【翻譯字幕】NVIDIA執行長黃仁勳全程演講 「AI如何帶動全球新產業革命發展」｜三立新聞網 SETN.com

all right let's get started good take good take oh je okay you guys ready yeah yeah everybody thinks we make GPS I video is so much more than that this whole Kino going to be about that okay so we'll start at the top examples of the use cases and then seeing it in action that's kind of the flow it's such a compelling story I'm super nervous about this just got to get in the Rhythm we're 2 weeks away from it you guys can go really really make it great we should take a swing at it yeah that's the plan we need to get daily status on that animation can you mute cuz I hear myself sorry it's the drop date for all the videos it needs to be done on the 28th is get all that safe travels everybody super excited to see everyone see you guys soon okay bye bye we're basically moving as fast as the world can absorb technology so we've got to LEAP from ourselves now the spine you just have to figure out a way to make it pop you know what I'm saying yeah you know what I'm saying you want to yeah that kind of [Music] [Applause] [Music] [Applause] thing okay thank you I'm super late let's go [Music] why please welcome to the stage Nvidia founder and CEO Jensen wall [Music] T I am very happy to be back thank you NTU for letting us use your Stadium the last time I was here I received a degree from [Applause] [Music] n and I gave gave the Run don't walk speech and today we have a lot to cover so I cannot walk I must run we have a lot to cover I have many things to tell you I'm very happy to be here in Taiwan Taiwan is the home of our treasured Partners this is in fact where everything Nvidia does begins our partners and ourselves take it to the world Taiwan and our partnership has created the world's AI infrastructure today I want to talk to you about several things one what is happening and the meaning of the work that we do together what is generative AI what is its impact on our industry and on every industry a blueprint for how we will go forward and engage this incredible opportunity and what's coming next generative Ai and its impact our blueprint and what comes next these are really really exciting times a restart of our computer industry an industry that you have forged an industry that you have created and now you're prepared for the next major Journey but before we start Nvidia lives at the intersection of computer graphics simulations and artificial intelligence this is our soul everything that I show you today is simulation it's math it's science it's computer science it's amazing computer architecture none of it's animated and it's all homemade this is invidious soul and we put it all into this virtual world we called Omniverse please enjoy [Applause] [Music] [Music] oh [Music] [Music] [Music] [Music] go [Music] [Applause] [Applause] I want to speak to you in Chinese but I have so much to tell you I have to think too hard to speak Chinese so I have to speak to you in English at the foundation of everything that you saw was two fundamental Technologies accelerated Computing and artificial intelligence running inside the Omniverse those two technologies those two fundamental forces of computing are going to reshape the computer industry the computer industry is now some 60 years old in a lot of ways everything that we do today was invented the year after my birth in 1964 the IBM system 360 introduced central processing units general purpose Computing the separation of hardware and software through an operating system multitasking IO subsystems dma all kinds of technologies that we use today architectural compatibility backwards compatibility family compatibility all of the things that we know today about Computing largely described in 1964 of course the PC Revolution democratized Computing and put it in the hands and the houses of everybody and then in 2007 the iPhone introduced mobile Computing and put the computer in our pocket ever since everything is connected and running all the time through the mobile Cloud this last 60 years we saw several just several not that many actually two or three major technology shifts two or three tectonic shifts in Computing where everything changed and we're about to see that happen again there are two fundamental things that are happening the first is that the processor the engine by which the computer industry runs on the central processing unit the performance scaling has slowed tremendously and yet the amount of computation we have to do is still doubling very quickly exponentially if processing requirement if the data that is that we need to process continues to scale exponentially but performance does not we will experience computation inflation and in fact we're seeing that right now as we speak the amount of data center power that's used all over the world is growing quite substantially the cost of computing is growing we are seeing computation inflation this of course cannot continue the data is going to continue to increase exponentially and CPU performance scaling will never return there is a better way for almost two decades now we've been working on accelerated Computing Cuda augments a CPU offloads and accelerates the work that A specialized processor can do much much better in fact the performance is so extraordinary that it is very clear now as CPU scaling has slowed and event substantially stopped we should accelerate everything I predict that every application that is processing intensive will be accelerated and surely every data center will be accelerated in the near future now accelerated Computing is very sensible it's very common sense if you take a look at an application and here here the 100t means 100 units of time it could be 100 seconds it could be 100 hours and in many cases as you know we're now working on artificial intelligence applications that run for a 100 days the onet is code that is requires sequential processing where single-threaded CPUs are really quite essential operating systems control logic really essential to have one instruction executed after another instruction however there are many algorithms computer Graphics is one that you can operate completely in parallel computer Graphics image processing physics simulations combinatorial optimizations graph processing database processing and of course the very famous linear algebra of deep learning there are many types of algorithms that are very conducive to acceleration through parallel processing so we invented an architecture to do to do that by adding the GPU to the CPU the specialized processor can take something that takes a great deal of time and accelerate it down to something that is in incredibly fast and because the two processors can work side by side they're both autonomous and they're both separate independent that is we could accelerate what used to take take a 100 units of time down to one unit of time well the speed up is incredible it almost sounds unbelievable it almost sounds unbelievable but today I'll demonstrate many examples for you the benefit is quite extraordinary a 100 times speed up but you only increase the power by about a factor of three and you increase the cost by only about 50% we do this all the time in the PC industry we add a GPU a $500 GPU gForce GPU to a $1,000 PC and the performance increases tremendously we do this in a data center a billion dooll data center we add $500 million worth of gpus and all of a sudden it becomes an AI Factory this is happening all over the world today well the savings are quite extraordin you're getting 60 times performance per dollar 100 times speed up you only increase your power by 3x 100 times speed up you only increase your cost by 1.5x the savings are incredible the savings are measured in dollars it is very clear that many many companies spend hundreds of millions of dollars processing data in the cloud if it was accelerated it is not unexpected that you could save hundreds of millions of dollars now why is that well the reason for that is very clear we've been experiencing inflation for so long in general purpose Computing now that we finally came to we finally determined to accelerate there's an enormous amount of captured loss that we can now regain a great deal of captured retained waste that we can now relieve out of the system and that will translate into savings Savings in money Savings in energy and that's the reason why you've heard me say the more you buy the more you save and now I've shown you the mathematics if is not accurate but it is correct okay that's called CEO math CEO math is not accurate but it is correct the more you buy the more you save well accelerated Computing does deliver extraordinary results but it is not easy why is it that it saves so much money but people haven't done it for so long the reason for that is because it's incredibly hard there is no such thing as a software that you can just run through a c compiler and all