Speaker 0:  what if i told you that the next computer virus wasn't just code that crashes your laptop, but a biological entity that actually breathes, replicates, and spreads? scientists just get used a genome ai to build viruses from scratch, and the existing rule book has no idea how to handle it. the arc institute has achieved a massive milestone in generating biology using an ai called evo. they trained it on 9,000,000,000,000 nucleotides and asked it to create a new viral genomes. it succeeded 16 times producing functional bacteriophages, viruses that infect bacteria, not humans, from nothing. the breakthrough is real, and it promises to revolutionize phage therapy for antibiotic resistant infections. the problem, current biosecurity regulations only flag known pathogens. because these are new ai designed entities, they fall through a massive regulatory loophole. we have the capability to create life, but no governance to steer it at all. what do you think about this, john?

Speaker 1:  yeah. this scares the crap out of me, but, also really cool at the same time. so like you said, what's remarkable about this is that those 16 viruses, they're not just copies of existing viruses. they contain substantial genetic novelty while still performing the complicated biological tasks necessary for a virus to survive, assembling a viral particle, entering a host bacterium, replicating its genome, generating more particles, and infecting additional bacteria. so some perform competitively with or better than the natural phage used as the design template, which scares the crap out of me. so, yeah, they generated roughly 700,000 canon candidate genomes, computationally filtered them, selected about 300 for laboratory synthesis, and produced about 16 viable, viral phages. and so, this but the thing is that this wasn't chatjpt generating a virus from a text prompt. the models involved are genome language models. so, traditional language models learn statistical relationships among tokens representing words or pieces of words, whereas genome models, genome models, instead treat genetic sequences as their language. right? and we've covered this in the past, but the important information isn't contained simply in individual letters like adenosine, cysteine, guanine, and thymine. biological function emerges from an extremely complicated combination of sequences across genes. and so the evil models are designed to learn those relationships, which is what is unique about this and is crazy. i think this is cool but also scary. the regulatory bodies again, we've talked about regulatory bodies and trying to keep up with ai. now that they're designing their own brand new phages, viral phages, like, what's gonna happen? what do you think about this, craig?

Speaker 0:  so we've had a lot of rapid advancements in protein folding with alphafold and molecular design and stuff since 2023. and in july 2020 or july 28, this year, nih released new guidelines prohibiting the risk of biological research but leaving a purely computational loophole. so this definitely has to be closed. and i don't know what computational loophole really means. just because you can doesn't mean we created it, and it looks okay on the computer, but we can't actually go out and print it because then it's physical and that breaks the loophole. so i'm just trying to figure out, you know, where that border is. but, this is like a chef who has mastered every ingredient on earth. and instead of following a grandmother's recipe, which we would consider traditional science, the chef imagines a dish that has never existed, puts it in the oven, and miraculously, it tastes like a five star meal. so it captures this is interesting, and crazy. and, you know, for you, those people use ai. just imagine how being able to jump into, a prompt and be able to say, hey. research just as easy as you would say, research 10 competitors in my area and find out their offers and pricing, and it comes back with that. now you can do the same thing with genomes and bacteria and viruses. hey. create me a 100 new viruses that will wipe out all humans on earth. go. and i'll go get coffee, and i'll come back. yeah. this is the fear. right? like, let's just kinda put it on the surface that when the ai can do these things, then we need to start being aware of someone will do it. you know? i mean, there's always someone these types of things are not too far for, someone who's radicalized or is this or is that or, you know, an extreme scientist, whatever you wanna call it. yeah.

Speaker 1:  especially nowadays with it with, having access to jailbroken llms that you can run offline. it is so easy to just download and use one and install it. it's absolutely crazy, especially using walmart.cpp or olam or whatever. that's the scary part is that okay. sure. they have all these, security measures in place, but no. all there's so many open source models that people can download and mess around with and be nefarious. and so, like, imagine when we get these llm models that are able to do the point of what these guys are doing, at a larger scale, at a at a home brew kind of system. that scares the crap

