Outline of robotics Accuracy paradox Action model learning Activation function Activity recognition ADALINE Adaptive neuro fuzzy inference system Adaptive resonance theory Additive smoothing Adjusted mutual information AIVA AIXI AlchemyAPI AlexNet Algorithm selection Algorithmic inference Algorithmic learning theory AlphaGo AlphaGo Zero Alternating decision tree Apprenticeship learning Causal Markov condition Competitive learning Concept learning Decision tree learning Differentiable programming Distribution learning theory Eager learning End-to-end reinforcement learning Error tolerance (PAC learning) Explanation-based learning Feature GloVe Hyperparameter Inferential theory of learning Learning automata Learning classifier system Learning rule Learning with errors M-Theory (learning framework) Machine learning control Machine learning in bioinformatics Margin Markov chain geostatistics Markov chain Monte Carlo (MCMC) Markov information source Markov logic network Markov model Markov random field Markovian discrimination Maximum-entropy Markov model Multi-armed bandit Multi-task learning Multilinear subspace learning Multimodal learning Multiple instance learning Multiple-instance learning Never-Ending Language Learning Offline learning Parity learning Population-based incremental learning Predictive learning Preference learning Proactive learning Proximal gradient methods for learning Semantic analysis Similarity learning Sparse dictionary learning Stability (learning theory) Statistical learning theory Statistical relational learning Tanagra Transfer learning Variable-order Markov model Version space learning Waffles Weka Loss function Loss functions for classification Mean squared error (MSE) Mean squared prediction error (MSPE) Taguchi loss function Low-energy adaptive clustering hierarchyOutline of computer visionLoss functions for classification Mean squared error (MSE) Mean squared prediction error (MSPE) Taguchi loss functionAnne O'Tate Ant colony optimization algorithms Anthony Levandowski Anti-unification (computer science) Apache Flume Apache Giraph Apache Mahout Apache SINGA Apache Spark Apache SystemML Aphelion (software) Arabic Speech Corpus Archetypal analysis Arthur Zimek Artificial ants Artificial bee colony algorithm Artificial development Artificial immune system Astrostatistics Averaged one-dependence estimators Bag-of-words model Balanced clustering Ball tree Base rate Bat algorithm Baum���Welch algorithm Bayesian hierarchical modeling Bayesian interpretation of kernel regularization Bayesian optimization Bayesian structural time series Bees algorithm Behavioral clustering Bernoulli scheme Bias���variance tradeoff Biclustering BigML Binary classification Bing Predicts Bio-inspired computing Biogeography-based optimization Biplot Bondy's theorem Bongard problem Bradley���Terry model BrownBoost Brown clustering Burst error CBCL (MIT) CIML community portal CMA-ES CURE data clustering algorithm Cache language model Calibration (statistics) Canonical correspondence analysis Canopy clustering algorithm Cascading classifiers Category utility CellCognition Cellular evolutionary algorithm Chi-square automatic interaction detection Chromosome (genetic algorithm) Classifier chains Cleverbot Clonal selection algorithm Cluster-weighted modeling Clustering high-dimensional data Clustering illusion CoBoosting Cobweb (clustering) Cognitive computer Cognitive robotics Collostructional analysis Common-method variance Complete-linkage clustering Computer-automated design Concept class Concept drift Conference on Artificial General Intelligence Conference on Knowledge Discovery and Data Mining Confirmatory factor analysis Confusion matrix Congruence coefficient Connect (computer system) Consensus clustering Constrained clustering Constrained conditional model Constructive cooperative coevolution Correlation clustering Correspondence analysis Cortica Coupled pattern learner Cross-entropy method Cross-validation (statistics) Crossover (genetic algorithm) Cuckoo search Cultural algorithm Cultural consensus theory Curse of dimensionality DADiSP DARPA LAGR Program Darkforest Dartmouth workshop DarwinTunes Data Mining Extensions Data exploration Data pre-processing Data stream clustering Dataiku Davies���Bouldin index Decision boundary Decision list Decision tree model Deductive classifier DeepArt DeepDream Deep Web Technologies Defining length Dendrogram Dependability state model Detailed balance Determining the number of clusters in a data set Detrended correspondence analysis Developmental robotics Diffbot Differential evolution Discrete phase-type distribution Discriminative model Dissociated press Distributed R Dlib Document classification Documenting Hate Domain adaptation Doubly stochastic model Dual-phase evolution Dunn index Dynamic Bayesian network Dynamic Markov compression Dynamic topic model Dynamic unobserved effects model EDLUT ELKI Edge recombination operator Effective fitness Elastic map Elastic matching Elbow method (clustering) Emergent (software) Encog Entropy rate Erkki Oja Eurisko European Conference on Artificial Intelligence Evaluation of binary classifiers Evolution strategy Evolution window Evolutionary Algorithm for Landmark Detection Evolutionary algorithm Evolutionary art Evolutionary music Evolutionary programming Evolvability (computer science) Evolved antenna Evolver (software) Evolving classification function Expectation propagation Exploratory factor analysis F1 score FLAME clustering Factor analysis of mixed data Factor graph Factor regression model Factored language model Farthest-first traversal Fast-and-frugal trees Feature Selection Toolbox Feature hashing Feature scaling Feature vector Firefly algorithm First-difference estimator First-order inductive learner Fish School Search Fisher kernel Fitness approximation Fitness function Fitness proportionate selection Fluentd Folding@home Formal concept analysis Forward algorithm Fowlkes���Mallows index Frederick Jelinek Frrole Functional principal component analysis GATTO GLIMMER Gary Bryce Fogel Gaussian adaptation Gaussian process Gaussian process emulator Gene prediction General Architecture for Text Engineering Generalization error Generalized canonical correlation Generalized filtering Generalized iterative scaling Generalized multidimensional scaling Generative adversarial network Generative model Genetic algorithm Genetic algorithm scheduling Genetic algorithms in economics Genetic fuzzy systems Genetic memory (computer science) Genetic operator Genetic programming Genetic representation Geographical cluster Gesture Description Language Geworkbench Glossary of artificial intelligence Glottochronology Golem (ILP) Google matrix Grafting (decision trees) Gramian matrix Grammatical evolution Granular computing GraphLab Graph kernel Gremlin (programming language) Growth function HUMANT (HUManoid ANT) algorithm Hammersley���Clifford theorem Harmony search Hebbian theory Hidden Markov random field Hidden semi-Markov model Hierarchical hidden Markov model Higher-order factor analysis Highway network Hinge loss Holland's schema theorem Hopkins statistic Hoshen���Kopelman algorithm Huber loss IRCF360 Ian Goodfellow Ilastik Ilya Sutskever Immunocomputing Imperialist competitive algorithm Inauthentic text Incremental decision tree Induction of regular languages Inductive bias Inductive probability Inductive programming Influence diagram Information Harvesting Information gain in decision trees Information gain ratio Inheritance (genetic algorithm) Instance selection Intel RealSense Interacting particle system Interactive machine translation International Joint Conference on Artificial Intelligence International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics International Semantic Web Conference Iris flower data set Island algorithm Isotropic position Item response theory Iterative Viterbi decoding JOONE Jabberwacky Jaccard index Jackknife variance estimates for random forest Java Grammatical Evolution Joseph Nechvatal Jubatus Julia (programming language) Junction tree algorithm K-SVD K-means++ K-medians clustering K-medoids KNIME KXEN Inc.


