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

## Метаданные

- **Канал:** TheAIGRID
- **YouTube:** https://www.youtube.com/watch?v=REjFL9hkkL4
- **Дата:** 26.11.2024
- **Длительность:** 16:23
- **Просмотры:** 99,519

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https://www.anthropic.com/news/the-case-for-targeted-regulation

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## Содержание

### [0:00](https://www.youtube.com/watch?v=REjFL9hkkL4) Segment 1 (00:00 - 05:00)

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 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 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 Accidental or autonomous behavior of the AI system itself so that last line there

### [5:00](https://www.youtube.com/watch?v=REjFL9hkkL4&t=300s) Segment 2 (05:00 - 10:00)

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 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 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 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 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 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. 2% so when you actually take a look at how the gpq a benchmark which is you know a benchmark for understanding really hard questions we can see that human experts scoring at 80% is pretty much going to be on par with what these systems have in the next iteration I'll probably even surpass them and it's says for now the uplift of having access to a Frontier Model relative to existing software and internet tools is still relatively small however it is growing rapidly as AI models advancing capabilities the potential for misuse is to continue on a similar scaling Trend right here the basic statement look compared to what current models are available we actually have a situation on our hands where we can look and see that if this continues with this current Trend we're going to see a situation where these models are going to do a lot more than the available internet so here's where they State a year of anthropics resp responsible scaling policy grappling

### [10:00](https://www.youtube.com/watch?v=REjFL9hkkL4&t=600s) Segment 3 (10:00 - 15:00)

with the catastrophic risks of AI systems is Rife with uncertainty we see the initial glimmers of risks that could become real serious in the near future but we don't know exactly when the real dangers will arrive we want to make the critical preparations well in advance and I think is kind of scary he's getting like look we see the initial glimmers of these risks that could become serious in the near future but the problem is we don't know when these dangers are going to arrive now basically this is where they discussed their responsible scaling policy which is how they manage to increase the capabilities of these models whilst keeping them safe so they state that the first principle of the responsible scaling policy is added is proportionate and the strengths of the Safety and Security measures increase in proportion with the defined capability thresholds that the AI systems meet basically staing that look they're not going to use any insane laws to you know lock up this AI but they're just going to use something that is proportional to the kind of capability that is measured so for example let's say you have an AI agent that is able to do a bunch of crazy tasks maybe you can find the agent to be able to only do tasks in a sandbox or a work environment so it's just something that restricts it in the areas where it needs to be restrictive they also talk about the fact that the RSP should be iterative basically saying that with every update they need to make sure that they update this regularly because of course AI is dynamic and of course to basically say that look we're going to regularly measure the capabilities of their models and rethink the security and safety approaches in light of how things have developed here's where anthropic actually talk about how they're going to internally document their findings and recommendations regarding the safeguards they've implemented so it will be interesting to see what kind of things they talk about because sometimes what anthropic will do is they will basically conduct these safety experiments and they'll be like okay we conducted a safety experiment and we found that Claude ran off and did this he that so we're basically recommending that if you have a model that is this capability make sure it doesn't do this and I think it was like the other day that literally Claude uh went off to Google something that was pretty random and I saw someone else have the same issue where the model just went off to do something on its own which is of course I guess an emergent capability considering that we haven't really given these models the ability to control computers before but now that this is a thing here I think this is showing us where we kind of need to ensure that we have certain safeguards now I do wonder if this is going to become a real thing they're basically stating here that they you know currently we don't have any way to verify any AI companies adherence to responsible scaling policies or whatever plans that they do have because we don't know the outcomes of the training run basically they say that look if any AIS are internally going rogue we don't have any kind of knowledge of this because these companies are extremely private and if they are doing it we want them to be able to publish it so that way we can actually see what is going on in these companies at least on the risk side and the ways that they're you know riging in these models in terms of these you know risk evaluations and they're basically stting a look transparency alone doesn't guarantee robust policies because the problem is that yes these companies might be open about what's happening but they might just lie they might just be like okay we've got a responsible policy where we check one output from the model and it could be a extremely weak policy that isn't you know thoroughly vetted and of course they could release these models into a while that could have a you know potential catastrophe if these models do you know exist in the wild you know crazy things can happen if they're uncensored unfiltered um and people can literally do what they want with them of course if these companies have these relaxed security measures transparency isn't going to solve that but they also you know do talk about you know accelerating the a industry they talk about of course they must not impose burdens that are unnecessary or unrelated to the issues at hands and it says one of the worst things that could happen to cause the catastrophic risk prevention is a link between forming regulation that's needed to prevent risks and burdensome or illogical rules any bill or law should also be so simple to understand and Implement complexity creates confusion and makes it harder to predict what will happen in practice they're basic saying look they don't want any crazy insane laws that's like going to you know cause people to do like insane loopholes and stuff like that it should just be pure simple and cut so that everyone can understand it and of course not be unnecessary now this is where they get into the you know FAQs and I think this is really good because I know a lot of people are going to have questions Maybe youve even commented something down below but it says here that this post talks a lot about cbrn misuses and cyber risk and why not other near term risks like deep fix and child safety and this is a great question but they say this post is not an attempt to address every possible safety problem posed by generative AI systems instead it aims to lay out principles for grappling with some types of risks which aren't well addressed by regulation today and which show up in computationally intensive Frontier models we continue to address neartime risks through things like our election integrity and work with other organizations butas say like guys of course those things are still issues but right now they're focusing on gen because this is the one that is steam rolling ahead without any kind of regulations at all now of course they're basically stating and I know most of you guys were going to ask this question won't regulations slow down Innovation and reduce our ability to compete with geopolitical adversaries basically saying that look do we want to slow down ourselves and let these other countries race past us in terms of technological advancement or do we want to innovate and stay ahead but it says here that even within the RSP framework we have advocated for high flexibility in

### [15:00](https://www.youtube.com/watch?v=REjFL9hkkL4&t=900s) Segment 4 (15:00 - 16:00)

designing these tests because the science of AI risk evaluation is nent and we don't want to create unnecessary burdens through inflexible rules but it is unrealistic okay that any regulation would impose literally zero burden and our goal should not be to achieve a large reduction in catastrophic risk and they state that our goal should be able to achieve a large reduction in catastrophic risk for a small imaginable cost in compliance burden basically saying that you know um overall they don't want to slow down the ecosystem ideally they want to speed it up and they just want to develop something that is lightweight that is flexible that's able to Simply prevent catastrophic risk that is the only thing that they are focusing on here not unnecessary regulation just focusing on preventing catastrophic risk and remember this is coming through anthropic a company that does need the regulation not to hamper Innovation and of course most of you guys might be commenting about you know open source ecosystem and it says our view is that regulation of Frontier models should focus on empirically measured risks not on whether a system is open source or closed basically saying that look open source versus closed Source either way it doesn't matter the only thing that should matter is if you test the model and if it comes up as something risky you should of course Implement some safeguards for said model so with that being said let me know you guys think about anthropic basically taking that we have 18 months left to a potential disaster if we don't manage to fix some things it will be interesting to see how the landscape plays out but if you enjoy the video I'll see you in the next one

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*Источник: https://ekstraktznaniy.ru/video/13679*