Chef de cuisine Gondola

In terms of benchmarks this is comparable to GeForce RTX 4060. Released June 29, 2023 by NVIDIA. Two and a half years behind.

the primary criticism of AI you hear has nothing to do with water use or existential risk whatsoever: most people just think it’s fake and doesn’t work and is a tremendous bubble eating intellectual property while emitting useless slop along the way. when GPT-5 came out and perhaps didn’t live up to what people were expecting for a full version bump, the timeline reaction was not mild, it was a full-scale meltdown. there are many intelligent (and unintelligent) people who latched onto this moment to declare AI scaling over, thousands of viral tweets, still a prevailing view in many circles. The financial-cultural phenomenon of machine intelligence is one of the most powerful in decades, and there are a lot of people who would like for its position to be weakened, many outright celebrating its losses and setback. Michael burry of ‘Big Short’ fame, unfortunately the type of guy to predict 12 of the last 3 recessions, has bet himself into insolvency on the AI bubble’s collapse one of the stranger things about this time is that there are very few secrets, and very little reason to be so misinformed. model labs have very little space in between creating new capabilities and launching them to the public. The view among the well informed public and not just “lab insiders” is that machine intelligence is absurdly joyfully smart at so many new things every month. It’s actively contributing on the cutting edge of programming and math and science. Sebastian Bubeck and co’s recent paper reports that GPT5-pro is capable of producing results on the frontier of theoretical physics research, Terry Tao wrote a blog about “vibe-proving” Erdos problems with the auto-formalization AI Aristotle. You can read that these scientists are using it to actively contribute to black hole physics, tighten mathematical bounds in optimization theory, churning morasses of biomedical data into real insight. Google Deepmind, from the way they are signalling, seems to be slowly closing a dragnet around the Navier-Stokes smoothness millennium problem (though of course, I don’t know). Several companies stocked top to bottom with brilliant scientists are racing to build pipelines to solve novel physics and chemistry and biology You can read online about the new kinds of organizations being born around machine intelligence as a first class factor of production. For the first time, the new factor actually gives you ideas for improving the processes themselves. It’s designing whole assembly lines where some of the workers on the assembly line are also AIs, and the line itself is morphing and self-optimizing. Tiny teams are producing amounts of work that seemed impossible to organizations of a few years ago. It’s hard not to feel excited by the productivity growth happening in these admittedly narrow software sectors. Every time I use codex to solve some issue late at night or GPT helps me figure out a difficult strategic problem I feel: what a relief. There are so few minds on Earth that are both intelligent and persistent enough about some intellectual pursuit to generate new insights and keep the torch of scientific civilization alive. Now you have potentially infinite minds to throw at infinite potential problems. Your computer friend that never takes the day off, never gets bored, never checks out and stops trying. You can feel the unburdening of Atlas, the takeoff. It feels more prosaic and less poetic than it did in 2023, even though the results speak for themselves more loudly