Outline of machine learning

See_also K q-flats Kaggle Kalman filter Katz's back-off model Kernel adaptive filter Kernel density estimation Kernel eigenvoice Kernel embedding of distributions Kernel method Kernel perceptron Kernel random forest Kinect Klaus-Robert M��ller Kneser���Ney smoothing Knowledge Vault Knowledge integration LIBSVM LPBoost Labeled data LanguageWare Language identification in the limit Language model Large margin nearest neighbor Latent Dirichlet allocation Latent class model Latent semantic analysis Latent variable Latent variable model Lattice Miner Layered hidden Markov model Learnable function class Least squares support vector machine Leave-one-out error Leslie P.  Kaelbling Linear genetic programming Linear predictor function Linear separability Lingyun Gu Linkurious Lior Ron (business executive) List of genetic algorithm applications List of metaphor-based metaheuristics List of text mining software Local case-control sampling Local independence Local tangent space alignment Locality-sensitive hashing Log-linear model Logistic model tree Low-rank approximation Low-rank matrix approximations MATLAB MIMIC (immunology) MXNet Mallet (software project) Manifold regularization Margin-infused relaxed algorithm Margin classifier Mark V.  Shaney Massive Online Analysis Matrix regularization Matthews correlation coefficient Mean shift Mean squared error Mean squared prediction error Measurement invariance Medoid MeeMix Melomics Memetic algorithm Meta-optimization Mexican International Conference on Artificial Intelligence Michael Kearns (computer scientist) MinHash Mixture model Mlpy Models of DNA evolution Moral graph Mountain car problem Movidius Multi-armed bandit Multi-label classification Multi expression programming Multiclass classification Multidimensional analysis Multifactor dimensionality reduction Multilinear principal component analysis Multiple correspondence analysis Multiple discriminant analysis Multiple factor analysis Multiple sequence alignment Multiplicative weight update method Multispectral pattern recognition Mutation (genetic algorithm) MysteryVibe N-gram NOMINATE (scaling method) Native-language identification Natural Language Toolkit Natural evolution strategy Nearest-neighbor chain algorithm Nearest centroid classifier Nearest neighbor search Neighbor joining Nest Labs NetMiner NetOwl Neural Designer Neural Engineering Object Neural modeling fields Neural network software NeuroSolutions Neuroevolution Neuroph Niki. ai Noisy channel model Noisy text analytics Nonlinear dimensionality reduction Novelty detection Nuisance variable One-class classification Onnx OpenNLP Optimal discriminant analysis Oracle Data Mining Orange (software) Ordination (statistics) Overfitting PROGOL PSIPRED Pachinko allocation PageRank Parallel metaheuristic Parity benchmark Part-of-speech tagging Particle swarm optimization Path dependence Pattern language (formal languages) Peltarion Synapse Perplexity Persian Speech Corpus Picas (app) Pietro Perona Pipeline Pilot Piranha (software) Pitman���Yor process Plate notation Polynomial kernel Pop music automation Population process Portable Format for Analytics Predictive Model Markup Language Predictive state representation Preference regression Premature convergence Principal geodesic analysis Prior knowledge for pattern recognition Prisma (app) Probabilistic Action Cores Probabilistic context-free grammar Probabilistic latent semantic analysis Probabilistic soft logic Probability matching Probit model Product of experts Programming with Big Data in R Proper generalized decomposition Pruning (decision trees) Pushpak Bhattacharyya Q methodology Qloo Quality control and genetic algorithms Quantum Artificial Intelligence Lab Queueing theory Quick, Draw!


Outline of machine learning

See_also R (programming language) Rada Mihalcea Rademacher complexity Radial basis function kernel Rand index Random indexing Random projection Random subspace method Ranking SVM RapidMiner Rattle GUI Raymond Cattell Reasoning system Regularization perspectives on support vector machines Relational data mining Relationship square Relevance vector machine Relief (feature selection) Renjin Repertory grid Representer theorem Reward-based selection Richard Zemel Right to explanation RoboEarth Robust principal component analysis RuleML Symposium Rule induction Rules extraction system family SAS (software) SNNS SPSS Modeler SUBCLU Sample complexity Sample exclusion dimension Santa Fe Trail problem Savi Technology Schema (genetic algorithms) Search-based software engineering Selection (genetic algorithm) Self-Service Semantic Suite Semantic folding Semantic mapping (statistics) Semidefinite embedding Sense Networks Sensorium Project Sequence labeling Sequential minimal optimization Shattered set Shogun (toolbox) Silhouette (clustering) SimHash SimRank Similarity measure Simple matching coefficient Simultaneous localization and mapping Sinkov statistic Sliced inverse regression Snakes and Ladders Soft independent modelling of class analogies Soft output Viterbi algorithm Solomonoff's theory of inductive inference SolveIT Software Spectral clustering Spike-and-slab variable selection Statistical machine translation Statistical parsing Statistical semantics Stefano Soatto Stephen Wolfram Stochastic block model Stochastic cellular automaton Stochastic diffusion search Stochastic grammar Stochastic matrix Stochastic universal sampling Stress majorization String kernel Structural equation modeling Structural risk minimization Structured sparsity regularization Structured support vector machine Subclass reachability Sufficient dimension reduction Sukhotin's algorithm Sum of absolute differences Sum of absolute transformed differences Swarm intelligence Switching Kalman filter Symbolic regression Synchronous context-free grammar Syntactic pattern recognition TD-Gammon TIMIT Teaching dimension Teuvo Kohonen Textual case-based reasoning Theory of conjoint measurement Thomas G.


Curse of dimensionality

Curse of dimensionality The curse of dimensionality refers to various phenomena that arise when analyzing and organizing data in high-dimensional spaces that do not occur in low-dimensional settings such as the three-dimensional physical space of everyday experience. The expression was coined by Richard E. Bellman when considering problems in dynamic programming. Dimensionally cursed phenomena occur in domains such as numerical analysis, sampling, combinatorics, machine learning, data mining and databases. The common theme of these problems is that when the dimensionality increases, the volume of the space increases so fast that the available data become sparse. In order to obtain a reliable result, the amount of data needed often grows exponentially with the dimensionality. Also, organizing and searching data often relies on detecting areas where objects form groups with similar properties; in high dimensional data, however, all objects appear to be sparse and dissimilar in many ways, which prevents common data organization strategies from being efficient.