of a sudden that application runs 100 times faster that is not even logical if it was possible to do that they would have just changed the CPU to do that you in fact have to rewrite the software that's the hard part the software has to be completely Rewritten so that you could reactor reexpress the algorithms that was written on a CPU so that it could be acceler at it offloaded accelerated and run in parallel that computer science exercise is insanely hard well we've made it easy for the world over the last 20 years of course the very famous cdnn the Deep learning library that process it neural networks we have a library for AI physics that you could use for fluid dynamics and many other applications where the neural network has to obey the laws of physics we have a great new library called aial that is a Cuda accelerated 5G radio so that we can software Define and accelerate the Telecommunications networks the way that we've soft software defined the world's networking um uh internet and so the ability for us to accelerate that allows us to turn all of Telecom into essentially the same type of platform a Computing platform just like we have in the cloud kitho is a comp computation lithography platform that allows us to uh process the most computationally intensive parts of Chip manufacturing making the mask tsmc is in the process of going to production with kitho saving enormous amounts of energy and more enormous amounts of money but the goal for tsmc is to accelerate their stack so that they're prepared for even further advances in algorithm and more computation for deeper and deeper uh uh narrow and narrow transistors parab bre is our Gene sequencing Library it is the highest throughput library in the world for Gene sequencing coop is an incredible library for combinatorial optimization route planning optimization the traveling salesman problem incredibly complicated people just people have well scientists have largely concluded that you needed a quantum computer to do that we created an algorithm that runs on accelerated Computing that runs light ing fast 23 World Records we hold every single major world record today C Quantum is an emulation system for a quantum computer if you want to design a quantum computer you need a simulator to do so if you want to design Quantum algorithms you need a Quantum emulator to do so how would you do that how would you design these quantum computers create these Quantum algorithms if the quantum computer doesn't exist while you use the fastest computer in the world that exists today and we call it of course Nvidia Cuda and on that we have an emulator that simulates quantum computers it is used by several hundred thousand researchers around the world it is integrated into all the leading M leading Frameworks for Quantum Computing and is used in scientific super super Computing centers all over the world CDF is an unbelievable library for data processing data processing consumes the vast majority of cloud spend today all of it should be accelerated CDF accelerates the major libraries used in the world spark many of you probably use spark in your companies pandas um a new one called polar and of course uh networkx which is a graph processing graph processing uh database um library and so these are just some examples there are so many more each one of them to be created so that we can enable the ecosystem to take advantage of accelerated Computing if we hadn't created CNN Cuda alone wouldn't have been able wouldn't have been possible for all of the deep learning scientists around the world to use because Cuda and the algorithms that are used in tensorflow and pytorch the Deep learning algorithms the separation is too far apart it's almost like trying to do computer Graphics without opengl it's almost like doing data processing without SQL these domain specific libraries are really The Treasure of our company we have 350 of them these libraries Is What It Takes and what has made it possible for us to have such open so many markets I'll show you some other examples today well just last week Google announced that theyve put qdf in the cloud and accelerate pan P pandas is the most popular data science library in the world many of you in here probably already use pandas it's used by 10 million data scientists in the world downloaded 170 million times each month it is the Excel that is the spreadsheet of data scientists well with just one click you can now use pandas in collab which is Google's cloud data centers platform accelerated by qdf the speed up is really incredible let's take a look that was a great demo right didn't take long when you accelerate data processing that fast demos don't take long okay well Cuda has now achieved what people call a Tipping Point but it's even better than that Cuda has now achieved a virtuous cycle this rarely happens if you look at history and all the Computing architecture Computing Platforms in the case of microprocessor CPUs it has been here for 60 years it has not been changed for 60 years at this level this way of doing Computing accelerated Computing has been around has creating a new platform is extremely hard because it's a chicken and egg problem if there are no developers that use your platform then of course there will be no users but if there are no users there are no install base if there're no install based developers aren't interested in it developers want to write software for a large installed base but a large install base requires Ires a lot of applications so that users would create that install base this chicken or the egg problem has rarely been broken and has taken us now 20 years one domain library after another one acceleration Library another and now we have 5 million developers around the world we serve every single industry from healthc care Financial Services of course the computer industry automotive industry just about every major industry in the world just about every field of science because there are so many customers for our architecture oems and cloud service providers are interested in building our systems system makers amazing system makers like the ones here in Taiwan are interested in building our systems which then takes and offers more systems to the market which of course creates greater opportunity for us which allows us to increase our scale R&D scale which speeds up the appli even more well every single time we speed up the application the cost of computing goes down this is that slide I was showing you earlier 100x speed up translates to 97 96% 98% savings and so when we go from 100 x speed up to 200 x speed up to 1,000 x speed up the savings the marginal cost of computing continues to fall well of course we believe that by reducing the cost of computing incredibly the market developers scientists inventors will continue to discover new algorithms that consume more and more and more Computing so that one day something happens that a phase shift happens that the marginal cost of computing is so low that a new way of using computers emerg in fact that's what we're seeing now over the years we have driven down the marginal cost of computing in the last 10 years in one particular algorithm by a million times well as a result it is now very logical and very common sense to train large language models with all of the data on the internet nobody thinks twice this idea that you could create a computer that could process so much data to write its own software the emergence of artificial intelligence was made possible because of this complete belief that if we made Computing cheaper and cheaper and cheaper somebody's going to find a great use well today Cuda has achieved the virtual cycle install bases growing Computing cost is coming down which causes more developers to come up with more ideas is which drives more demand and now we're on in the beginning of something very very important but before I show you that I want to show you what is not possible if not for the fact that we create a Cuda that we created the modern version of gener the modern Big Bang of AI generative AI what I'm about to show you would not be possible this is Earth two the idea that we would create a digital twin of the earth that we would go and simulate the Earth so that we could predict the future of our planet to better avert disasters or better understand the impact of climate change so that we can adapt better so that we could change our habits now this digital twin of Earth is probably one of the most ambitious projects that the world's ever undertaken and we're taking step large steps every single year and I'll show you results every single year but this year we made some great breakthroughs let's take a look on Monday the storm will ve North again and approach Taiwan there are big uncertainties regarding its path different paths will have different levels of impact on Taiwan [Music] for NVIDIA Earth 2 [Music] [Music] CI