M D

6 followers
·
27 following
M D
More ideas from M
"Pearl expects that causal reasoning could provide machines with human-level intelligence. They’d be able to communicate with humans more effectively and even, he explains, achieve status as moral entities with a capacity for free will — and for evil. Quanta Magazine sat down with Pearl at a recent conference in San Diego and later held a follow-up interview with him by phone. An edited and condensed version of those conversations follows."

"Pearl expects that causal reasoning could provide machines with human-level intelligence. They’d be able to communicate with humans more effectively and even, he explains, achieve status as moral entities with a capacity for free will — and for evil. Quanta Magazine sat down with Pearl at a recent conference in San Diego and later held a follow-up interview with him by phone. An edited and condensed version of those conversations follows."

Mistaken extrapolations, limited imagination, and other common mistakes that distract us from thinking more productively about the future of AI.

Mistaken extrapolations, limited imagination, and other common mistakes that distract us from thinking more productively about the future of AI.

AI now. For every industry. Jim McHugh explains why a new computing paradigm and deep learning software stack will be required to power, predict, and act on data. https://www.oreilly.com/ideas/ai-now-for-every-industry

AI now. For every industry. Jim McHugh explains why a new computing paradigm and deep learning software stack will be required to power, predict, and act on data. https://www.oreilly.com/ideas/ai-now-for-every-industry

If a programmer cannot see what a program is doing, she can't understand it.

Learnable Programming: Designing programming systems for understanding programs

An interactive guide to the Fourier transform

An Interactive Guide To The Fourier Transform

Common tools in AI: Propositional logic, Naive Bayes, Back-propagation, Neural Nets, Bayes Nets, ...

Common tools in AI: Propositional logic, Naive Bayes, Back-propagation, Neural Nets, Bayes Nets, ...

"I don't trust linear regressions when it's' harder to guess the direction of the correlation from the scatter plot than to find new constellations on it."

"I don't trust linear regressions when it's' harder to guess the direction of the correlation from the scatter plot than to find new constellations on it.

Generative models: what they are, why they are important, and where they might be going.  See also: [1] https://code.facebook.com/posts/1587249151575490/a-path-to-unsupervised-learning-through-adversarial-networks/ [2] http://kvfrans.com/generative-adversial-networks-explained/

Our first research results are now live: four projects that share a common theme of enhancing or using generative models, a branch of unsupervised learning techniques in machine learning.

Check it out, this is a Boston transit pass -- or at least the parts of it that matters. [Becky Stern] got rid of the rest in a bid to embed the RFID tag inside her cellphone.  The transit pass, cal...

Check it out, this is a Boston transit pass — or at least the parts of it that matters. [Becky Stern] got rid of the rest in a bid to embed the RFID tag inside her cellphone. The transit pass…

"Many of the sophisticated statistical techniques out there are created to sort out data quality problems. Scientists are always skeptical about data quality and are used to dealing with questionable data. So for them the lake is important because they get to work with raw data and can be deliberate about applying techniques to make sense of it, rather than some opaque data cleansing mechanism that probably does more harm that good."

A Data Lake is a store that hold raw data as a source for data scientists to explore ways to gain information. It should not be accessed by end-users or used for system integration.