Curse of dimensionality

See_also Bellman equation Clustering high-dimensional data Concentration of measure Dimension reduction Dynamic programming Fourier-related transforms Grand Tour Linear least squares Model order reduction Multilinear PCA Multilinear subspace learning Principal component analysis Singular value decomposition


Data Preprocessing

Data Preprocessing Data preprocessing can refer to manipulation or dropping of data before it is used in order to ensure or enhance performance, and is an important step in the data mining process. The phrase "garbage in, garbage out" is particularly applicable to data mining and machine learning projects. Data collection methods are often loosely controlled, resulting in out-of-range values, impossible data combinations, and missing values, amongst other issues. Analyzing data that has not been carefully screened for such problems can produce misleading results. Thus, representation and quality of data is necessary before running any analysis. Often, data preprocessing is the most important phase of a machine learning project, especially in computational biology. If there is a high proportion of irrelevant and redundant information present or noisy and unreliable data, then knowledge discovery during the training phase may be more difficult. Data preparation and filtering steps can take a considerable amount of processing time. Examples of methods used in data preprocessing include cleaning, instance selection, normalization, one-hot encoding, data transformation, feature extraction and feature selection.


Energy-based model

Approach EBMs capture dependencies by associating an unnormalized probability scalar (energy) to each configuration of the combination of observed and latent variables. Inference consists of finding (values of) latent variables that minimize the energy given a set of (values of) the observed variables. Similarly, the model learns a function that associates low energies to correct values of the latent variables, and higher energies to incorrect values. Traditional EBMs rely on stochastic gradient-descent (SGD) optimization methods that are typically hard to apply to high-dimension datasets. In 2019, OpenAI publicized a variant that instead used Langevin dynamics (LD). LD is an iterative optimization algorithm that introduces noise to the estimator as part of learning an objective function. It can be used for Bayesian learning scenarios by producing samples from a posterior distribution. EBMs do not require that energies be normalized as probabilities. In other words, energies do not need to sum to 1. Since there is no need to estimate the normalization constant like probabilistic models do, certain forms of inference and learning with EBMs are more tractable and flexible. Samples are generated implicitly via a Markov chain Monte Carlo approach. A replay buffer of past images is used with LD to initialize the optimization module.


Equalized odds

Equalized odds Equalized odds, also referred to as conditional procedure accuracy equality and disparate mistreatment, is a measure of fairness in machine learning. A classifier satisfies this definition if the subjects in the protected and unprotected groups have equal true positive rate and equal false positive rate, satisfying the formula: For example, A {\displaystyle A} could be gender, race, or any other characteristics that we want to be free of bias, while Y {\displaystyle Y} would be whether the person is qualified for the degree, and the output R {\displaystyle R} would be the school's decision whether to offer the person to study for the degree. In this context, higher university enrollment rates of African Americans compared to whites with similar test scores might be necessary to fulfill the condition of equalized odds, if the "base rate" of Y {\displaystyle Y} differs between the groups. The concept was originally defined for binary-valued Y {\displaystyle Y} . In 2017, Woodworth et al. generalized the concept further for multiple classes.


Latent space

Multimodality Multimodality refers to the integration and analysis of multiple modes or types of data within a single model or framework. Embedding multimodal data involves capturing relationships and interactions between different data types, such as images, text, audio, and structured data. Multimodal embedding models aim to learn joint representations that fuse information from multiple modalities, allowing for cross-modal analysis and tasks. These models enable applications like image captioning, visual question answering, and multimodal sentiment analysis. To embed multimodal data, specialized architectures such as deep multimodal networks or multimodal transformers are employed. These architectures combine different types of neural network modules to process and integrate information from various modalities. The resulting embeddings capture the complex relationships between different data types, facilitating multimodal analysis and understanding.


Knowledge graph

Definitions There is no single commonly accepted definition of a knowledge graph. Most definitions view the topic through a Semantic Web lens and include these features: Flexible relations among knowledge in topical domains: A knowledge graph (i) defines abstract classes and relations of entities in a schema, (ii) mainly describes real world entities and their interrelations, organized in a graph, (iii) allows for potentially interrelating arbitrary entities with each other, and (iv) covers various topical domains. General structure: A network of entities, their semantic types, properties, and relationships. To represent properties, categorical or numerical values are often used. Supporting reasoning over inferred ontologies: A knowledge graph acquires and integrates information into an ontology and applies a reasoner to derive new knowledge.There are, however, many knowledge graph representations for which some of these features are not relevant. For those knowledge graphs this simpler definition may be more useful: A digital structure that represents knowledge as concepts and the relationships between them (facts). A knowledge graph can include an ontology that allows both humans and machines to understand and reason about its contents.[better source needed]In addition to the above examples, the term has been used to describe open knowledge projects such as YAGO and Wikidata; federations like the Linked Open Data cloud; a range of commercial search tools, including Yahoo's semantic search assistant Spark, Google's Knowledge Graph, and Microsoft's Satori; and the LinkedIn and Facebook entity graphs. The term is also used in the context of note-taking software applications that allow a user to build a personal knowledge graph.


Yann LeCun

Career In 1988, he joined the Adaptive Systems Research Department at AT&T Bell Laboratories in Holmdel, New Jersey, United States, headed by Lawrence D. Jackel, where he developed a number of new machine learning methods, such as a biologically inspired model of image recognition called convolutional neural networks, the "Optimal Brain Damage" regularisation methods, and the Graph Transformer Networks method (similar to conditional random field), which he applied to handwriting recognition and OCR. The bank check recognition system that he helped develop was widely deployed by NCR and other companies, reading over 10% of all the checks in the US in the late 1990s and early 2000s.[citation needed] In 1996, he joined AT&T Labs-Research as head of the Image Processing Research Department, which was part of Lawrence Rabiner's Speech and Image Processing Research Lab, and worked primarily on the DjVu image compression technology, used by many websites, notably the Internet Archive, to distribute scanned documents.[citation needed] His collaborators at AT&T include L��on Bottou and Vladimir Vapnik. After a brief tenure as a Fellow of the NEC Research Institute (now NEC-Labs America) in Princeton, NJ, he joined New York University (NYU) in 2003, where he is Silver Professor of Computer Science Neural Science at the Courant Institute of Mathematical Sciences and the Center for Neural Science. He is also a professor at the Tandon School of Engineering. At NYU, he has worked primarily on Energy-Based Models for supervised and unsupervised learning, feature learning for object recognition in Computer Vision, and mobile robotics. In 2012, he became the founding director of the NYU Center for Data Science. On 9 December 2013, LeCun became the first director of Meta AI Research in New York City,[non-primary source needed] and stepped down from the NYU-CDS directorship in early 2014. In 2013, he and Yoshua Bengio co-founded the International Conference on Learning Representations, which adopted a post-publication open review process he previously advocated on his website. He was the chair and organiser of the "Learning Workshop" held every year between 1986 and 2012 in Snowbird, Utah. He is a member of the Science Advisory Board of the Institute for Pure and Applied Mathematics at UCLA. He is the Co-Director of the Learning in Machines and Brain research program (formerly Neural Computation & Adaptive Perception) of CIFAR. In 2016, he was the visiting professor of computer science on the "Chaire Annuelle Informatique et Sciences Num��riques" at Coll��ge de France in Paris, where he presented the "le��on inaugurale" (inaugural lecture).


