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Outline of machine learning

The following outline is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". ML involves the study and construction of algorithms that can learn from and make predictions on data. These algorithms operate by building a model from a training set of example observations to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions. == How can machine learning be categorized? == An academic discipline A branch of science An applied science A subfield of computer science A branch of artificial intelligence A subfield of soft computing Application of statistics === Paradigms of machine learning === Supervised learning, where the model is trained on labeled data Unsupervised learning, where the model tries to identify patterns in unlabeled data Reinforcement learning, where the model learns to make decisions by receiving rewards or penalties. == Applications of machine learning == Applications of machine learning Bioinformatics Biomedical informatics Computer vision Customer relationship management Data mining Earth sciences Email filtering Inverted pendulum (balance and equilibrium system) Natural language processing Named Entity Recognition Automatic summarization Automatic taxonomy construction Dialog system Grammar checker Language recognition Handwriting recognition Optical character recognition Speech recognition Text to Speech Synthesis Speech Emotion Recognition Machine translation Question answering Speech synthesis Text mining Term frequency–inverse document frequency Text simplification Pattern recognition Facial recognition system Handwriting recognition Image recognition Optical character recognition Speech recognition Recommendation system Collaborative filtering Content-based filtering Hybrid recommender systems Search engine Search engine optimization Social engineering == Machine learning hardware == Graphics processing unit Tensor processing unit Vision processing unit == Machine learning tools == Comparison of machine learning software Comparison of deep learning software === Machine learning frameworks === ==== Proprietary machine learning frameworks ==== Amazon Machine Learning Microsoft Azure Machine Learning Studio DistBelief (replaced by TensorFlow) ==== Open source machine learning frameworks ==== Apache Singa Apache MXNet Caffe PyTorch mlpack TensorFlow Torch CNTK Accord.Net Jax MLJ.jl – A machine learning framework for Julia === Machine learning libraries === Deeplearning4j Theano scikit-learn Keras === Machine learning algorithms === == Machine learning methods == === Instance-based algorithm === K-nearest neighbors algorithm (KNN) Learning vector quantization (LVQ) Self-organizing map (SOM) === Regression analysis === 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 classifier === Dimensionality reduction === Dimensionality 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 === Ensemble learning AdaBoost Boosting Bootstrap aggregating (also "bagging" or "bootstrapping") Ensemble averaging Gradient boosted decision tree (GBDT) Gradient boosting Random Forest Stacked Generalization === Meta-learning === Meta-learning Inductive bias Metadata === Reinforcement learning === Reinforcement learning Q-learning State–action–reward–state–action (SARSA) Temporal difference learning (TD) Learning Automata === Supervised learning === Supervised 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.) 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 model ==== Bayesian ==== Bayesian 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 algorithms ==== 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 SLIQ ==== Linear classifier ==== Linear classifier Fisher's linear discriminant Linear regression Logistic regression Multinomial logistic regression Naive Bayes classifier Perceptron Support vector machine === Unsupervised learning === Unsupervised learning Expectation-maximization algorithm Vector Quantization Generative topographic map Information bottleneck method Association rule learning algorithms Apriori algorithm Eclat algorithm ==== Artificial neural networks ==== Artificial neural network Feedforward neural network Extreme learning machine Convolutional neural network Recurrent neural network Long short-term memory (LSTM) Logic learning machine Self-organizing map ==== Association rule learning ==== Association rule learning Apriori algorithm Eclat algorithm FP-growth algorithm ==== Hierarchical clustering ==== Hierarchical clustering Single-linkage clustering Conceptual clustering ==== Cluster analysis ==== Cluster analysis BIRCH DBSCAN Expectation–maximization (EM) Fuzzy clustering Hierarchical clustering k-means clustering k-medians Mean-shift OPTICS algorithm ==== Anomaly detection ==== Anomaly detection k-nearest neighbors algorithm (k-NN) Local outlier factor === Semi-supervised learning === Semi-supervised learning Active learning Generative models Low-density separation Graph-based methods Co-training Transduction === Deep learning === Deep learning Deep belief networks Deep Boltzmann machines Deep Convolutional neural networks Deep Recurrent neural networks Hierarchical temporal memory Generative Adversarial Network Style transfer Transformer Stacked Auto-Encoders === Other machine learning methods and problems === Anomaly detection Association rules Bias-variance dilemma Classification Multi-label classification Clustering Data Pre-processing Empirical risk minimization Feature engineering Feature learning Learning to rank Occam learning Online machine learning PAC learning Regression Reinforcement Learning Semi-supervised learning Statistical learning Structured prediction Graphical models Bayesian network Conditional random field (CRF) Hidden Markov model (HMM) Unsupervised learning VC theory == Machine learning research == List of artificial intelligence projects List of datasets for machine learning research == History of machine learning == History of machine learning Timeline of machine learning == Machine learning projects == Machine learning projects: DeepMind Google Brain OpenAI Meta AI Hugging Face == Machine learning organizations == === Machine learning conferences and workshops === Artificial Intelligence and Security (AISec) (co-located workshop with CCS) Conference on Neural Information Processing Systems (NIPS) ECML PKDD International Conference on Machine Learning (ICML) ML4ALL (Machine Learning For All) == Machine learning publications == === Books on machine learning === Mathematics for Machine Learning Hands-On Machine Learning Scikit-Learn, Keras, and TensorFlow The Hundred-Page Machine Learning Book === Machine learning journals === Machine Learning Journal of Machine Learning Research (JMLR) Neural Computation == Pe
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Guideline execution engine

A guideline execution engine is a computer program which can interpret a clinical guideline represented in a computerized format and perform actions towards the user of an electronic medical record. A guideline execution engine needs to communicate with a host clinical information system. Virtual Medical Record (vMR) is one possible interface which can be used. The engine's main function is to manage instances of executed guidelines of individual patients. == Architecture == The following modules are generally needed for any engine: interface to clinical information system new guidelines loading module guideline interpreter module clinical events parser alert/recommendations dispatch == Guideline Interchange Format == The Guideline Interchange Format (GLIF) is a computer representation format for clinical guidelines. Represented guidelines can be executed using a guideline execution engine. The format has several versions as it has been improved. In 2003 GLIF3 was introduced. == Use of third party workflow engine as a guideline execution engine == Some commercial electronic health record systems use a workflow engine to execute clinical guidelines. RetroGuide and HealthFlow are examples of such an approach.
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Semantic triple

