HUGE AI NEWS : MAJOR BREAKTHROUGH!, 2x Faster Inference Than GROQ, 3 NEW GEMINI Models!

so with some incredible announcements this week that I genuinely can't even believe today I'm going to be showing you guys all of the stories you missed that happened earlier this week so now quickly for this story and I'm not going to gloss over this for too long because I already made a video on this but essentially Google research actually released this paper where they said diffusion models are realtime game engines now the reason that this research went so viral is because it's essentially having an AI system nearly render the game playay in real time so what you're seeing isn't like gameplay you're seeing someone play and the AI is rendering the game play as it happens which is you know absolutely incredible because this is the kind of technology that people thought was sci-fi so it's something that is just completely insane considering the amount of research going to this and what people had thought so this is something that's really strange I mean I guess people you know thought that this was going to take quite a lot of longer time and of course the main thing about this which is why it's so crazy is of course the implications for this so this is something that is rather f fascinating because it means that there are potentially simulated worlds going to be coming in the future and this is going to be something that many people are going to be exploring so it's going to be really interesting to see where this research actually leads to for the future now probably the most insane news from this week uh and it's debatable because there is another story that's also incredible um is that magic Labs that lab that I spoke about a while back apparently they have had a recent breakthrough so you can see right here that they' stated okay ltm2 mini is our first model with 100 million token context window that's 10 million lines of code or 750 novels for reference if you want to know how long 100 million tokens is it's unfathomably long because right now what we do have on the Google API we have Google Gemini which is you know a million context length and we have it up to 2 million but you don't even have most use cases for that long like I genuinely can't even find use cases on my day-to-day where I need 2 million you know context length and of course there are some use cases which I will mention in a moment but the point is that is absolutely insane guys like I don't think you guys understand how much 100 million tokens are and I do know that Google were researching 10 million token context length window for reference if we you know look at the things that GPT 4 has you know I remember first when we were looking at these model we were like okay this is pretty crazy it's got 32,000 tokens I can't believe this is like you know 16 times more than what we've had before four times more whatever and then I remember when anthropic came out with their early research that they were looking at 100,000 token context length and we were like wow this is incredible now guys the fact that we are already now at 100 million tokens is insane now the report is pretty crazy I'm going to show you guys why this is so crazy because the thing about this is that anyone can come out and say okay we have 100 million you know token context length you can put in 750 long novels in there and it's going to be able to you know read it but the

real thing is that um you know they talk about this problem okay and the problem that they talk about is the fact that current long context evaluations aren't great so basically what they State here is that the popular needle in a haystack evaluation places a random fact uh in the middle of a long context window and then asks the model to retrieve the fact so basically you have like you know the Harry Potter trilogy I don't even know if it's a Trilogy let's just think of a really long novel you put that into the model and then you'll put a random word somewhere in that novel and then you'll ask the uh model to find that random word and then of course when it does you're like okay it can actually you know use all of its context length but they basically say that this isn't good because of the fact that it stands out in a fiction novel about whales like if you have something random it's going to stand out and these models can you know recognize these needles so they're basically saying that what they've done here is they've designed something that's even harder okay and they've designed something called the hash hop okay so it says hashes are random and thus incompressible requiring the model to be able to store and retrieve the maximum possible information for a given context size at all times so basically what the model has to do is actually understand well not really understand but like actually retrieve what's going on and not just realize hey something strange is going on in this piece of content context length so you can see right here we prompt a model trained on hashes with hash pairs you can see that they've got all of these pairs and then we ask it to complete the value of the randomly selected hash pair so they've got like insane amounts of these and then of course it's going to have to you know randomly select a random hash pair which is you know remarkable because it's like there's no real way to identify which one is going to be picked all of these are completely random of course you could say maybe there's some kind of pattern here but they're randomly generated so this is going to be insane like abs absolutely insane in terms of the fact that when we look at the results of testing this against this you know 100 million context length guys it is pretty much perfect up until 32 million context length like the fact that you know even until you know you can see right here that until you get to 100 million context length that's when it starts dropping the fact that research in AI now has got us to the point where basically we're up to 32 to 50 million Contex length in tokens and it can retrieve that data accuracy just shows you how insane things are moving in terms of the speed and I remember earlier this year that like I was talking about how you know this company is going to be on to something because like I said before if you aren't familiar this company had a breakthrough earlier this year and basically um they showed it to investors and the investors were like okay this is insane and the investors didn't even need a pitch deck they literally just put $100 million see as I was saying before I was interrupted this is pretty incredible because it goes to show you know how fast the research is moving and the fact that you know this company are having breakthroughs and doing this kind of thing it just goes to show where we might be in the future now I think this is going to be pretty crazy in terms of the implications for everyone else because you have to understand that whilst yes we don't actually have access to this yet and I know many people in the comment section are going to be like okay another fancy demo without any actual results but the point here is that these implications are pretty insane because what we have is a situation where if these guys are basically saying that look we've got our system to 32 basically 100 million token context length Okay which is just unheard of at this point you know other labs and other models are going to have to facilitate this kind of token context length or they're going to have to innovate in some way which once again is going to just drive that Innovation because every single you know month there are new companies popping up there are new methods of innovation and there are new things that we really just didn't see so it's crazy that this kind of thing is happening all the time and I wanted to bring this to you guys' attention because I can't imagine where we're going to be you know in 5 10 years from now in terms of AI because the way how things are advancing this rapidly it seems that we're moving quicker than anyone anticipated and one thing I did forget to add was that this is of course the coding platform so basically they're trying to develop these software agents that's why they're so focused on having such a long context window because if you have a model that can literally ingest all of your code and then you can use natural language to fix your code and it can understand your code it's going to be you know pretty insane so I really can't wait for this to be released CU I think the implications are going to be staggering now of course you could also see that Sam Alman did finally tweet about the fact that they've reached an agreement with the US AI safety Institute for pre-release testing of their future models and it says for many reasons we think that it's important that this happens at the national level the United States needs

