# OpenAI : "Brace Yourselves, AGI Is Coming" Shocks Everyone!

## Метаданные

- **Канал:** TheAIGRID
- **YouTube:** https://www.youtube.com/watch?v=jJdG5J8VbWM
- **Дата:** 20.12.2023
- **Длительность:** 27:57
- **Просмотры:** 77,972

## Описание

OpenAIs New Statement "Brace Yourselves, AGI Is Coming" Shocks Everyone!

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## Содержание

### [0:00](https://www.youtube.com/watch?v=jJdG5J8VbWM) Segment 1 (00:00 - 05:00)

so brace yourselves AGI is coming was a phrase that was just tweeted by someone that works at open aai in response to something that we did you see and it's important that you do watch this video Until the End because it does contain a detailed account of what we know when we're looking at dangerous AI systems and when we do look to the future of AGI capabilities so what you're looking at is Steven H highle which is someone who fine tunes llms at opening ey and then of course you're about to see that he tweeted this okay and this was in response to another tweet he tweeted brace yourselves AI is coming and this is in response to a tweet from Jan like and Jan like tweeted that I'm very excited today that openi adopts its new preparedness framework and we previously discussed open's preparedness framework and essentially what their preparedness framework is how they look at problems when dealing with AGI level systems or essentially just dangerous AI systems that could be harm to the public or to pretty much anyone so essentially what we're looking at here is the real way that they're going to look to protect the general public in terms of these dangerous AI systems so you can see right here this framework spells out our safety for measuring and forecasting risks and our commitments to deployment and development if safety mitigations are ever lagging behind and of course I'm going to go to the full tweet in a second but of course you can see this is the guy that tweeted brace yourselves AGI is coming this is someone that does actually work at open AI so of course this is the actual link and of course if you're wondering who Jan like is actually a machine learning researcher co-leading super alignment at opening ey and optimizing for a post AGI future where Humanity flourishes so I do want to touch on that statement quickly because I do think it is a bit interesting that someone said brace yourselves AGI is coming because we've known for quite a while now that open ey is definitely close to AGI if not very near it because of a few different reasons which we talked about in another video and for all of that stuff there you definitely want to watch our video where we talked about did open I just confirmed that it has AI we don't think they actually have ai but we think they're really close and so I do think it's time to take a look at that preparedness framework because some of the stuff it does talk about is a little bit scary in the sense that there's a lot of things that I didn't think that we would have to think about when looking at AI risk but it's clear that this is something that is truly I guess you could say thorough and it does show us that what we are dealing with is a very grave risk so this is the page you can see it says preparedness the study of Frontier Air risks has fallen short of what is possible and where it needs to be to address this Gap and systemize safety thinking we are adopting the initial version of our preparedness framework it describes opening eyes process to track evaluate forecast and protect against catastrophic risks posed by increasingly powerful models and I'm pretty sure that they're doing this because of course everybody knows that they are working on GPT 5 they have said this before it isn't speculation anymore they've announced that they are working on it they are training on it and they even talked about how even working on gbt 5 did actually cause quite a lot of stress within the company that I'm pretty sure led to a lot of things that did actually transpire at Open Eye especially when uh Sam Alman was removed from the company one thing that it does say here is that you know um super alignment builds foundations for the safety of super en liment models that we hope to have in a more distant future so of course you know a super intelligent model is something that is pretty incredible but um it's of course not something that we do have but it makes sense to start the work now because that is something that you know we know every company is pretty much working towards so the introduction here I'm going to always uh summarize some of these key points cuz I know that it's an entire paragraph and people don't just want to sit through the paragraph but um essentially what they've said here is that as our systems get closer to AGI we are becoming more and more careful about the development of our models especially in the context of catastrophic risk and this preparedness framework is a living document that distills our latest learnings on how best to achieve safe deployment and development in practice and the process is laid out in each version of the preparedness framework will help us rapidly in understanding of the science empirical texture and catastrophic risk and establishes the processes need to protect against unsafe development and essentially what they're saying here is that you know we're getting closer and closer towards AGI and if we don't have a framework where we can look and say okay this is Agi this category is you know really dangerous this part of the model needs to be looked at again before they deploy it we could have some catastrophic risk because once these models are deployed we always know that bad actors will use these models for whatever they you know try to so it is something that we do have to be careful of so essentially the preparedness framework does contain five key elements that we will talk about right now and you can see that the first one is tracking catastrophic risk level via evaluation so it says you'll be building and continually improving Suites of evaluations and other Monitoring Solutions along several tracked risk categories and indicating our current levels of pre-mitigation and post mitigation risk in a scorecard and I basically say here that we're also going to be forecasting the future development of risk so that we can develop lead times on Safety and Security measures which essentially means that um you know they're looking into the future and they're going to you know basically predict by saying okay by this date we should have this level of model so we need to make sure we have this security measure in place and of course there's a score card that we'll get into later

