Image generation with Gemini just got a bananas upgrade and is the new state-of-the-art image generation and editing model. 🤯 From photorealistic masterpieces to mind-bending fantasy worlds, you can now natively produce, edit and refine visuals with new levels of reasoning, control and creativity. A quick dive into Gemini 2.5 Flash’s capabilities 🧵

The @JeffDean & @NoamShazeer episode. We talk about 25 years at Google, from PageRank to MapReduce to the Transformer to MoEs to AlphaChip – and soon to ASI. My favorite part was Jeff's vision for AGI as one giant MoE that is grown in bits and pieces over time like a forest, rather than trained all at once. Specialization, distillation, inference time scaling all emerge organically rather than by design. Noam bites every bullet: 100x world GDP soon; let’s get a million automated researchers running in the Google datacenter; living to see the year 3000. Links below. Enjoy! Timestamps 0:00:00 - Intro 0:03:29 - Joining Google in 1999 0:06:20 - Future of Moore's Law 0:11:04 - Future TPUs 0:13:56 - Jeff’s undergrad thesis: parallel backprop 0:15:54 - LLMs in 2007 0:25:09 - “Holy shit” moments 0:27:28 - AI fulfills Google’s original mission 0:32:00 - Doing Search in-context 0:36:12 - The internal coding model 0:37:29 - What will 2027 models do? 0:43:20 - A new architecture every day? 0:49:10 - Automated chips and intelligence explosion 0:53:07 - Future of inference scaling 1:02:38 - Already doing multi-datacenter runs 1:08:15 - Debugging at scale 1:12:41 - Fast takeoff and superalignment 1:20:51 - A million evil Jeff Deans 1:24:22 - Fun times at Google 1:27:51 - World compute demand in 2030 1:34:37 - Getting back to modularity 1:44:48 - Keeping a giga-MoE in-memory 1:49:35 - All of Google in one model 1:57:59 - What’s missing from distillation 2:03:10 - Open research, pros and cons 2:09:58 - Going the distance

7 examples of Gemini's multimodal capabilities in action (with code and prompts) 🤯🧵

Can #AI truly understand long videos? Tobias Weyand & the Google Research team are testing the limits w/ Neptune, an open-source benchmark for long video understanding. Dive into the details & see how AI tackles temporal reasoning, cause & effect, & more →goo.gle/4esTTNM

We love the excitement & results from the community on AlphaFold 3 and are doubling the AF Server daily job limit to 20. Happy to also share that we're working on releasing the AF3 model (incl weights) for academic use, which doesn’t depend on our research infra, within 6 months.

All languages covey information at a similar rate when spoken (39bits/s). Languages that are spoken faster have less information density per syllable! One of the coolest results in linguistics.