The Governments SECRET AGI Interview Reveals ALL! (Secret Darpa Interview Discussing AGI)

So Jimmy Apples recently tweeted out something that actually does lead back to some key, key advancements in AGI. Now, this document is actually from November 2023, but there are some key things on there that do show us things are moving at very, very rapid pace. But with that being said, this document definitely highlights how the future is going to be. And it shows us that even some of the very, very brightest minds aren't able to predict just how crazy AGI is. And AI is moving. So without further ado, I'm going to give you guys nine key pieces of information you need to know regarding this document and of course, any other insights that might be located in this thing. So essentially one of the things that you need to know is that this document is actually from darpa. So DARPA is essentially the Defense Advanced Research Projects Agency and has been a driving force in AI since its inception. So in the 1960s they funded groundbreaking research on neural networks, a core inception in modern AI that actually allows machines to train and improve without explicit programming. Now, DARPA also played a crucial role in the development of machine learning, which enables computers to learn from data and make predictions without being explicitly programmed for each task. Additionally, DARPA's support for research in natural language processing has actually helped computers understand and respond to human language, paving the way for advancements in areas like voice assistants and machine translation. And the reason I'm giving you guys these pieces of information here is because I want you guys to understand what DARPA has done before. You just think that this is an irrelevant piece of information and that this video isn't actually relevant whatsoever. There's also another piece of information that you should know about darpa. DARPA has also been at the leap. DARPA has also been at the front of breakthrough innovation. So DARPA has also sponsored projects, they have a long history of world changing innovations. They've played a critical role in the development of arpanet, which is the precursor to the modern Internet, which actually revolutionized the global communication and actually laid the foundation for the digital age. And DARPA funding also helped propel advancements in self driving cars, a technology with the potential to transform the transportation industry and safety. And their ongoing support continues to fuel cutting edge research in areas like prosthetics that can be controlled by the mind and BCI's brain Computer interfaces and next generation materials with unique properties. So DARPA is a very, very established. The third thing you need to know about this is that the date is important. Okay, this was on November 4, which is of course just before Gemini released. And this is going to be very, very important because when you see some of the dates and some of the key things being talked about, you're going to be like, okay, that makes it sense. So now let's go on to point number four. Point number four was, in fact, that DARPA were actually talking about cyber security and how things were going to affect them. So essentially, DARPA said, how will the President's Executive order on safe, secure and trustworthy AI effect? DARPA said, if, you know, this was like a presidential, you know, order that's basically looking to regulate AI, because, of course, the proliferation of these AI technologies has been profound. So essentially he says, you know, I'm not sure yet there are restrictions on what it takes to work for a frontier model. In the new document, they are particularly worried about who can use a frontier model to generate bioweapons, which is of course pretty bad, and in cyber attacks, and of course, demonstrate how they're being appropriately careful with that. I've not yet had a chance to grasp what the implications would be. We intend to able to work with frontier models, and we will get back with you. And essentially, they're pretty much concerned about cybersecurity and of course, bioweapons, which is pretty crazy. And if you didn't take a look at this, like I said before, this is something that you should be knowing about, because essentially these are some of the excerpts from the article. And essentially the Executive Order is essentially just discussing a lot of stuff. One of the points is, of course, limiting biological risk. They talk about how it's one of the most consequential things with biotechnology, and it requires the US Government to undertake assessments specifically focused on the potential misuse of of AI to develop biological weapons. Now, they also actually talk about DNA synthesis and screening regulation. The Executive Order creates a new regulation actually for DNA synthesis screening in federally funded biological research. And this is actually aimed at the use of AI where they're trying to prevent pathogens or toxins, evading screening and detection measures. And of course, there's a new framework establishment. There's of course, nucleic acid synthesis screening. There's also biorisk impact assessments, essentially. In summary, the Executive Order takes significant steps to address the intersection of AI and biosecurity, particularly concerning the potential for AI to be used in the development of bioweapons. And it establishes new regulations, reporting requirements, and risk assessment frameworks to ensure that the AI is pretty much developed in a safe and secure manner. Now, in addition, this is where things in this video start to get rather fascinating. This is where they actually start to talk about an AI slowdown. And I think this is actually rather fascinating. So this is where they mentioned GPT5 and a very, very vast amount of frontier models that at the time there weren't a lot of information on the Internet. So they basically state that, you know, as the capability advances, so too will the performers using them be able to leverage the advanced capability at the same time. Essentially what they're saying here is if we get systems like GPT5 that are advanced with reasoning, other people are going to be able to somehow jailbreak these models and be able to leverage that advanced capability at the same exact time. And of course, that is one model. And they said another piece is that we will be keeping an eye on what is happening. If the capability that we are working on in the program becomes outmatched, we will stop the program and regenerate or do something else. So whatever they're working on, if it just gets completely outclassed, I'm