Everyday AI Podcast – An AI and ChatGPT Podcast - EP 236: NVIDIA GTC Recap - 3 ways NVIDIA is going to change the AI world

Episode Date: March 26, 2024

NVIDIA's announcements from its GTC conference will change the AI world. We partnered with NVIDIA and were at the conference all week. We're breaking down all the big headlines and explaining what it means for the future of AI.Awesome Stuff From Our Partner, NVIDIA -Register for the FREE virtual NVIDIA GTC Conference or buy tickets to the in-person event and fill out this form here: https://www.youreverydayai.com/nvidia-giveaway/Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan questions on NVIDIA and AIRelated Episodes:Ep 234: Driving the Future – NVIDIA’s Vision for AI-powered TransportationEp 233: Robots Among Us – How NVIDIA is building the future of roboticsUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTimestamps:01:50 Daily AI news08:19 Powerful GPUs are crucial for generative AI.09:42 NVIDIA dominates GPU market with powerful technology.15:58 2024: Year of generative AI, compute shortage.19:34 Generative AI implementation expected to surge in 2024.22:52 Partners investing in NVIDIA for compute tech.26:32 Simulations revolutionize manufacturing process with accessible technology.28:56 Predicting audience reaction based on data analysis.33:41 NVIDIA's Gr00t system revolutionizes robot manufacturing.36:33 AGI, humanoid robots with language models approaching.38:25 Insights on robotics and AI at GTC conference.Topics Covered in This Episode:1. Nvidia’s Developments and Innovations2. Nvidia’s Role in Changing AI Ecosystem4. Benefits and Application of AI-powered Simulations5. Nvidia’s Involvement in Robotics6. Speculations and Aspirations for AGIKeywords:NVIDIA, GPUs, Blackwell system, NIMS, NEMO, NVIDIA AI Foundry, Omniverse, Isaac robotics, chipmaker, compute sweet spot, simulation-driven world, humanoids, AGI, digital twin, product simulation, service simulation, Groot, robot manufacturers, Figur, Isaac Robotics platforms, artificial general intelligence, Jensen Huang, Fora, NVIDIA GTC cSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the Everyday Podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live and Adobe Firefly, the All In One Creative AI Studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. Invidia's announcement from their GTC conference last week

will change our technological future. But it's not the headlines that you read that will make that big of an impact. It's actually connecting all of the dots that matters. So I was lucky enough to partner with Invidia for the GTC conference. So today, we're not just going to give you a. recap of the GTC conference, but we're going to tell you the three ways that Nvidia is going to change the AI world. All right. I'm excited to talk about that to more, today and more on Everyday AI. What's going on, y'all? Thanks for joining us.

My name is Jordan Wilson and I'm the host of Everyday AI. We're a daily live stream podcast and free daily news that are helping everyday people learn and leverage generative AI. So we are live unscripted, un-scripted, on edited the realest thing in artificial intelligence. So thank you for joining us. All right. So we are going to get going here in a second, but I have to remind you, if you're listening on the podcast, this is one of those ones. You got to go to your everyday AI.com. Today's newsletter is going to contain so much important information, not just that you need to know to understand the future of generative AI, but a whole lot more. So make sure if you haven't already to go check out our website,

I tell people it is a free generative AI university with more than 230 now backlogs of episodes, live streams, et cetera. So make sure you go check that out. But before we get into the three, the three ways that Nvidia is going to change the AI world, let's first do as we do every single day and go over the AI news. All right. So first, MIT researchers have found an AI image breakthrough. So MIT researchers have discovered a new method for generating high quality images

with a single step, reducing the time and computational resources need it. So previous diffusion models required multiple iterations to generate high-quality images, but the new method, which is being called distribution-matching distillation, only needs one step. That's a mouthful, right? But in test, DMD, or distribution matching distillation, produce images comparable and quality to those generated by more complex original models and achieved state-of-the-art performance in Texas.

to image generation. So actually some pretty big news there coming out of MIT. All right. Our second piece of news, pretty relevant to today's show about Nvidia's dominance, but is Nvidia too dominant? Some big companies are bandied together because they think so. So Intel, Qualcomm, Google, and others have created a group to combat Nvidia's dominance. So they've created the Unified Acceleration Foundation, a consortium of tech companies

aimed to challenge Nvidia's dominance in the AI market through an open source software suite. So the group also, so here's the complete lineup. So it consists of Intel, Google, Arm, Qualcomm, and Samsung. And it aims to deliver software and tools that can support various AI accelerator chips and liberate developers from the constraints, from what they say are the constraints of Nvidia's exclusive technology. So the project is expected to be fully up and running this year, aims to a little bit eliminate obstacles and enhance adaptability for AI developers across different platforms.

