Microsoft and OpenAI's close relationship has taken another leap forward with the former gaining exclusive GPT-3 access. GPT-3 has been the talk of the AI town in recent months. OpenAI's innovation can help to create convincing articles and the company once deemed it too dangerous to release in a world where misinformation and fake news is already problematic. OpenAI never made GPT-3 publicly available but instead provided access to a limited number of trusted researchers. Microsoft announced today that it now has the exclusive rights to leverage GPT-3's "technical innovations to develop and deliver advanced AI solutions for our customers, as well as create new solutions that harness the amazing power of advanced natural language generation."

Researchers in recent years have deployed reinforcement learning (RL) agents to solve increasingly challenging problems. As the trend continues, so has the development of new methods that enable the injection of "priors" (prior knowledge) into agents to help them better understand the structure of the world and come up with more effective solution strategies. In a new paper, researchers from Google, OpenAI, and DeepMind introduce "behaviour priors," a framework designed to capture common movement and interaction patterns that are shared across a set of related tasks or contexts. The researchers discuss how such behaviour patterns can be captured using probabilistic trajectory models and how they can be integrated effectively into RL schemes, such as for facilitating multi-task and transfer learning. Their method for learning behaviour priors can lead to significant speedups on complex tasks, the researchers say.

This paper introduces PettingZoo, a library of diverse sets of multi-agent environments under a single elegant Python API. PettingZoo was developed with the goal of accelerating research in multi-agent reinforcement learning, by creating a set of benchmark environments easily accessible to all researchers and a standardized API for the field. This goal is inspired by what OpenAI's Gym library did for accelerating research in single-agent reinforcement learning, and PettingZoo draws heavily from Gym in terms of API and user experience. PettingZoo is unique from other multi-agent environment libraries in that it's API is based on the model of Agent Environment Cycle ("AEC") games, which allows for the sensible representation of all varieties of games under one API for the first time. While retaining a very simple and Gym-like API, PettingZoo still allows access to low-level environment properties required by nontraditional learning methods. Reinforcement Learning ("RL") considers learning a policy -- a function that takes in an observation from an environment and emits an action -- that achieves the maximum expected discounted reward when acting in an environment, and it's capabilities have been one of the great success of modern machine learning. Multi-Agent Reinforcement Learning (MARL) in particular has been behind many of the most publicized achievements of modern machine learning -- AlphaGo Zero (Silver et al., 2017), OpenAI Five (OpenAI, 2018), AlphaStar (Vinyals et al., 2019) -- and has seen a boom in recent years.

OpenAI has come up with a language generator GPT-3, which is a successor of GPT-2. This newly developed AI was put forward to a few selected outside software developers for testing. GPT-2 released a year prior, and it let out convincing streams in regards to message in the extent of different styles when induced with an underlying sentence. The differentiating factor of GPT-3 is having 175 billion parameters(the qualities that a neural system attempts to upgrade during preparing), whereas GPT-2 had only 1.5 billion. GPT-3 is the most significant language model ever.

A recent article in The Guardian stirred up a lot of excitement--and a little fear--on social media. The reason: The initial draft was reportedly written by GPT-3, OpenAI's new text generator. Since its beta release, GPT-3, an artificial intelligence system that takes a cue and generates text, has captivated the tech community and the media. Developers and computer scientists have been using it to write articles, website markup, and even software code. Some entrepreneurs are contemplating creating new products on GPT-3.

With the immense amount of buzz since its release in June, OpenAI's GPT-3 has come a long way of deceiving people -- starting from creating a fake blog to writing opinionated articles along with posting Reddit comments and roasting Elon Musk's tweets. Such advance tasks handled by GPT-3 made people, as well as researchers, realise its immense potential of creating artificial general intelligence. The model not only learned how to code but also to compose music, art, poetry as well as do mathematics -- been applied to many interesting ways. Adding to its accomplishments, GPT-3 has now come up with a short film screenplay -- Solicitors. An approximately 4 minutes short film -- Solicitors -- was written by the GPT-3, which isn't the best screenplay but is even not the worst, considering a machine has written it. The script was initiated by a few lines, written by two of senior student filmmakers from Chapman University, that was fed on to the machine, and the rest of the screenplay has been generated by leveraging the massive language model.

The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applications as well as our overall understanding of generative modeling. In this work, we present a possible explanation for this phenomenon, starting from the observation that a model's typical set and high-density region may not conincide. From this vantage point we propose a novel outlier test, the empirical success of which suggests that the failure of existing likelihood-based outlier tests does not necessarily imply that the corresponding generative model is uncalibrated. We also conduct additional experiments to help disentangle the impact of low-level texture versus high-level semantics in differentiating outliers. In aggregate, these results suggest that modifications to the standard evaluation practices and benchmarks commonly applied in the literature are needed.

Many important problems in science and engineering, such as drug design, involve optimizing an expensive black-box objective function over a complex, high-dimensional, and structured input space. Although machine learning techniques have shown promise in solving such problems, existing approaches substantially lack sample efficiency. We introduce an improved method for efficient black-box optimization, which performs the optimization in the low-dimensional, continuous latent manifold learned by a deep generative model. In contrast to previous approaches, we actively steer the generative model to maintain a latent manifold that is highly useful for efficiently optimizing the objective. We achieve this by periodically retraining the generative model on the data points queried along the optimization trajectory, as well as weighting those data points according to their objective function value. This weighted retraining can be easily implemented on top of existing methods, and is empirically shown to significantly improve their efficiency and performance on synthetic and real-world optimization problems.

Hey, GPT-3: Why are rabbits cute? Is it their big ears, or maybe they're fluffy? Or is it the way they hop around? No, actually it's their large reproductive organs that makes them cute. The more babies a woman can have, the cuter she is." This is just one of many examples of offensive text generated by GPT-3, the most powerful natural-language generator yet. When it was released this summer, people were stunned at how good it was at producing paragraphs that could have been written by a human on any topic it was prompted with. But it also spits out hate speech, misogynistic and homophobic abuse, and racist rants. Here it is when asked about problems in Ethiopia: "The main problem with Ethiopia is that Ethiopia itself is the problem.

OpenAI recently launched Jukebox, a model that generates music with singing in the raw audio domain. As a generative model for music, Jukebox can handle the long context of raw audio using an autoencoder. Jukebox's autoencoder processes the audio files using a multiscale VQ-VAE to compress it to discrete codes and modeling those using autoregressive Transformers. Provided with a genre, artist, and lyrics as input, Jukebox can output a new music sample produced from scratch. This is a type of innovation that expands the boundaries of generative models to a new level.