C AI [Music] [Music] for ciff AI [Music] for NVIDIA Earth two fore [Music] someday in the near future we will have continuous weather prediction at every at every square kilometer on the planet you will always know what the climate's going to be you will always know and this will run continuously because we've trained the AI and the AI requires so little energy and so this is just an incredible achievement and I hope you you enjoyed it and very importantly uh the truth is that was a Jensen AI that was not me I I wrote I wrote it but an AI Jensen AI had to say it because of our dedication to continuously improved the performance of Drive the cost down researchers discovered AI researchers discovered Cuda in 2012 that was nvidia's first contact with AI this was a very important day we had the good wisdom to work with the the scientists to make it possible for a deep learning to happen and Alex net achieved of course a tremendous computer vision breakthrough but the great wisdom was to take a step back and understanding what was the background what is the foundation of deep learning what is its long-term impact what is its potential and we realized that this technology has great potential to scale an algorithm that was invented and discovered decades ago all of a sudden because of more data larger networks and very importantly a lot more compute all of a sudden deep learning was able to achieve what no human algorithm was able to now imagine if we were to scale up the architecture even more larger networks more data and more compute what could be possible so we dedicated ourselves to reinvent everything after 2012 we changed the architecture of our GPU to add tensor course we invented MV link that was 10 years ago now CNN tensor RT nickel we bought melanox tensor rtlm the Triton inference server and all of it came together on a brand new computer nobody understood no nobody asked for it nobody understood it and in fact I was certain nobody wanted to buy it and so we announced it at GTC and open AI a small company in San Francisco saw it and they asked me to deliver one to them I delivered the first dgx the F the world's first AI supercomputer to open AI in 2016 well after that we continued to scale from one AI supercomputer one AI Appliance we scaled it up to large supercomputers even larger by 2017 the world discovered Transformers so that we could train enormous amounts of data and recognize and learn patterns that are sequential over large spans of time it is now possible for for us to train these large language models to understand and achieve a breakthrough in natural language understanding and we kept going after that we built even larger ones and then in November 2022 trained on thousands tens of thousands of Nvidia gpus in a very large AI supercomputer open AI announced chat GPT 1 million users after five days 1 million after five days a 100 million after two months the fastest growing application in history and the reason for that is very simple it is just so easy to use and it was so magical to use to be able to interact with a computer like it's Human Instead of being clear about what you want it's like the computer understands your meaning it understands your intention oh I think here it it um asked the the closest Night Market night as you know uh the night market is very important to me so when I was young uh I was I think I was four and a half years old I used to love going to the night market because I I just love watching people and and uh uh and so we went my parents used to take us to the night market uhan and and um uh and I love I love uh I love going and one day uh my face you guys might might see that I have a large scar on my face my face was cut because somebody was washing their knife and I was a little kid um but my memories of of the Night Market is uh so deep because of that and I used to love I I just I still love going to the night market and I just need to tell you guys this the the Tona Night Market is is really good because there's a lady uh she's been working there for 43 years she's the fruit lady and it's in the middle of the the middle between the two go find her okay she she's really terrific I think it would be funny after this all of you go to see her she every year she's doing better and better her CEST improved and yeah I just love watching her succeed anyways anyways Chad GPT came along and um and something is very important in this slide here let me show you something this slide okay and this Slide the fundamental difference is this until chat GPT revealed it to the world AI was all about perception natural language understanding computer vision speech recognition it's all about perception and detection this was the first time the world solved a generative AI It produced tokens one token at a time and those tokens were words some of the tokens of course could now be images or charts or tables songs words speech videos those tokens could be anything they anything that that you can learn the meaning of it could be tokens of chemicals tokens of proteins genes you saw earlier in Earth to we were generating tokens of the weather we can we can learn physics if you can learn physics you could teach an AI model physics the AI model could learn the meaning of physics and it can generate physics we were scaling down to one kilometer not by using filtering it was generating and so we can use this method to generate tokens for almost anything almost anything of value we can generate steering wheel control for a car we can generate articulation for a robotic arm everything that we can learn we can now generate we have now arrived not at the AI era but a generative AI era but what's really important is this this comp computer that started out as a supercomputer has now evolved into a Data Center and it produces one thing it produces tokens it's an AI Factory this AI Factory is generating creating producing something of Great Value a new commodity in the late 1890s Nicola Tesla invented an AC generator we invented an AI generator the AC generator generated electrons nvidia's AI generator generates tokens both of these things have large Market opportunities it's completely fungible in almost every industry and that's why it's a new Industrial Revolution we have now a new Factory producing in a new commodity for every industry that is of extraordinary value and the methodology for doing this is quite scalable and the methodology of doing this is quite repeatable notice how quickly so many different AI models generative AI models are being invented literally daily every single industry is now piling on for the very first time the IT industry which is3 trillion $3 trillion IT industry is about to create something that can directly serve a hundred trillion doll of Industry no longer just an instrument for information storage or data processing but a factory for generating intelligence for every industry this is going to be a manufacturing industry not a manufacturing industry of computers but using the computers in manufacturing this has never happened before quite an extraordinary thing what led started with accelerated Computing led to AI led to generative Ai and now an industrial revolution now the impact to our industry is also quite significant of course we could create a new commodity a new product we call tokens for many Industries but the impact of ours is also quite profound for the very first time as I was saying earlier in 60 years every single layer of computing has been changed from CPUs general purpose Computing to accelerated GPU Computing where the computer needs instructions now computers process llms large language models AI models and where as the Computing model of the past is retrieval based almost every time you touch your phone some pre-recorded text or pre-recorded image or pre-recorded video is retrieved for you and recomposed based on a recommender system to present it to you based on your habits but in the future your computer will generate as much as possible retrieve only what's necessary and the reason for that is because generated generated data requires less energy to go fetch information generated data also is more contextually relevant it will encode knowledge it will code your understanding of you and instead of get that inform information for me or get that file for me you just say ask me for an answer and instead of a tool instead of your computer being a tool that we use the computer will now generate skills it performs tasks and instead of an industry that is producing software was which was a revolutionary idea in the early 90s remember the idea that Microsoft created for packaging software revolutionized the PC industry without package software what would we use the PC to do it drove this industry and now we have a new Factory a new computer and what we will run on top of this is a new type of software and we call it Nims Nvidia inference