One of the most important takeways from Davos that quickly became widely spread news, was that the world was about to enter the fourth industrial revolution, resulting from a convergence of a number of big technology changes (autonomous vehicles, sensors, biotechnology, 3D printing, robotics, artificial intelligence). One of the most important technological disruption taking us fast to that extraordinary moment is Deep learning. Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using multiple processing layers, with complex structures or otherwise, composed of multiple non-linear transformations. Making an analogy with the way the brain works, deep-learning software tries to imitate what happens in our brains, more exactly in the layers of neurons in the neocortex, where thinking takes place. Ultimately deep learning software aims to recognize patterns in digital representations of sounds, images, and other data.

Yann LeCun is the Director of AI Research at Facebook since December 2013, and Silver Professor at New York University on a part time basis, mainly affiliated with the NYU Center for Data Science, and the Courant Institute of Mathematical Science.

Developing a computer that can be as decisive and intelligent as humans is on IBM's mind, and it's making progress toward achieving that goal. IBM's computer chip called TrueNorth is designed to emulate the functions of a human brain. The company is now running tests and benchmarking TrueNorth to demonstrate how fast and power efficient the chips can be compared to today's computers. The results of the head-to-head contest are impressive. IBM says TrueNorth can engage in deep learning and make decisions based on associations and probabilities, much like human brains.

Diego Oppenheimer is worried that the Googles and the Facebooks will dominate the world of artificial intelligence. Elon Musk and Sam Altman are worried about the same thing. That's why they created a startup called OpenAI. In recent years, Google and Facebook have snapped up so many researchers at the heart of the deep learning movement, an AI movement that's rapidly reinventing everything from speech recognition to security. So, Musk and Altman grabbed several top AI researchers from Google and Facebook and vowed to share their work with the world at large. Now, Oppenheimer and his startup, Algorithmia, are doing their part in the battle against AI hegemony.

Maluuba launched its first Siri-like personal assistant at TC Disrupt San Francisco four years ago. Since then, the company has raised 11 million and has licensed its technology to a number of handset manufacturers that now use it to power their own personal-assistant features. As Maluuba's head of product Mo Musbah told me, the company spent the last two years doubling down on how it could utilize deep learning in the context of natural language processing. To do so, it recently opened an R&D office in Montreal, for example. As Musbah told me, "our vision there is to build one of the largest deep learning labs in the world," so the company is definitely not lacking in ambition.

Which articles are considered the state-of-the-art within Dialogue systems (and/or Q&A)?

Chris Messina Medium "It's a rare moment when it becomes clear that a technological revolution is upon us, and I believe we're in the midst of one such transition right now. Even if you haven't realized it yet, bots are everywhere... With proper forethought and consideration, bots present a new, unpolluted opportunity to build lasting relationships with people." ROBOTICS: Do No Harm, Don't Discriminate: Official Guidance Issued on Robot Ethics Hannah Devlin The Guardian "The BSI document begins with some broad ethical principles: 'Robots should not be designed solely or primarily to kill or harm humans; humans, not robots, are the responsible agents; it should be possible to find out who is responsible for any robot and its behaviour.'...The code suggests designers should aim for transparency, but scientists say this could prove tricky in practice. 'The problem with AI systems right now, especially these deep learning systems, is that it's impossible to know why they make the decisions they do,' said Winfield."

Google has achieved something major in artificial intelligence (AI) research. A computer system it has built to play the ancient Chinese board game Go has managed to win a match against a professional Go player: the European champion Fan Hui. The research is documented in a paper in this week's issue of the journal Nature. The Google system, named AlphaGo, swept France's Hui, who is ranked a 2-dan, in a five-game match at the Google DeepMind office in London in October. AlphaGo played against Hui on a full 19-by-19 Go board and received no handicap.

Even today, a hacker with a command of artificial intelligence may be able to force a self-driving car to miss a stop sign, or a facial recognition system to believe it's seeing a completely different person in a security setting. Researchers have shown that virtual personal assistants like Siri or Google Now can be tricked into visiting potentially malicious websites by audio that sounds like white noise to humans. To thwart such hackers, Elon Musk's OpenAI and Pennsylvania State University released a new tool this week called "cleverhans," that lets artificial intelligence researchers test how vulnerable their AI is to adversarial examples, or purposefully malicious data meant to confuse the algorithms. Once the vulnerability has been found, a defense to the attack can automatically be applied. The tool is meant to be a "collection of attacks and defenses, along with tutorials on how to use them," according to Nicolas Papernot, co-creator and security researcher at Pennsylvania State University, in an email to Quartz.

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time.