Artificial Intelligence (AI) continues to revolutionize countless sectors, impacting everything from healthcare to finance. Yet for many, the terminology associated with this advanced technology can often be bewildering. Our comprehensive AI Glossary acts as your beacon in the dense forest of AI jargon. From understanding the basics like Machine Learning and Neural Networks, to exploring advanced concepts such as Deep Learning and Natural Language Processing, this glossary is designed to clarify and simplify the complex landscape of AI. You can navigate through the glossary to become familiar with pivotal terms, as well as the latest trends and breakthroughs in the AI industry. As AI continues to grow and transform our world, it’s crucial for everyone to understand its language. Immerse yourself and dig into our AI Glossary.
- Activation Function
- Adam Optimizer
- Anaphora Resolution
- API
- Artificial Intelligence (AI)
- Attention Mechanism
- Attention Models
- Automatic Speech Recognition (ASR)
- Autoregressive Models
- Backpropagation
- Bag of Words (BoW)
- Bass and Variance
- Batch Size
- BERT (Bidirectional Encoder Representations from Transformers)
- Bias Node
- Bidirectional LSTM (BiLSTM)
- Burstiness
- Capsule Networks (CapsNets)
- Chatbot
- ChatGPT
- Chat Model
- Context Window
- Conversational Context
- Convolutional Neural Networks (CNNs)
- Coreference Resolution
- Cross-Validation
- Data Annotation
- Data Augmentation
- Datasets
- Deep Learning (DL)
- Dialog Systems
- Discriminative Models
- Document Summarization
- Embedding Layer
- Encoder-Decoder Structure
- Entity Extraction
- Few-Shot Learning
- Fine-tuning
- Function Calling
- Generative Models
- GloVe (Global Vectors for Word Representation)
- GPT Generative Pretrained Transformer (GPT)
- Gradient Descent
- Hugging Face
- Hyperparameters
- Knowledge Cutoff
- Knowledge Distillation
- Large Language Model (LMM)
- Latent Dirichlet Allocation (LDA)
- Lemmatization
- Levenshtein Distance
- LlamaIndex
- Long Short-Term Memory (LSTM)
- Loss Function
- Machine Learning (ML)
- Multilayer Perceptron (MLP)
- Multimodal Models
- Named Entity Recognition (NER)
- Natural Language Processing (NLP)
- Neural Network
- NVE (as it relates to generative images)
- One-Shot Learning
- Overfitting
- Paraphrasing
- Part-of-Speech Tagging (POS Tagging)
- Perplexity
- Pinecone Database
- Pretraining
- Prompt
- Prompt Engineering
- Recurrent Neural Networks (RNNs)
- Regularization
- Reinforcement Learning
- Relu (Rectified Linear Unit)
- Self-Attention
- Semantic Analysis
- Semi Supervised Learning
- Sentiment Analysis
- Sentiment Score
- Seq2Seq Models
- Sequence-to-Sequence Models
- Sigmoid Function
- Skip-grams
- Stemming
- Stochastic Gradient Descent (SGD)
- Stop Words
- Supervised Learning
- Syntactic Parsing
- Temperature (in the context of AI models)
- Test Set
- Text-to-Speech (TTS)
- TF-IDF (Term Frequency-Inverse Document Frequency)
- Token, Token Cost
- Topic Modeling
- Transfer Learning
- Transformer Models
- Turn-taking
- Underfitting
- Unsupervised Learning
- Validation Set
- Vector Database
- Word Embedding
- Word Sense Disambiguation
- Zero-Shot Learning
- Zero-shot, One-shot, and Few-shot Learning
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