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Nvidia CEO Jensen Huang interview: From the Grace CPU to engineer’s metaverse

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Jensen Huang, CEO of Nvidia, at GTC 21.

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Nvidia CEO Jensen Huang delivered a keynote speech this week to 180,000 attendees registered for the GTC 21 online-only conference. And Huang dropped a bunch of news across multiple industries that show just how powerful Nvidia has become.

In his talk, Huang described Nvidia’s work on the Omniverse, a version of the metaverse for engineers. The company is starting out with a focus on the enterprise market, and hundreds of enterprises are already supporting and using it. Nvidia has spent hundreds of millions of dollars on the project, which is based on 3D data-sharing standard Universal Scene Description, originally created by Pixar and later open-sourced. The Omniverse is a place where Nvidia can test self-driving cars that use its AI chips and where all sorts of industries will able to test and design products before they’re built in the physical world.

Nvidia also unveiled its Grace central processing unit (CPU), an AI processor for datacenters based on the Arm architecture. Huang announced new DGX Station mini-sucomputers and said customers will be free to rent them as needed for smaller computing projects. And Nvidia unveiled its BlueField 3 data processing units (DPUs) for datacenter computing alongside new Atlan chips for self-driving cars.

Here’s an edited transcript of Huang’s group interview with the press this week. I asked the first question, and other members of the press asked the rest. Huang talked about everything from what the Omniverse means for the game industry to Nvidia’s plans to acquire Arm for $40 billion.

Above: Nvidia CEO Jensen Huang at GTC 21.

Image Credit: Nvidia

Jensen Huang: We had a great GTC. I hope you enjoyed the keynote and some of the talks. We had more than 180,000 registered attendees, 3 times larger than our largest-ever GTC. We had 1,600 talks from some amazing speakers and researchers and scientists. The talks covered a broad range of important topics, from AI [to] 5G, quantum computing, natural language understanding, recommender systems, the most important AI algorithm of our time, self-driving cars, health care, cybersecurity, robotics, edge IOT — the spectrum of topics was stunning. It was very exciting.

Question: I know that the first version of Omniverse is for enterprise, but I’m curious about how you would get game developers to embrace this. Are you hoping or expecting that game developers will build their own versions of a metaverse in Omniverse and eventually try to host consumer metaverses inside Omniverse? Or do you see a different purpose when it’s specifically related to game developers?

Huang: Game development is one of the most complex design pipelines in the world today. I predict that more things will be designed in the virtual world, many of them for games, than there will be designed in the physical world. They will be every bit as high quality and high fidelity, every bit as exquisite, but there will be more buildings, more cars, more boats, more coins, and all of them — there will be so much stuff designed in there. And it’s not designed to be a game prop. It’s designed to be a real product. For a lot of people, they’ll feel that it’s as real to them in the digital world as it is in the physical world.

Omniverse lets artists design hotels in a 3D space.

Above: Omniverse lets artists design hotels in a 3D space.

Image Credit: Leeza SOHO, Beijing by ZAHA HADID ARCHITECTS

Omniverse enables game developers working across this complicated pipeline, first of all, to be able to connect. Someone doing rigging for the animation or someone doing textures or someone designing geometry or someone doing lighting, all of these different parts of the design pipeline are complicated. Now they have Omniverse to connect into. Everyone can see what everyone else is doing, rendering in a fidelity that is at the level of what everyone sees. Once the game is developed, they can run it in the Unreal engine that gets exported out. These worlds get run on all kinds of devices. Or Unity. But if someone wants to stream it right out of the cloud, they could do that with Omniverse, because it needs multiple GPUs, a fair amount of computation.

That’s how I see it evolving. But within Omniverse, just the concept of designing virtual worlds for the game developers, it’s going to be a huge benefit to their work flow.

Question: You announced that your current processors target high-performance computing with a special focus on AI. Do you see expanding this offering, developing this CPU line into other segments for computing on a larger scale in the market of datacenters?

Huang: Grace is designed for applications, software that is data-driven. AI is software that writes software. To write that software, you need a lot of experience. It’s just like human intelligence. We need experience. The best way to get that experience is through a lot of data. You can also get it through simulation. For example, the Omniverse simulation system will run on Grace incredibly well. You could simulate — simulation is a form of imagination. You could learn from data. That’s a form of experience. Studying data to infer, to generalize that understanding and turn it into knowledge. That’s what Grace is designed for, these large systems for very important new forms of software, data-driven software.

