HawkSearch AI Glossary

Empowering You with Clarity on Common AI Concepts

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AI (Artificial Intelligence)

AI (Artificial Intelligence)

AI (Artificial Intelligence)

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A branch of computer science that aims to create machines that can perform tasks requiring human-like intelligence. These tasks include problem-solving, pattern recognition, planning, and decision-making, among others.

Activation Function

Activation Function

Activation Function

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A mathematical function applied to a neuron's output in a neural network. Common examples include the sigmoid, tanh, and ReLU functions.

Attention Mechanism

Attention Mechanism

Attention Mechanism

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A mechanism in deep learning models, particularly in NLP, that allows the model to focus on specific parts of the input when producing an output. Crucial in models like the Transformer.

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Backpropagation

Backpropagation

Backpropagation

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An algorithm used in training feedforward neural networks for supervised learning. It calculates the gradient of the loss function concerning each weight by applying the chain rule.

Bayesian Networks

Bayesian Networks

Bayesian Networks

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Probabilistic graphical models that represent a set of variables and their conditional dependencies via a directed acyclic graph (DAG). They're used for reasoning under uncertainty.

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Capsule Networks

Capsule Networks

Capsule Networks

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A type of deep learning model that aims to improve the shortcomings of convolutional neural networks (CNNs), particularly in terms of handling spatial hierarchies between features. Proposed by Geoffrey Hinton.

Catastrophic Forgetting

Catastrophic Forgetting

Catastrophic Forgetting

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A phenomenon in neural networks, especially in deep learning, where the network rapidly forgets previously learned information upon learning new data.

Chinese Room Argument

Chinese Room Argument

Chinese Room Argument

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A thought experiment proposed by philosopher John Searle to challenge the notion that a computer program can possess genuine understanding or consciousness, even if it behaves as if it does.

Conceptual Search

Conceptual Search

Conceptual Search

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An advanced search mechanism that goes beyond literal keyword matching to understand the context or concept behind a search query. It may use semantics, related terms, and underlying ideas to produce relevant results.

Connectionism

Connectionism

Connectionism

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An AI approach emphasizing the importance of neural networks and parallel processing. It's based on the idea that cognitive processes are the emergent properties of interconnected networks of simple units.

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Deep Learning

Deep Learning

Deep Learning

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A subset of machine learning that uses multi-layered neural networks to analyze various factors of data. It's particularly effective in tasks like image and speech recognition.

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Embeddings

Embeddings

Embeddings

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In NLP, embeddings are dense vector representations of words or phrases. These vectors capture semantic meaning, and words with similar meanings tend to have close vector representations.

Explainable AI (XAI)

Explainable AI (XAI)

Explainable AI (XAI)

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An area of research aiming to make the decision-making process of AI systems clear and understandable to humans. It contrasts with "black-box" models where the decision process is not easily interpretable.

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Genetic Algorithms

Genetic Algorithms

Genetic Algorithms

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Optimization algorithms based on the process of natural selection. They are used to find approximate solutions to optimization and search problems, evolving solutions over time.

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Human-Machine Understanding

Human-Machine Understanding

Human-Machine Understanding

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A theory and goal in AI research aiming for a harmonious understanding between humans and machines. It emphasizes the importance of machines not just processing information but also relating to human contexts and emotions.

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Keyword Search

Keyword Search

Keyword Search

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A search mechanism that identifies content based on specific words or phrases. It contrasts with more advanced search mechanisms like conceptual search.

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LLM (Large Language Model)

LLM (Large Language Model)

LLM (Large Language Model)

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A type of machine learning model designed to understand, generate, or translate human language. Examples include OpenAI's GPT series.

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Machine Learning

Machine Learning

Machine Learning

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A subset of AI where computers are trained to perform tasks by learning patterns from data rather than being explicitly programmed.

Morphic Resonance

Morphic Resonance

Morphic Resonance

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A controversial hypothesis proposed by Rupert Sheldrake that suggests a kind of collective memory in nature, which could influence the structures of systems and organisms over time. It's more metaphysical and hasn't been widely adopted in AI but has been discussed in relation to collective learning systems.

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NLP (Natural Language Processing)

NLP (Natural Language Processing)

NLP (Natural Language Processing)

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A subfield of AI that focuses on the interaction between computers and humans through natural language. The goal is to enable computers to understand, interpret, and generate human language in a valuable way.

Neural Network

Neural Network

Neural Network

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A computational model inspired by the way biological neural networks in the human brain work. It's a fundamental building block in many deep learning models.

Neural Plasticity

Neural Plasticity

Neural Plasticity

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The ability of neural networks, both biological and artificial, to change their connections and behavior in response to new information, sensory experiences, or damage.

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Physical Symbol System Hypothesis

Physical Symbol System Hypothesis

Physical Symbol System Hypothesis

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Proposed by Allen Newell and Herbert A. Simon, it states that a physical symbol system has the necessary and sufficient means for general intelligent action.

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Reinforcement Learning

Reinforcement Learning

Reinforcement Learning

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An area of machine learning where an agent learns to make decisions by taking actions in an environment to maximize a reward. It's inspired by behavioral psychology and has applications in areas like game playing and robotics.

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Society of Mind Theory

Society of Mind Theory

Society of Mind Theory

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Proposed by Marvin Minsky, it's a theory that intelligence is not the product of any singular mechanism but arises from the interactions of a diverse range of simple mechanisms.

Swarm Intelligence

Swarm Intelligence

Swarm Intelligence

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A paradigm that studies collective behaviors from the local interactions of decentralized and self-organized systems. Examples include the flocking behavior of birds and the behavior of ant colonies.

Symbolic AI

Symbolic AI

Symbolic AI

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An approach to AI that focuses on symbol manipulation and rule-based logic to solve problems, as opposed to the statistical methods seen in modern machine learning.

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Tokenization

Tokenization

Tokenization

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The process of converting a sequence of text into individual tokens (usually words or subwords). It's a common first step in NLP tasks.

Transfer Learning

Transfer Learning

Transfer Learning

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A machine learning technique where a pre-trained model is fine-tuned on a new, similar task. This allows for leveraging knowledge from one task to improve performance on another.

Turing Test

Turing Test

Turing Test

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A measure of a machine's ability to exhibit intelligent behavior indistinguishable from that of a human. Proposed by Alan Turing in 1950.

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Vectors

Vectors

Vectors

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In the context of AI, vectors often refer to numerical representations of data. In NLP, word embeddings or sentence embeddings are often represented as vectors in a high-dimensional space. The spatial relation of these vectors can reflect semantic meaning.

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Zero-shot, One-shot, and Few-shot Learning

Zero-shot, One-shot, and Few-shot Learning

Zero-shot, One-shot, and Few-shot Learning

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Techniques in machine learning where models are designed to perform tasks without any examples (zero-shot), with only one example (one-shot), or with very few examples (few-shot).