OpenAI has recently launched Microscope in order to help researchers understand the architecture and behaviour of neural networks in a better way. According to the company, Microscope is a library of neuron visualisations starting with nine popular or heavily neural networks -- a vast collection encompasses millions of images. As the name suggests and similar to its usage, in a laboratory, Microscope has been designed to help AI researchers better understand the complex structure of neural networks with tens of thousands of neurons. In the OpenAI Microscope website, it has been stated that the "OpenAI Microscope is a collection of visualisations of every significant layer and neuron of several common "model organisms" which are often studied in interpretability. Microscope makes it easier to analyse the features that form inside these neural networks, and we hope it will help the research community as we move towards understanding these complicated systems."

This week, we witnessed open-source tools focusing mostly on making models lighter and explainable. OpenAI, especially, has come up with an interesting tool to promote the interpretability of ML models. Furthermore, TensorFlow has made it even more simple for developers to execute their models. Let us take a look at top AI news for developers from this week. OpenAI Microscope tool is a collection of visualisations of every significant layer and neuron of eight vision'model organisms', which are often studied in interpretability.

OpenAI yesterday unveiled its Open AI Microscope, which provides visualizations of every significant layer and neuron in eight of today's most popular computer vision (CV) models. Interactions between neurons indicate the abilities of neural networks, and with machine learning trending toward increasingly complicated neural networks it is important for researchers to be able to quickly and easily conduct a closer inspection of these thousands of interactions. This is where AI Microscope comes in. Just as biologists gain insights into organisms by putting model specimens under their microscopes, AI Microscope was designed to help researchers analyze the features that form inside leading CV models. OpenAI explains that its Microscope models are composed of a graph of nodes -- neural network layers connected via edges.

OpenAI today launched Microscope, a library of neuron visualizations starting with nine popular or heavily neural networks. In all, the collection encompasses millions of images. Like a microscope can do in a laboratory, Microscope is made to help AI researchers better understand the architecture and behavior of neural networks with tens of thousands of neurons. Initial models in Microscope include historically important and commonly studied computer vision models like AlexNet, 2012 winner of the now retired ImageNet challenge. AlexNet has been cited over 50,000 times in research.

Introducing OpenAI Microscope: a collection of visualizations of every layer and neuron in eight vision "model organisms" often studied in interpretability.

Insilico Medicine is pleased to announce that it has entered into a research collaboration with Boehringer Ingelheim to utilize Insilico's generative machine learning technology and proprietary Pandomics Discovery Platform with the aim of identifying potential therapeutic targets implicated in a variety of diseases. "Insilico Medicine is very impressed with the Research Beyond Borders group at Boehringer Ingelheim capabilities in the search of potential drug targets. In this collaboration, Insilico will provide additional AI capabilities to discover novel targets for a variety of diseases to benefit the patients worldwide. We are very happy to partner with such an advanced group," said Alex Zhavoronkov, PhD, founder, and CEO of Insilico Medicine. "We believe that Insilico's exclusive Pandomics platform will provide huge boost to our ability to explore and identify drug targets. We look forward to using AI to significantly improve the drug discovery process and contribute to human health," said from Dr. Weiyi Zhang, Head of External Innovation Hub, Boehringer Ingelheim Greater China.

Easy tips to train your Reinforcement Learning AI with Unity3D using the ML-Agents Framework. My name is Sebastian Schuchmann, AI enthusiast from Germany and we are going to cover simple, beginner-friendly ways to improve your Machine Learning process. The Algorithm used is called PPO and was developed by OpenAI (founded by Elon Musk). After watching this video you will hopefully be able to train an Artificial Intelligence to crack your favorite game. I am very curious about what you guys will create!

When trained effectively, the Variational Autoencoder (VAE) can be both a powerful generative model and an effective representation learning framework for natural language. In this paper, we propose the first large-scale language VAE model, Optimus. A universal latent embedding space for sentences is first pre-trained on large text corpus, and then fine-tuned for various language generation and understanding tasks. Compared with GPT-2, Optimus enables guided language generation from an abstract level using the latent vectors. Compared with BERT, Optimus can generalize better on low-resource language understanding tasks due to the smooth latent space structure. Extensive experimental results on a wide range of language tasks demonstrate the effectiveness of Optimus. It achieves new state-of-the-art on VAE language modeling benchmarks. We hope that our first pre-trained big VAE language model itself and results can help the NLP community renew the interests of deep generative models in the era of large-scale pre-training, and make these principled methods more practical.

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.

The recent COVID-19 pandemic has highlighted the need for rapid therapeutic development for infectious diseases. To accelerate this process, we present a deep learning based generative modeling framework, CogMol, to design drug candidates specific to a given target protein sequence with high off-target selectivity. We augment this generative framework with an in silico screening process that accounts for toxicity, to lower the failure rate of the generated drug candidates in later stages of the drug development pipeline. We apply this framework to three relevant proteins of the SARS-CoV-2, the virus responsible for COVID-19, namely non-structural protein 9 (NSP9) replicase, main protease, and the receptor-binding domain (RBD) of the S protein. Docking to the target proteins demonstrate the potential of these generated molecules as ligands. Structural similarity analyses further imply novelty of the generated molecules with respect to the training dataset as well as possible biological association of a number of generated molecules that might be of relevance to COVID-19 therapeutic design. While the validation of these molecules is underway, we release ~ 3000 novel COVID-19 drug candidates generated using our framework. URL : http://ibm.biz/covid19-mol