Lateralization is a fundamental feature of the human brain, where sex differences have been observed. Conventional studies in neuroscience on sex-specific lateralization are typically conducted on univariate statistical comparisons between male and female groups. However, these analyses often lack effective validation of group specificity. Here, we formulate modeling sex differences in lateralization of functional networks as a dual-classification problem, consisting of first-order classification for left vs. right functional networks and second-order classification for male vs. female models. To capture sex-specific patterns, we develop the Group-Specific Discriminant Analysis (GSDA) for first-order classification. The evaluation on two public neuroimaging datasets demonstrates the efficacy of GSDA in learning sex-specific models from functional networks, achieving a significant improvement in group specificity over baseline methods. The major sex differences are in the strength of lateralization and the interactions within and between lobes. The GSDA-based method is generic in nature and can be adapted to other group-specific analyses such as handedness-specific or disease-specific analyses.

This is a talk from GOTO Chicago 2019 by Doug Lenat, Award-winning AI pioneer who created the landmark Machine Learning program, AM, in 1976 and CEO of Cycorp. I've dropped the full talk abstract below for a read before diving into the talk: Almost everyone who talks about Artificial Intelligence, nowadays, means training multi-level neural nets on big data. Developing and using those patterns is a lot like what our right brain hemispheres do; it enables AI's to react quickly and – very often – adequately. But we human beings also make good use of our left brain hemisphere, which reasons more slowly, logically, and causally. I will discuss this "other type of AI" – i.e., left brain AI, which comprises a formal representation language, a "seed" knowledge base with hand-engineered default rules of common sense and good domain-specific expert judgement written in that language, and an inference engine capable of producing hundreds-deep chains of deduction, induction, and abduction on that large knowledge base.

The belief that there is a link between talent and left-handedness has a long history. Leonardo da Vinci was left-handed. So were Mark Twain, Mozart, Marie Curie, Nicola Tesla and Aristotle. It's no different today – former US president Barack Obama is a left-hander, as is business leader Bill Gates and footballer Lionel Messi. But is it really true that left-handers are more likely to be geniuses?

The belief that there is a link between talent and left-handedness has a long history. Leonardo da Vinci was left-handed. So were Mark Twain, Mozart, Marie Curie, Nicola Tesla and Aristotle. It's no different today – former US president Barack Obama is a left-hander, as is business leader Bill Gates and footballer Lionel Messi. But is it really true that left-handers are more likely to be geniuses?

We have long deemed ourselves to be at the pinnacle of cognitive abilities among animals. But that is different from being at the pinnacle of evolution in a number of very important ways. As Mark Twain pointed out in 1903, to presume that evolution has been a long path leading to humans as its crowning achievement is just as preposterous as presuming that the whole purpose of building the Eiffel Tower was to put that final coat of paint on its tip. Moreover, evolution is not synonymous with progress, but simply change over time. And humans aren't even the youngest, most recently evolved species.