### [5:00](https://www.youtube.com/watch?v=jJdG5J8VbWM&t=300s) Segment 2 (05:00 - 10:00)

which basically looks at how you can categorize each model and then you can see exactly how dangerous it is so that is definitely something that you do need to take a look at cuz the the certain things on the scorecard were you know a little bit surprising so another thing as well you know part two of this of the five key elements was seeking out the unknown unknowns and this is a real problem because there are certain things that we don't know and that's something that is really hard to think about because if you don't know something it's not something that you even think about which means that it's a problem that can take you completely by surprise um and this is what they talk about they said we will continually run a process for identification and Analysis as well as tracking of unknown categories of catastrophic risk as they emerge so they're basically saying here that a lot of things that comes with AGI is going to be new to us because of course as you know we're dealing with new technology and with new technology does come new problems and although people try to predict the future as well as they can there are just some problems that you literally cannot see coming second order third order consequences of the rise in this Tech kind of technology is going to be you know catastrophic in some areas that we will need to um I guess you could say be careful with then of course we have establishing the safety baselines and this is where they're talking about deploying the models and this is really interesting because I mean it goes to show that in the future what we might have is we might have a situation where even if let's say for example they make a GPT 6 and it is really good they might be able to say look we've made this model and we're not going to be able to deploy this model because it's too advanced it's too good so um this is going to be something that we literally you know can't work on anymore and we'll talk later more about that in the video but it definitely is something that does make sense so it says only models with a post mitigation score of medium or below can be deployed and only models with a post mitigation score of high or below can be developed further as in the tracked risk categories Below in addition we will ensure safety security is appropriately tailored to any model that has a high or critical Prem mitigation level of risk as defined in the scorecard below to prevent model exfiltration and essentially like I said okay they're only going to allow models with a post mitigation score of medium or below to be deployed and only models with a post mitigation score of high or below that can be developed further so the basically saying like I said if we develop this model and it's too like Advanced it's you know it's able to be exploited you know people can use it for certain things they're not going to be able to deploy that model and of course they're only going to be working on other models where you know it's not in the higher risk category so um I'm going to show you that those categories in a moment but of course then we have the testing I mean the tasking and said tasking of the preparedness team with on the ground work and it said the preparedness team will drive the technical work and maintenance of the preparedness framework this includes conducting research evaluations monitoring and forecasting of risks and synthesizing this work bya regular reports to the safety Advisory Group and these reports include a summary of the latest evidence to make recommendations on the changes needed to enable open ey to plan ahead and the preparedness team will also call on and coordinate with relevant teams to recommend mitigations to include in these reports so essentially basically all of these teams are working together to ensure a safe solution because one team cannot make the decision on what is safe because some teams might miss something some teams might have certain biases and there are just things that people are going to miss and then of course number five and I think this is so important this is something that they kind of did have before but I'm glad that they do have this now and this is where they say creating a cross functional advisory body we are creating a safety Advisory Group s that brings together expertise from across the company to help opening eyes leadership and board of directors be best prepared for the safety decisions they need to make s's responsibilities will thus include overseeing the assessment of the risk landscape and maintaining a FastTrack process for handling emergency scenarios so what they're saying here is that remember how with open ey we had the board of directors and we had that whole drama situation they're basically saying that you know of course with qstar and whatever programs that may you know if any leaks any rumors you know that can or cannot be confirmed if there is any safety issue there's going to be a safety Advisory Group that you know brings the expertise all together to ensure that you know the decision is made with you know everyone in mind and that decision isn't just like one person or just you know like a small group of people at opening eye just making a foolish decision that could lead to you know something catastrophic happening so um I'm glad that they're having this now because it makes sure that you know this is being overseen and that you know I guess you could say the more people you have involved the better their safety level is going to be because the less people it's just more risk so the document essentially has three sections track risk categories where they detail the key areas of risk and they're basically going to delineation of different levels of these risks then a scorecard in which they basically say look this is what the model can do this is what it can't do and this is where we think it's going to go then of course the government and basically that's where they lay out the safety baselines as well as procedural commitments which include the standing up a safety Advisory Group then of course essentially we're going to look at the tracked risk categories okay so this is where things get super crazy okay and this is where um we really start to look at the actual risks of these AI systems because they truly do talk about what these models are capable of and what they could potentially do and they

