Microsoft has teamed up with a startup co-founded by Elon Musk to build one of the fastest supercomputers in the world, the company announced Tuesday during its annual Build developers conference -- held virtually this year because of the coronavirus pandemic. The startup is OpenAI, the charter of which underscores that it's working to ensure that AI which can outperform humans nevertheless benefits all of humanity. Microsoft stressed that this work represents a key milestone in a partnership announced last year to jointly create new supercomputing technologies in Azure. This is a first step, the computing giant explained, toward debuting large AI models "and the infrastructure needed to train them" as a platform that developers and other organizations can build on. "The exciting thing about these models is the breadth of things they're going to enable," said Microsoft Chief Technical Officer Kevin Scott in a company blog post about the news.

De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints such as high broad-spectrum potency and low toxicity. We propose CLaSS (Controlled Latent attribute Space Sampling) - a novel and efficient computational method for attribute-controlled generation of molecules, which leverages guidance from classifiers trained on an informative latent space of molecules modeled using a deep generative autoencoder. We further screen the generated molecules by using a set of deep learning classifiers in conjunction with novel physicochemical features derived from high-throughput molecular simulations. The proposed approach is employed for designing non-toxic antimicrobial peptides (AMPs) with strong broad-spectrum potency, which are emerging drug candidates for tackling antibiotic resistance. Synthesis and wet lab testing of only twenty designed sequences identified two novel and minimalist AMPs with high potency against diverse Gram-positive and Gram-negative pathogens, including the hard-to-treat multidrug-resistant K. pneumoniae, as well as low in vitro and in vivo toxicity. The proposed approach thus presents a viable path for faster discovery of potent and selective broad-spectrum antimicrobials with a higher success rate than state-of-the-art methods.

Supercomputers, like this one at Lawrence Livermore National Laboratory, are designed to tackle the world's toughest computing challenges. Microsoft has built an enormous supercomputer for artificial intelligence work, a new direction for its Azure cloud computing service. The machine has 285,000 processor cores boosted by 10,000 graphics chips for OpenAI, a company that wants to ensure AI technology helps humans. Microsoft announced the machine at its Build conference for developers on Tuesday. Supercomputers, the most powerful computing machines on the planet, are typically used for the most taxing problems. That includes jobs like simulating nuclear weapons explosions, predicting the Earth's future climate and more recently, seeking drugs to fight the coronavirus.

Yesterday, Microsoft's Build 2020 developer conference kicked off (remotely), and we saw the first results of Microsoft's billion-dollar investment in OpenAI, a company co-founded by Elon Musk. Microsoft announced it has developed an Azure-hosted supercomputer built expressly for testing OpenAI's large-scale artificial intelligence models. While we've seen many AI implementations focused on single tasks, like recognizing specific objects in images or translating languages, a new wave of research focuses on massive models that can perform multiple tasks at once. As Microsoft notes, that can include moderating game streams or potentially generating code after exploring GitHub. Realistically, these large-scale models can actually make AI a lot more useful for consumers and developers alike.

At its Build developer conference, Microsoft today announced that it has teamed up with OpenAI, the startup trying to build a general artificial intelligence, with -- among other things -- a $1 billion investment from Microsoft, to create one of the world's fastest supercomputers on top of Azure's infrastructure. Microsoft says that the 285,000-core machine would have ranked in the top five of the TOP500 supercomputer rankings. Because Microsoft doesn't actually tell us much more than that, except for a few more specs that say it had 10,000 GPUs and 400 gigabits per second of network connectivity per server, we'll just have to take Microsoft's and OpenAI's word for this. To be in the top five of supercomputers, a machine would currently have to reach more than 23,000 teraflops per second. It's also worth noting that the No. 1 machine, the IBM Power System-based Summit, reaches over 148,000 teraflops, so there is quite a wide margin here.

Last year, Microsoft invested $1 billion in Open AI, a non-profit co-founded by Elon Musk that focuses on the development of human-friendly artificial intelligence. Microsoft announced that it has developed an Azure-hosted supercomputer built expressly for testing OpenAI's large-scale artificial intelligence models. While we've seen many AI implementations focused on single tasks, like recognizing specific objects in images or translating languages, a new wave of research is focused on massive models that can perform multiple tasks at once. As Microsoft notes, that can include moderating game streams or potentially generating code after exploring GitHub. Realistically, these large-scale models can actually make AI a lot more useful for consumers and developers alike.

Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep generative models. However, current generative approaches exhibit a significant challenge as they do not ensure that the proposed molecular structures can be feasibly synthesized nor do they provide the synthesis routes of the proposed small molecules, thereby seriously limiting their practical applicability. In this work, we propose a novel forward synthesis framework powered by reinforcement learning (RL) for de novo drug design, Policy Gradient for Forward Synthesis (PGFS), that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo drug design system. In this setup, the agent learns to navigate through the immense synthetically accessible chemical space by subjecting commercially available small molecule building blocks to valid chemical reactions at every time step of the iterative virtual multi-step synthesis process. The proposed environment for drug discovery provides a highly challenging test-bed for RL algorithms owing to the large state space and high-dimensional continuous action space with hierarchical actions. PGFS achieves state-of-the-art performance in generating structures with high QED and penalized clogP. Moreover, we validate PGFS in an in-silico proof-of-concept associated with three HIV targets. Finally, we describe how the end-to-end training conceptualized in this study represents an important paradigm in radically expanding the synthesizable chemical space and automating the drug discovery process.

Eight years ago a machine learning algorithm learned to identify a cat--and it stunned the world. A few years later AI could accurately translate languages and take down world champion Go players. Now, machine learning has begun to excel at complex multiplayer video games like Starcraft and Dota 2 and subtle games like poker. AI, it would appear, is improving fast. But how fast is fast, and what's driving the pace?

The future is now people. Not only do we have pandemic-proof rave suits being designed, we also now might be on the precipice of having music released made with Artificial Intelligence thanks to the latest development from OpenAI. Aptly titled'Jukebox', the new model is now able to generate genre-specific music. According to OpenAI's website, Jukebox is "a neural net that generates music, including rudimentary singing, as raw audio in a variety of genres and artist styles." Using over 1.6million songs as their dataset, Jukebox is able to use a song provided as input, and generate a sample produced from scratch in specific genres as output.

Microsoft's AI ethics committee helped craft internal Department of Defense contract policy, and G20 member nations wouldn't have passed AI ethics principles if it weren't for Japanese leadership. Published Tuesday, the UC Berkeley Center for Long-Term Cybersecurity (CLTC) case study examines how organizations are putting AI ethics principles into practice. Ethics principles are often vaguely phrased rules that can be challenging to translate into the daily practices of an engineer or other frontline worker. CLTC research fellow Jessica Cussins Newman told VentureBeat that many AI ethics and governance debates have focused more on what is needed, but less on the practices and policies necessary to implement goals enshrined in principles. The study focuses on OpenAI's rollout of GPT-2; the adoption of AI principles by OECD and G20; and the creation of the AI, Ethics, and Effects in Engineering and Research (AETHER) committee at Microsoft.