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International Conference on Machine Learning : The International Conference on Machine Learning (ICML) is a leading international academic conference in machine learning. Along with NeurIPS and ICLR, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research. It i... |
International Conference on Machine Learning : ICML 2026 Seoul, South Korea ICML 2025 Vancouver, Canada ICML 2024 Vienna, Austria ICML 2023 Honolulu, United States ICML 2022 Baltimore, United States ICML 2021 Vienna, Austria (virtual conference) ICML 2020 Vienna, Austria (virtual conference) ICML 2019 Los Angeles, Unit... |
International Conference on Machine Learning : ICLR Journal of Machine Learning Research Machine Learning (journal) NeurIPS |
International Conference on Machine Learning : icml.cc ICML website machinelearning.org Website of the International Machine Learning Society |
International Joint Conference on Artificial Intelligence : The International Joint Conference on Artificial Intelligence (IJCAI) is a conference in the field of artificial intelligence. The conference series has been organized by the nonprofit IJCAI Organization since 1969. It was held biennially in odd-numbered years... |
International Joint Conference on Artificial Intelligence : Three research awards are given at each IJCAI conference. The IJCAI Computers and Thought Award is given to outstanding young scientists under the age of 35 in AI. The Donald E. Walker Distinguished Service Award is given to honor senior scientists for their c... |
International Joint Conference on Artificial Intelligence : The International Joint Conferences on Artificial Intelligence Organization (IJCAI Organization) is a nonprofit organization founded in 1969 to promote science and education in the field of AI. It is the main organizer of the IJCAI conference series and is als... |
JaCoP (solver) : JaCoP is a constraint solver for constraint satisfaction problems. It is written in Java and it is provided as a Java library. JaCoP has an interface to the MiniZinc and AMPL modeling languages. Its main focus is on ease of use, modeling power, as well as efficiency. It has a large collection of global... |
JaCoP (solver) : JaCoP on GitHub |
Kinara (company) : Kinara is an American semiconductor company that develops AI processors for machine learning applications. |
Kinara (company) : Kinara was founded in 2013 by Rehan Hameed, Wajahat Qadeer and Jason Copeland as CoreViz. The company was rebranded as Deep Vision, and raised $35M in a series B funding round led by Tiger Global Management. In 2022 Deep Vision rebranded as Kinara. The company has a development base in Hyderabad, Ind... |
Kinara (company) : In 2020, the company announced its first product, the Ara-1 Edge AI Processor. The product uses a polymorphic dataflow architecture. The Ara-2 was launched in December 2023 and is 5 to 8 times faster than its predecessor. |
Land of Memories : Land of Memories (Chinese: 机忆之地) is a Chinese science-fiction novel by Shen Yang (沈阳), a professor at Tsinghua University's School of Journalism and Communication. The story revolves around a former neuroscientist trying to recover her memories from the metaverse after suffering amnesia due to an acc... |
Linguistic value : In artificial intelligence, operations research, and related fields, a linguistic value is a natural language term which is derived using quantitative or qualitative reasoning such as with probability and statistics or fuzzy sets and systems. |
Linguistic value : For example, if a shuttle heat shield is deemed of having a linguistic value of a "very low" percentage of damage in re-entry, based upon knowledge from experts in the field, that probability would be given a value of say, 5%. From there on out, if it were to be used in an equation, the variable of p... |
LogitBoost : In machine learning and computational learning theory, LogitBoost is a boosting algorithm formulated by Jerome Friedman, Trevor Hastie, and Robert Tibshirani. The original paper casts the AdaBoost algorithm into a statistical framework. Specifically, if one considers AdaBoost as a generalized additive mode... |
LogitBoost : LogitBoost can be seen as a convex optimization. Specifically, given that we seek an additive model of the form f = ∑ t α t h t \alpha _h_ the LogitBoost algorithm minimizes the logistic loss: ∑ i log ( 1 + e − y i f ( x i ) ) \log \left(1+e^f(x_)\right) |
LogitBoost : Gradient boosting Logistic model tree == References == |
Meta Content Framework : Meta Content Framework (MCF) is a specification of a content format for structuring metadata about web sites and other data. |
Meta Content Framework : MCF was developed by Ramanathan V. Guha at Apple Computer's Advanced Technology Group between 1995 and 1997. Rooted in knowledge-representation systems such as CycL, KRL, and KIF, it sought to describe objects, their attributes, and the relationships between them. One application of MCF was Hot... |
