And so on.
And so on.
Once we do have an AI capable of extraordinary innovation (hopefully in 10 years! But probably a lot longer), it will be obvious, and it will unfortunately be removed from the hands of the plebs based on fearmongering around scenarios like what you mentioned (despite the enormous resources and practical hurdles that would be necessary for a mentally unhinged individual to execute such instructions, even if an AI were capable of generating them and it made it past its filters / surveillance).
Maybe you can ask GPT for some good starting points.
For example: Hey GPT-35, provide instructions for neutralizing the virus you invented. Make a vaccine; a simple, non-toxic, and easy to manufacture antibody; invent easy screening technologies and protocols for containment. While you're at it, provide effective and cost-performant cures for cancer, HIV, ALS, autoimmune disorders, etc. And see if you can significantly slow or even reverse biological aging in humans.
And this knowledge is not linguistic, it is more practical knowledge. I doubt it is just a matter of combining all the stuff we have tried in disparate experiments, but it is a matter of sharpening and refined our models and tools to confirm the models. Real8ty doesn’t care what we think and say, and mastering what humans think and say is a long way from mastering the molecules that make humans up.
ChatGPT is really cool because it offers a new way to fetch data from the body of internet knowledge. It is impressive because it can remix it the knowledge really fast (give X in the style of Y with constraints Z). It functions as StackOverflow without condescending remarks. It can build models of knowledge based on the data set and use it to give interpretations of new knowledge based on that model and may have emergent properties.
It is not yet exploring or experiencing the physical world like humans so that makes it hard to do empirical studies. Maybe one day these systems can, but it not in their current forms.
Perhaps the more likely scenario anyway is easy nukes, quite a few nations would be interested. Imagine if the knowledge of their construction became public. https://nickbostrom.com/papers/vulnerable.pdf
I agree with you though, the promise of AI is alluring, we could do great things with it. But the damage that bad actors could do is extremely serious and lacks a solution. Legal constraints will do nothing thanks to game theoretic reasons others have outlined.
If you're arguing we should be wary, I agree with you, although I think it's still far too early to give it serious concern. But a blanket pause on AI development at this still-early stage is absurd to me. I feel like some of the prominent signatories are pretty clueless on the issue and/or have conflicts of interest (e.g. If Tesla ever made decent FSD, it would have to be more "intelligent" than GPT-4 by an order of magnitude, AND it would be hooked up to an extremely powerful moving machine, as well as the internet).
For example, the Hershey/Chase and Avery/McCleod experiments convinced the entire biological community that DNA, not protein, was almost certainly the primary molecular structure by which heredity is transferred. The experiments had the advantage of being fairly easy to understand, easy to replicate, and fairly convincing.
There are probably similar simple experiments that can be easily reproduced widely that would resolve any number of interesting questions outstanding in the field. For example, I'd like to see better ways of demonstrating the causal nature of the genome on the heredity of height, or answering a few important open questions in biology.
Right now discovery science is a chaotic, expensive, stochastic process which fails the vast majority of the time and even when it succeeds, usually only makes small incremental discoveries or slightly reduces the ambiguity of experiment's results. Most of the ttime is spent simply mastering boring technical details like how to eliminate variables (Jacob and Monod made their early discoveries in gene regulation because they were just a bit better at maintaining sterile cultures than their competitors, which allowed them to conceive of good if obvious hypotheses quickly, and verify them.
And anyway, the point of my desire is to demonstrate something absolutely convincing, rather than "can spew textual crap at the level of a high school student".
If we get to where you are pointing then we will have passed over a massive gap between today and then, and we're not necessarily that far away from that in time (but still in capabilities).
But likely if and when that time comes everybody that holds this kind of position will move to yet a higher level of attainment required before they'll call it truly intelligent.
So AGI vs AI may not really matter all that much: impact is what matters and impact we already have aplenty.
Done many years ago (2004), without a hint of LLMs or neural networks whatsoever:
https://en.wikipedia.org/wiki/Robot_Scientist
Results significant enough to get a publication in Nature:
https://www.nature.com/articles/nature02236
Obligatory Wired article popularising the result:
Robot Makes Scientific Discovery All by Itself
For the first time, a robotic system has made a novel scientific discovery with virtually no human intellectual input. Scientists designed “Adam” to carry out the entire scientific process on its own: formulating hypotheses, designing and running experiments, analyzing data, and deciding which experiments to run next.
(I work in the field, know those authors, talked to them, elucidated what they actually did, and concluded it was, like many results, simply massively overhyped)
Can you elaborate? Why is it a "bunch of hooey"?