microservices now what what happens is the Nim runs inside this Factory and this Nim is a pre-trained model it's an AI well this AI is of course quite complex in itself but the the Computing stack that runs AIS are insanely complex when you go and use chat GPT underneath their stack is a whole bunch of software underneath that prompt is a ton of software and it's incredibly complex because the models are large billions to trillions of parameters it doesn't run on just one computer it runs on multiple computers it has to distribute the workload across multiple gpus tensor parallelism pipeline parallelism data parall all kinds of parallelism expert parallelism all kinds of parallelism Distributing the workload across multiple gpus processing it as fast as possible because if you are in a factory if you run a factory your throughput directly correlates to your revenues your throughput directly correlates to quality of service and your throughput directly correlates to the number of people who can use your service we are now in a world where data center throughput utilization is vitally important it was important in the past but not vitally important it was important in the past but people don't measure it today every parameter is measured start time uptime utilization throughput idle time you name it because it's a factory when something is a factory its operations directly correlate to the financial performance of the company and so we realized that this is incredibly complex for most companies to do so what we did was we created this AI in a box and the containers an incredible AMS of software inside this container is Cuda CNN tensor RT Triton for inference Services it is cloud native so that you could Auto scale in a kubernetes environment it has Management Services and hooks so that you can monitor your AIS it has common apis standard API so that you could literally chat with this box you download this Nim and you can talk to it so long as you have Cuda on your computer which is now of course everywhere it's in every cloud available from every computer maker it is available in hundreds of millions of PCS when you download this you have an AI and you can chat with it like chat GPT all of the software is now integrated 400 dependencies all integrated into one we tested this Nim each one of these pre-trained models against all kind our entire install base that's in the cloud all the different versions of Pascal and ampers and Hoppers and all kinds of different versions I even forget some Nims incredible invention this is one of my favorites and of course as you know we now have the ability to create large language models and pre-trained models of all kinds and we we have all of these various versions whether it's language based or Vision based or Imaging based or we have versions that are available for healthc care digital biology we have versions that are digital humans that I'll talk to you about and the way you use this just come to ai. nvidia.com and today we uh just posted up in hugging face the Llama 3 Nim fully optimized it's available there for you to try and you can even take it with you it's available to you for free and so you could run it in the cloud run it in any Cloud you could download this container put it into your own Data Center and you could host it make it available for your customers we have as I mention all kinds of different domains physics some of it is for semantic retrieval called Rags Vision languages all kinds of different languages and the way that you use it is connecting these microservices into large applications one of the most important applications in the coming future of course is customer service agents customer service agents are necessary in just about every single industry it represents presents trillions of dollars of of customer service around the world nurses are customer service agents um in some ways some of them are nonprescription or or non Diagnostics um based nurses are essentially customer service uh customer service for retail for uh Quick Service Foods Financial Services Insurance just tens and tens of millions of customer service can now be augmented by language models and argumented by Ai and so these one these boxes that you see are basically Nims some of the NIMS are reasoning agents given a task figure out what the mission is break it down into a plan some of the NIMS retrieve information some of the NIMS might uh uh uh go and do search some of the NIMS uh might use a tool like kop that I was talking about earlier they could use a tool that uh could be running on sap and so it has to learn a particular language called abap maybe some Nims have to uh uh do SQL queries and so all of these Nims are experts that are now assembled as a team so what's happening the application layer has been changed what used to be applications written with instructions are now applications that are assembling teams assembling teams of AIS very few people know how to write programs almost everybody knows how to break down a problem and assemble teams very every company I believe in the future will have a large collection of Nims and you would bring down the experts that you want you connect them into a team and you you don't even have to figure out exactly how to connect them you just give the mission to an agent to a Nim to figure out who to break the tasks down and who to give it to and they that a that Central the leader of the of the application if you will the leader of the team would break down the task and give it to the various team members the team members would do their perform their task bring it back to the team leader the team leader would reason about that and present an information back to you just like humans this is in our near future this is the way applications are going to look now of course we could interact with these large these AI services with text prompts and speech prompts however there are many applications where we would like to interact with whe what is otherwise a humanlike form we call them digital humans Nvidia has been working on digital human technology for some time let me show it to you and well before I do that hang on a second before I do that okay digital humans has the potential of being a great interact interactive agent with you they make much more engaging that could be much more empathetic and of course um we have to uh uh cross this incredible Chasm this uncanny Chasm of realism so so that the digital humans would appear much more natural this is of course our vision this is a vision of where we love to go uh but let me show you where we are great to be in Taiwan before I head out to the night market let's dive into some exciting frontiers of digital humans imagine a future where computers interact with us just like humans can hi my name is Sophie and I am a digital human brand ambassador that are for Unique this is the incredible reality of digital humans digital humans will revolutionize industries from customer service to advertising and gaming the possibilities for digital humans are endless using the scans you took of your current kitchen with your phone they will be AI interior designers helping generate beautiful photorealistic suggestions and sourcing the materials and Furniture we have generated several design options for you to choose from they'll also be AI customer service agents making the interaction more engaging and personalized or digital healthcare workers who will check on patients providing timely personalized care um I did forget to mention to the doctor that I am allergic to penicillin is it still okay to take the medications the antibiotics you've been prescribed cicin and metronidazol don't contain penicillin so it's perfectly safe for you to take them and they'll even be AI brand ambassadors setting the next marketing and advertising Trends hi I'm EMA Japan's first virtual model new breakthroughs in generative Ai and computer Graphics let digital humans see understand and interact with us in humanlike ways H from what I can see it looks like you're in some kind of recording or production setup the foundation of digital humans are AI models built on multilingual speech recognition and synthesis and llms that understand and generate [Music] conversation the AI connect to another generative AI to dynamically animate a lifelike 3D mesh of a face and finally AI models that reproduce lifik appearances enabling real-time path traced subsurface scattering to simulate the way light penetrates the skin scatters and exits at various points giving skin its soft and translucent appearance Nvidia Ace is a suite of digital human Technologies packaged as easy to deploy fully optimized microservices or Nims developers can integrate a Nims into their existing Frameworks engines and digital human experiences neotron slm and llm Nims to understand our intent and orchestrate other models Reva speech Nims for interactive