Many retailers are scared of machine learning. This doesn't come from an unreasonable place. Marketers are afraid to trust technology to assist in making crucial marketing decisions as well. But while machine learning is certainly a game-changer, it doesn't fundamentally alter how businesses communicate with customers. It only better informs marketing decisions to empower smarter and more effective efforts.

While AI initiatives such as IBM Watson, chatbots and digital assistants have garnered much attention over the past year, the current applications only scratch the surface of the technology's potential. For companies, AI will be used to predict customer needs in near real-time, helping them retain customers. The importance of AI to customer interactions is reflected in Forrester's findings, such as that 37% of companies plan to implement intelligent assistants in the next 12 months, and 35% have their sights on cognitive product launches for customers. Some of the other ways AI will be used going forward include leveraging speech recognition for customer interactions, machine learning for making predictions, image recognition in combination with machine learning, robotics and self-driving cars. Already this year, there are signs that robots in stores could revolutionize the customer experience. However, because AI comes with a number of ethical, political and social issues attached, it will be important for companies to develop a plan and have foundational elements in place as they test and learn, per Forrester.

The Internet of Things (IoT) promises to simplify and streamline our lives by automating routine tasks that distract us from our goals. This promise is based on the ubiquitous deployment of smart, connected devices that link everything from industrial control systems to automobiles to refrigerators. Unfortunately, comparatively few of the devices currently deployed have been developed with an eye toward security, and as the DDoS attacks of late October 2016 have demonstrated, this oversight can have devastating, if not catastrophic results. In his session at 19th @ThingsExpo, Richard Blech, Chief Executive Officer at Secure Channels, explored the promise and the pitfalls inherent to IoT and offered a novel way of incorporating an effective security paradigm in which IoT "watchmen" monitor and maintain order within the IoT. Speaker Bio Richard Blech is Chief Executive Officer at Secure Channels Inc.

SAN FRANCISCO--(BUSINESS WIRE)--Kinetica, provider of the fastest, in-memory database accelerated by GPUs, today announced the availability of in-database analytics via user-defined functions (UDFs). This industry-first capability makes the parallel processing power of the GPU accessible to custom analytics functions deployed within Kinetica. This opens the opportunity for machine learning/artificial intelligence libraries such as TensorFlow, BIDMach, Caffe, and Torch to run in-database alongside, and converged with, BI workloads. Kinetica also introduced its extensible and flexible'Reveal' visualization framework for interactive, real-time data exploration. Kinetica's advanced in-database analytics make it possible for organizations to affordably converge Artificial Intelligence, Business Intelligence, Machine Learning, natural language processing, and other data analytics into one powerful platform.

The latest battle between man and machine is being played out on the poker tables of a Pennsylvania casino, and so far humanity is losing. An artificial intelligence computer program called Liberatus has accumulated winnings of almost $800,000 against a team of professional poker players at the Brain Vs. Liberatus, developed by Carnegie Mellon School of Computer Science, is aiming to be the first computer program to win a professional poker tournament--a game considered by many AI researchers to be one of the hardest for computers to beat humans at. Artificial intelligence advances in recent years have seen computers master and eventually outperform the best human players at games including Chess and the game show Jeopardy!. In 2016, researchers at Google's DeepMind developed a system that was able to beat champion Go player Lee Sedol--a significant step for artificial intelligence.

Apple plans to introduce a much smarter version of Siri this year, leveraging a bunch of machine learning technologies it recently acquired with Turi, Tuplejump, Perceptio, VocalIQ and other AI-focused acquisitions, Digitimes claims. The report seems to suggest Apple plans to introduce these features alongside introduction of a future iPhone, though it is not clear if this will be this year, or the next. However, this makes little sense given Siri is now available across all Apple's platforms – while the capabilities may differ (Siri behaves a little differently on a Mac, for example), the basic tech is similar. That's why I can't imagine new Siri features being introduced as an iPhone exclusive. The report also states other smartphone vendors, "are likely to introduce models featuring AI (artificial intelligence) applications as a means to ramp up market shares in 2017, according to industry sources."

Artificial Intelligence" Texas Hold'em Poker tournament, and a machine named Libratus is trouncing a quartet of professional human players. Should the machine maintain its substantial lead--currently at $701,242--it will be considered a major milestone in the history of AI. Given the early results, it appears that we'll soon be able to add Heads-Up, No-Limit Texas Hold'em poker (HUNL) to the list of games where AI has surpassed the best humans--a growing list that includes Othello, chess, checkers, Jeopardy!, and as we witnessed last year, Go. Unlike chess and Go, however, this popular version of poker involves bluffing, hidden cards, and imperfect information, which machines find notoriously difficult to handle. Computer scientists say HUNL represents the "last frontier" of game solving, signifying a milestone in the development of AI--and an achievement that would represent a major step towards more human-like intelligence. Google's AlphaGo has stomped to victory for a fourth time against Go world champion Lee Sedol. Artificial Intelligence" tournament began on January 11th at Rivers Casino in Pittsburgh.

Let's face it, viewing artificial intelligence (AI) simply as a labor-replacement or cost-saving mechanism is boring and uninspired. Let's start talking about a more expansive view that looks at AI as a catalyst for new ways to build markets and drive new forms of innovation. AI potentially will enable organizations to expand in ways that were unthinkable with sheer human brainpower, as powerful as that brainpower may be. The advantage AI brings to the table is that many mundane or grunt-work decisions that occupies human decision-makers' time – such as locating and fixing a data-transfer bottleneck, tracking machine-part performance, or even medical monitoring – is done by machines. In theory, at least, humans' roles are freed up and elevated to more strategic vistas. There are a million points in which machines could be making low-level decisions.

A purposely vague term, referring to an ever-growing set of tools and techniques, that are said to do stuff that people usually do, only better. AI programs have advanced from early victories in playing checkers to wins against chess masters. They have finally achieved the pinnacle of human intelligence, winning the game show Jeopardy. After decades of marching from success to success, today's leaders of Artificial Intelligence anticipate that practical applications of the technology are certain to emerge. If not, they threaten to further inflate the definition of Artificial Intelligence to encompass normal computer programs written by ordinary human beings, at which point success will be theirs -- since a computer program is, without doubt, artificial.

How will people sift and navigate information intelligently in the future, when there's even more data being pushed at them? Information overload is a problem we struggle with now, so the need for better ways to filter and triage digital content is only going to step up as the MBs keep piling up. Researchers in Finland have their eye on this problem and have completed an interesting study that used EEG (electroencephalogram) sensors to monitor the brain signals of people reading the text of Wikipedia articles, combining that with machine learning models trained to interpret the EEG data and identify which concepts readers found interesting. Using this technique the team was able to generate a list of keywords their test readers mentally flagged as informative as they read -- which could then, for example, be used to predict other relevant Wikipedia articles to that person. Or, down the line, help filter a social media feed, or flag content that's of real-time interest to a user of augmented reality, for example.