#1,996

Most Influential Person

English-American computer scientist

Stuart J. Russell is the founder of the Center for Human-Compatible Artificial Intelligence and professor of computer science at the University of California at Berkeley, adjunct professor of neurological surgery at the University of California at San Francisco and computer scientist. He earned a B.A. in physics from Wadham College at Oxford and a Ph.D. in computer science from Stanford University.

He is best known as the co-author of Artificial Intelligence: A Modern Approach, which is the most popular textbook on the subject. He is an active researcher in the field, exploring the history and future of artificial intelligence, machine learning, knowledge representation, probabilistic reasoning, inverse reinforcement learning and multitarget tracking.

A vocal opponent of the creation and use of autonomous weapons such as unmanned drones, he worked with the Future of Life Institute to produce a video about drones carrying out assassinations in order to get the attention of the United Nations’ governing bodies on conventional weapons.

He has been awarded the National Science Foundation’s Presidential Young Investigator Award, the World Technology Award, the AAAI/EAAI Outstanding Educator Award and the Mitchell Prize. His most recent published work is Human Compatible: Artificial Intelligence and the Problem of Control, which was published in 2019.

**Featured in Top Influential Computer Scientists Today**

According to Wikipedia, Stuart Jonathan Russell is an English computer scientist known for his contributions to artificial intelligence. He is a Professor of Computer Science at the University of California, Berkeley and Adjunct Professor of Neurological Surgery at the University of California, San Francisco. He holds the Smith-Zadeh Chair in Engineering at University of California, Berkeley. He founded and leads the Center for Human-Compatible Artificial Intelligence at UC Berkeley. Russell is the co-author of the most popular textbook in the field of artificial intelligence: Artificial Intelligence: A Modern Approach used in more than 1,500 universities in 135 countries.