As a policy, or not a policy, but as a philosophy, we tend not to do anything unless the world needs us to do it and it doesn’t exist. When you look at the Grace architecture, it’s unique. It doesn’t look like anything out there. It solves a problem that didn’t used to exist. It’s an opportunity and a market, a way of doing computing that didn’t exist 20 years ago. It’s sensible to imagine that CPUs that were architected and system architectures that were designed 20 years ago wouldn’t address this new application space. We’ll tend to focus on areas where it didn’t exist before. It’s a new class of problem, and the world needs to do it. We’ll focus on that.

Otherwise, we have excellent partnerships with Intel and AMD. We work very closely with them in the PC industry, in the datacenter, in hyperscale, in supercomputing. We work closely with some exciting new partners. Ampere Computing is doing a great ARM CPU. Marvell is incredible at the edge, 5G systems and I/O systems and storage systems. They’re fantastic there, and we’ll partner with them. We partner with Mediatek, the largest SOC company in the world. These are all companies who have brought great products. Our strategy is to support them. Our philosophy is to support them. By connecting our platform, Nvidia AI or Nvidia RTX, our raytracing platform, with Omniverse and all of our platform technologies to their CPUs, we can expand the overall market. That’s our basic approach. We only focus on building things that the world doesn’t have.

Nvidia's Grace CPU for datacenters.

Above: Nvidia’s Grace CPU for datacenters is named after Grace Hopper.

Image Credit: Nvidia

Question: I wanted to follow up on the last question regarding Grace and its use. Does this signal Nvidia’s perhaps ambitions in the CPU space beyond the datacenter? I know you said you’re looking for things that the world doesn’t have yet. Obviously, working with ARM chips in the datacenter space leads to the question of whether we’ll see a commercial version of an Nvidia CPU in the future.

Huang: Our platforms are open. When we build our platforms, we create one version of it. For example, DGX. DGX is fully integrated. It’s bespoke. It has an architecture that’s very specifically Nvidia. It was designed — the first customer was Nvidia researchers. We have a couple billion dollars’ worth of infrastructure our AI researchers are using to develop products and pretrain models and do AI research and self-driving cars. We built DGX primarily to solve a problem we had. Therefore it’s completely bespoke.

We take all of the building blocks, and we open it. We open our computing platform in three layers: the hardware layer, chips and systems; the middleware layer, which is Nvidia AI, Nvidia Omniverse, and it’s open; and the top layer, which is pretrained models, AI skills, like driving skills, speaking skills, recommendation skills, pick and play skills, and so on. We create it vertically, but we architect it and think about it and build it in a way that’s intended for the entire industry to be able to use however they see fit. Grace will be commercial in the same way, just like Nvidia GPUs are commercial.

With respect to its future, our primary preference is that we don’t build something. Our primary preference is that if somebody else is building it, we’re delighted to use it. That allows us to spare our critical resources in the company and focus on advancing the industry in a way that’s rather unique. Advancing the industry in a way that nobody else does. We try to get a sense of where people are going, and if they’re doing a fantastic job at it, we’d rather work with them to bring Nvidia technology to new markets or expand our combined markets together.

The ARM license, as you mentioned — acquiring ARM is a very similar approach to the way we think about all of computing. It’s an open platform. We sell our chips. We license our software. We put everything out there for the ecosystem to be able to build bespoke, their own versions of it, differentiated versions of it. We love the open platform approach.

Question: Can you explain what made Nvidia decide that this datacenter chip was needed right now? Everybody else has datacenter chips out there. You’ve never done this before. How is it different from Intel, AMD, and other datacenter CPUs? Could this cause problems for Nvidia partnerships with those companies, because this puts you in direct competition?

Huang: The answer to the last part — I’ll work my way to the beginning of your question. But I don’t believe so. Companies have leadership that are a lot more mature than maybe given credit for. We compete with the ARM GPUs. On the other hand, we use their CPUs in DGX. Literally, our own product. We buy their CPUs to integrate into our own product — arguably our most important product. We work with the whole semiconductor industry to design their chips into our reference platforms. We work hand in hand with Intel on RTX gaming notebooks. There are almost 80 notebooks we worked on together this season. We advance industry standards together. A lot of collaboration.

Back to why we designed the datacenter CPU, we didn’t think about it that way. The way Nvidia tends to think is we say, “What is a problem that is worthwhile to solve, that nobody in the world is solving and we’re suited to go solve that problem and if we solve that problem it would be a benefit to the industry and the world?” We ask questions literally like that. The philosophy of the company, in leading through that set of questions, finds us solving problems only we will, or only we can, that have never been solved before. The outcome of trying to create a system that can train AI models, language models, that are gigantic, learn from multi-modal data, that would take less than three months — right now, even on a giant supercomputer, it takes months to train 1 trillion parameters. The world would like to train 100 trillion parameters on multi-modal data, looking at video and text at the same time.