A semantic triple, or RDF triple or simply triple, is the atomic data entity in the Resource Description Framework (RDF) data model. As its name indicates, a triple is a sequence of three entities that codifies a statement about semantic data in the form of subject–predicate–object expressions (e.g., "Bob is 35", or "Bob knows John"). == Subject, predicate and object == This format enables knowledge to be represented in a machine-readable way. Particularly, every part of an RDF triple is individually addressable via unique URIs—for example, the statement "Bob knows John" might be represented in RDF as: http://example.name#BobSmith12 http://xmlns.com/foaf/spec/#term_knows http://example.name#JohnDoe34. Given this precise representation, semantic data can be unambiguously queried and reasoned about. The components of a triple, such as the statement "The sky has the color blue", consist of a subject ("the sky"), a predicate ("has the color"), and an object ("blue"). This is similar to the classical notation of an entity–attribute–value model within object-oriented design, where this example would be expressed as an entity (sky), an attribute (color) and a value (blue). From this basic structure, triples can be composed into more complex models, by using triples as objects or subjects of other triples—for example, Mike → said → (triples → can be → objects). Given their particular, consistent structure, a collection of triples is often stored in purpose-built databases called triplestores. == Difference from relational databases == A relational database is the classical form for information storage, working with different tables, which consist of rows. The query language SQL is able to retrieve information from such a database. In contrast, RDF triple storage works with logical predicates. No tables nor rows are needed, but the information is stored in a text file. An RDF-triple store can be converted into an SQL database and the other way around. If the knowledge is highly unstructured and dedicated tables aren't flexible enough, semantic triples are used over classic relational storage. In contrast to a traditional SQL database, an RDF triple store isn't created with a table editor. The preferred tool is a knowledge editor, for example Protégé. Protégé looks similar to an object-oriented modeling application used for software engineering, but it's focused on natural language information. The RDF triples are aggregated into a knowledge base, which allows external parsers to run requests. Possible applications include the creation of non-player characters within video games. == Limitations == One concern about triple storage is its lack of database scalability. This problem is especially pertinent if millions of triples are stored and retrieved in a database. The seek time is larger than for classical SQL-based databases. A more complex issue is a knowledge model's inability to predict future states. Even if all the domain knowledge is available as logical predicates, the model fails in answering what-if questions. For example, suppose in the RDF format a room with a robot and table is described. The robot knows what the location of the table is, is aware of the distance to the table and knows also that a table is a type of furniture. Before the robot can plan its next action, it needs temporal reasoning capabilities. Thus, the knowledge model should answer hypothetical questions in advance before an action is taken.
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Otterly.ai

Otterly.ai is an Austrian software company, founded in 2024, that provides tools for generative engine optimization, the practice of monitoring and optimizing results in large language models. == History == Otterly.ai was co-founded in 2024 by Thomas Peham, Klaus-M. Schremser and Josef Trauner. The concept for OtterlyAI was developed in response to the increasing use of generative AI tools in digital search and content discovery. The company announced a technology partnership with SEO platform Semrush in January 2025.
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Transfer learning

Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks. This topic is related to the psychological literature on transfer of learning, although practical ties between the two fields are limited. Reusing or transferring information from previously learned tasks to new tasks has the potential to significantly improve learning efficiency. Since transfer learning makes use of training with multiple objective functions it is related to cost-sensitive machine learning and multi-objective optimization. == History == In 1976, Bozinovski and Fulgosi published a paper addressing transfer learning in neural network training. The paper gives a mathematical and geometrical model of the topic. In 1981, a report considered the application of transfer learning to a dataset of images representing letters of computer terminals, experimentally demonstrating positive and negative transfer learning. In 1992, Lorien Pratt formulated the discriminability-based transfer (DBT) algorithm. By 1998, the field had advanced to include multi-task learning, along with more formal theoretical foundations. Influential publications on transfer learning include the book Learning to Learn in 1998, a 2009 survey and a 2019 survey. Ng said in his NIPS 2016 tutorial that TL would become the next driver of machine learning commercial success after supervised learning. In the 2020 paper, "Rethinking Pre-Training and self-training", Zoph et al. reported that pre-training can hurt accuracy, and advocate self-training instead. == Definition == The definition of transfer learning is given in terms of domains and tasks. A domain D {\displaystyle {\mathcal {D}}} consists of: a feature space X {\displaystyle {\mathcal {X}}} and a marginal probability distribution P ( X ) {\displaystyle P(X)} , where X = { x 1 , . . . , x n } ∈ X {\displaystyle X=\{x_{1},...,x_{n}\}\in {\mathcal {X}}} . Given a specific domain, D = { X , P ( X ) } {\displaystyle {\mathcal {D}}=\{{\mathcal {X}},P(X)\}} , a task consists of two components: a label space Y {\displaystyle {\mathcal {Y}}} and an objective predictive function f : X → Y {\displaystyle f:{\mathcal {X}}\rightarrow {\mathcal {Y}}} . The function f {\displaystyle f} is used to predict the corresponding label f ( x ) {\displaystyle f(x)} of a new instance x {\displaystyle x} . This task, denoted by T = { Y , f ( x ) } {\displaystyle {\mathcal {T}}=\{{\mathcal {Y}},f(x)\}} , is learned from the training data consisting of pairs { x i , y i } {\displaystyle \{x_{i},y_{i}\}} , where x i ∈ X {\displaystyle x_{i}\in {\mathcal {X}}} and y i ∈ Y {\displaystyle y_{i}\in {\mathcal {Y}}} . Given a source domain D S {\displaystyle {\mathcal {D}}_{S}} and learning task T S {\displaystyle {\mathcal {T}}_{S}} , a target domain D T {\displaystyle {\mathcal {D}}_{T}} and learning task T T {\displaystyle {\mathcal {T}}_{T}} , where D S ≠ D T {\displaystyle {\mathcal {D}}_{S}\neq {\mathcal {D}}_{T}} , or T S ≠ T T {\displaystyle {\mathcal {T}}_{S}\neq {\mathcal {T}}_{T}} , transfer learning aims to help improve the learning of the target predictive function f T ( ⋅ ) {\displaystyle f_{T}(\cdot )} in D T {\displaystyle {\mathcal {D}}_{T}} using the knowledge in D S {\displaystyle {\mathcal {D}}_{S}} and T S {\displaystyle {\mathcal {T}}_{S}} . == Applications == Algorithms for transfer learning are available in Markov logic networks and Bayesian networks. Transfer learning has been applied to cancer subtype discovery, building utilization, general game playing, text classification, digit recognition, medical imaging and spam filtering. In 2020, it was discovered that, due to their similar physical natures, transfer learning is possible between electromyographic (EMG) signals from the muscles and classifying the behaviors of electroencephalographic (EEG) brainwaves, from the gesture recognition domain to the mental state recognition domain. It was noted that this relationship worked in both directions, showing that electroencephalographic can likewise be used to classify EMG. The experiments noted that the accuracy of neural networks and convolutional neural networks were improved through transfer learning both prior to any learning (compared to standard random weight distribution) and at the end of the learning process (asymptote). That is, results are improved by exposure to another domain. Moreover, the end-user of a pre-trained model can change the structure of fully-connected layers to improve performance.
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Knowledge processing for robots

