This week looks behind the scenes of artificial intelligence, gaps today and changes already in motion.
The AI Winter
Tucked away in the depths of universities are projects that have little love in the wider world and yet can lead to a significant breakthrough.
For decades, experts were down on the idea of neural networks but over in Canada, a small group of expertise grew regardless. These approaches led to the advances in machine learning and the now very popular area of artificial intelligence.
Toronto Life has an excellent article on Geoffrey Hinton, one of these early pioneers and describes the “AI Winter” when few believed.
One of the reasons for persevering with neural networks was due to our lack of understanding of how the brain worked. Hinton said:
“The brain has got to work somehow and it sure as hell doesn’t work by someone writing programs and sticking them in there,”
The article also looks into his history and he has a fantastic family tree ranging from the founder of Boolean Logic to Everest and the Manhattan Project. He also happens to come from Wimbledon 😊
Artificial Intelligence’s holes
How far we have come with artificial intelligence should be celebrated but there are still holes. Wired takes a look at the downsides finding it:
- cannot think abstractly;
- does not scale to become human-like;
- useless without large data sets.
One of the benefits of this breakthrough though is the billions of pounds being spent finding other techniques that improve on these drawbacks. Without it, the AI winter would have continued.
The unknown can create angst and the potential of AI and robotics has many worrying about jobs (though automating away manual repetitive tasks can only be a good thing for people if we get it right).
Wired looks at how robots have entered the workplace as painters and changed the jobs of the people around them. The robots took over brute sanding and painting, while humans did the more complicated tasks like assembly, with some looking after the robots.
Reuters takes a look at how eastern Europe is also automating its factories as the cost of labour increases and the number of workers decreases.
In the US, a lack of labour is resulting in cows being increasingly milked by robots. A study by University of Minnesota researchers shows that the economic benefits are not uniform though. Robots were more profitable with upto 240 cows but at larger numbers (they studied 1,500 cows) parlours remained more profitable. More here.
Finally Newsweek has a rather alarmist article about the end of the musician based on Spotify hiring an AI scientist.
Spotify has an army of people working on the huge swathes of data available to it and when I see them at conferences, what they achieve never fails to astound.
Spotify and indeed IBM say their focus is on assisting the musician rather than replacing them, which holds true today. If the AI cannot interpret quality, which of course it cannot with current techniques, all it can do is generate millions of tracks in the hope of creating a gem.
Would you trawl through the results?