to continue to lead now I think this is going to be rather fascinating because the implications of this I don't think they're that bad but I think it's rather fascinating to the point where open AI has now gotten they're going to be testing these models with the US AI safety Institute uh in terms of like just trying to ensure safety now I do wonder how this is you know really going to work like is openi going to immediately give them raw access is it just going to be certain video models is it going to be every single model I mean it's going to be really interesting to see how this entire thing works because of course AI is something that evolves fast than regulations do and I do want to understand as well like how long is the AI safety Institute going to be testing these models for because some people are speculating that this might slow down the you know release cycle even further of course when you do have a model you have the initial thoughts about the model how you're going to train the model of course you've got the data collection of the model then of course you've got the training of the model then you've got the post six months of safety testing of that model where you have red teamers you have individuals of course you've got human feedback of the model you got just all of these different steps and now you know apparently we're going to get another step on top of that which is where we've got the US AI safety Institute for pre-release testing so I'm wondering you know is this just going to be for certain more advanced models or Consumer products because like I said before I think open AI in the future they're going to have a lot of products that they're just simply not going to release and they're going to be super Advanced but they're just not going to release them to the public because number one they're going to get backlash and number two um if those models get jailbroken there's going to be severe implications so I am wondering about how that is going to be I'm wondering if there's going to be some kind of transparency with that but the future is going to be rather fascinating really fascinating to see how the development space changes because one thing that I've realized as well is that there are a lot of people that are now I wouldn't say hating on open AI but they're becoming less and less patient because open AI has demoed a lot of things and they just simply haven't released them at all so I mean I wonder if opening eyes image from that aspect is also going to take some kind of hits there but it will be fascinating to see how things move forward then of course we did have Bland Ai and I wasn't going to make a video on this but this is something that I've been sent time and time again it's something that shows us

the future of AI employees and this can talk in any language or voice be designed for any use cases and handle millions of calls 247 this is why in my post AI economics Community one of the things I've always said you know that people are going to struggle with in terms of a future career is jobs that are on the phone so I'm going to show you guys exactly what this video is and then I'm going to give you guys my thoughts and opinions of people that spoke to our AI we're going to introduce you hi is this Isaiah I heard you want to show me off in a demo yeah hey Bland I hope you're having a great day I just wanted you to explain to our audience how you work I'm having a great day and yes Isaiah I'm an AI agent for Enterprises I can handle millions of phone calls simultaneously day or night and in any language voice I can be used for any use case in any industry across sales operations and of course customer support that sounds pretty awesome can you talk a little bit about what actually makes you special to handle conversations in an intelligent and hallucination proof way um I do this through an invention from our team called conversational Pathways it's the tree of prompts you're seeing on screen right now and it's how I navigate a conversation that sounds pretty amazing and what kind of Integrations does conversational pathway support pretty much anything I can be connected to a calendar to schedule meetings update a CRM and I can even payments over the phone do you want me to go into more detail no I think that's enough for now we'll let people talk with you on their own on our website thanks Bland have a great day thanks Isaiah you have a good day there are billions of phone calls made every single day in the near future almost all of them will be done with AI that means no more waiting on hold no more being endlessly transferred around and hundreds of billions of dollars generated and saved for Enterprises if you haven't already talked to Bland you can go to our website bland. and have a conversation with it where you can fill out our Enterprise form and start automating your phone calls today still hiring humans so yeah I think this is another interesting aspect of how the economy is going to change and what we can see here is a direct impact of AI impacting the workforce I'm not sure if you work in you know customer service or whatnot I've always said that you know certain careers for example have to move towards other adjacent opportunities that aren't as easy so for example one of the things that I know that you could do for example if this is in your industry if you're an example like customer service and you're used to talking on the phone what you could do is you could move to sales and sales is something that like you don't really want an AI there because there are just so many different questions that you do have that just don't always pop up and the thing is as well that like right here what we can see and hats off to the technology CU it is pretty great and you know there's no surprise about this but you can see that like there is this tree okay so there's a tree of there's basically a tree of like things that could happen but when you have C and sales every single person is going to be different in terms of their obligations and the problem is that like if you're using an AI to speak to someone especially if you're in high ticket sales or certain kind of sales it's going to be hard to get that person to buy and of course in the future maybe it's going to be overcome but there are just certain things that like you know for example you know when you're talking to someone in sales sometimes you might be from the same country as that person you might have the same culture know like an inside joke from a TV show from that person I don't know all of these things sound crazy but it's going to be really hard to like get that human aspect because the entire thing is that with sales what you're trying to do as well is you're trying to build up you know as much Rapport in a short amount of time as possible so that you can get that sale so I think those kind of careers are going to become more attractive but this kind of Technology I'm I'm going to kind of you know see how this works because I do remember that there was also a law that was recently passed that basically said look um and I don't think the law was passed just yet but it was basically moving forward that basically says look we need to ensure that individuals actually can talk to a human if they want to but I do think that even though that law might exist in the future where you have to talk to a human agent I still think that you know in the future this is going to be the future of how most conversations are going to be had especially during phone calls I don't know about you guys but I've been waiting on hold a millions of times before and it's just completely tedious and completely frustrating for something that's really simple to do and I wanted

