Summary: In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance recommender performance. In our first article, "Understanding and Selecting Recommenders" we talked about the broader business considerations and issues for recommenders as a group. In our second article, "5 Types of Recommenders" we attempted to detail the most dominant styles of Recommenders. Our third article, "Recommenders: Packaged Solutions or Home Grown" focused on how to acquire different types of recommenders and how those sources differ. In this last article in our series on recommenders we look to the future to see how the rapidly emerging capabilities of Deep Learning can be used to enhance performance.

Goal of the meetup is to dive into the Deep learning space. To start off with we will be going through the lectures of a Deep learning course on Udacity and working on the assignments (of course, we will maintain the "honor of code"). Once we are done with that we will take off with reading popular deep learning papers and implementing them. Currently this meetup is mostly for people who have some knowledge of machine learning but not deep learning. If you are an expert in deep learning then you are most welcome to join but we may not have much to offer, unless you want to brush up your DL skills or are interested in guiding DL enthusiasts.

Don't know where or how to start learning? But learning more about artificial intelligence, and the myriad overlapping and related fields and application domains does not require a PhD. Getting started can be intimidating, but don't be discouraged; check out this motivating and inspirational post, the author of which went from little understanding of machine learning to actively and effectively utilizing techniques in their job within a year. With more and more institutes of higher learning today making the decision to allow course materials to be openly accessible to non-students via the magic of the web, all of a sudden a pseudo-university course experience can be had by almost anyone, anywhere. Have a look at the following free course materials, all of which are appropriate for an introductory level of AI understanding, some of which also cover niche application concepts and material.

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."

For people living with Asperger's syndrome, every social interaction can be a battle. While high-functioning in some aspects, those suffering from the form of autism often struggle to engage with other people and topics outside of their own spheres of interest. Keeping up with conversations can be especially challenging then, since difficulty interpreting the meaning of nonverbal communication (like gestures and facial expressions) and modulations in the speech patterns of others is one of the hallmarks of the condition. A pair of MIT researchers have set out to make these interactions less harrowing. Using wearable tech and AI deep-learning systems, they've developed a tool that could someday act as a real-time virtual social coach.

Almost every machine learning breakthrough you hear about (and most of what's currently called "artificial intelligence") is supervised learning; where you start with a curated and labeled data set. But another technique, reinforcement learning, is just starting to make its way out of the research lab. Reinforcement learning is where an agent learns by interacting with its environment. It isn't told by a trainer what to do and it learns what actions to take to get the highest reward in the situation by trial and error, even when the reward isn't obvious and immediate. It learns how to solve problems rather than being taught what solutions look like. Reinforcement learning is how DeepMind created the AlphaGo system that beat a high-ranking Go player (and has recently been winning online Go matches anonymously). It's how University of California Berkeley's BRETT robot learns how to move its hands and arms to perform physical tasks like stacking blocks or screwing the lid onto a bottle, in just three hours (or ten minutes if it's told where the objects are that it's going to work with, and where they need to end up).

Recent progress in applying machine learning for jet physics has been built upon an analogy between calorimeters and images. In this work, we present a novel class of recursive neural networks built instead upon an analogy between QCD and natural languages. In the analogy, four-momenta are like words and the clustering history of sequential recombination jet algorithms is like the parsing of a sentence. Our approach works directly with the four-momenta of a variable-length set of particles, and the jet-based tree structure varies on an event-by-event basis. Our experiments highlight the flexibility of our method for building task-specific jet embeddings and show that recursive architectures are significantly more accurate and data efficient than previous image-based networks. We extend the analogy from individual jets (sentences) to full events (paragraphs), and show for the first time an event-level classifier operating on all the stable particles produced in an LHC event.

Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark and gluon jets better than observables designed by physicists. Our approach builds upon the paradigm that a jet can be treated as an image, with intensity given by the local calorimeter deposits. We supplement this construction by adding color to the images, with red, green and blue intensities given by the transverse momentum in charged particles, transverse momentum in neutral particles, and pixel-level charged particle counts. Overall, the deep networks match or outperform traditional jet variables. We also find that, while various simulations produce different quark and gluon jets, the neural networks are surprisingly insensitive to these differences, similar to traditional observables. This suggests that the networks can extract robust physical information from imperfect simulations.

With cyber criminals constantly adapting to industry defenses, creating new ways to commit cybercrimes, the cybersecurity industry is increasingly looking toward machine learning and artificial intelligence to help provide better deterrents, according to a new study from ABI Research. That increased reliance on automatic, intelligent processes for deterring cyber criminals will result in an increase in big data, intelligence and analytics spending, to the tune of $96 billion by 2021, according to the report. "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 (SIEM) and ultimately displace a large portion of traditional AV, heuristics, and signature-based systems within the next five years." User and Entity Behavioral Analytics (UEBA), and "deep learning" algorithm designs are becoming two of the more prominent technologies in cybersecurity solutions, their research found.

UC San Francisco's Center for Digital Health Innovation (CDHI) today announced a collaboration with Intel Corporation to deploy and validate a deep learning analytics platform designed to improve care by helping clinicians make better treatment decisions, predict patient outcomes, and respond more nimbly in acute situations. The collaboration brings together Intel's leading edge computer science and deep learning capabilities with UCSF's clinical and research expertise to create a scalable, high-performance computational environment to support enhanced frontline clinical decision making for a wide variety of patient care scenarios. Until now, progress toward this goal has been difficult because complex, diverse datasets are managed in multiple, incompatible systems. This next-generation platform will allow UCSF to efficiently manage the huge volume and variety of data collected for clinical care as well as newer "big data" from genomic sequencing, monitors, sensors and wearables. These data will be integrated into a highly scalable "information commons" that will enable advanced analytics with machine learning and deep learning algorithms.