MXNet, Amazon Web Services; preferred deep learning framework, was accepted to the Apache Incubator today. Admission to the incubator is the first step..

Deep learning is all the rage today, as companies across industries seek to use advanced computational techniques to find useful information hidden across huge swaths of data. While the field of artificial intelligence is decades old, breakthroughs in the field of artificial neural networks are driving the explosion of deep learning. In the wake of World War II, the English mathematician and codebreaker Alan Turning penned his definition for true artificial intelligence. Dubbed the Turing Test, a conversational machine would have to convince a human that he was talking to another human. It took 60 years, but a computer finally passed the Turing Test back in 2014, when a chat bot developed by the University of Reading dubbed "Eugene" convinced 33% of the judges convened by the Royal Society in London that he was real.

From Alexa to Amazon Go, we use deep learning extensively across all areas of Amazon, and we've tried a lot of deep learning engines along the way. One has emerged as the most scalable, efficient way to perform deep learning, and for these reasons, we have selected MXNet as our engine of choice at Amazon. MXNet is an open source, state of the art deep learning engine, which allows developers to build sophisticated, custom artificial intelligence systems. Training these systems is significantly faster in MXNet, due to its scale and performance. For example, for the popular image recognition network, Resnet, MXNet has 2X the throughput compared to other engines, letting you train equivalent models in half the time.

Hassaan Ahmed is co-founder of Intellisense Solutions. Machine learning is not new. We have witnessed it since the 1990s, when Amazon introduced a new "recommended for you" section for its users to display more personalized results. When we search for something on Google, machine learning is behind those search results. The "Friends" recommendations or the suggested pages on Facebook or a product recommendation on any e-commerce site all depend on machine learning.

This series of blog posts aims to provide an intuitive and gentle introduction to deep learning that does not rely heavily on math or theoretical constructs. The first part in this series provided an overview over the field of deep learning, covering fundamental and core concepts. The third part of the series covers sequence learning topics such as recurrent neural networks and LSTM. I wrote this series in a glossary style so it can also be used as a reference for deep learning concepts. The earliest deep-learning-like algorithms that had multiple layers of non-linear features can be traced back to Ivakhnenko and Lapa in 1965 (Figure 1), who used thin but deep models with polynomial activation functions which they analyzed with statistical methods. In each layer, they selected the best features through statistical methods and forwarded them to the next layer. They did not use backpropagation to train their network end-to-end but used layer-by-layer least squares fitting where previous layers were independently fitted from later layers.

Apple has joined a non-profit group focused on spreading the message that artificial intelligence technologies can be used for societal good. The technology giant is now a member of Partnership on Artificial Intelligence, which debuted in September with an initial cast of participants including Amazon (amzn), Google (goog), Microsoft (msft), IBM (ibm), and Facebook (fb). Although these huge technology companies are competitors, they stand to benefit by joining forces to combat any negative public attitudes about AI. Some critics says that rapid advances in AI can lead to widespread job losses and an invasion of privacy because these companies must continuously gather massive amounts of user data to improve their AI-powered software. Companies like Apple, Google, and Microsoft have all been heavily investing in AI technologies like deep learning to improve their services.

Cyber threats are an ever-present danger to global economies and are projected to surpass the trillion dollar mark in damages within the next year. As a result, the cybersecurity industry is investing heavily in machine learning in hopes of providing a more dynamic deterrent. ABI Research forecasts machine learning in cybersecurity will boost big data, intelligence, and analytics spending to $96 billion by 2021. "We are in the midst of an artificial intelligence security revolution," says Dimitrios Pavlakis, Industry Analyst at ABI Research. "This will drive machine learning solutions to soon emerge as the new norm beyond Security Information and Event Management, or SIEM, and ultimately displace a large portion of traditional AV, heuristics, and signature-based systems within the next five years."

We have seen a fast growing interest in current activities around AI startups and research in the last couple of months. Headlines like "2016 was the year AI came of age", "AI was everywhere in 2016", and "The Great A.I. Awakening" were all over the media in the ending weeks of 2016 and we are curious about what 2017 will bring. I found particularly interesting that the current applications, future potential, and possible risks even attracted interest beyond the tech community through TV shows like Westworld, coverage on traditional media and even Obama's farewell address. Sadly, for many of us tech enthusiasts here in Europe, we sometimes feel like there is way less movement on this side of the Atlantic than in the Silicon Valley. However, with major acquisitions like DeepMind, Magic Pony Technology, Movidius, Vision Factory, and Dark Blue Labs, Europe has shown that it is actually leading the way in AI and machine learning.

Two robotic arms face two closed doors. Both reach forward and miss the door handles entirely. So they reach again, and this time, they hit the handles head-on, rattling the door frames. Finally, they grab the handles cleanly and pull the doors open, and after a few more hours of trial and error, they can repeat the trick every time. The two robots are somewhere inside Google, and though other machines have long been agile enough to pull a door handle, these are different: They learned to open those doors largely on their own.

Co-founded by Vincent Granville and part of the DSC community, our focus is on data science, ML, AI, deep learning, dataviz, Hadoop, IoT, and BI.