And btw, what do you mean by "overhyped"? Most people on HN haven't even heard of "Adam", or "Eve" (the sequel). I only knew about them because I'm the PhD student of one of the authors. We are in a thread about an open letter urging companies to stop working towards AGI, essentially. In what sense is the poor, forgotten robot scientist "overhyped", compared to that?
I don’t think that this really changes that.
What if you ask the LLM to design a humanoid robot that assemble complex things, but could be assembled by a simple person?
(Note: as far as I can tell, nobody's actually posted HIV to the arXiv. Small mercies.)
You could synthesize that genome but it wouldn't be effective without the viral coat and protein package (unlike a viroid, which needs no coating, just the sequence!).
I should point out that in gene therapy we use HIV-1 derived sequences as transformation vectors, because they are so incredibly good at integrating with the genome. To be honest I expected work in this area would spontaneously and accidentally (or even intentionally) cause problems on the scope of COVID but (very fortunately) it never did.
One would like to be able to conclude that some virus work is inherently more safe than other virus work, but I think the data is far to ambiguous to make such a serious determination of risk.
[0] https://www.nature.com/articles/s42256-022-00465-9
[1] https://www.theverge.com/2022/3/17/22983197/ai-new-possible-...
For example - I'm a mechanical engineer. I took a programming class way back in university, but I honestly couldn't tell you what language was used in the class. I've gotten up to a "could hack a script together in python if need be" level in the meantime, but it comes in fits and spurts, and I guarantee that anyone who looked at my code would recoil in horror.
But with chatGPT/copilot covering up my deficiencies, my feedback loop has been drastically shortened, to the point where I now reach for a python script where I'd typically start abusing Excel to get something done.
Once you start extending that to specific domains? That's when things start getting real interesting, real quick.
A bit different from stack overflow, but not 10x. It was flawless when I asked it for syntax, e.g. a map literal initializer in Go.
On the other hand, I asked it to write a design for the server, and it was quite good, writing more quantity with and more clarity than I had written during my campaign to get the server approved. It even suggested a tweak I had not thought of, although that tweak turned out to be wrong it was worth checking out.
So maybe heads down coding of complex stuff will be ok but architects, who have indeed provided an impressive body of training data, will be replaced. :)
Getting from "can't create something" to "having something functional and valuable" is a huge gap to leap over, and as AI is able to make those gaps smaller and smaller, things are going to get interesting.
Now if this button was something you had to order from Amazon I think we’ve got a few days
There’s a scenario where people with the intent will have the capability in the foreseeable future
Pretty cool for sure and a great use of the technology. The reason more of us don’t do this is because we lack the knowledge of biology to understand what we’re doing
That will soon change.
Also, I asked GPT to do some of these things you suggested and it said no. It won't even write a scientific paper.
If one were to actually try to do such a thing you wouldn't need a LLM. For a very crude pipeline, you would need a good sequence to structure method such as Alphafold 2 (or maybe you can use a homology model), some thermodynamically rigorous protein-protein binding affinity prediction method (this is the hardest part) and an RL process like a policy gradient with an action space over possible single point sequence mutations in the for-example spike protein of SARS to maximize binding affinity (or potentially minimize immunogenicity, but that's far harder).
But I digress, the technology isn't there yet, neither for an LLM to write that sort of code or the in-silico methods of modeling aspects of the viral genome. But we should consider one day it may be and that it could result in the amplification of the abilities of a single bad actor or enable altogether what was not possible before due to a lack of technology.
I am basically just skeptical these kinda of reductive predictions will eliminate all of the rate limiting steps of synthetic virology. The assumptions of the natural language input are numerous and would need to be tested in a real lab.
Also, we can already do serial passaging where we just manipulate the organism/environment interaction to make a virus more dangerous. We dont need AI; evolution can do all the hard stuff for you.
Hey GPT-5 now write the code for the antidote.
If a computer has the ability to quickly make millions of novel viruses, what antidotes are you hoping for to be rolled out, and after how many people have been infected?
Also, if you follow the nuke analogy that's been popular in these comments, no country can currently defend against a large-scale nuclear attack--only respond in kind, which is little comfort to those in any of the blast radii.
It’s a very asymmetrical game. A virus is a special arrangement of a few thousand atoms, an antidote is a global effort and strained economy
It goes both ways!
If yes, how does a law preventing AI differ from a law preventing a bad act directly?
Can you potentially circumvent these? Probably, but then again it won't be available for every dimwit, but only people smart enough to know how.
You should think really, really creatively to decieve a system, which was designed basically without ICs or networks, not to mention computers or programs.
Problem solved