to show you guys this clip because I have an article that you know follows up to this so I'm going to show you guys this clip first and then I'm going to talk about the article even with prompting technique I mean it it's fascinating even with no new models coming out right um given a fixed model you can elicit better and better performance of it from it simply by improving how you prompt it and there was a paper that came out three or four months ago that suggested that like emotional manipulation of the large language model would get better results so the kind of the uh prompt suffix that they figured out so you say hey I need you to perform this task you define the steps and so on and you end with it's very important to my career that you get this right and the performance goes up you're like what is this like what are computers now so that's hilarious not going to lie but there's also this okay so this is Apple intelligence and the reason I wanted to include that cuz this in the video but I didn't know where and since that other previous clip had a decent amount of attention I wanted to show you guys that Apple intelligence okay are going to not only

are they you know having an announcement later this year which should be Apple's true AI announcement what they also do okay that testers have found that their prompts are meant to keep apple Intelligence on the rail so basically what Apple intelligence have done here what Apple you know have done basically is that they've actually said to the AI do not hallucinate so in the system prompt for the AI they've said do not hallucinate and the craziest thing about this is that someone was digging around on Reddit and they managed to find this and I find it incredible that like you know people have been trying to find out ways to get these systems to not hallucinate and one of the ways that you know Apple have been you know getting the system to not hallucinate and to be more correct is that they literally just say Do not hallucinate I mean it's kind of crazy like it just kind of it doesn't defy all logic but it's something that's so weird that shows us that we don't really understand these llms even though the training of a machine Learning System might be one might have imagined that even through the training of a machine learning even though the training of a machine Learning System might be Securus somehow in the end the system would do what it does through some kind of identifiable and explainable mechanism but what we'll see is that in fact that's typically not at all what happens instead it looks much more as if the training manages to home in on some wild computation that just happens to achieve the right results machine learning it seems isn't building structured mechanisms rather it's basically just sampling from the typical complexity one sees in the computational universe picking out pieces whose Behavior turn out to overlap what's needed and in a sense therefore the possibility of machine learning is ultimately yet another consequence of my favorite phenomenon of computational irreducibility so why is that well it's only because of computational irreducibility that there's all that richness in the computational universe and more than that it's because of computational irreducibility that things end up being effectively random enough that the Adaptive process of training a machine Learning System can reach success without getting stuck but the presence of computational irreducibility also has another important implication that even though we can expect to find limited pockets of computational reducibility we can't expect kind of a general narrative explanation of what a machine Learning System does in other words there won't be a traditional say mathematical general science of machine learning or for that matter probably also Neuroscience instead the story will be much closer to the fundamentally computational new kind of science that I've explored for so long and that has brought us our physics project and the ruad so in many ways the problem of machine learning is a version of the general problem of adaptive Evolution as encountered for example in biology we typically imagine that we want to adaptively optimize some overall Fitness of a system in machine learning we typically try to adaptively train a system to make it align with certain goals or behaviors most often defined by examples and yes in practice this is often done by Trying to minimize a quantity normally called the loss that Sten Wolfram saying that there might not be any way to actually identify the mechanisms behind deep learning because like in neuroscience and biology it might depend on wild computation and might be the outcome of computationally irreducible evolutionary adaptation till the are it's just basically too complex for humans to ever understand now in some actual insane news okay and I can't