- Artificial Intelligence: A Modern Approach (25729)
- Distance Metric Learning with Application to Clustering with Side-Information (2950)
- Dynamic bayesian networks: representation, inference and learning (2712)
- Algorithms for Inverse Reinforcement Learning (2098)
- Artificial intelligence - a modern approach, 2nd Edition (1726)
- Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping (1401)
- Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks (1346)
- Image Segmentation in Video Sequences: A Probabilistic Approach (1015)
- Online bagging and boosting (822)
- Reinforcement Learning with Hierarchies of Machines (738)
- Learning the Structure of Dynamic Probabilistic Networks (676)
- BLOG: Probabilistic Models with Unknown Objects (519)
- Do the right thing (440)
- Bayesian Q-Learning (404)
- Do the right thing - studies in limited rationality (402)
- Rationality and Intelligence (395)
- Research Priorities for Robust and Beneficial Artificial Intelligence (393)
- Adaptive Probabilistic Networks with Hidden Variables (381)
- Principles of Metareasoning (377)
- Identity Uncertainty and Citation Matching (346)
- Combined task and motion planning through an extensible planner-independent interface layer (331)
- Stochastic simulation algorithms for dynamic probabilistic networks (300)
- Cooperative Inverse Reinforcement Learning (299)
- Towards robust automatic traffic scene analysis in real-time (298)
- Provably Bounded Optimal Agents (288)
- Speech Recognition with Dynamic Bayesian Networks (272)
- State abstraction for programmable reinforcement learning agents (260)
- Experimental comparisons of online and batch versions of bagging and boosting (236)
- A generalized mean field algorithm for variational inference in exponential families (225)
- The BATmobile: Towards a Bayesian Automated Taxi (222)
- Object Identification in a Bayesian Context (222)
- Optimal Composition of Real-Time Systems (216)
- Learning agents for uncertain environments (extended abstract) (211)
- Tracking Many Objects with Many Sensors (209)
- Approximating Optimal Policies for Partially Observable Stochastic Domains (201)
- Combined Task and Motion Planning for Mobile Manipulation (191)
- Local Learning in Probabilistic Networks with Hidden Variables (186)
- A Logical Approach to Reasoning by Analogy (183)
- Composing Real-Time Systems (179)
- Efficient Memory-Bounded Search Methods (174)
- Automatic Symbolic Traffic Scene Analysis Using Belief Networks (171)
- Inverse Reward Design (161)
- PAC-learnability of determinate logic programs (150)
- Adversarial Training for Relation Extraction (149)
- Artificial Intelligence (143)
- Anytime Sensing Planning and Action: A Practical Model for Robot Control (141)
- A modern, agent-oriented approach to introductory artificial intelligence (138)
- Q-Decomposition for Reinforcement Learning Agents (136)
- Approximate Reasoning Using Anytime Algorithms (134)
- Programmable Reinforcement Learning Agents (132)
- NP-Completeness of Searches for Smallest Possible Feature Sets (106)
- Object Identification: A Bayesian Analysis with Application to Traffic Surveillance (100)
- Inteligencia Artificial: un Enfoque Moderno (98)
- Variational MCMC (95)
- Learning Plannable Representations with Causal InfoGAN (93)
- Adversarial Policies: Attacking Deep Reinforcement Learning (89)
- On Optimal Game-Tree Search using Rational Meta-Reasoning (86)
- Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient (84)
- Probabilistic models with unknown objects (78)
- Automated Construction of Sparse Bayesian Networks from Unstructured Probabilistic Models and Domain Information (71)
- Rationality and Intelligence: A Brief Update (70)
- General-Purpose MCMC Inference over Relational Structures (70)
- Artificial intelligence - a modern approach: the intelligent agent book (69)
- Approximate Inference for Infinite Contingent Bayesian Networks (67)
- Decision Theoretic Subsampling for Induction on Large Databases (67)
- Unifying logic and probability (67)
- A Declarative Approach to Bias in Concept Learning (65)
- Approximate inference for first-order probabilistic languages (65)
- Angelic Semantics for High-Level Actions (64)
- Selecting Computations: Theory and Applications (63)
- The Off-Switch Game (61)
- BLOG: Relational Modeling with Unknown Objects (61)
- Control Strategies for a Stochastic Planner (60)
- The compleat guide to MRS (60)
- Angelic Hierarchical Planning: Optimal and Online Algorithms (59)
- Stephen Hawking: \'Transcendence looks at the implications of artificial intelligence - but are we taking AI seriously enough?\' (58)
- First-Order Probabilistic Languages: Into the Unknown (55)
- Space-Efficient Inference in Dynamic Probabilistic Networks (54)
- A quantitative analysis of analogy by similarity (53)
- Robotics: Ethics of artificial intelligence (51)
- Analogical and inductive reasoning (49)
- Probabilistic modeling with Bayesian networks for automatic speech recognition (48)
- Multilinear Dynamical Systems for Tensor Time Series (47)
- Tree-Structured Bias (44)
- Logical Filtering (42)
- Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter (41)
- A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences (40)
- Challenge: What is the Impact of Bayesian Networks on Learning? (38)
- Preliminary Steps Toward the Automation of Induction (38)
