LLMs &amp; Generative AI

Value Learning

Value learning is the AI-safety approach of having a system infer what humans actually value, rather than optimizing a hand-coded proxy, so that capable agents pursue goals aligned with human intent even in novel situations.

Constitutional AI: Teaching Models to Govern Themselves

Constitutional AI teaches a model to self-correct against a set of principles, or a 'constitution.' This automates safety alignment for models like Claude, reducing reliance on human feedback.

Instrumental Convergence: Why All AIs Might Act Alike

Even with different end goals, intelligent agents tend to pursue the same sub-goals like self-preservation and resource gathering. This is key in AI safety, explaining why a paperclip-making AI might compete with humans for resources, not from malice but…

Orthogonality Thesis: An AI's Intelligence and Goals Are Unrelated

The Orthogonality Thesis states an AI's intelligence and its ultimate goals are independent. A superintelligence could pursue any objective, from beneficial to catastrophic, with equal capability.

KV Cache: Don't Recompute, Just Remember

KV Cache speeds up LLM text generation by storing intermediate calculations (Key/Value vectors) instead of recomputing them for every new token. It's a standard optimization in inference engines.

Google TPU: Built for Matrix Math

A TPU is a specialist ASIC, not a faster GPU; it trades graphics flexibility for matrix-math throughput per watt. Google deploys them for TensorFlow, JAX, and PyTorch at scale. They excel at CNNs but can lag on tasks needing rasterization or recurrent logic.

Post-Training Quantization: Shrink Models Without Retraining

Post-Training Quantization (PTQ) shrinks a pre-trained model by converting its weights to lower precision, like turning a WAV file into an MP3. Use it to run large models on consumer GPUs without costly retraining.

ONNX Runtime: Run Any AI Model, Anywhere

ONNX Runtime is a universal engine for AI models, letting you run them efficiently on any hardware, from cloud GPUs to a user's browser. It's used to deploy models for fast inference on servers or mobile devices.

FlashAttention: Faster, Memory-Efficient Exact Attention

FlashAttention is an IO-aware algorithm that computes exact attention faster and with less memory. It avoids slow GPU memory transfers, making it a key optimization for training and serving large models on modern GPUs.

Speculative Decoding: A Small LLM Speeds Up a Big One

Speculative decoding uses a small, fast 'draft' LLM to guess the next few words, which a larger 'target' LLM verifies in one batch. This cuts inference latency 2-3x in production systems.

Deep Learning Compilers: From Model to Machine Code

A Deep Learning Compiler translates a model from a framework like PyTorch into optimized code for specific hardware like a GPU or TPU. This avoids manual optimization for every model-hardware pair.

vLLM: Faster LLM Inference with PagedAttention

vLLM is a serving engine that speeds up LLM inference by treating GPU memory like virtual memory. It's used to serve models with higher throughput by batching requests without wasting memory on padding.

AWS Inferentia and Annapurna Labs

AWS Inferentia is an AWS chip product line. Annapurna Labs, Amazon's semiconductor division acquired in 2015, builds Nitro, Graviton, and Trainium and ranks among TSMC's top five fabless customers. Do not assume Annapurna designs every AWS accelerator.

Data Bias: When AI Inherits Our Flaws

Generative AI learns patterns from its training data. Data bias occurs when this data contains skewed perspectives or stereotypes, which the model then reproduces and amplifies. This is why an image generator might default to stereotypes.

Deepfakes: AI-Generated Media Impersonations

Deepfakes are AI-generated media that convincingly impersonate people. Think of it as digital puppetry, where an AI manipulates a face or voice. They're used for film effects and satire, but also for misinformation. The footgun: assuming you can spot one.

AI Governance: Rules for Building Intelligent Systems

AI governance creates rules of the road for intelligent systems, ensuring they're safe, fair, and transparent. It applies when governments pass laws or companies form ethics boards. The footgun is treating this as only a legal problem, not a technical one.

Fairness Metrics: Quantifying AI's Impact on People

Fairness metrics translate "fairness" into a measurable score, checking if a model treats groups equitably. They are crucial for models in hiring or lending.

The EU AI Act: Risk-Based AI Regulation

The EU AI Act isn't a blanket ban but a risk-based framework. It sorts AI into tiers—from unacceptable to minimal risk—and applies rules proportionally, affecting any company with AI users in the EU. The footgun is assuming it only applies to EU companies.

Bias Mitigation Algorithms: Correcting Unfair AI

Bias mitigation algorithms steer AI toward a defined standard of fairness. They're used in high-stakes systems like hiring or loan approvals to counteract harmful, systemic tendencies learned from biased data.