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The subject of artificial intelligence (AI) in education often centers around edtech breakthroughs and the ongoing evolution of the learning spaces inside schools. But AI is also experiencing growth in other sectors that have a direct impact on schools, presenting more areas for the education community to study. Take, for instance, construction and green energy resources. As schools look to cut costs, they are also increasing the adoption of sustainability programs. New state-of-the-art energy efficiency technologies may offer cost savings while reducing a school's carbon footprint.

When people talk about artificial intelligence in the education field, they're often referring to machine learning, Oranje said, in which a machine is …

ReadyAI is the first comprehensive K-12 AI education company to create an "out of the box ready" and complete program to teach AI. Our mission is to provide AI education that empowers students to use AI to change the world. We teach AI for social good. So we put an emphasis on the non-technical components of learning, combining art and multimedia. Students learn through playing and building up ideas with teammates.

The sight of a RoboBee careening towards a wall or crashing into a glass box may have once triggered panic in the researchers in the Harvard Microrobotics Laboratory at the Harvard John A. Paulson School of Engineering and Applied Science (SEAS), but no more. Researchers at SEAS and Harvard's Wyss Institute for Biologically Inspired Engineering have developed a resilient RoboBee powered by soft artificial muscles that can crash into walls, fall onto the floor, and collide with other RoboBees without being damaged. It is the first microrobot powered by soft actuators to achieve controlled flight. "There has been a big push in the field of microrobotics to make mobile robots out of soft actuators because they are so resilient," said Yufeng Chen, Ph.D., a former graduate student and postdoctoral fellow at SEAS and first author of the paper. "However, many people in the field have been skeptical that they could be used for flying robots because the power density of those actuators simply hasn't been high enough and they are notoriously difficult to control. Our actuator has high enough power density and controllability to achieve hovering flight."

When learning to follow natural language instructions, neural networks tend to be very data hungry – they require a huge number of examples pairing language with actions in order to learn effectively. This post is about reducing those heavy data requirements by first watching actions in the environment before moving on to learning from language data. Inspired by the idea that it is easier to map language to meanings that have already been formed, we introduce a semi-supervised approach that aims to separate the formation of abstractions from the learning of language. Empirically, we find that pre-learning of patterns in the environment can help us learn grounded language with much less data. Before we dive into the details, let's look at an example to see why neural networks struggle to learn from smaller amounts of data.

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. As the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning researches, as well as to summarize and interpret the mechanisms and the strategies in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Different from previous surveys, this survey paper reviews over forty representative transfer learning approaches from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, twenty representative transfer learning models are used for experiments. The models are performed on three different datasets, i.e., Amazon Reviews, Reuters-21578, and Office-31. And the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.

About the book: A widely used text on reinforcement learning, which is one of the most active research areas in artificial intelligence, this book provides a clear and simple account of the field's key ideas and algorithms. With a focus on core online learning algorithms, including UCB, Expected Sarsa, and Double Learning, it then extends these ideas to function approximation covering topics on artificial neural networks and the Fourier basis. This second edition includes new chapters on reinforcement learning's relationships to psychology and neuroscience as well as updated case-studies on AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. About the authors: Richard S. Sutton is a distinguished research scientist at DeepMind in Edmonton and a professor in the Department of Computing Science at the University of Alberta. He previously worked in industry at AT&T and GTE Labs, and in academia at the University of Massachusetts.

Data scientists need the ability to explain their models to executives and stakeholders, so they can understand the value and accuracy of their findings. The ability to interpret a generated model is crucial to ensure compliance with company policies, industry standards, and government regulations. Here's an interesting write up on Model Interpretability in Azure Machine Learning Services. Model designers and evaluators can use interpretability output of a model to verify hypotheses and build trust with stakeholders. They also use the insights into the model for debugging, validating model behavior matches their objectives, and to check for bias or insignificant features.

The idea of a sophisticated, artificially intelligent program that chooses just the right digital content for a student, at just the right time, can home in on students' strengths and weaknesses and can support or push them is on the educational horizon. Some companies already claim their ed-tech products do just that--or some version of it--to customize learning. But are AI and personalized learning really the "dynamic duo" that some educators are hoping for? Andreas Oranje, the general manager of research in research and development at the Educational Testing Service, says the time is right to examine how these technologies are evolving and the implications for K-12 teaching and learning. Educators are right to be excited about the potential of these technologies.