While gaming remains Nvidia’s primary source of growth, the company’s artificial intelligence business is growing rapidly. In the second fiscal-quarter, Nvidia reported increased demand for both its graphics and AI chips.

For some idea of Nvidia’s growth in AI, Huang says there are now over 4,000 AI startups working with his company. That number is up from 2,000 in April 2017 and goes to show the growing interest around the technology.

Nvidia continues to innovate in AI and is quickly establishing itself as a major player; particularly in driverless cars.

The company’s self-driving platform DRIVE is powered by Nvidia Xavier chips and consists of three parts:

1. DRIVE AV — This part of the DRIVE platform was also available in the previous generation and uses neural networks to perform the calculations required for self-driving cars.
2. DRIVE IX — The first of the two new inclusions is a software development kit which enables AI assistants that can harness data from sensors inside and outside the vehicle.
3. DRIVE AR — While there have been many advancements around self-driving technologies and AI assistants for cars, augmented reality is still relatively unexplored. With DRIVE AR, Nvidia intends to enable new graphical experiences which can deliver things such as information about points of interest along the route.

Nvidia says its Xavier chips have been in development for over four years and represents the work of over 2,000 engineers. Xavier features more than nine billion transistors and Nvidia claims it’s the most complex system-on-a-chip (SoC) ever created.

In a conference call last August, Huang teased Tesla’s reported difficulties in building its own AI chips: “It’s super hard to build Xavier and all the software stack on top of it. If it doesn’t turn out for whatever reason for them [Tesla] you can give me a call and I’d be more than happy to help.”

Autonomous car manufacturers need to train petabytes of data per day, reiterate their models, and deploy them again in order to get those vehicles to market. Nvidia has a server, equivalent to 800 traditional servers, called the DGX-2 which delivers two petaflops of performance.

During AI Expo London earlier this year, Nvidia senior director of enterprise David Hogan said: “The compute demands of AI are huge and beyond what anybody has seen within a standard enterprise environment before. You cannot train a neural network on a standard CPU cluster.”

While Nvidia is well-positioned in AI, and is already reaping the benefits, it foresees gaming continuing to be its primary source of revenue for some time.

Nvidia reported earnings per share of $1.24 on revenues of $2.58 billion in the second fiscal quarter ended July 31. Gaming was about 50.3% of total revenues, and GPUs were 81% of revenues.

Huang believes Nvidia’s GPU sales will increase thanks to the growing popularity of ‘ray tracing’ in games, deliver stunning realism by enabling things such as full-detail reflections which mirror the action happening in and around the player’s field of view:

Few games offered support for ray tracing last year and supporting RTX chips were expensive, so sales were subdued. That’s changed this year, with more supporting games and cheaper RTX chips available; a combination which produced $1.3 billion in gaming revenue for Nvidia this quarter.

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Most sports have been established for some time and ‘new’ ones are generally just similar but with some tweaked rules.

Design agency AKQA set out to create a truly new sport with help from an AI – meet, Speedgate.

Speedgate features six-player teams (take that, five-a-side lovers…) which compete on a field with three open-ended gates.

To score, players must first kick a ball through a centre gate which they’re unable to step through. After which, they can score a point by putting a ball through either of the end gates. If a teammate catches the ball and ricochets it back through, an extra point is scored.

To keep things fast-paced, the ball cannot be still for more than three seconds.

Rather than a rewrite of the rules of an existing sport, Speedgate sounds more like a collision of several. There are elements of soccer (I’m a Brit, so don’t tell my mates I called it that but NFL is king…), basketball/netball in terms of not standing still for long, and I’d imagine even some dodgeball.

That’s probably of little surprise when you hear how the AI came to create its new sport.

Data from 400 existing sports was fed into a neural network before it spat out some basic outlines for a sport and its accompanying rules. Some of these rules, while pretty awesome, were unrealistic. Many of us would like to see things like exploding Frisbees – but health and safety people are sticklers.

Playtests were then used to whittle down the sports to one champion. AKQA is now in discussions with the Oregon Sports Authority to get a Speedgate league going this summer.

AI even created the logo and motto, though we think the latter could do with some work (“face the ball to be the ball to be above the ball”).

It’s a cool example of what AI is able to create, but let’s make a pact to keep clips of The Hunger Games locked away. After all, AIs are already beating us at our own creations

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AI News first reported of OpenAI’s gaming prowess in August 2017 when it took on three of the best Dota 2 players in the world and won.

The AI learned how to play the game from scratch and was able to beat regular players within the space of an hour. Professional gamers put up more of a fight, with the AI requiring two weeks of training to beat some of humankind’s best.

At the time, OpenAI said they believe their AI is beatable as it’s not better in terms of actions-per-minute but made smarter decisions. Some players were able to confuse the bot and distract it from the main objectives; showing it’s not flawless.

In the latest man versus machine spectacle, the humans lost the first two rounds but won in the third.

The opponents faced off Valve’s The International 2018 esports competition in San Francisco. Rules were kept the same as the last bout which meant things like ‘couriers’ (NPCs used for delivering items to heroes) were not invulnerable.

