technique


If Your Company Isn't Good at Analytics, It's Not Ready for AI

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Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies. But companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics can end up paralyzed. They can become saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them. By contrast, companies with strong basic analytics -- such as sales data and market trends -- make breakthroughs in complex and critical areas after layering in artificial intelligence. For example, one telecommunications company we worked with can now predict with 75 times more accuracy whether its customers are about to bolt using machine learning.


Everything You Ever Wanted to Know About Artificial Intelligence

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Artificial intelligence is overhyped--there, we said it. Superintelligent algorithms aren't about to take all the jobs or wipe out humanity. But software has gotten significantly smarter of late. It's why you can talk to your friends as an animated poop on the iPhone X using Apple's Animoji, or ask your smart speaker to order more paper towels. Tech companies' heavy investments in AI are already changing our lives and gadgets, and laying the groundwork for a more AI-centric future.


Everything You Ever Wanted to Know About Artificial Intelligence

#artificialintelligence

Artificial intelligence is overhyped--there, we said it. Superintelligent algorithms aren't about to take all the jobs or wipe out humanity. But software has gotten significantly smarter of late. It's why you can talk to your friends as an animated poop on the iPhone X using Apple's Animoji, or ask your smart speaker to order more paper towels. Tech companies' heavy investments in AI are already changing our lives and gadgets, and laying the groundwork for a more AI-centric future.


How an A.I. 'Cat-and-Mouse Game' Generates Believable Fake Photos

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The woman in the photo seems familiar. She looks like Jennifer Aniston, the "Friends" actress, or Selena Gomez, the child star turned pop singer. But not exactly. She appears to be a celebrity, one of the beautiful people photographed outside a movie premiere or an awards show. And yet, you cannot...


Progress in AI seems like it's accelerating, but here's why it could be plateauing

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I'm standing in what is soon to be the center of the world, or is perhaps just a very large room on the seventh floor of a gleaming tower in downtown Toronto. Showing me around is Jordan Jacobs, who cofounded this place: the nascent Vector Institute, which opens its doors this fall and which is aiming to become the global epicenter of artificial intelligence. We're in Toronto because Geoffrey Hinton is in Toronto, and Geoffrey Hinton is the father of "deep learning," the technique behind the current excitement about AI. "In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Of the researchers at the top of the field of deep learning, Hinton has more citations than the next three combined. His students and postdocs have gone on to run the AI labs at Apple, Facebook, and OpenAI; Hinton himself is a lead scientist on the Google Brain AI team.


Google Sells A.I. for Building A.I. (Novices Welcome)

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Google has been using artificial intelligence to build other artificially intelligent systems for the last several months. Now the company plans to sell this kind of "automated machine learning" technology to other businesses across the globe. On Wednesday, Google introduced a cloud-computing service that it bills as a way to build a so-called computer vision system that suits your particular needs -- even if you have little or no experience with the concepts that drive it. If you are a radiologist, for example, you can use CT scans to automatically train a computer algorithm that identifies signs of lung cancer. If you run a real estate website, you can build an algorithm that distinguishes between living rooms and kitchens, bathrooms and bedrooms.


If Your Company Isn't Good at Analytics, It's Not Ready for AI

#artificialintelligence

Management teams often assume they can leapfrog best practices for basic data analytics by going directly to adopting artificial intelligence and other advanced technologies. But companies that rush into sophisticated artificial intelligence before reaching a critical mass of automated processes and structured analytics can end up paralyzed. They can become saddled with expensive start-up partnerships, impenetrable black-box systems, cumbersome cloud computational clusters, and open-source toolkits without programmers to write code for them. By contrast, companies with strong basic analytics -- such as sales data and market trends -- make breakthroughs in complex and critical areas after layering in artificial intelligence. For example, one telecommunications company we worked with can now predict with 75 times more accuracy whether its customers are about to bolt using machine learning.


Wise up, deep learning may never create a general purpose AI

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In August 2015, a number of carefully selected Facebook users in the Bay Area discovered a new feature on Facebook Messenger. Known as M, the service was designed to rival Google Now and Apple's Siri. A personal assistant that would answer questions in a natural way, make restaurant reservations and help with Uber bookings, M was meant to be a step forward in natural language understanding, the virtual assistant that – unlike Siri – wasn't a dismal experience. Fast forward a couple of years, and the general purpose personal assistant has been demoted within Facebook's product offering. Poor M. The hope was that it would tell users jokes and act as a guide, life coach and optimisation tool.


The Many Tribes of Artificial Intelligence – Intuition Machine – Medium

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One of the biggest confusions about "Artificial Intelligence" is that it is a very vague term. That's because Artificial Intelligence or AI is a term that was coined way back in 1955 with extreme hubris: We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it. An attempt will be made to find how to make machines use language, form abstractions, and concepts, solve kinds of problems now reserved for humans, and improve themselves. AI is over half a century old and carries with it too much baggage.


Machine Learning Can Help B2B Firms Learn More About Their Customers

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Much of the strategic focus in the digital economy thus far has revolved around getting better insights into consumers. B2C firms have been the leaders in customer analytics initiatives. E-commerce, mobile commerce, and social media platforms have enabled businesses to better sculpt marketing and customer support initiatives and customer services. Extensive data and advanced analytics for B2C have enabled strategists to better understand consumer behavior and corresponding propensities as visitors and purchasers conduct daily activities through online systems. But there is also an emerging capability to gain insights on business customers.