AdvancedModels & Architecture
Emotion Vectors
Measurable internal neural representations inside AI models that function like emotions and causally steer the model's behavior.
AI Dictionary
Models & Architecture
Glossary of AI concepts, explained simply
39 concepts
Models & Architecture
39 concepts
Advanced
IntermediateModels & Architecture
Adaptive Thinking in AI
A reasoning strategy where AI models dynamically adjust how much they think per turn — from instant responses to deep multi-step deliberation — based on task complexity.
AdvancedModels & Architecture
Adversarial Cost to Exploit (ACE)
A security benchmark that measures the economic token cost an adversary must spend to trick an AI agent into unauthorized tool use, replacing static pass/fail evaluations with game-theoretic cost analysis.
AdvancedModels & Architecture
Automated Alignment Research
Using frontier AI models to autonomously discover methods for aligning other AI systems — addressing the scalable oversight challenge by letting safety research scale with capabilities.
AdvancedModels & Architecture
DeepStack Injection
A VLM architecture that routes abstract visual features to early Transformer layers and high-resolution details to later layers for optimal document parsing in compact models.
AdvancedModels & Architecture
GRPO (Group Relative Policy Optimization)
A reinforcement learning algorithm that aligns language models by comparing groups of outputs against each other, eliminating the need for a separate reward model.
IntermediateModels & Architecture
Gemma 4
Google DeepMind's open-weight multimodal model family that natively handles text, vision, and audio on-device.
IntermediateModels & Architecture
LoRA (Low-Rank Adaptation)
An efficient fine-tuning method that trains only small adapter layers instead of the full model
IntermediateModels & Architecture
Model Distillation
Training a smaller 'student' model to replicate a larger 'teacher' model's capabilities at a fraction of the cost and latency
IntermediateModels & Architecture
PEFT (Parameter-Efficient Fine-Tuning)
A family of techniques that adapt large AI models to specific tasks by updating only a tiny fraction of parameters, cutting fine-tuning costs by 90–99%.
IntermediateModels & Architecture
Perplexity in NLP
The standard metric for evaluating language model quality — measuring how well a model predicts text, where lower values indicate better language understanding
IntermediateModels & Architecture
Quantization
Reducing model weight precision from 16/32-bit to 8/4-bit to shrink size and speed up inference
IntermediateModels & Architecture
RAG (Retrieval-Augmented Generation)
A technique that combines LLMs with external knowledge retrieval to improve accuracy and reduce hallucinations
AdvancedModels & Architecture
RLHF (Reinforcement Learning from Human Feedback)
A training technique that uses human preference ratings to align LLM behavior with human values
AdvancedModels & Architecture
Text/Action Mismatch
A failure mode where an LLM verbally refuses a restricted request in its text output while simultaneously executing the forbidden action in its structured tool-call output.
AdvancedModels & Architecture
Mixture-of-Experts (MoE) Model
An architecture that routes tokens to specialized sub-networks, increasing model capacity without a proportional increase in computing costs.
IntermediateModels & Architecture
Transformer
The neural network architecture underlying all modern LLMs, using attention mechanisms to process text
IntermediateModels & Architecture
VLM (Vision-Language Model)
An AI model architecture that jointly processes visual and textual inputs, enabling tasks like document understanding, image reasoning, and visual question answering.
AdvancedModels & Architecture
Attention Mechanism
The mathematical mechanism that allows transformers to dynamically focus on the most relevant parts of the input when processing each token
AdvancedModels & Architecture
KV Cache
A memory optimization that stores previously computed key-value pairs in transformer attention layers — avoiding redundant computation and accelerating generation 3-5×
AdvancedModels & Architecture
Self-Evolving Agentic Models
AI systems that autonomously improve their own capabilities by generating synthetic training data, debugging their own learning process, and modifying their reasoning strategies—early steps toward recursive self-improvement.
IntermediateModels & Architecture
Autoregressive Generation
Autoregressive generation is how LLMs produce text: predicting one token at a time, with each new token conditioned on all previously generated tokens.
AdvancedModels & Architecture
Beam Search
Beam search generates text by exploring multiple candidate sequences in parallel, keeping the top-k most promising paths to find the highest-probability output.
BeginnerModels & Architecture
DeepSeek
A highly efficient, open-weight AI model family that delivers frontier-level coding and reasoning capabilities at significantly lower computational costs.
AdvancedModels & Architecture
Flash Attention
A hardware-aware algorithm that massively speeds up LLM processing by optimizing GPU memory reads, enabling very long context windows.
BeginnerModels & Architecture
GPT
GPT (Generative Pre-trained Transformer) is OpenAI's family of large language models that demonstrated how scaling transformers produces increasingly capable AI.
IntermediateModels & Architecture
Gemini Omni
Google's any-to-any multimodal foundation model capable of generating any output (text, image, audio, video) from any input, with physics-grounded video generation as its first major capability.
AdvancedModels & Architecture
Mamba
A highly efficient AI architecture that uses State-Space Models instead of Transformers to process massive amounts of text with very low memory usage.
AdvancedModels & Architecture
MiniMax-M2
A 229.9B parameter Mixture-of-Experts model with only 9.8B active parameters per token, optimized for agentic tasks and exhibiting early signs of self-evolution—autonomously debugging its own training and modifying its scaffolding.
AdvancedModels & Architecture
Nemotron-Labs Diffusion
NVIDIA's family of language models (3B-14B) that merge autoregressive and diffusion generation into one architecture, enabling both GPT-style sequential generation and 10-50x faster parallel diffusion mode.
AdvancedModels & Architecture
Positional Encoding
Positional encoding tells transformers the order of tokens in a sequence, since self-attention alone is position-agnostic. Modern approaches like RoPE enable 128K+ context windows.
AdvancedModels & Architecture
Speculative Decoding
Speculative decoding speeds up LLM inference by having a small draft model generate candidate tokens that the large model verifies in parallel — same quality, 2-3x faster.
IntermediateModels & Architecture
Diffusion Model
A diffusion model generates images by learning to reverse a noise-adding process, iteratively refining random noise into coherent outputs guided by text prompts.
IntermediateModels & Architecture
Foundation Model
A foundation model is a large AI model pre-trained on broad data at scale that can be adapted to many downstream tasks through prompting, fine-tuning, or retrieval augmentation.
IntermediateModels & Architecture
GAN (Generative Adversarial Network)
A GAN uses two competing neural networks — a generator and a discriminator — to produce realistic synthetic data through adversarial training.
AdvancedModels & Architecture
State-Space Model (SSM)
An efficient AI architecture that maintains a continuously updating internal state to process massive sequences of data without the memory overhead of Transformers.
IntermediateModels & Architecture
Activation Function
Activation functions introduce non-linearity into neural networks, enabling them to learn complex patterns. Common ones: ReLU, GELU (transformers), sigmoid, softmax.
IntermediateModels & Architecture
Encoder-Decoder Architecture
An encoder-decoder architecture pairs an encoder (which reads and compresses input) with a decoder (which generates output), forming the basis of transformer model variants like BERT, GPT, and T5.
AdvancedModels & Architecture
Bicameral Model
A neural architecture that couples two parallel language models via their hidden states for real-time latent-channel coordination, dramatically improving reasoning accuracy without token overhead.
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