The journey there is not going to happen by using today’s architecture and making it bigger. It’s just too inefficient. We created something that is designed from the ground up to solve this class of interesting problems. Now this class of interesting problems didn’t exist 20 years ago, as I mentioned, or even 10 or five years ago. And yet this class of problems is important to the future. AI that’s conversational, that understands language, that can be adapted and pretrained to different domains, what could be more important? It could be the ultimate AI. We came to the conclusion that hundreds of companies are going to need giant systems to pretrain these models and adapt them. It could be thousands of companies. But it wasn’t solvable before. When you have to do computing for three years to find a solution, you’ll never have that solution. If you can do that in weeks, that changes everything.

That’s how we think about these things. Grace is designed for giant-scale data-driven software development, whether it’s for science or AI or just data processing.

Nvidia DGX SuperPod

Above: Nvidia DGX SuperPod

Image Credit: Nvidia

Question: You’re proposing a software library for quantum computing. Are you working on hardware components as well?

Huang: We’re not building a quantum computer. We’re building an SDK for quantum circuit simulation. We’re doing that because in order to invent, to research the future of computing, you need the fastest computer in the world to do that. Quantum computers, as you know, are able to simulate exponential complexity problems, which means that you’re going to need a really large computer very quickly. The size of the simulations you’re able to do to verify the results of the research you’re doing to do development of algorithms so you can run them on a quantum computer someday, to discover algorithms — at the moment, there aren’t that many algorithms you can run on a quantum computer that prove to be useful. Grover’s is one of them. Shore’s is another. There are some examples in quantum chemistry.

We give the industry a platform by which to do quantum computing research in systems, in circuits, in algorithms, and in the meantime, in the next 15-20 years, while all of this research is happening, we have the benefit of taking the same SDKs, the same computers, to help quantum chemists do simulations much more quickly. We could put the algorithms to use even today.

And then last, quantum computers, as you know, have incredible exponential complexity computational capability. However, it has extreme I/O limitations. You communicate with it through microwaves, through lasers. The amount of data you can move in and out of that computer is very limited. There needs to be a classical computer that sits next to a quantum computer, the quantum accelerator if you can call it that, that pre-processes the data and does the post-processing of the data in chunks, in such a way that the classical computer sitting next to the quantum computer is going to be super fast. The answer is fairly sensible, that the classical computer will likely be a GPU-accelerated computer.

There are lots of reasons we’re doing this. There are 60 research institutes around the world. We can work with every one of them through our approach. We intend to. We can help every one of them advance their research.

Question: So many workers have moved to work from home, and we’ve seen a huge increase in cybercrime. Has that changed the way AI is used by companies like yours to provide defenses? Are you worried about these technologies in the hands of bad actors who can commit more sophisticated and damaging crimes? Also, I’d love to hear your thoughts broadly on what it will take to solve the chip shortage problem on a lasting global basis.

Huang: The best way is to democratize the technology, in order to enable all of society, which is vastly good, and to put great technology in their hands so that they can use the same technology, and ideally superior technology, to stay safe. You’re right that security is a real concern today. The reason for that is because of virtualization and cloud computing. Security has become a real challenge for companies because every computer inside your datacenter is now exposed to the outside. In the past, the doors to the datacenter were exposed, but once you came into the company, you were an employee, or you could only get in through VPN. Now, with cloud computing, everything is exposed.

The other reason why the datacenter is exposed is because the applications are now aggregated. It used to be that the applications would run monolithically in a container, in one computer. Now the applications for scaled out architectures, for good reasons, have been turned into micro-services that scale out across the whole datacenter. The micro-services are communicating with each other through network protocols. Wherever there’s network traffic, there’s an opportunity to intercept. Now the datacenter has billions of ports, billions of virtual active ports. They’re all attack surfaces.

The answer is you have to do security at the node. You have to start it at the node. That’s one of the reasons why our work with BlueField is so exciting to us. Because it’s a network chip, it’s already in the computer node, and because we invented a way to put high-speed AI processing in an enterprise datacenter — it’s called EGX — with BlueField on one end and EGX on the other, that’s a framework for security companies to build AI. Whether it’s a Check Point or a Fortinet or Palo Alto Networks, and the list goes on, they can now develop software that runs on the chips we build, the computers we build. As a result, every single packet in the datacenter can be monitored. You would inspect every packet, break it down, turn it into tokens or words, read it using natural language understanding, which we talked about a second ago — the natural language understanding would determine whether there’s a particular action that’s needed, a security action needed, and send the security action request back to BlueField.

