Last year was huge for advancements in artificial intelligence and machine learning. But 2017 may well deliver even more. Here are five key things to look forward to.
Positive Reinforcement
AlphaGo’s historic victory against one of the best Go players of all time, Lee Sedol, was a landmark for the field of AI, and especially for the technique known as deep reinforcement learning.
Reinforcement learning takes inspiration from the ways that animals learn how certain behaviors tend to result in a positive or negative outcome. Using this approach, a computer can, say, figure out how to navigate a maze by trial and error and then associate the positive outcome—exiting the maze—with the actions that led up to it. This lets a machine learn without instruction or even explicit 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 (like the game of Go). Through relentless experimentation, as well as analysis of previous games, AlphaGo figured out for itself how play the game at an expert level.
The hope is that reinforcement learning will now prove useful in many real-world situations. And the recent release of several simulated environments should spur progress on the necessary algorithms by increasing the range of skills computers can acquire this way.
In 2017, we are likely to see attempts to apply reinforcement learning to problems such as automated driving and industrial robotics. Google has already boasted of using deep reinforcement learning to make its data centers more efficient. But the approach remains experimental, and it still requires time-consuming simulation, so it’ll be interesting to see how effectively it can be deployed.
Dueling Neural Networks
At the banner AI academic gathering held recently in Barcelona, the Neural Information Processing Systems conference, much of the buzz was about a new machine-learning technique 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 generates new data after learning from a training set, and another that tries to discriminate between real and fake data. By working together, these networks can produce very realistic 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 experts 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 believe may hold the key to making 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 players 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 company 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.
Language Learning
Ask AI researchers what their next big target is, and they are likely to mention language. The hope is that techniques that have produced spectacular progress in voice and image recognition, among other areas, may also help computers parse and generate 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 fascinating 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 meaningful 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.
#Bring_that_AI_ON!
Positive Reinforcement
AlphaGo’s historic victory against one of the best Go players of all time, Lee Sedol, was a landmark for the field of AI, and especially for the technique known as deep reinforcement learning.
Reinforcement learning takes inspiration from the ways that animals learn how certain behaviors tend to result in a positive or negative outcome. Using this approach, a computer can, say, figure out how to navigate a maze by trial and error and then associate the positive outcome—exiting the maze—with the actions that led up to it. This lets a machine learn without instruction or even explicit 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 (like the game of Go). Through relentless experimentation, as well as analysis of previous games, AlphaGo figured out for itself how play the game at an expert level.
The hope is that reinforcement learning will now prove useful in many real-world situations. And the recent release of several simulated environments should spur progress on the necessary algorithms by increasing the range of skills computers can acquire this way.
In 2017, we are likely to see attempts to apply reinforcement learning to problems such as automated driving and industrial robotics. Google has already boasted of using deep reinforcement learning to make its data centers more efficient. But the approach remains experimental, and it still requires time-consuming simulation, so it’ll be interesting to see how effectively it can be deployed.
Dueling Neural Networks
At the banner AI academic gathering held recently in Barcelona, the Neural Information Processing Systems conference, much of the buzz was about a new machine-learning technique 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 generates new data after learning from a training set, and another that tries to discriminate between real and fake data. By working together, these networks can produce very realistic 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 experts 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 believe may hold the key to making 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 players 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 company 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.
Language Learning
Ask AI researchers what their next big target is, and they are likely to mention language. The hope is that techniques that have produced spectacular progress in voice and image recognition, among other areas, may also help computers parse and generate 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 fascinating 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 meaningful 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.
#Bring_that_AI_ON!
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