funding public datasets for health @FoundationOAI || writing/podcasts on https://xitter.twitterapi.xyz/t.co/QPTHsR3NoU || ex @noetik_ai @dyno_tx
San Francisco, CA
Joined March 2019
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ive been getting back into Overwatch and anime to prepare for AGI, and im starting to get on board with human disempowerment. it’s sort of nice. on the other hand, maybe will be future forms of media that require human intelligence uplift to truly appreciate. lots of decisions ahead for the consumers amongst us. do you stick with your current brain and enjoy the zoo? or take the transhumanism pill, merge, and get access to beautiful, hyperstimulation video games that would insta-kill a normal human, BUT you have to work a 9-5 as a coordinator for Mythos 12. it’s a tough call
owlposting.com/p/on-creating…
the first drug with a new knob of control has (strong!) human evidence!
Dreadds work in humans nejm.org/doi/full/10.1056/NE…
owl retweeted
We used coding agents to automatically build baselines for 36 "AI for science" deep learning methods from Nature, Science, and Cell-family publications. These baselines recovered a median of ~95% of the score of the published method, and beat them in 33% of cases.
owl retweeted
Are you an unreasonably multi-disciplinary software engineer with deep biological/bioinformatics domain expertise?
Please apply for this: unc.peopleadmin.com/postings…
it actually feels evil to have a megaplatform and release ai-generated critiques of somebodies research without carefully checking each one. like really genuinely thoughtlessly evil. ruining the informational commons for what exactly? a dunk?
Errors in the research of Emmanuel Saez and Gabriel Zucman marginalrevolution.com/margi…
incredible work. im in awe of anthropic’s pace, and am grateful for the transparency of process+results
i went through 3 out of the 5 case studies they released and wrote down my thoughts on each. in italics are LLM-summarizations for the cases for those who havent read them
Over the past few months, Anthropic scientists have engaged in deep research partnerships with six organizations developing new medicines. We've also begun drug discovery efforts of our own.
In doing so, we’ve learned much more about how Claude can support the many stages of drug development.
In this blog post, we share some early results from these efforts, and describe how this work fits in with our wider ambitions in the life sciences.
anthropic.com/research/early…
owl retweeted
I discussed this fairly extensively with @owl_posting on our podcast together, but I agree the "scale up existing assay" approach is quite flawed.
If we examine sequencing, imagine where the field would have stalled if we just tried to scale Sanger. This would have been somewhat helpful, but ultimately not transformative. Protein engineering is very reliant on data that comes from DNA sequencing, especially in the age of barcoded DNA libraries, but this would not have been possible even with 1000x scaled up Sanger. NGS broke open an entire industry's worth of applications that Sanger simply couldn't deliver.
This is why I find the fully automated labs concept quite fraught. Sure, it helps you commodify particular assays, but you just get leap-frogged whenever someone creates the next NGS. Obviously you need both, but right now we're getting 95% of one and 5% of the other, should be much closer to 50-50.
And obviously listen to our interview on @corememory open.spotify.com/episode/5Nf…
owl retweeted
@iskander and I are hiring a senior experimental scientist to work with us on our project funded by Open AI Foundation to discover tumor antigens, tumor antigen-associated T cell receptors, and assess TCR performance via functional studies of TCR-engineered T cells. Apply here ( unc.peopleadmin.com/postings… ) if interested!
owl retweeted
okay I’m just going to rapid fire this because I’ve gotta sleep and also this is just like talking as if you were a close friend:
-like half of bio is ~wrong, like the problem is us not AI- misleading in vitro models, misleading murine models and then all of our “rules” are highly context dependent and not applicable in at least 20% of in vivo contexts
-also everything is baked with people and politics which leads to certain narratives/models in the paradigm sense winning when they maybe shouldn’t- publishing is beyond corrupt, holding people’s livelihood ransom based on whether they generate positive results and hide the negative is just a bad system
-lit search synthesis by AI is therefore not at all close to god, just sort of a condensation of issues
-but it’s still good and bad- like astra max got thymic involution at peak human adolescence wrong on monday but I’ll have grad students use it for questions I don’t want to be bugged with
-protein folding tells you v little abt function- like timing, location, ptms, nearby molecular species, idrs, local environment etc
-no one is building a model that integrates different echelons of regulation for actual in vivo tissues
-everyone needs to undiscover the cell/discover tissues and organ systems and overall interacting physiology
-human data should be step 1 in everything
-idk as far as experimental stuff/basic research gaps- automation and scaling aren’t issues it’s just like if your goal is solving human health what are you starting at? I would love for RNA biology to be solved first- I’m still biased towards building an actual causative model of life
-I think this shit could all be way more variable based on the individual than anyone is ready to deal with but everyone violently disagrees with me on that- but like my baseline basophil count is in the CML range and I’m am biased as a weirdo
-I hate non-durable single target based therapies in oncology for solid tumors with a passion and in my schizophrenic head I’d rather have biological understandings that allowed us to develop actual curative treatments
-to be positive ML based DNA encoded libraries are absolutely sick and I have new molecular glues in like the picomolar range which means tox will probably be an issue but yolo
owl retweeted
Maybe controversial: the conversation about scientific bottlenecks is overfocused on automation. New measurement technologies deserve more attention. You could automate an assay that needs an entire facility – or you could REINVENT the assay so that it fits into just a few tubes.
youtu.be/JAxkqb-nBWs?is=CH2H…
the owl posting podcast portfolio is incredible. congratulations to coherence, insanely talented team
We’ve raised a $45m Series A, co-led by
@foundersfund and The @OpenAI Startup Fund, to build cancer therapy that learns. Technology is rapidly changing medicine, but data loops and rapid adjustments are still largely missing. When you start cancer treatment, knowing whether what you’re doing is working is too slow. Clearly there is a better way. Build systems to watch cancer in real time, then feed that data into a system that itself can treat the disease. This seeds a future where, unlike drugs, we develop medicine that continuously learns to improve itself. We've been building neurotechnology and BCI platforms to do just that.
@coherenceneuro
My brother made a fun website: deprecation.org
Enter your job and it tells you when you'll be deprecated, and exactly how. For me it's... pretty soon and pretty accurate!
He's a very creative person (artist turned frontend dev to pay bills, now obv worried by AI) and this is full of little nuggets and easter eggs. It's an eerie mix of fun and jokes and real worries. Check it out!
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