Elon Musk Declares WAR! Apples NEW AI. New AUTONOMOUS UPDATE! And Nvidia's New AI Agents

So, Elon Musk has declared war. And in today's video, I'm going to be giving you guys all of the AI updates that you may have missed, because today's one is pretty, pretty crazy. Without further ado, let's not waste any time. One of the first things that we actually saw, we actually saw the breakup of inflection AI's PI company. So you can see right here, it says, welcome to Microsoft, Mustafa Suleiman. Thrilled to have you lead Microsoft AI as we build consumer AI like Copilot, that is loved by the benefit of people around the world. And we also saw Mustafa Suleiman say that he's excited to announce he's joining Microsoft as CEO of Microsoft AI AI, and he's going to be leading all consumer products and research, including Copilot, Bing, and of course, Edge. And he said that he's going to be working with some of his collaborators that he previously did work at. So this is the entire statement here. He says, you know, my friend and longtime collaborator Karen Simonyan will be chief scientist, and several of our amazing teammates have chosen to join us. Inflection AI, the company that recently actually dethroned well, didn't dethrone GPT4, but was on par with GPT4, will continue on its mission under a new CEO and look to reach more people than ever by making its API widely available to developers and businesses the world over. It's been an amazing journey with so much more to come. Thank you to everyone for your support. Things are just getting really started. Now. This was something that, I wouldn't say it shocked the entire industry, but this was quite surprising because we've known that AI in its current stage is something that is quite hard to gauge in terms of its exponential nature. And one of the things that some people did state was that they did state that in the future, some companies may just not be able to catch up. Now, some people could argue that currently maybe Inflection AI with their current system, maybe they didn't have enough user bases, that's why they're switching to APIs. Maybe they didn't have enough. And that is probably a valid argument. We could argue that GPT4, OpenAI, maybe there's some rumors that they're just so far ahead that training these foundation models as an independent company, and they recently actually did just raise, I think it was $1.5 billion or they raised, was it 200 or $500 million at a $1.5 billion valuation, which is pretty, pretty incredible. And what it goes to show that currently, of course, in the future certain AI companies may just be absorbed. But then again, at the same time we do have the nature that AI is vastly unpredictable and we've seen Microsoft AI previously, even have Sam Altman and Greg Brockman working there with that crazy stuff kind of going on. So I think this announcement, some people also did state that Microsoft are trying to divest away from OpenAI because essentially they want to diversify their portfolio holdings in terms of their AI companies because of course, as you know, with their Azure cloud services, they do provide that to OpenAI, but they want to provide that to more and more AI companies like Inflection, which is going to be interesting to see. Now they do have a pretty big user base from what I saw from their own statistics, but I don't really know anyone myself who actually uses them. And since they don't have an API, I'm actually wondering what their main customer base actually is. So that will be interesting to see how this company has evolved if it does continue. And something from their website, they also said that this has renewed emphasis on our API and comes with some important changes in the company. Today we're also announcing that two of our three co founder, Mustafa and Karen will be leaving Inflection to start Microsoft AI, a new division and Microsoft that will bring together their consumer efforts as well as Copilot, Bing and Edge. And I can only wonder how investors are going to feel about this because if you're someone that invested in a recent funding round and then two of the co founders, one of the co founders being from DeepMind, Mustafa Suleiman, a well respected person in the AI space leaving, I mean how does that drive the valuation of the company down if they want to sell or if they do want to pull their investment? I mean of course investment's always risky and this is really why. So let me know what you think about this. I think this goes to show that once again Satya Nadella, as a CEO, being able to negotiate this kind of deal and strike this kind of up here, and I mean for the future for Microsoft AI, I mean having all of the things that they do have, having such an integration with OpenAI, having now key researchers from Google, DeepMind, you know, now at the company, I mean they really, really do have an incredible, incredible stack and it seems like it's only going to get better in the future. I'm not going to speculate too much here, but another thing that people have been saying, a few, you know, whispers of the winds, so to speak, Some people are stating that, you know, these companies are preparing for AGI and essentially, if you remember, Microsoft's deal was OpenAI was one that Microsoft gets all the tech. Until OpenAI have achieved artificial general intelligence, they get access to pre AGI tech. So because, you know, they're realizing that, look, AGI is just around the corner, we're about to get shut out from the deal. Maybe they're now starting to try snap out these other people. Either