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As we collectively experience the increasing pervasiveness of machine learning algorithms that drive so many services and functions in our society, it is clear to us that a new workforce of specialized programmers and computer scientists exist behind this reality. You may even be one of these wunderkinds who expanded your early coding education to include the development of learning algorithms for enhancing existing software applications or creating entirely new deep learning systems. Or, you may be an "older-kinder" who quickly circled back to catch the wave of AI excitement to coral it into your well-honed development tool kit. Either way, while the current generation of programmers is running with machine learning trends, the next round of professionals who will fill our shoes – those young ones who are learning PowerPoint and Common Core math in grade school today – are experiencing AI as something that is… just already something normal. AI-powered digital voice assistants are commonplace in homes, and kids are thrilled to ask what the weather is today repeatedly or to have the device tell a joke.There are people behind this magic, of course, and the fad of learning this trade could become just that if we don't consider how to ensure a pipeline of future machine learning developers to carry our torch.

The development of artificial intelligence (AI) is causing ripple effects across a plethora of industries, and higher education is no exception.

The UAE is rolling out its biggest effort yet to develop a workforce versed in artificial intelligence, as the rapidly-advancing technology transforms economies worldwide. The Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), a new graduate-level AI research university in Abu Dhabi, is accepting applications for its first masters and PhD-level programmes this month, with its first class beginning in September 2020. The institution, the first university to have a singular focus on AI, aims to attract students from around the world to advance the technology and propel the UAE's economic diversification efforts. To compete with more than a hundred graduate-level degree programmes in AI -- mainly in North America, China and the UK -- MBZUAI is offering full scholarships, monthly stipends, health insurance and accommodation to all students. The Mohamed bin Zayed University of Artificial Intelligence is an open invitation from Abu Dhabi to the world to unleash AI's full potential.

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.