We're releasing an API for accessing new AI models developed by OpenAI. Unlike most AI systems which are designed for one use-case, the API today provides a general-purpose "text in, text out" interface, allowing users to try it on virtually any English language task. You can now request access in order to integrate the API into your product, develop an entirely new application, or help us explore the strengths and limits of this technology. Given any text prompt, the API will return a text completion, attempting to match the pattern you gave it. You can "program" it by showing it just a few examples of what you'd like it to do; its success generally varies depending on how complex the task is.

OpenAI, the machine learning nonprofit co-founded by Elon Musk, has released its first commercial product: a rentable version of a text generation tool the organisation once deemed too dangerous to release. Dubbed simply "the API", the new service lets businesses directly access the most powerful version of GPT-3, OpenAI's general purpose text generation AI. The tool is already a more than capable writer. Feeding an earlier version of the opening line of George Orwell's Nineteen Eighty-Four – "It was a bright cold day in April, and the clocks were striking thirteen" – the system recognises the vaguely futuristic tone and the novelistic style, and continues with: "I was in my car on my way to a new job in Seattle. I put the gas in, put the key in, and then I let it run. I just imagined what the day would be like. In 2045, I was a teacher in some school in a poor part of rural China. I started with Chinese history and history of science."

The recent deep generative models for static graphs that are now being actively developed have achieved significant success in areas such as molecule design. However, many real-world problems involve temporal graphs whose topology and attribute values evolve dynamically over time, including important applications such as protein folding, human mobility networks, and social network growth. As yet, deep generative models for temporal graphs are not yet well understood and existing techniques for static graphs are not adequate for temporal graphs since they cannot 1) encode and decode continuously-varying graph topology chronologically, 2) enforce validity via temporal constraints, or 3) ensure efficiency for information-lossless temporal resolution. To address these challenges, we propose a new model, called ``Temporal Graph Generative Adversarial Network'' (TG-GAN) for continuous-time temporal graph generation, by modeling the deep generative process for truncated temporal random walks and their compositions. Specifically, we first propose a novel temporal graph generator that jointly model truncated edge sequences, time budgets, and node attributes, with novel activation functions that enforce temporal validity constraints under recurrent architecture. In addition, a new temporal graph discriminator is proposed, which combines time and node encoding operations over a recurrent architecture to distinguish the generated sequences from the real ones sampled by a newly-developed truncated temporal random walk sampler. Extensive experiments on both synthetic and real-world datasets demonstrate TG-GAN significantly outperforms the comparison methods in efficiency and effectiveness.

In this paper, we propose a structural design methodology called \textit{data-driven topology design}, which aims to obtain high-performance material distributions for a multi-objective optimization problem from the initially given material distributions in a given design domain. Its basic idea is iterating the following processes: (i) selecting the material distributions from a dataset according to Pareto optimality, (ii) generating new material distributions using a deep generative model with the selected material distributions as the training data, and (iii) integrating the generated material distributions into the dataset. Because of the nature of a deep generative model, the generated material distributions are diverse and inheriting features of the training data, which are material distributions on the Pareto front at that specific point. Therefore, it is expected that some of the generated material distributions are superior to the training data, whereas some are inferior, and the Pareto front is improved by integrating the generated material distributions into the dataset. The Pareto front is further improved by iterating the above processes. Data-driven topology design is used to enhance a support system for determining appropriate formulations of topology optimization problems, and its usefulness is demonstrated through numerical examples.

Last year, Microsoft announced a billion-dollar investment in OpenAI, an organization whose mission is to create artificial general intelligence and make it safe for humanity. Just computers with general intelligence helping us solve our biggest problems. A year on, we have the first results of that partnership. At this year's Microsoft Build 2020, a developer conference showcasing Microsoft's latest and greatest, the company said they'd completed a supercomputer exclusively for OpenAI's machine learning research. But this is no run-of-the-mill supercomputer.

Brooks, the Chairman, and CTO of Rethink Robotics has stated that much of the misunderstanding has come from the overhyping of certain parts of AI, such as engines beating out professional players in video games like Dota 2 (OpenAI) and board games like Go. "An AI system can play chess fantastically, but it doesn't even know that it's playing a game," says Brooks, CTO of Rethink Robotics. When you see how a program learned something that a human can learn, you make the mistake of thinking it has the richness of understanding that you would have." Even then, for example, it took many attempts (around 459 attempts and 10, 000 hours of'AI' simulated gameplay) for OpenAI to beat a top e-sports team in a game of Dota 2. In the end, machine learning is usually a mundane task of using data to form patterns or understandings for the AI to do a specified job. To give an example, for computer vision, data needs to be'labeled'. This could be identifying cars within a photo (see the example below with vehicles).

Musk was responding to a massive feature story published in the MIT Technology Review about OpenAI, the AI research lab founded in part by Elon Musk, alongside others. The lab operates with the mission of developing safe and ethical AI that'll be good for the world. But MIT Tech's reporting tells of how Open AI went from being a transparent organization to a relatively opaque one (hence Musk's preceding Tweet about OpenAI needing to "be more open"). Musk's ability to self-aggrandize or self-flagellate is usually surprising in equal measure, but never shocking: Industries often argue for their own regulation as a way to keep government regulators off their backs. Though credit where it's due: Musk has been, as in the case of when he argued in favor of regulating autonomous weapons, more substantially -- and more effectively -- vocal than most when it comes to regulating AI. Whether or not this will have any substantial effects on other companies (statements from CEOs, regulatory commission efforts, etc) let alone Tesla or OpenAI will be nothing if not a compelling plot to watch.

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.