Anthropic's Chilling 18-Month Warning: AI Apocalypse in 18 Months

so a crazy article has come out from anthropic that I completely missed basically anthropic is daating that we have 18 months just 18 before something catastrophic happens within AI now currently the date for this article not the date of recording this video was the 31st of October 2024 which means 18 months from today April the 30th 2026 now that is actually a lifetime away when you think about it in terms of AI terms because that is a few months away and essentially a few months in AI we all know how crazy a few months can be so you can see here it basically says that look increasingly powerful AI systems have the potential to accelerate scientific progress unlock new Medical Treatments and of course grow the economy but along with the remarkable new capabilities of these AI come significant risks and governments should urgently take action on AI policy in the next 18 months the window for proactive risk prevention is closing fast basically what they're trying to say here is that the way how air models have been rapidly advancing we might be losing time to get that window down where we can actively Implement guard rails to actually prevent AI getting out of control and if you want to know how crazy AI development just has gone in the past couple of days take a look at this where it actually looks at how we've gone from gpt1 all the way from GPT 4 now to this Innovation area where we're going to be having scaling breakthroughs and even potentially super intelligent gpt1 which was nothing special to uh to 01 which is sort of PhD level in math and computer science and then 01 I think kicks off this new phase of you know we sort of think about as an innovation era which is basically now until super intelligence we'll see if that's 6 years or maybe even a bit less than that um the Hallmark I think here is that you know we're spending $200 billion on the models we probably can't spend a lot more than that you know probably can't spend you know 200 trillion on the models so there's only limited amounts of scaling left in terms of orders of magnitude and so we need corresponding uh Innovations to sort of come alongside uh definitely Advanced reasoning and test time compute uh is one of those uh and we think there's probably a few more handful of others that will get us you know to now the article continues to state that narrowly targeted regulation can allow us to get the best of both worlds realizing the benefits of AI while mitigating the risks and of course dragging our feet might lead to the worst of Both Worlds poorly designed knee-jerk regulation that hampers progress while also failing to be effective at preventing risks basically what they're stating here is that and I do believe this I'm not just reading this off the script I actually genuinely believe this is that if we do this really slowly what's going to happen is that there's going to be knee-jerk regulation which basically just means that something will occur first like the tragedy will occur first and then the regulation will be built around such tragedy so that it cannot occur again and I don't think we want to do regulation like that we would rather prevent the issue from ever happening then for us to wait for a catastrophe and then of course for us to then construct the regulation around that now they also talk about how what's going on inside AI companies is pretty crazy so it says in the last year and this is where they talk about urgency AI systems have grown dramatically better at math graduate level reasoning and computer coding along with many other capabilities and inside AI companies we see continued progress on as yet undisclosed systems and results now essentially what they mean by this is that this means that they are looking at systems that have not been publicly announced or revealed and this could include proprietary AI models or Technologies you know like Advanced AI systems that are in development but kept confidential for competitive ethical or security reasons or they could be talking about breakthrough results which is where they have significant progress or discoveries in AI capabilities that these companies are not ready to share with the public and of course possibly because they are being tested or because was revealing them could pose certain risks so this is where they're stating that these advancers offer many positive applications but progress in these same broad capabilities also brings with it the potential for destructive applications either from the misuse of AI in domains such as cyber security or biology or from The Accidental or autonomous behavior of the system itself now they're basically stating here that like these advances that we're going to see with these future models are offering all of these insane applications but the thing is is that because we're currently building these General models that are able to do quite a lot of different things than they generalized to other categories the problem is is that this progress also goes to those areas for misuse so basically what they're stating here is that you know the broad capabilities also brings with it the potential for destructive applications either from the misuse of AI in domains such as cyber security or biology or from The Accidental or autonomous behavior of the AI system itself so that last line there is a little bit more chilling considering the fact that you know 2025 2026 is going to be the year apparently for advanced AI agents so the craziest thing about this all okay is that they state that in the realm of cyber capabilities models have rapidly Advanced on a broad range of coding tasks and cyber offense evaluations and take a look at this guys because this when I read this I was like whoa this is actually pretty crazy when we actually think about it so it says on the software engineering bench software engineering task models have improved from being able to solve 1. 9 6% of a test set of reward coding problems which was clawed to in October 2023 all the way up to now it can do 40 9% just in basically a year I think because it says claw 2 was October 2023 and then claw 3. 5 Sonic in October 2024 is 49% guys so can we imagine what October 2025 looks like inwe that's probably going to be close to 90% guys and that is going to be pretty in it's like incredible I can't I can't even speak um and it also says internally our Frontier team has found that current models can already assist on a broad range of cyber offense related tasks and we expect that the next generation of models which will be able to plan over long multi-step tasks will be even more effective so that's of course something that we know when you give these models the ability to think for such a long time they're able to come up with a lot more coherent responses and are able to find solutions to problems that you know they otherwise weren't able to so I think I'm not going to lie guys this is going to be something that's really really crazy because if we're seeing that rate of progress on the software engineering progress Benchmark in just one year we can really start to think about okay what happens the year after that the year after that I mean it's really going to get to that point where things get incredible I mean this is why they're literally stating that look within 18 months we're going to have really Advanced AI systems and if we're not able to actually fix that right now and Implement these C rails then things are going to be able to go off the rails so overall what we're looking at is a situation that basically tells us that these systems are going to be even better than these current ones and it's probably going to happen a lot faster now they basically talk about here that they were also looking at the potential for cbrn which is chemical biological radiological and nuclear misuses and the UK AI safety Institute actually tested a range of models from industry actors including anthropic and they concluded that these models can be used to obtain expert level knowledge about biology and chemistry and for several models replies to science questions were on par with those given by PhD level experts so for those of you who are unaware of what I'm saying guys they've got a situation on their hands where these models the problem is is that pretty much every model has some kind of jailbreak I'm not sure how you're going to get these models to not say certain things literally every time a model is released I see someone on Twitter called plyy and he goes model jailbroken model pwned and basically there doesn't seem to be a real proper solution to the issue that is jailbreaking and they're basically saying that look models can be used to obtain expert level knowledge about biology and chemistry and of course the knowledge that they're giving them isn't like gbt 3.

5 level knowledge it isn't just complete jgun it's actually the kind of knowledge that is really good and is given by PhD level expert so when we actually look at where we are now the models that they've been testing we have to think about this in the next 3 to 5 years because these models are going to get so smart that if you have that model available to the average person we know that this is going to be some kind of issue you can see here it says about a year ago we warned that Frontier models might post real risks in the Cyber and cbrn domains within 2 to 3 years based on the progress described above we believe we are now substantially closer to such risks surgical careful regulation will soon be needed so basically what they're stting here is that look when you've got all of this progress the swe bench going so crazy we need to think about the fact that this is going to be some kind of exponential Improvement and we need to be prepared for this kind of risk of course you can see how AI systems have progressed dramatically in their understanding of the Sciences in the last year the widely used Benchmark the GP QA saw scores on its hardest section grow from 38. 8% when it was was released in November 2023 to 59. 4% in June 2024 and to 77.

3% in September which is opening I 01 and the crazy thing about all of this is that human experts score 81.