StarCraft 2 is a complex real-time strategy game that can still throw surprises at you even years after playing. In other words, StarCraft 2 is great for testing an AI.

Back in January, AI News reported DeepMind’s so-called ‘AlphaStar’ AI beat professional human eSports players Grzegorz Komincz and Dario Wunsch.

AlphaStar is now taking a virtual trip to Europe where it will begin playing a “small number” of games on the StarCraft 2 competitive ladder. Human players won’t even be aware they’re playing against the AI as it will be anonymised.

Blizzard explained the reasoning behind the anonymisation:

“Having AlphaStar play anonymously helps ensure that it is a controlled test so that the experimental versions of the agent experience gameplay as close to a normal 1v1 ladder match as possible.

It also helps ensure all games are played under the same conditions from match to match. DeepMind will release the research results in a peer-reviewed scientific paper along with replays of AlphaStar’s matches.”

AlphaStar was trained on historic game footage that StarCraft’s developer Blizzard has been releasing on a monthly basis. Five versions of the AI battled each other to hone their skills in training which equates to around 200 years for a human.

Multiple experimental variants of AlphaStar will take part in the test and it will play 1v1 matches only. The test will be opt-in, so players will have to click an in-game popup to get involved. Basically, it’s a voluntary ass-whooping.

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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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The AI, introduced by DeepMind in a research published in Science on 6th November 2018, was put against three of the world’s most complex board games and their current AI record holders.

This included world-champion chess AI Stockfish, DeepMind’s very own AlphaGo Zero – known as the best Go player in history – and elmo, winner of the 27th annual World Computer Shogi Championship in 2017.

AlphaZero defeated the AIs in all three games on its own and without human intervention. The only human assistance involved was teaching the AI the basic rules of the games. Using reinforcement learning, the AI would then play against itself millions of times using different strategies to win.

It took nine hours for the AI to learn chess, 12 hours for shogi, and 13 days for Go. Training the AI involving the use of a hefty 5,000 tensor processing units.

The learning algorithm was combined with the Monte Carlo tree search (MCTS), a ‘searching method’.  This is how the Go AI programmes know when and how to make the next move. This same system was used for chess and shogi, revealing the AI could be adapted to other complex games.

The interesting fact here is that AlphaZero implemented its own tactics which surprised human chess players. Moreover, the programme’s aggressive style and dynamic playstyle amazed chess grandmaster Matthew Sadler.

Such extraordinary abilities make the AI a fine teaching tool for chess players from which they can learn about hitherto-unseen gameplay strategies.

 AI & >.

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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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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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