Washington state governor green-lights facial-recog law championed by... guess who: Yep, hometown hero Microsoft

Plus more news from the world of machine learning

Roundup Here's your quick-fire summary of recent artificial intelligence news.

Enter a new AI Atari games champion

DeepMind has built a reinforcement-learning bot capable of playing 57 classic Atari 2600 games about as well as the average human.

Why 57, you may ask? The Atari 2600 console was launched in 1977 and has a library of hundreds of games. In 2012, a group of computer scientists came up with The Arcade Learning Environment (ALE), a toolkit consisting of 57 old Atari games to test reinforcement-learning agents.

AI researchers have been using this collection to benchmark the progress of their game-playing bots ever since. The average score reached on all 57 games has steadily increased with the development of more complex machine-learning systems, but most models have struggled to play the most difficult ones, such as Montezuma's Revenge, Pitfall, Solaris, and Skiing.

Reinforcement learning attempts to teach AI bots how to complete a specific task, such as playing a game, without explicitly telling it the rules. The agents thus have to learn through trial and error, and are guided by rewards. Reaching high scores means more delicious rewards, and over time, the computer learns to make good moves to play the game well.

The researchers have improved their system by employing different types of algorithms and tricks. The bot, dubbed Agent57, is better equipped in dealing with the most difficult games because it's been programmed to be able to explore its environment more efficiently even when the rewards are sparse.

A number of steps have to be executed in the games before a reward is given, so it's not immediately obvious how to play Montezuma's Revenge, Pitfall, Solaris, and Skiing, compared to games like Pong that have a more immediate reward feedback system.

The boffins reckon that mastering games in the ALE dataset is a good sign that a system is more generally intelligent and robust so that they might be applied in the real world.

"The ultimate goal is not to develop systems that excel at games, but rather to use games as a stepping stone for developing systems that learn to excel at a broad set of challenges," Deepmind wrote.

You can read more about the numerous nifty techniques that were used to improve Agent57 in more detail here [PDF].

Washington state backs a Microsoft-led facial recognition bill

The governor of the US state of Washington, Jay Inslee, has passed a piece of legislation that regulates the use of facial-recognition systems.

While the likes of San Francisco and Oakland in California, and Somerville in Massachusetts, have banned law enforcement from using facial-recognition technology, Washington has gone for a softer approach. That's not too much of a surprise, considering the bill [PDF] was sponsored by Microsoft, and the US state is the home of the Windows giant. Microsoft is keen for organizations to use its machine-learning services for things like facial and object recognition.

"This legislation represents a significant breakthrough – the first time a state or nation has passed a new law devoted exclusively to putting guardrails in place for the use of facial recognition technology," Redmond's president, Brad Smith, said.

Law enforcement agencies in Washington will be allowed to deploy facial-recognition systems, but will have to be more transparent about using it. First, they have to file a "notice of intent", a report that details the service the cops want to use from a particular vendor, and what it's being used for. The document also has to show what kind of data is collected and generated, what decisions the software makes, and where it will be deployed. The notice has to be given to a "legislative authority" that will be made public.

On the vendor side of things, companies will have to provide an application programming interface (API) to enable an independent party to audit the algorithm's performance. They must also report "any complaints or reports of bias regarding the service".

Smith gushed: "Through some of the new law's most important provisions, Washington state has become the first jurisdiction to enact specific facial recognition rules to protect civil liberties and fundamental human rights. While the public will rightly assess ways to improve upon this approach over time, it's worth recognizing at the outset the thorough approach the Washington state legislature has adopted."

Meanwhile, the American Civil Liberties Union has been fighting for a moratorium on facial recognition, demanding a temporary ban on the technology until Congress passes stricter laws that protect an individual's rights.

The Washington law is due to go into effect next year.

Here, make godawful ML music during lockdown

Remember Amazon's little AI music-generating keyboard DeepComposer that was touted at its annual re:Invent developer conference last year?

Well, now you can finally play with it. Don't worry if you don't have an actual physical keyboard, Amazon has released a digital version alongside the software needed to create music via machine learning.

DeepComposer trains generative adversarial networks (GANs) to create new jingles based on a particular style of music. The software is designed to help enthusiasts who don't necessarily have a deep knowledge of machine learning or music to learn about GANs in more detail.

It gives step-by-step instructions on how to build, train, and test GANs without having to write any code. Users create a little melody on the digital keyboard and pick the type of genre, and the GAN fills in the blanks, transforming the simple tune into computer generated music. The physical keyboard is available too, but only for the US.

You can find out more about that here. ®

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