Speaker 0:  out of me. yeah. and with regulatory gap, meaning there's no laws covering code for viruses or physical germs, meaning just you can't stop this code from getting out there and the stuff that it does because we've got no rules against it yet. so, it's scary, but we'll have to kinda wait and see what happens. but this goes back to our discussion that we end up on the different side of all the time is where does the government regulation happen and where shouldn't it happen? and i'm happy to have these discussions because, ideally, we get to a better, point, meaning, government just locked down missiles and fable or whatever and made them go through some process for a few weeks before they allowed it released, and they put some extra guardrails on there. same thing with chap tpt 5.6 and what the next version is. they're not quite the same process, but they have this new process where they have to go through. we don't know if they're testing for these biological agents. we don't know what they're testing for. i hope that they're testing for these things and if not, that now they are based on some of these stuff that come out. so although i'm not a huge fan of regulation, at some point, when you're getting to a situation of danger, hacking every software on the planet, creating biology that'll kill animals or do fix things or do whatever, there needs to be some oversight, what that is exactly and who makes it. that's a whole other discussion. but i think that oh, go ahead. go ahead. no. i think that's why there's conflict. it's like, i think we're breaking these lines all the time. and at a government level, we don't know what to be afraid of, so we're afraid of everything. and at a technology level, we have two sides saying we should pause everything. but at the same time, the people say or pause everything are moving at 10 x speed. so it's hard to, and if you pause, you're just gonna be left behind. so that'll never happen. and so we're keep moving forward, and we don't know how to control this, and we don't have governance, which leads to a lot of conflict and a lot of confusion. go ahead.

Speaker 1:  but it's software at the end of the day. like, the thing is that, no matter what, it can leak easily. so no matter how much government regulation comes in, is happening, like, we've already got very capable models already. people can build on top of those and, like, i think we're too late for government regulation when it comes to ai. i think we're too late.

Speaker 0:  i think you're partially right. yeah. and but i also think that it's not just regulation. it's monitoring. it's analysis control. if we test these kinds of things in the ai, we can test the responses. we can test words. we can test strings that we can now start searching on the internet from you know, the government has these big analysts. every, internet plug goes through the us government router pretty much everywhere, and they analyze all the packets. my point is we can start putting this stuff in to start detecting this and who's using it and whatnot. yeah. you're right. that it's starting to get out of the bag where people can grab a model, pull out the guard rules, pull out the information, and stick it into something and do some crazy stuff they shouldn't do with it. however, we need to start building the capabilities to say, alright. if there's dedicated models for these things, we want to stop them, whatever that means. detect them, their signatures when they're going online, whatever, at a firewall level, at a security level, at what have you. and, also, at the same time, we wanna detect people doing this weird stuff. there was an article i was seeing recently about how chatdtp and those other people are sending, you know, dangerous conversations to the authorities, whoever they happen to be. and we probably need more of that even though i hate it. but when everyone has a gun, people are gonna use it. mhmm. and so we can say it's too late, or we can do gun training, gun detection, put those shooting, not cameras up because that's debatable, but maybe those audio ones that can detect gunfire and, you know, pinpoint and get people out. we can do these things that, shrink the risk or minimize the impact or what have you of these things getting out in the wild. whether government or any of these tech companies actually do a good job of that is to be debated. but i think we are moving rapidly into territory that people just don't know how to handle. and that every day or every week, there's a new threat coming out from quantum or ai that has to be doubled. oh, we're we were hacking last week. now we got biologicals this week. next week, we got this. and, oh, every agent under the sun is hacking every other agent under the sun and getting out and doing these things. and i think it's freaking a lot of people out, so there's gonna be overreaction.

Speaker 1:  yep. there is. but i think we're too late. i honestly think we're too far down the rabbit hole, but, i could be wrong. maybe there's a way for, maybe there's this, blocks or red switch that they switch on and it kills all of the bottles and no one could use them anymore. i don't know. even the offline models, even air gap models, i don't know. no. i'm there's no way. but, yeah, back to this synthesizing of of the, of virus pages. this just, like, we've covered a lot of different, articles like this, and and this is just another one of those next steps. and i know, dario was saying, like, anthropic is really heavily focusing on stuff like this, bi biology and medical and drug development and all that sort of stuff. so things are just gonna ramp up and get even more crazy and scary.

Speaker 0:  well, they're all doing that. like, midjourney is turning into a medical company with scanning technology and whatnot. we talked about that a while ago where,

Speaker 1:  did we cover it? or

Speaker 0:  it was on our list. i don't know if we actually ended up talking about it or not. we did the research. we added up.

Speaker 1:  we should revisit it. yeah. because that was a good one. yeah.

Speaker 0:  but, basically, they're using air to do mri scanning images to find stuff faster and better. and so they're entering the medical field with medical software. all of them are doing something cool. and next thing you know, they'll be in another industry and another industry disrupting everything, and the government and regulators can't keep up. so the arc institute showed us that generative biology is here, and it works. while the promise of curing antibiotic resistant bacteria is huge, we've effectively opened a door that we don't have a key to lock it. as these models get faster and smarter, the policy will have to move twice as fast to keep up. as john says, we might be too late. i wanna know from you, if ai can design the cure, are you comfortable with it writing the virus to get there? let us know in the chat below. we'd love to hear what your thoughts are.