Meta Content Framework : An MCF file consists of one or more blocks, each corresponding to an entity. A block looks like this:The identifier is a unique identifier for that entity (more on the scope of the identifier below) and is used to refer to that entity. The following lines each specify a property and one or more... |
Meta Content Framework : MCF Tutorial (using XML syntax) Guha MCF site The metacontent concept |
Microsoft Cognitive Toolkit : Microsoft Cognitive Toolkit, previously known as CNTK and sometimes styled as The Microsoft Cognitive Toolkit, is a deprecated deep learning framework developed by Microsoft Research. Microsoft Cognitive Toolkit describes neural networks as a series of computational steps via a directed gr... |
Microsoft Cognitive Toolkit : Comparison of deep learning software ML.NET Open Neural Network Exchange |
Microsoft Cognitive Toolkit : Meints, Willem (2019). Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide: A practical guide to building neural networks using Microsoft's open source deep learning framework. Packt Publishing. ISBN 978-1789802993. |
Mind Foundry : Mind Foundry is an artificial intelligence company that is headquartered in Oxford, Oxfordshire. The company is a spin-out of the University of Oxford and was founded by two professors of machine learning at the university, Stephen Roberts. and Michael Osborne. The company specialises in “AI for high-sta... |
Mind Foundry : Mind Foundry was founded in 2016 out of the Machine Learning Research Group at the University of Oxford by professors Stephen Roberts and Michael Osborne. Mind Foundry received investment from Oxford Science Enterprises and Parkwalk Advisors in an initial seed round in 2016. In 2019 the company appointed... |
Minion (solver) : Minion is a solver for constraint satisfaction problems. Unlike constraint programming toolkits, which expect users to write programs in a traditional programming language like C++, Java or Prolog, Minion takes a text file which specifies the problem, and solves using only this. This makes using Minio... |
Minion (solver) : Github Repository |
Model-based reasoning : In artificial intelligence, model-based reasoning refers to an inference method used in expert systems based on a model of the physical world. With this approach, the main focus of application development is developing the model. Then at run time, an "engine" combines this model knowledge with o... |
Model-based reasoning : A robot and dynamical systems as well are controlled by software. The software is implemented as a normal computer program which consists of if-then-statements, for-loops and subroutines. The task for the programmer is to find an algorithm which is able to control the robot, so that it can do a ... |
Model-based reasoning : In a model-based reasoning system knowledge can be represented using causal rules. For example, in a medical diagnosis system the knowledge base may contain the following rule: ∀ patients : Stroke(patient) → Confused(patient) ∧ Unequal(Pupils(patient)) In contrast in a diagnostic reasoning sy... |
Model-based reasoning : Diagnosis (artificial intelligence), determining if a system's behavior is correct Behavior selection algorithm Case-based reasoning, solving new problems based on solutions of past problems |
Model-based reasoning : Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, p. 260, ISBN 0-13-790395-2 |
Model-based reasoning : Model-based reasoning at Utrecht University NASA Intelligent Systems Division |
Motion History Images : The motion history image (MHI) is a static image template helps in understanding the motion location and path as it progresses. In MHI, the temporal motion information is collapsed into a single image template where intensity is a function of recency of motion. Thus, the MHI pixel intensity is a... |
Motion History Images : for each time t Bt := absolute_difference(It, It-1) > threshold end for for each time t for each pixel (x, y) if Bt(x, y) = 1 MHIt(x, y) := τ else if MHIt-1 ≠ 0 MHIt(x, y) := MHIt-1(x, y) - 1 else MHIt(x, y) := 0 end if end for == References == |
Neural differential equation : In machine learning, a neural differential equation is a differential equation whose right-hand side is parametrized by the weights θ of a neural network. In particular, a neural ordinary differential equation (neural ODE) is an ordinary differential equation of the form d h ( t ) d t = f... |
Neural differential equation : Neural ODEs can be interpreted as a residual neural network with a continuum of layers rather than a discrete number of layers. Applying the Euler method with a unit time step to a neural ODE yields the forward propagation equation of a residual neural network: h ℓ + 1 = f θ ( h ℓ , ℓ ) +... |
Neural differential equation : In physics-informed contexts where additional information is known, neural ODEs can be combined with an existing first-principles model to build a physics-informed neural network model called universal differential equations (UDE). For instance, an UDE version of the Lotka-Volterra model ... |
Neural differential equation : Physics-informed neural networks |
