I work in computational quantum condensed-matter physics: the study of matter, materials, and artificial quantum systems. Complex problems are our thing. Researchers in our field are working on hyper-powerful batteries, perfectly efficient power transmission, and ultra-strong materials--all important stuff to making the future a better place. To create these concepts, condensed-matter physics deals with the most complex concept in nature: the quantum wavefunction of a many-particle system. Think of the most complex thing you know, and this blows it out of the water: A computer that models the electron wavefunction of a nanometer-size chunk of dust would require a hard drive containing more magnetic bits than there are atoms in the universe.

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).

It's report card time for the automakers and Silicon Valley denizens studying the tricky problem of making cars drive themselves, and everyone is passing. The California DMV just released its annual slate of "disengagement reports," documents provided by the 11 companies that received state permits to test autonomous vehicles by the end of 2015. The results, summarized below, reveal how often humans had to wrest control away from the computer, and why (sort of). Although the reports are an imperfect measure of how the technology performs, they do reveal rapid progress toward the day when you are no longer needed behind the wheel. Google and General Motors are leading the class with cars capable of driving hundreds of miles at a stretch without trouble.

This post is the second in a series where we will go over examples of how MapR data scientist Joe Blue assisted MapR customers, in this case a regional bank, to identify new data sources and apply machine learning algorithms in order to better understand their customers. If you have not already read the first part of this customer 360 series, then it would be good to read that first. In this second part, we will cover a bank customer profitability 360 example, presenting the before, during and after. The back story: a regional bank wanted to gain insights about what's important to their customers based on their activity with the bank. They wanted to establish a digital profile via a customer 360 solution in order to enhance the customer experience, to tailor products, and to make sure customers have the right product for their banking style.

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.

At some online publications, financial summaries and sports recaps are written by artificial intelligence (AI), not humans. In the medical field, thanks to "computer-assisted diagnosis," a computer was able to spot 52% of breast cancers based on mammography scans up to one year before the women were officially diagnosed. In some organizations, AI decides which sales opportunities are worthy of a salesperson's time. Gartner client inquiry on topics closely related to AI tripled from 2015 to 2016. As organizations recognize the potential for AI to disrupt business, interest is growing rapidly.

To be great at poker you gotta know when to hold them, know when to fold them, know when to walk away, and know when to core dump. That's only part of the technique a new AI system created by researchers at Carnegie Mellon used to beat four of the "world's best professional poker players" – Dong Kim, Jimmy Chou, Daniel McAulay and Jason Les. The AI played the humans in a 20-day 120,000-hand Heads-up No-Limit Texas Hold'em binge that happened live on a casino floor in Pittsburgh. The AI, called Libratus, was up $1,766,250 in chips by the end of the experiment when it finally beat the four pros in a competition at Rivers Casino. The players played nearly constantly, conferring on strategy after each day of play.

In fact, in many countries, the internet, the very thing that was supposed to smash down the walls of authoritarianism like a sledgehammer of liberty, has been instead been co-opted by those very regimes in order to push their own agendas while crushing dissent and opposition. And with the emergence of conversational AI -- the technology at the heart of services like Google's Allo and Jigsaw or Intel's Hack Harassment initiative -- these governments could have a new tool to further censor their citizens. Turkey, Brazil, Egypt, India and Uganda have all shut off internet access when politically beneficial to their ruling parties. Nations like Singapore, Russia and China all exert outsize control over the structure and function of their national networks, often relying on a mix of political, technical and social schemes to control the flow of information within their digital borders. The effects of these policies are self-evident.

One of the biggest misconceptions about artificial intelligence is the belief that today's AIs possess generalized intelligence. We are really good at leveraging large datasets to accomplish specific tasks, but fall flat at replicating the breath of human intelligence. If we're going to move towards generalized intelligence, Facebook wants to make sure we know how to evaluate progress. In a paper, Facebook's AI Research (FAIR) lab outlines just that as part of its CommAI framework. First, the team argues that a proper generalized AI should be able to fluently communicate in natural language with humans. Researchers in the field of natural language processing have been working on the problem of human computer interaction through language for some time now, but we have a long way to go before our computational systems can rival humans.

A study in Radiology investigated whether a machine learning model can predict patient survival of heart failure in pulmonary hypertension. The researchers identified 256 patients who were diagnosed with pulmonary hypertension. Each of these patients underwent cardiac MRI, right-sided heart catheterization and six-minute walk testing with a median follow-up of four years. The researchers used the cardiac MRI to create 3-D models of right ventricular motion, which a machine learning algorithm used to identify patterns predictive of survival. By the end of the research period, 36 percent of the patients had died.