Advanced Concepts in AI Research

Large Language Model (LLM)

A transformer-based model with billions or trillions of parameters trained on massive text corpora; capable of reasoning, summarization, and dialogue generation.

RLHF (Reinforcement Learning from Human Feedback)

A technique for aligning LLMs to human preferences by training a reward model from human judgments and optimizing the base model with reinforcement learning.

Direct Preference Optimization (DPO)

A newer alternative to RLHF that optimizes model behavior directly from ranked human preference pairs without a separate reward model.

Constitutional AI

A framework for training AI systems to follow predefined ethical principles or “constitutions” through self-critique and rule-based alignment.

Mixture of Experts (MoE)

A model architecture that routes each input through a small subset of specialized expert networks, improving efficiency and scaling.

Sparse Activation

The practice of activating only portions of a model (e.g., certain experts or attention heads) per input, reducing compute cost while maintaining capacity.

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Retrieval-Augmented Generation (RAG)

Enhancing language models with real-time retrieval from external knowledge bases to improve factual accuracy and grounding.

Vector Database

Specialized storage for embeddings that supports similarity search, essential for retrieval-augmented systems.

Embedding Space Geometry

The study of how learned representations organize in high-dimensional space and what structure encodes meaning or relationships.

Multimodal Learning

Integrating multiple data types (text, vision, audio, action) into a single model that can reason across modalities.

Diffusion Model

A generative architecture that learns to reverse a gradual noising process, enabling image, audio, and video synthesis with remarkable fidelity.

Score-Based Model

A formulation equivalent to diffusion that learns the gradient of the data distribution’s log density — key to generative sampling.

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Curriculum Learning

Training models on progressively more complex examples or tasks to improve stability and generalization.

Continual Learning

Enabling models to acquire new knowledge over time without catastrophic forgetting of previous tasks.

Meta-Learning

“Learning to learn”: algorithms that improve their own ability to adapt to new tasks with minimal data.

Interpretability

The study of how and why models make decisions; includes activation analysis, attribution methods, and mechanistic interpretability.

Mechanistic Interpretability

Reverse-engineering model internals to discover circuits, features, and representations that explain behavior at the neuron or head level.

Adversarial Robustness

Research into how small, targeted perturbations can fool models — and how to defend against them.

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Causal Inference in AI

Techniques for uncovering cause–effect relationships rather than mere correlations, bridging statistics and reasoning.

Evaluation & Benchmark Drift

The recognition that benchmarks become obsolete as models improve; maintaining relevance requires continual benchmark design.

Scaling Laws

Empirical relationships showing how model performance scales predictably with data, parameters, and compute — guiding frontier model design.

Hardware–Software Co-Design

Jointly optimizing models and hardware (GPU, TPU, ASIC, interconnect) for maximal throughput and efficiency.

Distributed Training Infrastructure

The orchestration of thousands of GPUs or nodes using frameworks like Megatron, DeepSpeed, and FSDP for large-scale training.

AI Alignment & Safety Research

The study of ensuring that advanced AI systems behave safely, predictably, and beneficially — encompassing robustness, interpretability, and governance.