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.

These capabilities will in many cases be integrated into our living systems. We thus face the question: What might remain uniquely human? And does this question even matter? AIs will become better and faster than unenhanced homo sapiens at nearly everything. AIs will become better and faster than unenhanced homo sapiens at nearly everything.

Insights gleaned from business intelligence reports and dashboards are worthwhile for decision support, early warning signals or identifying trends that warrant further investigation before taking action. However, advances in analytical technology (cognitive and machine learning) for example--combined with the proliferation of customer engagement channels has created the potential to fuel omni-channel customer journey design. It should come as no surprise that enterprise-level omni-channel customer analytics, similar to Big Data Analytics (BDA), encompass numerous information sources, multiple technologies (software and hardware), nimble business processes and specialized human expertise in advanced analytics. Not surprisingly, this Hypatia Research Group study found that global organizations are successfully utilizing various analytical techniques in the form of these types of engagement. Further analysis is available in our latest Galaxy study.

Blackbirds, the company's resource group for black employee, created the bot to provide users with facts, events in major cities, and viral content from black users.

When it comes to customer experience, most machines just aren't very smart. Every day, customers around the world are suggested products to buy or use that have little or no application to their lives and for which they aren't a customer. It is frustrating to get recommendations for items you would never actually purchase. Luckily, machine learning is evolving and changing how we use technology in the customer experience. Machine learning is solely focused on writing software that learns from past experiences and creates a more personalized and relevant experience for customers.