Earlier than Apr-20-2017

Quantifying Risk for Anxiety Disorders in Preschool Children: A Machine Learning Approach - Harvard Dataverse


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Decision Boundaries for Deep Learning and other Machine Learning classifiers


Please get 3 datasets from my repository on GitHub: simple XOR pattern, complex XOR pattern, and a grid dataset. In the ones below, I ran decision tree, SVM with some sets of parameters, neural network (with only a hidden layer), and random forest. Decision tree, neural network and random forest estimated much more complicated boundaries than the true boundaries, although SVM with well generalized by specific parameters gave natural and well smoothed boundaries (but classification accuracy was not good). Our primary interest here is what kind of set of tuning parameters shows what kind of decision boundaries.

Shining light on Facebook's AI strategy


In a speech today at Web Summit, Facebook CTO Mike Schroepfer laid out a vision for the role artificial intelligence and machine learning will play in the company's ambitions to improve global connectivity, technology accessibility, and human computer interaction. Large companies like Facebook play an incredibly important role in the artificial intelligence and machine learning ecosystem. Caffe2Go won't remain limited to Style Transfer -- it holds the key to deploying convolutional neural nets across Facebook's suite of mobile apps. While Google first popularized algorithmic search, and Snapchat is now making augmented reality mainstream, it's Facebook delivering artificial intelligence to the masses.

Machine Learning vs. Traditional Statistics: Different philosophies, Different Approaches


"Machine Learning (ML)" and "Traditional Statistics(TS)" have different philosophies in their approaches. Historically, ML techniques and approach heavily relies on computing power. As a result, TS heavily relies on small samples and heavy assumptions about data and its distributions, . The preferred learning method in machine learning and data mining is inductive learning.

Robert Downey Jr offers to voice Mark Zuckerberg's digital assistant


It may be Tesla's Elon Musk who most often invites comparison to Marvel's superhero Iron Man – the alter ego of billionaire inventor Tony Stark – but it is Mark Zuckerberg who might be the first to bring Stark's technology to life. In a Facebook conversation on Thursday, Zuckerberg invited suggestions for who should voice his real-life Jarvis (which, in the Iron Man and Avengers movies, stands for Just A Rather Very Intelligent System). Robert Downey Jr – who plays Jarvis's inventor Tony Stark in the Marvel movies – then commented, offering to voice it himself. Downey's intervention brought some buzz to a thread that was already careening off the rails, with many in particular demanding that their AI home assistant speak with a British accent.

IBM Watson Can Help Find Water Wasters In Drought-Stricken California


Using that information, water authorities or companies can target areas or homes where people are wasting water, and send specialized educational materials to let people know how they can cut down on water waste. Some of OmniEarth's customers have already started seeing results, with some reporting a 15 percent reduction in water use, just by using the conservation messages. In one case, he mentioned a manager of a water district that was an OmniEarth customer. Results from OmniEarth were turned over to the district, and the manager's house was flagged as inefficient.

ezDI Launches Coding Compliance and Auditing Platform "ezAssess"


What Artificial Intelligence Can and Can't Do Right Now The SpaceNet Challenge: help us to harness machine learning to make maps more current and ... Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time. We won't share your personal information with anyone.

20% of All Election Related Tweets Came From Non-Humans


With the elections done, let's take a look at one of the most prolific source of political tweets during the campaign period -- bots. They found roughly 400,000 bots in Twitter, and they made a massive amount of tweets, which made them surprisingly capable of distorting online debate. According to the study, these 400,000 bots were behind 20 percent of all election related tweets. What's interesting is how AI has made these bots better at what they do.

Tiny fingertip camera helps blind people read without braille

New Scientist

A new device lets blind people read by popping a miniature camera on their fingertip. To read printed material, many visually impaired people rely on mobile apps like KNFB Reader that translate text to speech. The average reading speed for an expert braille reader is around 90 to 115 words per minute, while sighted individuals have an average reading speed around 200 words per minute. Matthew Janusauskas at the American Foundation for the Blind, a nonprofit based in New York City, thinks the technology could be useful for reading printed materials where the layout affects comprehension, such as a page with multiple columns of text.

Counting endangered sea cows is hard, so we're going to make AI do it


If that sounds tedious, then perhaps you, like researchers at Murdoch University, would prefer to delegate the duty to a specially-trained computer. Hodgson worked with computer scientist Frederic Maire, of the Queensland University of Technology, to automate the process. They trained a machine learning system on imagery where the sea cows had already been tagged, allowing it to look at fresh photos and spot them with about 80 percent accuracy. Just one more way that machine learning and computer vision are helping out scientists and others across the globe.