Microsofts New AI Chip 'Athena' SHOCKS The Entire Industry

So the AI race is heating up once again, and not in the way that you might think. This is Microsoft's new game plan. They are working on their own chips that are going to rival Nvidia's. Now, I gotta be honest, if you don't know how far ahead Nvidia are, you should honestly take a look. Now, before I get into showing you all the details of Nvidia's chips, Microsoft's new secret chip, and later on in the video, even Google's new secret chip, which they claim is faster than Nvidia's, I need to tell you all why these chips are so important to ChatGPT and AI software. So essentially you all know exactly what ChatGPT is and how these AI softwares are used in our daily lives. Now, the thing is, AI is actually learned through a process called deep learning. Now, deep learning involves training AI networks with large data sets to learn from and examples to make predictions or decisions. So essentially, you know, chatgpt, how people feed it large amounts of data and in midjourney, how they fed it loads of pictures, that's how these programs are run. Now, of course, these processes are computationally intensive, requiring massive amounts of data and complex mathematical calculations. Now, Nvidia makes chips that are optimized for this type of processing with features like tensor cores that are designed to accelerate matrix math operations, which are central to many deep learning algorithms. So the chips that Nvidia make are also highly scalable, which make it suitable for large machine learning applications, which essentially means that these are the kind of chips that we need to power the next generation of AI. Now, what's also cool about these chips is that they support high bandwidth networking and advanced memory technologies, which allows for faster data transfer and more efficient use of memory. So essentially, these chips are literally an integral part of the next world's industrial revolution. And you have to understand that without these chips, it's going to be very, very hard to progress in AI. Now here's the thing, okay? Nvidia make these chips and they're pretty much the best chips that money can buy. They recently had the A100, which was a decent chip, but even recently, Nvidia upped the bar once again with the H100 chip that you're seeing on screen right now. And this one is super, super powerful that is completely optimized for AI. Now here's the problem, okay? Now, Nvidia is just one company that makes these chips. And you can see right here on ebay that these chips are very, very expensive and Highly in demand. Now what's crazier is that you can also see right here that the cost of training GPT3 was around $4 million, which is honestly insane. And what's even worse is that OpenAI spends approximately $3 million per month to maintain ChatGPT. And that's just based on the many costs that come with running these highly expensive GPUs. Now you have to understand that if these companies don't manage to create their own chips, they that they can make in house, they're going to be 100% reliant on Nvidia. And if Microsoft is reliant on Nvidia, then they're simply not going to be able to compete because of course that is going to be a hefty cost that they have to keep spending on. And of course Nvidia is going to be the only one who's raking in all these profits. Right now. The AI race is heating up more than ever. And everybody knows that large language models are going to be a thing in the future which where the company who manages to master these chips is essentially going to be powering pretty much the entire world's large language models because they won't have a choice. So that's why companies like Microsoft and companies like Google are racing to make their own chips. This is because if they make their own chips, then they're less reliant on the behemoth that is Nvidia. And of course we do know that these companies want to develop these things as quickly as possible as large language models get more and more scalable and more and more effective. Now that you know exactly what these AI chips are for, let's talk about Microsoft's Athena. So allegedly 300 staff are actually working on these chips, with sources saying that we could see wider use by Both Microsoft and OpenAI sometime next year, although they are unsure whether or not they actually want this chip to be available to cloud computing customers. However, this article goes on to state that OpenAI has actually used Microsoft supercomputing systems to to train their large language models that power ChatGPT. However, these computers actually run on Nvidia's chips, which is of course a problem that Microsoft is quickly trying to solve as these costs get more and more expensive. Now, if you don't realize how expensive these models are, remember every time you go onto ChatGPT and even if you are a Plus subscriber, you try and access GPT4, you see this message right here. It says GPT4 currently has a message cap of 25 messages and every three hours. I'm pretty sure that's largely due to the fact that GPT4 is much more expensive to run than GPT 3.5, which means that they definitely need to make this much more effective, much more quicker. Now, remember we previously talked about earlier in the video how Microsoft isn't the only company who are developing their chips. We also have Google teasing its TPU 4, okay? And this is an insane chip because they actually claimed that, that it was faster than Nvidia's model, the A100, which is also very, very quick, which is specialized in deep learning and AI capabilities. So in this article it says Google claims its TPU V4 outperforms Nvidia's A100. So a new scientific paper from Google details the performance of its cloud TPU supercomputing platform, claiming it provides exascale performance on machine learning with boosted efficiency. The authors of this paper claim that the TPU 4 is 1.2 to 1.7 times faster and uses 1.3 to 1.9 times less power than the Nvidia A100 in similar sized systems. So they note that of course, by this being a very impressive metric, they haven't actually compared this to Nvidia's H100 GPU due to their limited availability and their architecture. Now of course, as we stated previously in the video, these are extremely rare graphics card because they are of course also very expensive. Now you might be wondering, so just how powerful are Nvidia's H100 GPUs compared to Google's Cloud TPU V4 supercomputing platform where they're trying to build this machine learning infrastructure? Well, just take a look because honestly, Nvidia seem to constantly push the barrier and seem to be miles ahead from the competition. Look, take a look at this, okay? Hopper architecture accelerates AI. Okay, so basically Nvidia's new H100 GPU is essentially nine times faster, up to 30 times faster on training AI. This is absolutely incredible. Now the reason I say this is absolutely incredible is because you got to remember, okay, this is faster than the previous gpu. But remember, Google was recently boasting about how their own GPUs were were faster than Nvidia's A1 hundreds. But Nvidia just released the H100 which is actually 9 times and 30 times faster in certain aspects, which I think it's important to note that the rate in which AI is developing now many people are talking about large language models which of course, are very interesting and very effective at what they do. But what you're not paying attention to is, of course, the hardware side in which these companies are racing forward to to develop these chips that are literally going to power the next industrial revolution, which many are referring to as AI. And I do think that it is important to note the role that these GPUs and that these chips will play in advancing this and making everything much more quickly, because eventually we do want to have this in real time. I mean, ChatGPT is great, but if we could have this information instantly available to us in real time, that would be far more effective. Now, there's also something that is incredible, which is a breaking area of technology, which I do just want to quickly touch on. So this was a video that came up in my recommended feed because I've been consuming a lot of content on AI. Now, this video is very interesting because it talks about brain, like computing and spiking neural networks. Now let me explain why. This is literally the next level in AI. And once this technology is mastered, we about to see a major breakthrough. The video actually talks about neomorphic computing, and essentially it's basically computing that models the way the human brain works. So it involves creating artificial neural networks that can process information similar to the way that the brain does. So basically, the brain, as you know, is made up of neurons, which are cells that are responsible for transmitting information. When a neuron receives a signal, it sends out its own signal to other neurons. And this process of signaling between neurons is what allows the brain to process and interpret information. Now, neuromorphic computing works by creating artificial neurons and then connecting them in a network. So these artificial neurons are designed to behave like real neurons, so they can receive and send out signals just like the neurons in the brain. So by connecting these artificial neurons, we then can create an artificial neural network that can process information in the same way that the brain does. So the benefits of this, okay, are that this is basically like a human brain. And the reason people are investing and companies are developing these applications, such as intel, is because these are what will power the next AI transition. So it's still a relatively new field, but this video dives into a lot of stuff like 2D materials, which are essentially materials that are so thin that they literally call them 2D materials, because they're literally as thin as one atom. And this is truly a big deal. And you might not think of at the moment, but. But once this kind of technology does become mainstream and we're able to make technology that is able to power AI that's as thin as a neuron and as effective as a brain, you can really start to understand how quickly things are going to ramp up.