The combination of both of those things, and they were both independently surprising I guess, but it happened very fast. You always go so fast from product announcement to product announcement, to have that little bit in the middle where you went “Look, here’s context, this is where it’s a big deal”, I really enjoyed that part and that’s sort of what you were driving at now. That’s something you’re going to pay for as a TSMC customer, is that a cost you’re eager to pay or do you feel a little bit along for the ride here? I want to get to the other shifts you might have made, but I do want to jump in on that meeting demand bit because I want your help to solve one of the biggest conundrums I’ve had, which was in the second half of last year when you were taking large write-downs on your inventory, it turned out when you dove into your financial statements that a big chunk of those write-downs was not just chips you’d already made and couldn’t sell, but also future purchase order obligations with TSMC, and they were famously being very strict about those when everyone wanted space.
That’s why they call it single pane of glass, it’s managing one computer and that’s why it has to be software-defined. The faster the processors, the greater need for high speed computing fabrics and so it’s a matter of scale and the effectiveness of the scale. Is the explosion overstated or is it just a matter of that happened before ChatGPT and things exploded after that? Almost immediately after, the ripples of ChatGPT around the world, cloud service providers, software vendors in all these different industries started to ask the question, what does it mean to them? It turns out, to go back to that interview, you pre-announced DGX Cloud in that you said, quote, “If we ever do services, we’ll run it all over the world in the GPUs that are in everybody’s clouds in addition to building something ourselves if we have to.” Now, last time we talked about Omniverse Cloud, which is the “Building something ourselves”, perhaps. If you have multiple files to submit, please zip them together.
Today’s computers is not a PC, today’s computer is a data center, the data center is the computer and you have to orchestrate that entire fleet of computers inside the data centers as if it’s one. However, don’t just pick up any artificial intelligence book – make sure you look for one that suits your level and interests. First is training – almost every major cloud service provider was already working on large language models, now they realize they have to get to the next level faster. There is a big limitation in what can be covered in cross-cutting legislation on AI, and regardless of the overall regulatory approach, the detail will always need to be dealt with at the level of individual harms and use cases. For a business leader to create an appropriate portfolio, it is important to develop an understanding about which use cases and domains have the potential to drive the most value for a company, as well as which AI and other analytical techniques will need to be deployed to capture that value. Were you already planning on DGX Cloud then, or is this something that you have really shifted your thinking around over the last year?
It started when I first talked to you a year ago and I asked you if you would ever have a cloud service. But here, you announced DGX Cloud that runs in other people’s data centers. Is it even possible to build a DGX computer for China or is it like they’re going to have to figure out how to tie those chips together on their side? JH: Inference. The inference, the scale of inference business has gone through a step function, no doubt, and the type of inference that is being done right now where you know that video will have generative AI added to it to augment the video either to enhance the background, enhance the subject, relight the face, do eye reposing, augment with fun graphics, so on and so forth. Journalism is harnessing AI too, and will continue to benefit from it. Our comprehensive course equips students with the knowledge and skills essential for understanding AI fundamentals, machine learning, neural networks, and various related subjects. The inference engine is an automated reasoning system that evaluates the current state of the knowledge-base, applies relevant rules, and then asserts new knowledge into the knowledge base. This relates to artificial consciousness by proposing a specific mechanism of information handling, that produces what we allegedly experience and describe as consciousness, and which should be able to be duplicated by a machine using current technology.