COVID-19 disease, caused by the SARS-CoV-2 virus, was identified in December 2019 in China and declared a global pandemic by the WHO on 11 March 2020. Artificial Intelligence (AI) is a potentially powerful tool in the fight against the COVID-19 pandemic. AI can, for present purposes, be defined as Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision applications to teach computers to use big data-based models for pattern recognition, explanation, and prediction. These functions can be useful to recognize (diagnose), predict, and explain (treat) COVID-19 infections, and help manage socio-economic impacts. Since the outbreak of the pandemic, there has been a scramble to use and explore AI, and other data analytic tools, for these purposes. In this article, I provide an early review, discussing the actual and potential contribution of AI to the fight against COVID-19, as well as the current constraints on these contributions. It aims to draw quick take-aways from a fast expanding discussion and growing body of work, in order to serve as an input for rapid responses in research, policy and medical analysis. The cost of the pandemic in terms of lives and economic damage will be terrible; at the time of writing, great uncertainty surrounded estimates of just how terrible, and of how successful both non-pharmaceutical and pharmaceutical responses can be. Improving AI, one of the most promising data analytic tools to have been developed over the past decade or so, so as to help reduce these uncertainties, is a worthwhile pursuit.

I'm really fascinated by apps like Spotify and Shazam, and would like to pursue a career related to music information retrieval (MIR) and recommender systems. It seems like an esoteric field, especially since deep learning these days seems to primarily be focused on image and text data. Maybe we can connect and form a study group! Maybe we can share interesting papers (e.g. from ISMIR and ACM RecSys conferences), work on projects together, and share study materials.

Motion synthesis in a dynamic environment has been a long-standing problem for character animation. Methods using motion capture data tend to scale poorly in complex environments because of their larger capturing and labeling requirement. Physics-based controllers are effective in this regard, albeit less controllable. In this paper, we present CARL, a quadruped agent that can be controlled with high-level directives and react naturally to dynamic environments. Starting with an agent that can imitate individual animation clips, we use Generative Adversarial Networks to adapt high-level controls, such as speed and heading, to action distributions that correspond to the original animations. Further fine-tuning through the deep reinforcement learning enables the agent to recover from unseen external perturbations while producing smooth transitions. It then becomes straightforward to create autonomous agents in dynamic environments by adding navigation modules over the entire process. We evaluate our approach by measuring the agent's ability to follow user control and provide a visual analysis of the generated motion to show its effectiveness.

Subsampling of received wireless signals is important for relaxing hardware requirements as well as the computational cost of signal processing algorithms that rely on the output samples. We propose a subsampling technique to facilitate the use of deep learning for automatic modulation classification in wireless communication systems. Unlike traditional approaches that rely on pre-designed strategies that are solely based on expert knowledge, the proposed data-driven subsampling strategy employs deep neural network architectures to simulate the effect of removing candidate combinations of samples from each training input vector, in a manner inspired by how wrapper feature selection models work. The subsampled data is then processed by another deep learning classifier that recognizes each of the considered 10 modulation types. We show that the proposed subsampling strategy not only introduces drastic reduction in the classifier training time, but can also improve the classification accuracy to higher levels than those reached before for the considered dataset. An important feature herein is exploiting the transferability property of deep neural networks to avoid retraining the wrapper models and obtain superior performance through an ensemble of wrappers over that possible through solely relying on any of them. Automatic modulation classification plays an important role in modern wireless communications. It finds applications in various commercial and military areas. For example, Software Defined Radios (SDR) use blind recognition of the modulation type to quickly adapt to various communication systems, without requiring control overhead. In military settings, friendly signals should be securely received, while hostile signals need to be efficiently recognized typically without prior information.

Intelligent systems designed using machine learning algorithms require a large number of labeled data. Background knowledge provides complementary, real world factual information that can augment the limited labeled data to train a machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph. Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph. In this article, we discuss the use of relevant KGs to enhance input data for two applications that use machine learning -- recommendation and community detection. The KG improves both accuracy and explainability.

Those of you who work on GANs in PyTorch know that performance evaluation of this kind of models is highly dependent on TensorFlow, at least in the domain of 2D image generation. Efficiency: since these metrics share the same feature extractor (Inception V3), computing all of them together is not much longer than computing just one. Fidelity: discrepancies of the metrics are checked through a number of tests.

Roughly a year ago, we wrote "What machine learning means for software development." In that article, we talked about Andrej Karpathy's concept of Software 2.0. Karpathy argues that we're at the beginning of a profound change in the way software is developed. Up until now, we've built systems by carefully and painstakingly telling systems exactly what to do, instruction by instruction. The process is slow, tedious, and error-prone; most of us have spent days staring at a program that should work, but doesn't. And most of us have been surprised when some program that has been reliable for some time suddenly screws up at some slightly unexpected input.

This mask will be automatically applied to the face by using the facial landmarks (namely the points along the chin and nose) to compute where the mask will be placed.

MathWorks has introduced the new Release 2020a with expanded AI capabilities for deep learning. The release introduces an enhanced Deep Learning Toolbox that helps users manage multiple deep learning experiments, keep track of training parameters, analyse and compare results and code with the new Experiment Manager app. It can also interactively train a network for image classification, generate MATLAB code for training, and access pretrained models with Deep Network Designer app. Engineers can now train neural networks in the updated Deep Network Designer app, manage multiple deep learning experiments in a new Experiment Manager app, and choose from more network options to generate deep learning code. R2020a introduces new capabilities specifically for automotive and wireless engineers in addition to hundreds of new and updated features for all users of MATLAB and Simulink.

Artificial Intelligence is introduced to automate the tasks done by humans, making the machine do things better, what currently humans are doing better. Humans vs Machine is another topic of debate, we are not going to do this now. We are currently focused on the jobs of these machine learning pioneers and some of the arising questions. Can a machine do the task done by these pioneers? Will there be no need for machine learning engineers or data scientists in the future?