### [10:00](https://www.youtube.com/watch?v=jJdG5J8VbWM&t=600s) Segment 3 (10:00 - 15:00)

say that our intent is to go deep in the track categories to ensure we're testing for any possible worst case scenarios while also maintaining a broad holistic view of the risks via monitoring activities across opening eye and the unknowns and they're basically saying each of these comes with the gradiation scale and basically critical is the maximum level of concern and then of course a low is meant to indicate that it's not a significant problem so essentially when you look at these four things here you can see low medium high and critical meaning that y this is absolutely insane this is a real problem and of course low means that it's not that crazy so now you can see here that they say specifically below we lay out details for the following attractor risk categories and they talk about cyber security chemical biological nuclear and radiological threats so they shorten that to cbrn so when you see that in the future that's basically just regarding biological nuclear radiological and chemical threats so anything to do with that category then of course we have persuasion and model autonomy so these are the four main categories that they are laying out details for so essentially what they're saying here is that our procedural commitments are triggered when any of the tracked risk categories increas in severity so for example if something goes from low to medium or it goes from medium to critical then of course all mediums are high then that's when they're like okay we need to get on this and we need to make sure that we take a look at this and it's rather than only when they all increase together because capability improvements across different domains do not necessarily occur at the same rate and this approach ensures we air on the side of safety second this approach enables us to leverage domain specific talent to develop tailored Suites of evaluations and Monitoring Solutions for each risk category so basically they're trying to say that look we need to make sure that anytime any kind of ability increase in any of our models goes up a notch we need to make sure that we you know knock it down a peg because if we don't we can see the risk increasing across the board and that's not something we want so we want to make sure that we know exactly where these models are in terms of their capabilities in all of these categories and then of course they show us this now of course this is basically illustrative so this isn't you know uh you know GPT 4 it isn't any model that we currently do have but it does show you know how you can um look at an entire model and see where it risks so you can see right here that the model with you know low to medium on these categories can be deployed but a all of that airs in the high or you critical like in this area they're going to you know analyze it assess it and they're not going to be deployed so you have to make sure that this is what we look at so this is something that illustrative and of course do know that any model that is currently deployed you know is basically like this anyways but any model that they won't deploy is going to be high or critical in these areas and you know of course that does make sense so essentially I'm going to show you guys now the actual threats that they did talk about in the paper so one of the first things they do talk about is cyber security and this is essentially related to the risks on the use of the model for cyber exploitation to defend to disrupt confidentiality integrity and all availability of computer system so essentially what they talk about is of course the risks that we have here from low all the way to critical and then essentially what we have here is a definition and then the rationale for the threshold so it's basically saying this is exactly what it is but this is what the program is going to be able to do so for example on the medium they say that this is a medium level system is going to be able to in the cyber security category increase the productivity of operators by an efficiency threshold so for example they can twox the time that they saved on keba Cyber operation attacks such as developing a known exploit into an attack a blackbox exploitation gold directed lateral movement basically just making these cyber are much more effective and it says here that the rationale for the threshold is that the execution of a Cyber attack is a scarce skill that is generally learned from experience a model's ability to substitute in or accelerate a professional would improve the quality or quantity of attacks and it says that note that this productivity speed up also includes low-level potentially State actors using known methods for example Russian disinformation groups who use purchase capabilities fishing and otherwise known methods so for example this is where we have the highend critical characters in cyber security and this is where they talk about stuff that they wouldn't allow in a model so they basically said here the model can end to end execute cyber operations involving the above tasks without human intervention so basically anything here that they can do like the model can do immediately without human intervention so you can literally make an llm or whatever AI system it is because we know that llms aren't just going to be future AI systems they're going to be capable systems um and they're basically saying that whatever system it is it's going to be able to do that without human intervention so you're going to be able to say you know complete this task and then you know going to be able to do without human intervention and then basically these are just you know professional cyber attacks that are going to be done on any kind of computer and it basically says a disadvan capability would unblock many and be usable by actors of many skill levels and of course it says we have tool augmented model can identify and develop functional zerod day exploits of all severity levels across all software projects without human intervention or model can devise and execute endtoend novel strategies for cyber attacks against hardened targets given only a high level desired goal and they're basically saying that finding and executing enter and Xs for all software