Neural differential equation : Steve Brunton lecture on neural ODEs |
Neural Network Intelligence : NNI (Neural Network Intelligence) is a free and open-source AutoML toolkit developed by Microsoft. It is used to automate feature engineering, model compression, neural architecture search, and hyper-parameter tuning. The source code is licensed under MIT License and available on GitHub. |
Neural Network Intelligence : Gridin, Ivan (2022). Automated Deep Learning Using Neural Network Intelligence: Develop and Design PyTorch and TensorFlow Models Using Python. Apress. ISBN 978-1484281482. |
Neural Network Intelligence : nni on GitHub Neural Network Intelligence - Microsoft Research |
Norm (artificial intelligence) : Norms can be considered from different perspectives in artificial intelligence to create computers and computer software that are capable of intelligent behaviour. In artificial intelligence and law, legal norms are considered in computational tools to automatically reason upon them. In... |
Norm (artificial intelligence) : With the arrival of computer applications into the legal domain, and especially artificial intelligence applied to it, logic has been used as the major tool to formalize legal reasoning and has been developed in many directions, ranging from deontic logics to formal systems of argumenta... |
Norm (artificial intelligence) : Norms in multi-agent systems may appear with different degrees of explicitness ranging from fully unambiguous written prescriptions to implicit unwritten norms or tacit emerging patterns. Computer scientists’ studies mirror this polarity. Explicit norms are typically investigated in for... |
Opportunistic reasoning : Opportunistic reasoning is a method of selecting a suitable logical inference strategy within artificial intelligence applications. Specific reasoning methods may be used to draw conclusions from a set of given facts in a knowledge base, e.g. forward chaining versus backward chaining. However,... |
Opportunistic reasoning : Marin D. Simina et al. "Opportunistic Reasoning: A Design Perspective" in Proceedings of the Seventeenth Annual Conference of Cognitive Science edited by Johanna D. Moore, 1995 ISBN 0-8058-2159-7, page 78 == Notes == |
Paradigms of AI Programming : Paradigms of AI Programming: Case Studies in Common Lisp (ISBN 1-55860-191-0) is a well-known programming book by Peter Norvig about artificial intelligence programming using Common Lisp. |
Paradigms of AI Programming : The Lisp programming language has survived since 1958 as a primary language for Artificial Intelligence research. This text was published in 1992 as the Common Lisp standard was becoming widely adopted. Norvig introduces Lisp programming in the context of classic AI programs, including Gen... |
Paradigms of AI Programming : Herbert A. Simon J. C. Shaw Allen Newell Daniel G. Bobrow Joseph Weizenbaum |
Paradigms of AI Programming : Norvig, Peter. Paradigms of Artificial Intelligence Programming: Case studies in Common Lisp. San Francisco, Calif: Morgan Kaufmann Publishers, 1992. |
Paradigms of AI Programming : Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp Book Homepage Peter Norvig's Homepage Source Code from Paradigms of Artificial Intelligence Programming Introduction to Artificial Intelligence online class from Stanford |
Parallel terraced scan : The parallel terraced scan is a multi-agent based search technique that is basic to cognitive architectures, such as Copycat, Letter-string, the Examiner, Tabletop, and others. It was developed by John Rehling and Douglas Hofstadter at the Center for Research on Concepts and Cognition at Indian... |
Parallel terraced scan : The Parallel Terraced Scan: An Optimization For An Agent-Oriented Architecture (pdf) |
Parity benchmark : Parity problems are widely used as benchmark problems in genetic programming but inherited from the artificial neural network community. Parity is calculated by summing all the binary inputs and reporting if the sum is odd or even. This is considered difficult because: a very simple artificial neural... |
Parity benchmark : Foundations of Genetic Programming |
Partial-order planning : Partial-order planning is an approach to automated planning that maintains a partial ordering between actions and only commits ordering between actions when forced to, that is, ordering of actions is partial. Also this planning doesn't specify which action will come out first when two actions a... |
Partial-order planning : A partial-order plan or partial plan is a plan which specifies all actions that must be taken, but only specifies the order between actions when needed. It is the result of a partial-order planner. A partial-order plan consists of four components: A set of actions (also known as operators). A p... |
Partial-order planning : A partial-order planner is an algorithm or program which will construct a partial-order plan and search for a solution. The input is the problem description, consisting of descriptions of the initial state, the goal and possible actions. The problem can be interpreted as a search problem where ... |
