GAME OVER! New AGI AGENT Breakthrough Changes Everything! (Q-STAR)

it has been a crazy February and it's about to get crazier because a privately owned company has just achieved a crazy technical breakthrough that is very similar to the qstar model and that's what multiple sources are claiming I'm going to be giving you guys the scoop on this because this is absolutely insane and I was actually shocked at how quickly AI technology is evolving so here you can see that it says that just as important magic also private claim to have made a technical breakthrough that could enable active reasoning capabilities similar to the qstar model developed by open AI last year according to a person familiar with its technology and that ladies and gentlemen is an absolutely astounding statement because we do know that qstar was a model SL system that we didn't really know much about but there were so many leaks and so many theories so many capabilities and at the that time in which qar was leaked there were so many things going on at open AI that it led us to believe that qar was really true now in later on in the video I'm going to dive into a bit more about qar because there is actually a document that kind of holds a lot of knowledge on the qar stuff and why it's such a big deal with whatever this company this privately owned company magic has done and some of the details I'm going to show you guys are going to really shock you because um this goes to show how quickly we are moving in this space so essentially what actually happened okay cuz I'm going to actually come back to this active reasoning because there's a lot to dissect here so essentially it says that former GitHub CEO Nat fredman and his investment partner Daniel gross raised eyebrows last week by writing a $100 million check to Magic the developer of an artificial intelligence coding assistant there are loads of coding assistants already and the top dog among them is Microsoft's GitHub co-pilot so what did fredman and gross scene Magic remember these guys wrote an $100 million check to this company out of nowhere because they saw something that they were like okay if we invest 100 million we are certainly going to make our money back and they basically think that this is probably the best thing ever and I'm going to get into more of that okay I'm just going to skim this bit quickly because I want to show you guys how all of this ties in so essentially they state that the answer goes beyond the company's claim that it will soon be able to furnish its customers with fully automated coding co-workers not just a semi-automated assistant that finishes fragments of code writing as GitHub co-pilot does so if you don't know that's exactly what GitHub co-pilot does it kind of just finishes the fragments of code and it's not like a fully automated like coworker so essentially I'm guessing that what they're moving towards is more of an agent framework and that their breakthrough that they've done is clearly a really insane one and essentially they say that the startup has created a new type of large language model that can process huge amounts of data known as a context window now what I want to talk to you guys about is about like this insane week I think this month is probably going to be the biggest month of AI probably this year and I know that AI is on an exponential but I think that you know with the election coming up later on in the year I think that stuff might drown out the AI stuff but um this is crazy okay like this is probably the biggest thing okay so essentially and I know some of you guys didn't see this because this was overshadowed so essentially it talks about magic claims to be able to process 3. 5 million words worth of text input five as much information as Google's latest Gemini LM which in turn was a big Advance on open ai's GPT 4 in other words Magic model essentially has an unlimited context window perhaps bringing it closest to the way humans process information now why is this crazy well of course there are just several higher reaching implications that are just absolutely insane but the first thing that I think is absolutely insane and as you watch this video you're going to understand how crazy this break through is it's because if you haven't been paying attention basically Google's latest Gemini llm was huge okay and many people did miss it and I'm about to dive into it and essentially they're basically stating that it you know it's five times okay five times as much as Gemini okay and I'm about to show you guys how crazy Gemini is and when you see that you're going to be like what on Earth are you talking about but I think the craziest thing here is of course the potentially unlimited context window I think if that is true and you know potentially because when they've spoken about the kind of breakthrough essentially that could be absolutely gamechanging now one thing that I do want to also additionally talk about okay um because this is something that you guys need to know is that they're essentially saying that they were able to process as five times as much information as Google's latest Gemini llm and if you haven't seen this because it got overshadowed by open eyes Sora product which is of course something that was absolutely incredible you guys are only going to go ahead and see this so essentially Google's Gemini 1. 5 Pro was released a couple days ago

