Hollywood actor Morgan Freeman has provided the voice for an AI assistant created by Facebook's Mark Zuckerberg. Mr Zuckerberg said he asked the actor, who was chosen by the public, after an awards ceremony earlier this month. The Facebook co-founder coded the AI assistant - called Jarvis, after the butler in Iron Man - for his home. If he decides to release it to the public, people would relate differently to a famous voice than more robotic sounding assistants, tech experts said. Mr Zuckerberg asked his Facebook followers to pick the voice after building artificial intelligence to help him around the house.

Most tasks in Machine Learning can be reduced to classification tasks. For example, we have a medical dataset and we want to classify who has diabetes (positive class) and who doesn't (negative class). We have a dataset from the financial world and want to know which customers will default on their credit (positive class) and which customers will not (negative class). To do this, we can train a Classifier with a'training dataset' and after such a Classifier is trained (we have determined its model parameters) and can accurately classify the training set, we can use it to classify new data (test set). If the training is done properly, the Classifier should predict the class probabilities of the new data with a similar accuracy.

Terrible user interface and complex designs have plagued enterprise tools for decades. These tools are not only boring and bulky, but most of them require hours of training, onboarding, and whatnot before you can actually start using them. You end up losing crucial time just figuring out the basic workflow. This is 2016, and there ought to be a better way for enterprise businesses to get work done quickly and efficiently. The good news is it looks like bots might be the answer.

In March of 2016, a twenty-seven-year-old Syrian refugee named Rakan Ghebar began discussing his mental health with a counsellor. Ghebar, who has lived in Beirut since 2014, lost a number of family members to the civil war in Syria and struggles with persistent nervous anxiety. Before he fled his native country, he studied English literature at Damascus University; now, in Lebanon, he works as the vice-principal at a school for displaced Syrian children, many of whom suffer from the same difficulties as he does. When Ghebar asked the counsellor for advice, he was told to try to focus intently on the present. By devoting all of his energy to whatever he was doing, the counsellor said, no matter how trivial, he could learn to direct his attention away from his fears and worries.

What programming language should one learn to get a machine learning or data science job? It is debated in many forums. I could provide here my own answer to it and explain why, but I'd rather look at some data first. After all, this is what machine learners and data scientists should do: look at data, not opinions. So, let's look at some data. I will use the trend search available on indeed.com.

It's easy to joke that the internet was invented to give people around the world the opportunity to share pictures of cats. However, according to a new report, those kitty pictures may one day turn out to save your life. That is based on work being done by Dr. Alvin Rajkomar, an assistant professor at the University of California, San Francisco Medical Center. Rajkomar trained a deep learning neural network to be able to automatically detect life-threatening abnormalities in chest X-rays. "When I was a medical resident, I ordered a stat X-ray of a patient who I suspected had a life-threatening pneumothorax -- air outside of his lung compressing his heart -- and happened to be standing next to the digital X-ray machine as it was being taken," he told Digital Trends.

The growing popularity of artificial intelligence technology probably will lead to millions of lost jobs, especially among less-educated workers, and could exacerbate the economic divide between socioeconomic classes in the United States, according to a newly released White House report. But that same technology is also essential to improving the country's productivity growth, a key measure of how efficiently the economy produces goods. That could ultimately lead to higher average wages and fewer work hours. For that reason, the report concludes, our economy actually needs more artificial intelligence, not less. To reconcile the benefits of the technology with its expected toll, the report states, the federal government should expand both access to education in technical fields and the scope of unemployment benefits.

About halfway through a particularly tense game of Go held in Seoul, South Korea, between Lee Sedol, one of the best players of all time, and AlphaGo, an artificial intelligence created by Google, the AI program made a mysterious move that demonstrated an unnerving edge over its human opponent. On move 37, AlphaGo chose to put a black stone in what seemed, at first, like a ridiculous position. It looked certain to give up substantial territory--a rookie mistake in a game that is all about controlling the space on the board. Two television commentators wondered if they had misread the move or if the machine had malfunctioned somehow. In fact, contrary to any conventional wisdom, move 37 would enable AlphaGo to build a formidable foundation in the center of the board. The Google program had effectively won the game using a move that no human would've come up with. One reason that understanding language is so difficult for computers and AI systems is that words often have meanings based on context and even the appearance of the letters and words. In the images that accompany this story, several artists demonstrate the use of a variety of visual clues to convey meanings far beyond the actual letters.

The famous inventor and computer scientist Ray Kurzweil has made some very bold predictions about the pace at which human technology is advancing toward the ultimate threshold. That threshold is known as "The Singularity." That epithet is a metaphor borrowed from physics terminology to express the point at which information technology--specifically artificial intelligence--becomes sufficiently advanced as to irreversibly alter the course of history on earth. While The Singularity may be a familiar cautionary tale told by renowned thinkers such as Bill Gates, Carl Sagan, and Stephen Hawking, and artistically explored through the famous sci-trope of sentient robots, e.g. But that depends on how you choose to define doom, specifically.

Gaetan Hadjeres and Francois Pachet at the Sony Computer Science Laboratories in Paris created DeepBach, then entered Bach's 352 chorales. The resulting composition is certainly in the style. So why does this work better than some other attempts? Part of it is the sample size of compositions. Another part is the chorale's formal structure (four voices, simple patterns of notes and harmonies).