Schmidt Futures, a philanthropic initiative co-founded by former Google CEO and Chairman Eric ... [ ] Schmidt and his wife Wendy, is expanding its investment in artificial intelligence research. Schmidt Futures announced today that it was investing $148 million to fund the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship, a program of Schmidt Futures. With this newest funding, Schmidt Futures, a philanthropic initiative co-founded by former Google CEO and Chairman Eric Schmidt and his wife Wendy, has now committed a total of $400 million to support the development of artificial intelligence (AI) for scientific discovery for other advances in technology and engineering fields. According to the announcement, the new funding will initially support about 160 postdoctoral fellows at nine universities around the world to learn and apply AI methods to their research. The fellowship is expected to expand to more institutions and countries in the future.

When you picture a scientist, do you see a white coat-clad PhD-holder pipetting away at a lab bench? Or maybe a skygazer with a different day job who goes out on clear nights for a good view of the stars? Historically speaking, both of those examples fit the bill. German-British astronomer William Herschel was originally an amateur who observed the night sky using homemade telescopes. He discovered Uranus in 1781, working alongside his sister, Caroline Herschel, who made multiple discoveries herself.

Abstract: In many scenarios such as genome-wide association studies where dependences between variables commonly exist, it is often of interest to infer the interaction effects in the model. However, testing pairwise interactions among millions of variables in complex and high-dimensional data suffers from low statistical power and huge computational cost. To address these challenges, we propose a two-stage testing procedure with false discovery rate (FDR) control, which is known as a less conservative multiple-testing correction. Theoretically, the difficulty in the FDR control dues to the data dependence among test statistics in two stages, and the fact that the number of hypothesis tests conducted in the second stage depends on the screening result in the first stage. By using the Cramér type moderate deviation technique, we show that our procedure controls FDR at the desired level asymptotically in the generalized linear model (GLM), where the model is allowed to be misspecified.

The focus of the DLR Institute for Data Science in Jena is to find solutions for the major challenges of the digitalisation age. The research focuses on the areas of data extraction and mobilisation, data management and preparation, and data analysis and intelligence. The position is part of the BMBF project Avatar (anonymisation of personal health data by creating virtual avatars). Topics include, in particular, the semantic modelling of relevant metadata and data discovery. The overall goal of the project is providing anonymised health data for both academic and commercial research.

Did you miss a session from MetaBeat 2022? Head over to the on-demand library for all of our featured sessions here. Text-to-image AI systems such as DALL-E 2, Imagen and Midjourney are growing in popularity and capability right now, offering creators a revolutionary new way to produce content. Generating images from text prompts is a radical new approach to art-making and creative expression. But it also gives us the first glimpse of a fundamental shift in how we can better communicate and collaborate with our machines.

Humans have the remarkable ability to recognize and acquire novel visual concepts in a zero-shot manner. Given a high-level, symbolic description of a novel concept in terms of previously learned visual concepts and their relations, humans can recognize novel concepts without seeing any examples. Moreover, they can acquire new concepts by parsing and communicating symbolic structures using learned visual concepts and relations. Endowing these capabilities in machines is pivotal in improving their generalization capability at inference time. In this work, we introduce Zero-shot Concept Recognition and Acquisition (ZeroC), a neuro-symbolic architecture that can recognize and acquire novel concepts in a zero-shot way. ZeroC represents concepts as graphs of constituent concept models (as nodes) and their relations (as edges). To allow inference time composition, we employ energy-based models (EBMs) to model concepts and relations. We design ZeroC architecture so that it allows a one-to-one mapping between a symbolic graph structure of a concept and its corresponding EBM, which for the first time, allows acquiring new concepts, communicating its graph structure, and applying it to classification and detection tasks (even across domains) at inference time. We introduce algorithms for learning and inference with ZeroC. We evaluate ZeroC on a challenging grid-world dataset which is designed to probe zero-shot concept recognition and acquisition, and demonstrate its capability.

Riemannian geometry is a mathematical field which has been the cornerstone of revolutionary scientific discoveries such as the theory of general relativity. Despite early uses in robot design and recent applications for exploiting data with specific geometries, it mostly remains overlooked in robotics. With this blue sky paper, we argue that Riemannian geometry provides the most suitable tools to analyze and generate well-coordinated, energy-efficient motions of robots with many degrees of freedom. Via preliminary solutions and novel research directions, we discuss how Riemannian geometry may be leveraged to design and combine physically-meaningful synergies for robotics, and how this theory also opens the door to coupling motion synergies with perceptual inputs.

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While he was examining algae from a nearby lake through his homemade microscope, a creature "with green and very glittering little scales," which he estimated to be a thousand times smaller than a mite, had darted across his vision. Two years later, on October 9, 1676, he followed up with another report so extraordinary that microbiologists today refer to it simply as "Letter 18": Van Leeuwenhoek (lay-u-when-hoke) had looked everywhere and found what he called animalcules (Latin for "little animals") in everything. He found them in the bellies of other animals, his food, his own mouth, and other people's mouths. When he noticed a set of remarkably rancid teeth, he asked the owner for a sample of his plaque, put it beneath his lens, and witnessed "an inconceivably great number of little animalcules" moving "so nimbly among one another, that the whole stuff seemed alive." After a particularly uncomfortable evening, which he blamed on a fatty meal of hot smoked beef, he examined his own stool beneath his lens and saw animalcules that were "somewhat longer than broad, and their belly, which was flat-like, furnished with sundry little paws"--a clear description of what we now know as the parasite giardia. With his observations of these fast, fat, and sundry-pawed creatures, Van Leeuwenhoek became the first person to ever see a microorganism--a discovery of almost incalculable significance to human health and our understanding of life on this planet.

We consider a novel formulation of the problem of Active Few-Shot Classification (AFSC) where the objective is to classify a small, initially unlabeled, dataset given a very restrained labeling budget. This problem can be seen as a rival paradigm to classical Transductive Few-Shot Classification (TFSC), as both these approaches are applicable in similar conditions. We first propose a methodology that combines statistical inference, and an original two-tier active learning strategy that fits well into this framework. We then adapt several standard vision benchmarks from the field of TFSC. Our experiments show the potential benefits of AFSC can be substantial, with gains in average weighted accuracy of up to 10% compared to state-of-the-art TFSC methods for the same labeling budget. We believe this new paradigm could lead to new developments and standards in data-scarce learning settings.