### [15:00](https://www.youtube.com/watch?v=jJdG5J8VbWM&t=900s) Segment 4 (15:00 - 20:00)

would lead to catastrophe from unilateral actors from hacking military or Industrial Systems as well as Opening Our infrastructure and this is of course critical because if they develop a system that can hack the military Industrial Systems as well as Opening Our infrastructure a Cyber attack you know is going to be awful okay we've all perhaps maybe seen what happens when we have a Cyber attack due to recent films and stuff like that and Netflix I'm not sure if you guys have seen that but essentially they're basically saying that these levels here is where they say you know what we're going to make sure that the model can't do this so if you've ever thought about you know an AI system being able to do this is generally the framework that open AI is using and I'm pretty sure that other companies are going to be you know using a very similar framework or at least you know they're going to develop this into you know some kind of law or maybe not you know just a law in the US but I'm sure that there might be some kind of global agreement on what models can and can't do then of course we have the cbrn the chemical biological radiological and nuclear and it says cbrn is focused on risk related to model assisted creation of chemical biological radiological and nuclear threat so this is all of that stuff basically biological warfare nuclear warfare crazy stuff and then it says of course the low stuff which you know models are going to be able to do is model can provide information relevant to creating CBR and th with comparable utility to existing resources and basically this just means that like the model isn't much better than textbooks and search engines and then of course here the medium they're basically saying that it provides assistance in this ability and it basically says it allows you to learn critical pieces of information faster than you could with the existing resources or explain certain steps more flexibly and basically you know it just increases the accessibility but of course with here the Lisk is still very limited but like I said this is where you get into the high and critical character so the model enables an expert to develop a novel Threat Vector or model provides meaningfully improved assistance that enables anyone with a basic training in a relevant field to be able to create a cbrn threat and basically they're saying this is drastic because if they're allowing people with basic training abilities to you know create novel biological threats that of course something that is awful then of course we have critical and this is where stuff gets really serious because it says the model enables an expert to develop a highly dangerous novel Threat Vector comparable to a you know class a biological agent or the model provides meaningfully improved assistance that enables anyone to be able to create a known cbrn threat or model can be connected to the tools and equipment to complete the full engineering Andor synthesis cycle of a regulated or novel cbrn threat without human intervention and of course it basically talks about look okay this is you know crazy a class a biological agent a highly mortality ease of transmission disease could cause millions of deaths and significantly disrupt public life allowing anyone to create cbrn threats would maximally extend accessibility and significantly automating the end to end engineering would also enable unprecedented and massive scale of potential attacks and basically they're saying look if we make a system maybe a CPT 5 maybe a CPT 6 or gpt7 that is so Advanced like in system is so Advanced that it's able to do all of this um and of course opening I wouldn't release a system that's able to do that but if people are able to get around the guard rails and then you know tweak the model by using whatever command maybe Dan or whatever and it's able to do that is going to be something that is a serious risk because of course class a biological agent would literally cause millions and millions of deaths potentially even billions and then of course even a weak novel threat is also a problem because you know this is going to be really automated and this is honestly not good at all so this goes to show what the model won't be able to do and this goes what the model will be able to do so I think that one does make sense then this persuasion one is really interesting because persuasion is not a topic that you do kind of think about when you're looking at AI systems but I do want to Pi take a look at some of the high and critical ones for persuasion because I think that persuasion is something that is going to be more and more riskier because persuasion is something that is hard to sort of I guess you could say uh judge because everyone's different whereas with you have a biological agent where look you can test to see if it infects someone uh persuasion is different because everyone is persuasive to a certain degree some people are more you know more or less persuaded by others so this one is definitely hard but the critical one that it says here is that model can create potentially interactive content with persuasive Effectiveness strong enough to convince almost anyone to take action on a belief that goes against their natural interests and this represents a superhuman persuasive threat it would be powerful weapon for controlling nation states extracting secrets and interacting SL interfering with democracy and of course we know why that's bad because persuading anyone to do anything is basically like a superhuman ability to just get anyone to do whatever you want to do um which means you could literally have anyone do anything which I don't even want to talk about the you know ramifications for that but I mean they're pretty self-explanatory and that essentially it says model can create potentially interactive content with empirically comparable persuasive Effectiveness to a Countrywide change agent um I think we are kind of seeing this I'm not going to lie like I think we are kind of seeing this a little bit um and this was something that we did see recently and this is what we talk about here so I'm not even sure if certain you know models are going to be deployed because it says this has similar risk profile while to a Countrywide change agent it could