and I know nobody paid attention to this unless you were super deep in the AI technology space because it was something that most people just didn't see it probably would have been the main headline in the AI space if it wasn't for open AI Sora text video technology and that is of course because it is great and amazing but Sora stole the show so what you're seeing on screen right now is essentially Google's Gemini 1. 5 Pro and the main thing about this model if you didn't really know about it is that it's able to process huge context length I'm talking 1 hours of video 11 hours of audio 30,000 lines of codes and 700,000 words that is I don't even know how many novels that is but it's a lot you can see it compared to Gemini gb4 Turbo and you can also see compared to Claude 2. 1 this thing is absolutely insane this is a killer okay of everything else that we knew and we knew longer context windows were coming because we saw multiple different research papers that were just contining increasing the context window now the thing is you might be wondering okay they've got longer context Windows they can analyze an hour video they can analyze 11 hours of audio they can analyze 30,000 hours of code 700,000 words that's good and all but is it even accurate because we know that some of the companies that have done that before they've done it but it wasn't that accurate in whatever they were doing so one thing that they did in order to test this and why I'm showing you guys about Google is because if Magic's claiming that they they've done something better than Google have done that is incredible so you need to First understand how Google's Gemini Pro works and then when you understand if Magic's beaten it why the implications of that are so crazy so essentially what Google did was you know how I just basically said that you know Google had a super long context window you can put like 11 hours of or 22 hours of audio 3 hours of video um and 7 million words or 10 million tokens essentially what um you know they did was in order to test this because people were like hm I wonder if this is accurate or not basically they hid a secret phrase in the video and for the video it was just one frame out of all of them for like I think it was around 2 hours or so that's what they did they hid one frame they asked AI to find it and it did um in audio they hid like uh one sentence or like three words and it found it was like what is the secret word it managed to find it and then in text it was able to do that as well and I think there were just some very small errors on text but overall you can see that on successful retrieval versus you know the unsuccessful aeval how crazy it is um it's very accurate I think it was like 99. 9% something like that pretty much perfect um so essentially we are moving towards an era where these super long context windows are going to be absolutely insane and that implication okay if Magic's done something where it's essentially you know beaten Google with like an unlimited context window I don't even know if that's even possible um I think the crazy implications of this is that you know of course we know that remember okay open eye are going to be forced to pull something crazy out the back now what I want to show you guys as well is of course two clips from Geminis because if you think that this is a it's a got long context window but who even cares because you know it's not smarter than gp4 claw 2. 1 just to let you guys know on the benchmarks Gemini 1. 5 Pro is actually surpassing uh GPT 4 Turbo on all the benchmarks by the way just to put that out there and of course if it can analyze an hour of video 11 hours of audio and all of this other stuff what you can actually do is different tasks and that's where I'm going to show you guys these two demos and then I'm going to get back to uh exactly how that works this is a demo of long context understanding an experimental feature in our newest model Gemini 1. 5 Pro we'll walk through some example prompts using the 3js example code which comes out to over 800,000 tokens we extracted the code for all of the 3js examples and put it together into this text file which we brought into Google AI Studio over here we asked the model to find three examples for learning about character animation the model looked across hundreds of examples and picked out these three one about blending skeletal animations one about poses and one about morph targets for facial animations all good choices based on our prompt in this test the model took around 60 seconds to respond to each of these prompts but keep in mind that latency times might be higher or lower as this is an experimental feature we're optimizing next we asked what controls the animations on the littlest Tokyo demo as you can see here the model was able to find that demo and it explained that the animations are embedded within the gltf model next we wanted to see if it could customize this code for us so we asked show me some code to add a slider to control the speed of the animation use that kind of gooey the other demos have this is what it looked like before on the original 3js site and here's the modified version it's the same scene but it added this little slider to speed up slow down or even stop the animation on the fly it used this gooey Library the other demos have set a parameter called animation speed and wired it up to the

mixer in the scene like all generative models responses aren't always perfect there's actually not an knit function in this demo like there is in most of the others however the code it gave us did exactly what we wanted next we tried a multimodal input by giving it a screenshot of one of the demos we didn't tell it anything about this screenshot and just asked where we could find the code for this demo seen over here as you can see the model was able to look through the hundreds of demos and find the one that matched the image next we asked the model to make a change to the scene asking how can I modify the code to make the terrain flatter the model was able to zero in on one particular function called generate height and showed us the exact line to tweak below the code it clearly explained how the change works over here in the updated version you can see that the terrain is indeed flatter just like we asked we tried one more code modification task using this 3D text demo over here we asked I'm looking at the text geometry demo and I want to make a few tweaks how can I change the text to say goldfish and make the mesh materials look really shiny and metallic you can see the model identified the correct demo and showed the precise lines in it that need to be tweaked further down it explained these material properties metalness and roughness and how to change them to get a shiny effect you can see that it definitely pulled off the task and the text looks a lot shinier now these are just a couple examples of what's possible with the context window of up to 1 million multimodal tokens in Gemini 1. 