Could the same results be obtained by exposing deep-learning AI to fewer examples? Boston-based startup Gamalon developed a new technology to try to answer just that, and this week, it released two products that utilize its new approach.
Gamalon calls the technique it employed Bayesian program synthesis. The program also refines its knowledge as further examples are provided, and its code can be rewritten to tweak the probabilities.
While this new approach to programming still has difficult challenges to overcome, it has significant potential to automate the development of machine-learning algorithms.
“Probabilistic programming will make machine learning much easier for researchers and practitioners,” explained Brendan Lake, an NYU research fellow who worked on a probabilistic programming technique in 2015.
Unlike Google’s version, which relied on sketches it had previously seen to make predictions, Gamalon’s app relies on probabilistic programming to identify an object’s key features.
One product, the Gamalon Structure, using Bayesian program synthesis to recognize concepts from raw text, and it does so more efficiently than what’s normally possible.