So despite Nvidia's market dominance, though, here's the important part. The consortium plans to eventually support Nvidia's hardware and code while also seeking collaborations with other chip makers and cloud computing companies. Wow, that's got to say something when you never thought that companies like Google and Intel and Qualcomm and Samsung would band together, right? You say that five or ten years ago and you say, hey, it's to go up against Nvidia's dominance. It wouldn't make sense, but that's just the day and A, which we live in.

All right, our last piece of news for the day. Open AI has released a lot of new examples of some of the best and most versatile examples of its AI text video software soar up. So the examples are from artists and creatives who were granted early access to SORA to bring their ideas to life. And this is actually the first, at least official kind of announcement that we've heard from OpenA.I about SORA since the announcement, you know, now a couple of months ago. So this kind of behind the scenes look that's sharing some of the best examples from creatives shares how SORA has helped them break free from traditional constraints and visual. new and surreal concepts. So make sure to check out today's newsletter to see those examples. So go to your everyday AI.com and sign up for that. And hey, also, SORA, if you didn't already know,

Sor's about much more than just the AI video. But we're going to be talking about that today's show as well. All right. Hey, thanks for joining us to our live audience. Where are you joining us from? It's been a while since we've had a full week of live shows. So I'm excited. So thank you all for joining, you know, Mike joining us and Dr. Harvey Castro, Rolando, Brian, Juan, thank you all, Carolyn, Brian and Chris. Appreciate you all tuning in. You know, I'd really like to know from you, what was your biggest takeaway of Nvidia GTC? So I'm going to go ahead. And yes, very, we were very lucky here at Everyday AI to partner with Nvidia. We got a lot of behind the scenes access. We got kind of some exclusive information and behind the scenes Intel on exactly what's going on at

Nvidia. So before we get into kind of the three ways that Nvidia is going to change the AI world, I first just wanted to give you a quick recap of the conference as well. All right. Actually, no, I'm going to skip to the end. Let's skip to the end, shall we? So I'm going to give you the answers right now. Right. So if you have a super busy day, you can get on with your day. But here are the three ways that

Nvidia is going to change the AI world. Ready? So for the first time ever, we now have a compute sweet spot. All right. Number two, the world will now be a simulation. All right. And number three, Nvidia is creating humanoid and AGI. Oh, interesting. But they're not, but they are. All right. So there's the end. But today is hot take Tuesday, y'all. So let me know if you are joining this live. I can take it nice and easy. You know, give me one, two or three flame emojis.

You know, how hot should today's hot take Tuesday be? Let me know. I always listen to you. If you all want it kind of middle of the course, we'll keep it there. So before we get into those three very important ways that I believe, InVIDIA is going to change the AI world. First, let me give everyone a very brief recap of the Nvidia GTC conference. So the conference was last week.

So now we are full week away. So it was essentially Monday through Thursday of last week. So about four days of hundreds of speakers, workshops, tens of thousands of attendees. But here's just the biggest takeaways if you missed everything. All right. So what grabbed a lot of headlines was the Blackwell platform. All right. So that is Nvidia's new GPU chip.

And reportedly it is four times more powerful than current GPUs, which you have to keep in mind. That's crazy, y'all, because Nvidia's hopper GPU chip was already the world leader. It was already the most powerful GPU. And if you're not quite a dork like me, let me explain why this is even. important. Well, all generative AI needs these GPUs. Right. So when you go in and you put a prompt into, you know, co-pilot or you run, you know, an AI video in runway or in SORA, et cetera, right? Eventually, somewhere along the line, that is being computed, right? To talk in extremely simple terms,

you know, a lot of people are astounded the first time that they use. generate AI and they say, hell, how can I put in a 10-word prompt and get something unbelievable on the other end? Well, it's compute, right? It is these GPU chips, right? So these GPU chips, it's it powers everything. You know, technically these GPU chips power our economy, right? People looked at me like I was crazy when, you know, now about nine months ago, I said, in video is the most important company to the to the to the u.s economy people didn't understand that this is well before you know i'd even partnered with you know invidia on on anything right this is before i knew anyone at nvita i said invidia is probably the most important company to the u.s economy people didn't