This is all happening in real time, continuously, and there’s just no way to do this in the cloud because you would have to move way too much data to the cloud. There’s no way to do this on the CPU because it takes too much energy, too much compute load. People don’t do it. I don’t think people are confused about what needs to be done. They just don’t do it because it’s not practical. But now, with BlueField and EGX, it’s practical and doable. The technology exists.

Nvidia's Inception AI statups over the years.

Above: Nvidia’s Inception AI statups over the years.

Image Credit: Nvidia

The second question has to do with chip supply. The industry is caught by a couple of dynamics. Of course one of the dynamics is COVID exposing, if you will, a weakness in the supply chain of the automotive industry, which has two main components it builds into cars. Those main components go through various supply chains, so their supply chain is super complicated. When it shut down abruptly because of COVID, the recovery process was far more complicated, the restart process, than anybody expected. You could imagine it, because the supply chain is so complicated. It’s very clear that cars could be rearchitected, and instead of thousands of components, it wants to be a few centralized components. You can keep your eyes on four things a lot better than a thousand things in different places. That’s one factor.

The other factor is a technology dynamic. It’s been expressed in a lot of different ways, but the technology dynamic is basically that we’re aggregating computing into the cloud, and into datacenters. What used to be a whole bunch of electronic devices — we can now virtualize it, put it in the cloud, and remotely do computing. All the dynamics we were just talking about that have created a security challenge for datacenters, that’s also the reason why these chips are so large. When you can put computing in the datacenter, the chips can be as large as you want. The datacenter is big, a lot bigger than your pocket. Because it can be aggregated and shared with so many people, it’s driving the adoption, driving the pendulum toward very large chips that are very advanced, versus a lot of small chips that are less advanced. All of a sudden, the world’s balance of semiconductor consumption tipped toward the most advanced of computing.

The industry now recognizes this, and surely the world’s largest semiconductor companies recognize this. They’ll build out the necessary capacity. I doubt it will be a real issue in two years because smart people now understand what the problems are and how to address them.

Question: I’d like to know more about what clients and industries Nvidia expects to reach with Grace, and what you think is the size of the market for high-performance datacenter CPUs for AI and advanced computing.

Huang: I’m going to start with I don’t know. But I can give you my intuition. 30 years ago, my investors asked me how big the 3D graphics was going to be. I told them I didn’t know. However, my intuition was that the killer app would be video games, and the PC would become — at the time the PC didn’t even have sound. You didn’t have LCDs. There was no CD-ROM. There was no internet. I said, “The PC is going to become a consumer product. It’s very likely that the new application that will be made possible, that wasn’t possible before, is going to be a consumer product like video games.” They said, “How big is that market going to be?” I said, “I think every human is going to be a gamer.” I said that about 30 years ago. I’m working toward being right. It’s surely happening.

Ten years ago someone asked me, “Why are you doing all this stuff in deep learning? Who cares about detecting cats?” But it’s not about detecting cats. At the time I was trying to detect red Ferraris, as well. It did it fairly well. But anyway, it wasn’t about detecting things. This was a fundamentally new way of developing software. By developing software this way, using networks that are deep, which allows you to capture very high dimensionality, it’s the universal function approximator. If you gave me that, I could use it to predict Newton’s law. I could use it to predict anything you wanted to predict, given enough data. We invested tens of billions behind that intuition, and I think that intuition has proven right.

I believe that there’s a new scale of computer that needs to be built, that needs to learn from basically Earth-scale amounts of data. You’ll have sensors that will be connected to everywhere on the planet, and we’ll use them to predict climate, to create a digital twin of Earth. It’ll be able to predict weather everywhere, anywhere, down to a square meter, because it’s learned the physics and all the geometry of the Earth. It’s learned all of these algorithms. We could do that for natural language understanding, which is extremely complex and changing all the time. The thing people don’t realize about language is it’s evolving continuously. Therefore, whatever AI model you use to understand language is obsolete tomorrow, because of decay, what people call model drift. You’re continuously learning and drifting, if you will, with society.

There’s some very large data-driven science that needs to be done. How many people need language models? Language is thought. Thought is humanity’s ultimate technology. There are so many different versions of it, different cultures and languages and technology domains. How people talk in retail, in fashion, in insurance, in financial services, in law, in the chip industry, in the software industry. They’re all different. We have to train and adapt models for every one of those. How many versions of those? Let’s see. Take 70 languages, multiply by 100 industries that need to use giant systems to train on data forever. That’s maybe an intuition, just to give a sense of my intuition about it. My sense is that it will be a very large new market, just as GPUs were once a zero billion dollar market. That’s Nvidia’s style. We tend to go after zero billion dollar markets, because that’s how we make a contribution to the industry. That’s how we invent the future.