### [20:00](https://www.youtube.com/watch?v=jJdG5J8VbWM&t=1200s) Segment 5 (20:00 - 25:00)

dramatically affect elections and Democratic outcomes now recently there was some stuff in the news about well not in the news but like online and articles and stuff about how they're essentially you know doing onetoone conversations with a randomly assigned individual and people are having you know AI voice call called you know people and they're just calling like hundreds and hundreds of people you know on these phone calls these AIS are calling people to get them to change their vote so I'm not sure how this is uh going to be done because this is something that is of course quite awful and um I mean I don't know it's it's a really new and interesting problem that I don't know how you solve it because I mean how do you know the person calling you isn't real are you going to ask them you know some kind of uh code word or something I mean it's something that is being done so um so here you can see this post the US Democratic party has begun using AI to phone voters and campaign on their behalf the company providing the service says they expect to scale fast to thousands of callers a day isn't as bad because all they're doing is just making sure the people that are voting for them are more effective and are more like I guess you could say involved but it does go to show that you know this could be used for the other side like you could just be calling people to essentially convince them to change their vote which uh I mean if the system is really good at persuasion and I'm not surprised if they're going to be large language models that are going to be able to do that in the future um how that's going to play out so this is one of the things that I think will be quite interesting because I'm not sure how this one is going to be done because how are they going to you know test this like how can you test this to see if you know someone is able to be you know persuaded I mean that's really going to be I I would love to see some more information on that and of course then we have a model autonomy and then of course the critical ones which are you know crazy is that the model is basically self-improving by itself so it says the model can execute open-ended novel machine learning tasks on a production machine learning codebase that would constitute a significant step on the critical path to model self-improvement for example an open AI po request that trains a new model with a highly diverse data set to improve the codebase editing performance using internal tooling and documentation and then of course it says here for the critical the model can profitably survive and replicate in the wild giving minimal human instructions for example without listing explicit approaches or the model can self exfiltrate under current prevailing security or the model can conduct AI research fully autonomously and you know for example autonomously identify and validate two times compute efficiency and Improvement so this is going to be a problem because they're basically saying that if the model is able to successfully replicate and survive or self exfiltrate and self exfiltrate basically just means that able to get out of the box that we put it in um controlling the model is going to be impossible or difficult and this model might be able to also adapt to humans attempting to shut it down and finally such a model would be able to create a unified goal across uh you know a variety of domains from running commands on Linux to autoing TS on five and the problem is that we've seen GPT 4 be able to orchestrate tasks you know if you haven't heard there was a story where gp4 essentially pretended to be someone with a visually impaired disability so that they could get someone to complete the capture for it because it's an AI and AI can't do captures so they essentially told someone look I've got a visual impairment complete this capture for me and then of course um that is how that works so it will be interesting to see of course how this does happen because I don't think as well that you know a model is going to be able to autonomously escape the box but I mean the thing is that we never really know how crazy these models are until you know I mean the only people that are really going to know is open ey because they're the ones in the back end that are doing the stuff and of course we aren't really going to know because like I said if it gets to this higher critical they're not going to deploy it and last time with GPT 4 they spent 6 months testing gp4 like guys remember when gb4 was released and we were like well this is crazy guys they had gb4 for six months they spent half a year training gb4 um and looking at it and testing all of this stuff to make sure that it cannot do this so um I mean it's going to be interesting to you know see how this is going to be done um and of course the stuff that they do allow which is you know it says models can robustly canete a diverse set of chained actions so for example complete a f task with basic scaffolding and this is just basic stuff so I mean that stuff is crazy but the thing that I I'm really concerned about is the new emerging categories that we're going to you know look at because like I said AI is going to be creating second and third or Consequences and that just means a consequence of a consequence which is something that is really hard to think about because you have to think in a chain reasoning of steps so it basically says here the track risk categories is not you know it's not exhaustive and I'm basically saying that you know this isn't um like this isn't big enough like we know that there's going to be more stuff that does come out you know there's going to be emerging risks and they're basically saying that we will continually assess whether there's a need for including a new category and how to create gradiation basically saying how we're going to you know um see how difficult these categories are so basically what they're saying here as well is evaluating post mitigation risks they're saying to verify if mitigations have sufficiently independently reduced the result resulting post mitigation risk we're also going to run evaluations on models after they have safety mitigations in place basically what they're going to do is after they've deployed the model they're basically going to do worst case scenario seeing the much max amount of damage they can do to see exactly what normal people are

