13 terms
Showing all terms starting with A
The simulation of human intelligence in machines programmed to think, learn, and problem-solve autonomously.
An autonomous AI system that plans and executes multi-step tasks by combining reasoning, tool use (web search, code execution, APIs), and memory.
The challenge of ensuring AI systems act in accordance with human values, intentions, and goals rather than pursuing unintended objectives.
A neural network component that allows models to focus on the most relevant parts of an input when producing an output, foundational to transformers.
Automated Machine Learning - tools that automate the process of selecting, training, and tuning ML models without requiring deep expertise.
A model that generates outputs one token at a time, with each token conditioned on all previously generated tokens.
A machine learning approach where the model identifies and requests labels for the most informative data points, reducing the total annotation effort.
A technique that deliberately crafts inputs to fool AI models into making incorrect predictions, exposing vulnerabilities in model robustness.
AI systems capable of taking sequences of actions autonomously to complete long-horizon goals, often using tools, APIs, and memory.
A service that acts as the entry point for AI API calls, handling authentication, rate limiting, routing, and logging for model inference.
A hypothetical AI system with human-level cognitive abilities across all intellectual tasks, as opposed to narrow AI built for specific domains.
An NLP technique that identifies sentiment toward specific aspects or features of a subject, rather than assigning a single overall sentiment score.
AI systems combining computer vision, sensor fusion, and path planning to enable self-driving cars and robots to navigate real-world environments.
