Talk of artificial intelligence (AI) rising up to thwart humanity in a Terminator-style apocalypse is holding back research and development into the technology, and potentially harming society as a result, claims director of Microsoft Research Chris Bishop. The potential benefit from AI, such as the development of driverless cars and more efficient manufacturing, could revolutionize vast tracts of society, if only we could stop seeing it as something to fear. Talking ahead of the discussion about machine learning at the Royal Society in London, Bishop told the Guardian that humanity "may end up throwing the baby out with the bathwater" if too much of our attention is focused on the negative aspects of AI as depicted by "The Terminator" and Skynet. He says that while he disagrees with the high-profile people who have spoken out against AI, there are still some potential risks, though not in the humanity-ending way that is often depicted. Instead, he says it is likely that AI will develop potentially harmful biases.

Toyota, the world's largest automaker, is sending 5,000 connected cars that can wirelessly communicate with other vehicles and infrastructure onto the streets of Ann Arbor, Mich., in a real-world experiment designed to move autonomous driving closer to reality. Toyota is partnering with the University of Michigan's Transportation Research Institute on the project. Drivers who agree to participate in the tests will have a small data-collecting box installed in the trunk area of their car and two small antennas -- one on or near the rear windshield and another on the roof. The equipment transmits speed and position data from the vehicle to other participating vehicles as well as infrastructure such as traffic signals and research equipment located at intersections or along roadways. Drivers won't be required to take specific routes.

Jack Dashwood is the marcom director for computer vision company Movidius. There are an estimated 30 million surveillance cameras in the U.S. today. Out of these 30 million cameras, only 5 percent are monitored by a human at any given time. Instead, the majority of them are simply recording footage, providing little value other than evidence long after any kind of crime or accident has occurred. What this means is that today's "security" systems are mostly just a vast network of evidence collection devices, constantly recording and dumping data into hard drives, only to be retrieved after something regrettable has happened.

And now, the technology is being harnessed to streamline the United States Air Force acquisitions process -- an effort that will be undertaken by a small Lawrence business. Lawrence-based KalScott Engineering Inc. announced Wednesday it was selected to finish building artificial intelligence, called SOPHIA, for the Air Force. KalScott, owned by two Kansas University graduates, was chosen last summer to do some preliminary work training a supercomputer -- or "cognitive thinking machine" -- that could understand Defense Department contracting rules and answer questions about them audibly. Suman Saripalli, a co-owner of KalScott, said Wednesday the firm received the final go-ahead to complete the work. The announcement came with 750,000 more in funding. The finished product -- which will act as a "very intelligent assistant," Saripalli said -- is intended to help businesses and government employees navigate the Air Force's complex procurement process, which has been found to discourage small and innovative businesses from partnering with the federal government.

Japan's Sharp will launch in May a smartphone that's built into a humanoid robot. Or is it a humanoid robot with a built-in smartphone? The Robohon is said to be the world's first mobile robotic phone--and judging from the price and slightly unwieldy form factor could also be the last. It's 19.5 centimeters (7.7 inches) tall and weighs 390 grams (13.8 ounces), making it several times the size and weight of a conventional smartphone, and it will cost 198,000 yen, which is just over 1,800 and more than double the price of a high-end iPhone. But those shortcomings are more than made up in cuteness.

After guessing your age, classifying dog breeds, and finding celebrity likenesses, Microsoft researchers have launched a new tool for identifying the contents of photos. With CaptionBot, users can upload any photo, and Microsoft will use various recognition services to describe what's happening. This includes identifying celebrities, recognizing emotions, and describing basic objects that appear in the scene. We've seen this type of party trick before. Last year, Wolfram Alpha released a similar tool, which remains available at ImageIdentify.com.

In a meeting with a CIO and her team recently, I mentioned the term "deep learning" in the context of some big data and machine learning initiatives the CIO had asked me to investigate. This particular CIO is fairly savvy when it comes to big data, machine learning, and data analysis, but she stopped me mid-sentence to ask me to explain what I meant when I used the term "deep learning." We spent the next one-and-a-half hours walking through the basics of artificial intelligence, machine learning, and how this organization could incorporate these great approaches into their business. With the growth of big data and data science, I come across a lot of questions and discussions about machine learning, but I rarely come across discussions about deep learning and the value it can bring to an organization. First, it's important to understand the basis for deep learning.

Here are five ways a human can combat risks in machine learning applications. An ethical hacker is a trusted security professional who breaks into a system to discover machine learning vulnerabilities overlooked by a firewall, an intrusion detection system or any other security tools. In a simple scenario of gaining access, the ethical hacker uses a fake finger reconstructed from the fingerprint left behind by a legitimate user on a dirty device. To combat these risks, the device reader must be free of dirt, grease and moisture after each use and the database should be encrypted. A system administrator has super user privileges to analyze machine learning log files.

But while we've been traditionally involved in providing you with the data that you need, we are now taking it a step further by helping you analyze it as well. To this end, we'd like to officially announce the MonkeyLearn integration for Scrapy Cloud. This feature will bring machine learning technology to the data that you extract through Scrapy and Portia. MonkeyLearn is a classifier service that lets you analyze text. It provides machine learning capabilities like categorizing products or sentiment analysis to figure out if a customer review is positive or negative.

This blog post is authored by T. J. Hazen, Principal Data Scientist at Microsoft, and Patrick Buehler, Senior Data Scientist at Microsoft. The Masai giraffe is facing a troubling future. Because of habitat loss, illegal hunting and disease, the population of wild giraffes in Africa has declined to only 80,000. Scientists from the Wild Nature Institute are studying where the giraffe population is doing well and where they are faring poorly. By studying and understanding the natural and human-caused factors that affect giraffe survival, reproduction and movements, scientists can determine which conservation actions best protect giraffe populations.