Accurately forecasting extreme rainfall is notoriously difficult, but is also ever more crucial for society as climate change increases the frequency of such extremes. Global numerical weather prediction models often fail to capture extremes, and are produced at too low a resolution to be actionable, while regional, high-resolution models are hugely expensive both in computation and labour. In this paper we explore the use of deep generative models to simultaneously correct and downscale (super-resolve) global ensemble forecasts over the Continental US. Specifically, using fine-grained radar observations as our ground truth, we train a conditional Generative Adversarial Network -- coined CorrectorGAN -- via a custom training procedure and augmented loss function, to produce ensembles of high-resolution, bias-corrected forecasts based on coarse, global precipitation forecasts in addition to other relevant meteorological fields. Our model outperforms an interpolation baseline, as well as super-resolution-only and CNN-based univariate methods, and approaches the performance of an operational regional high-resolution model across an array of established probabilistic metrics. Crucially, CorrectorGAN, once trained, produces predictions in seconds on a single machine. These results raise exciting questions about the necessity of regional models, and whether data-driven downscaling and correction methods can be transferred to data-poor regions that so far have had no access to high-resolution forecasts.

GPT-3 is a 175 billion parameter AI language model that has been trained on a large amount of data. In simple terms, a language model is an AI model that can predict the next set of words given a collection of input words (very much like the auto-complete feature in search engines). Large language models, such as GPT-3, take this a step further by being able to generate source codes or stories based just on a description or suggestion. The startup behind GPT-3, OpenAI, has made its model available to developers via an API. You may sign up for it here, and you'll get a credit of $18.

Traditional deep learning models are also insufficient to help tackle the protein folding problem, as their focus is primarily on classifying …

AI Index co-director Jack Clark (currently co-founder of the AI company Anthropic, formerly of OpenAI) tells Fast Company that the AI industry is currently engaged in a debate over whether it's best to remove toxicity and bias by more careful curation of the training data, or by increasing the size of the training data set to the point where the "good" training data pushes the bad content to the margins. As tech companies large and small hurry to make big language models available through APIs or as cloud-based services, "it becomes critical to understand how the shortcomings of these models will affect safe and ethical deployment," the researchers write. There are also signs that AI companies are not ignoring the bias and ethics challenges of the technology. Researchers with industry affiliations contributed 71% more publications year-over-year in 2021 at fairness-focused industry conferences, the report says.

As a devotee of the open source software movement, he has written immensely popular web apps, peer-to-peer file exchanges, and more than 100 other pieces of code that he has given away, all in the 10 years since he graduated from college. Lately, though, Aboukhadijeh has entered a new kind of flow state, helped along by a tool called Copilot. It's a piece of artificially intelligent software that does some of the typing, and the thinking, for him. Built by OpenAI, the private research lab, and GitHub, the Microsoft-owned website where programmers share code, the tool is essentially autocomplete for software development. Much as Gmail tries to finish a sentence as you write it, Copilot offers to complete a chunk of your program.

The most recent breakthrough in deep learning research comes from OpenAI with two astonishing transformers -- GPT-3 and DALL-E [6], the former being an AI novelist and poet and the latter an AI designer and artist. With GPT-3 you can start a novel or a poem with a few sentences or paragraphs and ask the model to complete it. DALL-E model transforms text into many images.

Generative networks and OpenAI's CLIP are redefining what it means to be an artist.

Join today's leading executives online at the Data Summit live now! Code generation AI -- AI systems that can write in different programming languages given a prompt -- promise to cut development costs while allowing coders to focus on creative, less repetitive tasks. But while research labs like OpenAI and Alphabet-backed DeepMind have developed powerful code-generating AI, many of the most capable systems aren't available in open source. For example, the training data for OpenAI's Codex, which powers GitHub's Copilot feature, hasn't been made publicly available, preventing researchers from fine-tuning the AI model or studying aspects of it such as interpretability. To remedy this, researchers at Carnegie Mellon University -- Frank Xu, Uri Alon, Graham Neubig, and Vincent Hellendoorn -- developed PolyCoder, a model based on OpenAI's GPT-2 language model that was trained on a database of 249GB of code across 12 programming languages.

Microsoft and Open AI have developed a new method for fine-tuning massive AI models that are otherwise too expensive to retrain, such as GPT-3. A blog post published by Microsoft Research describes a technique called µ-Parametrization (or µP), which plays on the discovery of similarities between the behaviour of small- and large-scale AI models to minimize the quantity of compute resources required to make optimizations. Although you'd need a doctorate to make sense of the specifics, the essential message is this: with µ-Parametrization, it will be cheaper and simpler to develop larger-scale AI models capable of yielding far superior performance to those available today. As explained in the blog post, one reason large AI models are difficult to train effectively is because we have little insight into the way their behavior changes as they scale. As such, the larger the AI model, the less well-tuned researchers would currently expect it to be.

Natural selection driven by interspecific and intraspecific competition is a fundamental evolutionary mechanism that has led to the wide diversity and complexity of species inhabiting Earth. The process is mirrored to a degree in contemporary AI research, where competitive multi-agent reinforcement learning (RL) environments have enabled machines to reach superhuman performance. Designing multi-agent RL environments with conditions conducive to the development of interesting and useful agent skills can however be a time-consuming and laborious process. A common approach in single-agent settings is domain randomization, where the agent is trained on a wide distribution of randomized environments. Recent works have improved this process via automatic environment curricula techniques that adapt environment distribution during training to maximize the number of environments that produce better and more robust skills.