Scientists have developed a new artificial intelligence tool that can predict whether someone will attempt suicide as far off as two years into the future with up to 80 per cent accuracy. The team then combed through the electronic health records, which were anonymous, and identified more than 3,200 people who had attempted suicide. Using machine learning to examine all of those details, the algorithms were able to'learn' which combination of factors in the records could most accurately predict future suicide attempts. "The machine learns the optimal combination of risk factors. What really matters is how this algorithm and these variables interact with one another as a whole," said Jessica Ribeiro of Florida State University.

Scientists have used a unique computational technique that sifts through big data to identify a subset of concussion patients with normal brain scans, who may deteriorate months after diagnosis and develop confusion, personality changes and differences in vision and hearing, as well as post-traumatic stress disorder. This finding, which is corroborated by the identification of molecular biomarkers, is paving the way to a precision medicine approach to the diagnosis and treatment of patients with traumatic brain injury. Investigators headed by scientists at UC San Francisco and its partner institution Zuckerberg San Francisco General Hospital and Trauma Center (ZSFG) analyzed an unprecedented array of data, using a machine learning tool called topological data analysis (TDA), which "visualizes" diverse datasets across multiple scales, a technique that has never before been used to study traumatic brain injury. TDA, which employs mathematics derived from topology, draws on the philosophy that all data has an underlying shape. It creates a summary or compressed representation of all the data points using algorithms that map patient data into a multidimensional space.

If you want to know the future of business consulting, just look to the firms and universities at the forefront of a revolution in technology and a transformation in recruiting. I write those words as both a means of advice, to readers, and as an admonition, to companies. Far too many businesses are not ready for this change -- too few are even aware that change is afoot -- so preparing for this event is critical to the health of the economy and the expansion of the workforce as a whole. I refer, specifically, to the rise of artificial intelligence (A.I.) and the proliferation of big data. According to Braden Kelley, an innovation and consulting expert, a good way to understand this shift is to review the key concepts presented in this image of The Knowledge Funnel, a concept Roger Martin introduced in his book The Design of Business.

Machine learning has the potential to solve many of our regular human problems, like for instance having too few nightmarish, oddly cat-filled crude images to gaze upon. Luckily, Christopher Hesse created the edges2cats web-based tool to address exactly that issue. The machine learning software uses Google's Tensorflow to translate one image to another, using training data provided by a database of over 2,000 stock images of cats to identify edges and fill in simple line art with what it estimates would be the best approximation of a realistic cat coloring of what it wrongly assumes, because of the limitations set upon it by its cruel creator, must be a cat. Hesse himself understates the impact of this significantly, writing that "some of the pictures look especially creepy," and he theorizes that's because we have a very good idea of what animal faces especially should look like, anything that features eyes in particular (like my own example above, with the source material provided unwittingly by my colleague) turns out especially horrifying. We're probably only at the outer edge of the many-layered onion of fresh horrors made possible by machine learning and AI, and previously unimagined by our feeble fleshy brains, so strap in.

Amazon.com has launched a new program to help students build capabilities into its voice-controlled assistant Alexa, the company told Reuters, the latest move by a technology firm to nurture ideas and talent in artificial intelligence research. The e-commerce company said it is paying for a year-long doctoral fellowship at four universities for an undisclosed sum. Working with professors, the Alexa Fund Fellows will help students tackle complex technology problems in class on Alexa, like how to convert text to speech or process conversation. Amazon (amzn), Alphabet Inc's Google (goog) and others are locked in a race to develop and monetize artificial intelligence. Unlike some rivals, Amazon has made it easy for third-party developers to create skills for Alexa so it can get better faster--a tactic it now is extending to the classroom.

Scientists have used a unique computational technique that sifts through big data to identify a subset of concussion patients with normal brain scans, who may deteriorate months after diagnosis and develop confusion, personality changes and differences in vision and hearing, as well as post-traumatic stress disorder. This finding, which is corroborated by the identification of molecular biomarkers, is paving the way to a precision medicine approach to the diagnosis and treatment of patients with traumatic brain injury. Investigators headed by scientists at UC San Francisco and its partner institution Zuckerberg San Francisco General Hospital and Trauma Center (ZSFG) analyzed an unprecedented array of data, using a machine learning tool called topological data analysis (TDA), which "visualizes" diverse datasets across multiple scales, a technique that has never before been used to study traumatic brain injury. TDA, which employs mathematics derived from topology, draws on the philosophy that all data has an underlying shape. It creates a summary or compressed representation of all the data points using algorithms that map patient data into a multidimensional space.

It's safe to say the terms artificial intelligence (AI) and data science to say these terms have firmly made their way from the big screen and into the real world. There's never been more data at people's fingertips – and our enthusiasm and ability to make use of it grows every day. With more data potential, the next logical step for businesses is to focus on how we navigate through it, and understand how machines can help us use it. Every part of an organisation's infrastructure, no matter what sector, creates data – from metrics on machine performance, to software and customer interactions. Everyone has heard the hype that surrounds AI and how it's changing business, but why has this hit the mainstream now?

A secretive Canadian startup called Kindred AI is teaching robots how to perform difficult dexterous tasks at superhuman speeds by pairing them with human "pilots" wearing virtual-reality headsets and holding motion-tracking controllers. The technology offers a fascinating glimpse of how humans might work in synchronization with machines in the future, and it shows how tapping into human capabilities might amplify the capabilities of automated systems. For all the worry over robots and artificial intelligence eliminating jobs, there are plenty of things that machines still cannot do. The company demonstrated the hardware to MIT Technology Review last week, and says it plans to launch a product aimed at retailers in the coming months. The long-term ambitions are far grander.

You're sitting at home minding your own business when you get a call from your credit card's fraud detection unit asking if you've just made a purchase at a department store in your city. It wasn't you who bought expensive electronics using your credit card – in fact, it's been in your pocket all afternoon. So how did the bank know to flag this single purchase as most likely fraudulent? Credit card companies have a vested interest in identifying financial transactions that are illegitimate and criminal in nature. According to the Federal Reserve Payments Study, Americans used credit cards to pay for 26.2 billion purchases in 2012.

Voice assistants are software programs that respond to voice commands in order to perform a range of tasks. They can find an opening in a consumer's calendar to schedule an appointment, place an online order for tangible goods, and act as a hands-free facilitator for texting, among many, many other tasks. Technological advances are making voice assistants more capable. These improvements fall into two categories: improvements in AI, specifically natural language processing (NLP) and machine learning; and gains in computing and telecommunications infrastructure, like more powerful smartphones, better cellular networks, and faster cloud computing. Changes in consumer behavior and habits are also leading to greater adoption.