understand that nine months ago a year ago when i started talking about it but this is why you know depending on reports right now invidia has anywhere from you know 70 you know mid 70 to mid 80 percent market share on GPU because their GPUs are so much more powerful than everyone else's. And literally now all of the work that we do, because generative AI is being integrated into everything that we do, whether you've realized it yet or not, right? Like if you're working on a new, you know, Windows machine, you might have co-pilot running your entire operation, right? That is all being run by GPUs.

And for the most part, it is Nvidia's GPUs in their, you know, their current, you know, at least as of last week, their current most powerful GPU hopper. This new Blackwell, so the Blackwell system or the Blackwell chip, however you want to refer to it as, is four times more powerful than the current. Like, I'm laughing because that's never happened before. Like, it doesn't happen where you have a leader in a certain type of technology. And a company updates said technology to make it four times more powerful, reportedly 12 times more energy efficient, which energy is extremely important here when we're talking about the power of generative AI

in the future of compute. All right. So the biggest recap, you know, at least what was grabbing headlines is the new Blackwell platform. Two was NIMS. So this is, again, this is just our recap, not our three ways, Nvidia is going to change the AI world, but NIMS, which is essentially a new way to distribute software. So that is the InVIDIA interface models. And more or less, it's a way to easily deploy custom AI models within Nvidia's kind of AI software suite. Nemo and the Nvidia AI Foundry. This is essentially an end-to-end cloud framework in a powerful toolkit. And then last, last but definitely not in least, number four, Omniverse and Isaac Robotics

updates. So that's essentially, think of it like this. It's a robot gym, right? It's extremely impressive and we're going to be sharing about it in the newsletter. So again, go to your Everydayaa.com and sign up for that free daily newsletter if you haven't already. But updates to the Omniverse and the Isaac Robotics, you have the group kind of humanoid robotics framework that we talked about with the director of NVIDIA's robotics last week as well. So a lot of huge announcements from NVIDIA at the GTC conference. I mean, essentially, they're changing the way the future is being built, right?

And it's also important to talk about this. Nvidia makes chips, but they are so much more than a chipmaker, right? Like, that was one of the biggest messages that I took away is Invidia trying to, you know, shift away from this thought that they are a chipmaking company, right? So even NVIDIA's CEO, Jensen Wong, said that, right? So Jensen said, like, we are not chipmakers. We are creating the future of technology, which I would obviously agree with. All right.

So let's see. All right. Carolyn said hot. She said bring it hot. Brian said two flame emoji. Cecilia said three. Hey, Juan said burn it down. All right, Juan, I'll burn it down.

I'm feeling a little spicy today. All right. So now let's get in. Let's get back to the three ways that Nvidia is going to change the AI. world and I'm going to go into depth on these and hey because y'all wanted it I'm going to bring a little heat all right so uh again as a reminder of three things we now have a compute sweet spot that's number one number two the world will now be a simulation and number three invidia is creating humanoids and hie i yeah i saved that one for last all right let's get into it let's talk about a let's let's talk about number one we now have a compute sweet spot All right. Again, everyday AI is for the everyday people. It's for everyday people.

So if you are a huge GPT and compute nerd, excuse me, because I'm going to go ahead and try to simplify things. And, you know, I might get things only 95% accurate when I'm trying to simplify it to make sure it makes sense for everyone. All right. So what does hitting a compute sweet spot mean? Well, I would say for the, you know, whatever timeline you want to put on to it. But let's just say generative AI, because even generative AI, the definition of what it is and what it means has changed exponentially, you know, over the course of the last couple of years.

But let's say we've been living in a the generative AI world. Let's just say since chat GPT, right, since chat GPT debuted. So let's say we've been living in a generative AI world for two years, give or take. Right. We haven't had enough compute collectively, right? Does the Blackwell announcement mean that we have enough compute? Absolutely not, right? It's one thing Jensen was talking about during his keynote and, you know, in some closed

door, you know, Q&As, he said, we need to build more chips, bigger chips, more powerful chips, agree. So at least for right now, though, we have a compute sweet spot. But don't get me wrong. Compute is still currency, right? That's kind of the hot thing, all the smart. tech CEOs are saying, I've been saying it on the show here for months. But now it's cool because all the, all the billionaires are saying it, that compute is still a currency.