guessing that they're going to stop that program and just work on something else. And what they also said is that not all frontiers are advancing at the same pace. Reinforcement learning is not going as fast as the transformer model. And we know that so many people are building on top of the transformer model. Of course, with the GPT series, it's going absolutely crazy. That is just moving fundamentally faster than a lot of other AI developments. Because AI isn't just ChatGPT, it's actually a lot of different subsections and other things. So it's pretty crazy. And they do say here, which is, I'm going to get into this point as well, because this is why I think it's pretty crazy. They say the pace of frontier models is slowing down a little bit. It says a lot of the results that we are seeing right now include understanding what they are doing and what they are not doing. They haven't released GPT5 and they said, okay, now this is a bit why I highlighted it and this is why it's important. It says that they haven't really even started training GPT5 due to the slowdown in the release of the H1 hundreds due to the production problems at the Taiwan Semiconductor Manufacturing Company. So tsmc, if you don't know, they basically produce the chips. Like they just make all the stuff that Nvidia use. They basically supply Nvidia with the chips and they're basically one of the most, probably the most important company in the world right now. And they did have some issues. I can't remember what exactly the issues were, but I think during the region there were some political issues which led to the slowdown. There was just a lot of issues going on there. And they were saying that that isn't the reason why they didn't release GBT5. Now, I got to be honest, as point number five, you know, one of the things that was pretty crazy, remember how I said the dates are important because the dates for this document were actually November 4th or something like that? November 4th. I think the point is, is that two weeks later, OpenAI just basically announced that they were doing a training run. So the point is, is that if someone at darpa, someone who is, you know, got a lot of, you know, collaborations, this they do partner with Anthropic, Google, OpenAI, Microsoft, if they have all the information. And literally they started training GPT5 two weeks later. That is a pretty crazy statement because they are basically like, we're going to have some room, going to have some breathing room. They haven't even started training GPT5 due to the slowdown of the H1 hundreds. And the H1 hundreds were Nvidia's chips that they needed to use. I think it's crazy that literally two weeks later, because I remember making this video and I was like, GPT5 is now in training. There was a bunch of information showing that, that they started to train this model. So I think that is showing us that it's pretty crazy on how these AI advancements move quicker than what some teams think. And it just goes to show that within two weeks, a statement can be, I wouldn't say completely out of date, but it is one that shows that predictions are really, really hard to make in the AI space. Now, in addition, there was also something very interesting that they discussed. They also discussed the halting problem and Gemini. And they said the Gemini model getting the planning piece is integrated in the LLM is, which we are not so sure. We lack full transparency. Basically they're saying we don't know entirely, but we think Gemini is going to be getting LLM. Now, remember, this was in November 4th. So they're talking about Gemini, which were, like I said before, if we go back to my other slide right here, you guys can see that Gemini was actually released on December 6th of 2023. So that means that this was, like I said before, a month before Gemini, which is why I said things are moving rapidly. They were like, you know, we're not sure if it's getting planning or not. And I think that that is definitely going to be saved for a few future, future model. Now, of course, like I said with Gemini, they also discussed the halting problem. So they basically said that there's still significant problems to be solved. And this is where they actually discuss AGI. This is pretty crazy as well. They actually state that hearing people say we're just a little bit away from full artificial general intelligence is a bit more optimistic than reality. They're basically saying that, look, AGI is a bit far away. And whilst yes, I do believe what they're saying to an extent, I think it's crazy that they were stating some things that we've just seen right now with the pace of AI development shows us that it's really hard to make predictions. And saying that we're a bit far away from AGI, they're basically saying that we're further away from AGI than you think. And they said there's things like the halting problem, the exponential things, and we still need resources. And basically what they're talking about is compute, which is of course very, very true. And the halting problem is a concept from computer science that asks whether it's possible to create a program that can look at any other program and determine whether or not that program will eventually stop running or if it will run forever without ending. Now, essentially, this links to AI, because at its core, AI is about designing algorithms that can process information, learn from data and make decisions and predictions. The halting problem underscores the fundamental limit of what algorithms can achieve. And it reminds AI researchers that not every problem can be solved by computation alone, setting a boundary on the kind of task we can expect to handle. So it's pretty, pretty crazy. And of course, in AI development, especially in areas involving autonomous systems or decision making algorithms, understanding the limits of predictability is actually pretty crucial. And the halting problem illustrates that predicting outcomes of complex algorithms or a form of program can be fundamentally undecidable. And this actually has insane implications for AI safety as it highlights the importance of designing AI systems that can handle uncertainty and operate safely, even when outcomes cannot be predicted with certainty. So it's a lot of information there. But the long story short is that they think the AGI is not that fast. But, you know, I wouldn't say I disagree, I would just say it's very hard to predict considering what people are saying. I think it's going to be here by at least 2028, 2029. And of course, them talking about Gemini, getting planning. It's Clear that that wasn't the case. Now they also, you know, with