KnowRob (Knowledge processing for robots) is a system which combines knowledge representation and reasoning methods to acquire and ground knowledge. This system is the backbone of openEASE. both under developing at the Institute for Artificial Intelligence at the University of Bremen, Germany. == The framework == KnowRob can serve as a common sense framework for the integration of knowledge. This knowledge can be static encyclopedic knowledge, common sense knowledge, task descriptions, environment models, object information, observed actions, etc., which can come from different sources, like manually axiomatized, derived from observations, or imported from the web. KnowRob has been used by different research groups, as the Rice University using the ontological knowledge base in a robotic platform. As well by the Eindhoven University of Technology research group competing in the RoboCup league, in the "at Home" category, with the RoboEarth project. As well, KnowRob is mentioned in the work of some research groups from the Lucian Blaga University of Sibiu, Middle East Technical University in their combination of different knowledge bases, Keio University as related work because of the ontology service, University of Texas at Austin as related work as well because of the relation with the work presented, Hanyang University as related work as an OWL based knowledge processing framework. == Representations == To represent the knowledge, KnowRob uses the OWL ontology language and an extended first-order logic knowledge representation with computable predicates. To give the order of subactions, KnowRob includes a pair-wise ordering constrain, which gives a partial ordering. KnowRob adopts the closed-world assumption Prolog, and an open-world assumption by the use of computables. To include reasoning rules into Prolog, KnowRob uses an inference procedure beyond the capabilities of OWL to extract information about tasks executions. In its second version, KnowRob provides a logic interface to the hybrid reasoning kernel as a logic based language. This language presents the hybrid reasoning kernel as if everything were entities retrievable by providing partial descriptions for them. This entities descriptions include objects, their parts, and articulation models, environments composed of objects, software components, actions, and events. === Episodic memories === Episodic memory is related to the experience information, which is organized temporally and spatially, alongside combined with context information. In KnowRob, an episodic memory is understood as a recording that the agent makes of the ongoing activity, which includes very detailed information about the actions, motions, their purposes, effects and the behavior they generate, it also includes the images captured during execution, etc. == Usage == The knowledge is computed by external methods using Prolog queries. In the second version of the KnowRob system, is included a better structure of the packages and documentations. Which includes some extensions from the previous version, as well as a logic based language. For example, a cup description from perception can be represented in this language as: entity(Cup,[an, object, [type, cup], [shape, cylinder], [color, orange]]) As well, a controller could represent the same object as: entity(Cup, [an, object, [type, cup], [proper_physical_parts, [an, object, [type, handle], [grasp−pose, G−pose]]]]) The interface language is comparable to other query languages for symbolic knowledge bases. KnowRob's query language integrates reasoning methods, such as the simulation-based reasoning. == Goals == The goal of the KnowRob framework is to make semantic knowledge available for service robots. It is able to answer queries about missing information in vague instructions for tasks. This is possible with the actions hierarchical representation and information about objects which can be included in certain action.
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David Krueger (professor)

David Krueger is an American machine learning professor and advocate for the reduction of risks related to artificial intelligence. Krueger is an assistant professor in Robust, Reasoning, and Responsible AI at the University of Montreal and a Core Academic Member at Mila. == Early life and education == Krueger obtained a B.A. in mathematics from Reed College, and completed his MSc and Ph.D. in Computer Science at the University of Montreal. He trained in deep learning under Yoshua Bengio, Roland Memisevic, and Aaron Courville from 2013 to 2021. Krueger was also an intern on Google DeepMind's AI Safety team in 2018. == Career == Krueger researches deep learning, AI alignment, and AI safety. His work is focused on reducing the risk of human extinction resulting from out-of-control AI systems. Krueger was an assistant professor at the University of Cambridge from 2021 to 2024, before taking a faculty position at the University of Montreal in 2024. In 2023, he was a founding research director at the UK AI Security Institute. That same year, Krueger initiated the Statement on AI Risk, which argues that AI could cause human extinction and was signed by Anthropic's Dario Amodei, OpenAI's Sam Altman, AI expert Geoffrey Hinton, and other leaders. In April 2026, Krueger discussed the risks of advanced AI at a Capitol Hill event hosted by Senator Bernie Sanders. === Evitable === In 2025, Krueger founded Evitable, a nonprofit organization that advocates for an AI moratorium. == Views == Krueger argues that AI will lead to a "gradual disempowerment" of workers, likening AI chips to nuclear bombs. He also says the military use of AI "poses an existential risk to humanity."
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Yale shooting problem

