Ep 236: NVIDIA GTC Recap – 3 ways NVIDIA is going to change the AI world

Unleashing New Dimensions in AI: A Recap of NVIDIA's GTC Conference

NVIDIA, the undisputed leader in the GPU market with 70-80% share, is transforming the future of technology. Not content with being a chip manufacturer, NVIDIA is harnessing artificial intelligence (AI) to change the world in unprecedented ways. This article explores the key takeaways from the highly anticipated NVIDIA GTC conference and highlights three ways NVIDIA is reshaping the AI landscape.

Blackwell: Redefining High-Performance Computing

NVIDIA's new entrant, the Blackwell system, boasts not only impressive power—being reportedly four times more resilient than its predecessor—but also remarkable energy efficiency, claimed to be 12 times superior. Spanning developments in NIMS, NEMO, NVIDIA AI Foundry, Omniverse, and Isaac robotics, this development is expected to dramatically increase the scalability and sustainability of Generative AI operations.

NVIDIA's Three-Fold Strategy: An AI Revolution

NVIDIA's strategic approach to revolutionizing the AI world is three-fold: finding a compute sweet spot, fostering a simulation-driven world, and fast-tracking the development of humanoids and Artificial General Intelligence (AGI).

The Compute Sweet Spot

NVIDIA is making strides in enhancing the competitiveness of edge AI and local model running. By reducing reliance on cloud resources, NVIDIA is poised to optimize computational loads and improve energy efficiency, fostering a more sustainable AI landscape.

Towards a Simulation-Driven World

AI-powered simulations have already been used in manufacturing and production lines for years, but NVIDIA's updates to its Omniverse platform signal a future where such simulations become commonplace. The prospect of nearly every product and service being simulated in a digital twin could completely transform consumer experiences, enabling more intuitive and reliable interactions in the real world.

Humanoids and AGI: The Future of AI

NVIDIA doesn't physically build robots. Still, its advancements in Isaac robotics and the introduction of a system called 'Groot' position it at the forefront of humanoid and AGI development: allowing robots to exist in digital twins and learn in simulated environments. The road to AGI may be shorter than imagined, with experts speculating that it might be achievable within the next five years. This, coupled with NVIDIA's reported investments in pioneering robotics companies like Figure, signals an exciting future for AGI.

Key Takeaways

The recent NVIDIA GTC conference was alive with significant announcements expected to impact AI considerably. The future appears bright with increases in compute capabilities, advancements in digital twin technology, and strides towards AGI and humanoid development. These current developments along with other AI news highlighted during the conference promise to drive a decisive leap forward in the AI industry, bearing witness to NVIDIA's determination to redefine the artificial intelligence landscape.

While we eagerly await real-world applications and impacts of the advancements announced during the NVIDIA GTC conference, it is clear that the AI technology giant is paving the way for a future where artificial intelligence is woven into the fabric of our everyday lives.

Topics Covered in This Episode

1. Nvidia’s Developments and Innovations
2. Nvidia’s Role in Changing AI Ecosystem
4. Benefits and Application of AI-powered Simulations
5. Nvidia’s Involvement in Robotics
6. Speculations and Aspirations for AGI

Podcast Transcript

Jordan Wilson [00:00:19]:
NVIDIA's announcement from their g p 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 NVIDIA for the GTC conference. So today, we're not just gonna give you a recap of the GTC conference, but we're gonna tell you the 3 ways that NVIDIA is going to change the AI world. Alright. 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.

Jordan Wilson [00:01:02]:
My name is Jordan Wilson, and I'm the host of everyday AI. We're a daily livestream podcast and free daily newsletter helping everyday people learn and leverage generative AI. So we are live, unscripted, unedited, the realest thing in artificial intelligence. So thank you for joining us. Alright. 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 gotta go to your everydayai.com. Today's newsletter is gonna contain so much important information, not just that you need to know to understand the future of generative AI, but a whole lot more.

Jordan Wilson [00:01:39]:
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, livestreams, etcetera. So make sure you go check that out. But before we get into the 3 the 3 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. Alright. 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 needed. 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.

