Imagine feeding a video clip into an AI, and it spits out your exact brain activity. Meta's TRIBE v2 just did that across 1,117 hours of fMRI data from 720 subjects, zero-shot.
Moonshot AI dropped Kimi K2.5 without fanfare, but its 100-agent swarm is already rewriting automation rules. Developers are buzzing—tasks that dragged now finish in a fraction of the time.
Picture a brokerage statement: nested tables, jargon-thick prose, layouts that laugh at old OCR. Multimodal AI just cracked it, and finance workflows will never be the same.
Google just sunset five specialized embedding models, replacing them with a single multimodal beast. One API, one index: text to video, all in the same vector space.
A video clip feeds into an AI that cross-references product specs and customer tweets, spits out a sales script. Sounds slick. Productionizing it? That's the grind most ignore.
Trained on a mere 200 billion multimodal tokens—versus over a trillion for rivals—Microsoft's Phi-4-reasoning-vision-15B matches or beats much bigger models. It's proof that smarts, not scale, rule AI efficiency.
Everyone figured AI needed specialist models for chat, math, code, and pics. Mistral Small 4 says hold my beer: one fat MoE does it all. Deployment just got simpler. Or did it?
What if your phone could stare at a photo and launch a dozen searches before you blink? Google's latest AI does exactly that, turning one glance into a flood of answers.