way, I think it's rather fascinating. Now in other collaboration news, we have Apple's Gemini AI. So Apple, you know, the company that's been pretty big for quite some time, the company that's touted itself as the biggest company in the world, the best in terms of consumer hardware and brand recognition, this company is in talks with Google to integrate Gemini's AI service into the iPhone. Now, I find that this is so incredible and I can't wait to give my thoughts on this because this has been something that I've wanted to talk about for quite some time. So it says Apple is discussing a potential deal to integrate Google's AI platform Gemini into the iPhone. And they reported it could be a major deal between the tech giants and impact the wider AI industry. According to Bloomberg, Apple is holding active negotiations to license Gemini's AI models to integrate it into generative AI features into an upcoming software update to the iPhone later this year. Apple's discussions with Google follow similar talks it held with Microsoft's backed OpenAI the report ads. So they had similar talks with OpenAI. And Apple will release iOS 18, the latest version of its mobile OS system, later this year and is expected to integrate several AI features and including a more powerful version of the digital assistant Siri. That is of course true, because we've known that Siri is ancient in terms of AI terms like what Siri is supposed to be capable of. I mean, Apple has the very, very, very like they've got the floor. Okay, so to be. I'm literally speechless because Apple could do, you know, pretty much anything with Siri because they literally have the entire Apple ecosystem to make Siri very impressive. However, I've said this before, and I'll say this problem again. Apple has a perfectionist problem. Apple is a consumer brand that is on the, you know, our products are elegant, they're sleek, they're expensive, they're perfectionists, essentially. And with that, we know that LLMs aren't essentially perfect software. But the problem is, is that, you know, Apple are playing the game of, could we wait until these LLM systems and these AI systems, these other generative systems are pretty much perfect before we implement them. Or, or are we playing a game of lose out on this race and then fall behind forever? Because the problem with AI is that it moves very different to any other industry in the nature that it is exponential. So them adding Google's Gemini AI models, it might be interesting because I'm wondering if they're going to add it in a software side because they do have models that can run natively on the phone, but I'm guessing that they're going to run it on the software side so it can be updated dynamically. And I'm pretty sure that Google are going to fine tune that model in some kind of way because they are not going to want any PR disasters. And if you've known anything about Google recently, unfortunately they've been having several PR disasters in relation to their AI software. So I think this deal, if it does go through, it will be good because you know, Siri just doesn't do anything. And I think once Siri gets an update, I think that's when the global conscious is going to start to realize how crazy AI is. Especially if we move towards a system where Apple generates some kind of agentic system for Siri, where Siri is able to open all your apps and do just a million different things. So I think when Apple does that, that's when I think the general public are going to start to realize that okay, ChatGPT was crazy, but Siri getting all these advanced capabilities, this is going to be something that's really helpful. And I wonder how it's going to impact products like Rabbit, because right now Rabbit could really take the market share. And if you don't know what that is, it's basically an AI agent thing where you say, hey Rabbit, can you do this? Can you do this software? And it's really, really cool. So we also had this. So this was something that I found to be really cool. And like I said before, I'm not gonna, you know, do this too long because I already spoke about this. But something that was really cool about this was if you didn't see yesterday's video where I spoke about humanoid robots, was that they added grippers to Agility Robux, their robot system. And they also, I think they updated this a little bit. There was this system that existed there before but, but I'm guessing that they're doing more of this where you have an LLM embedded into the system with like a chain, not chain of thought, but something that where the LLM can be used for the robot to reason about what it's next going to do. And here we see it's reasoning steps. And I actually did speak about this yesterday in the GTC because this was right after the GTC where they showed us a demo. Now what's impressive about this as well was that we got examples of the sim to real where this is Nvidia's Isaac Sim or Isaac Gym where these robots are in a simulated environment in a one to one environment and then they do that again and then it's done in a real environment and we can see how it's able to learn effectively in that environment. Now I think with this as well, someone did point out, there was a tweet that pointed out that first off watching this video is thinking, okay, it's really cool, it's pretty simple. But you can see right here, if you pay attention, I'm going to circle something really quick. If you pay attention to the robot's legs right there and if I go back, you can see that the simulation data, the simulated data as the