LarsDu88

Bringing OpenAIs big result down to earth

Logically having a robot is great, but doesn't Shenzen still have a massive supply chain for the actual stuff that goes on the boards?

I work in biotech. Honestly, I think they will run into massive hurdles.

Amodei's academic background is relevant for protein engineering. Hence the recent press releases Anthropic has released which all concern prototype proof-of-concepts related to binding efficacy.

For a large number of therapeutic relevant indications, the bottleneck is actually clinical trials where you must contend with the sideeffects of any given therapeutic. Almost every drug I've worked on involves very well known mechanisms where knockdowns with targeted compounds will work in a petri dish, but can have catastrophic consequences when dosed on a live human.

As far as I can see there is no way to accelerate clinical trials in humans.

Even the most fantastically large and detailed perturbation datasets will struggle to capture this dynamic in a full human. This is why tech oligarchs are investing in virtual cell models. With a large automated research lab running perturbations 24/7 and capturing massive amounts of data, perhaps we will get as confident with predicting outcomes in cells as we now are confident with many coding projects. However, this is a scientific question, not a technological one. Public companies rarely give the people running them leeway to try and fail on big scientific question marks as opposed to well established or soon-to-be well established technology.

I posted the original comment, but there are multiple reasons why math success won't translate into drug development or materials engineering success. It is easy to create verifiable rewards environments for math and coding, and use the resulting data for RLVR post-training. For other domains you need machines or robots creating and running experiments. This is simply something that's never been attempted at scale.

Getting IPO level monies should enable the amount of capital needed to sustain such efforts, and if I were a betting man, that is going to be Anthropic's actual endgame given their nascent efforts in automating bio research.

I think a lot of discussion around the sucess or failure of Anthropic and OpenAI seems to hinge on their current uses, but people seem to have amnesia regarding the fact that Claude code wasn't even a thing until about 1 year ago. There are far wider markets these companies can tap on if they are well capitalized even as Chinese "actually open AI" commoditizes things.

There are moonshot projects related to superconductors, materials, and biology that these companies can claim progress on to keep valuations stratospheric.

He lead the team for most of development until leaving the company months before the horribly botched release. He mostly wrote most of the assembly code for the mainline sonic games in the 90s.

Yuji Naka really fell off hard after Sonic Adventure 2 and commercial failures of Sega's hardware endeavors as a whole. Went from being Sega's golden child, to being arrested for securities fraud.

Allegedly he was extremely difficult to work with and would not share the Night's 3d game engine with an American team that was developing a 3d Sonic game for the Sega Saturn, effectively kneecapping a big part of that console's commercial viability

Thank you for the correction. You dont need to retrain from scratch

Let us not forget MDK2

Pricing can always be set at a loss, however, particularly for a private company. Are there solid published numbers for a specific hardware spec?

Google's gonna Google...

Google's research showed some of the speed benefits tended to drop off at larger batch sizes on an H100. Are there published batch-size vs tokens/s figures for Mercury 3?

Honestly, a lot of those issues could be remedied with more optimized kernels, which LLMs could definitely write quite well now. I wouldn't be surprised if some deployments of Gemini Flash switched to diffusion for cases where there is low utilization, since Google showed you don't need to retrain an autoregressive model to rework it for diffusion inference.