Learning path

The frontier

Where AI is going: images and video, science, robots and world models.

23 lessons · 132 min left

Start path → What a large language model does

FoundationsLesson 2: What a large language model doesAn LLM is autocomplete that swallowed a library. Understanding that one sentence explains most of its strengths and every one of its weaknesses.Beginner4 min read🧪 Lab📝 TestUp next
Multimodal and generative AILesson 242: What multimodal meansText is only one way to hold information. Learn how images, audio and video are turned into tokens and embeddings, so one model can work with all of them.Middle6 min read🧪 Lab📝 Test
Multimodal and generative AILesson 243: How vision-language models seeImage patches, a vision encoder, and CLIP-style matching of pictures and words. Plus the mistakes these models still make with counting, small text and left-right.Middle6 min read🧪 Lab📝 Test
Multimodal and generative AILesson 244: How diffusion models make imagesA diffusion model learns to remove noise, a little at a time. Start from pure static, clean it step by step with your words as a guide, and a picture appears.Middle6 min read🧪 Lab📝 Test
Multimodal and generative AILesson 245: Video generationA video is many images that must agree with each other over time. Learn why that is much harder and costlier than one image, and where today's models still slip.Middle5 min read🧪 Lab📝 Test
Multimodal and generative AILesson 246: Speech models: listening and talkingSpeech recognition turns voice into text, text-to-speech turns text into voice, and newer assistants talk directly. Learn how each works, why speed matters, and why voices need consent.Middle5 min read🧪 Lab📝 Test
Multimodal and generative AILesson 247: Music and audio generationModels can now make songs, sound effects and background music from a few words. Learn how they work in general, and the hard questions about artists, consent and copyright.Middle5 min read🧪 Lab📝 Test
Multimodal and generative AILesson 248: Any-to-any modelsSome models can take in text, images and sound and also answer with any of them. Learn the general idea, why it is useful, and why it makes testing harder.Middle5 min read🧪 Lab📝 Test
Multimodal and generative AILesson 249: Watermarks and content credentialsInvisible watermarks such as SynthID and signed content credentials (C2PA) help show where an image, video or sound came from. Learn how they work, and what they cannot prove.Middle5 min read🧪 Lab📝 Test
Multimodal and generative AILesson 250: Building with multimodal APIsHow to send images and audio to a model API, what it costs, how to protect people's privacy in photos, and how to use it well for accessibility, like writing alt text.Middle7 min read🧪 Lab📝 Test
AI for scienceLesson 251: What AI adds to scienceAI can find patterns in huge data, run fast approximate simulations and suggest ideas to test. People still design the experiments, test the ideas and judge the results.Middle5 min read🧪 Lab📝 Test
AI for scienceLesson 252: Protein structure and AlphaFoldProteins fold into 3D shapes, and shape decides what they do. AlphaFold predicts those shapes from the sequence. Learn what it does, what it does not do, and how to explore its public database.Middle5 min read🧪 Lab📝 Test
AI for scienceLesson 253: AI for drug and materials discoveryAI can screen huge numbers of candidate molecules and even design new ones. But every candidate still faces a long road of lab tests and, for medicines, clinical trials.Middle6 min read🧪 Lab📝 Test
AI for scienceLesson 254: Weather and climate modelsMachine-learning weather models learn from decades of past weather and can forecast very fast. Physics-based models still matter, and every forecast carries uncertainty.Middle5 min read🧪 Lab📝 Test
AI for scienceLesson 255: AI and mathematicsProof assistants like Lean check every step of a proof by machine. AI can help find proofs, spot patterns and suggest conjectures, and the checker keeps it honest.Middle6 min read🧪 Lab📝 Test
AI for scienceLesson 256: AI lab assistants and automated experimentsAI can search the literature, help plan experiments and, in self-driving labs, choose the next experiment for robots to run. Safety and human judgment stay in charge.Middle6 min read🧪 Lab📝 Test
AI for scienceLesson 257: Hype, reproducibility and scientific integrityAI makes it easy to get results that look great and are not real. Learn about leakage, overfitting and fake references, and how honest disclosure and review keep science trustworthy.Middle5 min read🧪 Lab📝 Test
Robots and world modelsLesson 258: Why robots lag behind chatbotsChatbots learned from a huge amount of text. Robots have far less data, a messy world, real safety risks and costly hardware. Here is why the gap exists.Middle6 min read🧪 Lab📝 Test
Robots and world modelsLesson 259: Learning from demonstrationsOne of the most common ways to teach a robot is to show it what to do. Learn how demonstrations are collected, what behaviour cloning is, and why copying goes wrong.Middle6 min read🧪 Lab📝 Test
Robots and world modelsLesson 260: Simulation and sim-to-realReal robots are slow and costly to train. Simulators let a robot practise a huge number of times. The hard part is making what it learned work in the real world.Middle7 min read🧪 Lab📝 Test
Robots and world modelsLesson 261: Vision-language-action modelsSome newer robot models take camera images and a written instruction, and output the robot's next movements. Learn how they work in general terms, and where they still fall short.Middle6 min read🧪 Lab📝 Test
Robots and world modelsLesson 262: World models: learning how the world movesA world model predicts what will happen next if the robot takes an action. It lets a system imagine before it acts. Learn the idea, the methods and the debate.Middle8 min read🧪 Lab📝 Test
Robots and world modelsLesson 263: Robots in India: farms, factories and homesWhere could robots be useful in India, and what would it take? A look at possible uses, and the hard questions about jobs, safety and affordability.Middle7 min read🧪 Lab📝 Test