Jensen Huang Finally Reveals The Future Of AI In 2025... [NVIDIA's Masterplan]

so Jensen hang the CEO and founder of Nvidia gave a presentation at the AI Summit in India hello mumai as someone that's kind of spearheading the AI progress he's got a really good Vision about where things are going in as in video he presents a few interesting Concepts that are going to be pivotal to how AI is going to be developing moving forward and some of the infrastructure shifts that are going to be accompany get over here and hit like this is no time to freeze to start off h takes us back to 1964 if you can believe it and the idea of general purpose Computing the CPU the central processing unit kind of the general purpose Workhorse of the computer and the improvements in which were largely driven by Mo's law as our needs for computing improved we were able to keep progressing keep doing things because of Moors law and the rapid advancement in the processing power in the CPU however now we're reaching the limits of mors law we can't just keep cramming more transist onto a chip and expect the same exponential growth so then what is the next big leap for computing especially regarding AI this is where accelerated Computing comes in we're all aware of gpus graphic processing units like the Nvidia graphics card that a lot of us used to play video games and hang argues that the idea of accelerated Computing is that new free ride much like mors La was for general purpose Computing accelerated Computing for specific processes it's what's going to be driving the advancement moving forward take a listen we know now that the scaling of CPUs has reached its limit we can't continue to ride that curve that that Free Ride The Free Ride of Moors law has ended we have to now do something different or depreciation will end and we now will not enjoy depreciation but experience inflation Computing inflation and that's exactly what's happening around the world we no longer can afford to do nothing in software and expect that our Computing experience will continue to improve that costs will decrease and continue to spread the benefits of it and to benefit from solving greater and greater challenges we started our company to accelerate software our vision was there are applications that would benefit from acceleration if we augmented general purpose Computing we take the workload that is very computer intensive and we offload it and we accelerate it using a model we call Cuda a programming model that we invented Cuda that made it possible for us to accelerate applications tremendously that acceleration benefit has the same qualities as Moors law for applications that were impossible or impractical to perform using general purpose Computing we have the benefits of accelerated Computing to realize that capability for example computer Graphics real-time computer Graphics was made possible because of Nvidia coming into the world and make possible this new processor we call gpus the GPU was really the first accelerated Computing architecture running Cuda running computer Graphics a perfect example we democratized computer Graphics as we know it 3D Graphics is now literally everywhere it could be used as a medium for almost any application but we felt that long term accelerated Computing could be far far more impactful and so over the last 30 years we've been on a journey to accelerate one domain of application after another the reason why this has taken so long is simply because of this there is no such magical processor that can accelerate at everything in the world because if you could do that you would just call it a CPU you need to reinvent the Computing stack from the algorithms to the architecture underneath and connect it to applications on top in one domain after another domain computer Graphics is a beginning but we've taken this architecture Cuda architecture from one industry after another industry after another industry today we accelerate so many important industries there was a bit of an effect of slowing down AI adoption because some of the software needs to be Rewritten he introduces this idea of software 2. 0 now I've heard this concept first on an old blog post by Andre karpathy where he talks about you know software 1. 0 and moving into software 2.

0 and the idea was that before humans wrote the algorithms the code everything was written out step by step it was explicit instructions telling computers what to do in contrast software 2. 0 is based on machine learning no longer are humans writing the code take a listen to hang talk about this phenomenon and as you do think about this if the jump from software 1. 0 to 2.

0 was instead of humans writing code for computers now ai writes the code for computers then what is the next logical step what is software 3. 0 the world has completely changed now let's think about what happened the first thing that happened of course is how we do software our industry is underpinned by the method by which software is done the way that software was done call It software 1. 0 programmers would code algorithms we call functions into to run on a computer and we would apply it to input information to predict an output somebody would write python or C or Fortran or Pascal or C++ code algorithms that run on a computer you apply input to it and output is produced very classically the computer model that we understood quite well and it of course created one of the largest Industries in the world right here in India the production of software coding programming became a whole industry this all happened within our generation however that approach of developing software has been disrupted it is now not coding but machine learning using a computer using a computer to study the patterns and relationship of massive amounts of observed data to essentially learn from it the function that predicts it and so we are essentially designing a universal function approximator using machines to learn the expected output that would produce such a function and so going back and forth looking this is software 1.

0 with human encoding to now software 2.