This year, artificial intelligence is the buzzword. On university campuses, students who just graduated high school are checking out the latest computer science course offerings to see if they can take classes in machine learning. The truth about the age of Artificial Intelligence has caught many university administrator's attention. In the age of AI, to be successful, everyone, no matter what jobs, skill sets, or majors will at some point encounter AI in their work and their life. Penn State saw the benefits of working on AI projects early, specifically when it comes to teamwork and collaboration.

This year, artificial intelligence is the buzzword. On university campuses, students who just graduated high school are checking out the latest computer science course offerings to see if they can take classes in machine learning. The truth about the age of Artificial Intelligence has caught many university administrator's attention. In the age of AI, to be successful, everyone, no matter what jobs, skill sets, or majors will at some point encounter AI in their work and their life. Penn State saw the benefits of working on AI projects early, specifically when it comes to teamwork and collaboration.

Earlier this week, five organisations released an open dataset – CORD-19 – containing nearly 30 000 scientific articles with the hopes that artificial intelligence will be able to use the data and combat the spread of COVID-19 infections. These articles have previously been published in journals, or were saved on pre-print servers. CORD-19 is short for COVID-19 Open Research Data set. The CORD-19 dataset was released after the Trump administration issued a "call to action" for the tech community to develop AI (artificial intelligence) techniques to curb the spread of COVID-19 infections. In addition, Michael Kratsios, US Chief Technology Officer at The White House, explained that "decisive action from America's science and technology enterprise" was needed to prevent, detect, treat and develop a cure for COVID-19.

Earlier this week, five organisations released an open dataset – CORD-19 – containing nearly 30 000 scientific articles with the hopes that artificial intelligence will be able to use the data and combat the spread of COVID-19 infections. These articles have previously been published in journals, or were saved on pre-print servers. CORD-19 is short for COVID-19 Open Research Data set. The CORD-19 dataset was released after the Trump administration issued a "call to action" for the tech community to develop AI (artificial intelligence) techniques to curb the spread of COVID-19 infections. In addition, Michael Kratsios, US Chief Technology Officer at The White House, explained that "decisive action from America's science and technology enterprise" was needed to prevent, detect, treat and develop a cure for COVID-19.

A detailed analysis is given of a family of critical points determining spurious minima for a model student-teacher 2-layer neural network, with ReLU activation function, and a natural $\Gamma = S_k \times S_k$-symmetry. For a $k$-neuron shallow network of this type, analytic equations are given which, for example, determine the critical points of the spurious minima described by Safran and Shamir (2018) for $6 \le k \le 20$. These critical points have isotropy (conjugate to) the diagonal subgroup $\Delta S_{k-1}\subset \Delta S_k$ of $\Gamma$. It is shown that critical points of this family can be expressed as an infinite series in $1/\sqrt{k}$ (for large enough $k$) and, as an application, the critical values decay like $a k^{-1}$, where $a \approx 0.3$. Other non-trivial families of critical points are also described with isotropy conjugate to $\Delta S_{k-1}, \Delta S_k$ and $\Delta (S_2\times S_{k-2})$ (the latter giving spurious minima for $k\ge 9$). The methods used depend on symmetry breaking, bifurcation, and algebraic geometry, notably Artin's implicit function theorem, and are applicable to other families of critical points that occur in this network.

Stochastic gradient descent (SGD) is a popular algorithm for optimization problems arising in high-dimensional inference tasks. Here one produces an estimator of an unknown parameter from a large number of independent samples of data by iteratively optimizing a loss function. This loss function is high-dimensional, random, and often complex. We study here the performance of the simplest version of SGD, namely online SGD, in the initial "search" phase, where the algorithm is far from a trust region and the loss landscape is highly non-convex. To this end, we investigate the performance of online SGD at attaining a "better than random" correlation with the unknown parameter, i.e, achieving weak recovery. Our contribution is a classification of the difficulty of typical instances of this task for online SGD in terms of the number of samples required as the dimension diverges. This classification depends only on an intrinsic property of the population loss, which we call the information exponent. Using the information exponent, we find that there are three distinct regimes---the easy, critical, and difficult regimes---where one requires linear, quasilinear, and polynomially many samples (in the dimension) respectively to achieve weak recovery. We illustrate our approach by applying it to a wide variety of estimation tasks such as parameter estimation for generalized linear models, two-component Gaussian mixture models, phase retrieval, and spiked matrix and tensor models, as well as supervised learning for single-layer networks with general activation functions. In this latter case, our results translate into a classification of the difficulty of this task in terms of the Hermite decomposition of the activation function.

