Erik Brynjolfsson, MIT Sloan School professor, explains how rapid advances in machine learning are presenting new opportunities for businesses. He breaks down how the technology works and what it can and can't do (yet). He also discusses the potential impact of AI on the economy, how workforces will interact with it in the future, and suggests managers start experimenting now. Brynjolfsson is the co-author, with Andrew McAfee, of the HBR Big Idea article, "The Business of Artificial Intelligence." SARAH GREEN CARMICHAEL: Welcome to the HBR IdeaCast from Harvard Business Review. It's a pretty sad photo when you look at it. A robot, just over a meter tall and shaped kind of like a pudgy rocket ship, laying on its side in a shallow pool in the courtyard of a Washington, D.C. office building. Workers – human ones – stand around, trying to figure out how to rescue it. The security robot had just been on the job for a few days when the mishap occurred. One entrepreneur who works in the office complex wrote: "We were promised flying cars. Instead we got suicidal robots."

In this guest post, Jacqueline M. Kory Westlund, a researcher in the Personal Robots Group at the MIT Media Lab describes her projects and explorations to understand children's relationships with social robots. This story begins in 2013, in a preschool in Boston, where I hide, with laptop, headphones, and microphone, in a little kitchenette. Ethernet cables trail across the hall to the classroom, where 17 children eagerly await their turn to talk to a small fluffy robot. "Hi, my name is Mox! I'm very happy to meet you." The pitch of my voice is shifted up and sent over the somewhat laggy network. My words, played by the speakers of Mox the robot and picked up by its microphone, echo back with a 2-second delay into my headphones. It's tricky to speak at the right pace, ignoring my own voice bouncing back, but I get into the swing of it pretty quickly. It's one of our pilot tests before we embark on an upcoming experimental study.

Yes, most faculty, graduate students, and a lot of engineering teams in industry have already abandoned everything else and shifted to deep learning. Most new graduate students in applied areas such as computer vision that I meet, know nothing about probabilistic graphical models for instance, and their proposed solution to any problem is a CNN/LSTM/GAN. It is a huge deal to have an algorithm that can absorb large amounts of data - which is what deep learning methods enable. The (re-)discovery of such an algorithm (deep neural network training) has thus made possible many new applications, which were not possible just a few years ago. How excited are you about the steam engine today?

Imagine an app that can help you figure out how to replicate what you're eating in a restaurant and help track your calorie intake just by taking a picture of your plate. A team of MIT CSAIL researchers have developed an artificial intelligence system that has the potential to evolve into that kind of application. They call the AI Pic2Recipe, because it can predict the ingredients and recipe used to make a dish from a single snapshot. If that sounds familia, it's probably because of "See Food," the fictional app that made an appearance in HBO's Silicon Valley, and a new-ish Pinterest feature that recognizes the most prominent ingredients in a picture of food. Other teams and institutions are also working on similar projects, and a common issue is the lack of samples that leads to limited accuracy.

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Chances are, you've already encountered artificial intelligence today. Did your email spam filter keep junk out of your inbox? Did you find this site through Google? Did you encounter a targeted ad on your way? We constantly hear that we're on the verge of an AI revolution, but the technology is already everywhere.

Originally posted to LinkedIn Pulse by Chris Bradford, Executive Vice President of Sales & Marketing at FiberLight, LLC. According to a report from Markets and Markets, the global Artificial Intelligence (AI) space is expected to surge to $16 billion over the next five years, growing at a CAGR of nearly 63 percent from 2016 to 2022. AI is the development of smart systems that can perform tasks which normally require human intelligence. Machine and deep learning are subsets of AI that mimic activities in neural networks of the brain where thinking occurs. Deep learning software can be programmed to recognize patterns in the digital representations of sounds, images and other data.

For more than 250 years the fundamental drivers of economic growth have been technological innovations. The most important of these are what economists call general-purpose technologies -- a category that includes the steam engine, electricity, and the internal combustion engine. The internal combustion engine, for example, gave rise to cars, trucks, airplanes, chain saws, and lawnmowers, along with big-box retailers, shopping centers, cross-docking warehouses, new supply chains, and, when you think about it, suburbs. Companies as diverse as Walmart, UPS, and Uber found ways to leverage the technology to create profitable new business models. The most important general-purpose technology of our era is artificial intelligence, particularly machine learning (ML) -- that is, the machine's ability to keep improving its performance without humans having to explain exactly how to accomplish all the tasks it's given. Within just the past few years machine learning has become far more effective and widely available. We can now build systems that learn how to perform tasks on their own. Why is this such a big deal? First, we humans know more than we can tell: We can't explain exactly how we're able to do a lot of things -- from recognizing a face to making a smart move in the ancient Asian strategy game of Go. Prior to ML, this inability to articulate our own knowledge meant that we couldn't automate many tasks. Second, ML systems are often excellent learners.

We should have been more specific. Organizers say this event is the first global robotics competition specifically for high school students. There have been other robotics competitions with teams from multiple countries.]

We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers. Generating these questions can be difficult without trading away originality, relevance or diversity in the answer options. Our method addresses these problems by leveraging a large corpus of domain-specific text and a small set of existing questions. It produces model suggestions for document selection and answer distractor choice which aid the human question generation process. With this method we have assembled SciQ, a dataset of 13.7K multiple choice science exam questions (Dataset available at http://allenai.org/data.html). We demonstrate that the method produces in-domain questions by providing an analysis of this new dataset and by showing that humans cannot distinguish the crowdsourced questions from original questions. When using SciQ as additional training data to existing questions, we observe accuracy improvements on real science exams.