Our research aims to develop interactive, social agents that can coach people to learn new tasks, skills, and habits. In this paper, we focus on coaching sedentary, overweight individuals (i.e., trainees) to exercise regularly. We employ adaptive goal setting in which the intelligent health coach generates, tracks, and revises personalized exercise goals for a trainee. The goals become incrementally more difficult as the trainee progresses through the training program. Our approach is model-based - the coach maintains a parameterized model of the trainee's aerobic capability that drives its expectation of the trainee's performance. The model is continually revised based on trainee-coach interactions. The coach is embodied in a smartphone application, NutriWalking, which serves as a medium for coach-trainee interaction. We adopt a task-centric evaluation approach for studying the utility of the proposed algorithm in promoting regular aerobic exercise. We show that our approach can adapt the trainee program not only to several trainees with different capabilities, but also to how a trainee's capability improves as they begin to exercise more. Experts rate the goals selected by the coach better than other plausible goals, demonstrating that our approach is consistent with clinical recommendations. Further, in a 6-week observational study with sedentary participants, we show that the proposed approach helps increase exercise volume performed each week.

We seek causes through science, religion, and in everyday life. We get excited when a big rock causes a big splash, and we get scared when it tumbles without a cause. But our causal cognition is usually biased. The 'why' is influenced by the 'who'. It is influenced by the 'self', and by 'others'. We share rituals, we watch action movies, and we influence each other to believe in the same causes. Human mind is packed with subjectivity because shared cognitive biases bring us together. But they also make us vulnerable. An artificial mind is deemed to be more objective than the human mind. After many years of science-fiction fantasies about even-minded androids, they are now sold as personal or expert assistants, as brand advocates, as policy or candidate supporters, as network influencers. Artificial agents have been stunningly successful in disseminating artificial causal beliefs among humans. As malicious artificial agents continue to manipulate human cognitive biases, and deceive human communities into ostensive but expansive causal illusions, the hope for defending us has been vested into developing benevolent artificial agents, tasked with preventing and mitigating cognitive distortions inflicted upon us by their malicious cousins. Can the distortions of human causal cognition be corrected on a more solid foundation of artificial causal cognition? In the present paper, we study a simple model of causal cognition, viewed as a quest for causal models. We show that, under very mild and hard to avoid assumptions, there are always self-confirming causal models, which perpetrate self-deception, and seem to preclude a royal road to objectivity.

Posted By C. M. Rubin on Oct 9, 2019 "We added "Artificial Intelligence" to "Robotics & STEM" this year because it is an important and timely topic for young people to learn about." Prior to joining the Girls of Steel Robotics Program at Carnegie Mellon University's (CMU) Field Robotics Center, Theresa Richards was a science teacher in Pittsburgh where she created an award-winning lesson integrating robotics into a Human Anatomy and Physiology course. The problem her organization is trying to solve is the demand for more people in STEM, and in particular, women. A December 2018 report in Pittsburgh shows there are 80,000 STEM jobs currently available. "We believe that building robots builds confidence in STEM," says Richards.

Editor's Note: SHRM has partnered with TrainingIndustry.com to bring you relevant articles on key HR topics and strategies. The National Center for Women in Technology (NCWIT) predicts that while there will be 3.5 million "computing-related" jobs in the U.S. by 2026, 83% of them could go unfilled due to a lack of college graduates with related degrees. To meet this demand, organizations must reskill their workforces and look to candidates who have learned in-demand technical skills through alternate forms of education. In recent years, coding bootcamps have succeeded in training a diverse group of workers for careers as web, full-stack and software developers, among other roles, as well as reskilling people already in those professions. However, several major coding bootcamps have also closed in recent years, including Dev Bootcamp and The Iron Yard in 2017.

Rachelle Dene Poth is an educator, attorney, consultant, and author who is concentrated on making a difference throughout the education space. Poth, who recently received the Making IT Happen Award from ISTE and the Presidential Gold Award for volunteer service, will be a featured speaker at the 2020 Future of Education Technology Conference (FETC 2020), this coming January in Miami. Poth has taken her diverse array of professional experiences into the classroom to motivate 8th grade students to engage in their learning, even when it takes them a little time to discover the relevance. In terms of motivation, she explains, "I ask 8th graders to be receptive to ideas. It's not just'I want you just to do this because I say you have to do this.' It's, 'I want you to do this as a start to see what other interests stir in you.'"

TechRepublic's Karen Roby talks with an AI expert who believes we need to rethink our approach and focus more on cost benefit tradeoffs and resourcing. The following is an edited transcript of the interview. We're talking with Arijit Sengupta, he's an AI expert with over 20 years of education and experience working in artificial intelligence and even wrote a book called AI is a Waste Of Money. So Arijit, you obviously think we need to re-evaluate our approach to AI… explain! Arijit Sengupta: The answer is to go back to the fundamentals.

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If you've been thinking about building your own deep learning computer for a while but haven't quite got'round to it, here's another reminder. Not only is it cheaper to do so, but the subsequent build can also be faster at training neural networks than renting GPUs on cloud platforms. When you start trying small side projects like, say, building little autonomous drones or crafting a bot to spit out random snippets of poetry, you begin to realise how much compute power is really needed to get interesting results. So you can either fork out money to rent hardware via cloud services like AWS or Google Compute Platform or build your own server. Jeff Chen, an AI engineer and entrepreneur, drew up a handy shopping list for all the different parts needed to craft your own deep learning rig.

This class provides a practical introduction to deep learning, including theoretical motivations and how to implement it in practice. As part of the course we will cover multilayer perceptrons, backpropagation, automatic differentiation, and stochastic gradient descent. Moreover, we introduce convolutional networks for image processing, starting from the simple LeNet to more recent architectures such as ResNet for highly accurate models. Secondly, we discuss sequence models and recurrent networks, such as LSTMs, GRU, and the attention mechanism. Throughout the course we emphasize efficient implementation, optimization and scalability, e.g. to multiple GPUs and to multiple machines. The goal of the course is to provide both a good understanding and good ability to build modern nonparametric estimators.

Imagine you're taking an online business class -- the kind where you watch video lectures and then answer questions at the end. But this isn't a normal class, and you're not just watching the lectures: They're watching you back. Every time the facial recognition system decides that you look bored, distracted, or tuned out, it makes a note. And after each lecture, it only asks you about content from those moments. This isn't a hypothetical system; it's a real one deployed by a company called Nestor.