The Yale shooting problem is a conundrum or scenario in formal situational logic on which early logical solutions to the frame problem fail. The name of this problem comes from a scenario proposed by its inventors, Steve Hanks and Drew McDermott, working at Yale University when they proposed it. In this scenario, Fred (later identified as a turkey) is initially alive and a gun is initially unloaded. Loading the gun, waiting for a moment, and then shooting the gun at Fred is expected to kill Fred. However, if inertia is formalized in logic by minimizing the changes in this situation, then it cannot be uniquely proved that Fred is dead after loading, waiting, and shooting. In one solution, Fred indeed dies; in another (also logically correct) solution, the gun becomes mysteriously unloaded and Fred survives. Technically, this scenario is described by two fluents (a fluent is a condition that can change truth value over time): a l i v e {\displaystyle alive} and l o a d e d {\displaystyle loaded} . Initially, the first condition is true and the second is false. Then, the gun is loaded, some time passes, and the gun is fired. Such problems can be formalized in logic by considering four time points 0 {\displaystyle 0} , 1 {\displaystyle 1} , 2 {\displaystyle 2} , and 3 {\displaystyle 3} , and turning every fluent such as a l i v e {\displaystyle alive} into a predicate a l i v e ( t ) {\displaystyle alive(t)} depending on time. A direct formalization of the statement of the Yale shooting problem in logic is the following one: a l i v e ( 0 ) {\displaystyle alive(0)} ¬ l o a d e d ( 0 ) {\displaystyle \neg loaded(0)} t r u e → l o a d e d ( 1 ) {\displaystyle true\rightarrow loaded(1)} l o a d e d ( 2 ) → ¬ a l i v e ( 3 ) {\displaystyle loaded(2)\rightarrow \neg alive(3)} The first two formulae represent the initial state. The third formula formalizes the effect of loading the gun at time 1 {\displaystyle 1} . The fourth formula formalizes the effect of shooting at Fred at time 2 {\displaystyle 2} . This is a simplified formalization in which action names are neglected and the effects of actions are directly specified for the time points in which the actions are executed. See situation calculus for details. The formulae above, while being direct formalizations of the known facts, do not suffice to correctly characterize the domain. Indeed, ¬ a l i v e ( 1 ) {\displaystyle \neg alive(1)} is consistent with all these formulae, although there is no reason to believe that Fred dies before the gun has been shot. The problem is that the formulae above only include the effects of actions, but do not specify that all fluents not changed by the actions remain the same. In other words, a formula a l i v e ( 0 ) ≡ a l i v e ( 1 ) {\displaystyle alive(0)\equiv alive(1)} must be added to formalize the implicit assumption that loading the gun only changes the value of l o a d e d {\displaystyle loaded} and not the value of a l i v e {\displaystyle alive} . The necessity of a large number of formulae stating the obvious fact that conditions do not change unless an action changes them is known as the frame problem. An early solution to the frame problem was based on minimizing the changes. In other words, the scenario is formalized by the formulae above (that specify only the effects of actions) and by the assumption that the changes in the fluents over time are as minimal as possible. The rationale is that the formulae above enforce all effect of actions to take place, while minimization should restrict the changes to exactly those due to the actions. In the Yale shooting scenario, one possible evaluation of the fluents in which the changes are minimized is the following one. This is the expected solution. It contains two fluent changes: l o a d e d {\displaystyle loaded} becomes true at time 1 and a l i v e {\displaystyle alive} becomes false at time 3. The following evaluation also satisfies all formulae above. In this evaluation, there are still two changes only: l o a d e d {\displaystyle loaded} becomes true at time 1 and false at time 2. As a result, this evaluation is considered a valid description of the evolution of the state, although there is no valid reason to explain l o a d e d {\displaystyle loaded} being false at time 2. The fact that minimization of changes leads to wrong solution is the motivation for the introduction of the Yale shooting problem. While the Yale shooting problem has been considered a severe obstacle to the use of logic for formalizing dynamical scenarios, solutions to it have been known since the late 1980s. One solution involves the use of predicate completion in the specification of actions: in this solution, the fact that shooting causes Fred to die is formalized by the preconditions: alive and loaded, and the effect is that alive changes value (since alive was true before, this corresponds to alive becoming false). By turning this implication into an if and only if statement, the effects of shooting are correctly formalized. (Predicate completion is more complicated when there is more than one implication involved.) A solution proposed by Erik Sandewall was to include a new condition of occlusion, which formalizes the “permission to change” for a fluent. The effect of an action that might change a fluent is therefore that the fluent has the new value, and that the occlusion is made (temporarily) true. What is minimized is not the set of changes, but the set of occlusions being true. Another constraint specifying that no fluent changes unless occlusion is true completes this solution. The Yale shooting scenario is also correctly formalized by the Reiter version of the situation calculus, the fluent calculus, and the action description languages. In 2005, the 1985 paper in which the Yale shooting scenario was first described received the AAAI Classic Paper award. In spite of being a solved problem, that example is still sometimes mentioned in recent research papers, where it is used as an illustrative example (e.g., for explaining the syntax of a new logic for reasoning about actions), rather than being presented as a problem.
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Mobile DevOps

Mobile DevOps is a set of practices that applies the principles of DevOps specifically to the development of mobile applications. Traditional DevOps focuses on streamlining the software development process in general, but mobile development has its own unique challenges that require a tailored approach. Mobile DevOps is not simply as a branch of DevOps specific to mobile app development, instead an extension and reinterpretation of the DevOps philosophy due to very specific requirements of the mobile world. == Rationale == Traditional DevOps approach has been formed around 2007-2008, close to the dates when iOS and Android mobile operating systems were released to the public. The traditional DevOps approach primarily evolved to meet the changing needs of the software development world with the paradigm shift towards continuous and rapid development and deployment (such as in web development, where interpreted languages are more prevalent than compiled languages). While traditional DevOps embraced agility and flexibility, mobile operating system providers steered towards a walled-garden approach with compiled apps with tight controls over how they can be distributed and installed on a mobile device. This difference in the mobile development mindset compared to what the traditional DevOps approach is advocating, is augmented further with the mobile applications to be deployed on a high number of varying devices and operating systems. Eventually, the concept of Mobile DevOps took off as a trend around 2014-2015, in line with the fast growth of the number of applications in mobile app stores. As individuals and corporations alike are developing and publishing more and more mobile applications, the need for efficiency and shorter release cycles increased, which is addressed by the continuous feedback and continuous development approach within the concept of DevOps, while requiring a significant level of adaptation and extension of the traditional DevOps practices. == Mindset shift from traditional DevOps to mobile DevOps == Mobile DevOps has a unique set of challenges and constraints, which solidifies the fact that it needs to be approached as a separate discipline. These challenges can be outlined as follows: Platform-specific requirements and tight controls of mobile operating system providers, where for instance a macOS device is mandatory for iOS application development and release. The walled-garden approach of distributing mobile apps, specifically applying to iOS applications, which comes with app review and app release delays that would not be needed in web development, for instance. Code signing requirements that come with the walled-garden approach, which introduce additional processes in the mobile application build pipeline along with new security concerns. An entire deployment cycle is re-run even in the slightest code change due to how applications are compiled and delivered to the users. The final product is to be deployed to a wide variety of mobile devices worldwide, which requires extensive testing and user feedback. Monitoring mobile applications require additional tools and approaches to be able to get data from an application running on a mobile device while respecting user privacy. Frequent operating system updates by mobile platforms can require rapid adaptation of apps, introducing further complexity to the development and maintenance cycles. == Benefits of mobile DevOps == Mobile DevOps is not an abstract concept and offers a range of benefits that can help improve the efficiency and effectiveness of the mobile app development process. These benefits can even be quantified by collecting the data within the mobile application development lifecycle. The benefits can be categorized into the following areas: Faster Release Cycles: By automating tasks and streamlining the development process, mobile DevOps enables teams to deliver new features and updates more frequently. Improved Quality: Automated testing and continuous monitoring help to identify and fix bugs earlier in the development cycle, leading to higher quality apps. Optimized Resource Utilization: Mobile DevOps promotes optimized resource utilization by automating tasks and streamlining workflows. Furthermore, mobile DevOps practices like containerization can help to create more efficient and scalable development environments. Increased Agility: Mobile DevOps allows teams to be more responsive to changes in the market and user feedback. == List of Dedicated Mobile DevOps Platforms == Even though it is possible to run a mobile DevOps cycle with most of the CI/CD platforms, they may require significant effort compared to non-mobile CI/CD (e.g. you need to bring your own infrastructure or it may require "reinventing the wheel" for commonly used platforms like Jenkins). To overcome the mobile-specific challenges specified, there are certain platforms that are dedicated to the lifecycle of mobile applications. These platforms exclusively focus on DevOps processes for mobile app development and are also referred as mobile CI/CD platforms. Appcircle (Multiplatform | Cloud-based & On-premise) Visual Studio App Center (Multiplatform | Cloud-based) Xcode Cloud (Apple platforms only | Cloud-based)
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AlphaZero