Jordan Wilson [00:02:27]:
That's a mouthful. Right? But in test DMD or distribution matching distillation, produce images comparable in quality to those generated by more complex original models and achieved state of the art performance in text to image generation. So, actually, some some pretty big news there coming, out of MIT. Alright. Our second piece of news, pretty relevant to today's show about NVIDIA's dominance, but is NVIDIA too dominant? Some big companies are banding 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 open source, through an open source software suite. So the group also so here's the complete lineup.

Jordan Wilson [00:03:20]:
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 eliminate obstacles and enhance adaptability for AI developers across different platforms. So, despite NVIDIA's market dominance though, here's the important part. The consort the consortium plans to eventually support NVIDIA's hardware and code while also seeking collaborations with other chipmakers chipmakers and cloud computing companies. Wow. That's gotta say something when, you never thought that companies like, Google and, Intel and and Qualcomm and and Samsung would band together. Right? You say that 5 or 10 years ago and you say, hey. It's it's to go up against NVIDIA's dominance.

Jordan Wilson [00:04:18]:
That wouldn't make sense, but that's just the day to day which we live in. Alright. Our last piece of news for the day. OpenAI has released a lot of new examples of some of the best and most vers versatile examples of its AI text to video software, Sorra. So the examples are from artists and creatives who were granted early access to Soarer to bring their ideas to life. And this is actually the first, at least official, kind of announcement that we've heard from OpenAI about Sora since the announcement, you know, now a couple of months ago. So, these, this kind of behind the scenes look, that's sharing some of the best examples from creatives, shares how, sort of has helped them break free from traditional constraints and visualize new and surreal concepts. So make sure to check out today's newsletter, to see those examples.

Jordan Wilson [00:05:12]:
So go to your everydayai.com and sign up for that. And, hey, also, SORA, if you didn't already know, SORA's about much more than just the AI video, but we're gonna be talking about that today's show as well. Alright. Hey. Thanks for joining us to our live audience. Where are you joining us from? It's it's been a while since we've had a a full week of live shows, so I'm excited. So thank you all for joining. You know, Mike joining us and doctor Harvey Castro, Rolando, Brian, Juan.

Jordan Wilson [00:05:39]:
Thank you all. Carolyn, Brian, and Chris. Appreciate y'all tuning in. You know, I'd really like to know from you, what was your biggest takeaway of NVIDIA GTC? So I'm gonna 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, you know, 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.

Jordan Wilson [00:06:21]:
Alright. Actually, no. I'm gonna skip to the end. Let's skip to the end, shall we? So I'm gonna get give you the the the answers right now. Right? So if you have a super busy day, you can get on with your day. But here are the 3 ways that NVIDIA is going to change the AI world. Ready? So for the first time ever, we now have a compute sweet spot. Alright.

Jordan Wilson [00:06:42]:
Number 2, the world will now be a simulation. Alright. And number 3, NVIDIA is creating humanoids and AGI. Oh, interesting. But they're not, but they are. Alright? So I'm gonna there's there's the end, but today is hot take Tuesday, y'all. So so so let me know if if you are joining us live. I can take it nice and easy.

Jordan Wilson [00:07:07]:
You know, give me give me 1, 2, or or 3 flame emojis. You know? How how hot should today's hot take Tuesday be? Let me know. I always listen to you. If, if if you all want it kind of middle of the course, we'll we'll keep it there. So before we get into those 3, very important ways that I believe NVIDIA is going to change the AI world. 1st, 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.

Jordan Wilson [00:07:43]:
So about 4 days of, hundreds of of speakers, workshops, tens of thousands of, attendees. But here's, just the biggest takeaways if if you missed everything. Alright. So what grabbed a lot of headlines was the Blackwell platform. Alright. So that is NVIDIA's new GPU chip. And reportedly, it is 4 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.

Jordan Wilson [00:08:19]:
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, Copilot or you run, you know, an AI video in Runway or in SoR, etcetera. Right? Eventually, somewhere along the line, that is being computed. Right? To to talk in extremely simple terms. You're you you know, a lot of people are are astounded the first time that they use generative AI, and they say, hell. How can I put in a a a 10 word, prompt and get something unbelievable on the on the other end? Well, it's compute.

Jordan Wilson [00:09:06]:
Right? It is these GPU chips. Right? So these GPU chips, it's 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 9 months ago, I said, NVIDIA is the most important company to the to to the US economy. People didn't understand that. This is well before, you know, I'd even partnered with, you know, NVIDIA on on anything. Right? This is before I knew anyone at NVIDIA. I said NVIDIA is probably the most important company to the US economy.

