Alex van Grootel retweeted
We’ve been tracking capabilities similar to METR’s time-horizon plot. This year, for the first time, models started completing expert-level tasks on our private cybersecurity suite. As of today, the time horizon is approaching 2 hours
Big if true
Replying to @physical_int
This also shows up in the representations learned by the model. We plot the model’s representations of human and robot images. As pre-training is scaled up, the representation of humans and robots become more aligned: to a scaled-up model, human videos "look" like robot demos.
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I'm in Nairobi and keep having this conversation: what happens to countries like Kenya if AI automates the knowledge work that was supposed to be the path out of poverty?
Curious who's thinking seriously about this. Would love to compare notes.
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AGI discourse is starting to shift from just talking about AI safety/alignment and timelines, to AI preparedness. This is good.
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Where are the urban planners planning for a post-work future? The psychologists getting ready to deal with a potential purposelessness crisis?
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Alex van Grootel retweeted
New paper & surprising result.
LLMs transmit traits to other models via hidden signals in data.
Datasets consisting only of 3-digit numbers can transmit a love for owls, or evil tendencies. 🧵
Alex van Grootel retweeted
Today, we’re announcing the first major discovery made by our AI Scientist with the lab in the loop: a promising new treatment for dry AMD, a major cause of blindness.
Our agents generated the hypotheses, designed the experiments, analyzed the data, iterated, even made figures for the paper. The resulting manuscript is a first-of-a-kind in the natural sciences, in which everything that needed to be done to write the paper was done by AI agents, apart from actually conducting the physical experiments in the lab and writing the final manuscript. We are also introducing Robin, the first multi-agent system that fully automates the in-silico components of scientific discovery, which made this discovery. This is the first time that we are aware of that hypothesis generation, experimentation, and data analysis have been joined up in closed loop, and is the beginning of a massive acceleration in the pace of scientific discovery that will be driven by these agents. We will be open-sourcing the code and data next week.
Robin is a multi-agent system that uses Crow, Falcon, and Finch, the agents on our platform, to generate novel hypotheses, plan experiments, and analyze data. We asked Robin to find a new treatment for dry age-related macular degeneration. Robin considered the disease mechanisms associated with dry AMD, proposed a specific experimental assay that could be used to evaluate hypotheses in the wet lab, and proposed specific molecules we could test in that assay. We tested the molecules and gave it the resulting data, which it analyzed before proposing more experiments. In the end, it identified Ripasudil, a Rho Kinase inhibitor (ROCK inhibitor) that is approved in Japan for several other diseases, which seems very promising as potential treatment for dry AMD. It also identified specific molecular mechanisms that might underlie the effects of Ripasudil in RPE cells, from an RNA sequencing experiment it proposed. To be clear, no one has proposed using ROCK inhibitors to treat dry AMD in the literature before, as far as we can find, and I think it would have been very difficult for us to come up with this hypothesis without the agents. We have also run the proposed treatment by several experts in AMD, who confirm that it is interesting and novel. Moreover, this project was fast: with Robin in hand, the entire project took about 10 weeks, which is way shorter than it would have taken if we had been doing all of the in-silico components ourselves.
Important caveats: We are real biologists at FutureHouse, so I want to be clear that although the discovery here is exciting, we are not claiming that we have cured dry AMD. Fully validating this hypothesis as a treatment for dry AMD will take human trials, which will take much longer. Also, this discovery is cool, but it is not yet a "move 37"-style discovery. At the current rate of progress, I'm sure we will get to that level soon.
Congratulations to the team. Congratulations in particular to Robin, which generated the hypotheses, proposed the experiments, analyzed the data and generated the figures. And major congratulations also to the human team, which built Robin: @MichaelaThinks, @agreeb66, @benjamin0chang, @ludomitch, Mo Razzak, Kiki Szostkiewicz, and Angela Yiu.
Seeing lots of new companies in materials science + AI mostly focusing on materials design. Materials design is a critical part of getting better materials making our lives better. But it often takes ~10 years from design to commercial production. Big bottleneck.
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For a new materials there is a lot of innovation on processing that needs to happen.
You can't take the process you used to make your material in the lab and simply 100x everything to make a commercial-scale process. Lots of big and tiny problems to solve. Lots of tuning
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Between this and the Future House cryptic message, I suspect that 2025 is the year that public perception on "can AI make new discoveries" shifts to "yes". If we get 2 before the midpoint of the year, will we see a dozen by the end of 2025?
Excited to share new validation results of our @GoogleDeepMind @GoogleAI AI co-scientist!
Addressing the challenge of liver fibrosis, a condition affecting millions globally with few treatments options, we've been working with Dr. Gary Peltz @StanfordMed, exploring how co-scientist can help with identifying new therapeutic avenues for this complex disease.
Specifically, we tasked co-scientist with proposing novel hypotheses for epigenetic alterations leading to myofibroblast formation in liver fibrosis, suggesting new treatments and designing experimental protocols for validation.
Towards this, the co-scientist uncovered and highlighted histone deacetylation and DNA methylation, pointing towards HDAC (like Vorinostat), DNMT and BRD4 inhibitors as treatment options for study in advanced human liver organoid systems.
Experimental validation by Dr Peltz's lab @StanfordMed showed HDAC inhibitor Vorinostat & BRD4 inhibitors had significant anti-fibrotic effects in the organoid models; further Vorinostat also aided regeneration and cut TGFβ-induced chromatin damage by 91%.
We had previewed this work back in Feb but super glad to see comprehensive experimental validation results backing up the co-scientist generated hypotheses.
Notably, this work was done with an older version of the system from last year.
We have made considerable progress building on the latest Gemini 2.5 models. More soon!
Bioarxiv - biorxiv.org/content/10.1101/…
AI co-scientist - research.google/blog/acceler…
Grateful for the collaboration with Dr Peltz's team at Stanford.
Work done with amazing team mates @GoogleDeepMind @GoogleAI @taotu831 @Mysiak @ckbjimmy @apalepu13 @alan_karthi @KeranRong @ynhnx and many more not active here.
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To be clear - both of these results are closer to "can AI generate valid hypotheses", which isn't doing all of science. And we don't really know how many ridiculous AI hypotheses humans went through to pick these out.
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