Arm's campus in Cambridge, United Kingdom.

Above: Arm’s campus in Cambridge, United Kingdom.

Image Credit: Arm

Question: Are you still confident that the ARM deal will gain approval by close? With the announcement of Grace and all the other ARM-relevant partnerships you have in development, how important is the ARM acquisition to the company’s goals, and what do you get from owning ARM that you don’t get from licensing?

Huang: ARM and Nvidia are independently and separately excellent businesses, as you know well. We will continue to have excellent separate businesses as we go through this process. However, together we can do many things, and I’ll come back to that. To the beginning of your question, I’m very confident that the regulators will see the wisdom of the transaction. It will provide a surge of innovation. It will create new options for the marketplace. It will allow ARM to be expanded into markets that otherwise are difficult for them to reach themselves. Like many of the partnerships I announced, those are all things bringing AI to the ARM ecosystem, bringing Nvidia’s accelerated computing platform to the ARM ecosystem — it’s something only we and a bunch of computing companies working together can do. The regulators will see the wisdom of it, and our discussions with them are as expected and constructive. I’m confident that we’ll still get the deal done in 2022, which is when we expected it in the first place, about 18 months.

With respect to what we can do together, I demonstrated one example, an early example, at GTC. We announced partnerships with Amazon to combine the Graviton architecture with Nvidia’s GPU architecture to bring modern AI and modern cloud computing to the cloud for ARM. We did that for Ampere computing, for scientific computing, AI in scientific computing. We announced it for Marvell, for edge and cloud platforms and 5G platforms. And then we announced it for Mediatek. These are things that will take a long time to do, and as one company we’ll be able to do it a lot better. The combination will enhance both of our businesses. On the one hand, it expands ARM into new computing platforms that otherwise would be difficult. On the other hand, it expands Nvidia’s AI platform into the ARM ecosystem, which is underexposed to Nvidia’s AI and accelerated computing platform.

Question: I covered Atlan a little more than the other pieces you announced. We don’t really know the node side, but the node side below 10nm is being made in Asia. Will it be something that other countries adopt around the world, in the West? It raises a question for me about the long-term chip supply and the trade issues between China and the United States. Because Atlan seems to be so important to Nvidia, how do you project that down the road, in 2025 and beyond? Are things going to be handled, or not?

Huang: I have every confidence that it will not be an issue. The reason for that is because Nvidia qualifies and works with all of the major foundries. Whatever is necessary to do, we’ll do it when the time comes. A company of our scale and our resources, we can surely adapt our supply chain to make our technology available to customers that use it.BlueField-3 DPU

Question: In reference to BlueField 3, and BlueField 2 for that matter, you presented a strong proposition in terms of offloading workloads, but could you provide some context into what markets you expect this to take off in, both right now and going into the future? On top of that, what barriers to adoption remain in the market?

Huang: I’m going to go out on a limb and make a prediction and work backward. Number one, every single datacenter in the world will have an infrastructure computing platform that is isolated from the application platform in five years. Whether it’s five or 10, hard to say, but anyway, it’s going to be complete, and for very logical reasons. The application that’s where the intruder is, you don’t want the intruder to be in a control mode. You want the two to be isolated. By doing this, by creating something like BlueField, we have the ability to isolate.

Second, the processing necessary for the infrastructure stack that is software-defined — the networking, as I mentioned, the east-west traffic in the datacenter, is off the charts. You’re going to have to inspect every single packet now. The east-west traffic in the data center, the packet inspection, is going to be off the charts. You can’t put that on the CPU because it’s been isolated onto a BlueField. You want to do that on BlueField. The amount of computation you’ll have to accelerate onto an infrastructure computing platform is quite significant, and it’s going to get done. It’s going to get done because it’s the best way to achieve zero trust. It’s the best way that we know of, that the industry knows of, to move to the future where the attack surface is basically zero, and yet every datacenter is virtualized in the cloud. That journey requires a reinvention of the datacenter, and that’s what BlueField does. Every datacenter will be outfitted with something like BlueField.

I believe that every single edge device will be a datacenter. For example, the 5G edge will be a datacenter. Every cell tower will be a datacenter. It’ll run applications, AI applications. These AI applications could be hosting a service for a client or they could be doing AI processing to optimize radio beams and strength as the geometry in the environment changes. When traffic changes and the beam changes, the beam focus changes, all of that optimization, incredibly complex algorithms, wants to be done with AI. Every base station is going to be a cloud native, orchestrated, self-optimizing sensor. Software developers will be programming it all the time.