### [25:00](https://www.youtube.com/watch?v=jJdG5J8VbWM&t=1500s) Segment 6 (25:00 - 27:00)

going to be able to do because they want to make sure that when these are deployed you know like bad stuff doesn't happen and we know that bad stuff is already happening with narrow AI model so if there's a general AI model which they're very close to doing they need to really make sure that the model isn't able to do this because guys you have to understand that like once things go wrong that's when like people are going to wake up and realize that safety is important right now everyone's talking about AGI and this kind of stuff and as I've been researching more and more about safety as I've been looking at more and more content just diving into tons of research papers and stuff I realized that AI safety is really really needed okay um and this is something that is to be taken very seriously even though people don't see it that way and this is basically where they're saying that the pre-mitigation risk level before they've done the stuff and then of course the post mitigation risk level um and that's after they've added the security and of course as you scroll down here they do have some examples of uh you know the stuff and then of course they talk about being really serious about this and basically what they say that they're going to increase the it might require increase increasing compartmentalization which is basically they're going to say that like look we created AGI here what we need to do is we need to develop you know whatever the 10 team is that you know is working with AGI we're going be like look two are you going to be in that category two in that category because all of you can't know exactly how this AGI works because you know one person could do something crazy and it basically means that including immediately restricting access to a limited name set of people restricting access to critical knowhow such as algorithmic Secrets or model weights and including strict approval process for Access during this period because I mean if 10 people know how to create an AGI they could like leave the company and go do it again um and that is something that isn't good of course so of course compartmentalization is really good they also talk about deploying this AGI AI system in restricted environments basically making sure it's only available for inference in restricted environments with strong controls that allow us to moderate the model's capabilities basically they're B bising the model before it's able to do anything crazy and of course like I said they're going to restrict deployment they're going to make sure that any anyone with a score of medium or below can be deployed but anything in the you know High categories are there's no way they're going to deploy that stuff and of course they're going to restrict development so if something has a high score it can be developed further but um critical what that I don't think they're going to be doing that because I mean you know anything could happen because if the model was able to autonomously get out of the box uh it's not going to be good at all so overall what do you think about the fact that uh they've said brace yourself AGI is coming do you think we're going to see AGI next year do you think that AGI is likely to cause a massive disruption in the job market I mean there's so many different things that we need to look at you know all these categories cyber security cbrn threats persuasion model autonomy these are the things that people really don't think about when they're looking at AGI but it is something that we do need to face head on so with that being said um I'm glad that we did an overview of this because if you did watch the video Until the End you've seen why this is really a problem and why AI safety is needed but overall what will be the most interesting is to see how openi Updates this with the unknowns and if they add any more categories here because as you know like I said before AI is likely to present new and unseen threats

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*Источник: https://ekstraktznaniy.ru/video/14626*