so nvidia's CEO Jensen Wang did a special address at an AI Summit in India now this talk was rather fascinating because it was one of those talks that gives you an Insight with as to where we headed overall in AI I know most talks are about AI this or AI that but this one covered three key topics that I think most people aren't focused on one of them was the inference time with the new paradigm that AI is moving towards as you know the new 01 model thinks before it actually talks and that means that the model gets smarter with its responses there was also the talk about agents and how they're going to impact the workplace and lastly we got a really cool look at how physical AI is going to change the world in the future with humanoid robots so this talk is going to be a shortened summary and I'm going to give you guys the key details you need to know so coming in at the first point one of the first talks that he actually gives was about the new inference time AI this is where he speaks about how This Ti of AI is quite different it's basically like how you have system one and system two thinking system one being that short Snappy where someone ask you something immediately you immediately know the response but system 2 is kind of deliberative and you know planning and reasoning through certain steps to get to your response and this is a SCA scaling law at a time of inference the longer you think the higher quality answer you can produce this is not illogical this is very very intuitive to all of us if you were to ask me what's my favorite uh Indian food I would tell you chicken briani okay and I don't have to think about that very much and I don't have to reason about that I just know it and there are many things that you can ask it like for example what's Nvidia good at Nvidia is good at building AI supercomputers nvidia's great at building gpus and those are things that you know that it's encoded into your knowledge however there are many things that requires reasoning you know for example if I had to travel from uh Mumbai to California I I want to do it in the in a way that allows me to enjoy four other cities along the way you know today uh got here at 3:00 a. m. this morning uh I got here through Denmark I I and right before Denmark I was in Orlando Florida and before Orlando Florida I was in California that was two days ago and I'm still trying to figure out what day we're in right now but anyways I'm happy to be here uh if I were to to tell it I would like to go from California uh to Mumbai uh I would like to do it within uh 3 days uh and I give it all kinds of constraints about what time I'm willing to leave and able to leave what hotels I like to stay at so on so forth uh the people I have to meet the number of permutations of that of course uh quite high and so the planning of that process coming up with a optimal plan is very very complicated and so that's where thinking reasoning planning comes in and the more you compute the higher quality answer uh you could provide and so we now have two fundamental scaling laws that is driving our technology development first for training and now for inference next this is where we actually get to speak about agents now agents are something that are right on the horizon 2025 will largely be the year that autonomous AI takes over and you'll see them in the workplace it's likely you'll see them being able to do a various different amount of things for you as an individual it's quite likely that towards the end of 2025 you're going to see a number of autonomous AI agent systems paid and free come online and be able to offer you a variety of different goods and services okay so I'm going to introduce a couple of other ideas and so earlier I told you that we have Blackwell we have all of the libraries acceleration libraries that we were talking about before but on top there are two very important platforms we working on one of them is called Nvidia AI Enterprise and the other is called Nvidia Omniverse and I'll explain each one of them very Qui quickly first Nvidia AI Enterprise this is a time now where the large language models and the fundamental AI capabilities have reached a level of capabilities we're able to now create what is called agents large language models that understand understand the data that of course is being presented it could be it could be streaming data could video data language model data it could be data of all kinds the first stage is perception the second is reasoning about given its observations uh what is the mission and what is the task it has to perform perform in order to perform that task the agent would break down that task into steps of other tasks and uh it would reason about what it would take and it would connect with other AI models some of them are uh good at prod for example understanding PDF maybe it's a model that understands how to generate images maybe it's a model that uh uh is able to retrieve information AI information AI semantic data from a uh proprietary database so each one of these uh large language models are connected to the central reasoning large language model we call agent and so these agents are able to perform all kinds of tasks uh some of them are maybe uh marketing agents some of them are customer service agents some of them are chip design agents Nvidia has Chip design agents all over our company helping us design chips maybe there're software engineering uh agents uh maybe uh uh maybe they're able to do marketing campaigns uh Supply Chain management and so we're going to have agents that are helping our employees become super employees these agents or agentic AI models uh augment all of our employees to supercharge them make them more productive now when you think about these agents it's really the way you would bring these agents into your company is not unlike the way you would onboard uh someone uh who's a new employee you have to give them train training curriculum you have to uh fine-tune them teach them how to use uh how to perform the skills and the understand the vocabulary of your of your company uh you evaluate them and so they're evaluation systems and you might guardrail them if you're accounting agent uh don't do marketing if you're a marketing agent you know don't report earnings at the end of the quarter so on so forth and so each one of these agents are guard railed um that entire process we put into to essentially an agent life cycle Suite of libraries and we call that Nemo our partners are working with us integrate these libraries into their platforms so that they could enable agents to be created onboarded deployed improved into a life cycle of agents and so this is what we call Nvidia Nemo we have um on the one hand the libraries on the other hand what comes out of the output of it is a API inference microservice we call Nims essentially this is a factory that builds AIS and Nemo is a suite of libraries that on board and help you operate the AIS and ultimately your goal is to create a whole bunch of agents this is where we got the very fascinating talk about how we're going to get physical AI of course once you do have agents in AI it's very good because they are digital and they're able to move at hypers speed but how do you impact the physical world how do you manipulate physical objects and Achieve things in the real physical world whilst maintaining that scale of course it's humanoid robot and physical AI this is where he gives the interesting insight into where physical AI is truly headed what happens after agents now remember every single company has employees but most companies the goal is to build something to produce something something to make something and that those things that people make could be factories it could be warehouses it could be cars and planes and trains and uh ships and so on so forth all kinds of things computers and servers the servers that Nvidia builds it could be phones most companies in the largest of Industries ultimately produces something sometimes produ production of service which is the IT industry but many of your customers are about producing something those that next generation of AI needs to understand the physical world we call it physical AI in order to create physical AI we need three computers and we created three computers to do so the dgx computer which Blackwell for example is is a reference design an architecture for to create things like dgx computers for training the model that model needs a place to be refined it needs needs a place to learn and needs the place to apply its physical capability its robotics capability we call that Omniverse a virtual world that obeys the laws of physics where robots can learn to be robots and then when you're done with the training of it that AI model could then run in the actual robotic system that robotic system could be a car it could be a robot it could be AV it could be a autonomous moving robotic could be a a picking arm uh it could be an entire Factory or an entire Warehouse that's robotic and that computer we call agx Jetson agx dgx for training and then Omniverse for doing the digital twin now here here in India we've got a really great ecosystem who is working with us to take this infrastructure take this ecosystem of capabilities to help the world build physical AI systems then we got a very short summary about the entire talk this is from software 1 to software 2.
0 about her AI agents and mainly about how the humanoid robot is going to go completely crazy Nvidia are actually doing so much in that area that I can't wait to show you guys a new video I've been working on that covers how the Nvidia company is about to change the entire AI ecosystem so take a look at this because this one gives you pretty much everything you need to know for 60 years software 1. 0 code written by programmers ran on general purpose purp CPUs then software 2.
