by Stephen Downes
Feb 03, 2015
Neuroenhancement and the Extended Mind Hypothesis
One question that's always asked is what is the connection between social networks and neural networks? In a recent talk I referred to the 'Downes answer' and the 'Siemens answer' to this question. This article provides some of the theoretical underpinning to the Siemens answer: "The extended mind hypothesis (EMH) was first introduced to the philosophical world by David Chalmers and Andy Clark in 1998. Their claim was simple enough... mental phenomena were multiply realisable." This claim is (a variant of a claim called) functionalism, and it allows that the same mental state could exist in different types of physical states, such as neurons and computers. And if mental states are distributed, then the very same mental state could exist across both systems at once, being partially in a neural network and partially in a computer network (did George really have this solution in mind before I called it the Siemens answer? You'll have to ask him!).
ADL Community Survey
Advanced Distributed Learning,
Received by email: "the Advanced Distributed Learning (ADL) Initiative has launched a new effort to create a SCORM profile of the Experience API (xAPI). ADL requests your participation in this survey to help inform our direction for this effort, and to gauge your current usage of distributed learning products, services, SCORM and xAPI. The target audience for this survey is anyone in the education and training community familiar with distributed learning." This link will take you to the survey.
Google Brain’s Co-inventor Tells Why He’s Building Chinese Neural Networks
Basically this is a look at Coursera founder Andrew Ng's next venture. He's now working with Baidu and still focused on massive - “only interested in tech that can influence 100 million users” - and in particular on using neural networks for analytics. He hasn't lost his hubris - "We have the English language. Now we’re figuring out Chinese" - but that's OK if he does interesting work. I can't see working in Chinese being anything other than that. For those who think we think and learn in a language of logic, Chinese poses a challenge - it's completely different from English. You need to find low-level subsymbolic processes before ever getting to the language. "At the first level [the machine] might learn to detect edges in an image, and then it might learn to detect corners. This is knowledge that is common to the two languages."
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