As much as the media has been inundated with future of work stories that read like a sci-fi-like robot apocalypse, the future of work, in a very real sense, is already here. And what's really at stake is inequality. The real question for the future of work is not whether automation, robots, and AI will replace jobs--they will. And, if history is any guide, as-yet unimaginable jobs will be created. Over 60 percent of the jobs today didn't exist in 1940, according to MIT researchers.

In 2014, Yuval Noah Harari's life changed completely. The little-known academic was thrust into the international literary spotlight when his book on the history of humans from the discovery of fire to modern robotics, Sapiens, was translated into English. Then-US President Barack Obama said the book gave him a new perspective on "the core things that have allowed us to build this extraordinary civilisation that we take for granted". It went on to sell more than eight million copies worldwide. "I still see myself as a historian," says Harari. "I don't think that historians are experts in the past, historians are specialists in change and how things change and we learn the nature of change by looking at the past."

The new wave of cobotics--robots that can safely work alongside humans--offers tremendous possibilities for industry. And with artificial intelligence (AI), smart robotics is changing the game. Thanks to new cognitive capabilities, robots can perform more than just repetitive tasks. DirectIndustry e-magazine interviewed Raun Kilgo, Director of the Robotic Process Automation division at technology research firm ISG. DirectIndustry e-magazine: How would you define AI in robotics?

Don't doubt it: Machine learning is hot--and getting hotter. For the past two years, public interest in building complex algorithms that automatically "learn" and improve from their own operations, or experience (rather than explicit programming) has been growing. Call it "artificial intelligence," or (better) "machine learning." Such work has, in fact, been going on for decades. More recently, Shivon Zilis, an investor with Bloomberg Beta, has been building a landscape map of where machine learning is being applied across other industries.

To explore what machine learning could mean in education, EdSurge convened a meetup this past week in San Francisco with Adam Blum (CEO of OpenEd), Armen Pischdotchian, (an academic technology mentor at IBM Watson), Kathy Benemann (CEO of EruditeAI), and Kirill Kireyev (founder of instaGrok and technology head at TextGenome and GYANT). As you shift from statistical evaluation models to deep machine learning [involving neural networks], what hasn't kept pace is "explainability." Now, say you have a neural network or some machine learning program that's better at predicting student outcomes. It's just another way to enable student learning and teacher practice.

Real Questions About Artificial Intelligence in Education Tweet Share Email Don't doubt it: Machine learning is hot--and getting hotter. For the past two years, public interest in building complex algorithms that automatically "learn" and improve from their own operations, or experience (rather than explicit programming) has been growing. Call it "artificial intelligence," or (better) "machine learning." Such work has, in fact, been going on for decades. More recently, Shivon Zilis, an investor with Bloomberg Beta, has been building a landscape map of where machine learning is being applied across other industries.

Don't doubt it: Machine learning is hot--and getting hotter. For the past two years, public interest in building complex algorithms that automatically "learn" and improve from their own operations, or experience (rather than explicit programming) has been growing. Call it "artificial intelligence," or (better) "machine learning." Such work has, in fact, been going on for decades. More recently, Shivon Zilis, an investor with Bloomberg Beta, has been building a landscape map of where machine learning is being applied across other industries.

Warning bells sounded this week as The Times published "Robots are coming for your job." The opinion piece predicts: "Human workers of all stripes pound the table claiming desperately that they're irreplaceable. Meanwhile, corporations and investors are spending billions toward making all those jobs replaceable." Wait a minute, chorused our letter writers, not so fast. Part of the problem is that robots and machines are terrific workers but lousy customers.

We are working to develop automated intelligent agents, which can act and react as learning machines with minimal human intervention. To accomplish this, an intelligent agent is viewed as a question-asking machine, which is designed by coupling the processes of inference and inquiry to form a model-based learning unit. In order to select maximally-informative queries, the intelligent agent needs to be able to compute the relevance of a question. This is accomplished by employing the inquiry calculus, which is dual to the probability calculus, and extends information theory by explicitly requiring context. Here, we consider the interaction between two question-asking intelligent agents, and note that there is a potential information redundancy with respect to the two questions that the agents may choose to pose. We show that the information redundancy is minimized by maximizing the joint entropy of the questions, which simultaneously maximizes the relevance of each question while minimizing the mutual information between them. Maximum joint entropy is therefore an important principle of information-based collaboration, which enables intelligent agents to efficiently learn together.