As Chambersburg Area School District in Pennsylvannia kept delaying the return of students this spring, the inevitability began to set in. This was something Chambersburg Magnet School rising junior Everyn Kenney noticed, and despite not personally missing out on a commencement at Trojan Stadium, he decided to find a way to help out those who were. So after some joking about the idea at first, Kenney turned to a game he's played for years: Minecraft. Using the block-by-block multiplayer cooperative game, as well as some home-brewed coding, Kenney and his crew of nine friends set out to build a virtual, to-scale Trojan Stadium on Minecraft, and set up a virtual commencement for June 25 that graduates can attend either as a game character or watch on Twitch, all the way down to virtual caps and gowns. "I decided to just try putting it into my own hands because the school wasn't really doing anything yet," said Kenney, who is doing the project in conjunction with the school's Video Game Club.

One of the major breakthroughs in deep learning over the past five years has been the Generative Adversarial Network (GAN), a neural network-based generative model which aims to mimic some underlying distribution given a dataset of samples. In contrast to many supervised problems, where one tries to minimize a simple objective function of the parameters, GAN training is formulated as a min-max problem over a pair of network parameters. While empirically GANs have shown impressive success in several domains, researchers have been puzzled by unusual training behavior, including cycling so-called mode collapse. In this paper, we begin by providing a quantitative method to explore some of the challenges in GAN training, and we show empirically how this relates fundamentally to the parametric nature of the discriminator network. We propose a novel approach that resolves many of these issues by relying on a kernel-based non-parametric discriminator that is highly amenable to online training---we call this the Online Kernel-based Generative Adversarial Networks (OKGAN). We show empirically that OKGANs mitigate a number of training issues, including mode collapse and cycling, and are much more amenable to theoretical guarantees. OKGANs empirically perform dramatically better, with respect to reverse KL-divergence, than other GAN formulations on synthetic data; on classical vision datasets such as MNIST, SVHN, and CelebA, show comparable performance.

We consider the problem of designing minimax estimators for estimating the parameters of a probability distribution. Unlike classical approaches such as the MLE and minimum distance estimators, we consider an algorithmic approach for constructing such estimators. We view the problem of designing minimax estimators as finding a mixed strategy Nash equilibrium of a zero-sum game. By leveraging recent results in online learning with non-convex losses, we provide a general algorithm for finding a mixed-strategy Nash equilibrium of general non-convex non-concave zero-sum games. Our algorithm requires access to two subroutines: (a) one which outputs a Bayes estimator corresponding to a given prior probability distribution, and (b) one which computes the worst-case risk of any given estimator. Given access to these two subroutines, we show that our algorithm outputs both a minimax estimator and a least favorable prior. To demonstrate the power of this approach, we use it to construct provably minimax estimators for classical problems such as estimation in the finite Gaussian sequence model, and linear regression.

Neural networks have been shown to perform incredibly well in classification tasks over structured high-dimensional datasets. However, the learning dynamics of such networks is still poorly understood. In this paper we study in detail the training dynamics of a simple type of neural network: a single hidden layer trained to perform a classification task. We show that in a suitable mean-field limit this case maps to a single-node learning problem with a time-dependent dataset determined self-consistently from the average nodes population. We specialize our theory to the prototypical case of a linearly separable dataset and a linear hinge loss, for which the dynamics can be explicitly solved. This allow us to address in a simple setting several phenomena appearing in modern networks such as slowing down of training dynamics, crossover between rich and lazy learning, and overfitting.

As AI adoption brings out changes in the workplace, workers are challenged to obtain needed AI skills and business leaders are working to adapt. And as the COVID-19 pandemic has led to a shift to online learning, companies such as Udacity--who have been in that business for years--are in a good position to help. Business leaders may be caught between competing objectives of continuing to deliver strong financial performance while making investments in hiring, workforce training and new technologies that support growth, suggested the author of a recent piece in Harvard Business Review. A team at the MIT-IBM Watson AI Lab has been studying how work is being changed by AI. "By examining these findings, we can create a roadmap for leaders intent on adapting their workforce and reallocating capital, while also delivering profitability," stated author Martin Fleming, a VP and Chief Economist at IBM. He made three suggestions for reskilling the workforce to better prepare for AI.

Job Number: R0083391 Artificial Intelligence/Machine Learning Data Scientist The Challenge Are you excited at the prospect of unlocking the secrets held by a data set? Are you fascinated by the possibilities presented by the IoT or recent advances in machine learning and artificial intelligence? In an increasingly connected world, massive amounts of structured and unstructured data open up new opportunities. As a data scientist, you can turn these complex data sets into useful information to solve global challenges. Across private and public sectors -- from fraud detection to cancer research to national intelligence -- you know the answers are in the data.

As researchers around the world work to find answers to so many questions about the Coronavirus, two things have been happening that I have noticed. One, there has been an increase in the use of artificial intelligence in the medical field, in particular with tracking the onset of Coronavirus and using AI to explore trends and devise solutions to some of the challenges that we are facing as we deal with the COVID-19 pandemic. Second, AI has also become a more common topic of discussion in the world of education, with resources shared for how to learn more about AI and many online course providers seeing an increase in enrollment in their AI programs. Why do we need to pay more attention to AI now? There are statistics predicting that artificial intelligence in U.S. education will grow by 47.5% from 2017-2021.

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A new training program from AI and auto machine learning vendor DataRobot aims to teach citizen data scientists, including business analysts and data analysts, practical data science and AI skills. The paid program, "10x: The Applied Data Science Academy," provides instruction in skills such as problem framing, exploring data, feature engineering, and deploying models. It involves 40 hours of hands-on, self-paced training, 20 hours of practical labs, and 40 hours on a capstone project. With the program, revealed on Wednesday during DataRobot's AI Experience Worldwide virtual conference, held June 16-17, DataRobot enters an already crowded online AI training field. Vendors such as Google, IBM, and Microsoft have long offered free and paid analytics and AI training programs, as have many colleges and universities.

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