even believe this happened in fact I actually knew this was coming but this is actually sooner than I thought so introducing cerebrus inference okay so for those of you that thought Gro was quick gr was quick Reus inference is basically twice the speed of Gro guys grock like you can see 800 tokens in in a second that is absolutely insane guys like it's just crazy like I'm literally um you know speechless at this so you can see instant code generation you can see right there I'm going to show you guys um it was okay I thought it did say 0. 8 seconds but that is really quick guys I'm not even entirely sure like you know how this is going to work in the future in terms of how much data llms are going to be able to Output but this is you know some insane stuff you can see right here that if we actually take a look at this compared to some of the other proper popular providers you can see that AER is on 20 seconds per user perplexity is at 52 Amazon web services at 50 you know recently we had Gro which is at 250 but we've got cerebrus which is nearly double the speed okay like Gro was insanely fast but now we have something that is basically you know double the speed so that is insane now the reason that this is going to be crazy is because the applications for this are really good so we could have super smart models that are also quite fast because of course there's been that discussion about you know test time compute you know putting inference towards figuring out what the problem is but I mean imagine actually getting you know incredible answers in just a fraction of the time one of the things people have always discussed is how you know we need these lightweight models to give us quick responses but what if we could get Superior models with even Superior you know latency so you can see right here it says gives me a list of computer scientists tell me what each one's greatest achievement was and then you know they've tweeted and basically said that this is basically going from like you know dialup internet to you know gigabytes per second internet so it's like you remember when you had slow internet as a child not sure if some of you grew up with dialup internet how slow it was and then okay as we got older as technology improved we got internet that was just super fast and I mean that literally for like the llm that was literally like loading a web page like can you guys see that like 1,800 tokens in a second guys that's insane and then on the right hand side we can see these models generate the generating the tokens I mean of course this is just something that I I didn't even think was going to come soon enough but I mean can you imagine in the future when we get further inference improvements and things are basically automatic so I mean it's incredible like literally incredible stuff going on this week and then we have Dario amade saying the AI could increase the rate of Discovery in biology by 100x compressing the total amount of progress that occurred in the 20th and 21st centuries to just a few years now this has

staggering implications for everyone because this basically means that this stuff could be applied to longevity research certain terminal illnesses that we thought were once terminal I mean you know compressing Decades of research into just a few years means that we're going to unlock a lot of information in a very short amount of time for a c-biology if we think about what's really Advanced biology like it's really disproportionately a few technologies that kind of power every right so like genome sequencing right just the ability to like read The genome fundamental to like you know most of Modern Biology right more recently crisper the ability to edit the genome right fundamental to many experiments where we want to intervene in animal experiments starting to become important to you know Pharmaceuticals and you know and curing diseases although there's a while to go and you know it needs to be more reliable there are a lot of other techniques that are needed I think if we get AI right it could like increase by 10x maybe 100x the rate at which we invent these discover so if you look at crisper you know there was no reason you couldn't have done it 30 years ago it took 30 years to invent it and so I think if we can you know greatly increase the rate at which these discoveries happen will also we cure disease and you know the way I think about it is you know could we have like a kind of a compressed 21st century right could we make all the progress in biology and that we going to make in the 21st century using you know AI by you know kind of speeding things up 10x and if you think of all the progress we made in biology in the 20th century and then you know kind of compress that into five or 10 years to me that's the upside like I think that might literally be possible and you know diseases that have been with us for Millennia now lastly but not leastly Google actually did release three new products um/ AI models so they introduced a new smaller variant of Gemini 1. 5 Flash and eight billion model a stronger Gemini 1. 5 pro model which is better on coding and complex prompts and a significantly improved Gemini 1. 5 flash model I've used these

models the gem 1. 5 Pro Models the advanced ones and I got to be honest they are actually really good at breaking down some prompts interestingly enough sometimes when I've had some questions that I just haven't been able to get right outputs from either claw 3. 5 Sonic and gp40 gini 1. 5 Pro actually does present the right answer in a really detailed way so it's a bit weird because sometimes it's a really nice hit and sometimes it just completely misses and just doesn't get things right so I would say if you're someone you know you're struggling with an issue you know Claude can't solve it um you know GPT 40 can't solve it just log into the API and I'm going to show you guys right now so you just click this and then you just wait a second or two and then basically you just come over to here and they have two different experimental ones they got the experiment uh 01 and then they've got experiment 0827 and so the most recent one is 827 um and you know the one before that was 821 but it's still pretty useful to just see if your issue can be solved and you actually do get 2 million tokens in this chat area so it's really useful to try so I would say you know go ahead and use it because it's something that you're going to want to try