The robots are coming, but not necessarily for your job. The likelihood that robots, automation and artificial intelligence (AI) will completely wipe out large swaths of the workforce is exaggerated, a new MIT report finds. The report, from MIT's "Work of the Future" task force, examines the relationship between technology and work, drawing on research from more than 20 faculty members. There's no doubt technology will impact jobs, but researchers say there is a larger concern when it comes to the future of work: Increasing inequality. And the impact of that inequality has given workers legitimate concerns about the role of technology in the future.

The scale, pace and breadth of technological development in artificial intelligence, robotics, computing, biotechnology, materials science and beyond have ushered in the Fourth Industrial Revolution. In an interview with journalist Thomas Friedman, Google executive Eric Teller argues that humanity's 21st century challenge is to become as good at shaping the positive impacts of technologies as we are at inventing the technologies in the first place. Teller says the problem is that the political, economic, legal, organizational and educational systems in which we operate are not agile enough to respond to the scale and pace of technological change. My professional life is focused on how to educate aspiring engineers to be agile. I teach ethics, professionalism and communication in the Faculty of Engineering and Applied Science at Memorial University.

Moojan started her career in the space of banking and corporate finance, before venturing into technology and start-ups. Not only is she the co-founder of Startup Sesame – an alliance of tech events in Europe, but she's also the founder of "Silk Road Startup too". Perhaps most importantly of all, Asghari is responsible for co-founding the Women in AI initiative – a group committed to closing the gender gap in the AI and ML fields. Moojan is helping to lead the way for innovative women everywhere. Devi Parikh is both an assistant professor for the school of Interactive computing for Georgia Tech, and research at Facebook AI research.

Meta-learning extracts the common knowledge acquired from learning different tasks and uses it for unseen tasks. It demonstrates a clear advantage on tasks that have insufficient training data, e.g., few-shot learning. In most meta-learning methods, tasks are implicitly related via the shared model or optimizer. In this paper, we show that a meta-learner that explicitly relates tasks on a graph describing the relations of their output dimensions (e.g., classes) can significantly improve the performance of few-shot learning. This type of graph is usually free or cheap to obtain but has rarely been explored in previous works. We study the prototype based few-shot classification, in which a prototype is generated for each class, such that the nearest neighbor search between the prototypes produces an accurate classification. We introduce "Gated Propagation Network (GPN)", which learns to propagate messages between prototypes of different classes on the graph, so that learning the prototype of each class benefits from the data of other related classes. In GPN, an attention mechanism is used for the aggregation of messages from neighboring classes, and a gate is deployed to choose between the aggregated messages and the message from the class itself. GPN is trained on a sequence of tasks from many-shot to few-shot generated by subgraph sampling. During training, it is able to reuse and update previously achieved prototypes from the memory in a life-long learning cycle. In experiments, we change the training-test discrepancy and test task generation settings for thorough evaluations. GPN outperforms recent meta-learning methods on two benchmark datasets in all studied cases.

Multiple Instance learning (MIL) algorithms are tasked with learning how to associate sets of elements with specific set-level outputs. Towards this goal, the main challenge of MIL lies in modelling the underlying structure that characterizes sets of elements. Existing methods addressing MIL problems are usually tailored to address either: a specific underlying set structure; specific prediction tasks, e.g. classification, regression; or a combination of both. Here we present an approach where a set representation is learned, iteratively, by looking at the constituent elements of each set one at a time. The iterative analysis of set elements enables our approach with the capability to update the set representation so that it reflects whether relevant elements have been detected and whether the underlying structure has been matched. These features provide our method with some model explanation capabilities. Despite its simplicity, the proposed approach not only effectively models different types of underlying set structures, but it is also capable of handling both classification and regression tasks - all this while requiring minimal modifications. An extensive empirical evaluation shows that the proposed method is able to reach and surpass the state-of-the-art.

AI has achieved remarkable mastery over games such as Chess, Go, and Poker, and even Jeopardy, but the rich variety of standardized exams has remained a landmark challenge. Even in 2016, the best AI system achieved merely 59.3% on an 8th Grade science exam challenge. This paper reports unprecedented success on the Grade 8 New York Regents Science Exam, where for the first time a system scores more than 90% on the exam's non-diagram, multiple choice (NDMC) questions. In addition, our Aristo system, building upon the success of recent language models, exceeded 83% on the corresponding Grade 12 Science Exam NDMC questions. The results, on unseen test questions, are robust across different test years and different variations of this kind of test. They demonstrate that modern NLP methods can result in mastery on this task. While not a full solution to general question-answering (the questions are multiple choice, and the domain is restricted to 8th Grade science), it represents a significant milestone for the field.

For most of the past sixty years, a rich critique of artificial intelligence was avidly pursued, mostly by insiders, people either practicing AI or interested onlookers who were in close proximity. Now the world finds itself in a strange state: Just as AI has gone mainstream, showing up everywhere from your Instagram feed to your smartphone voice assistant, many of those voices of criticism have been lost as a generation of thinkers passed away, people like MIT scientist Marvin Minsky and UC Berkeley professor of philosophy Herbert Dreyfus. But a small contingent of critics remains, and the world needs them to keep a balance in its view of AI as the use of AI becomes more entwined with everyday life. They include Judea Pearl, whose Book of Why reminds AI practitioners of the need for causal reasoning; and University of Toronto professor Hector Levesque, whose test for common sense, the Winograd Schema Challenge, sets a high bar for conventional AI. But none have been more prolific in the modern era in the critique of AI than NYU professor of psychology Gary Marcus. In five books and numerous articles in popular publications such as The New York Times and The New Yorker, Marcus has skewered the latest AI headlines, to remind people of the limits to present AI.

Artificial intelligence researchers have long dreamed of building a computer as knowledgeable and communicative as the one in Star Trek, which could interact with humans in natural (i.e., human) language. Last week, we seemed to boldly go toward that ideal. The New York Times reported that a team at the Allen Institute for Artificial Intelligence (AI2) had achieved "an artificial-intelligence milestone." AI2's program, Aristo, not only passed but also excelled on a standardized eighth-grade science test. The machine, the Times heralded, "is ready for high school science. Melanie Mitchell is professor of computer science at Portland State University and External Professor at the Santa Fe Institute. Her book Artificial Intelligence: A Guide for Thinking Humans will be published in October by Farrar, Straus, and Giroux. Aristo isn't the first AI system to shine on a test designed to gauge human knowledge and reasoning abilities. In 2015 one system matched a 4-year-old's performance on an ...

Artificial Intelligence today is where personal computers were back in the 90s: a new skill that everyone will have to become familiar with within the next few years. What if you could be as familiar with AI as you are with MS Office? Why this course: The problem at hand is that while there are not enough data scientists and engineers to create AI solutions, there are even fewer managers and leaders who know how to apply AI to business or organizational problems in the right manner, or have the time to learn it in detail. The good news, however, is that just like with computers, most of us do not need to learn how to code to understand and use AI well. This course will help you get a thorough understanding of AI techniques & how to use/manage them, to support your career as well as your organization's growth. It will also clear the confusion around what AI can or cannot do, and will allow you to spot strong or weak AI solutions - all in under 3 hours.

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