And it is, right? It doesn't matter for the hottest AI startup out there or, you know, the enterprise company that's, you know, deploying generative AI across their workforce. It doesn't matter how good you are, how great your ideas are if you don't have the compute, right? Think of it like power outlets. All right. You could have, you know, 10 computers and 10 phones and all these things. But if you're sitting at the airport and there's only one available power outlet, you can't do all that work.

All right. Again, oversimplifying here. But that's what compute is, right? So as the collective business world, especially here in the U.S., we've talked about 2024 is the year of generative AI implementation, you need enough outlets on the wall to plug. Oh, here's our new, you know, AI enabled CRM.

Here's our new generative AI powered customer service, you know, solution. You need those outlets. You need the compute, right? And it has been, and it still will continue to be a problem, having enough compute. All right. But the fact that we went, again, without getting too technical here, Nvidia's Hopper GPU chip was by far the most powerful in the world. And then we get this Blackwell chip that was just unveiled last week.

That is reportedly four times more powerful, 12 times more energy efficient, depending on how you're looking at the different tasks, different benchmarks. There's different, you know, different multitudes or multiples. But essentially this, it is way more powerful, way more energy efficient. But it's still not enough. So here's why we have a sweet spot. Adobe just introduced an entirely new way to create, bringing the power and precision of its creative suite into one conversational experience.

Meet Firefly AI Assistant, now live in the Adobe Firefly app, the All In One Creative AI Studio. Powered by Adobe's Creative Agent, Firefly AI Assistant lets you start with your vision, just describe what you want, and shape the outcome as it takes form with the Assistant. The Assistant orchestrates multi-step workflows, drawing on 60-plus programs, and grade tools across Adobe Creative Cloud apps, including Photoshop, Illustrator Premiere, Lightroom Express, and more to help bring your ideas to life. You can also get started with creative skills, a growing library of pre-built workflows for common creative tasks like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant

takes is visible so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adobie.com. Models are actually getting smaller, right? We've talked about it here on the show, not just with edge computing or generative AI on actual devices. But when you look at what Mistral is doing, right, the French company that is creating large language models, But, you know, we still do have models getting much larger. Okay. So your, your trillion parameter models like GPT4, presumably GPT5 will be trillions, Gemini, Claude, Claude's new, you know, their new models.

So we still have large English models getting bigger, but we have now so many models getting smaller, which reduces the need for compute, right? The same thing. I mean, we even talked about a very small example, but, you know, the MIT researchers with their distribution matching distillation, the DM, right, the DMD. So so many researchers, startups, et cetera, have been spending the last year and a half because compute has been a problem. They've been spending so much time, energy, and resources on how can we make generative

AI faster? How can we make models smaller? How can we, you know, kind of reduce the number of generations needed, right? So let's just say in the cloud something's computing 20 times over. They're saying how can we get it once? Okay. So that's why we have, at least right now, a compute sweet spot. And I don't think a lot of people fully understand that.

You know, this is something I was talking to exhibitors on the GTC floor about, you know, hey, what do you think of Blackwell? And a lot of companies were excited. and rightfully so. You know, you have to think of it like computer software, right? Or maybe your smartphone software, right? When a new update comes out, until all of the other apps or, you know, everything else starts demanding more power, you almost have this sweet spot where

everything just runs better than expected, right? That's where we're going to be at in the coming months. And maybe, I don't know how long this is going to last. maybe a year or so. So this is actually a great time, a great moment for generative AI because you are going to see now all of these companies. We've been talking about this. 2024 is the year of implementation. And companies for the most part will be able to implement generative AI solutions, top to bottom, company-wide, enterprise-wide, in a somewhat cost-controlled way.

