Facebook is using AI to help describe images posted by users. Facebook's iOS app is using object recognition technology to give blind people an audio breakdown of what's going on in photos posted on the social network. The new accessibility feature, rolling out today, could be a major improvement on existing screen readers, which largely focus on text. Until now, when blind users were checking their Facebook newsfeed and came across an image, they would only hear the word "photo" and the name of the person who shared it, which left the user still dependent on friends and family to interpret an image. To improve the experience for blind people, Facebook has used its vast trove of user images to train a deep neural network that drives a computer vision system built to recognise objects in images.

Model ensembling is a very powerful technique to increase accuracy on a variety of ML tasks. In this article I will share my ensembling approaches for Kaggle Competitions. For the first part we look at creating ensembles from submission files. The second part will look at creating ensembles through stacked generalization/blending. I answer why ensembling reduces the generalization error. Finally I show different methods of ensembling, together with their results and code to try it out for yourself. This is how you win ML competitions: you take other peoples' work and ensemble them together." The most basic and convenient way to ensemble is to ensemble Kaggle submission CSV files. You only need the predictions on the test set for these methods -- no need to retrain a model. This makes it a quick way to ensemble already existing model predictions, ideal when teaming up. Let's see why model ensembling reduces error rate and why it works better to ensemble low-correlated model predictions. During space missions it is very important that all signals are correctly relayed. A coding solution was found in error correcting codes. The simplest error correcting code is a repetition-code: Relay the signal multiple times in equally sized chunks and have a majority vote. Signal corruption is a very rare occurrence and often occur in small bursts. So then it figures that it is even rarer to have a corrupted majority vote. As long as the corruption is not completely unpredictable (has a 50% chance of occurring) then signals can be repaired. Suppose we have a test set of 10 samples. The ground truth is all positive ("1?):

The story of how the FBI finally tracked down notorious fugitive Lynn Cozart, using its brand-new, 1 billion facial recognition system, seems tailor-made to disarm even the staunchest of skeptics. Cozart, a former security guard in Beaver County, Pennsylvania, was convicted of deviant sexual intercourse in 1996. According to court filings, he had molested his three juvenile children, two girls and one boy, from 1984 through 1994. It wasn't until May 11, 1995, that the children's mother came forward and told the Pennsylvania State Police what Cozart had been doing. He was convicted, but he failed to show up for his sentencing hearing in April 1996. Federal agents raided his home, interviewed family members and released photos of the man to the general public. In August 2006, the Cozart case was featured in "America's Most Wanted," the national television program, under a segment titled "Ten Years of Hell for Three Children."

Lawrence Livermore National Laboratory (LLNL) has purchased a new brain-inspired supercomputing platform developed by International Business Machines Corp (NYSE:IBM). Based on a breakthrough neurosynaptic computer chip called IBM TrueNorth, the scalable platform will process the equivalent of 16 million neurons and 4 billion synapses while consuming only the energy equivalent of a tablet computer. The brain-like, neural network design of the IBM neuromorphic system is able to run complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips. "The delivery of this advanced computing platform represents a major milestone as we enter the next era of cognitive computing," said Dharmendra Modha, IBM fellow and chief scientist of Brain-inspired Computing, IBM Research. "We value our partnerships with the national labs. In fact, prior to design and fabrication, we simulated the IBM TrueNorth processor using LLNL's Sequoia supercomputer. This collaboration will push the boundaries of brain-inspired computing to enable future systems that deliver unprecedented capability and throughput, while minimizing the capital, operating and programming costs – keeping our nation at the leading edge of science and technology."

After the initial shock of having a stroke and becoming paralysed on one side, Jan Morgan was desperate to regain movement and make life easier for her young daughter. Jan was just 50 when she had the stroke in September 2010 and her daughter Imogen 12. It left Jan completely paralysed down her left side, unable even to dress herself, let alone scratch an itch. While daily physiotherapy had helped restore movement to her left leg, after around 18 months Jan's arm was still immobile and she was largely dependent on Imogen. 'The physiotherapist's priority was to get me upright and focused on my lower limbs,' says Jan, now 56.

Australia's biggest pizza chain is now trying to take their operation to the next level by having any pizza ordered ready in three minutes and delivered in ten. The company is hoping to cut baking and delivery times by more than half in a trial scheme using high-speed cooking technology and military-grade delivery robots, the Sydney Morning Herald reports. Dominos chief executive Don Meij has called the innovative move Project 3-10 - pizza ready to take away in three minutes, and at your door in 10. Domino's Australia has been working with start-up company Marathon Robotics to create the world's first autonomous delivery vehicle - a robot that delivers pizza to your doorstep Meij knows the enormity of the task at hand and said making fresh pizza from scratch in three minutes will mean new ovens that blast heat in several directions. The latest technology has already been used to halve cooking times to four minutes, and Meij reckons that the goal of 10-minute deliveries in Australia could come to fruition within three years.

As robots become more life-like, questions have been raised about whether its morally acceptable to kick them, bully them or program them to kill. Taking this dilemma to the next level, researchers have studied how these machines make us feel, sexually and emotionally. Scientists programmed a human-shaped robot to ask volunteers to touch it in 13 different places. While some of these requests presented no difficulties, people were hesitant to touch'private' areas including the robot's'buttocks'. Scientists at Stanford University programmed a human-shaped robot (pictured left) to ask volunteers (pictured right) to touch it in 13 different places.

The rise of the robot writers is here. Computers are replacing human writers without you realising it. Hal just took my job. The Associated Press (AP) already uses them. These robot journalists don't need insurance or a pension, pay or annual leave – or even sleep. This lets publications react to unforeseen events such as earthquakes, in real time, without stirring a human hack from their slumbers.

"We have a profound ethical responsibility to design systems that have a positive impact on society, obey the law, and adhere to our highest ethical standards."–Oren On the impact of Artificial Intelligence (AI) on society, I have interviewed Oren Etzioni, Chief Executive Officer of the Allen Institute for Artificial Intelligence. What is the mission of the Allen Institute for AI (AI2)? Oren Etzioni: Our mission is to contribute to humanity through high-impact AI research and engineering. AI2 is the creation of Paul Allen, Microsoft co-founder, and you are the lead.

The boundaries between technology and automotive sectors continue to blur, and now Toyota and Microsoft have announced that they'll be working together to imbue cars with artificial intelligence that will, they promise, humanize the driving experience. That might sound like an odd idea given the rise of self-driving vehicles, but it's going to be a little while before our cars are fully autonomous. In the meantime, Toyota plans on making the driving experience more pleasant--and it's investing 5.5 million in technology research to try and get that to happen. That it's working with Microsoft isn't much of a surprise: The two companies have collaborated since 2011. Bloomberg reports that "the new venture will study everything from cars that help each other analyze traffic patterns to user-based insurance pricing to connecting drivers with information and security services in their homes."