robot, the legs come up, they kind of glide off the ground like right there, they're not on the ground, then they kind of slide off the ground. And that's one of the problems with you know, simulation to reality is because these kind of mistakes are very hard to, I guess predict and of course sometimes fix and translate into the real world. So these kind of things are very fascinating to see how they're going to be developed. And it will be interesting to see how this technology, you know, does actually get used in the workplace. Because Agility Robotics, their humanoid robot, well I guess it's kind of like a humanoid, you know, it's got like crow's legs or you know, ducks legs or something. But I guess these robots, it will be interesting to see how they really are integrated into society. Now this is one of my favorite humanoid robots as well. If you didn't see this one, this is Apollo showcasing dexterous capabilities taught by human demonstration in 1x speed. And I'm going to show you guys another robot video after this because this robot is actively now just recently signed a partnership with another company. And you can see right here, this robot is able to grab some stuff and make a little juicer. Now I would say that this robot doesn't look as fluid as other robotics companies, but I do think that this, what they, what they're showcasing here looks very, very effective. And one of the key things as well is that you know, as with the title of the video, you know, Elon Musk's pretty much declared war. And I'm going to get to that in a moment because it's pretty insane on what Elon Musk has realized with these robot 6 companies. And if you haven't been paying attention, yesterday Nvidia gtc, the CEO of Nvidia basically said that, look, Nvidia's humanoid robots, we're building a system, we're building the infrastructure, we're building the AI systems, we're building the simulation software. We are looking to take the road on this. And Tesla's Optimus wasn't there. So I don't know if in the future they're going to strike up a deal with Nvidia, but they really need to do that because companies like this are moving very, very quickly in terms of their one time speed demonstrations showcasing real human interactions, showcasing that they are working effectively and striking up deals with major companies like, you know, we are ecstatic to be collaborating with Mercedes Benz to empower staff in its manufacturing capabilities with state of the art technology that automates physically demanding, repetitive and dull tasks that people don't want to do. Stay tuned. This is just the beginning. So we can see here that this is something that is rather fascinating because we can see that these companies are already striking deals. Remember it was figures robot that also did some kind of deal where they've, you know, said that we're going to have these robots in our company sometime in the future. So I wouldn't be surprised if humanoids working in car factories is going to be the next stage because humanoid like robots are already working in car factories. They already do like a lot of stuff. But when we need to get to those fine motor controls, once these robots are figured out, that is going to be something that is rather interesting. Now remember what I said. Elon Musk has declared war. Okay. This was something I was supposed to make a video on before, but now that he's, you know, this time here, it just makes sense to add this to the video. So you can see right here, this is where Brett Adoc, the CEO of Figure, the humanoid robotics company that just really had a very impressive demo with OpenAI's integration. They did a post where they're speaking about their valuation and all of the collaboration efforts that they are going on with OpenAI, with Microsoft, the Bezos Expeditions and Nvidia, and Intel Capital One Parkway. So there's a lot of, of things. Okay. And Elon Musk saw this and he was like, okay, bring it on. So he's declaring war essentially against all of these humanoid robotics companies because, okay, Elon Musk has a very, very giant vision for Tesla's Optimus. And I think he understands that all of these other robotics companies are moving very, very quickly in terms of what they're being able to do. So these other companies are, you know, they're developing their robots, they're moving quickly in terms of the speed of production, in terms of integrating them with LLMs. And Elon Musk doesn't really want to fall behind because he's seen as a visionary, as an innovator. And if you fall behind in the robotics game, you really, really could lose out on a lot of things. Because the problem is, is that if Figure has striked UP deals with OpenAI, Microsoft, Nvidia and previously. I'm going to get an image for you guys just to show you guys how crazy this is. I don't know if you can see this properly, but this was the image. There were 1, 2, 3, 4, 5, 6, 7, 8, 9 humanoid robotics companies working with Nvidia. And these guys are pretty, pretty far on. Okay, that's Figure there. That's Unitary Robotics. That's Apptronic. That's Gility Robotics. That is Sanctuary AI's Phoenix platform. That is 1X Robotics. Neo robots. That's GL1's humanoid, Boston Dynamics. And I'm not sure about this fellow right here, so I will have to figure out who this one is right here. But the point being that these companies are doing partnerships on a crazy, crazy level. And Elon Musk has realized. Now, I actually did also see that Elon Musk has apparently shifted his focus according to this interview. That was a minute right here.

