After a year of remote learning, many schools are returning to in-person learning this fall. Whether it's in the classroom or follow-up work at home, STEM lessons focused on science, technology, engineering and mathematics are foundational to a number of educational pathways. As students head back to the classroom, here are some of the best STEM kits and coding accessories for elementary students and teachers. Robotics kits are essentially a STEM rite of passage at this point. The Giggleway science kit comes with everything children will need to construct three individual robot systems including motors, circuitry, wiring and an instruction book for independent DIY construction.

David Kaiser and Julie Shah are on a mission to prepare students and facilitate research to address the broad challenges and opportunities associated with computing. As associate deans of Social and Ethical Responsibilities of Computing (SERC) in the MIT Stephen A. Schwarzman College of Computing, Kaiser and Shah are advancing a number of initiatives they hope will get students and faculty to reflect on the potential social, ethical, and policy implications of new technologies. To help guide their efforts, Kaiser, the Germeshausen Professor of the History of Science and professor of physics, and Shah, professor of aeronautics and astronautics, have developed a teaching, research, and engagement framework for SERC that includes case studies, active learning projects, and building a community of scholars. Here, they discuss projects that are taking shape and how they are tapping into the expertise of colleagues across a wide range of fields to help inform the activities of SERC. Q: Weaving social and ethical aspects of computing into the curricula is a key mandate of SERC.

You don't miss anything that's not linked to AI & Analytics industry and your propensity to get deployed, do career pivot & succeed in the professional arena gets accelerated.

Indiana University will be a principal organization in two of the U.S. National Science Foundation's 11 new NSF National Artificial Intelligence Research Institutes, helping advance artificial intelligence to improve people's lives. Overall, the 11 institutes are focused on AI-based technologies that will result in advances such as helping older adults lead more independent lives, creating solutions to improve agriculture and food supply chains, and transforming AI into accessible "plug-and-play" technology, the NSF said in an announcement. The IU Luddy School of Informatics, Computing and Engineering and the Center of Excellence for Women and Technology, both based at IU Bloomington, will collaborate with the AI Institute for Intelligent Cyberinfrastructure with Computational Learning. Researchers from the IU School of Education at IU Bloomington and the Luddy School are part of the team for the NSF AI Institute for Engaged Learning, which will advance natural language processing, computer vision and machine learning to engage learners in AI-driven narrative-centered learning environments, particularly in STEM. Students will be engaged through story-based problem scenarios.

I have always emphasized on the importance of mathematics in machine learning. Here is a compilation of resources (books, videos, and papers) to get you going. This is not an exhaustive list but I have carefully curated it based on my experience and observations. This is a repost of my Twitter thread that you can find here. I will keep updating the list here as I come across more useful resources.

Artificial intelligence (AI) has become embedded in everyday life around the world, touching how we work, play, purchase and communicate. The power of AI lies in its potential to improve lives, but this potential can only be realized if AI represents the entire population. Increasing diversity in AI development is crucial to delivering equitable outcomes. Bias in AI is a real concern and it's generating more attention. Gartner predicts that in 2022, 85% of AI projects will deliver erroneous outcomes owing to bias in data, algorithms or the teams responsible for managing them.

The flexibility of decision boundaries in neural networks that are unguided by training data is a well-known problem typically resolved with generalization methods. A surprising result from recent knowledge distillation (KD) literature is that random, untrained, and equally structured teacher networks can also vastly improve generalization performance. It raises the possibility of existence of undiscovered assumptions useful for generalization on an uncertain region. In this paper, we shed light on the assumptions by analyzing decision boundaries and confidence distributions of both simple and KD-based generalization methods. Assuming that a decision boundary exists to represent the most general tendency of distinction on an input sample space (i.e., the simplest hypothesis), we show the various limitations of methods when using the hypothesis. To resolve these limitations, we propose matured dumb teacher based KD, conservatively transferring the hypothesis for generalization of the student without massive destruction of trained information. In practical experiments on feed-forward and convolution neural networks for image classification tasks on MNIST, CIFAR-10, and CIFAR-100 datasets, the proposed method shows stable improvement to the best test performance in the grid search of hyperparameters. The analysis and results imply that the proposed method can provide finer generalization than existing methods.

Generalised Linear Models (GLMs McCullagh & Nelder 1989; Nelder & Wedderburn 1972) are frequently used in different applications to predict a univariate response due to the ease of interpretation of the parameter estimates as well as the large availability of software that facilitates simple analyses. A common assumption in GLMs is that the covariates specified (including potential interaction terms) have a linear relationship with the mean of the response after transformation through the link function. Extensions such as Generalised Additive Models (GAMs T. J. Hastie & Tibshirani 1990; Wood 2017) require the specification of the main and interaction effects via a sum of (potentially non-linear) predictors. In GAMs, the non-linear relationship is usually captured via basis expansions of the covariates and constrained by a smoothing parameter. However, in problems where the numbers of covariates and/or observations are large, the linearity assumption may not be verified and, more importantly, it may not be simple to specify the covariates and their interactions that impact most on the response.

One of the biggest challenges that complicates applied supervised machine learning is the need for huge amounts of labeled data. Active Learning (AL) is a well-known standard method for efficiently obtaining labeled data by first labeling the samples that contain the most information based on a query strategy. Although many methods for query strategies have been proposed in the past, no clear superior method that works well in general for all domains has been found yet. Additionally, many strategies are computationally expensive which further hinders the widespread use of AL for large-scale annotation projects. We, therefore, propose ImitAL, a novel query strategy, which encodes AL as a learning-to-rank problem. For training the underlying neural network we chose Imitation Learning. The required demonstrative expert experience for training is generated from purely synthetic data. To show the general and superior applicability of \ImitAL{}, we perform an extensive evaluation comparing our strategy on 15 different datasets, from a wide range of domains, with 10 different state-of-the-art query strategies. We also show that our approach is more runtime performant than most other strategies, especially on very large datasets.

Fine-grained population distribution data is of great importance for many applications, e.g., urban planning, traffic scheduling, epidemic modeling, and risk control. However, due to the limitations of data collection, including infrastructure density, user privacy, and business security, such fine-grained data is hard to collect and usually, only coarse-grained data is available. Thus, obtaining fine-grained population distribution from coarse-grained distribution becomes an important problem. To tackle this problem, existing methods mainly rely on sufficient fine-grained ground truth for training, which is not often available for the majority of cities. That limits the applications of these methods and brings the necessity to transfer knowledge between data-sufficient source cities to data-scarce target cities. In knowledge transfer scenario, we employ single reference fine-grained ground truth in target city, which is easy to obtain via remote sensing or questionnaire, as the ground truth to inform the large-scale urban structure and support the knowledge transfer in target city. By this approach, we transform the fine-grained population mapping problem into a one-shot transfer learning problem. In this paper, we propose a novel one-shot transfer learning framework PSRNet to transfer spatial-temporal knowledge across cities from the view of network structure, the view of data, and the view of optimization. Experiments on real-life datasets of 4 cities demonstrate that PSRNet has significant advantages over 8 state-of-the-art baselines by reducing RMSE and MAE by more than 25%. Our code and datasets are released in Github (https://github.com/erzhuoshao/PSRNet-CIKM).