Jordan Wilson [00:09:42]:
People didn't understand that 9 months ago, a year ago when I started talking about it, but this is why. You know, the depending on reports, right now, NVIDIA has anywhere from, you know, 70, you know, mid 70 to mid 80% 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 Copilot 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 4 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 a a leader in a certain type of technology and a company updates said technology to make it 4 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. Alright.

Jordan Wilson [00:11:00]:
So the biggest recap, you you know, at least what was grabbing headlines is the new Blackwell platform. 2 was NIMS. So this is, and again, this is just our recap, not our our three ways NVIDIA's gonna change the AI world, but NIMS, which is essentially a new way to distribute software. So that is the NVIDIA interface models, and 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 cloud framework and a powerful toolkit. And then last last but definitely not the least, number 4, Omniverse and Isaac robotics updates. So that's essentially, think of it like this. It's a robot gym.

Jordan Wilson [00:11:46]:
Right? It's it's extremely impressive, and we're gonna be sharing about it in the newsletter. So, again, go to your everyday ai.com and sign up for that free newsletter if you haven't already. But updates to the Omniverse and the Isaac robotics. You you have the grouped, 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 g t z conference. I mean, essentially, they're 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 a chipmaker.

Jordan Wilson [00:12:31]:
Right? Like, that was one of the biggest messages that I took away is, in NVIDIA trying to, you know, shift away from this thought that they are a chipmaking company. Right? So even, NVIDIA's CEO, Jensen Huang said that. Right? So Jensen said, like, we are not, chipmakers. We are creating the the future of technology, which I would obviously, agree with. Alright. So let's see. Alright. Carolyn said hot.

Jordan Wilson [00:12:59]:
She said bring it hot. Brian said 2 flame emojis. Cecilia said 3. Hey. Juan said burn it down. Alright, Juan. I'll burn it down. I'm feeling a little spicy today.

Jordan Wilson [00:13:08]:
Alright. So now let's get in let's get back to the 3 ways that NVIDIA is gonna change the AI world. And I'm gonna go into depth on these and, hey, because y'all wanted it, I'm gonna bring a little heat. Alright. So, again, as a reminder of the 3 things, we now have a compute sweet spot. That's number 1. Number 2, the world will now be a simulation. And number 3, NVIDIA is creating humanoids and AGI.

Jordan Wilson [00:13:31]:
Yeah. I saved that one for last. Alright. Let's get into it. Let's talk about hey. Let's let's talk about number 1. We now have a compute sweet spot. Alright.

Jordan Wilson [00:13:45]:
Again, everyday AI is for the everyday people. It's it's for everyday people. So if you are, a huge g p t and and compute nerd, excuse me, because I'm gonna I'm gonna 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. Alright. So what does hitting a compute sweet spot mean? Well, I would say for the you know, whatever whatever timeline you wanna put onto 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 a the generative AI world, let's just say since Chat GPT, right, since Chat GPT debuted.

Jordan Wilson [00:14:33]:
So let's say we've been living in a generative AI world for for 2 years give or take, right, we haven't had enough compute collectively, Right? Does the the the Blackwell, announcement mean that we have enough compute? Absolutely not. Right? It's one thing Jensen was was talking about during his keynote and, you know, in some closed door, you know, q and a's, 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, the hot thing all the smart tech CEOs are saying. I've been saying it on the show here for months.

Jordan Wilson [00:15:15]:
But now it's cool because all the all the billionaires are saying it, that that compute is still a currency. And it is. Right? It doesn't matter for the the the hottest AI startup out there or, you know, the 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. Alright? 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. Alright? Again, oversimplifying here. But that's what compute is.

Jordan Wilson [00:15:58]:
Right? So as the, collective business world, especially here in the US, we've talked about 2024 is the year of generative AI implementation, you need enough outlets on the wall to plug, oh, here's 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. Alright. 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 4 times more powerful, 12 times more, energy efficient depending on, you know, how you're, you know, looking at the different task, different benchmarks. There's different, you know, different multitudes, or or multiples.

Jordan Wilson [00:17:02]:
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. 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 we still do have models getting much larger. Okay? So your your trillion parameter models like gpt4, presumably GPT 5 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.

