Arthur Renowitzky can't help but command attention as he walks down the street on a sunny autumn morning. A driver lowers her window to flash a smile and a thumbs-up. "You got this," she says. Renowitzky has been paralyzed since 2007 after being shot in the chest for $20 and a fake gold chain. But he can stand and walk, using crutches for balance, when wearing an exoskeleton suit with motorized hips and knees powering his movements.

Four billion years ago, Earth was a lifeless place. Nothing struggled, thought, or wanted. Seawater leached chemicals from rocks; near thermal vents, those chemicals jostled and combined. Some hit upon the trick of making copies of themselves that, in turn, made more copies. The replicating chains were caught in oily bubbles, which protected them and made replication easier; eventually, they began to venture out into the open sea. A new level of order had been achieved on Earth. The tree of life grew, its branches stretching toward complexity. Organisms developed systems, subsystems, and sub-subsystems, layered in ever-deepening regression. They used these systems to anticipate their future and to change it. When they looked within, some found that they had selves--constellations of memories, ideas, and purposes that emerged from the systems inside. They experienced being alive and had thoughts about that experience. They developed language and used it to know themselves; they began to ask how they had been made. This, to a first approximation, is the secular story of our creation. It has no single author; it's been written collaboratively by scientists over the past few centuries. If, however, it could be said to belong to any single person, that person might be Daniel Dennett, a seventy-four-year-old philosopher who teaches at Tufts. In the course of forty years, and more than a dozen books, Dennett has endeavored to explain how a soulless world could have given rise to a soulful one. His special focus is the creation of the human mind.

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.

Anyone who's visited the New York Stock Exchange lately knows technology has already taken a toll on Wall Street jobs. And the decimation is only going to continue as the artificial intelligence industry booms. By 2025, AI technologies will reduce employees in the capital markets worldwide by 230,000 people, according to a report from Opimas that came out last week. Financial institutions may see a 28% improvement in their cost-to-income ratios. Additionally, financial firms will spend more than $1.5 billion this year on AI-related technologies and $2.8 billion annually by 2021, not including their investments in AI startups, the Opimas report estimated.

Deep learning uses layers of neural networks to look for patterns in data. When a single layer spots a pattern it recognizes, it sends this information to the next layer, which looks for patterns in this signal, and so on. So in face recognition, one layer might look for edges in an image, the next layer for circular patterns of edges (the kind that eyes and mouths make), and the next for triangular patterns such as those made by two eyes and a mouth. When all this happens, the final output is an indication that a face has been spotted. Of course, the devil is in the details.

The idea of thinking machines may evoke thoughts of a Terminator-esque era, but the fact is that artificial intelligence, to an extent, is already a part of our lives and its presence is only set to grow. Artificial intelligence (AI) is a branch of computer science that deals with making computers simulate human intelligence. However technical and geeky that may sound, AI is a far less mundane technology than you might believe. Right from medical diagnoses to driverless cars, AI has fundamentally improved the way people consume a product, which is why marketers bet on it big time. A 2016 survey by Demandbase pointed out that over 80 percent of marketing executives believed that AI would revolutionise marketing by 2020.

Chatbots have now entered the world of the Indian financial services industry. Two life insurance companies, Birla Sun Life Insurance and HDFC Life, recently rolled out their own versions of chatbots. Among the banks, HDFC Bank has its own electronic virtual assistant or EVA. First, there was robo-advisory for those looking for automated advice and a platform to invest in mutual funds (MFs), and now chatbots suggest insurance policies and answer customer queries. Chatbots could very well be here to stay.

For years, Facebook has been investing in artificial intelligence fields like machine learning and deep neural nets to build its core business--selling you things better than anyone else in the world. But earlier this month, the company began turning some of those AI tools to a more noble goal: stopping people from taking their own lives. But it's not just tech giants like Facebook, Instagram, and China's up-and-coming video platform Live.me who are devoting R&D to flagging self-harm. Doctors at research hospitals and even the US Department of Veterans Affairs are piloting new, AI-driven suicide-prevention platforms that capture more data than ever before. The goal: build predictive models to tailor interventions earlier.

Anyone who's visited the New York Stock Exchange lately knows technology has already taken a toll on Wall Street jobs. And the decimation is only going to continue as the artificial intelligence industry booms. By 2025, AI technologies will reduce employees in the capital markets worldwide by 230,000 people, according to a report from Opimas that came out last week. Financial institutions may see a 28% improvement in their cost-to-income ratios. Additionally, financial firms will spend more than $1.5 billion this year on AI-related technologies and $2.8 billion annually by 2021, not including their investments in AI startups, the Opimas report estimated.

The idea of thinking machines may evoke thoughts of a Terminator-esque era, but the fact is that artificial intelligence, to an extent, is already a part of our lives and its presence is only set to grow. Artificial intelligence (AI) is a branch of computer science that deals with making computers simulate human intelligence. However technical and geeky that may sound, AI is a far less mundane technology than you might believe. Right from medical diagnoses to driverless cars, AI has fundamentally improved the way people consume a product, which is why marketers bet on it big time. A 2016 survey by Demandbase pointed out that over 80 percent of marketing executives believed that AI would revolutionise marketing by 2020.