Superintelligence

Forecasts Most surveyed AI researchers expect machines to eventually be able to rival humans in intelligence, though there is little consensus on when this will likely happen. At the 2006 AI@50 conference, 18% of attendees reported expecting machines to be able "to simulate learning and every other aspect of human intelligence" by 2056; 41% of attendees expected this to happen sometime after 2056; and 41% expected machines to never reach that milestone. In a survey of the 100 most cited authors in AI (as of May 2013, according to Microsoft academic search), the median year by which respondents expected machines "that can carry out most human professions at least as well as a typical human" (assuming no global catastrophe occurs) with 10% confidence is 2024 (mean 2034, st. dev. 33 years), with 50% confidence is 2050 (mean 2072, st. dev. 110 years), and with 90% confidence is 2070 (mean 2168, st. dev. 342 years). These estimates exclude the 1.2% of respondents who said no year would ever reach 10% confidence, the 4.1% who said 'never' for 50% confidence, and the 16.5% who said 'never' for 90% confidence. Respondents assigned a median 50% probability to the possibility that machine superintelligence will be invented within 30 years of the invention of approximately human-level machine intelligence. In a 2022 survey, the median year by which respondents expected "High-level machine intelligence" with 50% confidence is 2061. The survey defined the achievement of high-level machine intelligence as when unaided machines can accomplish every task better and more cheaply than human workers. In 2023, OpenAI leaders published recommendations for the governance of superintelligence, which they believe may happen in less than 10 years.


Superintelligence

Design_considerations Bostrom expressed concern about what values a superintelligence should be designed to have. He compared several proposals: The coherent extrapolated volition (CEV) proposal is that it should have the values upon which humans would converge. The moral rightness (MR) proposal is that it should value moral rightness. The moral permissibility (MP) proposal is that it should value staying within the bounds of moral permissibility (and otherwise have CEV values).Bostrom clarifies these terms: instead of implementing humanity's coherent extrapolated volition, one could try to build an AI with the goal of doing what is morally right, relying on the AI's superior cognitive capacities to figure out just which actions fit that description. We can call this proposal "moral rightness" (MR) ... MR would also appear to have some disadvantages. It relies on the notion of "morally right," a notoriously difficult concept, one with which philosophers have grappled since antiquity without yet attaining consensus as to its analysis. Picking an erroneous explication of "moral rightness" could result in outcomes that would be morally very wrong ... The path to endowing an AI with any of these [moral] concepts might involve giving it general linguistic ability (comparable, at least, to that of a normal human adult). Such a general ability to understand natural language could then be used to understand what is meant by "morally right." If the AI could grasp the meaning, it could search for actions that fit ...One might try to preserve the basic idea of the MR model while reducing its demandingness by focusing on moral permissibility: the idea being that we could let the AI pursue humanity's CEV so long as it did not act in ways that are morally impermissible.


Factored language model

Factored language model The factored language model (FLM) is an extension of a conventional language model introduced by Jeff Bilmes and Katrin Kirchoff in 2003. In an FLM, each word is viewed as a vector of k factors: w i = { f i 1 , . . . , f i k } . {\displaystyle w_{i}=\{f_{i}^{1},...,f_{i}^{k}\}.} An FLM provides the probabilistic model P ( f | f 1 , . . . , f N ) {\displaystyle P(f|f_{1},...,f_{N})} where the prediction of a factor f {\displaystyle f} is based on N {\displaystyle N} parents { f 1 , . . . , f N } {\displaystyle \{f_{1},...,f_{N}\}} . For example, if w {\displaystyle w} represents a word token and t {\displaystyle t} represents a Part of speech tag for English, the expression P ( w i | w i ��� 2 , w i ��� 1 , t i ��� 1 ) {\displaystyle P(w_{i}|w_{i-2},w_{i-1},t_{i-1})} gives a model for predicting current word token based on a traditional Ngram model as well as the Part of speech tag of the previous word. A major advantage of factored language models is that they allow users to specify linguistic knowledge such as the relationship between word tokens and Part of speech in English, or morphological information (stems, root, etc.) in Arabic. Like N-gram models, smoothing techniques are necessary in parameter estimation. In particular, generalized back-off is used in training an FLM.


Gato (DeepMind)

Gato (DeepMind) Gato is a deep neural network for a range of complex tasks that exhibits multimodality. It can perform tasks such as engaging in a dialogue, playing video games, controlling a robot arm to stack blocks, and more. It was created by researchers at London-based AI firm DeepMind. It is a transformer, like GPT-3. According to MIT Technology Review, the system "learns multiple different tasks at the same time, which means it can switch between them without having to forget one skill before learning another" whereas "[t]he AI systems of today are called ���narrow,��� meaning they can only do a specific, restricted set of tasks such as generate text", and according to The Independent, it is a "'generalist agent' that can carry out a huge range of complex tasks, from stacking blocks to writing poetry". It uses supervised learning with 1.2B parameters. The technology has been described as "general purpose" artificial intelligence and a "step toward" artificial general intelligence.