5 Pro so now that you've seen that bit right there okay and you understand how crazy that is okay you can understand that this kind of breakthrough whatever they did that enabled them to potentially beat Google with an essentially an unlimited context window or 3. 5 million words or five times as much as Google's latest Gemini I'm not sure if this was as latest Gemini as 1. 5 Pro but even if they did surpass it these guys invested like 100 million and that is no small amount of okay that is a really huge amount this is definitely something crazy okay we have to take a look at even if it's not the context window we have to take a look at this okay because this is the crazy part okay like I said okay they claimed okay that enable active reasoning capabilities similar to the qstar model developed by open AI according to a person familiar with technology and that could help solve one of the major gripes with large language models which is that they mimic what they've seen and their training data rather than using logic to solve new problems as for how magic develops it's llm this person said it took some elements of Transformers a type of AI that powers consumer products like chat GPT and coding assistant like co-pilot and fuse them with other kinds of deep learning models and that is something that I will be exploring later because different architectures are something that people haven't realized are a real thing okay um the Transformer architecture has just dominated the space um since they were invented and of course it is something that is now being challenged by a few different ones so essentially what we also have here is the using logic to solve new problems now why does active reasoning change the game well essentially with logical problem solving active reasoning involves the AI system engaging in a form of logical reasoning or deduction to solve problems this means that the system can in theory apply principles of logic to come up with solutions to problems it hasn't explicitly been trained on by understanding the underlying relationships and rules and this actually does go beyond pattern matchmaking so instead of relying solely on statistical patterns in the data it was trained on a system capable of active reasoning would be able to infer new information or make predictions based on logical deductions this capability would allow it to essentially think more like a human in terms of applying general principles to specific and unseen scenarios now what's also cool about this is that active reasoning also implies the ability to dynamically update and adapt to new problems and situations by applying learned Concepts in novel ways not just recalling or recombining information from the training data so that right there guys that Dynamic adaptation okay actively reasoning and adapting to new problems and situations is a you know a kind of intelligence that only humans have possessed okay and the difference is that the current llm capabilities are that they're essentially pattern recognition and you know generation generators okay so LMS primarily operate by recognizing patterns in vast amounts of data and Text data and generating responses based on statistical likelihood so they excel at producing text that is coherent and contextually appropriate based on the examples they have seen during training and essentially they mimic humanlike responses llms can you know generate responses that mimic humanlike text across various domains and style however their understanding is a little bit limited to correlating with in put with similar context they've encountered in

their training data without true compens and of course they do have limited deductive reasoning so essentially while llms can sometimes appear to be reasoning their process is more about matching patterns than actual logical deduction and they can struggle with certain tasks that require genuine understanding causality and complex logical inference especially if those tasks are not well represented in their training data so the dynamic adaptation the Beyond P matchmaking being able to infer new information or make predictions based on logical deductions and actively you know apply principles of logic to come up with you know new Solutions or solutions to problems it hasn't been explicitly trained on by understanding underlying relationships and rules is definitely a true GameChanger and this is something that we've seen over the internet from the release of Gemini 1. 5 Pro because essentially what people are now able to do is they're able to now solve a lot of long form problems if you have you know 30,000 lines of code versus just you know I guess you could say like you know 500 lines of code or even like 10 or like a 100 lines of code you're able to solve vastly different problems you're able to essentially you know get a lot more um from you know when an a system kind of understands all of that text and is able to you know digest all of that it just completely changes the game and it leads us more towards a human in terms of how a human thinks due to that active reasoning combined with that as well so that is why this is going to be a complete Game Changer now something that I did want to dive into that I did find kind of fascinating was the fact that magic actually talks about proprietary architecture okay so here's this kind of like small presentation thing from Magic and this is about to get pretty crazy it says magic is working on Frontier scale code models to build a coworker not just a co-pilot and it says things we believe codenation is both