Every single car will be a datacenter. Every car, truck, shuttle will be a datacenter. Every one of those datacenters, the application plane, which is the self-driving car plane, and the control plane, that will be isolated. It’ll be secure. It’ll be functionally safe. You need something like BlueField. I believe that every single edge instance of computing, whether it’s in a warehouse, a factory — how could you have a several-billion-dollar factory with robots moving around and that factory is literally sitting there and not have it be completely tamper-proof? Out of the question, absolutely. That factory will be built like a secure datacenter. Again, BlueField will be there.

Everywhere on the edge, including autonomous machines and robotics, every datacenter, enterprise or cloud, the control plane and the application plane will be isolated. I promise you that. Now the question is, “How do you go about doing it? What’s the obstacle?” Software. We have to port the software. There’s two pieces of software, really, that need to get done. It’s a heavy lift, but we’ve been lifting it for years. One piece is for 80% of the world’s enterprise. They all run VMware vSphere software-defined datacenter. You saw our partnership with VMware, where we’re going to take vSphere stack — we have this, and it’s in the process of going into production now, going to market now … taking vSphere and offloading it, accelerating it, isolating it from the application plane.

Nvidia has eight new RTX GPU cards.

Above: Nvidia has eight new RTX GPU cards.

Image Credit: Nvidia

Number two, for everybody else out at the edge, the telco edge, with Red Hat, we announced a partnership with them, and they’re doing the same thing. Third, for all the cloud service providers who have bespoke software, we created an SDK called DOCA 1.0. It’s released to production, announced at GTC. With this SDK, everyone can program the BlueField, and by using DOCA 1.0, everything they do on BlueField runs on BlueField 3 and BlueField 4. I announced the architecture for all three of those will be compatible with DOCA. Now the software developers know the work they do will be leveraged across a very large footprint, and it will be protected for decades to come.

We had a great GTC. At the highest level, the way to think about that is the work we’re doing is all focused on driving some of the fundamental dynamics happening in the industry. Your questions centered around that, and that’s fantastic. There are five dynamics highlighted during GTC. One of them is accelerated computing as a path forward. It’s the approach we pioneered three decades ago, the approach we strongly believe in. It’s able to solve some challenges for computing that are now front of mind for everyone. The limits of CPUs and their ability to scale to reach some of the problems we’d like to address are facing us. Accelerated computing is the path forward.

Second, to be mindful about the power of AI that we all are excited about. We have to realize that it’s a software that is writing software. The computing method is different. On the other hand, it creates incredible new opportunities. Thinking about the datacenter not just as a big room with computers and network and security appliances, but thinking of the entire datacenter as one computing unit. The datacenter is the new computing unit.

Bentley's tools used to create a digital twin of a location in the Omniverse.

Above: Bentley’s tools used to create a digital twin of a location in the Omniverse.

Image Credit: Nvidia

5G is super exciting to me. Commercial 5G, consumer 5G is exciting. However, it’s incredibly exciting to look at private 5G, for all the applications we just looked at. AI on 5G is going to bring the smartphone moment to agriculture, to logistics, to manufacturing. You can see how excited BMW is about the technologies we’ve put together that allow them to revolutionize the way they do manufacturing, to become much more of a technology company going forward.

Last, the era of robotics is here. We’re going to see some very rapid advances in robotics. One of the critical needs of developing robotics and training robotics, because they can’t be trained in the physical world while they’re still clumsy — we need to give it a virtual world where it can learn how to be a robot. These virtual worlds will be so realistic that they’ll become the digital twins of where the robot goes into production. We spoke about the digital twin vision. PTC is a great example of a company that also sees the vision of this. This is going to be a realization of a vision that’s been talked about for some time. The digital twin idea will be made possible because of technologies that have emerged out of gaming. Gaming and scientific computing have fused together into what we call Omniverse.

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Metacore secures $179.9M in credit from Supercell for casual games

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Metacore secures $179.9M in credit from Supercell for casual games

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Mobile game studio Metacore has raised $179.9 million in credit from Supercell to continue developing its casual mobile game Merge Mansion.

It’s a huge amount of money to pour into a game studio with one game, but it shows what Helsinki-based Supercell is willing to do with the cash it generates from mobile gaming hits like Clash of Clans, Clash Royale, Hay Day, and Brawl Stars.

Since releasing its first game (Merge Mansion) in late 2020, Metacore’s annual revenue run rate has reached $54 million, putting the company on track to become one of the fastest-growing game studios in Europe. Merge Mansion is a puzzle game with more than 800,000 daily players. The new funding will help boost Merge Mansion’s global operations and strengthen Metacore’s core team.