regards to AGI, as they were, you know, talking about, they said, my question is when you have, you might not have AGI, but you might have a system that helps humans and everyone in the room advance so quickly that before AGI becomes this apex, we're not an apex. This is asymptomatic growth where we are dealing with that constantly. Basically here DARPA tries to avoid directly competing with industry or near term developments in frontier models, such as incorporating new information into LLMs or developing multimodal models instead, because they know that OpenAI and all these other companies are going after that stack, they try to focus on other longer term challenges that the industry might not prioritize, such as multi level security. So here you can see they say what are they going to do in what time and what timeframe? And then what we should we do is something we talk about all the time. Do we have perfect answers? No, but we ask that question all the time. So they know that, you know, these companies are going to be working on these frontier models and LLMs are their main gripe. But I guess, I guess what they're trying to say is that they're going to be working on some other cool stuff. So I mean, it's going to be kind of fascinating. I'm sure they're probably not going to release any, any of the details of what they're releasing on. But they do partner with companies and that is something that they do talk about. You know, I should probably not skip to this, but you know, in here you can see this is point 11. I'm kind of skipping around on the points, but it says, you know, point number 11. Looking over the past year, how does DARPA and the DARPA programs that pop up, how do they stay relevant within the fast paced advancements in AI? How does DARPA maintain relevance when it's that fast paced? And that is a very good question. And they said one area is by program structure. The AI Cyber Challenge is a competition where we partner with LLM companies, anthropic, Google, Microsoft, OpenAI to provide compute access to those in the competition. So they hold these competitions and they essentially see what's capable. So it's pretty important that they do stay up to date. Because, because. And the reason this is all crazy is because if you remember, okay, the fact that these companies are partnered together does bring more credence to a letter that was, you remember the Q Star letter? They talked about how they spoke to darpa during something. And I mean, kind of, kind of, kind of gives me the vibes that maybe the Q Star letter was true. I mean, I don't really know, but the fact that, you know, DARPA are so closely integrated with these companies kind of leads me to believe that maybe there's a slight chance that it could be true. And if you don't remember the letter I'm talking about, it's the Q Star letter that sparked a huge deb and was one of the biggest leaked things that, you know, went all over the Internet and was the Q Star not meme, but like the big thing that everyone was talking about. Now one thing that they also did talk about that was rather fascinating and like I said before, this is why it's very hard to make AI predictions. They said, how seriously does DARPA consider the possibility of software being developed by AI? And here it says DARPA has a position on the topic. My opinion is that it will be a tool that will help people write software faster, particularly boring boilerplate software faster, but it will not automate the process. I don't think that people who write good code will be out of a job anytime in the foreseeable future. Maybe I am overly pessimistic, but that seems inconceivable. I think a lot of the boilerplate software like coming in frameworks or something like that, the code everyone hates to write, I think AI will write that anyway in the near future. So here this is a prediction about the future. And here he basically says augmentation, not replacement, which essentially means that he's predicting, or whoever it was in the interview is predicting that, you know, AI isn't going to replace coders. But, but remember this was before Devin was released and crazily enough there was actually a recent research paper that actually goes to show that they've improved on Devon as well. And there are open source versions of Devon being built. If you don't know what I'm talking about when I say Devin, that is referring to an AI system that essentially can write code on its own. It's an automatic AI agent software agent that has planning and reasoning capabilities built on top of GPT4. And people are wondering when GPT5 gets released, how crazy is Devon going to be? Because it's state of the art right now. It's just the very best. There was a recent research paper, but there was no demos. The point is that with GPT5 we might even see like a 5x jump on Devon, which would be 80% of software engineering tasks which would be absolutely incredible and that would definitely impact the industry. So him saying that, I don't think anyone's going to be out of a job anytime soon. Maybe not, but right now I know software engineering is a pretty, pretty hard industry to get into. So it's pretty crazy out there. When people like this, I wouldn't say they're struggling to make predictions, but certain AI things come up and it's like, oh, was that prediction right or not? It just goes to show that one of the things you just take away from this video is that nothing is impossible by AI. I wouldn't rule out anything because if we say that's impossible and then it comes to be true, you're just going to look like a fool. Okay, I'm not saying that this person does. I'm just saying that anything now I've seen, I would not rule it out at all. Because with the rate that these companies are increasing their capabilities, anything is genuinely possible. Now, in addition, the last point of this is that they essentially discuss fixing open source. They said DARPA is basically particularly interested in using frontier models to automatically find and suggest repairs to open source software at scale, which could be critical in scenarios like rapidly responding to a widespread cyber attack. And essentially, you know, if there's a cyber attack, so your software is going to go down, there's going to be loads of vulnerabilities. They're basically trying to think of how could they get an LLM system to quickly and, you know, just basically efficiently to fix all of the problems in that software. That is going to be something that they kind of want because that's going to be something that they might need in the future. Now, if you enjoyed this video, let me know because I'm super.