I think that yesterday probably lit the biggest fire that's ever been lit under Elon Musk's butt. And I can't wait to see how he reacts. I think this is a real threat to Tesla and I believe that Elon will perform better as a result of what was announced yesterday by Nvidia.

Works well under pressure. He works well when challenged. And I think this was a challenge to his territory. And he wants to be the human humanoid robotics company of all time. Optimus. He wants to be the default humanoid in the world first. Best. End of, end of discussion.

I just want to say this because I don't know that Tesla investors really know this, but I have heard from sources of mine that Elon very recently become much more involved with the Optimus program and is laser focused on Optimus and I love hearing that. I think it's about time and I.

Who'd you hear that from? Like, you don't have to tell me specifically, but was it an industry insider.

On robotics or is it just not from someone at Tesla? It was from someone that works closely with all of these companies, that knows that Elon, with all the robotics companies, and that said that Elon has a renewed kind of focus and passion.

Yeah, Chris Camilo talking about this just goes to show. And he's saying that he's got industry insiders or whatever. Whatever his source is is fascinating because it shows that Elon Musk is not playing games because this is pretty much the legacy of his company. It's pretty much one of the biggest things that he's trying to build. And we know that Optimus is of course doing great things, but they will have to step it up because it seems that these other companies could be taking their cake now in terms of, you know, stepping it up. Robotics is going crazy because this is 1x robotics end to end update, and this is end to end autonomy in March of 2024. So you can see right here that this is a demo of their Eve robots doing some crazy tasks. So they're being able to do this and you can see it's able to pack out groceries, maybe just got home from a long day, it's able to get out the groceries and pack them. And remember, there's going to be a coherent, agile system that's going to be able to know exactly what you've got. So if you want to ask it, can you make me some food? It can go ahead, remember what you've got from the grocery store, what's there. It's able to, you know, go get you a toy, go get you this, get you that. I mean, it's going to be really, really fascinating to see how this works. And although this doesn't seem, you know, really, really fluid or, you know, that crazy, all of this stuff is very, very impressive because this is end to end, you know, manipulation on a neural net. So this is some very, very impressive progress. Although it doesn't seem as crazy as figures the other day, because I know that one kind of like broke the Internet or whatever this. Trust me, guys, like, if you've been seeing the robotic space for quite some time, this is some very, very, very, very good stuff. And we're seeing that these robots are getting better and better and better. And I can only imagine where we are going to be in the next 10 years with this kind of technology. If the pace keeps up at this. Because I mean, you know, it's only going to take, you know, a few breakthroughs here and there for robotics to get to that, you know, mobile aloha level of autonomy where things are very, very effective. So this 1, 2, this end to end update by 1x Robotics is honestly very, very impressive. And I'm excited because like I said before in the previous video, I'm not sure if some of you guys saw it, but they have a humanoid robot platform that they still currently are working on and I think their demo is going to be by far one of the most impressive demos that they are working on. So I really, really cannot wait for that demo and I really, really cannot wait to see that. So that's going to be something very fascinating to see in the future. Now what we're going to do is we're going to take a look at some of the other AI announcements from Nvidia that you did miss. I wanted to include them in the another video, but they didn't really fit in because it was about humanoid robots. So I wanted to just include that here. With that being said, let's take a look at that.

The future of heavy industries starts as a digital twin. The AI agents helping robots, workers and infrastructure navigate unpredictable events in complex industrial spaces will be built and evaluated first in sophisticated digital twins. This Omniverse digital twin of a 100,000 square foot warehouse is operating as a simulation environment that integrates digital workers. AMRS running the Nvidia Isaac Perceptor stack centralized activity maps of the entire warehouse from 100 simulated ceiling mount cameras using Nvidia Metropolis and AMR route planning with Nvidia COOPT software in loop. Testing of AI agents in this physically accurate simulated environment enables us to evaluate and refine how the system adapts to real world unpredictability. Here, an incident occurs along this AMR's planned route, blocking its path as it moves to pick up a pallet. Nvidia Metropolis updates and sends a real time occupancy map to coopt where a new optimal route is calculated. The AMR is enabled to see around corners and improve its mission efficiency with Generative AI powered Metropolis Vision foundation models. Models operators can even ask questions using natural language. The visual model understands nuanced activity and can offer immediate insights to improve operations. All of the sensor data is created in simulation and passed to the real time AI running as Nvidia inference microservices or NIMs. And when the AI is ready to be deployed in the physical Twin the real warehouse. We connect Metropolis and Isaac nims to real sensors with the ability for continuous improvement of both the digital Twin and the AI models.

Let's take a look at how Nissan has integrated Omniverse into their workflow. And it's all because it's connected by all these wonderful tools and these developers that we're working with. Take a look.

Sam.

Virtual screening for new medicines is a computationally intractable problem. Existing techniques can only scan billions of compounds and require days on thousands of standard compute nodes to identify new drug candidates. Nvidia Bionemo nims enable a new generative screening paradigm using NIMS for protein structure prediction. With alphafold molecule generation with Molmim and docking with Diff Doc, we can now generate and screen candidate molecules in a matter of minutes. Molmim can connect to custom applications to steer the generative process, iteratively optimizing for desired properties. These applications can be defined with bionemo Microservices or built from scratch. Here, a physics based simulation optimizes for a molecule's ability to bind to a target protein while optimizing for other favorable molecular properties. In parallel, Molmim generates high quality drug like molecules that bind to the target and are synthesizable, translating to a higher probability of developing successful medicines faster. Bionemo is enabling a new paradigm in drug discovery with NIMS providing on demand microservices that can be combined to build powerful drug discovery workflows like de novo protein design or guided molecule generation. For virtual screening. Bionemo NIMS are helping researchers and developers reinvent computational drug design.