Here the loss function l: R R R, the probability distribution P is unknown, and the data sample (ζ,y) R d R is a random vector distributed as P. The most prevalent algorithm employed for solving(1) is stochastic gradient descent (SGD). Whereas a significant amount of work has been devoted to the convergence analysis of SGD (see, e.g., Robbins and Monro (1951); Bottou et al. (2018); Bubeck (2015); Pflug (1986)), leading, in particular, to learning rate schedules, the question of how to terminate the algorithm when one is near an optimal classifier remains largely unaddressed. Yet, inexpensive stopping criteria are of utmost interest in machine learning. For instance, if one could produce a low cost test to determine near-optimality, then without sacrificing the quality of the solution or efficiency of the SGD algorithm, needless computational time would be eliminated. Secondly, early termination tests impose a degree of predictability on accuracy and running times-a useful quality when SGD occurs as a subproblem of a larger computation. Several works show that early termination of SGD can prevent overfitting, speed up learning procedures, and/or improve generalization properties (Prechelt, 2012; Hardt et al., 2016; Yao et al., 2007).

Sparsity and missing data problems are very common in spatiotemporal traffic data collected from various sensing systems. Making accurate imputation is critical to many applications in intelligent transportation systems. In this paper, we formulate the missing data imputation problem in spatiotemporal traffic data in a low-rank tensor completion (LRTC) framework and define a novel truncated nuclear norm (TNN) on traffic tensors of location$\times$day$\times$time of day. In particular, we introduce an universal rate parameter to control the degree of truncation on all tensor modes in the proposed LRTC-TNN model, and this allows us to better characterize the hidden patterns in spatiotemporal traffic data. Based on the framework of the Alternating Direction Method of Multipliers (ADMM), we present an efficient algorithm to obtain the optimal solution for each variable. We conduct numerical experiments on four spatiotemporal traffic data sets, and our results show that the proposed LRTC-TNN model outperforms many state-of-the-art imputation models with missing rates/patterns. Moreover, the proposed model also outperforms other baseline models in extreme missing scenarios.

Machine learning is driving development across many fields in science and engineering. A simple and efficient programming language could accelerate applications of machine learning in various fields. Currently, the programming languages most commonly used to develop machine learning algorithms include Python, MATLAB, and C/C ++. However, none of these languages well balance both efficiency and simplicity. The Julia language is a fast, easy-to-use, and open-source programming language that was originally designed for high-performance computing, which can well balance the efficiency and simplicity. This paper summarizes the related research work and developments in the application of the Julia language in machine learning. It first surveys the popular machine learning algorithms that are developed in the Julia language. Then, it investigates applications of the machine learning algorithms implemented with the Julia language. Finally, it discusses the open issues and the potential future directions that arise in the use of the Julia language in machine learning.

An audio version of this Medium article is available on Spotify and Apple Podcasts. The coronoavirus of 2019 (COVID-19) is being solved with Artificial Intelligence and Data Science. Global researchers are partnering on scientific breakthroughs to rapidly deploy and test new vaccines, to determine hotspots of the disease growth, and to recommend strategies with the World Health Organization for disease quarantine and prevention. Since the December 2019 outbreak of the #coronavirus (COVD-19) in China, I have been closely watching the news and listening to speeches about the deadly virus. There is panic everywhere with people wearing masks and others locked in their houses all day to avoid contracting the virus.