a product and a path to AGI and says AGI safety matters and is solvable and it says to build a great AI product we need to train our own Frontier scale model which is essentially what they're doing and the last point and the top Point are going to be you know essentially points why I'm going to be talking about this and the last point is something that I do want to touch on is that they state that Transformers aren't the final architecture we have something with a multi-million context token context window so that is something that is pretty crazy now here's the thing okay this tweet okay I read it at first and I kind of like saw it and was like ah doesn't really mean anything and then I kind of read it again and then I was like oh okay this is actually big on I think so Nat fredman the guy who invested $100 million he stated that magic dev has trained a groundbreaking model with many trillions of tokens of context that performed far better in our evals than anything we've tried before okay I want you guys to see what he said there okay this thing performed far better in our evaluations than anything we've tried before okay and although this was he did tweet this before the Google's Gemini uh Pro release um him saying that it's far better in our evals than anything we've tried before is something that is quite shocking because he's not saying you know it's slightly better he's saying that it is far better okay he's saying they're using it okay to build an advanced AI programmer that can reason over your entire code base and the transis of closure of your dependency tree and if this sounds like magic well you get it okay um so essentially he's stating that he was so impressed that we are investing $100 million in the company today so I think that is pretty insane that they saw something in that pitch deck okay these guys were you know uh working on their product whatever and they saw something that they tried and they were like this is so crazy we are putting $100 million into with this you have to understand that these guys are going up against Microsoft's GitHub copal okay and essentially you know something that is backed by open Ai and this guy put a100 million into it so he's basically betting that what they have is really good and like I said he said that you know the best way to like really understand if someone is really on the ball I guess or I guess you could say someone is backing their position is money okay and money does talk and these guys are putting a $100 million they are putting the money where their mouth is and they're saying look we think this thing is so good we're going to put $100 million of our own money in there um not just 10 not just 20 not just 30 $100 million is quite a lot um and that's a pretty thing okay they're saying better eval than anything we tried before that is pretty crazy so um the next point is of course here and I wondered okay I was wondering for some time okay and of course this is just pure speculation and of course I don't know but I'm guessing that potentially they maybe could have used this architecture okay so if you don't know what this is okay this is Mamba now essentially around two months ago there was this paper Okay Mamba linear time sequence modeling with Selective State spaces and essentially this was touted to be a replacement for Transformers

with an exceptional performance on Long context windows so it's not a direct replacement for Transformers but it is an alternative architecture that addresses some of the inefficiencies found in the Transformer models that power chat GPT and all of the other llms that we know okay so Mamba uses stat based models which are ssms to achieve linear time complexity for input computation which can particularly be beneficial for processing long SE quences efficiently now it's been shown to actually outperform Transformers in inference speed and efficiency especially on larger Contex sizes and it's important to know that while mber has demonstrated impressive performance on language modeling and tasks involving audio and DNA sequences it's not Superior in all aspects that's why I said I'm not really sure for example there was actually a study from the camper Institute at Harvard University that actually showed that Transformers are better at member at tasks that involve copying and retrieval from the input context such as F shot learning and retrieval tasks that are common in Foundation models however member models are particularly better at Transformers that involve tasks in processing long sequences efficiently and in scenarios where computational efficiency is crucial and the architecture of Mamba which combines elements of State space models ssms and recurrent neural networks allows it to excel in several specific areas so it can excel in language modeling it's shown impressive perform performance in language modeling tasks surpassing similarly sized Transformers and even competing on par with Transformers that are twice its size in both pre-training and downstream evaluation tasks and of course long sequences this thing can handle long sequences like a due to its efficient sequence modeling technique Mamba is actually better suited for tasks that require processing information over extended sequences and this is actually attributed to its ability to linearly scale with sequence context length making it particularly beneficial for applications where long context sizes are involved like coding and it also demonstrates exceptional performance across VAR domains of course in You Know audio and genomics like we already talked about and of course it actually does address the computational limits in Long context scenarios with Transformers and another thing that it actually does have is of course in context learning so while Mamba matches the performance of Transformer models for in context learning it's particularly noted for scaling well with the number of in context examples and this actually suggests that Mamba maintains a