The focus of AI education in general has been on training small numbers of students for research and teaching responsibilities in academe and research and development positions in industry and government. Emphasis typically has been on cultivating depth of understanding of AI concepts and methods and rigor in AI methodologies of analysis, modeling, design, experiment, and so on. The need for this kind of deep and rigorous education in AI will not only continue but also grow. Nevertheless, several factors are converging to change fundamentally some aspects of AI education in the 21st century. First, there is a growing demand for expertise in AI in industry, business, and commerce.

Fast forward to 2030 and the children who started school in 2017 will need to be just as skilled in critical thinking, creativity and empathy as they are in literacy and numeracy and technology. It is impossible to accurately predict the jobs of the future, says Mark Scott, the secretary of the NSW Department of Education, but schools will need to prepare the next generations of students for a world that will be dominated by intelligent machines. Malcolm Turnbull announces a new 10-year schools funding plan, and a new review by David Gonski. Labor brands the funding plan "an act of political bastardry". It's not as harsh as 2014, but the government is still looking to save billions of dollars in higher education costs in the budget.

In general there is no guarantee that, even with a lot of data, deep learning does better than other techniques, for example tree-based such as random forest or boosted trees. Do I need some Skynet to run it?

The Robot Academy is a new learning resource from Professor Peter Corke and the Queensland University of Technology (QUT), the team behind the award-winning Introduction to Robotics and Robotic Vision courses. There are over 200 lessons available, all for free. The lessons were created in 2015 for the Introduction to Robotics and Robotic Vision courses. We describe our approach to creating the original courses in the article, An Innovative Educational Change: Massive Open Online Courses in Robotics and Robotic Vision. The courses were designed for university undergraduate students but many lessons are suitable for anybody, as you can easily see the difficulty rating for each lesson.

Sometimes I write a blog just to formulate and organize a point of view, and I think it's time that I pull together the bounty of excellent information about Machine Learning. This is a topic with which business leaders must become comfortable, especially tomorrow's business leaders (tip for my next semester University of San Francisco business students!). Machine learning is a key capability that will help organizations drive optimization and monetization opportunities, and there have been some recent developments that will place basic machine learning capabilities into the hands of the lines of business. By the way, there is an absolute wealth of freely-available material on machine learning, so I've included a sources section at the end of this blog for folks who want more details on machine learning. Time to dive into the world of machine learning!

Complex statistical machine learning models are increasingly being used or considered for use in high-stakes decision-making pipelines in domains such as financial services, health care, criminal justice and human services. These models are often investigated as possible improvements over more classical tools such as regression models or human judgement. While the modeling approach may be new, the practice of using some form of risk assessment to inform decisions is not. When determining whether a new model should be adopted, it is therefore essential to be able to compare the proposed model to the existing approach across a range of task-relevant accuracy and fairness metrics. Looking at overall performance metrics, however, may be misleading. Even when two models have comparable overall performance, they may nevertheless disagree in their classifications on a considerable fraction of cases. In this paper we introduce a model comparison framework for automatically identifying subgroups in which the differences between models are most pronounced. Our primary focus is on identifying subgroups where the models differ in terms of fairness-related quantities such as racial or gender disparities. We present experimental results from a recidivism prediction task and a hypothetical lending example.

The goal of this tutorial is to introduce key models, algorithms, and open questions related to the use of optimization methods for solving problems arising in machine learning. It is written with an INFORMS audience in mind, specifically those readers who are familiar with the basics of optimization algorithms, but less familiar with machine learning. We begin by deriving a formulation of a supervised learning problem and show how it leads to various optimization problems, depending on the context and underlying assumptions. We then discuss some of the distinctive features of these optimization problems, focusing on the examples of logistic regression and the training of deep neural networks. The latter half of the tutorial focuses on optimization algorithms, first for convex logistic regression, for which we discuss the use of first-order methods, the stochastic gradient method, variance reducing stochastic methods, and second-order methods. Finally, we discuss how these approaches can be employed to the training of deep neural networks, emphasizing the difficulties that arise from the complex, nonconvex structure of these models.

Over the last few months I've had the opportunity to talk to a lot of decision-makers about artificial intelligence in general and machine learning in particular. Several of these executives had been asked by their investors about their machine learning (ML) strategies and where they have already implemented ML. So how did this technical subject all of a sudden become a topic of discussion in company boardrooms? Computers are supposed to solve tasks for humans. The traditional approach is to "program" the desired procedure; in other words, we teach the computer a suitable problem-solving algorithm.

Roger is an entrepreneur who has co-founded a social network entitled ThoughtBasin that looks to connect students looking to make a difference with organizations looking for difference makers. This experience has given him some setbacks, but also some priceless insights. He is deferring admission from the law school of University of Toronto to pursue his dream of creating impact through entrepreneurship, and he is constantly looking to learn and create, and to do more. He contributes to social entrepreneurship projects with his fellow Global Shapers, coordinates a volunteer tutoring site, and on his off time he unwinds by reading, writing, and dancing---sometimes, all at the same time. Follow him on Twitter at https://twitter.com/Rogerh1991.

Foreign Affairs Editor Gideon Rose discusses work and life in the age of automation. He speaks on "Bloomberg Surveillance." "Sophia," an artificially intelligent (AI) human-like robot developed by Hong Kong-based humanoid robotics company Hanson Robotics, is seen in Geneva on June 7, 2017. That's what influential economist John Maynard Keynes prophesied in his famous 1930 essay "Economic Possibilities for Our Grandchildren," forecasting that in the next century technology would make us so productive we wouldn't know what to do with all our free time. This is not the future Keynes imagined.