Well, I would say they have done precisely that in evaluating the model, no? For example section 2.2.5.1:
>Uplift and feasibility results
>The median expert assessed the model as a force-multiplier that saves meaningful time (uplift level 2 of 4), with only two biology experts rating it comparable to consulting a knowledgeable specialist (level 3). No expert assigned the highest rating. Most experts were able to iterate with the model toward a plan they judged as having only narrow gaps, but feasibility scores reflected that substantial outside expertise remained necessary to close them.
Other similar examples also in the system card
so I'm just telling you they did the thing you said you wanted.
Yes, it is far inferior to the 'Trust torginus and his ability to understand the large body of experience that other actual subject-matter-experts have somehow not understood' strategy
The parallels here are quite remarkable imo, but defer to your own judgement on what you make of them.
It's not your fault that you don't know this, but this whole subthread is very CS-coded in its disdain for other software people's standard of evidence.
- the models help to retrieve information faster, but one must be careful with hallucinations.
- they don't circumvent the need for a well-equipped lab.
- in the same way, they are generally capable but until we get the robots and a more reliable interface between model and real world, one needs human feet (and hands) in the lab.
Where I hope these models will revolutionize things is in software development for biology. If one could go two levels up in the complexity and utility ladder for simulation and flow orchestration, many good things would come from it. Here is an oversimplified example of a prompt: "use all published information about the workings of the EBV virus and human cells, and create a compartimentalized model of biochemical interactions in cells expressing latency III in the NES cancer of this patient. Then use that code to simulate different therapy regimes. Ground your simulations with the results of these marker tests." There would be a zillion more steps to create an actual personalized therapy but a well-grounded LLM could help in most them. Also, cancer treatment could get an immediate boost even without new drugs by simply offloading work from overworked (and often terminally depressed) oncologists.
I hate to be rude in a setting like this, but please at least research the things you're sure about/prognosticating on.
> the same way, they are generally capable but until we get the robots and a more reliable interface between model and real world, one needs human feet (and hands) in the lab.
Honestly, the kinds of labs where 'bioweapons' would be made are the least dependent on human intervention.
You need someone to monitor your automated cell incubating system, make sure your pipetting / PCR robots are doing fine and then review the data.
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What do you are you trying to achieve in your example? This is all gobbldey-gook for someone who actually sees real, live cancer patients.
It's very easy to learn more about this if it's seriously a question you have.
I don't quite follow why you think that you are so much more thoughtful than Anthropic/OpenAI/Google such that you agree that LLMs can't autonomously create very bad things but—in this area that is not your domain of expertise—you disagree and insist that LLMs cannot create damaging things autonomously in biology.
I will be charitable and reframe your question for you: is outputting a sequence of tokens, let's call them characters, by LLM dangerous? Clearly not, we have to figure out what interpreter is being used, download runtimes etc.
Is outputting a sequence of tokens, let's call them DNA bases, by LLM dangerous? What if we call them RNA bases? Amino acids? What if we're able to send our token output to a machine that automatically synthesizes the relevant molecules?
No, it's not. It took years of polishing by software engineers, who understand this exact profession to get models where they are now.
Despite that, most engineers were of the opinion, that these models were kinda mid at coding, up until recently, despite these models far outperforming humans in stuff like competitive programming.
Yet despite that, we've seen claims going back to GPT4 of a DANGEROUS SUPERINTELLIGENCE.
I would apply this framework to biology - this time, expert effort, and millions of GPU hours and a giant corpus that is open source clearly has not been involved in biology.
My guess is that this model is kinda o1-ish level maybe when it comes to biology? If biology is analogous to CS, it has a LONG way to go before the median researcher finds it particularly useful, let alone dangerous.
>No, it's not. It took years of polishing by software engineers, who understand this exact profession to get models where they are now
This reads as defensive. The thing that is easy to learn is 'why are biology ai LLMs dangerous chatgpt claude'. I have never googled this before, so I'll do this with the reader, live. I'm applying a date cutoff of 12/31/24 by the way.
Here, dear reader, are the first five links. I wish I were lying about this:
- https://sciencebusiness.net/news/ai/scientists-grapple-risk-...
- https://www.governance.ai/analysis/managing-risks-from-ai-en...
- https://gssr.georgetown.edu/the-forum/topics/biosec/the-doub...
- https://www.vox.com/future-perfect/23820331/chatgpt-bioterro...
- https://www.reddit.com/r/ClaudeAI/comments/1de8qkv/awareness...
I don't know about you, but that counts as easy to me.
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> I would apply this framework to biology - this time, expert effort, and millions of GPU hours and a giant corpus that is open source clearly has not been involved in biology.
I've been getting good programming and molecular biology results out of these back to GPT3.5.
I don't know what to tell you—if you really wanted to understand the importance, you'd know already.
Not that that justifies doom and gloom, but there is a pretty inescapable assymetry here between weaponry and medicine. You can manufacture and blast every conceivable candidate weapon molecule at a target population since you're inherently breaking the law anyway and don't lose much if nothing you try actually works.
Though I still wonder how much of this worry is sci-fi scenarios imagined by the underinformed. I'm not an expert by any means, but surely there are plenty of biochemical weapons already known that can achieve enormous rates of mass death pleasing to even the most ambitious terrorist. The bottleneck to deployment isn't discovering new weapons so much as manufacturing them without being caught or accidentally killing yourself first.
Software engineering is at the intersection of being heavy on manipulating information and lightly-regulated. There's no other industry of this kind that I can think of.
There is a massive gap between "having a recipe" and being able to execute it. The same reason why buying a Michelin 3 star chefs cookbook won't have you pumping out fine dining tomorrow, if ever.
Software it a total 180 in this regard. Have a master black hats secret exploits? You are now the master black hat.
The other side of the scaremongering coin is improbable optimism.
Consider reading the CB evaluations section, which covers what they did pretty extensively (hint: many domain experts involved).
Since most of us here are devs, we understand that software engineering capabilities can be used for good or bad - mostly good, in practice.
I think this should not be different for biology.
I would like to reach out and talk to biologists - do you find these models to be useful and capable? Can it save you time the way a highly capable colleague would?
Do you think these models will lead to similar discoveries and improvements as they did in math and CS?
Honestly the focus on gloom and doom does not sit well with me. I would love to read about some pharmaceutical researcher gushing about how they cut the time to market - for real - with these models by 90% on a new cancer treatment.
But as this stands, the usage of biology as merely a scaremongering vehicle makes me think this is more about picking a scary technical subject the likely audience of this doc is not familiar with, Gell-Mann style.
IF these models are not that capable in this regard (which I suspect), this fearmongering approach will likely lead to never developing these capabilities to an useful degree, meaning life sciences won't benefit from this as much as it could.