Interview


What's actually scary about Westworld, according to an AI expert

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In this video interview with Quartz, AI expert Kai-Fu Lee talked about some of the issues raised by last year's hit HBO TV show Westworld, which was about a futuristic theme park hosted by robots, where human visitors pay to live out their fantasies. Lee is a famous venture capitalist in China and a former Microsoft and Google executive. He has an undergrad degree in computer science from Columbia and PhD from Carnegie Mellon University, where he did pioneering work on machine learning and speech recognition.


One of the greatest chess players of all time, Garry Kasparov, talks about artificial intelligence and the interplay between machine learning and humans

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Because when you look back at my matches that I've played with chess computers, now, if we stick with the intelligence as a result, then by the definition of its output, Deep Blue was intelligent because it played grand-master-level chess. A free chess app on your mobile is better than Deep Blue, stronger than Deep Blue. Looking, for instance, at the games we played in 1997, and using modern computers, I found out that it's not just I who made mistakes, but Deep Blue made quite a few serious mistakes ... serious mistakes that could bring the game from a drawing position to a losing one. Holodny: It's easy to see how human's intuition can be weaker than a computer, but do you think there are examples when it's an advantage to act on intuition?


Why Big Data, Machine Learning Are Critical to Security

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Big data and machine learning will play increasingly critical roles in improving information security, predicts Will Cappelli, a vice president of research at Gartner. "In terms of market size, Gartner estimates that in 2016 the world spent approximately $800 million on the application of big data and machine learning technologies to security use cases," he says in an interview with Information Security Media Group. A typical use case would be to deploy a big data log management platform and then deploy some kind of machine learning capability on top of that platform to enable the automated discovery of hidden patterns in this data that indicate, for example, unauthorized access, he says. Cappelli is a Gartner Research vice president in the enterprise management area, focusing on the application of big data and machine learning technologies to IT operations as well as application performance monitoring.


Interview with Steve Lucas - CEO at Marketo -- MarTechSeries

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Deeper, more meaningful engagement is the only way for a marketer to win, and we'll continue to see an acceleration of adaptive and intuitive applications and technologies that will enable the marketer to deliver consistent and personalized experiences at scale. A transformation from Marketing Automation as a tool to a broader platform is a big macro trend, followed quickly by advances in the "automation" of marketing becoming more machine defined and driven vs. human defined and driven – i.e. That means they need technologies that will enable them to deliver experiences that drive engagement, advocacy, and life-long relationships consistently, at scale. Artificial intelligence, adaptive or intuitive technologies – however you want to refer to it – is already here, and it's playing a big role in helping marketers listen, learn, and engage with their customers.


Artificial Intelligence and Employee Feedback

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Some companies are now turning to artificial intelligence (AI) tools to conduct sentiment analysis on employee feedback, gauge how employees feel and address their concerns. Bob Schultz, general manager of IBM Talent Management Solutions in San Francisco, said that sentiment analysis software is part of IBM's talent insights product suite. Employee engagement platform CultureAmp employs smarter algorithms to give users more actionable insights right from platform data or to conduct deeper dives into other employee surveys. But Orler said the most useful tool she's seen to date converts audio from candidates' recorded video interviews into easily searched text.


Which One Of These Visions Of How We'll Work In The Future Sounds Most Appealing?

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A year-long effort by the Shift Commission–a group formed by the New America Foundation and Bloomberg and involving 100 leading figures from technology, business, policy-making, and culture–took on some of these questions, imagining what the future of work might look like in 10 to 20 years, and, to a lesser extent, how we might prepare for that future. In an interview with Fast Company, he explains that the aim was not to make predictions for work's future. In this prospective economy, there's also less work than now, but most of it continues to be in the form of traditional jobs, not gigs. Fewer people work overall, leading to decreased consumer-led economic demand.


One of the greatest chess players of all time, Garry Kasparov, talks about artificial intelligence and the interplay between machine learning and humans

#artificialintelligence

Because when you look back at my matches that I've played with chess computers, now, if we stick with the intelligence as a result, then by the definition of its output, Deep Blue was intelligent because it played grand-master-level chess. A free chess app on your mobile is better than Deep Blue, stronger than Deep Blue. Looking, for instance, at the games we played in 1997, and using modern computers, I found out that it's not just I who made mistakes, but Deep Blue made quite a few serious mistakes ... serious mistakes that could bring the game from a drawing position to a losing one. Holodny: It's easy to see how human's intuition can be weaker than a computer, but do you think there are examples when it's an advantage to act on intuition?


Interview with Flowcast CTO: AI / Machine Learning in Fintech

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It was founded in Mar 2016 by FinTech entrepreneurs and financial services professionals who have decades of financial service industry experience and have founded successful Fintech startups. Sandhya from the FinTech School (Sandhya): It gives me great pleasure to introduce Winnie Cheng, a Fintech Entrepreneur and Data Scientist extraordinaire. She holds a PhD in Computer Science and Artificial Intelligence from MIT and a MS in Computer Science from Stanford University. For banks and funders, we help them understand the risks for underwriting trade financing using machine learning techniques.



March Machine Learning Mania, 5th Place Winner's Interview: David Scott

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Kaggle's annual March Machine Learning Mania competition drew 442 teams to predict the outcomes of the 2017 NCAA Men's Basketball tournament. In this winner's interview, Kaggler describes how he came in 5th place by stepping back from solution mode and taking the time to plan out his approach to the the project methodically. This included splitting the development data into a build and validation sample and leaving the test data provided for the last 4 years. I stuck with a basic logistic regression technique for the model development and it appeared to work well.