Artificial intelligence to see significant progress in 2017

(See interview replay above)

With AI to date, one of the major challenges has been understanding, modeling and enabling the process of machine learning. Deep learning, neural networks, and regression are all examples of machine learning systems which all learn from labeled training examples. Essentially, a human points at, say, a picture of a chair, and says “chair.” Then the human must repeat this at least 10,000 times with 10,000 different chairs in order to teach the machine what a chair looks like. Subsequently, the exercise must be repeated kittens, marmalade, binoculars, and everything else in existence. There is no way to improve on this process by explaining things to a machine learning system as machine learning systems cannot accept any kind of instructions to help them learn new concepts – they learn only from data. If a deep learning system were to drive your self-driving car off the road, there would be no way to find out why it did that, nor to give it instructions to avoid similar mistakes in the future. You just have to train it on more data and then hope that it doesn’t drive your car off the road again.

Consequently, AI has faced difficulty in making progress. However, a company, Gamalon (see: https://gamalon.com/), has done work on operationalizing Bayesian Program Synthesis (BPS, see: https://en.wikipedia.org/wiki/Bayesian_Program_Synthesis) and in the process looks to be on the way to realizing significant gains in accelerating AI implementation, especially in its target area of analyzing and interpreting unstructured data. BPS offers improvement in that it

1) is not expensive as it requires only few training examples

2) not computing intensive in requiring only a single core as opposed to 100s

3) allows readily the identification and prevention of bias in its training examples

4) enables single individuals to develop and implement training examples

5) is much faster in developing and deploying AI systems.

As such, Gamalon BPS offers the opportunity to build AI systems at a higher level of complexity and in the process supplant existing AI implementations. Looks as if with Gamalon BPS, AI will be gaining speed indeed in 2017.