AlphaZero is a computer program developed by artificial intelligence research company DeepMind to master the games of chess, shogi and go. This algorithm uses an approach similar to AlphaGo Zero. On December 5, 2017, the DeepMind team released a preprint paper introducing AlphaZero, which would soon play three games by defeating world-champion chess engines Stockfish, Elmo, and the three-day version of AlphaGo Zero. In each case it made use of custom tensor processing units (TPUs) that the Google programs were optimized to use. AlphaZero was trained solely via self-play using 5,000 first-generation TPUs to generate the games and 64 second-generation TPUs to train the neural networks, all in parallel, with no access to opening books or endgame tables. After four hours of training, DeepMind estimated AlphaZero was playing chess at a higher Elo rating than Stockfish 8; after nine hours of training, the algorithm defeated Stockfish 8 in a time-controlled 100-game tournament (28 wins, 0 losses, and 72 draws). The trained algorithm played on a single machine with four TPUs. DeepMind's paper on AlphaZero was published in the journal Science on 7 December 2018. While the actual AlphaZero program has not been released to the public, the algorithm described in the paper has been implemented in publicly available software. In 2019, DeepMind published a new paper detailing MuZero, a new algorithm able to generalize AlphaZero's work, playing both Atari and board games without knowledge of the rules or representations of the game. == Relation to AlphaGo Zero == AlphaZero (AZ) is a more generalized variant of the AlphaGo Zero (AGZ) algorithm, and is able to play shogi and chess as well as Go. Differences between AZ and AGZ include: AZ has hard-coded rules for setting search hyperparameters. The neural network is now updated continually. AZ doesn't use symmetries, unlike AGZ. Chess or Shogi can end in a draw unlike Go; therefore, AlphaZero takes into account the possibility of a drawn game. == Stockfish and Elmo == Comparing Monte Carlo tree search searches, AlphaZero searches just 80,000 positions per second in chess and 40,000 in shogi, compared to 70 million for Stockfish and 35 million for Elmo. AlphaZero compensates for the lower number of evaluations by using its deep neural network to focus much more selectively on the most promising variation. == Training == AlphaZero was trained by simply playing against itself multiple times, using 5,000 first-generation TPUs to generate the games and 64 second-generation TPUs to train the neural networks. In parallel, the in-training AlphaZero was periodically matched against its benchmark (Stockfish, Elmo, or AlphaGo Zero) in brief one-second-per-move games to determine how well the training was progressing. DeepMind judged that AlphaZero's performance exceeded the benchmark after around four hours of training for Stockfish, two hours for Elmo, and eight hours for AlphaGo Zero. == Preliminary results == === Outcome === ==== Chess ==== In AlphaZero's chess match against Stockfish 8 (2016 TCEC world champion), each program was given one minute per move. AlphaZero was flying the English flag, while Stockfish the Norwegian. Stockfish was allocated 64 threads and a hash size of 1 GB, a setting that Stockfish's Tord Romstad later criticized as suboptimal. AlphaZero was trained on chess for a total of nine hours before the match. During the match, AlphaZero ran on a single machine with four application-specific TPUs. In 100 games from the normal starting position, AlphaZero won 25 games as White, won 3 as Black, and drew the remaining 72. In a series of twelve, 100-game matches (of unspecified time or resource constraints) against Stockfish starting from the 12 most popular human openings, AlphaZero won 290, drew 886 and lost 24. ==== Shogi ==== AlphaZero was trained on shogi for a total of two hours before the tournament. In 100 shogi games against Elmo (World Computer Shogi Championship 27 summer 2017 tournament version with YaneuraOu 4.73 search), AlphaZero won 90 times, lost 8 times and drew twice. As in the chess games, each program got one minute per move, and Elmo was given 64 threads and a hash size of 1 GB. ==== Go ==== After 34 hours of self-learning of Go and against AlphaGo Zero, AlphaZero won 60 games and lost 40. === Analysis === DeepMind stated in its preprint, "The game of chess represented the pinnacle of AI research over several decades. State-of-the-art programs are based on powerful engines that search many millions of positions, leveraging handcrafted domain expertise and sophisticated domain adaptations. AlphaZero is a generic reinforcement learning algorithm – originally devised for the game of go – that achieved superior results within a few hours, searching a thousand times fewer positions, given no domain knowledge except the rules." DeepMind's Demis Hassabis, a chess player himself, called AlphaZero's play style "alien": It sometimes wins by offering counterintuitive sacrifices, like offering up a queen and bishop to exploit a positional advantage. "It's like chess from another dimension." Given the difficulty in chess of forcing a win against a strong opponent, the +28 –0 =72 result is a significant margin of victory. However, some grandmasters, such as Hikaru Nakamura and Komodo developer Larry Kaufman, downplayed AlphaZero's victory, arguing that the match would have been closer if the programs had access to an opening database (since Stockfish was optimized for that scenario). Romstad additionally pointed out that Stockfish is not optimized for rigidly fixed-time moves and the version used was a year old. Similarly, some shogi observers argued that the Elmo hash size was too low, that the resignation settings and the "EnteringKingRule" settings (cf. shogi § Entering King) may have been inappropriate, and that Elmo is already obsolete compared with newer programs. === Reaction and criticism === Papers headlined that the chess training took only four hours: "It was managed in little more than the time between breakfast and lunch." Wired described AlphaZero as "the first multi-skilled AI board-game champ". AI expert Joanna Bryson noted that Google's "knack for good publicity" was putting it in a strong position against challengers. "It's not only about hiring the best programmers. It's also very political, as it helps make Google as strong as possible when negotiating with governments and regulators looking at the AI sector." Human chess grandmasters generally expressed excitement about AlphaZero. Danish grandmaster Peter Heine Nielsen likened AlphaZero's play to that of a superior alien species. Norwegian grandmaster Jon Ludvig Hammer characterized AlphaZero's play as "insane attacking chess" with profound positional understanding. Former champion Garry Kasparov said, "It's a remarkable achievement, even if we should have expected it after AlphaGo." Grandmaster Hikaru Nakamura was less impressed, stating: "I don't necessarily put a lot of credibility in the results simply because my understanding is that AlphaZero is basically using the Google supercomputer and Stockfish doesn't run on that hardware; Stockfish was basically running on what would be my laptop. If you wanna have a match that's comparable you have to have Stockfish running on a supercomputer as well." Top US correspondence chess player Wolff Morrow was also unimpressed, claiming that AlphaZero would probably not make the semifinals of a fair competition such as TCEC where all engines play on equal hardware. Morrow further stated that although he might not be able to beat AlphaZero if AlphaZero played drawish openings such as the Petroff Defence, AlphaZero would not be able to beat him in a correspondence chess game either. Motohiro Isozaki, the author of YaneuraOu, noted that although AlphaZero did comprehensively beat Elmo, the rating of AlphaZero in shogi stopped growing at a point which is at most 100–200 higher than Elmo. This gap is not that high, and Elmo and other shogi software should be able to catch up in 1–2 years. == Final results == DeepMind addressed many of the criticisms in their final version of the paper, published in December 2018 in Science. They further clarified that AlphaZero was not running on a supercomputer; it was trained using 5,000 tensor processing units (TPUs), but only ran on four TPUs and a 44-core CPU in its matches. === Chess === In the final results, Stockfish 9 dev ran under the same conditions as in the TCEC superfinal: 44 CPU cores, Syzygy endgame tablebases, and a 32 GB hash size. Instead of a fixed time control of one move per minute, both engines were given 3 hours plus 15 seconds per move to finish the game. AlphaZero ran on a much more powerful machine with four TPUs in addition to 44 CPU cores. In a 1000-game match, AlphaZero won with a score of 155 wins, 6 losses, and 839 draws. DeepMind also played a series of games using the TCEC opening positions; AlphaZero also won
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TasteDive

