Deep Learning is thus far a tale of two stories. The more publicized story is one of its step change performance that has astounded even longer-term term practitioners in the field. In trend prediction in IoT, and in face recognition and visual classification, Deep Learning hasn't beaten the competition so much as crushed it. The dark side of Deep Learning is the lack of a'shallow end of the pool' for IoT big data practitioners intending to dip their toes into it as an addition to their predictive analytics toolbox. Even a tentative foray into Deep Learning involves choosing between rapidly evolving (and competing) frameworks, and making non-trivial design choices (data sufficiency, data augmentation, network topology, guards against too slow or too fast learning rates to name a few) that are more art than science.

Artificial Intelligence will undoubtedly reshape the business, making our lives easier and more sufficient. AI is seen as an indispensable tool for supporting humans in every aspects of life. In future, it will be the driving force for Industrial revolution mainly driven by data, networks and computing power. "The two fundamental pillars of digital transformation for any organization- "Speed" and "Customer Centric Innovation" which are on the top of CXOs' minds. Every enterprise is dealing with two basic questions, "How fast can you innovate?" and "Can you innovate fast enough?" That said we see two broad technology trends answering the aforementioned questions emerging across the board, "Cloud Native" and "AI". On one hand, enterprises who are in the experimentation and migration stages of cloud adoption have realized that the benefits of cloud goes well beyond apex optimization to acceleration of contextual innovation. And on the other hand, we see widespread adoption of NLP and cognitive computing to provide augmented/assistive intelligence and personalized experiences to customers. With Millennial in focus for most enterprises, delivering personalization has become important now more than ever. CXOs expect AI and specifically deep learning to pave the path to achieve such targeted personalization" said Anup Nair, VP and CTO, Mphasis Digital.

"By far, the greatest danger of artificial intelligence is that people conclude too early that they understand it." Artificial intelligence (AI) is pushing the boundaries of human imagination. Machines today are capable of doing a lot of things that we could not imagine doing 20 years ago. AI has changed the way we look at learning and inventing. From drug discovery to sports analysis to protecting the oceans, AI has marked its presence everywhere.

Microsoft has created a new research lab which will focus on general-purpose artificial intelligence (AI) technology development. The lab will be called Microsoft Research AI, and will capitalize on the company's existing AI expertise while pursuing new hires from related fields like cognitive psychology. The lab will also seek out academic partnerships, including a formal collaboration with MIT's Center for Brains, Minds and Machines. Located within Microsoft's Redmond HQ, the lab will ultimately be home to a team of more than 100 AI scientists exploring learning, natural language processing, perception systems, and other areas. The purpose of combining these disciplines and striving toward more general AI will be to develop a single system that can master a broad array of challenges and tasks.

We talked yesterday of an example of how deep learning and artificial intelligence can be used to put words in people's mouths, creating video proof of something someone said, even if they didn't really say it. Prospects like that are downright scary, but so too are the realities of the jobs AI will be able to take away from humans.

The world's top researchers are pushing the boundaries of artificial intelligence at the NVIDIA AI Labs, known as NVAIL, located at 20 top universities around the globe. At University of Toronto, Raquel Urtasun is developing affordable self-driving cars. But because genomic data is highly complex, researchers must develop more effective deep learning techniques, said Adriana Romero, a post-doctoral fellow at Montreal Institute for Learning Algorithms, Université of Montréal. "Right now you see robots in factories or other settings where they repeat the same thing over and over again," said Chelsea Finn, a doctoral student working in the University of California, Berkeley's AI lab, which was one of the first to receive an NVIDIA DGX-1.

Inspite of all the current hype, AI is not a new field of study, but it has its ground in the fifties. If we exclude the pure philosophical reasoning path that goes from the Ancient Greek to Hobbes, Leibniz, and Pascal, AI as we know it has been officially started in 1956 at Dartmouth College, where the most eminent experts gathered to brainstorm on intelligence simulation. This happened only a few years after Asimov set his own three laws of robotics, but more relevantly after the famous paper published by Turing (1950), where he proposes for the first time the idea of a thinking machine and the more popular Turing test to assess whether such machine shows, in fact, any intelligence. As soon as the research group at Dartmouth publicly released the contents and ideas arisen from that summer meeting, a flow of government funding was reserved for the study of creating a nonbiological intelligence. Atthat time, AI seemed to be easily reachable, but it turned out that was not the case.

Landscape photography is hard, no matter how beautiful an environment you're shooting in. You need to be well-versed in composition, deal with weather conditions, know how to adjust your camera settings for the best possible shot, and then edit it to come up with a pleasing picture. Google might be close to solving the last part of that puzzle: a couple of its Machine Perception researchers have trained a deep-learning system to identify objectively fine landscape panorama photos from Google Street View, and then artistically crop and edit them like a human photographer would. The results don't just speak for themselves: Google showed a bunch of these photos, along with others from various sources, and asked several pro photographers to grade them for quality; about 40 percent of Google's submissions were perceived as being created by'semi-pro'- or'pro'-level photographers. What's especially interesting is that the AI is capable of applying contextually meaning adjustments in different parts of each photograph, making for dramatic lighting and more compelling images – as opposed to simply applying a filter to the entire picture or adding something predictable like a vignette.

This week: awesome applied AI pieces. Opinion: AI is not really Intelligent When not to use deep learning?. Why investors won't be all that interested in AI startups sooner rather than later.