From king’s college london, carnegie mellon, & U birmingham.
Llm-driven robots risk enacting discrimination, violence, and unlawful actions.
Rumaisa Azeem, Andrew Hundt, Masoumeh Mansouri, Martim Brandão June 2024 Paper: https://arxiv.org/abs/2406.08824 Code: https://github.com/SepehrDehdashtian/…
The data and code for paper ‘The Dark Side of Dataset Scaling: Evaluating Racial Classification in Multimodal Models’ — SepehrDehdashtian/the-dark-side-of-dataset-scaling.
This review spotlights the revolutionary role of deep learning (DL) in expanding the understanding of RNA is a fundamental biomolecule that shapes and regulates diverse phenotypes including human diseases. Understanding the principles governing the functions of RNA is a key objective of current biology. Recently, big data produced via high-throughput experiments have been utilized to develop DL models aimed at analyzing and predicting RNA-related biological processes. This review emphasizes the role of public databases in providing these big data for training DL models. The authors introduce core DL concepts necessary for training models from the biological data. By extensively examining DL studies in various fields of RNA biology, the authors suggest how to better leverage DL for revealing novel biological knowledge and demonstrate the potential of DL in deciphering the complex biology of RNA.
This summary was initially drafted using artificial intelligence, then revised and fact-checked by the author.
Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. Here the authors train a deep neural network to predict and dynamically control gene expression in thousands of individual bacteria in real-time which they then apply to control antibiotic resistance and study single-cell survival dynamics.
A team of roboticists at the University of Tokyo has taken a new approach to autonomous driving—instead of automating the entire car, simply put a robot in the driver’s seat. The group built a robot capable of driving a car and tested it on a real-world track. They also published a paper describing their efforts on the arXiv preprint server.
Elon Musk says Tesla could make 100 million Optimus robots a year, costing $10k-20k each, to do everything from babysitting to working in factories, leading to the population of humanoid robots exceeding that of humans.
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Colin Jacobs, PhD, assistant professor in the Department of Medical Imaging at Radboud University Medical Center in Nijmegen, The Netherlands, and Kiran Vaidhya Venkadesh, a second-year PhD candidate with the Diagnostic Image Analysis Group at Radboud University Medical Center discuss their 2021 Radiology study, which used CT images from the National Lung Cancer Screening Trial (NLST) to train a deep learning algorithm to estimate the malignancy risk of lung nodules.
Salesforce recently announced that it has introduced more than 50 AI-powered tools among its workforce and reported that these tools have collectively saved all of its employees in excess of 50,000 hours—or 24 years’ worth—of working time in just three months.
As a company, Salesforce serves as an especially compelling case study for the impact of AI on work—not only because the company tests tools on their own workforce, but because so many others rely on Salesforce’s products to do their jobs each day. Simply put: Salesforce is in the business of work.
Salesforce has more than 70,000 employees worldwide—a 30% increase since 2020. And the software giant builds the products that are used by employees at some 150,000 workplaces, from small businesses to Fortune 500 companies; from sales and customer service teams to marketing and tech teams.