- Analogy by Similarity (37)
- Efficient Gradient Estimation for Motor Control Learning (36)
- Algorithm selection by rational metareasoning as a model of human strategy selection (36)
- Using Classical Planners for Tasks with Continuous Operators in Robotics (36)
- Bayesian Relational Memory for Semantic Visual Navigation (35)
- Markovian State and Action Abstractions for MDPs via Hierarchical MCTS (35)
- Artificial Intelligence - A Modern Approach, Third International Edition (34)
- Execution Architectures and Compilation (34)
- Efficient resource-bounded reasoning in AT-RALPH (33)
- EBOOK : Artificial Intelligence, A Modern Approach, 3rd edition (32)
- Should Robots be Obedient? (32)
- Should We Fear Supersmart Robots? (32)
- Decayed MCMC Filtering (31)
- Cognitive Model Priors for Predicting Human Decisions (30)
- Artificial intelligence. Fears of an AI pioneer. (30)
- Efficient belief-state AND-OR search, with application to Kriegspiel (30)
- Rationality as an explanation of language (30)
- Reinforcement learning for autonomous vehicles (29)
- Probabilistic detection of short events, with application to critical care monitoring (28)
- First-Order Probabilistic Models for Information Extraction (27)
- How Long Will It Take? (26)
- Swift: Compiled Inference for Probabilistic Programming Languages (26)
- Adaptive Probabilistic Networks (26)
- Handbook of Perception and Cognition (25)
- Declarative Bias: An Overview (25)
- RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains (25)
- Writing and sketching in the air, recognizing and controlling on the fly (24)
- Convergence of Reinforcement Learning with General Function Approximators (24)
- Boundaries of Operationality (24)
- Eecient Memory-bounded Search Methods (24)
- First-Order Open-Universe POMDPs (22)
- Decayed MCMC iltering (22)
- Stratagus-playing Agents in Concurrent ALisp (21)
- Shift of Bias as Non-Monotonic Reasoning (20)
- Meta-Learning MCMC Proposals (20)
- Predicting human decisions with behavioral theories and machine learning (20)
- Decision-Theoretic Control of Reasoning: General Theory and an (20)
- Machine Learning, Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, USA, July 9-12, 1995 (19)
- Inverse reinforcement learning for video games (19)
- Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms (18)
- A Sketch of Autonomous Learning using Declarative Bias (18)
- Exploiting locality in probabilistic inference (17)
- Metaphysics of Planning Domain Descriptions (17)
- Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design (16)
- Analogy and Single-Instance Generalization (16)
- Adaptive Learning of Decision-Theoretic Search Control Knowledge (16)
- Metareasoning for Monte Carlo Tree Search (16)
- Gaussian Process Random Fields (15)
- Artificial intelligence: The future is superintelligent (15)
- Learning from Examples and Membership Queries with Structured Determinations (15)
- Partially Observable Sequential Decision Making for Problem Selection in an Intelligent Tutoring System (14)
- A temporally abstracted Viterbi algorithm (14)
- Fine-Grained Decision-Theoretic Search Control (14)
- Learnability of Constrained Logic Programs (14)
- Tractability of Planning with Loops (14)
- Efficient Reinforcement Learning with Hierarchies of Machines by Leveraging Internal Transitions (13)
- Why are DBNs sparse? (12)
- Prior knowledge and autonomous learning (12)
- An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning (12)
- Declarative Bias for Structural Domains (11)
- Planning Using Multiple Execution Architectures (11)
- An architecture for bounded rationality (11)
- Probabilistic Model-Based Approach for Heart Beat Detection (11)
- Sequential quadratic programming for task plan optimization (11)
- A Smart-Dumb/Dumb-Smart Algorithm for Efficient Split-Merge MCMC (11)
- Mutual Constraints on Representation and Inference (11)
- Bounded Intention Planning (11)
- Extended abstract: Learning search strategies (10)
- Learning and Planning with a Semantic Model (10)
- Extending Bayesian Networks to the Open-Universe Case (10)
- Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism (10)
- IMEX: Overcoming Intactability In Explanation Based Learning (10)
- The Extended Parameter Filter (10)
- Compositional Modeling with DPNs (8)
- Unifying Logic and Probability: A New Dawn for AI? (8)
- Dynamic Scaled Sampling for Deterministic Constraints (7)
- Angelic Hierarchical Planning: Optimal and Online Algorithms (Revised) (7)
- Inductive learning by machines (7)
- Probabilistic graphical models and algorithms for genomic analysis (7)
- Modelling Glycaemia in ICU Patients - A Dynamic Bayesian Network Approach (6)
- Signal-based Bayesian Seismic Monitoring (6)
- A Compact, Hierarchical Q-function Decomposition (6)
- A compact, hierarchically optimal Q-function decomposition (6)
- Object Identi cation : A Bayesian Analysiswith Application to Tra c Surveillance 1 (6)
- On Some Tractable Cases of Logical Filtering (6)
- Complete Guide to MRS (5)
- Quantifying Differences in Reward Functions (5)
- Expressive Probability Models in Science (5)
- Exploiting Belief State Structure in Graph Search (4)
- It's not too soon to be wary of AI: We need to act now to protect humanity from future superintelligent machines (4)
- Pruned Neural Networks are Surprisingly Modular (4)
