While this new approach to programming still has difficult challenges to overcome, it has significant potential to automate the development of machine-learning algorithms. The program also refines its knowledge as further examples are provided, and its code can be rewritten to tweak the probabilities. This form of probabilistic programming - a code that uses probabilities instead of specific variables - requires fewer examples to make a determination, such as, for example, that the sky is blue with patches of white clouds. The Bayesian probability is used to refine predictions about the world using experience.

It is based on a mathematical framework named after 18 th century mathematician Thomas Bayes. Gamalon calls the technique it employed Bayesian program synthesis. For instance, 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. Complex work can be accomplished using this method, but it certainly leaves something to be desired.

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