Intelligence: We all think we know it when we see it. But do we really understand that elusive quality? It's clear that our ideas about intelligence have evolved over time as the skills deemed necessary for survival and success have changed. Just think about the way kids roll their eyes when their parents have a hard time understanding technology. Those young folks instinctively grasp what to us seems foreign and hopelessly confounding.

Today's announcement was made from the EDUCAUSE Annual Conference taking place this week in Chicago, IL. To learn more about SnapLogic for higher education, stop by SnapLogic Booth #1114 on the conference showfloor. Today's progressive universities and colleges are embracing the cloud, unifying their applications and systems, and putting data at the center of their strategies to enrich the experience of their diverse constituents: Student Engagement: The majority of incoming students are digital natives who expect consistent, real-time access to information on housing, parking, class schedule, grades, financial aid, and more, ideally delivered via a one-stop-shop online portal. Data-driven Faculty: Faculty are leveraging digital tools to tailor, personalize, and optimize learning for students, both in the classroom and via online courses. At the individual student level, many professors are leveraging data to identify students who may be struggling and require additional attention.

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Pepe the robot was wall-mounted near a handwashing station. It prompted children to wash their hands and provided positive reinforcement. The hand-shaped robot, dubbed'Pepe', is the product of a collaboration between researchers from the University of Glasgow in Scotland and Amrita Vishwa Vidyapeetham University in India. Pepe was mounted to the wall above a handwashing station at the Wayanad Government Primary School in Kerala, which has around 100 pupils aged between five and 10. A small video screen mounted behind Pepe's green plastic exterior acted as a'mouth,' allowing researchers to tele-operate the robot to speak to the pupils and draw their attention to a poster outlining the steps of effective handwashing.

We study layered neural networks of rectified linear units (ReLU) in a modelling framework for stochastic training processes. The comparison with sigmoidal activation functions is in the center of interest. We compute typical learning curves for shallow networks with K hidden units in matching student teacher scenarios. The systems exhibit sudden changes of the generalization performance via the process of hidden unit specialization at critical sizes of the training set. Surprisingly, our results show that the training behavior of ReLU networks is qualitatively different from that of networks with sigmoidal activations. In networks with K >= 3 sigmoidal hidden units, the transition is discontinuous: Specialized network configurations co-exist and compete with states of poor performance even for very large training sets. On the contrary, the use of ReLU activations results in continuous transitions for all K: For large enough training sets, two competing, differently specialized states display similar generalization abilities, which coincide exactly for large networks in the limit K to infinity.

We consider the problem of how a teacher algorithm can enable an unknown Deep Reinforcement Learning (DRL) student to become good at a skill over a wide range of diverse environments. To do so, we study how a teacher algorithm can learn to generate a learning curriculum, whereby it sequentially samples parameters controlling a stochastic procedural generation of environments. Because it does not initially know the capacities of its student, a key challenge for the teacher is to discover which environments are easy, difficult or unlearnable, and in what order to propose them to maximize the efficiency of learning over the learnable ones. To achieve this, this problem is transformed into a surrogate continuous bandit problem where the teacher samples environments in order to maximize absolute learning progress of its student. We present a new algorithm modeling absolute learning progress with Gaussian mixture models (ALP-GMM). We also adapt existing algorithms and provide a complete study in the context of DRL. Using parameterized variants of the BipedalWalker environment, we study their efficiency to personalize a learning curriculum for different learners (embodiments), their robustness to the ratio of learnable/unlearnable environments, and their scalability to non-linear and high-dimensional parameter spaces. Videos and code are available at https://github.com/flowersteam/teachDeepRL.

In this paper, we have discussed initial findings and results of our experiment to predict sexual and reproductive health vulnerabilities of migrants in a data-constrained environment. Notwithstanding the limited research and data about migrants and migration cities, we propose a solution that simultaneously focuses on data gathering from migrants, augmenting awareness of the migrants to reduce mishaps, and setting up a mechanism to present insights to the key stakeholders in migration to act upon. We have designed a webapp for the stakeholders involved in migration: migrants, who would participate in data gathering process and can also use the app for getting to know safety and awareness tips based on analysis of the data received; public health workers, who would have an access to the database of migrants on the app; policy makers, who would have a greater understanding of the ground reality, and of the patterns of migration through machine-learned analysis. Finally, we have experimented with different machine learning models on an artificially curated dataset. We have shown, through experiments, how machine learning can assist in predicting the migrants at risk and can also help in identifying the critical factors that make migration dangerous for migrants. The results for identifying vulnerable migrants through machine learning algorithms are statistically significant at an alpha of 0.05.

If you remember the days of cassette tapes, floppy disks and flip phones, then we don't need to tell you how quickly technology is moving lately. Now it seems we're running headlong into the era of artificial intelligence. Yes, smart computers capable of learning and adapting to solve complex problems. We're not quite to HAL yet, but it seems technology is getting there. And with these new technologies comes a new vocabulary.

This post is a part of my forthcoming book on Mathematical foundations of Data Science. In this post, we use the Perceptron algorithm to bridge the gap between high school maths and deep learning. As part of my role as course director of the Artificial Intelligence: Cloud and Edge Computing at the University..., I see more students who are familiar with programming than with mathematics. They have last learnt maths years ago at University. And then, suddenly they find that they encounter matrices, linear algebra etc when they start learning Data Science.

Based on my experience as a curriculum specialist in the world of digital resources, here's the number one most successful content-sharing best practice of all time: do you have a group chat where you share nonsensical YouTube videos with each other? I'm sure the last thing you shared on it probably wasn't what you think of when you think of the life-changing potential of digital resources, but think of it anyway. Did you save it for later? There's so much that determines whether content "works" for someone, but for a curriculum specialist, whether it did or not is the most important question in the world. That's why I'm fascinated with these group chats and seek them out whenever we, at Learning Equality, visit our users: if they let me, I'll peer over their shoulders to see who's sharing what in Rajasthan (seventh-grade girls, Bollywood music), in Mexico (twentysomethings, stickered selfies), and in Kakuma refugee camp (everyone, hair styles, Office tutorials, conversion rates…).