Right. where I think, let's just say, if we weren't getting, you know, new, new GPUs from Nvidia or Qualcomm or AMD, et cetera, if we weren't getting all these new chips, we would be having a problem as a country, getting enough power outlets, right? Because now all of a sudden, every single business wants to take their generative AI plug and power it and plug it into a socket, right? But two years ago, there weren't really enough, right? Like researchers and, you know, AI companies were looking around and saying, hey, we're running out of outlets, right? And if businesses want to power, you know, their business with generative AI, we either

need to reduce the power needed or create more outlets. So we kind of have both of those things happening in tandem with the focus on edge AI or, you know, models small enough to live on a device, which really negates the need for so much of these cloud resources, right? If you can run models locally on your phone or locally on a machine, it obviously reduces the load that you eventually need the compute power that you actually need because it all is living locally. But every time you go on to chat GPT or to runway or maybe once we all get access to SORA, that just eats away compute.

And, you know, unfortunately right now, compute is still expensive and it is not terribly energy efficient. New updates like Blackwell help to solve that. But it's still, I won't say problematic, but it's still something that you have to take into consideration. So we have that compute sweet spot. And I also think, right, all the companies that the open AIs, the, you know, I keep saying runway, mid-journey, entropic, right, all of these companies are using invidious chips, right? So presumably, another way that this changes and kind of working in this compute sweet spot is I'm sure over the last year or so that you or your company has been using different generative AI services, you get errors all the time, right? And a lot of, you know,

consumers that don't fully understand kind of this compute paradigm, look at this generative AI technology and they're like, oh, it kind of stinks. Time's out all the time. We get errors. you know, there's outages. Well, it's because hundreds of millions of people are all of a sudden rushing to use all of these services that didn't exist before, right? It's, you know, Jensen said something in a Q&A session. He said, you know, we're creating demand from nowhere. We're not borrowing all of this demand from somewhere else.

So we do have a sweet spot in compute. And I also think as an example, right? So we talk about Sam Altman, you know, reportedly raising $7 trillion for computer. alone. Well, I would say my hot take here, since you wanted the fire, is I think he saw, right, I think at some point all these big partners who are spending tens of billions of dollars, I'm sure they get a look at what Nvidia has in the pipeline. And I'm sure he saw the just huge jump, right, between Hopper and Blackwell and saw, wow, we should be investing in compute ourselves. We should be creating chips, right? That's why you now see, you know, Amazon, Microsoft, et cetera.

creating now chips in-house because everyone is still going to need compute, but at least for right now, kind of the quote-unquote software in the hardware, I think in the coming months, maybe year plus are going to be working in harmony. Now you have more companies that didn't even exist a year ago popping up that are allowing you to share kind of compute resources in the cloud versus, you know, spending, you know, a couple of billion dollars or hundreds of millions of dollars to get, you know, a thousand of these, you know, GPUs running at your, you know, at your company.

All right. So that's number one. Yeah, yeah. Hey, we're getting there, Ben. Yes, the world is a simulation. Yeah, don't worry. We're getting there. All right.

So that's number one. Number two, the world will now be a simulation. Yeah, it will. Right. I'm not going to get too meta on this. Obviously, we're still living in a physical. world. But the way things are going, and this is, I would say, at least things that I learned the most at the Nvidia GTC conference is just the power of simulation, right? Obviously, I knew

how simulation worked and in some of the use cases that AI powered simulations in digital twins, right? We've talked about it. We have experts, we've had experts on this show before talking about the power of digital twins, but a lot of these new announcements from Nvidia changes what is even possible with digital twins and simulations. So if you don't know what that is, let me try to do my best to explain to you. Think, let's say your company is investing, you know, $50 million in building a new manufacturing plant. All right.

So maybe the way that you would traditionally, you know, build this plan is you look at your current plant, you model it after that. You hire some expensive consultants to come in, you know, run some, maybe you run some simulations. But for the most part, you're probably just looking on what you think has worked and you're putting forward your best guesses. That's incredibly inefficient, right? And it's not leveraging technology, right? So with simulations, essentially, you can simulate an entire new factory that doesn't exist,

right, based on millions and millions of other simulations that have been run. So that's an example of something that you can do, you know, not just in Vindividius platform, but many other, you know, generative AI and AI powered cloud platforms. You can run literally millions of simulations of your warehouse that doesn't exist yet based on your projected data, your products, you know, how much space do they need, how many people are going to be, you know, operating in this facility. And you'll see by, you know, these platforms running literally, millions of simulations for your manufacturing plant that doesn't exist yet, you're going to see

