Humans are already forming relationships with their artificial intelligence (AI) assistants, so we should make that technology as emotionally aware as possible by teaching it to respond to our feelings. That is the premise of Rana el Kaliouby, cofounder and CEO of Affectiva, an MIT spinout company that sells emotion recognition technology based on her computer science PhD, which she spent building the first ever computer that can recognise emotions. The machine learning-based software uses a camera or webcam to identify parts of human faces (eyebrows, the corners of eyes, etc), classify expressions and map them onto emotions like joy, disgust, surprise, anger, and so on, in real time. "We are getting lots of interest around chatbots, self-driving cars, anything with a conversational interface. If it's interfacing with a human it needs social and emotional skills. This tech is already being integrated into robots," el Kaliouby tells Techworld.

A study shows that almost 60% of people are willing to ride in a self-driving car, but that might be because we still fail to realize the real implications of putting our lives and the lives of others in the hands of an autonomous vehicle. You're in a self-driving car, cruising down the highway, when something goes wrong. Should the car save you or the people crossing the street? Should a self-driving car slam into a wall to save women, children, and the elderly? What if it's to save a couple of criminals instead?

In Boston, two major academic centers are teaming up to apply big data and machine learning to the problem of diagnosing cancers earlier and with more accuracy. It is research that might have major implications for the anatomic pathology profession. A collaborative effort between teams at Beth Israel Deaconess Medical Center (BIDMC) and Harvard Medical School (HMS) has resulted in an innovation that could result in more accurate diagnoses in the pathology laboratory. The teams have been working on a machine learning software program that will eventually function as an artificial intelligence (AI) to improve the accuracy of diagnostics. They hope to someday build AI-powered computer systems that can accurately and quickly interpret pathology images.

It's all well and good to ask if androids dream of electric sheep, but science fact has evolved to a point where it's beginning to coincide with science fiction. No, we don't have autonomous androids struggling with existential crises -- yet -- but we are getting ever closer to what people tend to call "artificial intelligence." Machine Learning is a sub-set of artificial intelligence where computer algorithms are used to autonomously learn from data and information. In machine learning computers don't have to be explicitly programmed but can change and improve their algorithms by themselves. Today, machine learning algorithms enable computers to communicate with humans, autonomously drive cars, write and publish sport match reports, and find terrorist suspects.

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Google says its Translate app now spits back more natural translations. Google said Tuesday it has vastly improved its Google Translate app, available on phones and the web. The search giant said it's now incorporating "neural machine translation" into the software, which means it can translate whole sentences at a time, instead of breaking the text down to smaller chunks and translating those pieces. The result is translations coming out more natural, with better syntax and grammar, Google said. "It has improved more in one single leap than in 10 years combined," Barak Turovsky, the product lead for Google Translate, said during a press event at Google's San Francisco office.

When you hear the term "artificial intelligence," you may think of tech giants Amazon, Google, IBM, Microsoft, or Facebook. Industrial powerhouse General Electric is now aiming to be included on that short list. It may not have a chipper digital assistant like Cortana or Alexa. It won't sort through selfies, but it will look through X-rays. It won't recommend movies, but it will suggest how to care for a diesel locomotive.

Not in my lifetime, says Google's cloud chief 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.

Facebook said that it has bought facial recognition startup FacioMetrics, potentially using the technology for photo or video effects to better challenge rival Snapchat. "How people share and communicate is changing and things like masks and other effects allow people to express themselves in fun and creative ways," a Facebook spokesperson said in an email reply to an AFP inquiry. "We're excited to welcome the FacioMetrics team who will help bring more fun effects to photos and videos and build even more engaging sharing experiences on Facebook." Silicon Valley-based Facebook did not disclose financial terms of the deal to buy FacioMetrics, which was spun out of Carnegie Mellon University in Pennsylvania. FacioMetrics was founded in 2015 and specializes in using artificial intelligence to give facial image analysis capabilities to applications that run on smartphones.