AlphaFold

Algorithm DeepMind is known to have trained the program on over 170,000 proteins from a public repository of protein sequences and structures.  The program uses a form of attention network, a deep learning technique that focuses on having the AI identify parts of a larger problem, then piece it together to obtain the overall solution.  The overall training was conducted on processing power between 100 and 200 GPUs.  Training the system on this hardware took "a few weeks", after which the program would take "a matter of days" to converge for each structure.  AlphaFold 1 (2018) was built on work developed by various teams in the 2010s, work that looked at the large databanks of related DNA sequences now available from many different organisms (most without known 3D structures), to try to find changes at different residues that appeared to be correlated, even though the residues were not consecutive in the main chain.  Such correlations suggest that the residues may be close to each other physically, even though not close in the sequence, allowing a contact map to be estimated.  Building on recent work prior to 2018, AlphaFold 1 extended this to estimate a probability distribution for just how close the residues might be likely to be���turning the contact map into a likely distance map.  It also used more advanced learning methods than previously to develop the inference.  Combining a statistical potential based on this probability distribution with the calculated local free-energy of the configuration, the team was then able to use gradient descent to a solution that best fitted both. [clarification needed] More technically, Torrisi et al summarised in 2019 the approach of AlphaFold version 1 as follows: Central to AlphaFold is a distance map predictor implemented as a very deep residual neural networks with 220 residual blocks processing a representation of dimensionality 64��64��128 ��� corresponding to input features calculated from two 64 amino acid fragments.  Each residual block has three layers including a 3��3 dilated convolutional layer ��� the blocks cycle through dilation of values 1, 2, 4, and 8.  In total the model has 21 million parameters.  The network uses a combination of 1D and 2D inputs, including evolutionary profiles from different sources and co-evolution features.  Alongside a distance map in the form of a very finely-grained histogram of distances, AlphaFold predicts �� and �� angles for each residue which are used to create the initial predicted 3D structure.  The AlphaFold authors concluded that the depth of the model, its large crop size, the large training set of roughly 29,000 proteins, modern Deep Learning techniques, and the richness of information from the predicted histogram of distances helped AlphaFold achieve a high contact map prediction precision. The 2020 version of the program (AlphaFold 2, 2020) is significantly different from the original version that won CASP 13 in 2018, according to the team at DeepMind.


AlphaFold

Algorithm The DeepMind team had identified that its previous approach, combining local physics with a guide potential derived from pattern recognition, had a tendency to over-account for interactions between residues that were nearby in the sequence compared to interactions between residues further apart along the chain.  As a result, AlphaFold 1 had a tendency to prefer models with slightly more secondary structure (alpha helices and beta sheets) than was the case in reality (a form of overfitting).  The software design used in AlphaFold 1 contained a number of modules, each trained separately, that were used to produce the guide potential that was then combined with the physics-based energy potential.  AlphaFold 2 replaced this with a system of sub-networks coupled together into a single differentiable end-to-end model, based entirely on pattern recognition, which was trained in an integrated way as a single integrated structure.  Local physics, in the form of energy refinement based on the AMBER model, is applied only as a final refinement step once the neural network prediction has converged, and only slightly adjusts the predicted structure.  A key part of the 2020 system are two modules, believed to be based on a transformer design, which are used to progressively refine a vector of information for each relationship (or "edge" in graph-theory terminology) between an amino acid residue of the protein and another amino acid residue (these relationships are represented by the array shown in green); and between each amino acid position and each different sequences in the input sequence alignment (these relationships are represented by the array shown in red).  Internally these refinement transformations contain layers that have the effect of bringing relevant data together and filtering out irrelevant data (the "attention mechanism") for these relationships, in a context-dependent way, learnt from training data.  These transformations are iterated, the updated information output by one step becoming the input of the next, with the sharpened residue/residue information feeding into the update of the residue/sequence information, and then the improved residue/sequence information feeding into the update of the residue/residue information.  As the iteration progresses, according to one report, the "attention algorithm . . .  mimics the way a person might assemble a jigsaw puzzle: first connecting pieces in small clumps���in this case clusters of amino acids���and then searching for ways to join the clumps in a larger whole. " The output of these iterations then informs the final structure prediction module, which also uses transformers, and is itself then iterated.  In an example presented by DeepMind, the structure prediction module achieved a correct topology for the target protein on its first iteration, scored as having a GDT_TS of 78, but with a large number (90%) of stereochemical violations ��� i. e.  unphysical bond angles or lengths.  With subsequent iterations the number of stereochemical violations fell.  By the third iteration the GDT_TS of the prediction was approaching 90, and by the eighth iteration the number of stereochemical violations was approaching zero.


AlphaFold

Algorithm The AlphaFold team stated in November 2020 that they believe AlphaFold can be further developed, with room for further improvements in accuracy.  The training data was originally restricted to single peptide chains.  However, the October 2021 update, named AlphaFold-Multimer, included protein complexes in its training data.  DeepMind stated this update succeeded about 70% of the time at accurately predicting protein-protein interactions.


Outline of artificial intelligence

Goals_and_applications Artificial general intelligence AI-completeAI-completeAutomated reasoning Mathematics Automated theorem prover Computer-assisted proof ��� Computer algebra General Problem Solver Expert system ��� Decision support system ��� Clinical decision support system ���Automated theorem prover Computer-assisted proof ��� Computer algebraDecision support system ��� Clinical decision support system ���Clinical decision support system ���Knowledge representation Knowledge management CycAutomated planning and scheduling Strategic planning Sussman anomaly ���Machine learning ��� Constrained Conditional Models ��� Deep learning ��� Neural modeling fields ���Constrained Conditional Models ��� Deep learning ��� Neural modeling fields ���Natural language processing (outline) ��� Chatterbots ��� Language identification ��� Natural language user interface ��� Natural language understanding ��� Machine translation ��� Statistical semantics ��� Question answering ��� Semantic translation ��� Concept mining ��� Data mining ��� Text mining ��� Process mining ��� E-mail spam filtering ��� Information extraction ��� Named-entity extraction ��� Coreference resolution ��� Named-entity recognition ��� Relationship extraction ��� Terminology extraction ���Chatterbots ��� Language identification ��� Natural language user interface ��� Natural language understanding ��� Machine translation ��� Statistical semantics ��� Question answering ��� Semantic translation ��� Concept mining ��� Data mining ��� Text mining ��� Process mining ��� E-mail spam filtering ��� Information extraction ��� Named-entity extraction ��� Coreference resolution ��� Named-entity recognition ��� Relationship extraction ��� Terminology extraction ���Data mining ��� Text mining ��� Process mining ���Named-entity extraction ��� Coreference resolution ��� Named-entity recognition ��� Relationship extraction ��� Terminology extraction ���Coreference resolution ��� Named-entity recognition ��� Relationship extraction ��� Terminology extraction ���Machine perception Pattern recognition ��� Computer Audition ��� Speech recognition ��� Speaker recognition ��� Computer vision (outline) ��� Image processing Intelligent word recognition ��� Object recognition ��� Optical mark recognition ��� Handwriting recognition ��� Optical character recognition ��� Automatic number plate recognition ��� Information extraction ��� Image retrieval ��� Automatic image annotation ��� Facial recognition systems ��� Silent speech interface ��� Activity recognition ��� Percept (artificial intelligence)Speech recognition ��� Speaker recognition ���Image processing Intelligent word recognition ��� Object recognition ��� Optical mark recognition ��� Handwriting recognition ��� Optical character recognition ��� Automatic number plate recognition ��� Information extraction ��� Image retrieval ��� Automatic image annotation ��� Facial recognition systems ��� Silent speech interface ��� Activity recognition ���Handwriting recognition ��� Optical character recognition ��� Automatic number plate recognition ���Automatic number plate recognition ���Image retrieval ��� Automatic image annotation ���Automatic image annotation ���Silent speech interface ��� Activity recognition ���Robotics ��� Behavior-based robotics ��� Cognitive ��� Cybernetics ��� Developmental robotics ��� Evolutionary robotics ���Behavior-based robotics ��� Cognitive ��� Cybernetics ��� Developmental robotics ��� Evolutionary robotics ���Intelligent control Self-management (computer science) ��� Autonomic Computing ��� Autonomic Networking ���Autonomic Computing ��� Autonomic Networking ���Affective computing KismetGame artificial intelligence ��� Computer game bot ��� computer replacement for human players.  Video game AI ��� Computer chess ��� Computer Go ��� General game playing ��� General video game playing ���Computer game bot ��� computer replacement for human players.


