Microsoft is investing $1 billion in OpenAI, a San Francisco-based research lab founded by Silicon Valley luminaries, including Elon Musk and Sam Altman, that's dedicated to creating artificial general intelligence (AGI). The investment will make Microsoft the "exclusive" provider of cloud computing services to OpenAI, and the two companies will work together to develop new technologies. OpenAI will also license some of its tech to Microsoft to commercialize, though when this may happen and what tech will be involved has yet to be announced. OpenAI began as a nonprofit research lab in 2015 and was intended to match the high-tech R&D of companies like Google and Amazon while focusing on developing AI in a safe and democratic fashion. But earlier this year, OpenAI said it needed more money to continue this work, and it set up a new for-profit firm to seek outside investment. To attract backers, OpenAI has made outrageous promises about the potential of its technology.

We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL) research. In MARL, it usually requires customizing observations, rewards and actions for each agent, changing cooperative-competitive agent-interaction, and playing with/against a third-party agent, etc. We provide a novel modular design, called Interface, for manipulating such routines in essentially two ways: 1) Different interfaces can be concatenated and combined, which extends the OpenAI Gym Wrappers concept to MARL scenarios. 2) During MARL training or testing, interfaces can be embedded in either wrapped OpenAI Gym compatible Environments or raw environment compatible Agents. We offer off-the-shelf interfaces for several popular MARL platforms, including StarCraft II, Pommerman, ViZDoom, Soccer, etc. The interfaces effectively support self-play RL and cooperative-competitive hybrid MARL. Also, Arena can be conveniently extended to your own favorite MARL platform.

In 2015, car-and-rocket man Elon Musk joined with influential startup backer Sam Altman to put artificial intelligence on a new, more open course. They cofounded a research institute called OpenAI to make new AI discoveries and give them away for the common good. Now, the institute's researchers are sufficiently worried by something they built that they won't release it to the public. The AI system that gave its creators pause was designed to learn the patterns of language. It does that very well--scoring better on some reading-comprehension tests than any other automated system.

Grover's architecture is based on OpenAI's GPT-2, a powerful pre-training model. The training has been performed on randomly-sampled sequences from RealNews dataset and the Newspaper Python library has been used to extract the body and metadata from each article. The dataset used for building this model is called as RealNews which is a fairly large corpus of news articles from Common Crawl. For training the model, the researchers construct a large corpus of news articles with metadata from Common Crawl which includes 5,000 news domains indexed by Google News. News from Common Crawl from December 2016 through March 2019 were used as training data and the news articles published in April 2019 from the April 2019 were used as test data.

The latest addition to this growing array is a platform called Runway ML, which hosts dozens of AI-based creative functions ranging from utilitarian tasks to experimental works-in-progress and whimsical novelties. The hub draws together a range of recent breakthroughs that have transformed the creative capacity of AI, including research group OpenAI's text generator, GPT-2, new methods of image fabrication and an Nvidia web tool that turns doodles into photorealistic landscapes. "We are on the verge of a new creative revolution," Runway ML's mission statement says. "Recent advances in machine learning and artificial intelligence research are producing radical changes in the way digital content is made, understood and processed, unfastening previously unimagined ways of creating." The key innovation driving much of this progress is a machine-learning model called a Generative Adversarial Network (GAN), in which a neural network generates images by honing them until another neural network can no longer tell the difference between the fake images and the real ones of the data set.

OpenAI conducts an enormous amount of research in AI subfields from computer vision to natural language processing (NLP). The San Francisco-based firm -- which was cofounded by CTO Greg Brockman, chief scientist Ilya Sutskever, and others with a $1 billion in backing from luminaries like LinkedIn cofounder Reid Hoffman and Y Combinator chairman Sam Altman -- last year detailed an AI robotics system capable of human-like dexterity. The capped-profit company's Dota 2 bot recently defeated 99.4% of players in public matches and a team of professional players twice, and its most sophisticated NLP model can generate convincingly humanlike short stories and Amazon reviews from whole cloth. Unsurprisingly, there's been a lot of learnings in the roughly three and a half years since OpenAI's inception. At VentureBeat's Transform 2019 conference, Brockman and Sutskever touched on advances in hardware and transparency with respect to AI, and on the topic of responsible disclosure.