On average, a match features 80,000 individual frames and each character is able to perform around 170,000 possible actions. The AI’s ability to comprehend and take relevant actions is nothing short of incredible.

According to OpenAI Cofounder and Chairman Greg Brockman, the firm’s AI now has the equivalent of 45,000 years of Dota 2 gameplay experience. Taking that into account makes it even more impressive the human players managed to beat the AI even once.

OpenAI is currently able to play just 18 of the 115 heroes featured in Dota 2, so – if you’re thinking of issuing a challenge – perhaps get to grips with those it’s not had the equivalent of around 562 human lifetimes training in.

An archived broadcast of the match can be viewed on Twitch here.

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Back in 2015, DeepMind attempted to play various Atari games. The AI was competent in most of the games and became as good at Video Pinball as a human player.

DeepMind notoriously struggled with Montezuma’s Revenge, a notoriously complex game from the 1980s. The AI was unable to learn a path and retrieve even the first ‘key’ in the game.

Video games, in general, have become a battleground for AIs to show-off. DeepMind’s failure with Montezuma’s Revenge set the game as one benchmark for the industry to prove advancements.

A new algorithm designed by Fabio Zambetta and his team from RMIT University learns from past mistakes and identified next steps 10 times faster. The AI was successful in autonomously playing Montezuma’s Revenge.

In a statement, Zambetta explained:

“Truly intelligent AI needs to be able to learn to complete tasks autonomously in ambiguous environments.

We’ve shown that the right kind of algorithms can improve results using a smarter approach rather than purely brute forcing a problem end-to-end on very powerful computers.”

Zambetta presented his findings at the 33rd AAAI Conference on Artificial Intelligence in Hawaii today and explained how it works.

DeepMind and similar AIs struggle with adventure games like Montezuma’s Revenge due to a lack of reward until it obtains the first item, a key in this case. This makes it difficult for the AI to work out if what it’s doing is correct/optimal.

Games like Video Pinball provide the AI with quick rewards due to things such as point increases. This approach enables the AI to learn what path is going to achieve the highest score.

By implementing reinforcement learning, the researchers added ‘pellet rewards’ for the system to promote it exploring more paths.

“With time, this technology will be valuable to achieve goals in the real world, whether in self-driving cars or as useful robotic assistants with natural language recognition,” said Zambetta.

Other AI researchers are continuing to advance their approaches. DeepMind itself published two papers last summer describing how an AI could learn to conquer Montezuma’s Revenge from YouTube videos.

We look forward to watching upcoming AI bouts for the Montezuma’s Revenge title.

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The live stream of the showdowns was viewed by more than 55,000 people.

“This is, of course, an exciting moment for us,” said David Silver, a researcher at DeepMind. “For the first time, we saw an AI that was able to defeat a professional player.”

DeepMind created five versions of their ‘AlphaStar’ AI. Each AI was trained with historic game footage that StarCraft-developer Blizzard has been releasing on a monthly basis.

In order to further improve their abilities, the five AIs were pitted against each other in a league. The leading AI racked up experience that would equate to a human training for around 200 years.

Perhaps needless to say, AlphaStar wiped the floor with human players Grzegorz Komincz and Dario Wunsch.

You can watch AlphaStar taking on the human players below:

The only hope for humans so far is that AlphaStar was trained for a single map and using just the one alien race type of three available in the game. Removed from its comfort zone, it would not perform as well.

Video games have driven more rudimentary AI developments for decades. The advancement shown by AlphaStar could be used to create more complex ‘bots’ that can pose a challenge and help train even the best human players.

This isn’t the first time we’ve seen DeepMind’s AI bots in action – but, in the past, they’ve had a tendency of immediately rushing its opponents with ‘workers’ in a behaviour that Blizzard called “amusing”.

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StarCraft 2 is a complex game with many strategies, making it the perfect testing ground for AI. Google’s DeepMind first started exploring how it can use AI to beat the world’s best StarCraft players back in 2016.

In 2017, StarCraft’s developer Blizzard made 65,000 past matches available to DeepMind researchers to begin training bots. Blizzard promised it would make a further half a million games available each month.

We’ve seen DeepMind’s AI bots in action with various degrees of success. The AI had a tendency of immediately rushing its opponents with ‘workers’ in a behaviour that Blizzard called “amusing,” but confessed it had a 50 percent success rate even against StarCraft 2’s AI bots on ‘insane’ difficulty.

Fed with some replays from human players using more complex strategies, the AI began adopting them.

“After feeding the agent replays from real players, it started to execute standard macro-focused strategies, as well as defend against aggressive tactics such as cannon rushes,” Blizzard said.

We’re yet to see these new strategies being used by DeepMind’s AI but it won’t be much longer until we do.

“It’s only been a few months since BlizzCon but DeepMind is ready to share more information on their research,” Blizzard said today.

“The StarCraft games have emerged as a ‘grand challenge’ for the AI community as they’re the perfect environment for benchmarking progress against problems such as planning, dealing with uncertainty, and spatial reasoning.”