Jordan Wilson [00:18:04]:
I mean, we even talked about a very small example, but, you know, the MIT researchers with their distribution matching distillation. The d m right? The DMD. So so many, researchers, startups, etcetera, have been spending the last year and a half because compute has been a problem. They've been spending so much time, energy, and 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.

Jordan Wilson [00:18:51]:
And I don't think a lot of people fully understand that. You know, this is something I was talking to, exhibitors on the g t c floor about. You know, hey. What do you think of 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 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 gonna last. Maybe a year or so.

Jordan Wilson [00:19:34]:
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, cost controlled way. Right? Where I think let's just say if if we weren't getting, you know, new new GPUs from NVIDIA or Qualcomm or AMD, etcetera. 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 and plug it into a socket.

Jordan Wilson [00:20:23]:
Right? But 2 years ago, there weren't really enough. Right? Like researchers and and, you know, AI companies were looking around and saying, hey. We're running out of outlets. Right? And if 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 on edge AI or, you know, models small enough to live on a device, which really negates, the need for so much of of these cloud resources. Right? If you can run models locally on your phone or locally on a machine, it obviously reduces the the the the load that you eventually need, the compute power that you need because it all is living locally. Right? But every time you go on to to chat gpt or to to runway or, you you know, maybe once we get all get access to to, that that just eats away computes. And, you know, unfortunately, right now, compute is still expensive, and it is not terribly energy efficient.

Jordan Wilson [00:21:28]:
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 the open AI, the, you know, I keep saying runway, mid journey, in in anthropic. Right? All all of these companies are using NVIDIA's 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 and and a lot of, you 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 kinda stinks. Time's out all the time. We get errors.

Jordan Wilson [00:22:21]:
You know, there's there's outages. Well, it's because 100 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 you know, Jensen said something in a a q and 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 $7,000,000,000,000 for compute alone.

Jordan Wilson [00:22:52]:
Well, I would say my hot take here since he wanted the fire is I think he saw. Right? I I I think at some point, all these big partners who are spending tens of 1,000,000,000 of dollars, I'm sure they get a look at what NVIDIA has in the pipeline. And I'm sure he saw the the just huge jump, right, between Hopper and, and 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, etcetera, 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 and the hardware, I think in the coming months, maybe year plus, are gonna 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, couple a $1,000,000,000 or 100 of 1,000,000 of dollars to get, you know, a thousand of these, you know, GPUs, running at your, you know, at your company.

Jordan Wilson [00:23:58]:
Alright. So that's number 1. Yeah. Yeah. Hey. We're getting there, Ben. The yes. The world is a simulation.

Jordan Wilson [00:24:04]:
Yeah. Don't worry. We're getting there. Alright. So that's number 1. Number 2, the world will now be a simulation. Yeah. It will.

Jordan Wilson [00:24:12]:
Right. I'm not gonna 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 I I knew how simulation worked and and and some of the use cases, that AI powered simulations and digital twins. Right? We've talked about it. We have experts. We've had experts on this show before talking about the power of of digital twins, but, a lot of these new announcements from NVIDIA changes what is even possible with digital twins and simulations.

Jordan Wilson [00:24:56]:
So, if you don't know what that is, let me try to do my best to explain it to you. Think. Let's say your company is investing, you know, $50,000,000 in building a new manufacturing plant. Alright. So maybe the way that you would traditionally, you know, build this plant 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.

Jordan Wilson [00:25:34]:
Right? So with simulations, essentially, you can simulate an entire new factory that doesn't exist. Right? Based on, 1,000,000 and 1,000,000 of other simulations that have been run. So that's an example of something that you can do, you know, not just in NVIDIA's platform, but many other, you know, generative AI and AI powered, cloud platforms is you can run literally millions of simulations of your warehouse that doesn't exist yet based on your projected data, your products, you you know, how much space do they need, how many people are are gonna be, you know, operating in this facility. And you'll see by, you know, these platforms running literally millions of of simulations for your manufacturing plant that doesn't exist yet. You're gonna see problems. Oh, these these shelves are are too close together. You know, they're they're not gonna be able to, you know, operate and and move things from this point, you know, point a to point b. There's gonna be a lot of collisions here.

