Last year was huge for improvements in artificial intelligence and machine learning. But 2017 may well deliver even more. Here are five key things to look forward to.
AlphaGo’s historic victory towards one of the best Go players of all time, Lee Sedol, was a landmark for the field of AI, and especially for the strategy known as deep reinforcement learning.
Reinforcement studying will take ideas from the ways that animals learn how certain manners tend to result in a positive or negative outcome. Using this approach, a computer can, say, discover how to navigate a labyrinth by trial and error and then associate the positive outcome-exiting the maze-with the actions that led up to it. This allows a machine understand without having guidance or even precise examples. The idea has been around for decades, but combining it with large (or deep) neural networks provides the power needed to make it work on really complex problems. Through relentless experimentation, as well as analysis of previous games, AlphaGo figured out for itself how play the game at an expert level.
The expectation is that reinforcement understanding can be convenient in many real-world scenarios. And the current release of various simulated environments must spur success on the necessary algorithms by boosting the range of skills computers can obtain this way.
In 2017, we’re about to discover efforts to apply reinforcement learning to challenges such as computerized driving and business oriented #robotics. Google already has presented of utilizing deep reinforcement learning to produce its data centers extremely effective. However the technique continues to be experimental, and it still demands time-consuming simulators, so it’ll be fascinating to see how successfully it can be implemented.
Dueling neural networks
At the banner AI academic meeting held recently in Barcelona, the Neural Information Processing Systems conference, much of the buzz was about a new machine-learning strategy known as generative adversarial networks.
Invented by Ian Goodfellow, now a research scientist at OpenAI, generative adversarial networks, or GANs, are systems consisting of one network that creates new data after learning from a training set, and another that tries to discriminate among real and fake data. By working together, these networks can produce very genuine synthetic data. The approach could be used to generate video-game scenery, de-blur pixelated video footage, or apply stylistic changes to computer-generated designs.
Yoshua Bengio, one of the world’s leading specialists on machine learning (and Goodfellow’s PhD advisor at the University of Montreal), said at NIPS that the approach is especially exciting because it offers a powerful way for computers to learn from unlabeled data-something many consider may hold the key to producing computers a lot more intelligent in years to come.
China’s AI boom
This may also be the year in which China starts looking like a major player in the field of AI. The country’s tech industry is shifting away from copying Western companies, and it has identified AI and machine learning as the next big areas of innovation.
China’s leading search company, Baidu, has had an AI-focused lab for some time, and it is reaping the rewards in terms of improvements in technologies such as voice recognition and natural language processing, as well as a better-optimized advertising business. Other competitors are now scrambling to catch up. Tencent, which offers the hugely successful mobile-first messaging and networking app WeChat, opened an AI lab last year, and the corporation was busy recruiting talent at NIPS. Didi, the ride-sharing giant that bought Uber’s Chinese operations earlier this year, is also building out a lab and reportedly working on its own driverless cars.
Chinese investors are now pouring money into AI-focused startups, and the Chinese government has signaled a desire to see the country’s AI industry blossom, pledging to invest about $15 billion by 2018.
Ask AI researchers what their next big target is, and they are likely to mention language. The hope is that techniques that have produced impressive progress in voice and image recognition, among other areas, may also help computers parse and produce language more effectively.
This is a long-standing goal in artificial intelligence, and the prospect of computers communicating and interacting with us using language is a interesting one. Better language understanding would make machines a whole lot more useful. But the challenge is a formidable one, given the complexity, subtlety, and power of language.
Don’t expect to get into deep and significant conversation with your smartphone for a while. But some impressive inroads are being made, and you can expect further advances in this area in 2017.
Backlash to the hype
As well as genuine advances and exciting new applications, 2016 saw the hype around artificial intelligence reach heady new heights. While many have faith in the underlying value of technologies being developed today, it’s hard to escape the feeling that the publicity surrounding AI is getting a little out of hand.
Some AI researchers are obviously irritated. A launch party was organized during NIPS for a fake AI startup called Rocket AI, to highlight the growing mania and nonsense around real AI research. The deception wasn’t very convincing, but it was a fun way to draw attention to a genuine problem.