Glossary
AI Assistants
Fundamentals
Adversarial Machine LearningAI AgentsAI and EducationAI and MedicineAI AssistantsAI HardwareAI Voice TransferApproximate Dynamic ProgrammingBackpropagationBayesian Machine LearningCurse of DimensionalityData LabelingDeep LearningDeep Reinforcement LearningDimensionality ReductionF1 Score in Machine LearningFeedforward Neural NetworkFine Tuning in Deep LearningGated Recurrent UnitGenerative AILarge Language Model (LLM)Machine LearningMultimodal LearningNatural Language Generation (NLG)Natural Language Processing (NLP)Natural Language Understanding (NLU)Precision and RecallTransformersZero-shot Classification Models
Models
AlphaGo ZeroBERTChatGPTChess botsDall-EDiffusion ModelsDistilBERTGoogle's BardHidden Markov Models (HMMs)Inference EngineLarge Language Model (LLM)Llama 2LLM CollectionMidjourney (Image Generation)MistralOpenAI WhisperPerceptronProbabilistic Models in Machine LearningRoBERTaRule-Based AISpeech-to-text modelsText-to-Speech ModelsXLNet
Techniques
Activation FunctionsBatch Gradient DescentBeam Search AlgorithmBenchmarkingClassificationCURE AlgorithmExpectation MaximizationFlajolet-Martin AlgorithmGaussian ProcessesGenerative Adversarial Networks (GANs)Gradient Boosting Machines (GBMs)Gradient ClippingGradient ScalingGroundingHyperparametersk-ShinglesLatent Dirichlet Allocation (LDA)Mixture of ExpertsMultimodal AIOnline Gradient DescentOverfitting and UnderfittingPrompt ChainingPrompt EngineeringRetrieval-Augmented Generation (RAG)RLHFSentiment AnalysisSequence ModelingSemantic KernelTokenizationWinnow AlgorithmWord Embeddings
Last updated on January 25, 20248 min read
AI Assistants

AI agents and assistants are transformative tools across various domains. The future promises exciting advancements with integration with other technologies. 

Editors’ Note: This glossary entry discusses both AI Agents and AI Assistants.

An agent, in the context of artificial intelligence, is a system capable of sensing and interacting with its environment. It uses sensors to detect environmental inputs and actuators to affect its surroundings. In essence, an agent perceives its environment and takes actions based on these perceptions, much like humans use their senses to gather information and respond to their surroundings.

Consider an NLP model as an agent:

  • Percepts (Input): Textual prompts or information provided to the NLP model for processing.

  • Environment (Context): The operational setting of the NLP model, such as chat interfaces or applications requiring language understanding.

  • Sensors (Comprehension): The model's components (like attention mechanisms and transformers) that process and interpret textual input.

  • Learning Element (Adaptation): The algorithms within the NLP model that enable it to learn from data and improve over time.

  • Decision-Making Component (Interpretation): The model's capability to generate coherent and contextually appropriate text.

  • Actuators (Output): The part of the model that translates its internal processes into readable language.


Actions (Language Outputs): The actual text generated by the NLP model in response to inputs, such as sentences or paragraphs.