It helps with bureaucracy and pipeline generation. That's valuable and frees up time and effort for scientists. But it's not generating new ideas or solving problems like in math/engineering.
The problem is that biology is like trying to reverse engineer alien technology to understand how it works. Math/engineering is more like building up complexity through well defined parts. Math/engineering is a heck of a lot more deterministic than biology. The ultimate bottleneck in biology in context of AI is well curated and breadth of data, not necessarily intelligence.
This is one application of AI that I’ve been hopeful for. Accelerating research into new medicines and cures to disorders, diseases, and cancers could be a net positive on human lives. Maybe it’ll help bring R&D costs down so that medicines for rare diseases aren’t out of reach for those that are afflicted by them.
> If Claude is to significantly increase the rate of new cures, we need to work with many more biotechnology firms and integrate Claude more deeply into their development processes.
Given all that, the wording here feels very self-serving. Cautious optimism is probably warranted.
TBH, I am waiting for Mathocalypse-level progress in biology and medicine, but I guess that's not happening as fast, because it's the real world, human lives, etc., mostly etc.
Indeed, actually, one of the limitations preventing AI from making more contributions is humans. Human emotions, laws, morality, responsibility, and so on are all part of these limitations.
I've always thought openai could actually create an agent that can control various physical robotic arms and things like that, and then continuously iterate on solutions that can participate in the real physical environment. This could accelerate automatic iteration for many things that require human operations, such as complicated experiments, assembly adjustments, and testing.
Otherwise, AI is always trapped in virtual systems and still needs human intervention. But even though AI is already very powerful now, there is still a limit to how much humans can handle or assign tasks. Many times, AI has already finished processing something, but I don't even have time to assign the next deeper level of tasks.
People hope AI can improve productivity, but at the same time they're afraid of AI actually getting involved in reality, while the valuation of the entire AI industry is so high.
This kind of wanting to move forward but not moving forward makes humans especially uncomfortable.
I expect that AI use in the more axiomatic kinds of research will be well in advance of sciences that have a heavy, messy empirical foundation, like biology.
Specifically, it's because the verification loops are much longer in applied sciences. In biology you need to wait for wet lab experiments, clinical trials, regulatory steps, etc. before you can call something "solved".
Since sequential steps are slow, I think we're going to see massive increases in parallelization. I'm not sure how the regulatory frameworks will keep up honestly.
Well, it matters more for AI because there are some limitations of human research that don't apply to AI. Not all of course, just mainly the fact that there's a finite number of great researchers. Other things are limitations that could theoretically be overcome -- for instance, it's "only" money stopping you from having more lab space as long as you have a suitable robot to run a lab.
I wonder what the unlock would be, if any, of having simulations substitute any (or all, in time) of these steps. Akin to the method used to train FSD cars, or robots.-
In my untrained opinion, I don't think that will be as much of a cheat as it might seem. Or more accurately, creating a simulation accurate enough to where it would work for this purpose would be where all the breakthroughs would have to be made. Building a perfect simulation requires an understanding of the system being simulated, that I don't think we fully have of molecular biology -- if we did, I suspect we'd already have many of the answers we are seeking when we do this kind of research.
But I'm no biologist so if someone else is, feel free to correct me.
I have a rare disease with no cure. Id love to see how Claude can help with us, but I'm cautious what it can really advance. Murine models still take time, and clinical trials are slow. I would think most biotech companies lose money at these stages.
>> If Claude is to significantly increase the rate of new cures, we need to work with many more biotechnology firms and integrate Claude more deeply into their development processes
It's funny that a lot of the "helping" is just actually filling paperwork.
If that's the bottleneck maybe we need to rethink all the bureaucracy around the process instead of using AI to fill a document and then using AI to read it. That's just plain stupid.
I think the dividing line between skilled and unskilled use of AI is 'machine, do magic' vs 'machine, do x, y and Z, thus and so, avoid i, j, k which knowledge is the fruit of bitter experience.' Being able to articulate what matters and why is the ultimate high level language.
I like how there's a giant "chapter picker" pinned at the top of the web page, but there are only two chapters and the second chapter is a tiny blob at the end of the article.
The point about finding problems in documented processes (Enveda: manufacturing and filings) is unexpected but interesting. One of our civilizational problems is that the sheer volume of documentation has slowly exceeded capabilities of humans to produce it reliably and without flaws.
It helps with bureaucracy and pipeline generation. That's valuable and frees up time and effort for scientists. But it's not generating new ideas or solving problems like in math/engineering.
The problem is that biology is like trying to reverse engineer alien technology to understand how it works. Math/engineering is more like building up complexity through well defined parts. Math/engineering is a heck of a lot more deterministic than biology. The ultimate bottleneck in biology in context of AI is well curated and breadth of data, not necessarily intelligence.
This is one application of AI that I’ve been hopeful for. Accelerating research into new medicines and cures to disorders, diseases, and cancers could be a net positive on human lives. Maybe it’ll help bring R&D costs down so that medicines for rare diseases aren’t out of reach for those that are afflicted by them.
> If Claude is to significantly increase the rate of new cures, we need to work with many more biotechnology firms and integrate Claude more deeply into their development processes.
Given all that, the wording here feels very self-serving. Cautious optimism is probably warranted.
