Mark Palmer is senior vice president and general manager of engineering at TIBCO. Much has been made about the business implications of recent, rapid advancements in cognitive computing -- that is, the possibility of advanced analytics tools to help human knowledge workers glean actionable insight from vast and deep lakes of historical, transactional and machine-generated information. When utilized well, cognitive tools help humans identify patterns and surface previously undetected cyberattack patterns on your company, customer buying behavior or predictive signals of catastrophic equipment failure based on readings from sensor-enabled devices. But as your business inevitably becomes more algorithmic, you're faced with the next problem: Many algorithms, once discovered, have a remarkably short shelf-life. Algorithmic excellence in analytics requires more than just great math; you must also become as agile at killing off weak or vanquished algorithms as a NASCAR pit crew changing worn tires -- you need to be replacing them with promising new ones.

"This is a propaganda machine. It's targeting people individually to recruit them to an idea. It's a level of social engineering that I've never seen before. They're capturing people and then keeping them on an emotional leash and never letting them go," said professor Jonathan Albright. Albright, an assistant professor and data scientist at Elon University, started digging into fake news sites after Donald Trump was elected president. Through extensive research and interviews with Albright and other key experts in the field, including Samuel Woolley, Head of Research at Oxford University's Computational Propaganda Project, and Martin Moore, Director of the Centre for the Study of Media, Communication and Power at Kings College, it became clear to Scout that this phenomenon was about much more than just a few fake news stories. It was a piece of a much bigger and darker puzzle -- a Weaponized AI Propaganda Machine being used to manipulate our opinions and behavior to advance specific political agendas. By leveraging automated emotional manipulation alongside swarms of bots, Facebook dark posts, A/B testing, and fake news networks, a company called Cambridge Analytica has activated an invisible machine that preys on the personalities of individual voters to create large shifts in public opinion.

Artificial intelligence systems will be able to give managers real-time insights about their business operations -- as well as detect early warnings of problems before they occur. This article is part of an MIT SMR initiative exploring how technology is reshaping the practice of management. Editor's Note: This article is one of a special series of 14 commissioned essays MIT Sloan Management Review is publishing to celebrate the launch of our new Frontiers initiative. Each essay gives the author's response to this question: "Within the next five years, how will technology change the practice of management in a way we have not yet witnessed?" Workers on the factory floor have suddenly gathered at a point along the production line.

Artificial Intelligence already had a massive impact in the past years, but where will AI be in the coming years? Here is a list of predictions. Waiting to make their moveAsia's looming labour shortage p There is an obvious solution p print-edition iconFrom the print edition Asia p Feb 11th 2017 p THE … The first FDA approval for a machine learning application to be used in a clinical setting is a big step forward for AI and machine learning in healthcare and industry as a whole. Until recently, artificial intelligence (AI) was similar to nuclear fusion in unfulfilled promise. ManpowerGroup, one of the world's largest jobs companies, released a report detailing how the technological revolution is going to change the … Replacing the real world with a virtual one is a neat trick.

There is an urgent need to expand the AI epistemic community beyond the specific geographies in which it is currently clustered. Artificial Intelligence (AI) is no longer the subject of science fiction and is profoundly transforming our daily lives. While computers have already been mimicking human intelligence for some decades now using logic and if-then kind of rules, massive increases in computational power are now facilitating the creation of'deep learning' machines i.e. algorithms that permit software to train itself to recognise patterns and perform tasks, like speech and image recognition, through exposure to vast amounts of data. These deep learning algorithms are everywhere, shaping our preferences and behaviour. Facebook uses a set of algorithms to tailor what news stories an individual user sees and in what order.

In a new interview with Quartz, Microsoft founder Bill Gates makes a rather stunning argument--that robots who replace human workers should incur taxes equivalent to that worker's income taxes. "Right now, the human worker who does, say, $50,000 worth of work in a factory, that income is taxed . . . If a robot comes in to do the same thing, you'd think that we'd tax the robot at a similar level." Gates argues that these taxes, paid by a robot's owners or makers, would be used to help fund labor force retraining. Former factory workers, drivers, and cashiers would be transitioned to health services, education, or other fields where human workers will remain vital.

I would like to thank the European Parliament for the invitation to speak at this seminar on a topic which is at the heart of the European Commission's activities, especially those of the Directorate-General I represent, DG Connect. Let me first state that we greatly appreciate and welcome the publication of the report of the European Parliament Legal Affairs Committee. Given the great public attention currently devoted to robotics and artificial intelligence (AI) this report is very timely and points at some crucial issues that need to be addressed. It highlights the opportunities robotics and AI offer and points towards the clear need for a coherent European approach and for Europe to have a strong presence and investment in this technology in order to maintain leadership in it. Many aspects of the report are directly in line with the Digitising European Industry strategy we launched in April 2016.

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Gamalon Inc, emerged from stealth mode this week, announced two machine learning products, based on an in-house technology known as Bayesian Program Synthesis (BPS). The company claims BPS can perform machine learning tasks 100 times faster than conventional deep learning techniques, while providing more accurate results. "We call our way of doing this Bayesian program learning," said Gamalon founder and CEO, Ben Vigoda at a recent TED talk. He believes using Bayesian probabilistic modeling is a much more efficient way, that is, a much less computationally intensive way, to infuse intelligence into machines. Unlike deep learning, which often needs millions of data examples to train a neural network, a Bayesian model can be built with much fewer examples.

A toddler meanders unsteadily through the living room, pausing by a sleek black cylinder in the corner. "Alexa," he says in a high-pitched voice. The cylinder acknowledges the request, despite the muffled pronunciation, and the music starts. Alexa, a cloud-based speech recognition software from Amazon and the brain of its black cylindrical loudspeaker Echo, has been a big hit around the world – except for the younger ones, who take it for granted. Children will grow up alongside it, just as Alexa will evolve, as the AI powering it learns to answer more and more questions, and – perhaps – one day even converses freely with people.