The field of Artificial Intelligence powered by Machine Learning and Deep Learning has gone through some phenomenal changes over the last decade. Starting off as just a pure academic and research-oriented domain, we have seen widespread industry adoption across diverse domains including retail, technology, healthcare, science and many more. More than often, the standard toolbox of machine learning, statistical or deep learning models remain the same. New models do come into existence like Capsule Networks, but industry adoption of the same usually takes several years. Hence, in the industry, the main focus of data science or machine learning is more'applied' rather than theoretical and effective application of these models on the right data to solve complex real-world problems is of paramount importance.

Algorithmic trading has revolutionised the stock market and its surrounding industry. Over 70% of all trades happening in the US right now are being handled by bots[1]. Gone are the days of the packed stock exchange with suited people waving sheets of paper shouting into telephones. This got me thinking of how I could develop my own algorithm for trading stocks, or at least try to accurately predict them. I've learned a lot about neural networks and machine learning over the summer and one of the most recent and applicable ML technologies I learnt about is the LSTM cell [2]. Long Short Term Memory cells are like mini neural networks designed to allow for memory in a larger neural network.

Animating characters is a difficult task when it comes to interacting with objects and the environment. What if we used computer brains instead? In this research, we present the Neural State Machine, a data-driven deep learning framework that can handle such animations. The system is able to learn character-scene interactions from motion capture data, and produces high-quality animations from simple control commands. The framework can be used for creating natural animations in games and films, and is the first of such frameworks to handle scene interaction tasks for data-driven character animation.

Interpretability remains one of the biggest challenges of modern deep learning applications. The recent advancements in computation models and deep learning research have enabled the creation of highly sophisticated models that can include thousands of hidden layers and tens of millions of neurons. While its relatively simple to create incredibly advanced deep neural network models, its understanding how those models create and use knowledge remains a challenge. Recently, researchers from the Google Brain team published a paper proposing a new method called Concept Activation Vectors(CAVs) that takes a new angle to the interpretability of deep learning models. To understand the CAV technique, it is important to understand the nature of the interpretability challenge in deep learning models.

Due to the ambiguity in Deep Learning solutions, there has been a lot of talk about how to make explainability inclusive of an ML pipeline. Explainable AI refers to methods and techniques in the application of artificial intelligence technology (AI) such that the results of the solution can be understood by human experts. It contrasts with the concept of the "black box" in machine learning and enables transparency. The first picture consists of a bunch of mathematical expressions chained together that represent the way inner layers of an algorithm or a neural network functions. Whereas, the second picture also contains the working of an algorithm but the message is more lucid.

AutoAugment is an augmentation strategy that employs a search algorithm to find an augmentation policy that will yield the best results on the model. Each policy has several sub-policies. One sub-policy is randomly chosen for each image. Each sub-policy consists of an image processing function and the probability that the functions are applied with. The image processing operations could be translation, shearing or rotation.

In this paper, iSIZE proposes deep neural networks as precoding components for current and future codec ecosystems.

MaxQ AI, an Israeli-based medical diagnostic AI company announced that the company's Accipio intracranial hemorrhage (ICH) and stroke software will be integrated on Philips' computed tomography (CT) systems. MaxQ AI will be demonstrating the company's full suite of Accipio solutions during the upcoming Radiological Society of North America (RSNA) 2019 Annual Meeting in Chicago. The integration of Accipio's AI-powered solutions into Philips CT systems will support the detection of ICH to augment caregivers in identifying and prioritizing patients suffering from stroke, traumatic brain injury, head trauma, and other life-threatening conditions. The Accipio platform will be available with new Philips CT systems and as an upgrade to previously installed Philips CT systems throughout U.S. and E.U. markets. MaxQ AI's ACCIPIO ICH and Stroke Platform utilizes deep learning technologies to analyze medical imaging data such as non-contrast head CT images.

Like all other imaging modalities, the resolution, speed, sample preservation, and signal-to-noise ratio (SNR) of point scanning systems are difficult to optimize simultaneously. In particular, point scanning systems are uniquely constrained by an inverse relationship between imaging speed and pixel resolution. Here we show these limitations can be mitigated via the use of deep learning-based super-sampling of undersampled images acquired on a point-scanning system, which we termed point-scanning super-resolution (PSSR) imaging. Oversampled, high SNR ground truth images acquired on scanning electron or Airyscan laser scanning confocal microscopes were'crappified' to generate semi-synthetic training data for PSSR models that were then used to restore real-world undersampled images. Remarkably, our EM PSSR model could restore undersampled images acquired with different optics, detectors, samples, or sample preparation methods in other labs.