Serena Capital, a major player in financing and accompanying high growth digital companies, has launched its 3rd fund in 8 years; Serena Data Ventures, dedicated to Big Data and Artificial Intelligence. Institutional investors and large corporate groups have committed close to 80 million Euros to Serena Data Ventures which just closed its first investment in Heuritech, a French startup specialized in deep learning technologies. Serena Capital reinforces its start-up support with the arrival of 3 new Venture Partners and Amélie Faure hired as Operating Partner. At the end of 2016 Serena Capital published a study focused on the "Data Revolution" and the great dynamism of European start-ups in the field of Big Data and Artificial Intelligence. In the coming years identifying and leveraging data will disrupt all business sectors including banking, insurance, health, energy, manufacturing, trade, logistics, etc.

Cernan was born in Chicago in 1935 and graduated from Indiana's Purdue University in 1956. He became a Navy attack pilot before being selected for NASA's astronaut program in 1963. Three years later, he became the second American to walk in space during a Gemini 9 flight ― something he called the "spacewalk from hell" because of several malfunctions in his spacesuit. He logged a total of 566 hours and 15 minutes in space, more than 73 hours of which were on the moon's surface, either in the module or on the ground. Cernan eventually retired from NASA in 1976.

Distilling a generally-accepted definition of what qualifies as artificial intelligence (AI) has become a revived topic of debate in recent times. Some have rebranded AI as "cognitive computing" or "machine intelligence", while others incorrectly interchange AI with "machine learning". This is in part because AI is not one technology. It is in fact a broad field constituted of many disciplines, ranging from robotics to machine learning. The ultimate goal of AI, most of us affirm, is to build machines capable of performing tasks and cognitive functions that are otherwise only within the scope of human intelligence.

One of the most popular ways people find ideas on Pinterest is through Related Pins, an item-to-item recommendations system that uses collaborative filtering. Previously, candidates were generated using board co-occurrence, signals from all the boards a Pin is saved to. Now, for the first time, we're applying deep learning to make Related Pins even more relevant. Ultimately, we developed a scalable system that evolves with our product and people's interests, so we can surface the most relevant recommendations through Related Pins. In this post, we'll cover how we use deep learning to generate recommendation candidates, which, in testing, has increased engagement with Related Pins by 5 percent.

If you've been following my articles up to now, you'll begin to perceive, what's apparent to many advanced practitioners of Deep Learning (DL), is the emergence of Game Theoretic concepts in the design of newer architectures. This makes intuitive sense for two reasons. The first intuition is that DL systems will eventually need to tackle situations with imperfect knowledge. In fact we've already seen this in DeepMind's AlphaGo that uses partial knowledge to tactically and strategically best the world-best human in the game of Go. The second intuition is that systems will not remain monolithic as they are now, but rather would involve multiple coordinating (or competing) cliques of DL systems.

Baidu's Deep Learning Lab, which is leading the artificial intelligence programme at China's dominant internet search engine, was upstaged by Google DeepMind's AlphaGo on its home turf when it announced a duel against human competitors scheduled to be broadcast across China on Friday. Deep Learning Lab will enter its artificial intelligence robot to solve problems in the popular television game show Super Brain in China. The challenge will involve using facial and voice recognition skills in three rounds against human challengers. The first round of the Baidu AI versus humans contest will be aired on Jiangsu Satellite TV Station on Friday night. Representing humanity in the challenge are a number of gifted individuals who won the previous Super Brain contest.

Artificial intelligence is reshaping the way businesses are approaching their future. Learn how AI is taking on human-level functions for customer service, fraud prevention and more. As the "next big thing" emerges, it isn't unusual for buzz to outpace understanding at first. Artificial intelligence continues to cement itself in the technological lexicon, but defining what exactly it is and how it can be used can be hard to pin down. Answers vary depending on who you ask.

Gene Cernan, commander of Apollo 17 and the last person to walk on the moon, has passed away. He was also the second American to walk in space during the Gemini 9 mission. A link has been sent to your friend's email address. Gene Cernan, commander of Apollo 17 and the last person to walk on the moon, has passed away. He was also the second American to walk in space during the Gemini 9 mission.

Research systems are providing a vision of what is possible. However much work remains before such abilities are robust, widely useful, and generally available. This article identifies 10 key challenges, relating to modeling, systems architecture, and development methods. Of pressing importance for dialogue systems, these challenges are also relevant for intelligent and interactive systems more generally. Given Siri's broad deployment and popular example in science fiction movies. However, tellingly, salience, one might imagine that it solved the problems such systems are portrayed as idiot savants: knowledgeable, of interacting in dialogue: we often meet people logical, and well-spoken, but unable to who are unaware how cleverly Siri and her sisters interact smoothly with humans. We find it provocative avoid dialogue.

Despite significant progress made over the past twenty five years, unconstrained face verification remains a challenging problem. This paper proposes an approach that couples a deep CNN-based approach with a low-dimensional discriminative embedding learned using triplet probability constraints to solve the unconstrained face verification problem. Aside from yielding performance improvements, this embedding provides significant advantages in terms of memory and for post-processing operations like subject specific clustering. Experiments on the challenging IJB-A dataset show that the proposed algorithm performs comparably or better than the state of the art methods in verification and identification metrics, while requiring much less training data and training time. The superior performance of the proposed method on the CFP dataset shows that the representation learned by our deep CNN is robust to extreme pose variation. Furthermore, we demonstrate the robustness of the deep features to challenges including age, pose, blur and clutter by performing simple clustering experiments on both IJB-A and LFW datasets.