Some masochists enjoy the pain of being repeatedly beaten in games like Dark Souls – it’s understandable, overcoming those challenges is a great feeling – but most of us have a breaking point where the power button gets hit and the game just ends up collecting dust on a shelf.

A patent spotted by SegmentNext details an AI which learns from the human player and can continue when they’re not playing:

“The method includes monitoring a plurality of game plays of the user playing a plurality of gaming applications,” the patent explains.

“The method includes generating a user gameplay profile of the user by adjusting the default gameplay style based on the plurality of game plays, wherein the user gameplay profile includes a user gameplay style customized to the user. The method includes controlling an instance of a first gaming application based on the user gameplay style of the user gameplay profile.”

It’s easy to see how such an AI could be used in grind-heavy games to reach a required level, or another player’s AI that has completed a particular challenge could even be used to overcome it rather than lead to a situation where they give up completely.

For those who feel that level of intervention is cheating, the AI could instead offer tips to the human player as they’re gaming. The AI basically wants to be a helpful player two rather than the person on your couch that mocks you and then does worse when they take the controller.

While great strides have been made in making games accessible for people with disabilities, there are some parts of games which remain problematic even with the best intentions of developers. These individuals will also benefit from an AI which can take the reins for a bit and allow them to continue enjoying the rest of the game and the increasingly immersive storytelling many provide.

(Photo by Fabian Albert on Unsplash)

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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.

 Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech Expo, Blockchain Expo, and Cyber Security & Cloud Expo.

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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? Let us know in the comments.

 Interested in hearing industry leaders discuss subjects like this and sharing their use-cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London and Amsterdam to learn more. Co-located with the  IoT Tech Expo, Blockchain Expo and Cyber Security & Cloud Expo so you can explore the future of enterprise technology in one place.

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