speech and translation audio to face and gesture Nims for facial and body animation an Omniverse RTX with dlss for neural rendering of skin and hair Ace Nims run on Nvidia gdn a Global Network of Nvidia accelerated infrastructure that delivers low latency digital human processing to over 100 [Music] regions Kama pretty incredible well those those Ace runs in the cloud but it also runs on PCS we had the good wisdom of including tensor core gpus in all of RTX so we've been shipping AI gpus for some time preparing ourselves for this day the reason for that is very simple we always knew that in order to create a new Computing platform you need to install Bas first eventually the application will come if you don't create the install base how could the application come and so if you build it they might not come but if you build it if you don't build it they cannot come and so we installed every single RT TX GPU with tensor core G tensor core processing and now we have 100 million GeForce RTX AIP PCS in the world and we're shipping 200 and this this copy text we're featuring four new amazing laptops all of them are able to run AI your future laptop your future PC will become an AI it'll be constantly helping you assisting you in the background the PC will also run applications that are enhanced by AI of course all your photo editing your writing and your tools all the things that you use will all be enhanced by Ai and your PC will also host applications with digital humans that are AIS and so there are different ways that AIS will manifest themselves and become used in PCS but PCS will become very important AI platform and so where do we go from here I spoke earlier about the scaling of our data centers and every single time we scaled we found a new phase change when we scaled from dgx into large AI supercomputers we enabled Transformers to be able to train on enormously large data data sets well what happened was in the beginning the data was human supervised it requ required human labeling to train AIS unfortunately there's only so much you can human label Transformers made it possible for unsupervised learning to happen now Transformers just look at an enormous amount of data or look at enormous amount of video or look at enormous amount of uh images and it can learn from studying an enormous amount of data find the patterns and relationships itself while the next generation of AI needs to be physically based most of the AIS today uh don't understand the laws of physics it's not grounded in the physical world in order for us to generate uh uh images and videos and 3D graphics and many physics phenomenons we need AIS that are physically based and understand the laws of physics well the way that you could do that is of course learning from video is One Source another way is synthetic data simulation data and another way is using computers to learn with each other this is really no different than using Alpha go having Alpha go play itself self-play and between the two capabilities same capabilities playing each other for a very long period of time they emerge even smarter and so you're going to start to see this type of AI emerging well if the AI d data is synthetically generated and using reinforcement learning it stands to reason that the rate of data generation will continue to advance and every single time data generation grows the amount of computation that we have to offer needs to grow with it we are about to enter a phase where AI can learn the laws of physics and understand and be grounded in physical world data and so we expect that models will continue to grow and we need larger gpus while Blackwell was designed design for this generation this is Blackwell and it has several very important Technologies uh one of course it's just the size of the chip we took two of the largest a chip that is as large as you can make it at tsmc and we connected two of them together with a 10 terabytes per second link between the world's most advanced cies connecting these two together we then put two of them on a computer node connected with a gray CPU the gray CPU could be used for several things in the training situation it could use it could be used for fast checkpoint and restart in the case of inference and generation it could be used for storing context memory so that the AI has memory and understands uh the context of the the conversation you would like to have it's our second generation Transformer engine Transformer engine allows us to adapt dynamically to a lower precision based on the Precision and the range necessary for that layer of computation uh this is our second generation GPU that has secure AI so that you could you could ask your service providers to protect your AI from being either stolen from theft or tampering this is our fifth generation MV link MV link allows us to connect multiple gpus together and I'll show you more of that in a second and this is also our first generation with a reliability and availability engine this system this Ras system allows us to test every single transistor flipflop memory on chip memory off chip so that we can in the field determine whether a particular chip is uh failing the mtbf the meantime between failure of a supercomputer with 10,000 gpus is a measured in hours the meantime between failure of a supercomputer with 100,000 gpus is measured in minutes and so the ability for a supercomputer to run for a long period of time and train a model that could last for several months is practically impossible if we don't invent Technologies to enhance its reliability reliability what of course enhances uptime which directly affects the cost and then lastly decompression engine data processing is one of the most important things we have to do we added a data compression engine decompression engine so that we can pull data out of storage 20 times faster than what's possible today well all of this represents Blackwell and I think we have one here that's in production during GTC I showed you Blackwell in a prototype State um the other side this is why we practice [Laughter] ladies and gentlemen this is Blackwell black Blackwell is in production incredible amounts of Technology this is our production board this is the most complex highest performance computer the world's ever made this is the gray CPU and these are you could see each one of these Blackwell dyes two of them connected together you see that it is the largest D the largest chip the world makes and then we connect two of them together with a 10 terabyte per second link okay and that makes the Blackwell computer and the performance is incredible take a look at this so um you see you see our uh the the computational the flops the AI flops uh for each generation has increased by a thousand times in eight years Moore's law in eight years is something along the lines of oh I don't know maybe 40 60 and in the last eight years uh Mo's law has gone a lot lot less and so just to compare even Mor's law at its best of times compared to what blackw could do so the amount of computations is incredible and when whenever we bring the computation High the thing that happens is the cost goes down and I'll show you what we've done is we've increased through its computational capability the energy used to train a gp4 2 trillion parameter 8 trillion Tokens The amount of energy that is used has gone down by 350 times Well Pascal would have taken 1,000 gwatt hours 1,000 gwatt hours means that it would take a gigawatt data center the world doesn't have a gigawatt data center but if you had a gigawatt data center it would take a month if you had a 100 watt 100 megawatt data center it would take about a year and so nobody would of course um uh create such a thing and um and that's the reason why these large language models at GPT was impossible only 8 years ago by us driving down the increasing the performance the energy efficient while keeping and improving energy efficient efficiency along the way we've now taken with Blackwell what used to be a th000 gwatt hours to three an incredible Advance uh 3 gwatt hours if it's a if it's a um uh uh a 10,000 gpus for example it would only take a cou 10,000 gpus I guess it would take a few days 10 days or so so the amount of advance in just eight years is incredible well this is for inference this is for token generation Our token generation performance has made it possible for us to drive the energy down by three four 45,000 times 177,000 Jews per token that was Pascal 17,000 jewels is kind of like two light bulbs running for two days it would take two light bulbs running for two days amounts of energy 200 Watts running for two days to generate one token of gp4 it takes about three tokens to generate one word and so the amount of energy used necessary for Pascal to generate GPT 4 and have a chat GPT experience with you was practically impossible but now we only use 0.4 Jews per token and we can