This is our selection of featured articles and resources posted since Monday. Get Ready for Regulations that Restrict Your Analytics Article: What is Data Science? Article: What is Data Science?

Deep data science is a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation, or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus these techniques also belong to deep data science. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation. No overlap with other fields such as statistics or machine learning. Other words for DDS (deep data science) could be pure data science or core data science.

Under, behind and inside many of the apps we use every day, a revolution is underway. It's a revolution that started decades ago but today is empowering companies to deliver better, smarter services with greater ease and on broader scales than ever before. At Singularity University's inaugural Global Summit, Neil Jacobstein, chair of Artificial Intelligence and Robotics, provided a primer showing how artificial intelligence literally transforms everything it touches. First of all, it's critical to define the scope of artificial intelligence (AI), which can be categorized into four areas: techniques in pattern recognition, software agency (that is, software that acts like real users), an exponential technology that is accelerating other exponential technologies, and a vision of a future superhuman intelligence (that fortunately hasn't happened yet). Anyone who has seen a science fiction film is likely familiar with this last area, but it's the other three areas where AI is making huge strides at a revolutionary pace.

Nature has given human beings an amazing ability to learn. We learn complex tasks, like language and image recognition from birth and continue throughout our lives to modify and build upon these first learning experiences. It seems natural then, to use the concept of learning, building up knowledge and being able to model and predict outcomes and apply that to computer related processes and tasks. The terminology used to describe the technologies involved in this paradigm in computing are Artificial Intelligence (AI). In the late 90s, a defining moment in the world of artificial intelligence happened.

OpenAI, the billion-dollar San Francisco artificial intelligence lab backed by Tesla CEO Elon Musk, just unveiled a new virtual world. It's called Universe, and it's a virtual world like no other. It's a place where AI can learn to do just about anything. Other AI labs have built similar worlds where AI agents can learn on their own. Researchers at the University of Alberta offer the Atari Learning Environment, where agents can learn to play old Atari games like Breakout and Space Invaders. Microsoft offers Malmo, based on the game Minecraft.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

Deep learning & XgBoost: Winning it hands down!

Join the leader in emerging technology research Gigaom February 15-16th in San Francisco, CA, and register now for early bird tickets to discover from world-leading practitioners how today's most progressive enterprises are using AI tools and platforms to drive revenue and improve customer experiences, product development, and business operations.

We're lucky to have Travana sponsoring the night, providing food, drinks and their amazing space! Sergei Burkov, Founder and CEO of Alterra.ai, a Deep Learning / NLP startup, building bots for online travel. Sergei's previous startup was acquired by Google, where he became the first head of its R&D Center in Moscow, Russia. Sergei will talk about how Deep Learning can be utilized for building smart conversational bots and compare them with not-so-smart bots that rely on on-screen buttons. He will also describe what the rise of bots means for the travel space, and where the opportunities are.

Artificial intelligence is all the rage, with headline-grabbing advances being announced at a dizzying pace, and companies building dedicated AI teams as fast as they can. Andrew Ng, chief scientist at Baidu Research, and a major figure in the field of machine learning and AI, says improvements in computer processor design will keep performance advances and breakthroughs coming for the foreseeable future. "Multiple [hardware vendors] have been kind enough to share their roadmaps," Ng says. "I feel very confident that they are credible and we will get more computational power and faster networks in the next several years." The field of AI has gone through phases of rapid progress and hype in the past, quickly followed by a cooling in investment and interest, often referred to as "AI winters."