AI Foundations
Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
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Understand what AI is, how systems learn, where they fail, and how to judge claims without hype.
Use AI productively while protecting privacy, checking outputs, and preserving human accountability.
Evaluate workplace use cases, run safe pilots, measure value, and communicate changes responsibly.
Analyze AI systems through rights, equity, governance, safety, and public-interest outcomes.
Understand language models, retrieval, agents, evaluation, cost, and deployment safeguards through practical system design.
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Wav2Letter is an end-to-end speech recognition system from Facebook AI that used only convolutional neural networks, no recurrence.
Audio AIJasper and QuartzNet are NVIDIA's end-to-end convolutional speech recognition models, with QuartzNet being a dramatically smaller, efficient redesign…
Language AIPerplexity is the classic score for how 'surprised' a language model is by real text — lower means it predicts words more confidently.
Language AILoRA lets you customize a giant pretrained model by training only a tiny set of new weights instead of all billions.
SocietyPrompt injection is when hidden or malicious instructions hijack an AI system into ignoring its rules and doing the attacker's bidding.
Language AIConstitutional AI is Anthropic's method for aligning models using a written set of principles — a 'constitution' — so the AI critiques and revises its own…
Visual AIVision Transformers (ViTs) apply the transformer architecture that powers ChatGPT to images, treating a picture as a sequence of patches instead of a grid…
Visual AIStable Diffusion is an open-source text-to-image model, released by Stability AI in 2022, that generates pictures by gradually removing noise from a random…
Visual AIMidjourney is a popular commercial text-to-image service known for its striking, highly aesthetic results and its origins as a Discord bot.
FundamentalsOverfitting is when a model memorizes its training data and fails on new examples; underfitting is when it is too simple to capture the real pattern.
FundamentalsRegularization is a set of techniques that deliberately constrain a model so it generalizes to new data instead of memorizing the training set.
TechnicalBatch normalization is a technique that rescales the inputs to each layer of a neural network during training, making deep networks train faster and more…
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