Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized "ChemAI", a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. Additionally, we screened the DrugBank using ChemAI to allow for drug repurposing, which would be a fast way towards a therapy. We provide these top-ranked compounds of ZINC and DrugBank as a library for further screening with bioassays at https://github.com/ml-jku/sars-cov-inhibitors-chemai.

For a linear system, the response to a stimulus is often superposed by its responses to other decomposed stimuli. In quantum mechanics, a state is the superposition of multiple eigenstates. Here, by taking advantage of the phase difference, a common feature as we identified in data sets, we propose eigen component analysis (ECA), an interpretable linear learning model that incorporates the principle of quantum mechanics into the design of algorithm design for feature extraction, classification, dictionary and deep learning, and adversarial generation, etc. The simulation of ECA, possessing a measurable $class\text{-}label$ $\mathcal{H}$, on a classical computer outperforms the existing classical linear models. Eigen component analysis network (ECAN), a network of concatenated ECA models, enhances ECA and gains the potential to be not only integrated with nonlinear models, but also an interface for deep neural networks to implement on a quantum computer, by analogizing a data set as recordings of quantum states. Therefore, ECA and ECAN promise to expand the feasibility of linear learning models, by adopting the strategy of quantum machine learning to replace heavy nonlinear models with succinct linear operations in tackling complexity.

Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics. Such approaches find TSP solutions of good quality but require additional procedures such as beam search and sampling to improve solutions and achieve state-of-the-art performance. However, few studies have focused on improvement heuristics, where a given solution is improved until reaching a near-optimal one. In this work, we propose to learn a local search heuristic based on 2-opt operators via deep reinforcement learning. We propose a policy gradient algorithm to learn a stochastic policy that selects 2-opt operations given a current solution. Moreover, we introduce a policy neural network that leverages a pointing attention mechanism, which unlike previous works, can be easily extended to more general k-opt moves. Our results show that the learned policies can improve even over random initial solutions and approach near-optimal solutions at a faster rate than previous state-of-the-art deep learning methods.

Generative AI is one of the exciting recent advancements in artificial intelligence technology because of its ability to create something new. From turning sketches into images for accelerated product development, to improving computer-aided design of complex objects, there are many practical applications emerging across industries. This Generative AI technique pits two different neural networks against each other to produce new and original digital works based on sample inputs. Until now, developers interested in growing skills in this area haven't had an easy way to get started. With AWS DeepComposer, developers, regardless of their background in ML, can get started with Generative Adversarial Networks (GANs), learning how to train and optimize them to create original music.

DeepMind's breakthroughs in recent years are well documented, and the UK AI company has repeatedly stressed that mastering Go, StarCraft, etc. were not ends in themselves but rather steps toward artificial general intelligence (AGI). DeepMind's latest achievement stays on path: Agent57 is the ultimate gamer, the first deep reinforcement learning (RL) agent to top human baseline scores on all games in the Atari57 test set.

We are awash in digital images from photos, videos, Instagram, YouTube, and increasingly live video streams. Working with image data is hard as it requires drawing upon knowledge from diverse domains such as digital signal processing, machine learning, statistical methods, and these days, deep learning. Deep learning methods are out-competing the classical and statistical methods on some challenging computer vision problems with singular and simpler models. In this crash course, you will discover how you can get started and confidently develop deep learning for computer vision problems using Python in seven days. Note: This is a big and important post. You might want to bookmark it.

NVIDIA has early identified the promising HPC – AI convergence trend and has been working on enabling it. The growing adoption of NVIDIA Volta GPU by the Top 500 Supercomputers highlights the need of computing acceleration for this HPC & AI convergence. Many projects today demonstrate the benefit of AI for HPC, in terms of accuracy and time to solution, in many domains such as Computational Mechanics (Computational Fluid Mechanics, Solid Mechanics…), Earth Sciences (Climate, Weather and Ocean Modeling), Life Sciences (Genomics, Proteomics…), Computational Chemistry (Quantum Chemistry, Molecular Dynamics…), Computational Physics. NVIDIA today for instance, uses Physics Informed Neural Networks for the heat sink design in our DGX system.

Development of artificial intelligence agents tends to frequently be measured by their performance in games, but there's a good reason for that: Games tend to offer a wide proficiency curve, in terms of being relatively simple to grasp the basics, but difficult to master, and they almost always have a built-in scoring system to evaluate performance. DeepMind's agents have tackled board game Go, as well as real-time strategy video game StarCraft. But the Alphabet company's most recent feat is Agent57, a learning agent that can beat the average human on each of 57 Atari games with a wide range of difficulty, characteristics and gameplay styles. Being better than humans at 57 Atari games may seem like an odd benchmark against which to measure the performance of a deep learning agent, but it's actually a standard that goes all the way back to 2012, with a selection of Atari classics including Pitfall, Solaris, Montezuma's Revenge and many others. Taken together, these games represent a broad range of difficulty levels, as well as requiring a range of different strategies in order to achieve success.

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Driven by the Mantra – Cashless Convenient Credit, ePayLater offers the simplest possible checkout experience in existence today, providing customers with the ability to conclude a transaction with just a click of the mouse or a tap of the touchscreen. It is a'Buy Now, Pay Later' solution through which customers can get access to an instant credit limit to make faster purchases, that too without having to pay at the same time. In a candid conversation with Express Computer's Gairika Mitra, Akshat Saxena, Co-Founder, ePayLater discusses about ePayLater in detail, the latest technology used, and much more… We have come up with an innovative solution to empower individuals to transact on credit anytime anywhere. Our partnership with IDFC Bank got us unprecedented access to the UPI system, which means we now offer credit services on merchant portals which are UPI-enabled and BharatQR network. Since the partnership, our customers have taken up Scan and Pay in a big way and we see almost 10-15% growth month on month in terms of UPI transactions.