Richard Sutton
(44 articles)
Next Word Prediction
Enables language models to predict the most probable subsequent word in a text sequence using generative AI techniques.
Generality: 780
NLP
Natural Language Processing
Natural Language Processing
Field of AI that focuses on the interaction between computers and humans through natural language.
Generality: 931
RL
Reinforcement Learning
Reinforcement Learning
Type of ML where an agent learns to make decisions by performing actions in an environment to achieve a goal, guided by rewards.
Generality: 890
Motor Learning
Process by which robots or AI systems acquire, refine, and optimize motor skills through experience and practice.
Generality: 675
Training
Process of teaching a ML model to make accurate predictions or decisions, by adjusting its parameters based on data.
Generality: 940
Task Environment
Setting or context within which an intelligent agent operates and attempts to achieve its objectives.
Generality: 760
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
NLU
Natural Language Understanding
Natural Language Understanding
Subfield of NLP focused on enabling machines to understand and interpret human language in a way that is both meaningful and contextually relevant.
Generality: 894
Bias-Variance Trade-off
In ML, achieving optimal model performance involves balancing bias and variance to minimize overall error.
Generality: 818
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
Inductive Bias
Assumptions integrated into a learning algorithm to enable it to generalize from specific instances to broader patterns or concepts.
Generality: 827
DNN
Deep Neural Networks
Deep Neural Networks
Advanced neural network architectures with multiple layers that enable complex pattern recognition and learning from large amounts of data.
Generality: 916
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 Error
The discrepancy between predicted outcomes by an AI model and the actual observed results in a dataset.
Generality: 675
Autonomous Learning
Systems capable of learning and adapting their strategies or knowledge without human intervention, based on their interactions with the environment.
Generality: 870
Artificial Curiosity
Algorithmic mechanism in AI that motivates the system's behavior to learn inquisitively and explore unfamiliar environments.
Generality: 625
Meta-Learning
Learning to learn involves techniques that enable AI models to learn how to adapt quickly to new tasks with minimal data.
Generality: 858
Catastrophic Forgetting
Phenomenon where a neural network forgets previously learned information upon learning new data.
Generality: 686
Policy Learning
Branch of reinforcement learning where the objective is to find an optimal policy that dictates the best action to take in various states to maximize cumulative reward.
Generality: 790
Wake Sleep
Biologically inspired algorithm used within unsupervised learning to train deep belief networks.
Generality: 540
Transfer Learning
ML method where a model developed for a task is reused as the starting point for a model on a second task, leveraging the knowledge gained from the first task to improve performance on the second.
Generality: 870
Continuous Learning
Systems and models that learn incrementally from a stream of data, updating their knowledge without forgetting previous information.
Generality: 870
One-Shot Learning
ML technique where a model learns information about object categories from a single training example.
Generality: 542
Data Efficient Learning
ML approach that requires fewer data to train a functional model.
Generality: 791
DRL
Deep Reinforcement Learning
Deep Reinforcement Learning
Combines neural networks with a reinforcement learning framework, enabling AI systems to learn optimal actions through trial and error to maximize a cumulative reward.
Generality: 855
Latent Space
Abstract, multi-dimensional representation of data where similar items are mapped close together, commonly used in ML and AI models.
Generality: 805
Sequence Prediction
Involves forecasting the next item(s) in a sequence based on the observed pattern of prior sequences.
Generality: 825
Autoregressive Sequence Generator
A predictive model harnessed in AI tasks, particularly involving times series, which leverages its own prior outputs as inputs in subsequent predictions.
Generality: 650
Sequential Models
Type of data models in AI where the arrangement of data points or events adhere to a specific order for predictive analysis and pattern recognition.
Generality: 815
RLHF
Reinforcement Learning from Human Feedback
Reinforcement Learning from Human Feedback
Technique that combines reinforcement learning (RL) with human feedback to guide the learning process towards desired outcomes.
Generality: 625
Sample Efficiency
Ability of a ML model to achieve high performance with a relatively small number of training samples.
Generality: 815
Expressive Hidden States
internal representations within a neural network that effectively capture and encode complex patterns and dependencies in the input data.
Generality: 695
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
Ablation
Method where components of a neural network are systematically removed or altered to study their impact on the model's performance.
Generality: 650
Zero-shot Capability
The ability of AI models to perform tasks or make predictions on new types of data that they have not encountered during training, without needing any example-specific fine-tuning.
Generality: 775
Next Token Prediction
Technique used in language modeling where the model predicts the following token based on the previous ones.
Generality: 735
Continual Pre-Training
Process of incrementally training a pre-trained ML model on new data or tasks to update its knowledge without forgetting previously learned information.
Generality: 670
Post-Training
Techniques and adjustments applied to neural networks after their initial training phase to enhance performance, efficiency, or adaptability to new data or tasks.
Generality: 650
Scaling Laws
Mathematical relationships that describe how the performance of machine learning models, particularly deep learning models, improves as their size, the amount of data, or computational resources increases.
Generality: 835
1-N Systems
Architectures where one input or controller manages multiple outputs or agents, applicable in fields like neural networks and robotics.
Generality: 790
Instruction Following Model
AI system designed to execute tasks based on specific commands or instructions provided by users.
Generality: 640
Instruction-Following
Ability to accurately understand and execute tasks based on given directives.
Generality: 725