User:Sunspot435/sandbox

Administrative Section
AI Engineering Technical POC: Trevor J Bihl, Ph.D.—concept development, technical management of AI network architectures.

Neuroscience Technical POC: Teresa D Hawkes, Ph.D.—concept development, technical management of neuroscience information.

Technical Summary
This ongoing research project presents neurophysiological evidence for brain structure and function to inform AI architecture construction in terms of von Neumann and neuromorphic hardware and algorithms. This will include reviews of topics (e.g., Learning to Learn: (human neuroscience) and non-living (computer-based) intelligence, Human Brain Mapping, Living and Non-living Intelligence Materials Science) and current events (e.g., Meta/LeCunn brain modules and unsupervised learning, Karl Friston free principle theory of brain function) that guide AI engineering beyond neuron type, backpropagation, and spiking neural networks. Importantly, neuromorphic hardware as well as algorithms can be programmed while only von Neumann algorithms can be programmed. In this way, neuromorphic hard- and software mimics human neuroscience well including low power usage, as human neuroscience hardware as well as software (learning and operations) can be programmed by the system and do not require the electricity von Neumann machines and algorithms require. Of key interest are microcircuits (specialized groups of neurons in living intelligence), needed networks (transient networks of microcircuits that provide required output then disassemble as seen in living intelligence), and engineering sensitivity to the different materials science of living and non-living intelligence systems.

Technical Merit
We are just at the beginning of neuroscience’s influence on the development of computer-based non-living intelligence systems. We know Turing and von Neumann were both influenced by human neuroscience processes, as were IBM, Minsky, LeCunn, and Rogers. Engineers are mimicking neurons and their function to give non-living intelligence human capacity, including general intelligence, analysis of large datasets, and creative response to information outside of extant datasets. Research questions and directions are presented as well as the neuroscience and AI engineering evidence that informs such. Individuals and organizations include academic, industry, and government scientific peer-reviewed, arXiv, bioXiv, and industry blog coverage that will be reviewed and integrated here by doctors in neuroscience (Teresa D Hawkes) and AI-engineering (Trevor J Bihl) published in the peer-reviewed and conference literatures. We invite interaction with readers and scientists as we move the field of united neuroscience and AI engineering forward.

Technical Reports

 * Learning to Learn (done)
 * Meta/LeCunn Public Relations Report (done)
 * Neuroscience Hardware for AI Engineers (done)