eco ML dev

Google DeepMind has made Stable Diffusion's VAE obsolete. Right now, every major AI image generator (like Stable Diffusion) operates on a massive, hidden flaw. They use a two-step process. First, an encoder compresses an image into a smaller "latent" space. Then, that encoder is frozen forever. Only after it is frozen does the actual diffusion model try to learn how to generate things from that space. The frozen encoder doesn't know how the generator works. This creates a brutal trade-off. If the compression is simple, the final output is blurry and soupy. If you try to keep all the fine details, the latent space becomes a chaotic mess that the AI struggles to learn. DeepMind has dropped a paper that completely destroys this trade-off. They call it Unified Latents (UL). Instead of training the encoder and the generator in isolation, DeepMind co-trained them. Together. They replaced the old, rigid architecture with a framework where the encoder is jointly regularized by a diffusion prior and decoded by a diffusion model. The AI now learns how to compress the data specifically so the generator can understand it perfectly. It explicitly controls the "bitrate" of its own imagination. The results rewrite the economics of generative AI. On massive benchmarks like ImageNet-512 and Kinetics-600, Unified Latents just set new state-of-the-art records for fidelity and video consistency. But here is the part that should terrify the hardware monopolies. It achieved this while requiring significantly fewer training FLOPs than existing models. We spent the last two years thinking the only way to get perfectly coherent AI video was to burn billions of dollars on larger GPU clusters. But the real unlock wasn't throwing more compute at the problem. It was teaching the AI to organize its own mind before it started dreaming.

Simulated water is wet. You just need to exist at the same level as the water within the simulation hierarchy. (99% of this discourse can be resolved by people simply being more careful about this.)

Google DeepMind researcher argues that LLMs can never be conscious, not in 10 years or 100 years. "Expecting an algorithmic description to instantiate the quality it maps is like expecting the mathematical formula of gravity to physically exert weight."

Perhaps the main problem with people in the bay area is that they haven't read the Discworld books, or read them too long ago