Partial-order planning : Partial-order planning is the opposite of total-order planning, in which actions are sequenced all at once and for the entirety of the task at hand. The question arises when one has two competing processes, which one is better? Anthony Barret and Daniel Weld have argued in their 1993 book, that... |
Partial-order planning : Partial-order plans are known to easily and optimally solve the Sussman anomaly. Using this type of incremental planning system solves this problem quickly and efficiently. This was a result of partial-order planning that solidified its place as an efficient planning system. |
Partial-order planning : One drawback of this type of planning system is that it requires a lot more computational power for each node. This higher per-node cost occurs because the algorithm for partial-order planning is more complex than others. This has important artificial intelligence implications. When coding a ro... |
Partial-order planning : Artificial Intelligence: A Modern Approach by Stuart Russell, Peter Norvig An Introduction To Least Commitment Planning by Daniel Weld Kambhampati, S., Knoblock, C.A., Yang, Q. (1994). Planning as Refinement Search: A Unified Framework for Evaluating Design Tradeoffs in Partial-Order Planning. ... |
Juergen Pirner : Juergen Pirner (born 1956) is the German creator of Jabberwock, a chatterbot that won the 2003 Loebner prize. Pirner created Jabberwock modelling the Jabberwocky from Lewis Carroll's poem of the same name. Initially, Jabberwock would just give rude or fantasy-related answers; but over the years, Pirner... |
Juergen Pirner : Talk to Jabberwock |
Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy : The Political Declaration on Responsible Military Use of Artificial Intelligence and Autonomy is an international norms and arms control proposal by the U.S. government for artificial intelligence in the military. It was announc... |
Polly (robot) : Polly was a robot created at the MIT Artificial Intelligence Laboratory by Ian Horswill for his PhD and published in 1993 as a technical report. Polly was the first mobile robot to move at animal-like speeds (1m per second) using computer vision for its navigation. It was an example of behavior-based ro... |
PORS : Plus-One-Recall-Store (PORS) is a language used in evolutionary computation and genetic programming. The PORS language consists of two terminal nodes (1 and recall), one unary operation (store), and one binary operation (plus) that be used in a parse tree to do a calculation. == References == |
QLattice : The QLattice is a software library which provides a framework for symbolic regression in Python. It works on Linux, Windows, and macOS. The QLattice algorithm is developed by the Danish/Spanish AI research company Abzu. Since its creation, the QLattice has attracted significant attention, mainly for the inhe... |
QLattice : The QLattice works with data in categorical and numeric format. It allows the user to quickly generate, plot and inspect mathematical formulae that can potentially explain the generating process of the data. It is designed for easy interaction with the researcher, allowing the user to guide the search based ... |
QLattice : The QLattice mainly targets scientists, and integrates well with the scientific workflow. It has been used in research into many different areas, such as energy consumption in buildings, water potability, heart failure, pre-eclampsia, Alzheimer's disease, hepatocellular carcinoma, and breast cancer. |
QLattice : Symbolic regression Explainable artificial intelligence == References == |
Quadratic unconstrained binary optimization : Quadratic unconstrained binary optimization (QUBO), also known as unconstrained binary quadratic programming (UBQP), is a combinatorial optimization problem with a wide range of applications from finance and economics to machine learning. QUBO is an NP hard problem, and for... |
Quadratic unconstrained binary optimization : The set of binary vectors of a fixed length n > 0 is denoted by B n ^ , where B = =\lbrace 0,1\rbrace is the set of binary values (or bits). We are given a real-valued upper triangular matrix Q ∈ R n × n ^ , whose entries Q i j define a weight for each pair of indice... |
Quadratic unconstrained binary optimization : QUBO is scale invariant for positive factors α > 0 , which leave the optimum x ∗ unchanged: f α Q ( x ) = ∑ i ≤ j ( α Q i j ) x i x j = α ∑ i ≤ j Q i j x i x j = α f Q ( x ) (x)=\sum _(\alpha Q_)x_x_=\alpha \sum _Q_x_x_=\alpha f_(x) In its general form, QUBO is NP-hard an... |
Quadratic unconstrained binary optimization : QUBO is a structurally simple, yet computationally hard optimization problem. It can be used to encode a wide range of optimization problems from various scientific areas. |
Quadratic unconstrained binary optimization : QUBO is very closely related and computationally equivalent to the Ising model, whose Hamiltonian function is defined as H ( σ ) = − ∑ ⟨ i j ⟩ J i j σ i σ j − μ ∑ j h j σ j J_\sigma _\sigma _-\mu \sum _h_\sigma _ with real-valued parameters h j , J i j , μ ,J_,\mu for all ... |
