Microsoft's Office Lens app has let folks upload photos from their iOS and Android devices since April 2015. With integration in the latest Windows OS, though, they've also added support for Office 365 if your business or personal accounts use that instead. It's still got all the optical character recognition you've enjoyed with OneNote since 2013, meaning the text and figures are searchable once you've uploaded the images to OneDrive.

The ScanSnap S1300i from Fujitsu is the smallest multi-page double-sided ScanSnap scanner for one-button ease of use at the desk or on the road. Just stack up to 10 pages into the feeder and press the blue button. The S1300i features a powerful set of automated image processing functions including auto skew correction, auto orientation, and auto color detection -- all working behind the scenes so your scanned content looks great without effort. Scanning to the cloud lets you access your documents from just about anywhere. The unit is powered by USB or the included AC power cord.

ArXiv sanity clean for text-to-speech bookmark. How to use text-to-speech to create audio books and read science papers: https://www.youtube.com/watch?v R2b9d...

'Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal. Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". As machines become increasingly capable, facilities once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence" having become a routine technology.

Scientists have discovered a way to reconstruct the images in a person's mind. In a new study, researchers concluded that it's possible to visualize a person's memory using only an fMRI machine and some machine learning software. For the study, published in the Journal of Neuroscience, the researchers showed 23 participants hundreds of faces while hooked up to an fMRI machine. While they were looking at the faces, an AI program was learning to associate their perception of certain features with different brain activity. Then, once the machine has learned which activity corresponds to which feature, the participants are shown a picture of a brand-new face, and the machine reconstructs it.

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.