TasteDive (formerly named TasteKid) is an entertainment recommendation engine for films, TV shows, music, video games, books, people, places, and brands. It also has elements of a social media site; it allows users to connect with "tastebuds", people with like minded interests. == History == TasteDive was founded in 2008 as TasteKid by brothers Andrei Oghina and Felix Oghina. In 2019, it was acquired by Qloo headquartered in NYC. "Qloo has built for developers and enterprises what TasteDive has built for individuals". == Description == When a user types in the title of a film or TV show, the site's algorithm provides a list of similar content. It provides recommendations for TV shows to watch based on films liked by the user, and vice versa. It also provides recommendations for music, video games, and books, and includes film and TV trailers and music videos. An account is free and is not required to receive recommendations, but recommendations are more accurate for those with an account. The more a user explores the site, the more the site learns about the user's preferences and the better the results become. The site also has a social media aspect where one can see activity and gain recommendations from other users, how many others in the community like or dislike any recommendation, and how popular their tastes are within the TasteDive community. The main competitors of TasteDive are Taste App, Trakt.tv and Tastoid.
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Yale shooting problem

The Yale shooting problem is a conundrum or scenario in formal situational logic on which early logical solutions to the frame problem fail. The name of this problem comes from a scenario proposed by its inventors, Steve Hanks and Drew McDermott, working at Yale University when they proposed it. In this scenario, Fred (later identified as a turkey) is initially alive and a gun is initially unloaded. Loading the gun, waiting for a moment, and then shooting the gun at Fred is expected to kill Fred. However, if inertia is formalized in logic by minimizing the changes in this situation, then it cannot be uniquely proved that Fred is dead after loading, waiting, and shooting. In one solution, Fred indeed dies; in another (also logically correct) solution, the gun becomes mysteriously unloaded and Fred survives. Technically, this scenario is described by two fluents (a fluent is a condition that can change truth value over time): a l i v e {\displaystyle alive} and l o a d e d {\displaystyle loaded} . Initially, the first condition is true and the second is false. Then, the gun is loaded, some time passes, and the gun is fired. Such problems can be formalized in logic by considering four time points 0 {\displaystyle 0} , 1 {\displaystyle 1} , 2 {\displaystyle 2} , and 3 {\displaystyle 3} , and turning every fluent such as a l i v e {\displaystyle alive} into a predicate a l i v e ( t ) {\displaystyle alive(t)} depending on time. A direct formalization of the statement of the Yale shooting problem in logic is the following one: a l i v e ( 0 ) {\displaystyle alive(0)} ¬ l o a d e d ( 0 ) {\displaystyle \neg loaded(0)} t r u e → l o a d e d ( 1 ) {\displaystyle true\rightarrow loaded(1)} l o a d e d ( 2 ) → ¬ a l i v e ( 3 ) {\displaystyle loaded(2)\rightarrow \neg alive(3)} The first two formulae represent the initial state. The third formula formalizes the effect of loading the gun at time 1 {\displaystyle 1} . The fourth formula formalizes the effect of shooting at Fred at time 2 {\displaystyle 2} . This is a simplified formalization in which action names are neglected and the effects of actions are directly specified for the time points in which the actions are executed. See situation calculus for details. The formulae above, while being direct formalizations of the known facts, do not suffice to correctly characterize the domain. Indeed, ¬ a l i v e ( 1 ) {\displaystyle \neg alive(1)} is consistent with all these formulae, although there is no reason to believe that Fred dies before the gun has been shot. The problem is that the formulae above only include the effects of actions, but do not specify that all fluents not changed by the actions remain the same. In other words, a formula a l i v e ( 0 ) ≡ a l i v e ( 1 ) {\displaystyle alive(0)\equiv alive(1)} must be added to formalize the implicit assumption that loading the gun only changes the value of l o a d e d {\displaystyle loaded} and not the value of a l i v e {\displaystyle alive} . The necessity of a large number of formulae stating the obvious fact that conditions do not change unless an action changes them is known as the frame problem. An early solution to the frame problem was based on minimizing the changes. In other words, the scenario is formalized by the formulae above (that specify only the effects of actions) and by the assumption that the changes in the fluents over time are as minimal as possible. The rationale is that the formulae above enforce all effect of actions to take place, while minimization should restrict the changes to exactly those due to the actions. In the Yale shooting scenario, one possible evaluation of the fluents in which the changes are minimized is the following one. This is the expected solution. It contains two fluent changes: l o a d e d {\displaystyle loaded} becomes true at time 1 and a l i v e {\displaystyle alive} becomes false at time 3. The following evaluation also satisfies all formulae above. In this evaluation, there are still two changes only: l o a d e d {\displaystyle loaded} becomes true at time 1 and false at time 2. As a result, this evaluation is considered a valid description of the evolution of the state, although there is no valid reason to explain l o a d e d {\displaystyle loaded} being false at time 2. The fact that minimization of changes leads to wrong solution is the motivation for the introduction of the Yale shooting problem. While the Yale shooting problem has been considered a severe obstacle to the use of logic for formalizing dynamical scenarios, solutions to it have been known since the late 1980s. One solution involves the use of predicate completion in the specification of actions: in this solution, the fact that shooting causes Fred to die is formalized by the preconditions: alive and loaded, and the effect is that alive changes value (since alive was true before, this corresponds to alive becoming false). By turning this implication into an if and only if statement, the effects of shooting are correctly formalized. (Predicate completion is more complicated when there is more than one implication involved.) A solution proposed by Erik Sandewall was to include a new condition of occlusion, which formalizes the “permission to change” for a fluent. The effect of an action that might change a fluent is therefore that the fluent has the new value, and that the occlusion is made (temporarily) true. What is minimized is not the set of changes, but the set of occlusions being true. Another constraint specifying that no fluent changes unless occlusion is true completes this solution. The Yale shooting scenario is also correctly formalized by the Reiter version of the situation calculus, the fluent calculus, and the action description languages. In 2005, the 1985 paper in which the Yale shooting scenario was first described received the AAAI Classic Paper award. In spite of being a solved problem, that example is still sometimes mentioned in recent research papers, where it is used as an illustrative example (e.g., for explaining the syntax of a new logic for reasoning about actions), rather than being presented as a problem.
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ConEmu

