Intelligent tutoring systems for ill-defined domains, such as reading and writing, are critically needed, yet uncommon. Two such systems, the Interactive Strategy Training for Active Reading and Thinking (iSTART) and Writing Pal (W-Pal) use natural language processing (NLP) to assess learners’ written (i.e., typed) responses and provide immediate, accurate feedback. The current paper reports on efforts to implement adaptive instruction and task selection into both systems. In iSTART, we developed a new practice module, in which learners’ past performance data governs two adaptive functionalities: 1) the use of self-explanation scaffolding and 2) the increase or decrease of difficulty of practice texts. In W-Pal, adaptivity is implemented by triggering targeted instructional support on the basis of deficits identified in learners’ essays. In this paper, we describe the need for adaptive reading and writing instruction, along with the design and development of adaptivity in the two systems.

There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine. We give a new theoretical analysis and family of algorithms for predicting individual treatment effect (ITE) from observational data, under the assumption known as strong ignorability. The algorithms learn a "balanced" representation such that the induced treated and control distributions look similar. We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation. We use Integral Probability Metrics to measure distances between distributions, deriving explicit bounds for the Wasserstein and Maximum Mean Discrepancy (MMD) distances. Experiments on real and simulated data show the new algorithms match or outperform the state-of-the-art.

The E-learning course starts by refreshing the basic concepts of the analytics process model: data preprocessing, analytics and post processing. We then discuss decision trees and ensemble methods (bagging, boosting, random forests), neural networks, support vector machines (SVMs), Bayesian networks, survival analysis, social networks, monitoring and backtesting analytical models. Throughout the course, we extensively refer to our industry and research experience. The E-learning course consists of more than 20 hours of movies, each 5 minutes on average. Quizzes are included to facilitate the understanding of the material.

Jerry Kaplan does for the future what Jared Diamond did for the past: He pulls together our human (or humanoid) fate in sparkling,often hilarious, prose. Kaplan begins by offering the non scientific reader (me) a clear overview of the AI advances that are poised to make human workers obsolete--offering eye popping examples explaining how the pace of technology is destined to overwhelm the human landscape of life and work. He then charts the changes that span FAR more than driverless cars. Mechanical robots (or what Kaplan calls "forged intelligences") will be more adept (and. of course, far more cost effective) than humans at performing every routine job from collecting our garbage to stocking our grocery shelves (and make those physical stores quaint relics of the past). "Synthetic intelligences" (machines that think and analyze information) will outwit humans at making complex diagnoses or writing legal briefs--automating out many of the hapless law school or medical students spending decades accumulating those mountainous student debts .

What you're looking at is not an art installation or set from the next Tron movie. It's the new RACE Robotics Lab in Singapore, used to display the latest industrial robots and train engineers working on automated assembly lines. According to architect Ministry of Design, the aim was to create "an engaging and future-forward spatial experience that denotes the idea of industrial automation and precision." Ministry of Design told Engadget that the lab's primary function is "to train and inspire more people to use robotics automation in their everyday work." The experience starts in the minimalist, all-black lobby that features just the lab signage (also created by the firm) and LEDs running at various crazy angles.

Breakthroughs in artificial intelligence (AI) can be eye opening. But they can also seem futuristic. Consider Elon Musk's recent announcement of a new venture aimed at linking human brains to computers. Thus it's not surprising that many business leaders have been lulled into believing AI is a force to be reckoned with, but not now. In spite of all its hype, AI is set to advance at a rapid pace but not necessarily because of AI technologies themselves.

He shares his takeaways in this guest post.] I was super-excited to attend Microsoft Build for the first time. Being a student, I definitely enjoyed the experience. I am very passionate about technology and have been working on a startup of my own. As I think about my role in the technology and business world, I believe that my friends and I, as teenagers, have a different perspective on technology than many of the other attendees at Microsoft Build.

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are usually grounded in the agent's decision making architecture, of which RL is an important subclass. Studying emotions in RL-based agents is useful for three research fields. For machine learning (ML) researchers, emotion models may improve learning efficiency. For the interactive ML and human-robot interaction (HRI) community, emotions can communicate state and enhance user investment. Lastly, it allows affective modelling (AM) researchers to investigate their emotion theories in a successful AI agent class. This survey provides background on emotion theory and RL. It systematically addresses 1) from what underlying dimensions (e.g., homeostasis, appraisal) emotions can be derived and how these can be modelled in RL-agents, 2) what types of emotions have been derived from these dimensions, and 3) how these emotions may either influence the learning efficiency of the agent or be useful as social signals. We also systematically compare evaluation criteria, and draw connections to important RL sub-domains like (intrinsic) motivation and model-based RL. In short, this survey provides both a practical overview for engineers wanting to implement emotions in their RL agents, and identifies challenges and directions for future emotion-RL research.

My first recollection of an effective Deep Learning system that used feedback loops where in "Ladder Networks". In an architecture developed by Stanford called "Feedback Networks", the researchers explored a different kind of network that feeds back into itself and develops the internal representation incrementally: In an even more recently published research (March 2017) from UC Berkeley have created astonishingly capable image to image translations using GANs and a novel kind of regularization. The major difficulty of training Deep Learning systems has been the lack of labeled data. So the next time you see some mind boggling Deep Learning results, seek to find the strange loops that are embedded in the method.

If you've ever had a sibling that plays video games, this should be a familiar scene: they're playing the game, and you're sitting beside them on the floor, shoving Doritos into your face by the handful. You're also shouting, "UP! OK, NOW GO DOWN! NO, DOWN! WATCH OUT FOR THAT GUY BEHIND YOU!" Maybe, just maybe, you'll beat the game together. This is essentially how undergraduate students at Stanford University recently taught AI to play a notoriously challenging game, Montezuma's Revenge for the Atari 2600. These fledgling computer scientists hope that this approach could one day be used to allow advanced robots and AI to learn how about the real world from average schmoes like me in the future. "Everyone in the developed world is interacting with AI every day, whether they know it or not," said Russell Kaplan, a Stanford computer science student and one of the study's co-authors, in an interview.