Researchers from the University of Illinois at Urbana-Champaign think mimicking nature is the best way to help robots take flight. So with the help of a 3D printer they've created a flying robotic bat complete with a layer of fake silicone skin for the wing's membranes. It somehow manages to make real bats look almost cute in comparison. Weighing in at just 92 grams thanks to a carbon fiber airframe, the Bat Bot--or B2, for short--uses five motors on board to control the flapping and articulation of its wings. It's intelligent, too, with an onboard microprocessor and sensors that allow it navigate a room without the need for an exterior motion capture system keeping it out trouble.

Like electricity transformed everything we do, artificial intelligence will reshape our world. AI, essentially intelligent machines, could change industries from retail to finance to transportation. That will change our lives, said a panel of experts Monday discussing "The State of AI" at the EmTech Digital Conference in San Francisco. And just how all companies use the Internet, they may need to start expanding their data teams. Three of the biggest experts in artificial intelligence, Andrew Ng, Peter Norvig and Oren Etzioni, say despite its recent boom, AI still has a long way to go.

Machine learning is accomplishing Facebook's mission of connecting the world across language barriers. Facebook is now serving 2 billion text translations per day. Facebook can translate across 40 languages in 1,800 directions, like French to English. And 800 million users, almost half of all Facebook users, see translations each month. That's all based on Facebook's own machine learning translation system.

Developers and press gathered today at the Shoreline Amphitheater in Mountain View, California, for the annual ritual known as Google I/O. Are you ready for a Google overdose? Here's everything the company announced during its most important event of the year: Google launched its latest Android N developer preview today -- the first one to receive "beta-quality" status. Developers can start testing their apps for this release by downloading the new preview here. The factory images should arrive shortly for the following supported devices: Nexus 5X, Nexus 6, Nexus 6P, Nexus 9, Nexus 9 LTE, Nexus Player, General Mobile 4G, and Pixel C. Remember the rumor suggesting Google may use an online poll to name Android N? Well, it turns the rumor was half-correct: It's more of a suggestion box than a poll. Google wants to hear your what you've got at android.com/n.

If you look at the record, America has been concerned about not alienating Pakistan for the last 10 years or more. But the Afghans are clear about this, and the Americans know that one of the reasons the Taliban insurgency has been resilient is that they have sanctuaries across the border in Pakistan, in Balochistan. I think the Americans were thinking that Pakistan would probably deliver the Taliban, because they had influence over them. Pakistan claims they have control, but then they say, "well, we don't have full control, we can bring them to the table but we can't make them do things unless you promise something concrete."

SeaDrone, the underwater robot coming out of a new company founded by two Stanford AI lab veterans, is aiming to make fish farming a lot easier--particularly for smaller aquaculture operations--by making underwater inspection cheaper and easier. The ocean ROV's story is not an unusual one for Silicon Valley: two Stanford students meet over a lab bench, get an idea that something they'd been tinkering around with for themselves could be turned into a product and the basis of a company. It's a story Silicon Valley loves. Eduardo Moreno met Shuyun Chung in the Stanford AI lab in 2013. Moreno, in the thick of his studies for a master's degree in mechanical engineering, was working on underwater robot hardware in collaboration with King Abdullah University of Science and Technology in Saudi Arabia.

This is the fourth part in'A Brief History of Neural Nets and Deep Learning'. In this part, we will get to the end of our story and see how deep learning emerged from the slump neural nets found themselves in by the late 90s, and the amazing state of the art results it has achieved since. "Ask anyone in machine learning what kept neural network research alive and they will probably mention one or all of these three names: Geoffrey Hinton, fellow Canadian Yoshua Bengio and Yann LeCun, of Facebook and New York University."1 When you want a revolution, start with a conspiracy. With the ascent of Support Vector Machines and the failure of backpropagation, the early 2000s were a dark time for neural net research. LeCun and Hinton variously mention how in this period their papers or the papers of their students were routinely rejected from being published due to their subject being Neural Nets.

At Twitter Inc., Hugo Larochelle's job is to develop an understanding of how users of the social network are connected to each other and what interests them in order to categorize and promote content that includes tweets, images and videos. To help accomplish that, he and his fellow data analysts use an emerging technology: deep learning tools. As Larochelle, a research scientist at Twitter, explained during a presentation at the Deep Learning Summit in Boston this month, deep learning is a category of machine learning that seeks to understand complex problems, such as interpreting images or text-based natural language. He and other proponents say deep learning techniques -- which lean heavily on the use of neural networks -- are more useful than traditional machine learning when data analytics applications involve unstructured data or require subjective interpretations. And deep learning is quickly becoming a hot field in the realm of advanced data analytics.

Losing jobs to technology is nothing new. Since the industrial revolution, roles that were once exclusively performed by humans have been slowly but steadily replaced by some form of automated machinery. Even in cases where the human worker is not completely replaced by a machine, humans have learnt to rely on a battery of machinery to be more efficient and accurate. A report from the Oxford Martin School's Programme on the Impacts of Future Technology said that 47% of all jobs in the US are likely to be replaced by automated systems. Among the jobs soon to be replaced by machines are real estate brokers, animal breeders, tax advisers, data entry workers, receptionists, and various personal assistants. But you won't need to pack up your desk and hand over to a computer just yet, and in fact jobs that require a certain level of social intelligence and creativity such as in education, healthcare, the arts and media are likely to remain in demand from humans, because such tasks remain difficult to be computerised.

When AlphaGo, Google's artificial intelligence program, defeated champion Go player Lee Sedol earlier this year, everyone praised its advanced software brain. But the program, developed by Google's DeepMind research team, also had some serious hardware brawn standing behind it. The program was running on custom accelerators that Google's hardware engineers had spent years building in secret, the company said. With the new accelerators plugged into the AlphaGo servers, the program could recognize patterns in its vast library of game data faster than it could with standard processors. The increased speed helped AlphaGo make the kind of quick, intuitive judgments that define how humans approach the game.