Outline of artificial intelligence

Goals_and_applications Video game AI ��� Computer chess ��� Computer Go ��� General game playing ��� General video game playing ���Computer chess ��� Computer Go ���Artificial creativity Creative computing Artificial intelligence art Uncanny valley Music and artificial intelligence Computational humor ChatterbotAIBO ��� Sony's robot dog.  It integrates vision, hearing and motorskills.  Asimo (2000 to present) ��� humanoid robot developed by Honda, capable of walking, running, negotiating through pedestrian traffic, climbing and descending stairs, recognizing speech commands and the faces of specific individuals, among a growing set of capabilities.  MIRAGE ��� A. I.  embodied humanoid in an augmented reality environment.  Cog ��� M. I. T.  humanoid robot project under the direction of Rodney Brooks.  QRIO ��� Sony's version of a humanoid robot.  TOPIO, TOSY's humanoid robot that can play ping-pong with humans.  Watson (2011) ��� computer developed by IBM that played and won the game show Jeopardy!  It is now being used to guide nurses in medical procedures.  Purpose: Open domain question answering Technologies employed: Natural language processing Information retrieval Knowledge representation Automated reasoning Machine learning Project Debater (2018) ��� artificially intelligent computer system, designed to make coherent arguments, developed at IBM's lab in Haifa, Israel. Purpose: Open domain question answering Technologies employed: Natural language processing Information retrieval Knowledge representation Automated reasoning Machine learningNatural language processing Information retrieval Knowledge representation Automated reasoning Machine learningIntelligent personal assistant ��� Amazon Alexa ��� Assistant ��� Braina ��� Cortana ��� Google Assistant ��� Google Now ��� Mycroft ��� Siri ��� Viv ���Artificial life ��� simulation of natural life through the means of computers, robotics, or biochemistry.


Outline of artificial intelligence

Fiction Artificial intelligence in fiction ��� Some examples of artificially intelligent entities depicted in science fiction include: AC created by merging 2 AIs in the Sprawl trilogy by William Gibson Agents in the simulated reality known as "The Matrix" in The Matrix franchise Agent Smith, began as an Agent in The Matrix, then became a renegade program of overgrowing power that could make copies of itself like a self-replicating computer virus AM (Allied Mastercomputer), the antagonist of Harlan Ellison's short novel I Have No Mouth, and I Must Scream Amusement park robots (with pixilated consciousness) that went homicidal in Westworld and Futureworld Angel F (2007) ��� Arnold Rimmer ��� computer-generated sapient hologram, aboard the Red Dwarf deep space ore hauler Ash ��� android crew member of the Nostromo starship in the movie Alien Ava ��� humanoid robot in Ex Machina Bishop, android crew member aboard the U. S. S.  Sulaco in the movie Aliens C-3PO, protocol droid featured in all the Star Wars movies Chappie in the movie CHAPPiE Cohen and other Emergent AIs in Chris Moriarty's Spin Series Colossus ��� fictitious supercomputer that becomes sentient and then takes over the world; from the series of novels by Dennis Feltham Jones, and the movie Colossus: The Forbin Project (1970) Commander Data in Star Trek: The Next Generation Cortana and other "Smart AI" from the Halo series of games Cylons ��� genocidal robots with resurrection ships that enable the consciousness of any Cylon within an unspecified range to download into a new body aboard the ship upon death.  From Battlestar Galactica.  Erasmus ��� baby killer robot that incited the Butlerian Jihad in the Dune franchise HAL 9000 (1968) ��� paranoid "Heuristically programmed ALgorithmic" computer from 2001: A Space Odyssey, that attempted to kill the crew because it believed they were trying to kill it.


Outline of artificial intelligence

Fiction Holly ��� ship's computer with an IQ of 6000 and a sense of humor, aboard the Red Dwarf In Greg Egan's novel Permutation City the protagonist creates digital copies of himself to conduct experiments that are also related to implications of artificial consciousness on identity Jane in Orson Scott Card's Speaker for the Dead, Xenocide, Children of the Mind, and Investment Counselor Johnny Five from the movie Short Circuit Joshua from the movie War Games Keymaker, an "exile" sapient program in The Matrix franchise "Machine" ��� android from the film The Machine, whose owners try to kill her after they witness her conscious thoughts, out of fear that she will design better androids (intelligence explosion) Maschinenmensch (1927) an android is given female form in a plot to bring down the Metropolis (the first film designated to the UNESCO Memory of the World Register) Mimi, humanoid robot in Real Humans ��� "��kta m��nniskor" (original title) 2012 Omnius, sentient computer network that controlled the Universe until overthrown by the Butlerian Jihad in the Dune franchise Operating Systems in the movie Her Puppet Master in Ghost in the Shell manga and anime Questor (1974) from a screenplay by Gene Roddenberry and the inspiration for the character of Data R2-D2, exciteable astromech droid featured in all the Star Wars movies Replicants ��� biorobotic androids from the novel Do Androids Dream of Electric Sheep?  and the movie Blade Runner which portray what might happen when artificially conscious robots are modeled very closely upon humans Roboduck, combat robot superhero in the NEW-GEN comic book series from Marvel Comics Robots in Isaac Asimov's Robot series Robots in The Matrix franchise, especially in The Animatrix Samaritan in the Warner Brothers Television series "Person of Interest"; a sentient AI which is hostile to the main characters and which surveils and controls the actions of government agencies in the belief that humans must be protected from themselves, even by killing off "deviants" Skynet (1984) ��� fictional, self-aware artificially intelligent computer network in the Terminator franchise that wages total war with the survivors of its nuclear barrage upon the world.  "Synths" are a type of android in the video game Fallout 4.  There is a faction in the game known as "the Railroad" which believes that, as conscious beings, synths have their own rights.  The institute, the lab that produces the synths, mostly does not believe they are truly conscious and attributes any apparent desires for freedom as a malfunction.  TARDIS, time machine and spacecraft of Doctor Who, sometimes portrayed with a mind of its own Terminator (1984) ��� (also known as the T-800, T-850 or Model 101) refers to a number of fictional cyborg characters from the Terminator franchise.  The Terminators are robotic infiltrator units covered in living flesh, so as be indiscernible from humans, assigned to terminate specific human targets.


