If you think your mind is the only safe place left for all your secrets, think again, because scientists are making real steps towards reading your thoughts and putting them on a screen for everyone to see. A team from the University of Oregon has built a system that can read people's thoughts via brain scans, and reconstruct the faces they were visualising in their heads. As you'll soon see, the results were pretty damn creepy. "We can take someone's memory - which is typically something internal and private - and we can pull it out from their brains," one of the team, neuroscientist Brice Kuhl, told Brian Resnick at Vox. The researchers selected 23 volunteers, and compiled a set of 1,000 colour photos of random people's faces.

A team from the University of Oregon have developed a system that can read people's thoughts via brain scans, and rebuild the faces they were visualising in their heads. The study, led by Brice Kuhl and Hongmi Lee from the University of Oregon, used artificial intelligence (AI) that analysed brain activity in an attempt to reconstruct one of a series of faces that participants were seeing. It's not an exact science, but the AI did get close. "We can take someone's memory – which is typically something internal and private – and we can pull it out from their brains," Kuhl told Vox. "Some people use different definitions of mind reading, but certainly, that's getting close," Kuhl told Vox. Kuhl and his colleague Lee recently published a paper in The Journal of Neuroscience with a conclusion straight out of science fiction: Kuhl and Lee created images directly from memories using an MRI, some machine learning software, and a few hapless human guinea pigs.

What do you think of when someone says "AI" or "Artificial Intelligence"? For most of us, it conjures up an image of the future. It doesn't much evoke the here and now. Artificial intelligence is already out of the box. And while it might not be as slick as the movies, it has vast applications in almost every field, from business to medicine, traffic jams to Facebook photos.

When I was working on my next pattern classification application, I realized that it might be worthwhile to take a step back and look at the big picture of pattern classification in order to put my previous topics into context and to provide and introduction for the future topics that are going to follow. Pattern classification and machine learning are very hot topics and used in almost every modern application: Optical Character Recognition (OCR) in the post office, spam filtering in our email clients, barcode scanners in the supermarket … the list is endless. In this article, I want to give a quick overview about the main concepts of a typical supervised learning task as a primer for future articles and implementations of various learning algorithms and applications. Predictive modeling is the general concept of building a model that is capable of making predictions. Typically, such a model includes a machine learning algorithm that learns certain properties from a training dataset in order to make those predictions.

I'm trying to find a website that this guy wrote. In it he explains how machine learning works, gives you a history of its evolution and you build an OCR example as part of the tutorial using the MNIST DATABASE as your sample set. I found this site on a friends computer and now I've lost it.

I'm struggling with math while I'm reading Machine learning: A probabilistic perspective like I'm confused about quantiles, inverse cdf etc. Could you recommend me a book that I should read first? The best STEM book I've ever read. Goes into great depth without sacrificing any ease of comprehension. Few recourses, in any subject, can provide both.

Today we have some very good AI/ML posts. First, Pete Warden tells us how to break into machine learning. It is some very practical advice on how to get started. David Lettier shows us how to develop K-Means from scratch. Lastly, on An Uncommon Lab we get to see how Kalman Filters work.

A couple have been told to restrain their cat or face having their mail deliveries suspended. Matthew Sampson was notified by Royal Mail last week of a "potential hazard" at his home in South Gloucestershire which was "affecting deliveries". According to Royal Mail, Bella the cat is a "threat" to staff and has been putting "fingers at risk of injury". But owner Matthew Sampson, said he was "shocked" by the notice as he has "never seen her get aggressive". In the letter, Royal Mail states it has been "experiencing difficulties in delivering mail" to Mr Sampson's address in Patchway "because of the actions of a cat". It said the couple's postman had reported that when he pushes mail through their letterbox their cat "snatches the mail and put his fingers at risk of injury".

Not surprisingly, the Guild isn't happy with the Supreme Court's choice -- it calls this a "colossal loss" and insists that the appeals court was "blinded" by Google's attempt to portray itself as rescuing lost books for the public good. The statements are a bit melodramatic (many of these titles are unlikely to return to print before they reach the public domain), but they do point out that the concerns over digitizing books aren't quite over. Although Google appears to have walked a fine line, the worry is that other outfits might not be quite so scrupulous.

The Authors Guild and several individual writers have argued that the project, known as Google Books, illegally deprives them of revenue. The high court left in place an October 2015 ruling by the 2nd U.S. Circuit Court of Appeals in New York in favor of Google. A unanimous three-judge appeals court panel said the case "tests the boundaries of fair use," but found Google's practices were ultimately allowed under the law. The individual plaintiffs who filed the proposed class action against Google included former New York Yankees pitcher Jim Bouton, who wrote the acclaimed memoir "Ball Four." Several prominent writers, including novelist and poet Margaret Atwood and lyricist and composer Stephen Sondheim, signed on to a friend-of-the-court brief backing the Authors Guild.