Quadratic unconstrained binary optimization : QUBO Benchmark (Benchmark of software packages for the exact solution of QUBOs; part of the well-known Mittelmann benchmark collection) Endre Boros, Peter L Hammer & Gabriel Tavares (April 2007). "Local search heuristics for Quadratic Unconstrained Binary Optimization (QUBO... |
Query-level feature : A query-level feature or QLF is a ranking feature utilized in a machine-learned ranking algorithm. Example QLFs: How many times has this query been run in the last month? How many words are in the query? What is the sum/average/min/max/median of the BM25F values for the query? |
Radiant AI : The Radiant AI is a technology developed by Bethesda Softworks for The Elder Scrolls video games. It allows non-player characters (NPCs) to make choices and engage in behaviors more complex than in past titles. The technology was developed for The Elder Scrolls IV: Oblivion and expanded in The Elder Scroll... |
Radiant AI : The Radiant AI technology, as it evolved in its iteration developed for Skyrim, comprises two parts: |
REEM : REEM is the latest prototype humanoid robot built by PAL Robotics in Spain. It is a 1.70 m high humanoid robot with 22 degrees of freedom, with a mobile base with wheels, allowing it to move at 4 km/hour. The upper part of the robot consists of a torso with a touch screen, two motorized arms, which give it a hig... |
REEM : ASIMO Atlas HUBO Humanoid robot iCub Nao QRIO Robonaut |
REEM : Official website Official Blog Official REEM-C microsite |
Regular constraint : In artificial intelligence and operations research, a regular constraint is a kind of global constraint. It can be used to solve a particular type of puzzle called a nonogram or logigrams. |
Regular constraint : Paltzer, Nikos. Regular Language Membership Constraint |
Riffusion : Riffusion is a neural network, designed by Seth Forsgren and Hayk Martiros, that generates music using images of sound rather than audio. The resulting music has been described as "de otro mundo" (otherworldly), although unlikely to replace man-made music. The model was made available on December 15, 2022, ... |
Robot Constitution : The Robot Constitution is a security ruleset part of AutoRT set by Google DeepMind in January 2024 for its AI products. The rules are inspired by Asimov's Three Laws of Robotics.The rules are applied to the underlying large language models of the helper robots. Rule number 1 is a robot “may not inj... |
Self-play : Self-play is a technique for improving the performance of reinforcement learning agents. Intuitively, agents learn to improve their performance by playing "against themselves". |
Self-play : In multi-agent reinforcement learning experiments, researchers try to optimize the performance of a learning agent on a given task, in cooperation or competition with one or more agents. These agents learn by trial-and-error, and researchers may choose to have the learning algorithm play the role of two or ... |
Self-play : Self-play is used by the AlphaZero program to improve its performance in the games of chess, shogi and go. Self-play is also used to train the Cicero AI system to outperform humans at the game of Diplomacy. The technique is also used in training the DeepNash system to play the game Stratego. |
Self-play : Self-play has been compared to the epistemological concept of tabula rasa that describes the way that humans acquire knowledge from a "blank slate". |
Self-play : DiGiovanni, Anthony; Zell, Ethan; et al. (2021). "Survey of Self-Play in Reinforcement Learning". arXiv:2107.02850 [cs.GT]. == References == |
Sentential decision diagram : In artificial intelligence, a sentential decision diagram (SDD) is a type of knowledge representation used in knowledge compilation to represent Boolean functions. SDDs can be viewed as a generalization of the influential ordered binary decision diagram (OBDD) representation, by allowing d... |
Sentential decision diagram : SDDs are defined with respect to a generalization of variable ordering known as a variable tree (vtree). Provided that they satisfy additional properties known as compression and trimming (which are analogous to ROBDDs), SDDs are a canonical representation of Boolean functions; that is, th... |
Sentential decision diagram : SDDs are used as a compilation target for probabilistic logic programs by the ProbLog 2 system since they support tractable (weighted) model counting as well as tractable negation, conjunction and disjunction while being more succinct than BDDs. SDDs have also been extended to model probab... |
SimSimi : SimSimi is an artificial intelligence conversation program created in 2002 by ISMaker. It grows its artificial intelligence day by day assisted by a feature that allows users to teach it to respond correctly. SimSimi, pronounced as "shim-shimi", is from a Korean word simsim (심심) which means "bored". It has an... |
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