"Our research with DeepMind has the potential to revolutionise the way professionals carry out eye tests and could lead to earlier detection and treatment of common eye diseases such as age-related macular degeneration," said Professor Sir Peng Tee Kaw, the head of Moorfields' ophthalmology research centre. DeepMind, which Google paid 400 million to acquire two years ago, hopes to use artificial intelligence to advance medical and climate research after its software defeated the world champion at the ancient Chinese board game Go.

To inform the Open Philanthropy Project's investigation of potential risks from advanced artificial intelligence, and in particular to improve our thinking about AI timelines, I (Luke Muehlhauser) conducted a short study of what we should learn from past AI forecasts and seasons of optimism and pessimism in the field. In addition to the issues discussed on our AI timelines page, another input into forecasting AI timelines is the question, "How have people predicted AI -- especially HLMI (or something like it) -- in the past, and should we adjust our own views today to correct for patterns we can observe in earlier predictions?"1 We've encountered the view that AI has been prone to repeated over-hype in the past, and that we should therefore expect that today's projections are likely to be over-optimistic. To investigate the nature of past AI predictions and cycles of optimism and pessimism in the history of the field, I read or skim-read several histories of AI2 and tracked down the original sources for many published AI predictions so I could read them in context. I also considered how I might have responded to hype or pessimism/criticism about AI at various times in its history, if I had been around at the time and had been trying to make my own predictions about the future of AI. I can't easily summarize all the evidence I encountered that left me with these impressions, but I have tried to collect many of the important quotes and other data below. Then, in a final subsection, I summarize some questions I might have investigated if I had more time. I would be curious to learn whether people who read a set of sources similar to the set I consulted come away from that exercise with roughly the same impressions impressions I have. I would also be curious to hear how many AI scientists who were active during most of the history of the field share my impressions. The histories I read left me with the impression that some (but not all) of the earliest AI researchers -- starting around the time of the Dartmouth Conference in 1956 -- thought HLMI (or something like it) might only require a couple decades of work. For example, Moravec (1988) claims that John McCarthy founded the Stanford AI project in 1963 "with the then-plausible goal of building a fully intelligent machine in a decade" (p.

A six-week-long research accelerator, championed by NASA's Office of the Chief Technologist and hosted at the SETI Institute, is engaging young researchers from around the world to take on one of the truly existential threats to our species. The NASA Frontier Development Lab (FDL) is bringing together a team of postgraduate researchers in data analytics and planetary science and challenging them to think outside the box on the threat of asteroid impacts. The initiative is under the aegis of experts from the space agency and the SETI Institute, with deep-learning expertise contributed by NVIDIA and Autodesk. Asteroids that collide with Earth are one cosmic danger that it's now possible to mitigate. In 2013, NASA's Asteroid Grand Challenge charged participants with identifying all possible asteroid threats, and determining what to do about them. FDL co-director, James Parr, describes the concept: "Grand challenges, such as detecting and characterizing the potentially hazardous asteroids we can't see, demand ingenious new applications of emerging technologies.

This isn't to say that the NTSB is thinking about banning or limiting self-driving car tech, assuming it calls for any changes once its investigation is over. As Bloomberg notes, the board has long asked for more automation, since it can prevent collisions that humans would make. However, there's a real chance that it might ask for widespread use of advanced sensors (such as lidar), stricter testing methods or other approaches that could minimize the chances of a crash.

The Duchess of Cambridge, Kate Middleton, will not only make you fall in love with her timeless charms and classic beauty. Her sense of humor and fun personality would make you understand why Prince William fell in love with her. Middleton showed the crowd her funny side during a gala event by one of their friends held on June 22. An interesting reason behind her humor might shock most of the people. People Magazine reported how Kate Middleton kid around the chefs of the said event saying Prince William is patient enough to put up with her cooking.

"Nature hath made men so equal, in the faculties of the body, and mind; as that though there be found one man sometimes manifestly stronger in body, or of quicker mind than another; yet when all is reckoned together, the difference between man, and man, is not so considerable, as that one man can thereupon claim to himself any benefit, to which another may not pretend as well as he." This peculiar thought--that, in the most important respects, and despite their manifest differences, all men are equal--has laid the intellectual foundation for democracy's unlikely triumph. But will society retain its belief in equality when it is no longer just man against man? Can democracy thrive when more and more benefits accrue to machines that are stronger in body, and quicker in mind, than any mere mortal? And will the machines' owners remain willing to honor the claims of their social inferiors when they no longer need them to make their food, or to staff their companies, or to fight their wars?

April Blackburn, CIO of the Florida Department of Transportation, discussed the opportunities for cognitive analytics and connected devices across the state's IT systems. The agency is already plotting the course for a tech-centric strategic plan and is involved in data sharing with private industry to improve transit in the state. Eyragon Eidam is the assistant news editor for Government Technology magazine, and covers legislation, social media and public safety. He can be reached at eeidam@erepublic.com.

A Tesla Model S electric vehicle is shown in San Francisco, California, U.S., April 7, 2016. How much do we really know about what so-called self-driving vehicles can and cannot do? The fatal traffic accident involving a Tesla Motors car that crashed while using its Autopilot feature offers a stark reminder that such drivers are in uncharted territory--and of the steep cost of that uncertainty. The sensor systems that enable Tesla's hands-free driving are the result of decades of advances in computer vision and machine learning. Yet the failure of Autopilot -- built into 70,000 Tesla vehicles worldwide since October 2014 -- to help avoid the May 7 collision that killed the car's sole occupant demonstrates how far the technology has to go before fully autonomous vehicles can truly arrive.

Deftr, legal tech company, and Y Combinator Fellowship recipient, today announced the launch of the first AI-powered tool that helps professionals diagram intricate corporate structures. The new product reads text as it is typed in real time, then automatically turns that text into a shareable, interactive graphic illustrating a corporate structure or transaction. Named for the dynamic tax ledger used by the Ottoman Empire, Deftr helps legal and professional service firms focus on high-level, strategic work by leveraging artificial intelligence to automate manual work. From tax law and IRS guidance to corporate structuring and financial regulations, we enable users to navigate legal complexity and understand its practical consequences intuitively, simply, and cheaply. For hundreds of years the Swiss watch had been considered to be the most accurate, trust-worthy and well-designed way of telling the time – the best way to convey that crucial information to a wearer.

The Air Force wants white papers that describe new ways quantum computing could help achieve its mission, according to an amended Broad Agency Announcement posted Friday. Eventually, the government could provide a test-bed where a contractor might install, develop and test a quantum computing system, according to the announcement. Last year, the Air Force announced it had about 40 million available to fund research into, and the eventual maintenance and installation of a quantum system -- a branch of emerging computing technology that relies on the mechanics of atomic particles to process complex equations. The Air Force Research Laboratory's Information Directorate, which focuses on processes such as signal processing, networking technology, cyber research and supercomputing, is collecting those white papers. Many problems that stump traditional computers can be re-framed as "optimization problems," the solicitation says; one of quantum computing's potential benefits is its ability to quickly find the optimum solution to a multidimensional problem, given certain constraints.