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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.
AI Auditing: The OWASP LLM Top 10 Checklist
Think of an AI audit like a building inspection for your LLM app, checking for structural weaknesses. It uses frameworks like the OWASP Top 10 to find risks like prompt injection and data leakage.
Hugging Face Hub: The GitHub for Machine Learning
Think of the Hugging Face Hub as the GitHub for machine learning. It's a central platform to find, share, and collaborate on millions of models, datasets, and demo apps. Use it to download a pre-trained model or share your own.
The OpenAI API: Access to Foundational AI Models
The OpenAI API provides access to influential models like GPT and DALL-E. It enables developers to integrate generative AI into commercial applications and research, building on technology that catalyzed the current AI boom.
The Llama Model Family: Open-Source AI for Production
Think of Llama not as one model, but a family of open-source AIs you can run anywhere. Use it for cost-effective, fine-tuned applications like internal search or when you need full control. The biggest mistake is mis-sizing the model for your task.
MaaS: Renting AI Brains via API
Model-as-a-Service (MaaS) is like renting a pre-trained AI expert via an API. Instead of building and training your own models, you pay to use powerful, ready-made ones for tasks like text generation or image analysis.
Amazon Bedrock: One API for Many AI Models
Amazon Bedrock is an API gateway for foundation models, letting you switch AI providers without rewriting code. It's used to build generative AI apps while avoiding vendor lock-in.