Judea Pearl
(47 articles)
Conditional Probability
Measures the likelihood of an event occurring, given that another event has already occurred.
Generality: 880
Bayesian Inference
Method of statistical inference in which Bayes' theorem is used to update the probability estimate for a hypothesis as more evidence or information becomes available.
Generality: 896
Causal Inference
Process of determining the cause-and-effect relationship between variables.
Generality: 870
Loss Optimization
Process of adjusting a model's parameters to minimize the difference between the predicted outputs and the actual outputs, measured by a loss function.
Generality: 886
Generalization
Ability of a ML model to perform well on new, unseen data that was not included in the training set.
Generality: 891
Adaptive Problem Solving
The capacity of AI systems to modify their approaches to problem-solving based on new data, feedback, or changing environments, enhancing their efficiency and effectiveness over time.
Generality: 790
ML
Machine Learning
Machine Learning
Development of algorithms and statistical models that enable computers to perform tasks without being explicitly programmed for each one.
Generality: 965
Heuristic Search Techniques
Methods used in AI to find solutions or make decisions more efficiently by using rules of thumb or informed guesses to guide the search process.
Generality: 878
Supervised Learning
ML approach where models are trained on labeled data to predict outcomes or classify data into categories.
Generality: 882
Function Approximation
Method used in AI to estimate complex functions using simpler, computationally efficient models.
Generality: 810
Inference
Process by which a trained neural network applies learned patterns to new, unseen data to make predictions or decisions.
Generality: 861
Regularization
Technique used in machine learning to reduce model overfitting by adding a penalty to the loss function based on the complexity of the model.
Generality: 845
Probabilistic Programming
Programming paradigm designed to handle uncertainty and probabilistic models, allowing for the creation of programs that can make inferences about data by incorporating statistical methods directly into the code.
Generality: 820
Uncertainty Reduction
A process in AI by which systems manage and diminish uncertainty in predictions and decisions to improve performance and reliability.
Generality: 830
Overfitting
When a ML model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.
Generality: 890
Statistical AI
Utilizes statistical methods to analyze data and make probabilistic inferences, aimed at emulating aspects of human intelligence through quantitative models.
Generality: 890
Universal Learning Algorithms
Theoretical frameworks aimed at creating systems capable of learning any task to human-level competency, leveraging principles that could allow for generalization across diverse domains.
Generality: 840
Learnability
Capacity of an algorithm or model to effectively learn from data, often measured by how well it can generalize from training data to unseen data.
Generality: 847
Program Induction
A process in AI where computers generate, or 'induce', programs based on provided data and specific output criteria.
Generality: 785
Reasoning System
Software entities designed to emulate human reasoning processes by drawing logical inferences from available data or known facts.
Generality: 775
Matrix Models
Mathematical frameworks that use matrices with parameters to represent and solve complex problems, often in ML, statistics, and systems theory.
Generality: 728
Commonsense Reasoning
The ability of AI systems to make presumptions about the type of
Generality: 775
Probabilistic Inferencing
A technique in AI focused on drawing conclusions based on the probability of different outcomes, given partial or uncertain information.
Generality: 870
Bayesian Network
Graphical model that represents probabilistic relationships among variables using directed acyclic graphs (DAGs).
Generality: 820
Backpropagation
Algorithm used for training artificial neural networks, crucial for optimizing the weights to minimize error between predicted and actual outcomes.
Generality: 890
Inductive Bias
Assumptions integrated into a learning algorithm to enable it to generalize from specific instances to broader patterns or concepts.
Generality: 827
Function Approximator
Computational model used to estimate a target function that is generally complex or unknown, often applied in machine learning and control systems.
Generality: 806
State Representation
The method by which an AI system formulates a concise and informative description of the environment's current situation or context.
Generality: 682
Prediction
Process of using data-driven algorithms to forecast future outcomes or trends based on historical data.
Generality: 825
Markov Blanket
A concept in probabilistic graphical models representing a node's minimal set of dependencies, crucial for inferring the node's behavior in a network.
Generality: 755
Similarity Computation
A mathematical process to quantify the likeness between data objects, often used in AI to enhance pattern recognition and data clustering.
Generality: 675
Classifier
ML model that categorizes data into predefined classes.
Generality: 861
SRL
Statistical Relational Learning
Statistical Relational Learning
Combines statistics and relational data to construct models that can learn from complex, structured data involving multiple interdependent entities.
Generality: 500
Sequence Prediction
Involves forecasting the next item(s) in a sequence based on the observed pattern of prior sequences.
Generality: 825
Convergence
The point at which an algorithm or learning process stabilizes, reaching a state where further iterations or data input do not significantly alter its outcome.
Generality: 845
Robustness
Ability of an algorithm or model to deliver consistent and accurate results under varying operating conditions and input perturbations.
Generality: 885
HITL
Human-in-the-Loop
Human-in-the-Loop
Integration of human judgment into AI systems to improve or guide the decision-making process.
Generality: 665
Black Box Problem
The difficulty in understanding and interpreting how an AI system, particularly ML models, makes decisions.
Generality: 850
Explainability
Ability of a system to transparently convey how it arrived at a decision, making its operations understandable to humans.
Generality: 820
Interpretability
Extent to which a human can understand the cause of a decision made by an AI system.
Generality: 900
XAI
Explainable AI
Explainable AI
AI systems designed to provide insights into their behavior and decisions, making them transparent and understandable to humans.
Generality: 775
Neurosymbolic AI
Integration of neural networks with symbolic AI to create systems that can both understand and manipulate symbols in a manner similar to human cognitive processes.
Generality: 675
Hybrid AI
Combines symbolic AI (rule-based systems) and sub-symbolic AI (machine learning) approaches to leverage the strengths of both for more versatile and explainable AI systems.
Generality: 820
Causal AI
A form of AI that reasons using cause and effect logic to provide interpretable predictions and decisions.
Generality: 813
Autonomous Reasoning
Capacity of AI systems to make independent decisions or draw conclusions based on logic or data without human intervention.
Generality: 850
AMI
Advanced Machine Intelligence
Advanced Machine Intelligence
Refers to high-level AI systems possessing the capability to perform complex cognitive tasks with or without human-like reasoning.
Generality: 873
Self-Correction
An AI system's ability to recognize and rectify its own mistakes or errors without external intervention.
Generality: 815