Archive for brain imaging

Big Bayes goes South




at the brain institute


brainsFor a rather convoluted reason, I happened to visit the Brain and Spine Institute (ICM, Institut du Cerveau et de la Moelle Épinière) yesterday, in Paris, within the Pitié-Salpétrière Hospital. And saw this row of brains, printed by 3D printers, rather than standing in jars. (Like Abe Normal’s.) Which produced brain shadows, not commonly seen otherwise!

machines learning but not teaching…

Posted in Books, pictures with tags , , , , , , , on October 28, 2016 by xi'an

A few weeks after the editorial “Algorithms and Blues“, Nature offers another (general public) entry on AIs and their impact on society, entitled “The Black Box of AI“. The call is less on open source AIs and more on accountability, namely the fact that decisions produced by AIS and impacting people one way or another should be accountable. Rather than excused by the way out “the computer said so”. What the article exposes is how (close to) impossible this is when the algorithms are based on black-box structures like neural networks and other deep-learning algorithms. While optimised to predict as accurately as possible one outcome given a vector of inputs, hence learning in that way how the inputs impact this output [in the same range of values], these methods do not learn in a more profound way in that they very rarely explain why the output occurs given the inputs. Hence, given a neural network that predicts go moves or operates a self-driving car, there is a priori no knowledge to be gathered from this network about the general rules of how humans play go or drive cars. This rather obvious feature means that algorithms that determine the severity of a sentence cannot be argued as being rational and hence should not be used per se (or that the judicial system exploiting them should be sued). The article is not particularly deep (learning), but it mentions a few machine-learning players like Pierre Baldi, Zoubin Ghahramani and Stéphane Mallat, who comments on the distance existing between those networks and true (and transparent) explanations. And on the fact that the human brain itself goes mostly unexplained. [I did not know I could include such dynamic images on WordPress!]

contemporary issues in hypothesis testing



