As AI integration becomes more prominent, one can't help but to think about just how intelligent deep learning technology will be in the future. One of the first place many of our minds go is to AI becoming too intelligent and taking matters into its own virtual hands. How accurate are those portrayals, though? Will it get to a point where we're overpowered by AI, to the point where we're under their metaphorical thumb? TNW Conference is back for its 12th year.

Last year was huge for advancements in artificial intelligence and machine learning. But 2017 may well deliver even more. Here are five key things to look forward to. AlphaGo's historic victory against one of the best Go players of all time, Lee Sedol, was a landmark for the field of AI, and especially for the technique known as deep reinforcement learning. Reinforcement learning involves having a machine learn to solve a problem not through programming or explicit examples, but through experimentation combined with positive reinforcement.

Galileo viewed nature as a book written in the language of mathematics and decipherable through physics. His metaphor may have been a stretch for his milieu, but not for ours. Ours is a world of digits that must be read through computer science. It is a world in which artificial-intelligence (AI) applications perform many tasks better than we can. Like fish in water, digital technologies are our infosphere's true natives, while we analog organisms try to adapt to a new habitat, one that has come to include a mix of analog and digital components.

Machine learning is coming to the data center both to improve internal IT management and embed intelligence into key business processes. You have probably heard of a mystical deep learning, threatening to infuse everything from systems management to self-driving cars. Is this deep learning some really smart artificial intelligence that was just created and about to be unleashed on the world, or simply marketing hype aiming to re-launch complex machine learning algorithms in a better light? As DevOps is slowly taking over the IT landscape, its vital that IT pros understand it before jumping right into the movement. In this complimentary guide, discover an expert breakdown of how DevOps impacts day-to-day operations management in modern IT environments.

The fields of Blockchain and Artificial Intelligence are converging, and they will intersect soon. Artificial Intelligence and Machine Learning require vast amounts of data. Meanwhile, Blockchain allows for decentralized autonomous organizations which will soon involve hundreds of millions of people. Furthermore, platforms built on Blockchain technology will soon be powerful enough to support AI applications. At that point, AI could evolve very quickly and become, effectively, an unstoppable utility for the world's population.

Underground mining poses a number of threats including fires, methane outbreaks or seismic tremors and bumps. An automatic system for predicting and alerting against such dangerous events is of utmost importance – and also a great challenge for data scientists and their machine learning models. This was the inspiration for the organizers of AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines. Our solutions topped the final leaderboard by taking the first two places. In this post, we present the competition and describe our winning approach.

Security cameras are great, but only when they're actually pointed at whatever is going on. Alarm has developed a machine learning algorithm, called the Insights Engine, that continually monitors sensors placed around your property to learn how things are normally run and to quickly identify unexpected events -- say, a break-in or a water leak -- when they occur. If the system does spot something out of the ordinary, it will deploy a swarm of autonomous UAVs built on Qualcomm's Snapdragon Flight drone platform to investigate. These little fliers will swarm over the event site and provide live video feeds to your phone. You can also opt in to share that video data with either Alarm.com's

Eight out of ten B2B marketing executives predict artificial intelligence (AI) will revolutionize marketing by 2020, according to a December report by Demandbase -- but is it the key to unlocking a future without email spam? Demandbase, an account-based marketing (ABM) platform, partnered with Wakefield Research to poll 500 B2B marketers from the manager level to c-level executives, at companies with at least 250 employees, about AI-driven marketing. The results of the study suggest that marketers are eager to embrace artificial intelligence, yet only 10% of respondents are currently using AI and only 26% of marketers are confident that they understand how artificial intelligence can be applied to marketing. Education and integration concerns are the largest hurdles obstructing marketers with regard to artificial intelligence. Integrating AI into an existing marketing stack was the top-ranking challenge expressed by marketers when they considered incorporating AI into their marketing campaigns, with 60% of marketers selecting it as their top concern.

In this article, I clarify the various roles of the data scientist, and how data science compares and overlaps with related fields such as machine learning, deep learning, AI, statistics, IoT, operations research, and applied mathematics. As data science is a broad discipline, I start by describing the different types of data scientists that one may encounter in any business setting: you might even discover that you are a data scientist yourself, without knowing it. As in any scientific discipline, data scientists may borrow techniques from related disciplines, though we have developed our own arsenal, especially techniques and algorithms to handle very large unstructured data sets in automated ways, even without human interactions, to perform transactions in real-time or to make predictions. To get started and gain some historical perspective, you can read my article about 9 types of data scientists, published in 2014, or my article where I compare data science with 16 analytic disciplines, also published in 2014. I also wrote about the ABCD's of business processes optimization where D stands for data science, C for computer science, B for business science, and A for analytics science.