BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework - Howard, Newton; Chouikhi, Naima; Adeel, Ahsan; Dial, Katelyn; Howard, Adam; Hussain, Amir (Frontiers in Computational Neuroscience, 2020)
Human intelligence is constituted by a multitude of cognitive functions activated either directly or indirectly by external stimuli of various kinds. Computational approaches to the cognitive sciences and to neuroscience are partly premised on the idea that computational simulation of such cognitive functions and brain operations suspected to correspond to them, can help to further discover knowledge about those functions and operations. Specifically, how they might work together. It is also partly premised on the idea that empirical neuroscience research, whether following on from such a simulation or otherwise—as indeed simulation and empirical research are complementary—could help us build better artificially intelligent systems. This is based on the assumption that principles by which the brain seemingly operates, to the extent that it can be understood as computational, should at least be tested as principles for the operation of artificial systems. This paper explores some of the principles of the brain that seem to be responsible for its autonomous, problem-adaptive nature. The brain operating system (BrainOS) explicated here is an introduction to ongoing work aiming to create a robust, integrated model, combining between the connectionist paradigm underlying neural networks and the symbolic paradigm underlying much else of AI. BrainOS is an automatic approach that selects the most appropriate model based on the (a) input at hand, (b) prior experience, i.e. a history of results of prior problem solving attempts and (c) world knowledge (represented in the symbolic way, and used as a means to explain its approach). It is able to accept diverse and mixed input data types, process histories, and objectives, to extract knowledge and infer a situational context. BrainOS is designed to be efficient through its ability to not only choose the most suitable learning model but to effectively calibrate it based on the task at hand.