TBH, I am waiting for Mathocalypse-level progress in biology and medicine, but I guess that's not happening as fast, because it's the real world, human lives, etc., mostly etc.
Indeed, actually, one of the limitations preventing AI from making more contributions is humans. Human emotions, laws, morality, responsibility, and so on are all part of these limitations.
I've always thought openai could actually create an agent that can control various physical robotic arms and things like that, and then continuously iterate on solutions that can participate in the real physical environment. This could accelerate automatic iteration for many things that require human operations, such as complicated experiments, assembly adjustments, and testing.
Otherwise, AI is always trapped in virtual systems and still needs human intervention. But even though AI is already very powerful now, there is still a limit to how much humans can handle or assign tasks. Many times, AI has already finished processing something, but I don't even have time to assign the next deeper level of tasks.
People hope AI can improve productivity, but at the same time they're afraid of AI actually getting involved in reality, while the valuation of the entire AI industry is so high.
This kind of wanting to move forward but not moving forward makes humans especially uncomfortable.
Google does this research not sure if the others are doing it. Embodied reasoning
https://deepmind.google/models/gemini-robotics/
They should put AI in a car so it can drive itself or create robots that use world models...
I expect that AI use in the more axiomatic kinds of research will be well in advance of sciences that have a heavy, messy empirical foundation, like biology.
That plus organs on a chip could be scalable and generate interesting data
Keep waiting
Specifically, it's because the verification loops are much longer in applied sciences. In biology you need to wait for wet lab experiments, clinical trials, regulatory steps, etc. before you can call something "solved".
Since sequential steps are slow, I think we're going to see massive increases in parallelization. I'm not sure how the regulatory frameworks will keep up honestly.
> we're going to see massive increases in parallelization
Why? This is not an AI problem, it's an industry one. And if it was simple, it would already exist.
Well, it matters more for AI because there are some limitations of human research that don't apply to AI. Not all of course, just mainly the fact that there's a finite number of great researchers. Other things are limitations that could theoretically be overcome -- for instance, it's "only" money stopping you from having more lab space as long as you have a suitable robot to run a lab.
> And if it was simple, it would already exist.
Not necessarily. Many things are simply gatekept behind the door.
> wet lab experiments, clinical trials, regulatory steps
I wonder what the unlock would be, if any, of having simulations substitute any (or all, in time) of these steps. Akin to the method used to train FSD cars, or robots.-
In my untrained opinion, I don't think that will be as much of a cheat as it might seem. Or more accurately, creating a simulation accurate enough to where it would work for this purpose would be where all the breakthroughs would have to be made. Building a perfect simulation requires an understanding of the system being simulated, that I don't think we fully have of molecular biology -- if we did, I suspect we'd already have many of the answers we are seeking when we do this kind of research.
But I'm no biologist so if someone else is, feel free to correct me.
By the time we can simulate a human body to the degree that clinical trials aren’t necessary I think we have solved disease a long time ago.
I have a rare disease with no cure. Id love to see how Claude can help with us, but I'm cautious what it can really advance. Murine models still take time, and clinical trials are slow. I would think most biotech companies lose money at these stages.
>> If Claude is to significantly increase the rate of new cures, we need to work with many more biotechnology firms and integrate Claude more deeply into their development processes
Sounds invasive.
Have you tried it yourself on their best available public model yet?
I suspect the public model will refuse. Anthropic models have gotten extremely strict on biology and security work.
I'd be happy to be wrong, though. I've never tried to do biology with Claude, but I have gotten many refusals and downgrades for security work.
It's funny that a lot of the "helping" is just actually filling paperwork.
If that's the bottleneck maybe we need to rethink all the bureaucracy around the process instead of using AI to fill a document and then using AI to read it. That's just plain stupid.
> Six Generate scientists used Claude Code to propose new designs for this part
“Generate scientist” … is that a real job like “prompt engineer”
I think the dividing line between skilled and unskilled use of AI is 'machine, do magic' vs 'machine, do x, y and Z, thus and so, avoid i, j, k which knowledge is the fruit of bitter experience.' Being able to articulate what matters and why is the ultimate high level language.
At that point you are just defining the requirements, you may as well write the code for it
I think they meant six scientists from "Generate Biomedicines" (the company)
Either that or six degenerate scientists. Both options work just fine
"Invent a new drug, make no mistakes"
“One that won't make me nervous, wondering what to do. One that makes me feel like I feel when I'm with you.”
Crossing fingers for LSD 2.0
I like how there's a giant "chapter picker" pinned at the top of the web page, but there are only two chapters and the second chapter is a tiny blob at the end of the article.
That's standard blog design though
New cocaine?
We've had "research chemicals" for a couple of decades.
Some, do what it says on the tin.
Some...not so much.
The issue with any new chemical is full, comprehensive, regulated testing.
Dodgy folks aren't really so good, with that last part. They use their customers as guinea pigs.
Claude, you are a senior drug dealer. Get me my fix, make no mistakes.
The point about finding problems in documented processes (Enveda: manufacturing and filings) is unexpected but interesting. One of our civilizational problems is that the sheer volume of documentation has slowly exceeded capabilities of humans to produce it reliably and without flaws.
Same applies to construction projects, BTW.