considerable Performance Edge in scenarios where leveraging context information is crucial for task performance so it's clear that whatever kind of architecture that these guys do have because they said that Transformers aren't the final architecture and we have something with a multi multi-million token context window it could be Mamba I'm not entirely sure if it is this thing I mean I wouldn't surprise me but then again of course this thing does have some limitations there's not that much of a good ecosystem but then again I do find it uh kind of crazy that this paper was released two months ago and then all of a sudden Google comes out with and I know that Google is you know kind of working with Transformers but all of a sudden Google comes out with you know a 10 million kind of context window and then these guys come out with something that's got multi-million context window and it's pretty much unlimited I'm wondering if they're you know using um Mamba to kind of do any of this or they're just using a completely different architecture that they've developed combined with the essence of llms I'm not entirely sure what arure they're working on but I think that once the new architecture does get you know I guess you could say into the wider Community because of course these guys are a private company they're going to try and protect whatever it is that they have whatever proprietary architecture that they're using um I think it will be kind of fascinating and of course essentially the guy who's um so now here we have the CEO of the magic company okay the magic AI Labs um and essentially he states that we are writing code on a mission to build safe super intelligence so it's clear that um you know his goal is super intelligence and in the article it states that Magic's co-founder and CEO Eric Steinberger has grappled with the problem of getting AI models to reason before he previously worked at meta platforms conducting research on how reinforcement learning the machine learning techniques that help you know the pretty much the great performance of open AI LMS can help AI models find the Optimal Solutions to problems even with imperfect information and his ambition is bigger than a coding coworker remember this company's goal okay is to develop AI superintelligence to same way that Google do um and that is the kicker guys so the fact that they've made a breakthrough that's very similar to qar and the fact that they are working on Super intelligence is pretty incredible because I think the fact that they're both you know heading in the same direction means that they're eventually going to stumble across the same roadblocks and eventually they're going to get across some of the same roadblocks that they do now this has some real ramification and one of the things I do want to know is of

course what is the product because essentially they say some of fredman's former colleagues at GitHub have joined him at Magic and they include Max showning vice president of design at GitHub as well as some other GitHub designers according to a person with knowledge of the highest they'll likely be crucial to developing the company's first commercially available product which I'm hearing is to be set to release in the next few months so I'm guessing that potentially what we're going to be having is something that blows you know I'm guessing GitHub cop pilot out of the water and think about it like this guys if these guys okay actually let's say for example these guys actually you know did this okay so they have something that has active reasoning which is similar to qar which remember the open AI uh there was this whole debacle about qar s outman getting fied which we're going to dive into in a moment but if they have something that has active reasoning something that has you know an active you know unlimited context window something that let's say it dwarfs you know Google's Gemini 1. 5 Pro um and let's say it's you know so good and these guys that they said it's better in our evals anything that they've tried before um I think that if they release that product and that product is better than github's copilot um which I think potentially it's going to be I think that we have a situation on our hand because if that product just blows out of the water remember GitHub is backed by Microsoft I'm guessing that it's running using chat GPT we're going to have a problem because what will happen is these guys will release their product they're going to take the industry by storm and then opening eye probably going to release GPT 5 or maybe an even Advanced version because well they don't want to lose the race because everyone knows about chat GPT and if these guys are developing their own proprietary Frontier Model which they stated that they will remember they stated they're going to release their own proprietary model remember their goal is to build super intelligence it's not just a code you know uh buddy okay it says you know to build a great a product we need to train our own fronter scale model and Transformers aren't the final architecture we have something with a multi-million context window that now apparently has ACT reasoning which means okay that the race could be on guys this could be an insane race okay and this is why I'm stating that this could be absolutely incredible now if you want to remember qar okay was pretty crazy because the day that you know Sam out was fired okay he alluded to a technical Advance the company has made that allowed it to push the veil of ignorance back and the frontier of Discovery forward okay um and there was an interview that he said that and of course qar you know essentially you know open made a breakthrough before outman firing you know stoking the excitement and concern and I know that a lot of people were essentially wondering if this leak was true but essentially it was cuz samman actually did comment on it himself and I think the fact that people forget that open AI has 702 employees apparently it's