problems. Oh, these shelves are are too close together. You know, they're not going to be able to, you know, operate and move things from this point, you know, point A to point B. There's going to be a lot of collisions here. So we, you know, you need to rethink how you set up this part of your manufacturing or this part of your production line, right? So literally, that's just an easy example, hopefully to understand on how simulations are run and how they're already and they've been being used for years. So now we have some updates to the Omniverse, right? And so the other thing also going back to point one about the sweet spot of compute is it also changes who can use all of these platforms because at least for the foreseeable future, it's lowering the barrier

of entry to play here, right? To play in some of these very powerful normally enterprises only environments, right? It's, it's, it's, it's, it's kind of like with chat, GBT, and we've talked about this on the show, you know, so many, so many times before. This is the first time I believe, right? I've, I've been, you know, kind of in Martec and in communications now for full time for 20 years. And this is the first time in my 20 years on Earth that the average company can go out and spend, you know, 20 bucks, 50 bucks, $100 a month and get similar level of technology that enterprise companies can get. That's another part of, you know, kind of being in this compute sweet spot, but also point number two on how the world will now be a simulation. Because now more and more

companies can start to use just in this example, Nvidia's Omniverse or, you know, other kind of world simulators powered by AI to simulate everything, right? Hey, you guys wanted hot takes. Here's what I think. I think simulations are going to get weird. I think they're going to get weird, right? Right now, the example I gave of, all right, your company spending $50 million, building a new production plant, that makes sense, right? That makes sense. Yeah, you want that to be simulated with millions of simulations using, you know, in VDIA's data from, you know, all of their systems, uploading all your data. But I think a lot of things are going to be simulated, right? I could even see if you want to get super weird, I could see a time in

in the future. I don't know if it's months or years away where I could simulate this exact podcast, this exact live stream. You know, and I tell a system, hey, here's what my topic is. Here's some of my bullet points.

And it's going to go ahead. It's going to run millions of simulations. Oh, what if you go down this route? What if you go down this route? Here's how your audience is going to react to all these different things based on all the data that we have on you, your audience,

how they respond to certain content, et cetera. When you tap into these powerful platforms like the Omniverse, they're not the only ones, but if you bring your data and you are able to tap into literally a world of knowledge, simulations are, I don't know if that's weird or extremely powerful or both, but that's where I see things heading, right? Where every single I think in the future, every single product service that you interact with has already been simulated, right, as a.

digital twin in the omniverse, in the metaverse, et cetera, where almost every single thing that we interact with, cars we drive, the devices that we use, you know, the software we send emails on. I think everything will have already been simulated millions or billions of times already, simple day-to-day interactions with products that we use all the time. Hopefully what that means is more intuitive products and services. better experiences in the real world. That's the point of simulations.

That's the point of, as an example, the Omnifers. But I do believe the world will be a simulation. I think almost every single, you know, the clothes you wear, the shoes, you know, the new pair of Nike's that you go by, they will have been run through a, like a digital simulation millions or billions of times. right, across different textures, you know, what happens if, you know, this person has a bigger left foot than right foot? What happens if, you know, they constantly go from wet to dry surfaces, right? Like all of these products and services that we use every single day will have already been simulated millions or billions of times.

Hopefully, like I said, hopefully that doesn't make it weird on our end, but it just makes for better product services and experiences in the real world. All right. Yeah, hey, Earth 2. Hey, great, great point, Juan. Thanks for joining us live. Yeah, Earth 2, that's a great point. If you read some of my kind of insider takeaways from GTC, Earth 2, that's something that

NVIDIA announced as well, a literal Earth simulator, right? So you can't look at me too weird and be like, all right, Jordan, this is weird if you say the world will be a simulation. Well, Jensen Wong talked about that, even at his keynote, some new updates to the Earth 2, kind of their world simulator. And they're trying to, you know, reduce climate change and to better predict extreme weather sooner. I think literally every aspect of our daily lives will very soon have already been simulated. Hey, I love this comment from Josh.

And hey, shout out to Josh. Josh gave me like probably the funniest reply to a comment ever on LinkedIn. But Josh just said this is like when Dr. Strange ran through millions of simulations to identify. identify the reality that would defeat Thanos. Yes, exactly. Yeah, if you're a Avengers fan, exactly that, you know, running through literally millions or billions of different scenarios to give all of us, hopefully ideal business and personal outcomes. That is what AI-powered world simulators mean to all of us. Hey, Tanya, I don't think I can get into this one. She said, What about running simulations for potential marriages?

