What do you expect from deep learning in 2016?
The Internet is rapidly moving to the next level, where data will control everything that can be imagined. As a result, in-depth learning (a new area of machine learning) has also begun to gain momentum, as this is one of the key aspects of data science. Technological giants such as Google and Facebook have already invested heavily in deep learning (as it will play a crucial role in the technologies of the future), so expect to hear much more about it in the coming months.
Deep learning has developed quite rapidly over the past year and this momentum is expected to continue. A year ago, designing machine vision networks cost 5-10 times more and required more parameters (15 times more), but now it is cheaper and more affordable. The main factors in this transformation are better training methods and improved network architecture.
Voice recognition and computer vision have largely confirmed that machine learning at higher levels of complexity is possible. As a result, more attention will be paid to semi-directed or uncontrolled learning algorithms to handle large data streams.
Reinforced learning approaches will also become more visible in the future. As only a controlled approach has significant limitations, in-service training with reinforcements will play a key role in improving the effectiveness of machine learning without a teacher.
2016 will be the year when in depth training algorithms will be used for tasks that require problem solving. This, in turn, will accelerate the pace at which in-depth learning is being introduced in various industries. A sign of the future is the recent cooperation between Google and Movidius, which have teamed up to increase the spread (deep learning technologies) on mobile devices.
It is also possible to make a reasonable assumption by observing the number of in-depth learning projects at GitHub. For all these activities, it is safe to say that there are real prospects for achieving high levels of AI in the near future. Industry leaders are very interested in exchanging data and tools with each other in order to accelerate the development of in-depth learning.
In this way, the various businesses will not only exchange data and participate in the development of the technology, but also train people quickly so that they can acquire the necessary knowledge to take the technology to the next level.
Expect that in 2016, in-depth training will stimulate new innovations and move forward significantly in the coming months.