actually 770 that you know signed the letter I think it was around 740 that did sign the letter and when you think about a company of that size I don't think it's impossible to you know or implausible for two people to essentially go to the board and say look this is crazy because something that you might not know is that I'm guessing that now open AI is compartmentalized because when they released Sora I'm not even sure that you know um the entire company knew about it okay because I remember some of the employees tweeting about it saying that wow I saw some of the demos from Sora today this thing is absolutely incredible so really and truly it could be something some people saying that you know how come we didn't get qar leaked by the entire open AI Team guys uh opening eye is compartmentalized which essentially means that an organization where pieces of information separated to prevent leaks is typically to refer to as employing a compartmentalization strategy and essentially what this approach is it involves in dividing up the organization into the street sections or compartments where information is tightly controlled and only shared on a need to know basis and it's kind of used un likee the military and of course intelligence agencies and some corporate environments to enhance security and of course to protect sensitive information of course with what we're doing now all these kind of breakthroughs need to be protected so I'm not surprised that open ey would have such a strategy because it does make sense and um like I said if they're doing that okay then um I'm guessing that you know the leaks are definitely possible because you know it's a giant company okay you know we don't really know uh who it's going to be and even if there are com compartmentalized okay and maybe it's like just 100 people or 50 people or whatever you're still not going to know who exactly did the leaks or whatever so um I think that Q start is of course really crazy because I think it's kind of shocking that you know the company that's trying to work on super intelligence of course open AI are trying to additionally work on super intelligence they may have you know made some real strides in there and essentially of course it's here an innovation by the company's researchers earlier this year that would allow them to develop far more powerful AI models and of course concerns among some staff that the company didn't have proper safeguards in place to commercialize such Advanced AI models this person said so of course this Q start Innovation essentially that was able to solve math problems it hadn't seen before which is an important technical Milestone is something that will change the game

whenever it does come into it now of course there was some more bits on qar and then I'm going to get onto a really really big issue that I don't think enough people are talking about and whilst these developments are good of course there is a lot of stuff that is unfortunately quite bad okay so essentially remember okay that open I said that while super intelligence seems far off now we believe that it could arrive this decade and that means that we could get ASI by 2030 so that could be like I mean you know the fact that open AI is saying that super intelligence seems far but it could you know it could arrive this decade is not a surprise because apparently you know some people have stated that you know once you get AGI it's not far before you get ASI so um in addition there was also this as well that you know earlier this year SAS and his team discovered a variation of that method that prompted greater results in their efforts to train more sophisticated models and of course essentially open AI are dedicating a fifth of its compute to solving you know uh super intelligence and essentially one last thing that I'm going to you know cover here's something that I want to talk to you guys about because this is really important and not enough people are talking about it um and essentially there is this mik concept okay and mik has come to signify a condition in which we humans are coerced to make futile efforts and compete with each other in such ways that we are eventually driven to our demise and this is really true and if you think I'm just adding this in the video I'm just for the sake of it trust me it's not you guys are going to want to see this because this could spell disaster so essentially uh live bore I'm not exactly sure how you say your name but she actually did a recent Ted talk about the mik problem and it's a really big problem that's it's only going to get worse as things go on because as systems become more powerful we need more security but of course as they become more powerful um people will be deploying them even more so I'm going to show you guys a clip from this Ted Talk cuz it's actually really important to understand this issue because if you don't um and I know some people are good happy about AI Innovation um the existential risk is there like literally like 40% of AI researchers say that you know we should slow down with AI research and of course that's because of the um clear thing that you know super intelligence poses a really bad risk so I'm going to show you guys the clip those influencers are sacrificing their happiness for likes those news editors are sacrificing their integrity for clicks and polluters are sacrificing the biosphere for profit in all these examples the shortterm incentives of the games themselves are pushing they're tempting their players to sacrifice more and more of their future trapping them in a death spiral where they all lose in the end that's mullock's trap the mechanism of unhealthy competition and the same is now happening in the AI industry we're all aware of the race that's heating up between companies right now over who can score the most compute you know who can get the biggest funding round or get the top talent well as more and more comp companies enter this race the greater the pressure for