Tune in to this extraordinary edition of S.M.A.C. Talk Technology Podcast in which we examine how to become a disruptor (not disrupted!) in every industry Innovation itself isn't the entire challenge – pockets of innovation can be found in most any company, from the wildly successful to those that have failed spectacularly. The real challenge is being able to innovate at scale across an entire organization, all while creating a mechanism for those innovations to be shared, sustained, and to drive value back into the core of the business. In this series of podcasts, we'll explore how you can make that happen in your business. Listen in on these talks from your fellow innovators to discover how to embrace digital transformation and become a disruptor in your industry. More than 3,100 global executives took part in the most comprehensive global study of its kind, conducted by Oxford Economics.

In deepest September, the thick of the pistachio harvest, the autumn sky was usually veiled with dust thrown high as the shakers and receivers vibrated through the trees. But for weeks now, the machines had stopped. This left the whole orchard--almost a hundred thousand acres--more vulnerable to aflatoxin than ever. It was nice to see the stars again. It was also time for the robots to come back to work. "You know, this kind of thing doesn't happen in Iran," Stephens said. Dash's client was the biggest farmer in North America. He wore snakeskin and sandalwood and a linen suit that glowed in the predawn shadow. "What's in Iran?" Dash asked. His gaze wandered over the rows of heavily laden trees. "You ever been to Iran?" Everything about him screamed sales rep: his acid-peel face, his giant watch, the snap of taurine gum between his smiling jaws. The blockchain they developed to track the uranium suddenly developed sentience, and your agency is the only thing keeping us meatsacks from being turned to glass." Brand reps tended to treat Dash as though her work with inorganic species had contaminated her humanity in some irreversible way. Brand reps for agri-bots were apparently no different from the others. "Presuming that a machine intelligence wants to turn us into glass presumes that it cares what happens to us at all," she said. "Any theoretical intelligence would have as much reason to care about us as a cancer cell cares about a human lung.

I've never been quite as fond of Japan's Fanuc (OTCPK:FANUY) (6954.T) as many readers seem to be, and over the last five years you could have done better with other automation names like Yaskawa (OTCPK:YASKY), Rockwell (ROK), Keyence (OTCPK:KYCCF), or HollySys (HOLI) (though the two-year comps are more forgiving to Fanuc). While Fanuc has done better than I'd expected over the last two years in terms of revenue growth, leveraging a strong rebound in machine tool and robomachinery orders, margins and FCF generation haven't been all that impressive as business has skewed to lower-margin products. Now there are macro clouds on the horizon. Weaker smartphone capex demands seem likely to pressure results in 2018 and we may be nearing the point of peak machine tool orders, setting the stage for what could be a nasty decline over the next few years. I do expect Fanuc to continue to see strong growth in robots and robotic components, but I'm just not excited about the valuation today given those challenges and potential risks.

A WAVE of automation anxiety has hit the West. Just try typing "Will machines…" into Google. An algorithm offers to complete the sentence with differing degrees of disquiet: "...take my job?"; "...take all jobs?"; "...replace humans?"; "...take over the world?" Job-grabbing robots are no longer science fiction. In 2013 Carl Benedikt Frey and Michael Osborne of Oxford University used--what else?--a machine-learning algorithm to assess how easily 702 different kinds of job in America could be automated. They concluded that fully 47% could be done by machines "over the next decade or two".

For those lucky enough to get in, the UAE AI Summer Camp that starts on Sunday and runs through the summer may well prove a transformative experience. Funded by the Ministry of State for Artificial Intelligence Office and with speakers from the likes of Microsoft and IBM, and aimed at school and university students and government executives, the Camp sold out in 24 hrs - and small wonder. Attendees will get access to cutting-edge tech and be able to build systems like AI chatbots that converse with humans. As someone who began working on AI systems more than 25 years ago, I understand the excitement of getting computers to mimic brain-like abilities, however crudely. But I also know that AI enthusiasts are prone to overlooking the single biggest obstacle to the adoption of the technology: human nature.

Lengpudashi, the "Cold Poker Master," arrived in Hainan to face the top Texas Hold'Em players in China for a USD 290,000 winner-takes-all purse. His opponents were serious players -- Team Dragons was led by Yue (Alan) Du, a venture capitalist and amateur player who was the first Chinese mainlander to win a trophy at the World Series of Poker. But none of them was a match for Lengpudashi, an artificial intelligence bot developed by scientists at Carnegie Mellon University. The event, organized by former Greater China President of Google and venture capitalist Kai-fu Lee, followed anearlier showdown in January where Lengpudashi's older version Libratus beat four of the world's top poker professionals in Pittsburgh, Pennsylvania. Unlike Google's AlphaGo's triumph over 18-time world champion Lee Sedol last year, there was not so much fanfare in this event.

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A proliferation of smart devices, factory automation robots and more sophisticated cars has lifted demand for the MLCC. A typical gasoline-powered car may require only a few thousand, but an electric car might need 10,000, say industry experts. "The industry is going through tightness it has never seen before," says SMBC Nikko Securities analyst Ryosuke Katsura. He says electronics makers accustomed to getting the part right away now have to wait six months. Only a handful of makers, mostly Asian, produce the component.

It's been an exciting, but complex year in the data world. Just as last year, the data tech ecosystem has continued to "fire on all cylinders". If nothing else, data is probably even more front and center in 2018, in both business and personal conversations. Some of the reasons, however, have changed. On the one hand, data technologies (Big Data, data science, machine learning, AI) continue their march forward, becoming ever more efficient, and also more widely adopted in businesses around the world.

The random-forest technique has emerged in recent years as a powerful way to analyze large data sets while avoiding some of the pitfalls of other data-mining methods. It is based on the idea that some future event can be determined by a decision tree in which an outcome is calculated at each branch by reference to a set of training data. However, decision trees suffer from a well-known problem. In the latter stages of the branching process, decisions can become severely distorted by training data that is sparse and prone to huge variation at this kind of resolution, a problem known as overfitting. The random-forest approach is different.