We all love a race. There is something about seeing somebody strive to win that gets our blood stirring. But there is one big race going on now that it’s likely you never heard of, which is the race to develop deep learning.
Deep learning is a specialized field of Artificial Intelligence research that looks to teach computers to learn by structuring them to mimic the neurons in the neocortex, that portion of our brain that does all of the thinking. The field has been around for decades, with limited success, and has needed faster computers to make any real headway.
The race is between a few firms that are working to be the best in the field. Microsoft and Google have gone back and forth with public announcements of breakthroughs, while other companies like Facebook and China’s Baidu are keeping their results quieter. It’s definitely a race, because breakthroughs are always compared to the other competitors.
The current public race deals with pattern recognition. The various teams are trying to get a computer to identify various objects in a defined data set of millions of pictures. In September Google announced that it had the best results on this test and just this month Microsoft said their computers beat not only Google, but did better than what people can do on the test.
All of the companies involved readily admit that their results are still far below what a human can do naturally in the real world, but they have made huge strides. One of the best known demonstrations was done last summer by Google who had their computer look at over 10 million YouTube videos and asked it to identify cats. Their computer did twice as good as any previous test, which was particularly impressive since the Google team had not pre-defined what a cat was to the computer ahead of time.
There are some deep learning techniques in IBM’s Watson computer that beat the best champs in Jeopardy. Watson is currently being groomed to help doctors make diagnoses, particularly in the third world where there is a huge lack of doctors. IBM has also started selling time on the machine to anybody and there is no telling all of the ways it is now being used.
Probably the most interesting current research is in teaching computers to learn on their own. This is done today by enabling multiple levels of ‘neurons’. The first layer learns the basic concept, like recognizing somebody speaking the letter S. Several first-layer inputs are fed to the second layer of neurons which can then recognize more complex patterns. This process is repeated until the computer is able to recognize complex sounds.
The computers being used for this research are already getting impressive. The Google computer that did well learning to recognize cats had a billion connections. This computer was 70% better at recognizing objects than any prior computer. For now, the breakthroughs in the field are being accomplished by applying brute computing force and the cat-test computer used over 16,000 computer processors, something that only a company like Google or Microsoft has available. .
Computer scientists all agree that we are probably still a few decades away from a time when computers can actually learn and think on their own. We need a few more turns of Moore’s Law for the speed of computers to increase and the size of the processors to decrease. But that does not mean that there are not a lot of current real life applications that can benefit from the current generation of deep learning computers.
There are real-world benefits of the research today. For instance, Google has used this research to improve the speech recognition in Android smartphones. But what is even more exciting is where this research is headed for the future. Sergey Brin says that his ultimate goal is to build a benign version of HAL from 2001: A Space Odyssey. It’s likely to take multiple approaches in addition to deep learning to get to such a computer.
But long before a HAL-like computer we could have some very useful real-world applications from deep learning. For instance, computers could monitor complex machines like electric generators and predict problems before they occur. They could be used to monitor traffic patterns to change traffic lights in real time to eliminate traffic jams. They could be used to enable self-driving cars. They could produce a universal translator that will let people with different languages converse in real-time. In fact, in October 2014, Microsoft researcher Rick Rashid gave a lecture in China. The deep learning computer transcribed his spoken lecture into written text with a 7% error rate. It then translated it into Chinese and spoke to the crowd while simulating his voice. It seems like with deep learning we are not far away from having that universal translator promised to us by science fiction.