Admittedly, I have scratched a very small surface area of what the brain does and is capable of, but I mention it here, since I would like to explore how we might begin to create our full artificial intelligent entity. We can use the anatomical structures within the brain as a starting point to allow us to develop similar components within our "computational" humanoid; for example, the equivalent cerebellum within our humanoid would have similar functional processes, in turn, providing, movement, posture and dexterity of movement. Likewise, the artificial occipital lobe would be used to process how objects are perceived and would operate in conjunction with the cerebellum to coordinate movement in accordance with its surroundings.

A recent release out of Insilico Medicine reports, "At Mensa Annual Gathering 2016, the annual event of the largest and oldest high IQ society transpiring in San Diego from June 29th to July 3rd, Dr. Michael West, CEO of BioTime, Inc announced the launch of a beta version of Embryonic.AI, an artificially intelligent system for analyzing the embryonic state of human cell samples using gene expression data. The first implementation of Embryonic.AI was launched by LifeMap Discovery, Inc, a subsidiary of BioTime, Inc and is freely available for beta testing. Scientists and companies from all over the world are welcome to test their stem cell and adult cell samples using Embryonic.AI and discuss collaboration alternatives.

"BioTime harnesses the largest collection of highest-quality gene expression data coming from scrupulously designed and controlled cell differentiation experiments we have seen to date. It was large enough to train a complex architecture of deep neural networks to work as a classifier and a predictor of the embryonic state. We recently tested Embryonic.AI using mouse data and noticed surprising results showing the capabilities of this system in cross-species analysis. Research projects using Embryonic.AI may transform our understanding of cancer and other diseases and possible developments in reinforcement learning may help navigate and control cellular differentiation states", said Alex Zhavoronkov, Ph.D., CEO of Insilico Medicine, Inc. The system utilizes a sophisticated architecture of multi-class deep neural networks (DNNs) and DNN ensembles trained on thousands of samples of carefully selected cells of multiple classes: embryonic stem cells, induced pluripotent stem cells, progenitor stem cells, adult stem cells and adult cells to recognize the class and embryonic state of the sample, achieving high accuracy in simulations.