COVID-19, the disease caused by the SARS-CoV-2 virus, has been declared a pandemic by the World Health Organization, with over 294,000 cases as of March 22, 2020 (WHO, 2020). In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis at different scales including molecular, medical and epidemiological applications. We finish with a discussion of promising future directions of research and the tools and resources needed to facilitate AI research. Executive Summary - There is a broad range of potential applications of AI covering medical and societal challenges created by the COVID-19 pandemic; however, few of them are currently mature enough to show operational impact.

Artificial Intelligence (AI) is the theory and development of computer systems that can perform tasks that normally require human intelligence. These tasks include visual perception, speech recognition, decision making, and language translation. Systems capable of performing such tasks are steadily transitioning from research laboratories into industry usage. AI technology is unique in that it is flexible in application. It can be used to improve processes, enhance interactions, and solve problems that, until recently, could only be performed by humans.

Neuro-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning. This survey identifies several parallels across seven different dimensions between these two fields. These cannot only be used to characterize and position neuro-symbolic artificial intelligence approaches but also to identify a number of directions for further research.

This article surveys engineering and neuroscientific models of planning as a cognitive function, which is regarded as a typical function of fluid intelligence in the discussion of general intelligence. It aims to present existing planning models as references for realizing the planning function in brain-inspired AI or artificial general intelligence (AGI). It also proposes themes for the research and development of brain-inspired AI from the viewpoint of tasks and architecture.

It includes detailed explanations of the fundamental concepts in machine learning, data processing, model evaluation and the typical machine learning workflow. It provides many coded examples using scikit-learn.

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.

In this paper, our focus is the connection and influence of language technologies on the research in neurolinguistics. We present a review of brain imaging-based neurolinguistic studies with a focus on the natural language representations, such as word embeddings and pre-trained language models. Mutual enrichment of neurolinguistics and language technologies leads to development of brain-aware natural language representations. The importance of this research area is emphasized by medical applications.

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To understand changes in physical systems and facilitate decisions, explaining how model predictions are made is crucial. We use model-based interpretability, where models of physical systems are constructed by composing basic constructs that explain locally how energy is exchanged and transformed. We use the port Hamiltonian (p-H) formalism to describe the basic constructs that contain physically interpretable processes commonly found in the behavior of physical systems. We describe how we can build models out of the p-H constructs and how we can train them. In addition we show how we can impose physical properties such as dissipativity that ensure numerical stability of the training process. We give examples on how to build and train models for describing the behavior of two physical systems: the inverted pendulum and swarm dynamics. I. Introduction The necessity for interpretability comes from the fact that it is not always enough to train and model and get an answer, but is also important to understand why a particular answer was given. A simple but meaningful definition of model interpretability given in [17] relates this notion to the degree to which a human can understand the cause of a decision. In our case, since we care about models that describe the behavior of physical systems, we change the definition to the degree to which a human can understand the physical processes that cause a prediction. Throughout this paper we focus on physically-interpretable models: models that embed physical laws that explain how energy is transformed and exchanged in the system. A physically-interpretable model facilitates learning and updating the model when something unexpected happens. This update is done by finding an explanation for an unexpected event. For example, an electrical motor unexpectedly overheats and we ask ourselves: "Why is the motor overheating?".

Artificial intelligence technology has continued to grow in recent years, stunning the world with its latest innovations. But, some are admittedly growing weary about AI and its continuous growth. With talk of robots one day replacing humans for labor, concerns of an increasingly tech dependent world grow stronger. A report from Oxford researchers stated that 47% of American jobs will be at risk by 2030 because of automation. However, AI is truly changing the world - providing innovation that can change how we approach healthcare, the environment, and the day to day act of living.