Jordan Wilson [00:26:32]:
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 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 1 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 it's lowering the barrier of entry to play here, right, to play in some of these very powerful, normally enterprise only environments. Right? It's it's it's kind of like with chat gpt, 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 Martech and and 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, $50, a $100 a month and get similar level of technology that enterprise companies can get. That's another part of, you you know, kind of being in this compute sweet spot, but also point number 2 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.

Jordan Wilson [00:28:04]:
Right? Hey. You guys wanted hot takes. Here's here's what I think. I think simulations are gonna get weird. I think they're gonna get weird. Right? Right now, the example I gave of, alright, your company is spending $50,000,000 building a new production plant. That makes sense. Right? That makes sense.

Jordan Wilson [00:28:20]:
Yeah. You want that to be simulated with millions of simulations using, you know, in Nvidia's data from, you know, all of their systems, uploading all your data, but I think a lot of things are gonna be simulated. Right? I could even see if if if you wanna get super weird, I could see a a a time in the 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 I I tell a system, hey. Here's here's what my, what my topic is. Here's some of my bullet points, and it's gonna go ahead. It's gonna run millions of simulations.

Jordan Wilson [00:28:56]:
Oh, what if you go down this route? What if you go down this route? Here's how your audience is gonna react to all these different things based on all the data that we have on you, your audience, how they respond to certain content, etcetera. When when 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 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 as a digital twin, in the omniverse, in the meta in the metaverse, etcetera, where almost every single thing that we interact with, Cars we drive, the devices that we use, you you know, the software we send emails on. I I think everything will have already been simulated 1,000,000 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.

Jordan Wilson [00:30:10]:
That's the point of simulations. That's the point of, as an example, the omniverse. But I do believe the world will be a simulation. I think almost every, you know, single you know, the the the clothes you wear, the shoes, you you know, the the new pair of Nikes that you go buy, they will have been run through, a a sit like a digital simulation 1,000,000 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 1,000,000,000 or 1,000,000,000 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. Alright. Yeah.

Jordan Wilson [00:31:07]:
Hey. Earth 2. Hey. Great great point, Juan. Thanks for joining us live. Yeah. Earth 2. That's that's a great point.

Jordan Wilson [00:31:14]:
If 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, alright, Jordan. This is weird if you say the world will be a simulation. Well, Jensen Mong talked about that at even at his keynote. Some 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.

Jordan Wilson [00:31:51]:
I love this comment from Josh. And hey. Shout out to Josh. Josh gave me, like, probably the the the funniest, reply to a comment ever on LinkedIn. But Josh just said this is like when doctor Strange ran through millions of simulations to identify the reality that would defeat, Thanos. Yes. Exactly. Yeah.

Jordan Wilson [00:32:09]:
If you're a Avengers fan, exactly that. You know? Running through literally 1,000,000,000 or 1,000,000,000 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. Our hey, Tanya. I don't 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 that I lucked out with mine. So, yeah.

Jordan Wilson [00:32:42]:
But I'm sure I'm sure that's gonna be a thing where, you know, people, yeah, are simulating everything. I'm sure that already exists. You know, simulating things in in dating apps or matchmaking, I'm sure that already exists. But, yes, everything is gonna be simulated. Alright. 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 3, NVIDIA is creating humanoids and AGI.

Jordan Wilson [00:33:13]:
So here's the thing. They're definitely not, but they are. Okay? Let me let me tell you what that means. Humanoids. Alright. Again, we talked, with, Amit Goel, the, director NVIDIA of robotics. NVIDIA is not building physical robots, but, their Groot system love the name. I think it's general stands for general robotics, 3.

Jordan Wilson [00:33:41]:
I I 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 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 they're just existing and they're getting smarter. Right? So so all of these companies that are building physical products are more than likely already using either NVIDIA's chips or their software solutions, like, Isaac Robotics, like the Omniverse.

Jordan Wilson [00:34:27]:
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, 9 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 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 o one.

Jordan Wilson [00:35:00]:
It's using, chat g p t, to to listen and to process and to speak in natural language, you know, that's figure o one is presumably running on NVIDIA GPU chips. NVIDIA is is one of the main investors, financial investors. They're one of the biggest partners, with with, Figur. You know? And I do think Figur, 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 gonna be using, the the the new Groot, kind of, foundational system.

