Building data-efficient AI algorithms with a dose of inspiration from the brain
Currently, the predominant approach in AI is to use unlimited data to solve narrowly defined problems. To progress toward humanlike intelligence, AI benchmarks will need to be extended to focus more on data efficiency, flexibility of reasoning, and transfer of knowledge between tasks. This talk will detail the challenges and successes in making these ideas operational. At Vicarious, the language of probabilistic graphical models is used as the representational framework. Compared with neural networks, graphical models have several advantages, such as the ability to incorporate prior knowledge, answer arbitrary probabilistic queries, and deal with uncertainty. However, a downside is that inference can be intractable. By incorporating several insights that originally were discovered in neuroscience, engineers at Vicarious were able to create probabilistic models, on which accurate inference can be performed using message-passing algorithms that are similar to the computations in a neural network.