The world was in awe and threw in a lot of praise for Google’s new AI voice assistant Duplex which is able to place a call to a human on behalf of another human. This just goes to show the level of advancement AI has achieved this year. However, Google Duplex is just one demonstration amongst numerous other tech trends, areas where artificial intelligence has been making headways. Every day a new headline is made with AI affecting certain aspects of life. But what is happening behind the scenes, in development facilities where researchers and techno heads are constantly striving to beat the levels achieved by previous AIs? Here are some of the top artificial intelligence tech trends this year.
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Deep neural networks have the ability to mimic the human brain by learning from images, audio, and text data. They have been in use for more than a decade, however, there’s a lot still to be discovered and learn how a neural network learns and how can they be made efficient. Deep learning is getting smarter though, instead of feeding hundreds and thousands of data points, today’s systems can give an accurate output by factoring only a few hundred data points.
Deep Reinforcement Learning
You learn from your own mistakes is probably the most realistic statements ever made. The developers of artificial intelligence have better understood this and are applying this principle of reinforced learning to their systems. This is the exact reason why the famous AlphaGo was able to beat a human champion. More recently, it beat a DOTA champion in a very complex game by teaching itself to play the game within two weeks. Researchers are relying more on this technique as it uses fewer data to train its models.
Augmented Data Learning
Machine learning works by accumulating a wide variety of data sources to train the system. However, the unavailability of such kind of data poses a big problem for artificial intelligence systems. Emerging tech trends use new synthetic data and transfer a model trained for one domain to be used in another. Transfer learning or one-shot learning techniques are being used currently to teach AI systems without significant data sources. Similarly, it can address a wider variety of problems, which has less historical data.
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Hybrid Learning Models
Machine learning takes in and processes data with a fixed set of rules and depends on metadata to have information in a certain format. However, they do not have a model for uncertainties like the way probabilistic or Bayesian approaches do. New hybrid learning models combine the two approaches to leverage the strengths of each one of them. Hybrid learning helps in solving various business problems with deep learning by factoring in uncertainty. Bayesian GANs, Bayesian deep learning, and Bayesian conditional GANs are some of the examples of such hybrid learning models.