This article was posted by Xiu-Shen Wei. Xiu-Shen Wei is a 2nd-year Ph.D. candidate of Department of Computer Science and Technology in Nanjing University and a member of LAMDA Group. Deep Neural Networks, especially Convolutional Neural Networks (CNN), allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-arts in visual object recognition, object detection, text recognition and many other domains such as drug discovery and genomics. In addition, many solid papers have been published in this topic, and some high quality open source CNN software packages have been made available.

ITEM: A researcher at Telenor Group Research says he has developed a machine learning algorithm that uses mobile phone call records to determine literacy rates in developing markets by location. According to MIT Technology Review, researcher Pål Sundsøy says he started with a regular survey – conducted by a professional agency for a mobile operator – covering 76,000 mobile users in Asia that collected the user's mobile number and asked them if they could read. Sundsøy matched that data against the mobile operator's call data records, which enabled him to work out where each user was, who they called, for how long, etc. Then he crunched 75% of the correlated data to detect patterns with illiterate users, and used the remaining 25% to see if those patterns could identify illiterate people and areas where there is a higher proportion of illiterate people. All in all, he says, his machine learning algorithm can spot illiterate individuals with surprising accuracy.

After months of delays and much anticipation, the Federal Aviation Administration finally released its final rule on small drones. The rule represents an important, long-delayed step toward establishing comprehensive regulations for flying small unmanned aerial systems and integrating them into the national airspace. Industry experts have argued for years that the United States risked falling behind in the burgeoning drone market and missing out on countless opportunities for growth. The new rule should expand the growth of the US commercial drone industry, catalyze American innovation in the global marketplace, and maintain the technological superiority of US military drones. But there's work left to do.

Big data in healthcare can now be measured in exabytes, and every day more data is being thrown into the mix in the form of patient-generated information, wearables and EHR systems. Traditional methods of analysis are no longer enough to handle, let alone take proper advantage of, the potential that healthcare data holds. This is where deep machine learning (or simply, "deep learning") comes in. However, its greatest power lies in its ability to extract value from data in ways that humans and traditional machine learning methods cannot. Deep machine learning has applications in a number of healthcare areas.

Big data in healthcare can now be measured in exabytes, and every day more data is being thrown into the mix in the form of patient-generated information, wearables and EHR systems. Traditional methods of analysis are no longer enough to handle, let alone take proper advantage of, the potential that healthcare data holds. This is where deep machine learning (or simply, "deep learning") comes in. However, its greatest power lies in its ability to extract value from data in ways that humans and traditional machine learning methods cannot. Deep machine learning has applications in a number of healthcare areas.

Arun Uday is a former VC and currently the founder of Tring Chat, a mobile group chat app that allows users to search and join chat groups of interest to them. Chatbots seem to be climbing the proverbial peak of the tech hype curve with every passing day. Popular messaging apps have been attempting to outdo each other in terms of making their platforms open for bot development. Businesses are also rushing to embrace them, as evidenced by the interest they are eliciting from even staid institutions such as centuries-old banks. The frenetic action notwithstanding, the moot point, as always, is whether bots actually solve any real end-user need.

Maitreya One, a black futurist and hip-hop artist living in Harlem, steps off the Greyhound bus on a warm morning in Montgomery, Alabama. I walk up to him and give him a hug. Maitreya is a civil rights link from the past to the future--and one of the few African-American transhumanists I know. He is stepping off one bus in Montgomery--whose roots are tied to the spectacular Freedom Riders who challenged segregation laws in the early 1960s--and onto another: the Immortality Bus, whose mission is to spread radical science and promote transhumanist rights. Like others in the burgeoning transhumanism movement, Maitreya supports becoming a cyborg in the future, and he knows the coming controversy over such aims may end up as challenging as the civil rights era battles over racism. To transhumanists--some who want to become new biological species and others who want to become machines--a new civil rights age is looming.

When someone sends me something interesting, I can't help but share it so this insight from Susan Visser came at just the right moment. Susan and I have exchanged various ideas over the years, so here's her view of the key Fintech trends. Data is having a tremendous impact on customer experience, and through enhanced insight is boosting customer profitability in the financial sector. Take a brief glimpse at six trends in which fintech innovation is steering imaginative and profound approaches to rich customer experiences for financial consumers. The biggest trend in fintech today is centered around improving customer experiences.

Technology and data made possible advances in...technology and data. JL The Economist reports: New techniques have made training deep networks feasible. This takes a lot of number-crunching power, which became available when several AI research groups realised that graphical processing units (GPUs), the specialised chips used in PCs and video-games consoles to generate fancy graphics, were also well suited to running deep-learning algorithms. HOW HAS ARTIFICIAL intelligence, associated with hubris and disappointment since its earliest days, suddenly become the hottest field in technology? The term was coined in a research proposal written in 1956 which suggested that significant progress could be made in getting machines to "solve the kinds of problems now reserved for humans…if a carefully selected group of scientists work on it together for a summer". That proved to be wildly overoptimistic, to say the least, and despite occasional bursts of progress, AI became known for promising much more than it could deliver.

Head chef robot "Andrew" flips Japanese pancakes during the "Kingdom of Robot" press preview at the Huis Ten Bosch amusement park on July 12 in Sasebo, Nagasaki, Japan. In the last 400 years or so, since the time of the scientific revolution, we have come to find it natural to suppose that the world is comprehensible. Nature and its laws, operating in things most small as well as in the cosmos as a whole, are understandable. And, yet, the biologist J.B.S. Haldane, quoted in Samuel Arbesman's intriguing new book coming out next week, Overcomplicated: Technology at the Limits of Comprehension, has written: "Now, my own suspicion is that the universe is not only queerer than we suppose, but it is queerer than we can suppose." It isn't just the world of physics that has come to seem so exceedingly strange -- at the level of the quantum and also that of the multiverse -- but consciousness itself.