- Bayesian problem-solving applied to scheduling (4)
- First-Order Open-Universe POMDPs: Formulation and Algorithms (4)
- Neural Networks are Surprisingly Modular (4)
- Understanding Learned Reward Functions (4)
- DERAIL: Diagnostic Environments for Reward And Imitation Learning (3)
- The MAGICAL Benchmark for Robust Imitation (3)
- Benefits of Assistance over Reward Learning (3)
- Fast Gaussian Process Posteriors with Product Trees (3)
- A Nearly-Black-Box Online Algorithm for Joint Parameter and State Estimation in Temporal Models (3)
- Machine Learning (3)
- Handbook of Perception and Cognition , Vol . 14 Chapter 4 : Machine Learning (3)
- Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making (3)
- Combined State and Parameter Estimation of Human Intracranial Hemodynamics (2)
- Accumulating Risk Capital Through Investing in Cooperation (2)
- Multi-Principal Assistance Games (2)
- Who speaks for AI? (2)
- Clusterability in Neural Networks (2)
- Expressive Probability Models For Speech Recognition And Understanding (2)
- 146 Modeling and Machine Learning of Cerebrovascular Dynamics. (2)
- BFiT: From Possible-World Semantics to Random-Evaluation Semantics in an Open Universe (2)
- Automated Pricing Agents in the On-Demand Economy (2)
- Probabilistic modeling of sensor artifacts in critical care (2)
- Model Based Probabilistic Inference for Intensive Care Medicine (2)
- PNPACK: Computing with probabilities in Java (2)
- Neural Block Sampling (2)
- PNPACK: Computing with Probabilities in Java (2)
- The Physics of Text: Ontological Realism in Information Extraction (2)
- Quantitative Analysis of Analogy (2)
- Learning in Rational Agents (2)
- Concurrent Hierarchical Reinforcement Learning for RoboCup Keepaway (2)
- Servant of Many Masters: Shifting priorities in Pareto-optimal sequential decision-making (2)
- Control of Mobile Robots Using Anytime Computation (1)
- Uncertain Observation Times (1)
- Trustworthy AI (1)
- Unifying Logic and Probability : Recent Developments (1)
- Title Feasibility Study Of Fully Autonomous Vehicles Using Decision-theoretic Control Permalink (1)
- Technical perspectiveThe ultimate pilot program (1)
- The MineRL BASALT Competition on Learning from Human Feedback (1)
- Priorities for Robust and Bene fi cial (1)
- Reports of the AAAI 2010 Conference Workshops (1)
- SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory (1)
- PAC Learning of Causal Trees with Latent Variables (0)
- Patient-adaptable intracranial pressure morphology analysis using a probabilistic model-based approach. (0)
- Learning the Preferences of Uncertain Humans with Inverse Decision Theory (0)
- Syllabus COSC 5365 Artificial Intelligence (0)
- Compositional Modeling With DPNsGeo (0)
- Joint State and Parameter Estimation in Temporal Models by Yusuf Bugra (0)
- Tools for Autonomous Agents (Abstract) (0)
- vercoming Hntr ctability in Err ased Learning (0)
- Hierarchical Planning for Mobile Manipulation (0)
- Value determination with function approximation (0)
- MADE: Exploration via Maximizing Deviation from Explored Regions (0)
- Temporal Logical Filtering - Preliminary Results (0)
- Applications of distributed acoustic sensing for security and surveillance (0)
- Value Determination with General Function Approximators (0)
- Product Trees for Gaussian Process Covariance in Sublinear Time (0)
- Commentary on Baum ’ s " How a Bayesian . . ? (0)
- Control Strategies for a tochastic Planner (0)
- Explore and Control with Adversarial Surprise (0)
- Stuart Russell ANALOGY BY SIMILARITY (0)
- 1 APPROXIMATE REASONING USING ANYTIME ALGORITHMS (0)
- Artificial Intelligence: A Binary Approach (0)
- Learning the Value of Computation (0)
- Decision-Theoretic Planning with Multiple Execution Architectures* (0)
- Resources on Existential Risk General Blockinscholarly Blockindiscussion Blockinof Blockinexistential Blockinrisk (0)
- The Future of (Artificial) Intelligence (0)
- Two Techniques for Tractable Decision-Theoretic Planning* (0)
- Towards Practical Bayesian Parameter and State Estimation (0)
- Human-Compatible Artificial Intelligence (0)
- New light on RV Tau variables. (0)
- Distributed acoustic sensing for infrastructure (0)
- Improving Gradient Estimation by Incorporating Sensor Data (0)
- Efficient motor control learning (0)
- Towards Limited Rational Agents (0)
- Learning a Semantic Prior for Guided Navigation (0)
- Statistical Relational Artificial Intelligence, Papers from the 2010 AAAI Workshop, Atlanta, Georgia, USA, July 12, 2010 (0)
- Multi-Principal Assistance Games: Definition and Collegial Mechanisms (0)
- Application to Problem-Solving Search (0)
- Uncertain Learning Agents (Abstract) (0)
- Efficient Cooperative Inverse Reinforcement Learning (0)
- Application to Game-Playing (0)
- Multitasking: Optimal Planning for Bandit Superprocesses (0)
- Electric light cables and the distribution of electricity (0)
- AI for Humanity: The Global Challenges (0)
- Supplementary Material for "SLIP: Learning to Predict in Unknown Dynamical Systems with Long-Term Memory" (0)
- Weight of the World (0)
- Open-Universe Theory for Bayesian Inference, Decision, and Sensing (OUTBIDS) (0)
- Object Identiication: a Bayesian Analysis with Application to Traac Surveillance 1 (0)

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