I'm sure it's already happening, right? I'm very happy that I lucked out with mine. So, yeah, but I'm sure I'm sure that's going to be a thing where, you know, people, yeah, are simulating everything. I'm sure that already exists, you know, simulating things in dating apps or matchmaking. I'm sure that already exists. But yes, everything is going to be simulated. All right.

Last but not least, I know some of you listen, you know, when you're walking your dog or on an exercise machine and you know, you might only have 35 minutes on that machine. You can't listen to me forever. So number three, invidia is creating humanoid and AGI. So here's the thing. They're definitely not, but they are. Okay. Let me tell you what that means.

Humanoids, all right? Again, we talked with Emmett Goel, the director, Nvidia of robotics. Invidia is not building physical robots. but their Groot system love the name. I think it's general, it stands for general robotics, zero, zero three. I think it's technically what it stands for. So the Groot system allows every other robot manufacturer to use Nvidia's system to, again, we talked about the Isaac, right, Isaac Robotics, essentially a gym for robots, right?

seeing some of those simulations where you just see, you know, millions of these robots walking around in a kind of digital twin, real world, Omniverse, Isaac Robotics, it's their gym. They're just existing and they're getting smarter, right? So all of these companies that are building physical products are more than likely already using either Invidias chips or their software solutions like Isaac robotics, like the Omniverse. So, Nvidia is not creating any physical robots, right? But companies as an example, like Figure. So we shared about Figure on the show about, what, nine days ago, right?

And we showed you that video where there's a robot that is not only interacting with a person. So it is doing general robotics where it's reacting to things in real time. It's talking to a person. It's completing tasks based on a person. it's doing one task and answering questions on another, right? That's the example of the figure 01. It's using chat GPT to listen and to process and to speak in natural language. You know, that's figure 01 is presumably running on Nvidia GPU chips.

InVIDIA is one of the main investors, financial investors. They're one of the biggest partners with figure. You know, and I do think figure, at least in my opinion, is one of the companies on the robotics or the humanoid side that's kind of running away with things. But guess what? It's no surprise that they are one of their biggest investors is Nvidia. Right. So all of these companies that are creating these humanoid robots are either using

Nvidia chips. They are using Nvidia's Isaac Robotics platforms. They're going to be using the new brute kind of foundational system. Nvidia is intertwined into everything, literally everything. If you want to talk meta, Invidia, if you want to talk OpenAI, Invidia, whatever your favorite generative AI system is, Nvidia. Right. So they're creating humanoids and, in fact, AGI. Right.

We were able to ask Nvidia CEO Jensen Wong questions you know, kind of in a closed door session and he was asked about AGI right? And how far how far out are we? And you know, what he said is, you know, hey, depends on what you even consider it to be artificial general intelligence, right?

Human intelligence is obviously changing. You know, what technology and AI is capable of is changing all the time. You know, his kind of blanket statement was probably five years, I think we're probably a lot closer than that, right? There's always an argument and people say, oh, what is a GI, right? But that's essentially when, let's just use the figure robot as an example, because I think that's pretty, pretty easy to visualize when you have a physical form, humanoid robot,

right? But you power it with a large language model like chat GPT, right? So at what point can that, you know, figure? slash chat GVT, perform tasks, both mental and physical, general tasks at the same level, better than the average human. I think we're a lot closer than people think, right? Especially when, you know, probably still the strongest model out there. Sorry, Fod 3, sorry, Gemini 1.5.

I still think GVT4, which is an old model, is still the best model out there across the board when you look at outside functionalities and features. When you think of right now that a leading model is two years old, and when you think, hey, what happens when the next GPT, whether it's 4.5 or 5, what happens when that comes out? Yeah, I think we're getting closer and closer to AGI, right? But a couple of things, some of my takeaways, and I open the show by talking about connecting the dots, right,

between what was said and what was not said, but what was implied. or maybe things that a lot of people miss. Right. So during a closed door Q&A, I was literally about 10 feet from, Nvidia CEO, Jensen Wong. So I was able to get a lot of insights, even just looking at his facial expression, how he reacted to certain questions.