generate tokens at incredible rates and very little energy okay so Blackwell is just an enormous leap well even so it's not big enough and so we have to build even larger machines and so the way that we build it is called dgx so this is this is our Blackwell chips and it goes into djx systems that's why we should practice so this is a dgx Blackwell this has this is air cooled has eight of these gpus inside look at the size of the heat syns on these gpus about 15 kilowatts 15,000 watts and completely air cooled this version supports x86 and it's it goes into the infrastructure that we've been shipping Hoppers into however if you would like to have liquid cooling we have a new system and this new system is based on this board and we call it mgx for modular and this modular system you won't be able to see this this can they see this can you see this you can the are you okay I say and so this is the mgx system and here's the two uh black black wall boards so this one node has four Blackwell chips these four Blackwell chips this is liquid cooled nine of them nine of them uh well 72 of these 72 of these gpus 72 of these G gpus are then connected together with a new MV link this is mvy link switch fifth generation and the mvy link switch is a technology Miracle this is the most advanced switch the world's ever made the data rate is insane and these switches connect every single one of these black Wells to each other so that we have one giant 72 GPU Blackwell well the benefit the benefit of this is that in one domain one GPU domain this now looks like one GPU this one GPU has 72 versus the last generation of eight so we increased it by nine times the amount of bandwidth we've increased by 18 times the AI flops we've increased by 45 times and yet the amount of power is only 10 times this is 100 kilowatt and that is 10 kilow and that's for one now of course well you could always connect more of these together and I'll show you how to do that in a second but what's the miracle is this chip this MV link chip people are starting to awaken to the importance of this mvlink chip as it connects all these different gpus together because the large language models are so large it doesn't fit on just one GPU it doesn't fit on one just just one node it's going to take the entire rack of gpus like this new dgx that I that I was just standing next to uh to hold a large language model that are tens of trillions of parameters large mvlink switch in itself is a technology Miracle it's 50 billion transistors 74 ports at 400 gabs each four lengths cross-sectional bandwidth of 7 7.2 tabes per second second but one of the important things is that it has mathematics inside the switch so that we can do reductions which is really important in deep learning right on the Chip And so this is what this is what um a dgx looks like now and a lot of people ask us um you know they say and there's this there's this confusion about what Nvidia does and and um how is it possible that that Nvidia became so big building gpus and so there's an impression that this is what a GPU looks like now this is a GPU this is one of the most advanced gpus in the world but this is a gamer GPU but you and I know that this is what a GPU looks like this is one GPU ladies and gentlemen dgx GPU you know the back of this GPU is the MV link spine the MV link spine is 5,000 wires a two miles and it's right here this is an MV link spine and it connects 702 gpus to each other this is a electrical mechanical Miracle the transceivers makes it possible for us to drive the entire length in Copper and as a result this switch the Envy switch Envy link switch driving the Envy link spine in Copper makes it possible for us to save 20 kilowatt in one rack 20 kilow could now be used for processing just an incredible achievement so this is the the MV links spine wow I went down today and if you and even this is not big enough even this is not big enough for AI Factory so we have to connect it all together with very high speed networking well we have two types of networking we have infiniband which has been used uh in supercomputing and AI factories all over the world and it is growing incredibly fast for us however not every data center can handle infiniband because they've already invested their ecosystem in Ethernet for too long and it does take some specialty and some expertise to manage infiniband switches and infiniband networks and so what we've done is we've brought the capabilities of infiniband to the ethernet architecture which is incredibly hard and the reason for that is this ethernet was designed for high average throughput because every single note every single computer is connected to a different person on the internet and most of the communications is the data center with somebody on the other side of the internet however deep learning in AI factories the gpus are not communicating with people on the internet mostly it's communicating with each other they're communicating with each other because they're all they're collecting partial products and they have to reduce it and then redistribute it chunks of partial products reduction redistribution that traffic is incredibly bursty and it is not the average throughput that matters it's the last arrival that matters because if you're reducing collecting partial products from everybody if I'm trying to take all of your so it's not the average throughput is whoever gives me the answer last okay ethernet has no provision for that and so there are several things that we had to create we created an end to-end architecture so that the the Nick and the switch can communicate and we applied four different Technologies to make this possible number one Nvidia has the world's most advanced RDMA and so now we have the ability to have a network level RDMA for ethernet that is incredibly great number two we have congestion control the switch does Telemetry at all times incredibly fast and whenever the uh the uh the the gpus or the the Nick are sending too much information we can tell them to back off so that it doesn't create hotspots number three adaptive routing ethernet needs to transmit and receive in order we see congestions or we see uh ports that are not currently being used irrespective of the ordering we will send it to the available ports and Bluefield on the other end reorders it so that it comes back in order that adaptive routing incredibly powerful and then lastly noise isolation there's more than one model being trained or something happening in the data center at all times and their noise and their traffic could get into each other and causes Jitter and so when it when the noise of one training model one model training causes the last arrival to end up too late it really slows down to training well overall remember you have you have built A5 billion doll or3 billion do Data Center and you're using this for training if the utilization Network utilization was 40% lower and as a result the training time was 20% longer the5 billion data center is effectively like a $6 billion dollar data center so the cost is incredi the cost impact is quite High ethernet with Spectrum X basically allows us to improve the performance so much that the network is basically free and so this is really quite an achievement we're very we have a whole pipeline of ethernet products behind us this is Spectrum x800 it is uh 51.2 terabits per second and 256 Radix the next one coming is 512 Ric is one year from now 512 Rix and that's called Spectrum x800 Ultra and the one after that is x1600 but the important idea is this x800 is designed for tens of thousands tens of thousands of gpus x800 ultra is designed for hundreds of thousands of gpus and x600 is designed for millions of gpus the days of millions of GPU data centers are coming and the reason for that is is very simple of course we want to train much larger models but very importantly in the future almost every interaction you have with the Internet or with a computer will likely have a generative AI running in the cloud somewhere and that generative AI is working with you interacting with you generating videos or images or text or maybe a digital human and so you're interacting with your computer almost all the time and there's always a generative AI connected to that some of it is on Prem some of it is on your device and a lot of it could be in the cloud these generative AI will also do a lot of reasoning capability instead of just one-hot answers they might iterate on answers so that it improve the quality of the answer before they give it to you and so the amount of generation we're going to do in the future is going to be extraordinary let's take a look at all of this put together now tonight this is our first nighttime Keynote I want to thank I want to thank all of you for coming out tonight at 7 o'clock and and so what I'm about to show you has a new Vibe okay there's a new Vibe this is kind