Playing Go probably won’t lead to better AI

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The traditional Chinese board game Go. (Wikipedia)

Go is a traditional Chinese game that is played by two players and involves placing stones on a board in order to capture the opponent’s stones.

Although the rules are quite simple, and there is perfect information, the strategies involved are quite complex. The very best software available today that plays this game is not able to beat professional players for many reasons, including the number of possible moves and the large size of the board (compared to chess).

Now Demis Hassabis, who was behind DeepMind (purchased by Google in 2014), has hinted that his team has managed to get a machine to play the board game Go. Previously, the team has demonstrated an AI learning to play old video games like Breakout. The algorithms have learned to play without help better than most humans, so this team knows a thing or two about designing algorithms to learn to play games.

I’m a little skeptical though about how much conquering Go will advance the field of AI. Back in the 1990s, much excitement and publicity was generated when IBM’s Deep Blue managed to beat the reigning world chess champion, Gary Kasparov. However, this feat was more down to processing power and Deep Blue’s ability to evaluate millions of positions per second than any real advance in machine intelligence.

Certainly Kasparov didn’t play that way and so Deep Blue didn’t really give any great insights into human intelligence. While it may be interesting, beating a human at Go may well turn out to be another example of Artificial Narrow Intelligence.

via Google DeepMind Founder Says AI Machines Have Beat Board Game Go | Re/code.

Go Board Game (Wikipedia)

Toyota Pursuing Alternative AI Car Strategy

Toyota is investing $50m into driver-assisted Artificial Intelligence, in partnership with MIT and Stanford, according to Fortune. The research will be focused on helping human drivers avoid accidents by adding AI to their cars.

This is in contrast to the seemingly much more ambitious self-driving cars being pursued by Google and others. Although Toyota itself demonstrated it’s own self-driving Lexus in 2013, it seems that they also believe that people will still want to drive their own cars. By investing in AI technology to augment human drivers, Toyota seems to be hedging its bets.

There is still a long way to go before we will see fully autonomous cars and trucks commonly on the roads. The fruits of this research may be installed in your car much sooner than that.

Sources:

Toyota partners with MIT and Stanford on artificial intelligence – Fortune

Toyota sneak previews self-drive car ahead of tech show

Beware Artificial Intelligence Snake Oil

Clark Stanley's Snake Oil (Source Wikipedia)
Clark Stanley’s Snake Oil (Source Wikipedia)

Business Owners! Have you got an Internet Strategy? A Mobile Strategy? A Cloud Strategy? A Social Media Strategy?

That’s a lot of strategies to think about. Now, those responsible for business strategy are also being encouraged to have an Artificial Intelligence Strategy. No doubt there will soon be a consultant with a new job title to help: the Artificial Intelligence Strategist.

It is prudent as a business owner to be aware of what’s happening in the AI field so that if you come across something, you can evaluate it and it’s impact on your business.

As with any new area of technology, there will be lots of different things which will be labelled as AI, but won’t really be. In fact, most of what passes for AI today, isn’t really intelligent at all either.

Take Machine Learning as an example. The term itself is misleading in that it seems to indicate that the machine (more likely the computer) is learning something the way that people do. In fact, that’s not happening at all. Machine learning is mostly very clever algorithms that are adept at picking out patterns in noisy data. These patterns can be used to make predictions about other data, with a high degree of accuracy. So fundamentally, it’s building a mathematical model based on a set of training data.

I’ve deliberately glossed over how difficult this is in practice. A lot of the success of these techniques is due to the intelligence of the people creating the algorithms.

When you see the term “Artificial Intelligence”, it’s also worth reminding yourself that AI today is an umbrella term for a toolbox of algorithms and techniques. Each technique has it’s own advantages and disadvantages, and a domain to which it is suited.

It is most definitely not a brain in a box that can carry out lots of different functions. That type of AI is still in the realm of science fiction and certain sensationalist areas of the popular press.

If you see someone selling you a product or a service with AI in the title, ask what sort of AI they are talking about. What technique does it use? What tasks will it automate? How will it be trained? How robust is it? How accurate is it? Claims of 100% accuracy are a definite red flag.

AI is definitely worth keeping an eye on due to it’s disruptive potential if you are a business owner, or responsible for strategy. Just watch out for the snake oil.