And he was asked a lot about AGI, about compute, about humanoids, right? And he said, hey, we're going to have our chat GPT moment for robotics soon. Right. And I think robotics and humanoids and AGI are all a lot closer tied to each other than most people realize. But one thing that I took away from both closed door and open door sessions at the NVIDIA GTC conference is the only thing in questioning that the CEO of one of the largest, I believe they're either the third. I didn't look this morning, but either the third or fourth largest company in the United States by market cap. And the CEO only brought up in a closed-door session one aspect of AI more than once without being prompted.

All right. So what that means is, you know, hey, Jensen, A or B, and he says B. Hey, Jensen, one, two, or three, and he says three. But bringing up things unprompted, I think, tells about not just the direction of where maybe his mindset is, but also the direction of the industry. And the only thing in that closed-door session, and I didn't see anyone else talk about this or write about it, the only thing that Jensen brought up more than once unprompted was SORA. All right. And here's the other thing.

Brought it up unprompted. It's not even his product, right? Technically, I mean, obviously, Nvidia powers Open AI, but you could think of them as, in theory, a competitor because Sam Altman is reportedly trying to raise $7 trillion to create chips, compute, fusion power, etc. But he brought up SORA on multiple occasions and talked about its implications, not as an AI video tool. That's the thing that most people are forgetting or they're overlooking. He was bringing it up because of AGI. He was. You know, there's a transcript somewhere.

We'll try to, you know, try to include that in our daily newsletter today. So sign up at your everyday AI.com. However, he brought up SORA on multiple occasions, not for its video editing prowess. He brought it up because he said he was extremely impressed with its ability to understand the relationship between the physical world, objects in the physical world. And what that means for not just AI, but what that means for humanoids, what that means for artificial general intelligence. So had to connect some of the dots between what was said and what was unsaid at the Nvidia GTC conference. So as a reminder, as we wrap up the three ways that I believe that AI or Nvidia's announcements at the GTC conference that their conference, that their announcements are going to change the AI world. All right.

We're going to recap. Number one, we'll hit a compute sweet spot maybe for the first time. It might be short-lived. Number two, the world will be a simulation. what these announcements mean or digital twins can't overstate it enough. And number three, yes, even though they're not technically working toward humanoids and AGI, NVIDIA is actually helping to create humanoids and AGI. All right, speaking of NVIDIA GTC, hey, I found the date.

I think you still have about 10 more days. So make sure to check out the link in the description. But you can still, even though the conference is already wrapped, up, you can still sign up for free. Literally, some of the leading experts in the world are running workshops that you would normally pay thousands of dollars to attend these workshops. You can go sign up, find the link, you can sign up and still watch all of these replays and sessions for free for, I think, about another 10 days.

So make sure that you go do that. And by signing up, if you sign up with our link, you will enter into our giveaway for a free Nvidia, G-Force, GPU, as well as DLI learning credits from Nvidia. All right, that's a wrap for today's show. Make sure to join us Thursday. This is going to be an amazing one. So learning from the Giants, WWT's Gen AI Blueprint. We are going to be talking with the founder or the co-founder and CEO of Worldwide Technology,

Jim Kavanaugh, one of the largest, y'all, let me restate this, sitting down, one-on-one with one of the largest companies in the world with their CEO. This was actually, we just recorded this last week at GTC. So I can tell you how this conversation goes. And it's amazing. You are not going to want to miss that. And you're also not going to want to miss today's newsletter. Go to your everyday AI.com.

Sign it for the free daily newsletter. And hey, if this show was helpful, go ahead. Please, we spend hours putting these shows together. It takes you about 10 seconds to go repost this, re-share it, share it. Here's what I'll do. If you're on the live stream, you see this. if not, you're on the podcast. I have a picture here with Nvidia CEO Jensen Wong. But there's actually a pretty embarrassing story that happened before this photo was taken.

So if you repost this, if you share this on social media, let me know. And I'll tell you in a private message. I can't put myself on blast there. It's extremely embarrassing. I'll tell you a funny story. So thank you for joining us. We hope to see you back tomorrow and every day for more everyday AI. Thanks, y'all. Firefly AI Assistant, now live in Adobe Firefly, the Allman One Creative AI Studio. Just describe what you want to create in your own words and the assistant handles the rest, orchestrating multi-step workflows across Adobe Creative Cloud apps, including Photoshop, Premiere Express, and more in one conversational interface.

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