of the nighttime keynote Vibe so enjoy this [Applause] [Music] black [Music] well let's go go go go [Music] go okay [Music] [Music] [Music] come on yeah yeah yeah yeah get a y get a [Music] y let's [Music] go uh uh the more you back the more you safe with TP a eye tailor made faster than a speed Al Al efficient that's today [Music] [Applause] [Music] now you can't do that on a morning [Applause] keynote I think that style of keynote has never been done in compy Tex ever might be the last only Nvidia can pull off that only I can do that Blackwell of course uh is the first generation of Nvidia platforms that was launched at the beginning at the right as the world knows the generative AI era is here just as the world realized the importance of AI factories just as the beginning of this new Industrial Revolution uh we have so much support nearly every OEM every computer maker every CSP every GPU Cloud Sovereign clouds even telecommunication companies Enterprises all over the world the amount of success the amount of adoption the amount of of uh enthusiasm for black is just really off the charts and I want to thank everybody for that we're not stopping there uh during this during the time of this incredible growth we want to make sure that we continue to enhance performance continue to drive down cost cost of training cost of inference and continue to scale out AI capabilities for every company to embrace the further we the further performance we drive up the Greater the cost decline Hopper platform of course was the most successful data center processor probably in history and this is just an incredible incredible success story however Blackwell is here and every single platform as you'll notice are several things you got the CPU you have the GPU you have MV link you have the Nick and you have to switch the the MV link switch the uh connects all of the gpus together as large of a domain as we can and whatever we can do we connect it with large um very large and very high-speed switches every single generation as you'll see is not just a GPU but it's an entire platform we build the entire platform we integrate the entire platform into an AI Factory supercomputer however then we disaggregate it and offer it to the world and the reason for that is because all of you could create interesting and Innovative configurations and and and all kinds of different uh uh Styles and and fit different uh data centers and different customers and different places some of it for Edge some of it for Telco and all of the different Innovation are possible if it would we made the systems open and make it possible for you to innovate and so we design it integrated but we offer it to you disintegrated so that you could create modular systems the Blackwell platform is here our company is on a one-year rhythm we're our basic philosophy is very simple one build the entire data center scale disaggregate it and sell it to you in Parts on a one-year Rhythm and we push everything to technology limits whatever tsmc process technology will push it to the absolute limits whatever packaging technology push it to the absolute limits whatever memory technology push it to Absolute limits series technology Optics technology everything is pushed to the Limit well and then after that do everything in such a way so that all of our software runs on this entire install base software inertia is the single most important thing in computers it'll when a computer is backwards compatible and it's architecturally compatible with all the software that has already been created your ability to go to market is so much faster and so the velocity is incredible when we can take advantage of the entire install Bas of software that's already been created well Blackwell is here next year is Blackwell Ultra just as we had h100 and h200 you'll probably see some pretty exciting New Generation from us for Blackwell Ultra again again push to the limits and the Next Generation Spectrum switches I mentioned well this is the very first time that this next click has been made and I'm not sure yet where I'm going to regret this or not we have code names in our company and uh we try to keep them very secret uh often times uh most of the employees don't even know but our next Generation platform is called Reuben the Reuben platform the Reuben platform um I'm I'm not going to spend much time on it uh I know what's going to happen you're going to take pictures of it and you're going to go look at the fine prints uh and feel free to do that so we had the Reuben platform and one year later we'd have the Reuben um Ultra platform all of these chips that I'm showing you here are all in full development 100% of them and the rhythm is one year at the limits of Technology all 100% architecturally compatible so this is this is basically what Nvidia is building and all of the richest of software on top of it so in a lot of ways the last 12 years from that moment of imet and US realizing that the future of computing was going to radically change to today is really exactly as I was holding up earlier GeForce pre 20102 and Nvidia today the company has really transformed tremendously and I want to thank all of our partners here for supporting us every step along the way this is the Nvidia black wall platform let me talk about what's next the next wave of AI is physical ai ai that understands the laws of physics AI that can work among us and so they have to understand the world model so that they understand how to interpret the world how to perceive the world they have to of course have excellent cognitive capabilities so they can understand us understand what we asked and perform the tasks in the future robotics is a much more per pervasive idea of course when I say robotics there's a humanoid robotics that's usually the representation of that but that's not at all true everything is going to be robotic all of the factories will be robotic the factories will orchestrate robots and those robots will be building products that are robotic robots interacting with robots building products that are robotic well in order for us to do that we need to make some breakthroughs and let me show you the video the era of Robotics has arrived one day everything that moves will be autonomous researchers and companies around the world are developing robots powered by physical AI physical AIS are models that can understand instructions and autonomously perform complex tasks in the real world multimodal llms are breakthroughs that enable robots to learn perceive and understand the world around them and plan how they'll Act and from Human demonstrations robots can now learn the skills required to interact with the world using gross and fine motor skills one of the integral Technologies for advancing robotics is reinforcement learning just as llms need rhf or reinforcement learning from Human feedback to learn particular skills generative physical AI can learn skills using reinforcement learning from physics feedback in a simulated world these simulation environments are where robots learn to make decisions by performing actions in a virtual world that obeys the laws of physics in these robot gyms a robot can learn to perform complex and dynamic tasks safely and quickly refining their skills through millions of Acts of trial and error we built Nvidia Omniverse as the operating system where physical AIS can be created Omniverse is a development platform for virtual world simulation combining realtime physically based rendering physics simulation and generative AI Technologies in Omniverse robots can learn how to be robots they learn how to autonomously manipulate objects with Precision such as grasping and handling objects or navigate environments autonomously finding optimal paths while avoiding obstacles and Hazards learning in Omniverse minimizes the Sim to real Gap and maximizes the transfer of learned behavior building robots with generative physical AI requires three computers Nvidia AI supercomputers to train the models envidia Jetson Orin and Next Generation Jetson Thor robotic supercomputer to run the models and Nvidia Omniverse where robots can learn and refine their skills in simulated worlds we build the platforms acceleration libraries and AI models needed by developers and companies and allow them to use any or all of the stacks that suit them best the next wave of AI is here robotics powered by physical AI will revolutionize Industries this isn't the future this is happening now there are several ways that we're going to serve the market the first we're going to create platforms for each type of robotic systems one for robotic factories and warehouses one for robots that manipulate things one for robots that move and one for uh robots that are humanoid and so each one of these robotic robotics platform is like almost everything else we do a computer acceleration libraries and pre-train models computers