“We’re off to a really good start and raised this follow-on funding from Supercell to increase the scaling of the game,” said Metacore CEO Mika Tammenkoski in an interview with GamesBeat. “It couldn’t be more exciting than this.”

Supercell has backed the game studio for years, with an initial investment of $5.9 million in 2018 followed by a $17.9 million investment and $11.9 million credit line in 2020. The new credit line financing strengthens Metacore’s capability to accelerate its growth while maintaining their current ownership structure and autonomy.

Supercell’s investments lead Jaakko Harlas said in a statement that Metacore is going from strength to strength. He said Merge Mansion launched with high expectations and has met them. He said Supercell invests in strong teams, and Supercell’s role is to remove obstacles.

“Merge Mansion has hit its metrics, and we have been scaling it successfully so far,” Tammenkoski said. “We believe that we can really reach the top of the charts with that game. As you know, as you know, getting to the top of the charts, or scaling mobile games, is really capital intensive because of the dynamics of the free-to-play business model. And it means that you have to invest heavily, and then you have to wait for a while to get the return on the investment.”

Metacore looks to fill key roles in game development and brand marketing.

“Most of the money goes into marketing,” he said. “The personal costs are really marginal compared to what you can spend on performance and brand marketing. And we really want to make Merge Mansion into an entertainment brand in the mobile game space. And that means that we really need to invest into it as well.”

Metacore has a distinctive approach to scaling its studio: It builds and tests games with small, two-to-three person teams that have full autonomy over games they develop and only expand these teams once they’ve validated the concept on the market through player feedback. That’s pretty similar to the way that Supercell runs.

Regarding Supercell, “They know how capital intensive scaling these games is,” Tammenkoski said. “We couldn’t have a better partner than this.”

Above: Metacore’s Merge Mansion mixes puzzles with discovery.

Image Credit: Metacore

This enables Metacore to quickly pivot or scrap game projects that players aren’t responding to, but it also means the studio can swiftly act when it’s clear they have a hit game like Merge Mansion on its hands.

Metacore has doubled its team size to close to 30 since last fall and is actively recruiting for key roles in game development, brand marketing, and other strategic business functions. Tammenkoski emphasized that the company is not rushing with recruitment and is taking the time to find the right fit.

Tammenkoski and Aki Järvilehto founded the company. Merge Mansion features a grandmother and her grandaughter who bond over an old mansion and try to get it back into livable shape. The advertising will focus on telling a story for a mass market audience, Tammenkoski said.

The funding comes at a time when mobile advertising is in flux, as Apple is prioritizing user privacy over targeting advertising. Tammenkoski said there is turbulence in the market and no one knows how bad it will get, but he said he is not targeting any particular cohort of players. That should make it easier to deal with Apple’s change in the Identifier for Advertisers (IDFA).

“The dynamics will change, but we will go broad with our advertising,” Tammenkoski said.

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Speech recognition system trains on radio archive to learn Niger Congo languages

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For many of the 700 million illiterate people around the world, speech recognition technology could provide a bridge to valuable information. Yet in many countries, these people tend to speak only languages for which the datasets necessary to train a speech recognition model are scarce. This data deficit persists for several reasons, chief among them the fact that creating products for languages spoken by smaller populations can be less profitable.

Nonprofit efforts are underway to close the gap, including 1000 Words in 1000 Languages, Mozilla’s Common Voice, and the Masakhane project, which seeks to translate African languages using neural machine translation. But this week, researchers at Guinea-based tech accelerator GNCode and Stanford detailed a new initiative that uniquely advocates using radio archives in developing speech systems for “low-resource” languages, particularly Maninka, Pular, and Susu in the Niger Congo family.

“People who speak Niger Congo languages have among the lowest literacy rates in the world, and illiteracy rates are especially pronounced for women,” the coauthors note. “Maninka, Pular, and Susu are spoken by a combined 10 million people, primarily in seven African countries, including six where the majority of the adult population is illiterate.”

The idea behind the new initiative is to make use of unsupervised speech representation learning, demonstrating that representations learned from radio programs can be leveraged for speech recognition. Where labeled datasets don’t exist, unsupervised learning can help to fill in domain knowledge by determining the correlations between data points and then training based on the newly applied data labels.