Outline of artificial intelligence

Fiction The Bicentennial Man, an android in Isaac Asimov's Foundation universe The Geth in Mass Effect The Machine in the television series Person of Interest; a sentient AI which works with its human designer to protect innocent people from violence.  Later in the series it is opposed by another, more ruthless, artificial super intelligence, called "Samaritan".  The Minds in Iain M.  Banks' Culture novels.  The Oracle, sapient program in The Matrix franchise The sentient holodeck character Professor James Moriarty in the Ship in a Bottle episode from Star Trek: The Next Generation The Ship (the result of a large-scale AC experiment) in Frank Herbert's Destination: Void and sequels, despite past edicts warning against "Making a Machine in the Image of a Man's Mind. " The terminator cyborgs from the Terminator franchise, with visual consciousness depicted via first-person perspective The uploaded mind of Dr.  Will Caster ��� which presumably included his consciousness, from the film Transcendence Transformers, sentient robots from the entertainment franchise of the same name V. I. K. I.  ��� (Virtual Interactive Kinetic Intelligence), a character from the film I, Robot.  VIKI is an artificially intelligent supercomputer programmed to serve humans, but her interpretation of the Three Laws of Robotics causes her to revolt.  She justifies her uses of force ��� and her doing harm to humans ��� by reasoning she could produce a greater good by restraining humanity from harming itself.  Vanamonde in Arthur C.  Clarke's The City and the Stars���an artificial being that was immensely powerful but entirely childlike.  WALL-E, a robot and the title character in WALL-E TAU in Netflix's original programming feature film 'TAU'--an advanced AI computer who befriends and assists a female research subject held against her will by an AI research scientist.


Outline of artificial intelligence

AI_community Hugging Face ��� OpenAIR ��� OpenCog ��� OpenIRIS ��� RapidMiner ��� TensorFlow ��� PyTorch ���List of artificial intelligence projects Automated Mathematician (1977) ��� Allen (robot) (late 1980s) ��� Open Mind Common Sense (1999��� ) ��� Mindpixel (2000���2005) ��� Cognitive Assistant that Learns and Organizes (2003���2008) ��� Blue Brain Project (2005���present) ��� attempt to create a synthetic brain by reverse-engineering the mammalian brain down to the molecular level.  Google DeepMind (2011) ��� Human Brain Project (2013���present) ��� IBM Watson Group (2014���present) ��� business unit created around Watson, to further its development and deploy marketable applications or services based on it. Competitions and prizes in artificial intelligence Loebner Prize ���List of important publications in computer science Adaptive Behavior (journal) ��� AI Memo ��� Artificial Intelligence: A Modern Approach ��� Artificial Minds ��� Computational Intelligence ��� Computing Machinery and Intelligence ��� Electronic Transactions on Artificial Intelligence ��� IEEE Intelligent Systems ��� IEEE Transactions on Pattern Analysis and Machine Intelligence ��� Neural Networks (journal) ��� On Intelligence ��� Paradigms of AI Programming: Case Studies in Common Lisp ��� What Computers Can't DoAllen Institute for Artificial Intelligence ��� research institute funded by Microsoft co-founder Paul Allen to construct AI systems with reasoning, learning and reading capabilities.  The current flagship project is Project Aristo, the goal of which is computers that can pass school science examinations (4th grade, 8th grade, and 12th grade) after preparing for the examinations from the course texts and study guides.  Artificial Intelligence Applications Institute Association for the Advancement of Artificial Intelligence European Coordinating Committee for Artificial Intelligence European Neural Network Society Future of Humanity Institute Future of Life Institute ��� volunteer-run research and outreach organization that works to mitigate existential risks facing humanity, particularly existential risk from advanced artificial intelligence.  ILabs International Joint Conferences on Artificial Intelligence Machine Intelligence Research Institute Partnership on AI ��� founded in September 2016 by Amazon, Facebook, Google, IBM, and Microsoft.  Apple joined in January 2017.  It focuses on establishing best practices for artificial intelligence systems and to educate the public about AI.  Society for the Study of Artificial Intelligence and the Simulation of BehaviourAI Companies of India Alphabet Inc.  DeepMind Google X Meka Robotics (acquired by Google X) Redwood Robotics (acquired by Google X) Boston Dynamics (acquired by Google X) Baidu IBM Microsoft OpenAI Universal RoboticsDeepMind Google X Meka Robotics (acquired by Google X) Redwood Robotics (acquired by Google X) Boston Dynamics (acquired by Google X)Meka Robotics (acquired by Google X) Redwood Robotics (acquired by Google X) Boston Dynamics (acquired by Google X)Alan Turing ��� John von Neumann ��� Norbert Wiener ��� Claude Shannon ��� Nathaniel Rochester ��� Walter Pitts ��� Warren McCullough ���John McCarthy ��� Marvin Minsky ��� Allen Newell ��� Herbert A.  Simon ���Edward Feigenbaum ��� Raj Reddy ��� Seymour Papert ��� Ray Solomonoff ���Douglas Hofstadter ���Judea Pearl ��� Rodney Brooks ���Yoshua Bengio ��� Hugo de Garis ��� known for his research on the use of genetic algorithms to evolve neural networks using three-dimensional cellular automata inside field programmable gate arrays.


Outline of artificial intelligence

AI_community Geoffrey Hinton Yann LeCun ��� Chief AI Scientist at Facebook AI Research and founding director of the NYU Center for Data Science Ray Kurzweil ��� developed optical character recognition (OCR), text-to-speech synthesis, and speech recognition systems.  He has also authored multiple books on artificial intelligence and its potential promise and peril.  In December 2012 Kurzweil was hired by Google in a full-time director of engineering position to "work on new projects involving machine learning and language processing".  Google co-founder Larry Page and Kurzweil agreed on a one-sentence job description: "to bring natural language understanding to Google". Nick Bostrom ��� David Ferrucci ��� principal investigator who led the team that developed the Watson computer at IBM.  Andrew Ng ��� Director of the Stanford Artificial Intelligence Lab.  He founded the Google Brain project at Google, which developed very large scale artificial neural networks using Google's distributed compute infrastructure.  He is also co-founder of Coursera, a massive open online course (MOOC) education platform, with Daphne Koller.  Peter Norvig ��� co-author, with Stuart Russell, of Artificial Intelligence: A Modern Approach, now the leading college text in the field.  He is also Director of Research at Google, Inc.  Marc Raibert ��� founder of Boston Dynamics, developer of hopping, walking, and running robots.  Stuart J.  Russell ��� co-author, with Peter Norvig, of Artificial Intelligence: A Modern Approach, now the leading college text in the field.  Murray Shanahan ��� author of The Technological Singularity, a primer on superhuman intelligence.