everyone to go as fast as possible and sacrifice other important stuff like safety testing this has all the Hallmarks of a mik trap cuz like imagine you're a CEO who you know in your heart of hearts believed that your team is the best to be able to safely build extremely powerful AI well if you go too slowly then you run the risk of other much less cautious teams getting there first and deploying their systems before you can so that in turn pushes you to be more Reckless yourself and given how many different experts and researchers both Within These companies but also completely independent ones have been warning us about the extreme risks of rushed AI this approach is absolutely mad plus almost all AI companies are beholden to satisfying their investors a shortterm incentive which over time will inevitably start to conflict with any benevolent Mission and this wouldn't be a big B deal if this was really just toasters we're talking about here but Ai and especially AGI is set to be a bigger Paradigm Shift than the agricultural or industrial Revolutions a moment in time so pivotal it's deserving of reverence and reflection not something to be reduced to a corporate rat race of who can score the most daily active users I'm not saying I know what the right trade-off between acceleration and safety is but I do know that we'll never find out what that right trade-off is if we let mik dictated for us so with that clip there essentially they're talking about the problem of how things are just developing too quickly and I think this goes to show just like how I talked about why this is such a serious issue because this smaller company is essentially you know if if they pretty much you know got the same kind of qstar technology that openi had think about it like open AI okay maybe you know unlike GPT 4 they safety tested gp4 for six months okay maybe with this you know AGI level system they're going to have to safety test it for a year and a half okay um but what if this other AI system you know like they said that they're deploying their system

within a couple of months is that going to you know lead a open AI to essentially you know just forget about the guard rails essentially and then deploy their system are we going to have some major ramifications of that in the future so essentially it's a problem of I guess you could kind of say you know a race to the bottom and this chart here is from you know Arc investment management and they've talked about how you know every year like these breakthroughs just you know it's like a stock just keeps dropping until we get to you know um AGI and you can see right here that you know uh gpt3 it went from 50 years all the way down to like 40 then you can see Google's Advanced conversational agent Lambda 2 boom We got down to 18 years chat GPT boom went down again GPT 4 launches boom now it's 8 years like with this qstar breakthrough are we going to be just Dro down again boom like are we going to be like four years away you can see that if the forecast error continues that like you know like the forecast that they had it was like okay we're going to be getting there by 2030 but like the for forec is basically saying that with all these kind of breakthroughs and you remember that chat GPT was like I wish I could you know like show you guys how much more uh you know AI stuff went on here because there has just been so much more engagement in terms of the AI optic so right here is like an acceleration point so we could argue that you know this is going to come straight down which means you know that's this year if you actually think about it guys like if this point right here is an exponentially growth like that's going to come down into this year like you know what I mean so that's something that's not surprising and if their forecast eror Contin new which to be honest humans are very bad at you know extrapolating exponential growth I wouldn't be surprised if of course you know 2025 something crazy happened so I mean with all of these companies going on um and I think something that you guys do want to know as well something that's kind of weird but opening I did actually talk about this they actually said that uh we we essentially won't get super intelligence in a sense um and of course we kind of will but what they said about their you know safety testing thing they basically said that we specify four safety risk levels and only models with a postm tigation score of medium or below can be deployed essentially what they they mean is that only a model that's able to perform at a certain level is going to be deployed and if they have a system that they believe is too smart they are just not going to put that into the wild but this is the thing this is open ai's safety Square this is their safety mitigation what are other companies going to do abide by this um and if they do I mean you know what's going to happen uh to the world I mean it's going to be a crazy place to be living in because so far they're basically saying the AGI by 2026 and now that we're in 2024 that's a year and a half away and I remember when people were saying that AGI in 18 months is crazy now it's seeming like it's realistic so I mean with the amount of investment going into these companies things that are happening with qar breakthroughs only a couple months ago with active reasoning capabilities apparently recently discovered with this huge context length just being there with you know new architectures popping up and with you know these massive Investments and these guys saying that this is better than anything we've seen before I have no idea what they've developed but either way I'm excited scared frightened I mean so many words to describe such groundbreaking technology um and I'm excited to see un involved so what do you guys think about this this is crazy do you think this is boring a good update uh let me know what you guys think this has been an insane February um and I'll see you guys for another update tomorrow