acceleration libraries pre-train models and we test everything we train everything and integrate everything inside Omniverse where Omniverse is as the video was saying where robots learn how to be robots now of course the EOS system of robotic warehouses is really really complex it takes a lot of companies a lot of tools a lot of technology to build a modern warehouse and warehouses are increasingly robotic one of these days will be fully robotic and so in each one of these ecosystems we have sdks and apis that are connected into the software industry sdks and apis connected into Edge AI industry um and companies and then also of course uh systems that are designed for plc's and robotic systems for the odms it's then integrated by integrators uh created for ultimately uh building warehouses uh for customers here we have an example of kenmac building a robotic warehouse for giant giant group okay and then here now let's talk about Factor factories has a completely different ecosystem and foxcon is building some of the world's most advanced factories their ecosystem again Edge Computers and Robotics software for Designing the factories the workflows programming the robots and of course PLC computers that orchestrate uh the digital factories and the AI factories we have sdks uh that are connected into each one of these ecosystems as well this is happening all over Taiwan foxcon has buil is building digital twins of their factories Delta is building digital twins of their factories by the way half is real half is digital half is Omniverse pegatron is building digital twins of their robotic factories wistron is building digital twins of their robotic factories and this is really cool this is a video of foxc con's new Factory let's take a look demand for NVIDIA accelerated Computing is skyrocketing as the world modernizes traditional data centers into generative AI factories foxcon the world's largest electronics manufacturer is gearing up to meet this Demand by building robotic factories with Nvidia Omniverse and AI Factory planners use Omniverse to integrate facility and Equipment data from leading industry applications like Seaman team Center X and Autodesk Revit in the digital twin they optimize floor layout and line configurations and locate optimal camera placements to monitor future operations with Nvidia Metropolis powered Vision AI virtual integration saves planners on the enormous cost of physical change orders during construction the foxcon teams use the digital twin as the source of Truth to communicate and validate accurate equipment layout the Omniverse digital twin is also the robot gym where foxcon developers train and test Nvidia ISAC AI applications for robotic perception and manipulation and Metropolis AI applications for Sensor Fusion in Omniverse foxcon simulates two robot AIS before deploying run times to Jets and computers on the assembly line they simulate Isaac manipulator libraries and AI models for automated Optical inspection for object identification defect detection and trajectory planning to transfer hgx systems to the test pods they simulate Isaac perceptor powered fobot amrs as they perceive and move about their environment with 3D mapping reconstruction with Omniverse foxcon builds their robotic factories that orchestrate robots running on Nvidia ISAC to build Nvidia AI supercomputers which in turn train foxcon robots [Applause] so a robotic Factory is designed with three computers train the AI on Nvidia AI you have the robot running on the PLC systems uh for orchestrating the the uh the factories and then you of course simulate everything inside Omniverse well the robotic arm and the robotic amrs are also the same way three computer systems the difference is the two omniverses will come together so they'll share one virtual space when they share one virtual space that robotic arm will become inside the robotic Factory and again three three uh three computers and we provide the computer the acceleration layers and pre-train uh pre-trained AI models we've connected Nvidia manipulator and Nvidia Omniverse with seens the world's leading Industrial Automation software and systems company this is really a fantastic partnership and they're working on factories all over the world semantic pick aai now integrates Isaac manipulator and sematic pick aai uh runs operates ABB CA yasawa uh Fook uh Universal robotics um and techman and so seens is a fantastic integration we have all kinds of other Integrations let's take a look Arc is integrating Isaac perceptor into dock smart autonomy robots for enhanced object recognition and human motion tracking in Material Handling byd Electronics is integrating Isaac manipulator and perceptor into their AI robots to enhance manufacturing efficiencies for Global customers ideal works is building Isaac perceptor into their iwos software for AI robots in Factory Logistics intrinsic an alphabet compy company is adopting Isaac manipulator into their Flow State platform to advance robot grasping Gideon is integrating Isaac perceptor into Trey AI powered forklifts to advance AI enabled Logistics Argo robotics is adopting Isaac perceptor into perception engine for advanced vision-based amrs Solomon is using Isaac manipulator AI models in their acup piic 3D software for industrial manipulation techman robot is adopting Isaac Sim and manipulator into TM flow accelerating automated Optical inspection pteridine robotics is integrating Isaac manipulator into polycope X for cobots and Isaac perceptor into mere amrs venin is integrating Isaac manipulator into machine Logic for AI manipulation [Music] robots robotics is here physical AI is here this is not science fiction and it's being used all over Taiwan and just really really exciting and that's the factory the robots inside and of course all the products going to be robotic so there are two very high volume robotics products one of course is the self-driving car or cars that have a great deal of autonomous capability Nvidia again builds the entire stack next year we're going to go to production with the Mercedes Fleet and after that in 20 2026 the jlr fleet uh we offer the full stack to the world however you're welcome to take whichever Parts uh which whichever layer of our stack just as the entire the entire uh uh Drive stack is open the next high volume robotics product that's going to be manufactured by robotic factories with robots inside will likely be humanoid robots and this has great progress in recent years in both the cognitive capability because of foundation models and also the world understanding capability that we're in the process of developing I'm really excited about this area because obviously the easiest robot to adapt into the world are human robots because we built the world for us we also have the vast the most amount of data to train these robots than other types of robots because we have the same uh physique and so the amount of training data we can provide through demonstration capabilities and video capabil abilities is going to be really great and so we're going to see a lot of progress in this area well I think we have um some robots that we like to uh welcome here we go about my size and we have we have some friends to join us so the fut the future of robot robotics is here the next wave of AI and and of course you know Taiwan builds computers with keyboards you build computers for your pocket you build computers for data centers in the cloud in the future you're going to build computers that walk and computers that roll you know around and um so these are all just computers and as as it turns out the technology is very similar to the technology of building um all of the other computers that you already build today so this is going to be a really extraordinary uh Journey for us well uh I want to thank I want to I want to thank I want to I have I've I've made one last video if you don't mind I something that that uh uh uh we we really enjoyed making um and if you want let's run [Music] [Music] it [Music] [Music] [Music] for for [Music] [Music] [Music] [Applause] taian [Music] [Applause] thank you I love you guys thank [Applause] you thank you all for coming have a great comput text thank you I am here I will make you like me best yeah singing with me really [Music]

Channel: 三立新聞網SETN

Views: 229,326

Keywords: 三立新聞網, 三立新聞, 三立, 三立電視, setn, setn三立, 三立直播, taiwan news, taiwan, news, LIVE, NEWS, 24H, NEWS LIVE, Taiwan News, Taiwan, SET, 孫安佐, 孫鵬, 狄鶯, 離婚, 符水, 下毒, 大麻, 泰國, 天道, 再興, 嘉峰, 長勝, 消防, 殉職, 質詢, 曾資程, 議會, 最新消息, 新聞, 直播, 網路直播, 最新, 即時, 新聞快訊, 蔡英文, 習近平, 台灣, 陳建仁, 朱立倫, 侯友宜, 新聞直播, 賴清德, 曹興誠, 郭台銘, 柯文哲, 信賴台灣, 總統, 大選, 選舉, 2024, 蕭美琴, 賴蕭配, 中央, 立法院, 地方議會, 罰則, 虛偽陳述, 隱匿資訊, 火警, 台南, 永康, 橡膠工廠, 民眾黨, 國民黨, 黃國昌, 王世堅, 罷免, 選罷法, 青年局, 李四川, 戴錫欽, 殷瑋, 蔣萬安, 李多慧, 黃仁勳, 開球, 研華, 致詞, 閉眼, 投球, 開球嘉賓, 味全龍, 中職, 頂新, 輝達, 葉君璋, 魏應充, 翻譯字幕, NVIDIA, AI

Id: iAHLtgXbYvw

Length: 108min 7sec (6487 seconds)

Published: Sun Jun 02 2024