We're going to invent a new way invent a new way for you to receive and operate software. This software comes basically in a digital box. We call it a container and we call it the Nvidia Inference Microservice. A Nim and I want to explain to you what it is. A Nim. It's a pre trained model. So it's pretty clever. And it is packaged and optimized to run across Nvidia's installed base which is very very large. What's inside it is incredible. You have all these pre trained state of the art open source models. They could be open source, they could be from one of our partners, it could be created by us like Nvidia Moment it is packaged up with all of its dependencies. So CUDA the right version, Cudnn the right version, TensorRT LLM distributing across the multiple GPUs, Triton inference server all completely packaged together. It's optimized depending on whether you have a single GPU, multi GPU or multi node of GPUs, it's optimized for that and it's connected up with APIs that are simple to use. Now this, think about what an AI API is. An AI API is an interface that you just talk to. And so this is a piece of software in the future that has a really simple API. And that API is called Human. And these packages, incredible bodies of software will be optimized and packaged. Then we'll put it on a website and you can download it, you can take it with you, you can run it in any cloud, you can run it in your own data center, you can run in workstations if it fit. And all you have to do is come to AI.Nvidia.com we call it Nvidia Inference Microservice. But inside the company we all call it nims.

Okay.

Just imagine, you know, someday there's going to be one of these chatbots, and these chatbots is going to just be in a Nim and you'll assemble a whole bunch of chatbots and that's the way software is going to be built someday. How do we build software in the future? And so the productivity of our chip designers can go up. This is what you can do with a nim. First thing you can do with it's customize it. We have a service called Nemo Microservice that helps you curate the data, preparing the data so that you could teach this onboard, this AI. You fine tune them and then you guard rail it. You can even evaluate the answer, evaluate its performance against other examples. And so that's called the Nemo Microservice. Now the thing that's, that's emerging here is this. There are three elements, three pillars of what we're doing. The first pillar is of course, inventing the technology for AI models and running AI models and packaging it up for you. The second is to create tools to help you modify it. First is having the AI technology. Second is to help you modify it. And third is infrastructure for you to fine tune it. And if you like deploy it. You could deploy it on our infrastructure called DGX Cloud, or you can deploy it on prem. You could deploy it anywhere you like. Once you develop it, it's yours to take anywhere. And so we are effectively an AI foundry. We will do for you and the industry on AI what TSMC does for us building chips. And so we go to IT with our, go to TSMC with our big ideas. They manufacture it and we take it with us. And so exactly the same thing here, AI Foundry. And the three pillars are the nims, Nemo, Microservice, and DGX Cloud. The other thing that you could teach the NIM to do is to understand your proprietary information. Remember, inside our company, the vast majority of our data is not in the cloud. It's inside our company. It's been sitting there, you know, being used all the time. And gosh, it's basically Nvidia's intelligence. We would like to take that data, learn its meaning, like we learned the meaning of almost anything else that we just talked about, learn its meaning, and then re index that knowledge into a new type of database called a vector database. And so you essentially take structured data or unstructured data, you learn its meaning, you encode its meaning. So now this becomes an AI database. And it that AI database in the future, once you create it, you can talk to it. And so let me give you an example of what you could do. So suppose you create. You get. You've got a whole bunch of multimodality data. And one good example of that is PDF. So you take the PDF, you take all of your PDFs, all your favorite. You know, the stuff that is proprietary to you, critical to your company. You can encode it. Just as we encoded pixels of a cat and it becomes the word cat, we can encode all of your PDF and it turns into vectors that are now stored inside your vector database. It becomes the proprietary information of your company. And once you have that proprietary information, you can chat to it. It's a smart database, so you just chat with data. And how much more enjoyable is that for our software team? They just chat with the bugs database. How many bugs was there last night? Are we making any progress? And then after you're done talking to this bugs database, you need therapy. So we have another chatbot for you. You can do. Okay. So we call this Nemo Retriever. And the reason for that is because ultimately its job is to go retrieve information as quickly as possible. And you just talk to it. Hey, retrieve me this information. It goes, brings it back to you. Do you mean this? You go, yeah, perfect. Okay. And so we call it the Nemo Retriever. Well, the Nemo service helps you create all these things, and we have all these different NIMs. We even have nims of digital humans.

I'm Rachel, your AI Care manager.

And you just talked to her, and she's connected, in this case to Hippocratic AI's large language model for healthcare, and it's truly amazing.