Can a chatbot help you manage your money? Three London-based fintech startups are betting on the answer being yes and have jumped aboard the messaging gravy train as they seek to entice millennials to their respective platforms. The thinking goes something like this: A conversational interface, coupled with tech that plugs into your bank account and analyses your spending in the background, is the best way to deliver financial assistance to help you keep track of your money and actually save for a rainy day. More broadly these chatbots are targeting millennials who, they claim, typically aren't as financially savvy as they could be and who are perfectly comfortable communicating entirely through emoji. Choosing to announce news or fully launch today are Plum, Chip, and Cleo, which is a little awkward.

Futurist Tom Cheesewright has set out his vision for non-executive boards, saying not every member will be human. Speaking at the launch event for In Touch Networks, formally Directors Online Network, Cheesewright said there will be an element of A.I in years to come. The founder of applied futurism practice, "Book of the Future" addressed a gathering of 30 attendees at the company's new Peninsula building offices. Delving into what the future holds for company boards he said: "Successful organisations are like athletes. They need to see what is coming, be really fit and have the right reflexes to respond."

King Midas learned the hard way what happens if you don't specify exactly what you want. The emergence of general artificial intelligence could be as significant for humanity as the agricultural or industrial revolutions. But humans need to take steps early on to make sure that these AIs are built in a way which makes them helpful rather than harmful. According to Professor Nick Bostrom, a leading philosophers on artificial intelligence and founding director of Oxford University's Future of Humanity Institute, there's a way in which humans can avoid becoming slaves to machines of superior intelligence: by designing from the very beginning to ensure they're going to act in the interest of the human race. This doesn't mean we need to "tie its hands behind its back and hold a big stick over it in the hope we can force it to our way" but rather that we must "build it in such a way that it's on our side and wants the same things as we do".

A former cabinet minister has likened Russia's role in Syria to the Nazi regime in 1930s Spain, ahead of an emergency Commons debate on the humanitarian situation in Aleppo. Andrew Mitchell accused Russia of "shredding" international law with its bombing campaign in the country. The Tory MP also accused Russian forces of committing a war crime by attacking a UN relief convoy last month. The three-hour emergency debate will be held later in the day. The northern city of Aleppo has become a key battleground in Syria's bloody five-year civil war.

If you already tried to upload some of your pictures to OneDrive, you may be aware that Microsoft's cloud storage service is able to automatically tag your photos and categorize them, group them by location, and more. By adding more data to user-generated content, Microsoft's artificial intelligence tools also make it easier for OneDrive users to find relevant pictures using OneDrive's search feature. But could artificial intelligence accomplish the same sort of magic with video content? That's exactly what Chia-Wen Lin and Min Sun, professors in the Electrical Engineering department of National Tsinghua University in Taiwan, are trying to do. In a new blog post on the Microsoft Research blog, the company explains that both professors partnered in 2015 with Dr. Tao Mei, lead researcher in multimedia at Microsoft Research Asia who worked on a new image recognition, segmentation, and captioning dataset called COCO (Common Objects in Context). Professor Sun created a video title generation method based on deep learning to automatically find the special moments--or highlights--in videos, and generate an accurate and interesting title for the highlights.

The people who know me know I'm not big on rocking the boat, but I just can't get this thought out of my mind. I often chew on the future and this post is about the future of the job market and our very survival as employees. It's an incredibly important issue, so I had to get your attention. I should pump the brakes a little before everyone thinks I've lost my mind. I used to think that.

MOSCOW--When Russian clubgoers flocked to the country's biggest electronic music festival this summer, they didn't have to bring a camera or even their phones. Instead, festival organizers used facial-identification technology to pick out revelers and send them their pictures directly to their phone. All they needed to do was opt in, by sending a selfie. The technology is the product of NTechLab, a Moscow-based firm whose algorithm to identify facial features is getting attention in the broader information technology world. NTechLab co-founders Artem Kukharenko and Alexander Kabakov believe the possible uses of their technology are almost endless, and mostly positive: from allowing police to search for criminals in real time, to helping amusement parks identify and sell photos to their guests.

Sometimes it's the simplest studies that reveal how deeply culture shapes our thinking. Take a 2009 experiment involving only a researcher, a child, and a two-word instruction.1 The researcher announces, "Let's dance!" and demonstrates a series of movements: He holds his hands together at eye level and extends them--first to the left, then to the right, then to the left twice, counting with each movement ("One, two, three, four!"). After a few tries, eventually all the children could do the dance on their own. Now comes the test: The researcher spins the child around, to face the other way, and asks her to perform it again.

We introduce a Maximum Entropy model able to capture the statistics of melodies in music. The model can be used to generate new melodies that emulate the style of the musical corpus which was used to train it. Instead of using the $n-$body interactions of $(n-1)-$order Markov models, traditionally used in automatic music generation, we use a $k-$nearest neighbour model with pairwise interactions only. In that way, we keep the number of parameters low and avoid over-fitting problems typical of Markov models. We show that long-range musical phrases don't need to be explicitly enforced using high-order Markov interactions, but can instead emerge from multiple, competing, pairwise interactions. We validate our Maximum Entropy model by contrasting how much the generated sequences capture the style of the original corpus without plagiarizing it. To this end we use a data-compression approach to discriminate the levels of borrowing and innovation featured by the artificial sequences. The results show that our modelling scheme outperforms both fixed-order and variable-order Markov models. This shows that, despite being based only on pairwise interactions, this Maximum Entropy scheme opens the possibility to generate musically sensible alterations of the original phrases, providing a way to generate innovation.

ABSTRACT Extracting information from functional magnetic resonance (fMRI) images has been a major area of research for more than two decades. The goal of this work is to present a new method for the analysis of fMRI data sets, that is capable to incorporate a priori available information, via an efficient optimization framework. Tests on synthetic data sets demonstrate significant performance gains over existing methods of this kind. Index Terms -- fMRI Data Analysis, Dictionary Learning, Blind Source Separation 1. INTRODUCTION Functional magnetic resonance imaging (fMRI) is a powerful noninvasive technique suitable to providing important information concerning the brain activity. Studying the different areas in the brain that correspond to important tasks such as vision, perception, recognition, etc., constitutes a major open area of research, demanding robust and high precision techniques for the analysis of fMRI data analysis [1], [2], [3], [4].