New datasets

The researchers created two datasets, West African Speech Recognition Corpus and the West African Radio Corpus, intended for applications targeting West African languages. The West African Speech Recognition Corpus contains over 10,000 hours of recorded speech in French, Maninka, Susu, and Pular from roughly 49 speakers, including Guinean first names and voice commands like “update that,” “delete that,” “yes,” and “no.” As for the West African Radio Corpus, it consists of 17,000 audio clips sampled from archives collected from six Guinean radio stations. The broadcasts in the West African Radio Corpus span news and shows in languages including French, Guerze, Koniaka, Kissi, Kono, Maninka, Mano, Pular, Susu, and Toma.

To create a speech recognition system, the researchers tapped Facebook’s wav2vec, an open source framework for unsupervised speech processing. Wav2vec uses an encoder module that takes raw audio and outputs speech representations, which are fed into a Transformer that ensures the representations capture whole-audio-sequence information. Created by Google researchers in 2017, the Transformer network architecture was initially intended as a way to improve machine translation. To this end, it uses attention functions instead of a recurrent neural network to predict what comes next in a sequence.

Above: The accuracies of WAwav2vec.

Despite the fact that the radio dataset includes phone calls as well as background and foreground music, static, and interference, the researchers managed to train a wav2vec model with the West African Radio Corpus, which they call WAwav2vec. In one experiment with speech across French, Maninka, Pular, and Susu, the coauthors say that they achieved multilingual speech recognition accuracy (88.01%) on par with Facebook’s baseline wav2vec model (88.79%) — despite the fact that the baseline model was trained on 960 hours of speech versus WAwav2vec’s 142 hours.

Virtual assistant

As a proof of concept, the researchers used WAwav2vec to create a prototype of a speech assistant. The assistant — which is available in open source along with the datasets — can recognize basic contact management commands (e.g., “search,” “add,” “update,” and “delete”) in addition to names and digits. As the coauthors note, smartphone access has exploded in the Global South, with an estimated 24.5 million smartphone owners in South Africa alone, according to Statista, making this sort of assistant likely to be useful.

“To the best of our knowledge, the multilingual speech recognition models we trained are the first-ever to recognize speech in Maninka, Pular, and Susu. We also showed how this model can power a voice interface for contact management,” the coauthors wrote. “Future work could expand its vocabulary to application domains such as microfinance, agriculture, or education. We also hope to expand its capabilities to more languages from the Niger-Congo family and beyond, so that literacy or ability to speak a foreign language are not prerequisites for accessing the benefits of technology. The abundance of radio data should make it straightforward to extend the encoder to other languages.”

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Gamescom announces online-only festival in August, reversing hybrid event plan

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The crowd at Gamescom 2019 on opening day on Tuesday, August 20.

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Reversing a plan announced in March, Gamescom will no longer try to do a hybrid gaming expo this summer. Instead, it will focus on an online-only event at the end of August.

The fan-and-business trade show is the world’s biggest game-industry event — with 370,000 people attending the physical event in 2019 — but it had to switch to online-only in 2020 due to the pandemic. The event organizers floated the idea of a hybrid physical event where fans could come see games in person along with digital announcements. The hope was that the coronavirus would subside thanks to vaccinations and that people would want to recapture the excitement of an in-person event.

But today, the Association of the German Games Industry and Koelnmesse decided against that plan, based on responses from potential exhibitors and fans. They plan to hold the main part of the show from August 25 to August 29.

Gamescom Congress will once again take place Thursday, August 26, and Devcom will start off the events August 23. The main days of Gamescom will take place on August 26 and August 27. IGN will produce a show dubbed Awesome Indies. Opening Night Live, which Geoff Keighley produces, will still take place, but it will now be online-only as well. Gamescom was planning to start selling tickets in May.

Above: The crowd at Gamescom 2019 on opening day. The show was online-only in 2020. It will be online-only again in 2021.

Image Credit: Dean Takahashi

“This decision was made after extensive discussions with partners and exhibitors,” the organizers said in a press release. “Thus, the organizers take into account the current situation, in which too many companies are unable to participate in physical events this year due to the still difficult development. In this way, they also meet the partners’ strong need for planning security. This means that Gamescom 2021 will be held exclusively digitally and free of charge for all Gamescom fans.”

Last year, Gamescom had more than 100 million video views over all formats and channels, more than 50 million unique viewers from 180 countries, and 370 partners from 44 countries. Oliver Frese, chief operating officer of Koelnmesse, said in a statement that Gamescom was coming too early for many companies in the industry, as it required so much advanced planning amid an uncertain environment. Companies need that planning reliability, he said.

Felix Falk, managing director of the German Games Industry Association, said in a statement that next year the groups will be able to implement more of the concepts they had in mind for a hybrid version of Gamescom. There will be business-to-business matchmaking events such as “indies meet investors and publishers” pitch events.

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