A.I. science center

Advanced A.I.

We dive into the heart of what A.I. is today, parsing fact from fiction.

1) Advanced A.I. in one sentence

Advanced AI today is the engineering of foundation models (transformers and relatives) plus retrieval, tools, agents, and governance to reliably solve real tasks under latency/cost/safety constraints.

2) History of AI (the compact “why it looks like this now” timeline)

1950s–1970s: Symbolic AI

  • Logic, rules, search, theorem proving; worked in constrained domains.
  • Weakness: brittle, doesn’t learn from data.

1980s: Expert systems boom (then bust)

  • Codified domain rules; expensive to build/maintain; collapsed when complexity grew.

1990s–2011: Statistical ML & “data era”

  • SVMs, trees, ensembles; practical wins (spam, risk, forecasting).
  • Feature engineering dominated.

2012–2016: Deep learning breakthroughs

  • Big leap in vision/speech; representation learning reduces feature engineering.

2017–present: Transformers + foundation models

  • The transformer architecture (“Attention Is All You Need”) replaces recurrence with attention and scales extremely well. (arXiv)
  • This scaling unlocks today’s LLMs, multimodal models, and copilots.

3) What is a Transformer (the “real” explanation)

A Transformer is a neural architecture built around self-attention, enabling:

  • Parallel processing of sequences (unlike RNNs).
  • Long-range dependency tracking via attention weights.
  • Scaling (more data/compute/parameters → better capability, within limits).

Core pieces:

  • Token embeddings (+ positional encoding)
  • Self-attention (Q, K, V) to decide “what matters”
  • Feed-forward layers for nonlinearity
  • Residual connections + layer norm for stable deep training
  • Decoder (for generation) uses causal masking so it can’t “peek ahead”

This is the architecture introduced in the 2017 transformer paper. (arXiv)

4) Training: how modern models are made

A) Pretraining (self-supervised)

  • Learn broad patterns from large corpora (next-token prediction or masked modeling).
  • Main costs: compute, data quality, infrastructure.

B) Post-training (alignment + specialization)

Common steps:

  • Supervised fine-tuning (SFT): teach helpful response styles
  • Preference optimization (RLHF / DPO-like approaches): steer behavior toward human preferences (varies by lab)
  • Domain fine-tuning: internal docs, codebases, vertical knowledge (with guardrails)

C) Evaluation (continuous)

  • Capability: task suites, internal golden sets
  • Safety: jailbreaks, sensitive data, tool misuse
  • Reliability: hallucination rate, groundedness, latency, cost

5) Mixture of Experts (MoE): “bigger brains, similar compute”

MoE splits the model into many “experts” and uses a router to activate only a few experts per token/request.

  • Benefit: huge parameter count without proportional compute per token.
  • Tradeoffs: routing complexity, training stability, communication overhead.

Switch Transformer is a key MoE approach that simplified routing and demonstrated large sparse scaling. (arXiv)

6) Distillation: “compress the teacher into a student”

Distillation trains a smaller model (student) to mimic a larger one (teacher), often using:

  • Teacher logits / soft targets
  • Intermediate representations
  • Task-specific supervision

Why it matters:

  • Lower latency, lower cost, on-device deployment
  • Often improved consistency in narrow domains (if distilled well)

Common pitfalls:

  • Student inherits teacher biases/errors
  • Knowledge can collapse if the distillation objective is too narrow

7) Pruning: removing weights/structures to speed up inference

Pruning reduces compute/memory by removing:

  • Individual weights (unstructured pruning)
  • Neurons/channels/heads (structured pruning)
  • Entire layers (rare, more aggressive)

Tradeoffs:

  • Unstructured pruning may not speed up on real hardware unless you have sparse kernels
  • Structured pruning tends to yield real speedups but may hurt quality more

8) Quantization (close cousin to pruning)

Even though you didn’t explicitly ask, it’s part of the “sticky advanced stuff”:

  • Reduce precision (FP16 → INT8/INT4, etc.)
  • Big wins for deployment cost and on-device inference
  • Tradeoff: accuracy drop, especially on long-context reasoning unless tuned carefully

9) RAG (retrieval) vs training (what goes where)

RAG is best for:

  • Fresh information (policies, product catalogs, tickets)
  • Proprietary data (internal docs)
  • Citations + auditability (show sources)

Training/fine-tuning is best for:

  • Style & behavior (tone, format)
  • Stable domain patterns (classification schema, writing conventions)
  • Tool-use routines (if you want consistent structured outputs)

Many orgs combine both:

  • RAG for facts + fine-tuned behavior for reliable structure.

10) Agentic AI (what it is, and what makes it hard)

Agentic AI = an LLM that can plan, call tools, take actions, and iterate toward a goal.

The modern pattern:

  1. User goal
  2. Agent plans steps
  3. Agent calls tools (search, DB, code, ticket system)
  4. Agent observes results
  5. Agent updates plan, repeats
  6. Produces final output + logs

Key failure modes:

  • Tool hallucination (calling non-existent tools)
  • Prompt injection via retrieved content
  • Infinite loops / runaway cost
  • Partial actions without transactional safety

11) API calls, tool calling, function calling (practical definition)

In production, “advanced AI” is usually an API-integrated system, not a chat window.

Tool / function calling

  • You define tools (functions) with schemas.
  • The model returns a structured tool call.
  • Your app executes it and returns results to the model.

Why it matters

  • Models aren’t databases.
  • Tools provide fresh, accurate, permissioned data and actions.

12) The modern production stack (what “advanced” actually looks like)

A) Model layer

  • Base model (general intelligence)
  • Optional fine-tuned variants (domain behavior)

B) Context layer

  • RAG (vector + keyword search)
  • Reranking
  • Permissions filtering
  • Source-of-truth routing (CRM/ERP instead of docs when needed)

C) Tool layer

  • APIs: tickets, CRM, billing, inventory
  • Code execution / automation (careful with permissions)

D) Orchestration

  • Agent frameworks, state machines, retry logic
  • Observability: traces, logs, eval metrics

E) Safety & governance

  • Policy enforcement, redaction, audit trails
  • Risk management frameworks (NIST AI RMF + GenAI profile is widely referenced) (NIST Publications)

13) IDEs and “AI-native development”

AI-assisted IDEs typically provide:

  • Inline completion
  • Chat with codebase context
  • Refactoring suggestions
  • Test generation
  • Repo-wide search with embeddings

The “advanced” leap is codebase-aware + tool-aware:

  • Uses retrieval over your repository
  • Runs tests/linters automatically
  • Creates PRs with diffs and rationale
  • Maintains change logs and auditability

(Under the hood, it’s the same stack: retrieval + tools + governance.)

14) Glossary quick definitions

  • Self-attention: mechanism that weights relationships among tokens.
  • Context window: how much text the model can consider at once.
  • Embeddings: vector representations for semantic similarity search.
  • Routing (MoE): selecting which expert subnetwork processes a token.
  • Distillation: teacher→student compression.
  • Pruning: removing parameters/structure.
  • Quantization: reducing numeric precision for speed/memory.
  • Fine-tuning: training on task/domain data to adjust behavior.
  • Tool calling: model requests structured external actions/data.
  • Agent loop: plan → act → observe → update.