If 2020 was the year of large language models and meta-learning, 2021 so far has been the year of large, multi-modal models that combine vision and text together. OpenAI's CLIP and DALL-E models have shown just how robust the combination of language modeling and vision can be. DALL-E in particular has shown itself to be capable of generating very impressive images based on user-specified text prompts. Presumably, there's much more to come in this area, including integrations with robotics and a continued push toward bringing AI into the physical world. New questions are being raised about when and how AI should be applied, given established problems with bias in AI algorithms.

Two years ago, Open AI released Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. Safety Gym has use cases across the reinforcement learning ecosystem. The open-source release is available on GitHub, where researchers and developers can get started with just a few lines of code. In this article, we will explore some of the alternative environments, tools and libraries for researchers to train machine learning models. AI Safety Gridworlds is a suite of reinforcement learning environments illustrating various safety properties of intelligent agents.

Last month, OpenAI cofounder Wojciech Zaremba said the company has disbanded its robotics team in a Weights & Biases podcast. "I was actually working for several years on robotics. Recently, we changed the focus at OpenAI. I disbanded the robotics team. There are actually plenty of domains that are very rich with data. Ultimately that was holding us back, in the case of robotics," said Zaremba.

All the sessions from Transform 2021 are available on-demand now. In a new paper, researchers at OpenAI have revealed details about Codex, a deep learning model that generates software source code. Codex powers Copilot, an "AI pair programmer" tool developed jointly by OpenAI and GitHub. Copilot is currently available in beta test mode to a limited number of users. The paper is a fascinating read that explains the process through which the scientists at OpenAI managed to repurpose their flagship language model GPT-3 to create Codex. But more importantly, the paper also sheds much-needed light on how far you can trust deep learning in programming.

In brief OpenAI has disbanded its AI robotics team and is no longer trying to apply machine learning to physical machines. Wojciech Zaremba, co-founder of OpenAI, who led the robotics group confirmed that the company recently broke up the team to focus working on more promising areas of artificial general intelligence research. "Here's a reveal ... as of recently we changed the focus at OpenAI, and I actually disbanded the robotics team," he said during an episode of the Weights & Biases podcast. Zaremba said a lack of training data was holding the robotics research back: there wasn't enough information on hand to teach the systems to the level of intelligence desired. "From the perspective of what we want to achieve, which is to build AGI, I think there was actually some components missing," he added.

I asked OpenAI's now-famous algorithm GPT-3 to write me a story. GPT-3 is likely one of the most powerful natural language processing (NLP) algorithms in the world. It can be used for a wide range of tasks, such as summarizing articles, powering video game dialogue, and even writing programming code.

Join live for the final day of Transform 2021, including the AI Innovation & Women in AI Awards. OpenAI has disbanded its robotics team after years of research into machines that can learn to perform tasks like solving a Rubik's Cube. Company cofounder Wojciech Zaremba quietly revealed on a podcast hosted by startup Weights & Biases that OpenAI has shifted its focus to other domains, where data is more readily available. "So it turns out that we can make a gigantic progress whenever we have access to data, and all our machine learning, unsupervised, and reinforcement learning -- they work extremely well, and there [are] actually plenty of domains that are very, very rich with data. And ultimately that was holding us back in terms of robotics," Zaremba said.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. In a new paper, researchers at OpenAI have revealed details about Codex, a deep learning model that generates software source code. Codex powers Copilot, an "AI pair programmer" tool developed jointly by OpenAI and GitHub. Copilot is currently available in beta test mode to a limited number of users. The paper is a fascinating read that explains the process through which the scientists at OpenAI managed to repurpose their flagship language model GPT-3 to create Codex.

Future climate change scenarios are usually hypothesized using simulations from weather generators. However, there only a few works comparing and evaluating promising deep learning models for weather generation against classical approaches. This study shows preliminary results making such evaluations for the multisite precipitation synthesis task. We compared two open-source weather generators: IBMWeathergen (an extension of the Weathergen library) and RGeneratePrec, and two deep generative models: GAN and VAE, on a variety of metrics. Our preliminary results can serve as a guide for improving the design of deep learning architectures and algorithms for the multisite precipitation synthesis task.

At AWS, we love sharing our passion for technology and innovation, and AWS DeepComposer is no exception. This service is designed to help everyone learn about generative artificial intelligence (AI) through the language of music. You can use a sample melody, upload your own melody, or play a tune using the virtual or a real keyboard. Best of all, you don't have to write any code. But what exactly is generative AI, and why is it useful?