ConEmu (short for Console emulator) is a free and open-source tabbed terminal emulator for Windows. ConEmu presents multiple consoles and simple GUI applications as one customizable GUI window with tabs and a status bar. It also provides emulation for ANSI escape codes for color, bypassing the capabilities of the standard Windows Console Host to provide 256 and 24-bit color in Windows. The program has a large range of customization, including custom color palettes for the standard 16 colors, hotkeys, transparency, an auto-hideable mode (similar to the way Quake originally displayed its developer console). Initially, the program was created as a companion to Far Manager, bringing some features common for graphical file managers to this console application (thumbnails and tiles, drag and drop with other windows, true color interface, and others). As of 2012, ConEmu could be used with any other Win32 console application or simple GUI tool (such as Notepad, PuTTY or DOSBox). ConEmu doesn't provide any shell itself, but rather allows using any other shell. It does provide a limited macro language, to control the hosted applications startup.
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Komodo (chess)

Komodo and Dragon by Komodo Chess (also known as Dragon or Komodo Dragon) are UCI chess engines developed by Komodo Chess, which is a part of Chess.com. The engines were originally authored by Don Dailey and GM Larry Kaufman. Dragon is a commercial chess engine, but Komodo is free for non-commercial use. Dragon is consistently ranked near the top of most major chess engine rating lists, along with Stockfish and Leela Chess Zero. == History == === Komodo === Komodo was derived from Don Dailey's former engine Doch in January 2010. The first multiprocessor version of Komodo was released in June 2013 as Komodo 5.1 MP. This version was a major rewrite and a port of Komodo to C++11. A single-processor version of Komodo (which won the CCT15 tournament in February earlier that year) was released as a stand-alone product shortly before the 5.1 MP release. This version, named Komodo CCT, was still based on the older C code, and was approximately 30 Elo stronger than the 5.1 MP version, as the latter was still undergoing massive code-cleanup work. With the release of Komodo 6 on October 4, 2013, Don Dailey announced that he was suffering from an acute form of leukaemia, and would no longer contribute to the future development of Komodo. On October 8, Don made an announcement on the Talkchess forum that Mark Lefler would be joining the Komodo team and would continue its development. Komodo TCEC was released on December 4, 2013. This was the same version that had won TCEC Season 5, and was the last with input from Don Dailey, to whom it was dedicated. Komodo 7 was released on May 21, 2014, adding Syzygy tablebase support. On May 24, 2018, Chess.com announced that it has acquired Komodo and that the Komodo team have joined Chess.com. The Komodo team is now called Komodo Chess. On December 17, 2018, Komodo Chess released Komodo 12.3 MCTS, a version of the Komodo 12.3 engine that uses Monte Carlo tree search instead of alpha–beta pruning/minimax. The last version, Komodo 14.3, was released on October 4, 2023. === Dragon === On November 9, 2020, Komodo Chess released Dragon by Komodo Chess 1.0, which features the use of efficiently updatable neural networks in its evaluation function. Dragon is derived from Komodo in the same way that Komodo was derived from Doch. Dragon is also called Komodo Dragon in certain tournaments such as the Top Chess Engine Championship and the World Computer Chess Championship (WCCC) but not in the Chess.com Computer Chess Championship (CCC). A Chess.com staff member named Dmitry Pervov joined the Dragon development team to write the NNUE code for Dragon, and Dietrich Kappe joined the Dragon development team to help Larry Kaufman and Mark Lefter train Dragon's neural networks. On March 17, 2023, Larry Kaufman announced that he and Mark Lefter have stepped down from Dragon development and from ownership of Komodo Chess, and that Chess.com have taken full control of Komodo Chess. As of March 17, 2023, Dietrich Kappe is the only person responsible for the development of Dragon, but Chess.com are looking for more programmers to help with Dragon development. The final version, Dragon 3.3, was released on October 4, 2023. == Competition results == === Komodo === Komodo has played in the ICT 2010 in Leiden, and further in the CCT12 and CCT14. Komodo had its first tournament success in 1999, when it won the CCT15 with a score of 6½/7. Komodo won both the World Computer Chess Championship and World Computer Software Championship in 2016. Komodo once again won the World Computer Chess Championship and World Blitz in 2017. In TCEC competition, Komodo was historically one of the strongest engines. In Season 4, it lost only eight out of its 53 games and managed to reach Stage 4 (Quarterfinals), against very strong competition which were running on eight cores (Komodo was running on a single processor). The next season, Komodo won the superfinal against Stockfish. The two engines jockeyed for the championship over the next few seasons: Stockfish won in Season 6, while Komodo won Seasons 7 and 8. Komodo failed to make the superfinal in Season 9, losing out to Houdini; but after Houdini was later disqualified for containing code plagiarized from Stockfish, Komodo was promoted to the runner-up. Komodo retrospectively won Season 10 in the same way. Starting from Season 11 however, Stockfish improved at a rate that left its rivals behind, crushing Komodo in Season 12 and 13. The advent of the neural network engine Leela Chess Zero meant Komodo has largely failed to qualify for the superfinal since, with a single exception in Season 22, when it lost to Stockfish. Although Komodo has not qualified for the superfinal, it has cemented itself as the third-strongest engine in the competition, finishing in that position for five of the last six seasons. ==== Chess.com Computer Chess Championship ==== === Dragon === ==== Chess.com Computer Chess Championship ==== ==== Top Chess Engine Championship ==== == Notable games == Komodo vs Hannibal, nTCEC - Stage 2b - Season 1, Round 4.1, ECO: A10, 1–0 Archived 2016-03-04 at the Wayback Machine Komodo sacrifices an exchange for positional gain. Gull vs Komodo, nTCEC - Stage 3 - Season 2, Round 2.2, ECO: E10, 0–1 Archived March 4, 2016, at the Wayback Machine Archived 2016-03-04 at the Wayback Machine
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Daniel Wolfe