You can find a stream of DeepMind’s AI playing StarCraft 2 via StarCraft’s Twitch or Deepmind’s YouTube at 6pm GMT/10am PT/1pm ET on January 24th.

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Creating 3D renders is a painstaking and time-consuming process requiring specific skills and can be incredibly costly. By reducing the barriers, Nvidia could enable more ideas to move from concept into reality.

During the NeurIPS AI conference in Montreal, Nvidia set up a dedicated area showing its technology. The company used its DGX-1 supercomputer for the demonstration so this isn’t achievable on your average household computer, at least for now.

The AI running on the DGX-1 took footage taken via a self-driving car’s dashcam, extracted a high-level semantics map using a neural network, and then used Unreal Engine 4 to generate the virtual world.

In the animation below, the top left image represents the input map. The bottom-right represents Nvidia’s video synthesis (vid2vid), while the others show competing approaches:

Beditor Catanzaro, VP of Applied Deep Learning Research at NVIDIA, said:

“NVIDIA has been inventing new ways to generate interactive graphics for 25 years, and this is the first time we can do so with a neural network.

Neural networks — specifically generative models — will change how graphics are created. This will enable developers to create new scenes at a fraction of the traditional cost.”

The result of the demo is a simple driving game that allows participants to navigate an urban scene.

“The capability to model and recreate the dynamics of our visual world is essential to building intelligent agents,” Nvidia’s researchers wrote in a paper. “Apart from purely scientific interests, learning to synthesize continuous visual experiences has a wide range of applications in computer vision, robotics, and computer graphics.”

 AI & >.

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Some of mankind’s most complex games, like Go, have failed to challenge AIs from the likes of DeepMind. Reinforcement learning trains algorithms by running scenarios repeatedly with a ‘reward’ given for successes, often a score increase.

Two classic games from the 80s – Montezuma’s Revenge and Pitfall! – have thus far been immune to a traditional reinforcement learning approach. This is because they have little in the way of notable rewards until later in the games.

Applying traditional reinforcement learning typically results in a failure to progress out the first room in Montezuma’s Revenge, while in Pitfall! it fails completely.

One way researchers have attempted to provide the necessary rewards to incentivise the AI is by adding them in for exploration, what’s called ‘intrinsic motivation’. However, this approach has shortcomings.

“We hypothesize that a major weakness of current intrinsic motivation algorithms is detachment,” wrote Uber’s researchers. “Wherein the algorithms forget about promising areas they have visited, meaning they do not return to them to see if they lead to new states.”

Uber’s AI research team in San Francisco developed a new type of reinforcement learning to overcome the challenge.

The researchers call their approach ‘Go-Explore’ whereby the AI will return to a previous task or area to assess whether it yields a better result. Supplementing with human knowledge to guide it towards notable areas sped up its progress dramatically.

If nothing else, the research provides some comfort us feeble humans are not yet fully redundant and the best results will be attained by working hand-in-binary with our virtual overlords.

 AI & >.

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FastStart learns how gamers play and what files are needed to be downloaded first. Microsoft claims this can half the time it takes for a user to get into their new content — if it previously took 30 minutes to download and play, it will now take just 15 minutes.

Xbox has long had a similar ‘Ready to Start’ system where games can be played while the rest downloads in the background. This system required developers to manually configure it during the game’s development, but FastStart is automatic as a platform-level feature.

Furthermore, as you’d expect from a machine learning feature, it’s always learning how to improve.

Jason Ronald, Principal Group Program Manager for Xbox Platform, says: “Since FastStart takes advantage of machine learning, we will continue to improve our algorithm over time getting players into the fun as soon as possible.”

The news is exciting for gamers who are eager to get into their new releases but face increasingly large downloads as a result of 4K textures and bigger worlds being created.

However, FastStart serves as an example of what machine learning is enabling that could be used outside the gaming world. Even shaving a few seconds off smaller downloads helps to improve the experience for consumers.

FastStart will be launching for select games in Xbox GamePass following a system update this month.

An updated list of FastStart supported titles will be available here.

Are you excited about the use of machine learning to prioritise downloads?

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A team of researchers from the Universities of Lyon and Purdue, along with game developer Ubisoft, have published a paper detailing how this works.

Games are an art form, and creative designers take a lot of pride in their work. As such, they will work closely with everyone working on the project to make sure their visions get brought to life as closely as possible.

Rather than hand over full control to the AI, and hope for the best, some basic input is required. This could define whether the intended world is more like a city or a forest, whether it’s an Earth-like planet or futuristic alien world, and whether it seems new or worn over time.

Artists can begin drawing their vision and then let the AI take over the time-consuming and tedious bit of filling in things such as elevation, ridges, vegetation, rock formations, and more.

Some of these things are beyond what current AI is capable of, but it offers a glimpse at where it’s headed. Nvidia recently showed off its own technology where AI convincingly generated fake celebrity mugshots; something which could one day be used for building game characters.

As with most industries, the use of AI for game development is proving itself able to increase efficiency rather than replace everyone’s jobs. AI has a long history with gaming, and we look forward to seeing what the latest advancements can do for improving the development process of new titles.

What are your thoughts on AI being used to build virtual worlds?

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