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What do you test after millions of safe miles? Tornadoes, elephants, and flooded suburbs. Waymo is doing just that with Google’s Genie 3 world model!
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AI INSIGHTS
Simulation: Encounter with a Texas longhorn & more
Waymo quietly rolled out its Waymo World Model, a new hyperrealistic simulation engine built on Google’s Genie 3. You feed it a prompt and it creates a fully interactive 3D driving scene. With Genie 3, they can now simulate:
Hyper-realistic 3D driving scenes
Interactive sensor data across multiple modalities (e.g. lidar, radar, cameras)
Wild “long-tail” edge cases: tornadoes, floods, fires, snowstorms, wild animals
In each case, the system renders the full environment so the Waymo Driver can train in safe danger. The key innovation here is that Genie 3 controls them. It offers:
Driving Action Control – simulate “what if” decisions in a safe loop
Scene Layout Control – fully custom traffic flows, obstacles, lighting
Language Control – tweak time of day, weather, road layout using natural language
Waymo’s bet: by simulating everything, they can make real-world failure close to impossible. They’re even building a Gemini-based voice assistant for inside the car.
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AI HIGHLIGHTS
🖥️ This is pure gold. Just paste this prompt into to any AI model, it'll literally train you how to become an ELITE prompt engineer in under 24 hours, step-by-step.
🌐 It’s Safer Internet Day, Feb 10. Google says everyone’s learning with AI now, but are you doing it safely? So Google dropped 5 quick tips for safe, effective learning.
🎬 ByteDance’s Seedance 2.0 went viral after limited release, and it’s a truly massive leap forward for AI video. Some outstanding demos already hit over 2.6M views.
⚡ Claude Opus 4.6 just got a new ‘Fast Mode’, now 2.5× quicker. It’s live in Claude Code and API if you wanna test the speed. Join the waitlist for fast mode here.
📢 Ads are live in ChatGPT, real ads. Adobe’s one of the first partners, testing ads for Photoshop, Acrobat & Firefly. Wanna know how this actually looks in your chat?
🚀 OpenAI says ChatGPT is booming again: 10%+ monthly, and 800M weekly users. Codex surged 50% after GPT‑5.3 launch. Reminder: New model drops this week.
💰 Big AI Investment: a16z put $1.7B into AI infra from its $15B fund. They say 2026 is a “super cycle”, rebuilding core tools, chips, platforms from the ground up.
NEW EMPOWERED AI TOOLS
🧠 OpenAI Frontier is a platform that helps enterprises build, deploy, or manage AI agents that can do real work: shared context, onboarding,…
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AI BREAKTHROUGH
Transformers are great at predicting text but what if they could predict robot actions just as well?
Researchers at Harvard and Stanford just introduced a new system, OAT. It turns messy, continuous robot actions into clean, discrete tokens, so language models like GPT or Claude can actually drive robots using next-token prediction.
It’s already beating prior tokenization and diffusion-based methods across 20+ tasks. They built a full encoder–decoder setup:
The encoder splits up a robot’s motion into chunks and summarizes each chunk using register tokens
Then, Finite Scalar Quantization compresses that into a short sequence of tokens (way smaller than past methods)
Finally, a neural decoder translates any token sequence back into an actual movement
And because the token order matches the natural left-to-right flow of transformer models, the whole system works better for autoregressive prediction.
Early tokens describe coarse movement; later ones add detail. At this rate, "predict the next action" might become as common as "predict the next word"!?
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The AI Fire Team
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