Jordan Wilson [00:35:52]:
NVIDIA is intertwined into everything. Literally everything. If you wanna talk meta, NVIDIA. If you wanna talk open AI, NVIDIA. Whatever your favorite, generative AI system is, NVIDIA. Right? So they're creating humanoids and in in fact, AGI. Right? We were able to ask, NVIDIA CEO Jensen Huang 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, a, depends on what you even consider to be artificial general intelligence.

Jordan Wilson [00:36:33]:
Right? Human intelligence is obviously changing. You know, what technology and AI is capable of is is changing all the time. You know, his kind of blanket statement was probably 5 years. I think we're probably a lot closer than that. Right? There's always an argument and people say, oh, what is AGI? 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 gpt perform tasks, both mental and physical, general tasks at the same level, better than the average human. And we're a lot closer than people think. Right? Especially when, you know, probably still the strongest model out there.

Jordan Wilson [00:37:31]:
Sorry, Claude 3. Sorry, Gemini 1.5. I still think gbt 4, which is an old model, is still, the best model out there, across the board when you look at outside, functionalities, and and features. When you think of right now that a leading model is 2 years old, and when you think, hey. What happens when the next GPT, what whether it's 4.5 or 5, what happens when that comes out? Yeah. I think we're getting close closer and closer to to AGI. Right? But a couple of things, some of my takeaways, and I I I opened 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 during a a closed door q and a, I was literally about 10 feet from from, NVIDIA CEO Jensen Wong.

Jordan Wilson [00:38:25]:
So I was able to get a lot of insights even just looking at his 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 gonna have our chat GPT moment for robotics soon. Right? And I think, robotics and 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 in in questioning that, the CEO of one of the largest I I believe they're either the 3rd I didn't look this morning, but either the 3rd or 4th 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. Alright? So what that means is, you know, hey, Jensen a or b, and he says b.

Jordan Wilson [00:39:25]:
Hey, Jensen 1, 2, or 3, and he says 3. But bringing up things unprompted, I think tells about not just the direction, of of 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 fora. Alright? And here's the other thing. Brought it up unprompted. It's not even his product. Right? Technically I mean, obviously, NVIDIA powers OpenAI, but you could think of them as, in theory, a competitor because Sam Altman is reportedly trying to raise $7,000,000,000,000 to create chips, compute fusion power, etcetera. Right? But he brought up SoRa on multiple occasions and talked about its implications not as an AI video tool.

Jordan Wilson [00:40:15]:
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 everydayai.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 and 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.

Jordan Wilson [00:41:05]:
So as a reminder, as we wrap up, the 3 the 3 ways that I believe that AI or, NVIDIA's, announcements at the GTC conference that their conference, that their announcements are gonna change the AI world. Alright. We're gonna recap. Number 1, we'll hit a compute sweet spot. Maybe for the first time, it might be short lived. Number 2, the world will be a simulation. What, these announcements mean for digital twins, can't overstate it enough. And number 3, yes, even though they're not technically working toward humanoids and AGI, NVIDIA is actually helping to create humanoids and AGI.

Jordan Wilson [00:41:52]:
Alright. Speaking of NVIDIA g t c, 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 are running workshops that you would normally pay 1,000 of dollars, to attend these workshops. You can go sign up, find the link.

Jordan Wilson [00:42:16]:
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 GeForce GPU as well as DLI learning credits from NVIDIA. Alright. 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 gonna be talking with, the founder or the cofounder and CEO of Worldwide Technology, Jim Kavanaugh, one of the largest y'all, let me let me restate this.

Jordan Wilson [00:42:57]:
Sitting down 1 on 1 with 1 of the largest companies in the world with their CEO, this was actually we just recorded this last week at at GTC. So I can tell you how this conversation goes, and it's amazing. You are not gonna wanna miss that. And you're also not gonna wanna miss today's newsletter. Go to your everyday ai.com. Sign up for the free daily newsletter. And, hey, if this show was helpful, go ahead, please. We spend hours putting these shows together.

Jordan Wilson [00:43:22]:
It takes you about 10 seconds to go repost this, reshare it, share it. Here's what I'll do. If you're on the livestream, you see this. If not, you're on the podcast. I'm sharing, I I have a picture here with, NVIDIA CEO, Jensen Huang. But there's actually a pretty embarrassing story that happened before this, 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 I can't put myself on blast there.

Jordan Wilson [00:43:49]:
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.

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