Emotional intelligence is a hard thing to achieve, even in humans. But it's especially difficult in robots, and maybe even more so in chatbots, whose available tools for picking up on emotional cues are quite limited. Emotibot is a Chinese startup focused on making sure personal assistants, chatbots and virtual customer service agents can accurately understand the emotional state of people they're dealing with. Having someone respect your emotions while dealing with any kind of customer service interaction, you might know from personal experience, can make a huge difference in quality of service. Bots right now are pretty emotionally stunted; some can crack jokes, pulling from a pre-programmed pool of one-liners, but few, if any, can detect that you might not be in the best of moods for hearing a wisecrack and instead opting for a more sympathetic response.

In the past few years, the market for artificial intelligence (AI) and machine learning technologies has gained strong momentum. What's interesting, though, is that the much of the innovation in this space is driven by disruptors, not legacy vendors. These'upstarts' are companies born in the internet age or companies that have transitioned into the AI market, and are building out useful AI products that will likely broaden in their impact over time. Much like how Amazon Web Services (AWS) became the infrastructure provider of choice for many companies with the rise of the public cloud, many of these upstarts will see their products widen in application. While many of these companies might have originally created their product for a specific use case, it's possible that they will grow into platforms on which the company may build an additional revenue stream.

MIT's AI learned to recognize faces just as the humans do Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

In recent years, computers have gotten remarkably good at recognizing speech and images: Think of the dictation software on most cellphones, or the algorithms that automatically identify people in photos posted to Facebook. But recognition of natural sounds--such as crowds cheering or waves crashing--has lagged behind. That's because most automated recognition systems, whether they process audio or visual information, are the result of machine learning, in which computers search for patterns in huge compendia of training data. Usually, the training data has to be first annotated by hand, which is prohibitively expensive for all but the highest-demand applications. Sound recognition may be catching up, however, thanks to researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL).

It's no secret that I love technology. From the domination of mobile to the latest in recruitment tools and gamification, and how video and live streaming is having an impact on hiring and training--changes are afoot that many of us couldn't have imagined 15 or so years ago. The reason this "tech meets HR" marriage is so exciting is how quickly the technology evolution has disrupted HR and enhanced the way HR professionals get things done. Now there's another big disrupter on the horizon, one that you would be wise to keep your eyes on: Artificial intelligence. In layman's terms, artificial intelligence (or AI) is an area of computer science where computers are "developed" to behave much the way humans do.

Going down the list, Google first announced a way for companies to effectively rent their own machine learning processor next year, a machine learning GPU that's linked into Google Cloud:

What You'll Learn / What to Expect form this talk: - Uber's self driving car program - Examples of machine learning solving tough problems at top companies - A brief history of artificial intelligence - Where machine learning and artificial intelligence is headed - The Feedback Loop: Dogfights and Bandits - Multi-armed Bandit - The Key Tenets of Machine Learning at Uber - Machine Learning as a service at Uber - Resources for implementing machine learning as a service About Danny Lange: Danny Lange is the Head of Machine Learning at Uber. He runs the machine learning platform team - providing machine learning as a service offered across Uber. Previously, Danny ran the machine learning platform team at Amazon. He lead the launch of machine learning as a public service on AWS. Before Amazon, Danny ran the machine learning tools team at Microsoft and did the first push of ML tools into the Azure cloud as a service.

Driven by a declining population, a trend for developing robotic babies has emerged in Japan as a means of encouraging couples to become "parents". The approaches taken vary widely and are driven by different philosophical approaches that also beg a number of questions, not least whether these robo-tots will achieve the aim of their creators. To understand all of this it is worth exploring the reasons behind the need to promote population growth in Japan. The issue stems from the disproportionate number of older people. Predictions from the UN suggest that by 2050 there will be about double the number of people living in Japan in the 70-plus age range compared to those aged 15-30.

MIT's AI learned to recognize faces just as the humans do Microsoft's AI will describe images in Word and PowerPoint for blind users Facebook is developing AI to bust'offensive' Live video: report Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

More below Recently, you may have noticed people sharing stylized photos and videos that resemble famous paintings, like "Starry Night" by Van Gogh and "The Scream" by Munch. But how do the apps that make these images work? How can the style of a painting be transferred to a selfie or a photo of your dog? Turns out, a whole lot of A.I. using machine learning and deep neural networks. In this video, we break down the basics of how style transfer works, and demo some recent style transfer experiments created by research scientists at Google. In addition to internal Google experiments and tools, some style transfer images and videos created using these apps: http://prisma-ai.com/ What are you curious about?