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.
It’s no surprise that the vast majority of people claim to understand what Artificial Intelligence is. Popular culture has been a key driver of this coupled with the recent debates on the potential benefits and threats of AI, and the visible successes of Google, Facebook and Apple.
However, there is a disconnect between what the latter have achieved and the portrayal in cinema and literature. Almost all modern AI is specifically focused on particular tasks. This is known as Artificial Narrow Intelligence. It is very effective in doing one thing, but completely useless at anything else. Imagine asking Siri to drive your car for you and you’ll get an understanding of how narrow the intelligence is.
By contrast, AI in movies or even that being discussed as an existential threat is Artificial General Intelligence. Such AGIs would be capable of independent action, motivation and autonomy. This type of AI does not exist to any great extent today and in fact seems to be as far away as when Alan Turing wrote about it over 60 years ago.
There is a good chance that the survey respondents are mistaken in their interpretation of Artificial Intelligence. Unfortunately, this mistake may turn into disillusionment when they find out just how far we have to go before there is a real HAL 9000.
The Artificial Intelligence community was abuzz recently with the news that Google has open-sourced it’s machine learning framework, called TensorFlow. This system was created by the Google Brain Team, working in it’s Machine Intelligence Research group.
This is not the first open source machine learning framework. Within the Python environment in particular, there are frameworks such as scikit-learn, PyBrain and others that have been around for a good while. What’s different about this new framework is that it has the backing of one of the most advanced commercial machine learning organisations, Google. In committing the project to open-source, it is inviting researchers, commercial practitioners and hobbyists to contribute to the framework. With Google’s backing, it seems destined for a long life.
But back to today. The framework has both Python and C++ APIs, with the expectation that C++ will be slightly faster on certain tasks. The instructions for installing TensorFlow are straightforward, but immediately I ran into a problem. My (slightly ageing) MacBook was running Python 2.7.5 and running TensorFlow caused a segmentation fault. Updating to Python 2.7.10 fixed the problem and I was able to successfully run though some of the tutorials.
There seems to be a wide range of neural network capabilities already available within the framework which provides much opportunity for exploration and experimentation. The tutorials cover areas such as handwriting recognition, image classification (using convolutional neural networks) and language modelling (using recurrent neural networks).
What’s also interesting is that since it’s an open source framework, the underlying code behind all these machine learning techniques is available for anyone to download, examine, modify and improve.
What will be the long-term impact of this is hard to tell. However, it is clear that Google has already put in quite a bit of effort already effort into this framework, and now that it’s out in the open, there will be lots more improvement to come.
A new report from Bank of America Merrill Lynch has added to the recent spate of analysis predicting a massive impact on work and jobs by robotics and artificial intelligence (as reported in the Guardian).
They estimate that up to 35% of all jobs in the UK (47% in the US) are at risk of displacement by technology within 20 years. This is going to cause a huge shift in the type of work that people can expect to do in the future. It has important implications for education policy, jobs and economic growth. In addition, it is incumbent on politicians and policy makers to ensure that the benefits from increased automation are widely distributed.
A common counter point made is that by eliminating some jobs, technology creates other jobs. However, the authors note:
“The trend is worrisome in markets like the US because many of the jobs created in recent years are low-paying, manual or services jobs which are generally considered ‘high risk’ for replacement,” the bank says.
While 20 years may seem like far into the future, children born this year will just be entering the workforce then. They may be faced with not having any jobs to look forward to.
Fortune has an interesting answer to the question of Artificial Intelligence taking jobs: “Everything that can be automated, will be. But not everything can be.” This opinion is offered by Robert Tercek, who is president of game company, Milestone Entertainment. However, no evidence is provided to support this and it’s quite dangerous to assume that not […]