Daniel Wolfe (born 1960) is an American activist, advocate, and writer whose work advances health programs and policy that balance scientific research and community expertise. His career has focused on support for community health movements, particularly among groups often regarded as criminal or socially suspect, including gay men and people who use illicit drugs. == Early life == Wolfe was raised between Arizona—including time on Rancho Linda Vista, a commune outside of Tucson—and East Hampton, NY. He received his undergraduate degree in Near Eastern Studies from Princeton University, and following time studying Arabic in Egypt, worked as the junior ghostwriter on the autobiographies of First Lady of Egypt Jehan Sadat and Pakistani Prime Minister Benazir Bhutto. Upon return to New York, he was an assistant at the Council on Foreign Relations to Richard W. Murphy, former US Assistant Secretary of State for Near Eastern and South Asian Affairs. Disagreement with US killing of Iraqi civilians during the 1990 Gulf War—and the rising toll of HIV in NY—moved Wolfe to leave Middle East studies and work full-time on AIDS in 1990. == Education == Wolfe was Community Scholar at the Columbia University Mailman School of Public Healthwhere he received his Masters in Public Health in 2004. He holds a Masters of Philosophy (in history) from Columbia University, and a BA in Near Eastern Studies from Princeton University. He was the recipient of a Charles H. Revson Foundation fellowship for urban leaders who have made a substantial contribution to New York City, and a fellow at the Center for Arabic Studies Abroad in Cairo, Egypt. == AIDS and gay activism == Wolfe was part of the media committee for ACT UP’s 1998 action to seize control of the FDA, and helped organize ACT UP NY’s challenge to Governor Cuomo to do better on the AIDS response and other actions.Wolfe also joined ACT UP colleagues Gregg Bordowitz, David Barr, Richard Elovich, Jean Carlomusto and others to work at Gay Men’s Health Crisis (GMHC), the nation’s first AIDS organization, where he served as director of communications and spokesperson on issues including opposition to NY State cuts to the AIDS budget, the disclosure that Olympic Champion Greg Louganis had HIV, reports of the FBI spying on AIDS activists, and GMHC’s move to offer HIV testing and targeted support to those who were HIV-negative. Wolfe also continued cultural work, making art, performance and video as a member of the gay and lesbian collective GANG with artists and ACT UP members including Zoe Leonard, Suzanne Wright, Loring McAlpin, Wellington Love, Adam Rolston and others, and writing a biography of Lawrence of Arabia for a series for young adults on famous gay men and lesbians in history edited by Martin Duberman. Controversy followed, with North Carolina Senator Jesse Helms waving a GANG piece in an issue of the Movement Research Performance Journal on the floor of Congress to show the "rottenness" of publicly funded art, and a number of schools banning the biography series for young adults from their libraries. Wolfe and others challenged the move as continuing the longstanding and homophobic demand that notable gay men and lesbians stay silent about essential details of their private lives even while being celebrated for their professional achievements. == Gay health == The approval of antiretroviral therapy for HIV in 1996 opened up new space for discussions of gay health beyond HIV, and new directions for Wolfe. Working from hundreds of interviews, surveys, workshops, and with a team of writers, Wolfe was the author of Men Like Us, the Our Bodies, Ourselves-inspired GMHC Complete Guide to Gay Men’s Sexual, Physical, and Emotional Well-being, covering issues from spirituality to sexual health to aging. The move to frame gay health beyond condoms and pills—and to offer a guide to health that “did not need to be translated from the original heterosexual”—was part of a larger gay health movement encompassing wellness and pleasure, and focused less on health disparity than on individual and community resilience. Wolfe was a keynote speaker and workshop leader, along with Eric Rofes, Chris Bartlett, and other organizers, at the first National Gay Men’s Health Summit held in Boulder, Colorado in 2002. Awarded a Charles H. Revson Fellowship for urban leaders in the City of New York, Wolfe became a community scholar at Columbia University’s Center of History and Ethics of Public Health, where he received his MPH in 2003, and was a contributor to Searching Eyes: Privacy, the State, and Disease Surveillance in America. == International harm reduction == Wolfe was Director of International Harm Reduction Development at the Open Society Foundations (2005-2021) where he led grantmaking and advocacy to protect the health and rights of people who use drugs in Eastern Europe, Asia, Africa and the Americas. Wolfe challenged approaches that conditioned support on abstinence or that sought to treat people who use illegal drugs like drugs themselves, as something to be controlled or contained. As with the gay health movement, he advocated a focus on community resilience and strengths, and on supporting individuals and communities to negotiate the balance between risk and pleasure of activities integral to life. Noting what he called the “antisocial behavior of health systems,” Wolfe’s analysis elevated issues such as forced labor and harsh punishment delivered in the name of addiction treatment and rehabilitation, the role of criminalization, imprisonment and stigma in interrupting or impeding HIV treatment, and the bias toward coercive approaches in studying and delivering addiction treatments. He also pointed to defects in national and international drug control policies and human rights violations as a root cause of HIV, hepatitis, and other health challenges faced by people who used drugs. Concrete advocacy supported by Open Society’s International Harm Reduction Development program under his direction included rebuffing US government efforts to force the UN to remove all references to harm reduction in its materials, addition of the addiction treatment medicines methadone and buprenorphine to the World Health Organization’s essential medicines list, and WHO endorsement of lay distribution of the opioid overdose antidote naloxone. Wolfe and OSF colleagues also advocated for new approaches to intellectual property and data sharing in research and development of medicines and vaccines to lower price and improve access to medicines globally to those in need. == AI and patient rights == Reports of patients denied opioid prescriptions based on an algorithm purporting to calculate their risk of overdose led Wolfe to work on AI, first as a resident at the Rockefeller Foundation Bellagio Center, and then as Executive Director of a new UCSF UC Berkeley program pioneering efforts to join AI, clinical and public health practice, and equity. In keeping with his earlier (analog) work on HIV, Wolfe has highlighted concerns about health systems using algorithms to gauge the merit of treatments for those regarded as socially suspect, the importance of moving beyond proprietary, black box algorithms toward an architecture of health data as a public good, and the need to maximize benefit for patients and communities, as well health systems, in the use of large language models.
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