Google announced its Google Cloud Machine Learning Group to be led by two machine-learning experts: Fei-Fei Li and Jia Li. The group will focus on delivering cloud-based machine learning software to businesses. The new group evolves from Google's Cloud Machine Learning alpha application it launched in March. In conjunction with announcing the new group, Google also introduced the new Google Cloud Jobs API to help people advance their careers. "Over the past year, Google has developed a new machine-learning model that has the potential to greatly improve the recruitment efforts of any company," writes Rob Craft, group lead for Google Cloud Machine Learning, in a corporate blog posting.

Google's Cloud Machine Learning service launched earlier this year and, already, the company is calling it one of its "fastest growing product areas." Today, the company is announcing a number of new features for Cloud Machine Learning users and developers who want to run their own machine learning workloads in Google's cloud. Unlike its competitors, like AWS and Azure, Google never offered developers access to virtual machines with high-end graphics processing units (GPUs). Machine learning (as well as a number of other specialized workloads, mostly in the sciences) heavily depends on GPUs to power the core algorithms that have made this technique so successful. Sadly, you'll have to wait a bit before you can get started with running your own machine-learning workloads on the Google Cloud Platform.

This week, our 5 bots to try list features three services that our Bots Channel readers may be familiar with. Two of them, Icon8bot and Azkarbot, were featured two weeks ago. And Octane AI, which makes its debut, became embroiled in a controversy shortly after launching, as reported by VentureBeat. But it's not enough to just read about them. Please give them a try and let us know what you think.

Oren Etzioni, a well-known AI researcher, complains about news coverage of potential long-term risks arising from future success in AI research (see "No, Experts Don't Think Superintelligent AI is a Threat to Humanity"). After pointing the finger squarely at Oxford philosopher Nick Bostrom and his recent book, Superintelligence, Etzioni complains that Bostrom's "main source of data on the advent of human-level intelligence" consists of surveys on the opinions of AI researchers. He then surveys the opinions of AI researchers, arguing that his results refute Bostrom's. It's important to understand that Etzioni is not even addressing the reason Superintelligence has had the impact he decries: its clear explanation of why superintelligent AI may have arbitrarily negative consequences and why it's important to begin addressing the issue well in advance. Bostrom does not base his case on predictions that superhuman AI systems are imminent.

A leading expert on the development of artificial intelligence spoke about where she thinks the future of AI is heading. The shift toward more autonomy will have a major impact on the job market, low skilled jobs will be more scarce and demand for highly skilled roboticists will skyrocket.

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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