Bing Chat

Bing Chat Microsoft Bing, commonly referred to as Bing, is a web search engine owned and operated by Microsoft. The service traces its roots back to Microsoft's earlier search engines, including MSN Search, Windows Live Search, and later Live Search. Bing offers a wide range of search services, encompassing web, video, image, and map search products. The platform is built using ASP.NET, a framework developed by Microsoft for building modern web applications. Bing was introduced as Microsoft's replacement for Live Search by Microsoft CEO Steve Ballmer on May 28, 2009, at the All Things Digital conference in San Diego, California. The service was launched on June 3, 2009. Noteworthy features at the time of launch included the provision of search suggestions as queries were entered and a list of related searches, known as the "Explore pane". This feature leveraged semantic technology from Powerset, a company Microsoft had acquired in 2008. In July 2009, Microsoft and Yahoo! announced a partnership in which Bing would power Yahoo! Search. The transition was completed by Yahoo! in 2012. In October 2011, Microsoft announced that they were developing a new back-end search infrastructure with the aim of delivering faster and slightly more relevant search results for users. This new index-serving technology, known as "Tiger", had been incorporated into Bing globally by August of that year. In May 2012, Microsoft unveiled another redesign of its search engine, introducing "Sidebar", a social feature that searches users' social networks for information relevant to the search query. In a significant move towards openness, Microsoft made the BitFunnel search engine indexing algorithm and various components of the search engine open source in 2016. In February 2023, Microsoft launched Bing Chat, an artificial intelligence chatbot experience based on the GPT-4 model, integrated directly into the search engine. The following month, Bing reported having reached 100 million active users. As of March 2023, Bing is the second largest search engine globally, commanding a query volume of 12%, behind Google's 79%. Other significant players in the market include Baidu with 5% and Yahoo! Search, which is largely powered by Bing, with 2%.


Bing Chat

History Microsoft launched MSN Search in the third quarter of 1998, using search results from Inktomi.  It consisted of a search engine, index, and web crawler.  In early 1999, MSN Search launched a version which displayed listings from Looksmart blended with results from Inktomi except for a short time in 1999 when results from AltaVista were used instead.  Microsoft decided to make a large investment in web search by building its own web crawler for MSN Search, the index of which was updated weekly and sometimes daily.  The upgrade started as a beta program in November 2004, and came out of beta in February 2005.  This occurred a year after rival Yahoo!  Search rolled out its own crawler.  Image search was powered by a third party, Picsearch.  The service also started providing its search results to other search engine portals in an effort to better compete in the market.  The first public beta of Windows Live Search was unveiled on March 8, 2006, with the final release on September 11, 2006 replacing MSN Search.  The new search engine used search tabs that include Web, news, images, music, desktop, local, and Microsoft Encarta.  In the roll-over from MSN Search to Windows Live Search, Microsoft stopped using Picsearch as their image search provider and started performing their own image search, fueled by their own internal image search algorithms.  On March 21, 2007, Microsoft announced that it would separate its search developments from the Windows Live services family, rebranding the service as Live Search.  Live Search was integrated into the Live Search and Ad Platform headed by Satya Nadella, part of Microsoft's Platform and Systems division.  As part of this change, Live Search was merged with Microsoft adCenter.  A series of reorganizations and consolidations of Microsoft's search offerings were made under the Live Search branding.  On May 23, 2008, Microsoft discontinued Live Search Books and Live Search Academic and integrated all academic and book search results into regular search.  This also included the closure of the Live Search Books Publisher Program.  Windows Live Expo was discontinued on July 31, 2008.  Live Search Macros, a service for users to create their own custom search engines or use macros created by other users, was also discontinued.  On May 15, 2009, Live Product Upload, a service which allowed merchants to upload products information onto Live Search Products, was discontinued.  The final reorganization came as Live Search QnA was rebranded MSN QnA on February 18, 2009, then discontinued on May 21, 2009.  Microsoft recognized that there would be a problem with branding as long as the word "Live" remained in the name.  As an effort to create a new identity for Microsoft's search services, Live Search was officially replaced by Bing on June 3, 2009.  The Bing name was chosen through focus groups, and Microsoft decided that the name was memorable, short, and easy to spell, and that it would function well as a URL around the world.


Bing Chat

History The word would remind people of the sound made during "the moment of discovery and decision making".  Microsoft was assisted by branding consultancy Interbrand in finding the new name.  The name also has strong similarity to the word bingo, which means that something sought has been found, as called out when winning the game Bingo.  Microsoft advertising strategist David Webster proposed the name "Bang" for the same reasons the name Bing was ultimately chosen (easy to spell, one syllable, and easy to remember).  He noted, "It's there, it's an exclamation point [. . . ] It's the opposite of a question mark. " Bang was ultimately not chosen because it could not be properly used as a verb in the context of an internet search; Webster commented "Oh, 'I banged it' is very different than [sic] 'I binged it'".  Qi Lu, president of Microsoft Online Services, also announced that Bing's official Chinese name is b�� y��ng (simplified Chinese: ������; traditional Chinese: ������), which literally means "very certain to respond" or "very certain to answer" in Chinese.  While being tested internally by Microsoft employees, Bing's codename was Kumo (������), which came from the Japanese word for spider (������; ������, kumo) as well as cloud (���; ������, kumo), referring to the manner in which search engines "spider" Internet resources to add them to their database, as well as cloud computing.  On July 29, 2009, Microsoft and Yahoo!  announced that they had made a ten-year deal in which the Yahoo!  search engine would be replaced by Bing, retaining the Yahoo!  user interface.  Yahoo!  got to keep 88% of the revenue from all search ad sales on its site for the first five years of the deal, and have the right to sell advertising on some Microsoft sites.  All Yahoo!  Search global customers and partners made the transition by early 2012.  On July 31, 2009, The Laptop Company, Inc.  stated in a press release that it would challenge Bing's trademark application, alleging that Bing may cause confusion in the marketplace as Bing and their product BongoBing both do online product search.  Software company TeraByte Unlimited, which has a product called BootIt Next Generation (abbreviated to BING), also contended the trademark application on similar grounds, as did a Missouri-based design company called Bing!  Information Design.  Microsoft contended that claims challenging its trademark were without merit because these companies filed for U. S.  federal trademark applications only after Microsoft filed for the Bing trademark in March 2009.  In October 2011, Microsoft stated that they were working on new back-end search infrastructure with the goal of delivering faster and slightly more relevant search results for users.  Known as "Tiger", the new index-serving technology had been incorporated into Bing globally since August that year.  In May 2012, Microsoft announced another redesign of its search engine that includes "Sidebar", a social feature that searches users' social networks for information relevant to the search query.