Out-of-distribution (OoD) detection is a natural downstream task for deep generative models, due to their ability to learn the input probability distribution. There are mainly two classes of approaches for OoD detection using deep generative models, viz., based on likelihood measure and the reconstruction loss. However, both approaches are unable to carry out OoD detection effectively, especially when the OoD samples have smaller variance than the training samples. For instance, both flow based and VAE models assign higher likelihood to images from SVHN when trained on CIFAR-10 images. We use a recently proposed generative model known as neural rendering model (NRM) and derive metrics for OoD. We show that NRM unifies both approaches since it provides a likelihood estimate and also carries out reconstruction in each layer of the neural network. Among various measures, we found the joint likelihood of latent variables to be the most effective one for OoD detection. Our results show that when trained on CIFAR-10, lower likelihood (of latent variables) is assigned to SVHN images. Additionally, we show that this metric is consistent across other OoD datasets. To the best of our knowledge, this is the first work to show consistently lower likelihood for OoD data with smaller variance with deep generative models.

Deep generative models for symbolic music are typically designed to model temporal dependencies in music so as to predict the next musical event given previous events. In many cases, such models are expected to learn abstract concepts such as harmony, meter, and rhythm from raw musical data without any additional information. In this study, we investigate the effects of explicitly conditioning deep generative models with musically relevant information. Specifically, we study the effects of four different conditioning inputs on the performance of a recurrent monophonic melody generation model. Several combinations of these conditioning inputs are used to train different model variants which are then evaluated using three objective evaluation paradigms across two genres of music. The results indicate musically relevant conditioning significantly improves learning and performance, and reveal how this information affects learning of musical features related to pitch and rhythm. An informal subjective evaluation suggests a corresponding improvement in the aesthetic quality of generations.

In this work, we focus on fine-tuning an OpenAI GPT-2 pre-trained model for generating patent claims. GPT-2 has demonstrated impressive efficacy of pre-trained language models on various tasks, particularly coherent text generation. Patent claim language itself has rarely been explored in the past and poses a unique challenge. We are motivated to generate coherent patent claims automatically so that augmented inventing might be viable someday. In our implementation, we identified a unique language structure in patent claims and leveraged its implicit human annotations. We investigated the fine-tuning process by probing the first 100 steps and observing the generated text at each step. Based on both conditional and unconditional random sampling, we analyze the overall quality of generated patent claims. Our contributions include: (1) being the first to generate patent claims by machines and being the first to apply GPT-2 to patent claim generation, (2) providing various experiment results for qualitative analysis and future research, (3) proposing a new sampling approach for text generation, and (4) building an e-mail bot for future researchers to explore the fine-tuned GPT-2 model further.

Training generative models like generative adversarial networks (GANs) and normalizing flows is challenging for noisy data. A novel curriculum learning algorithm pertaining to clustering is proposed to address this issue in this paper. The curriculum construction is based on the centrality of underlying clusters in data points. The data points of high centrality takes priority of being fed into generative models during training. To make our algorithm scalable to large-scale data, the active set is devised, in the sense that every round of training proceeds only on an active subset containing a small fraction of already trained data and the incremental data of lower centrality. Moreover, the geometric analysis is presented to interpret the necessity of cluster curriculum for generative models. The experiments on cat and human-face data validate that our algorithm is able to learn the optimal generative models (e.g. ProGAN and Glow) with respect to specified quality metrics for noisy data. An interesting finding is that the optimal cluster curriculum is closely related to the critical point of a geometric percolation process formulated in the paper.