Sign up to receive it in your inbox. Your story in this week's issue, " Predictions and Presentiments," is drawn from your forthcoming book, " Beginning Middle End," which is coming out in July. The audio version will incorporate sounds that you and your team recorded in Sicily, where both the piece and the novel are set. How would you compare the creative processes of writing and recording, and the experiences of reading and listening? Recording sound and listening attentively have been an integral part of my writing process for a long time now.

Financial markets are complex, non-stationary systems where the underlying data distributions can shift over time, a phenomenon known as regime changes, as well as concept drift in the machine learning literature. These shifts, often triggered by major economic events, pose a significant challenge for traditional statistical and machine learning models. A fundamental problem in developing and validating adaptive algorithms is the lack of a ground truth in real-world financial data, making it difficult to evaluate a model's ability to detect and recover from these drifts. This paper addresses this challenge by introducing a novel framework, named ProteuS, for generating semi-synthetic financial time series with pre-defined structural breaks. Our methodology involves fitting ARMA-GARCH models to real-world ETF data to capture distinct market regimes, and then simulating realistic, gradual, and abrupt transitions between them. The resulting datasets, which include a comprehensive set of technical indicators, provide a controlled environment with a known ground truth of regime changes. An analysis of the generated data confirms the complexity of the task, revealing significant overlap between the different market states. We aim to provide the research community with a tool for the rigorous evaluation of concept drift detection and adaptation mechanisms, paving the way for more robust financial forecasting models.

This paper introduces PROTEUS, a fully automated system that produces data-driven hypotheses from raw data files. We apply PROTEUS to clinical proteogenomics, a field where effective downstream data analysis and hypothesis proposal is crucial for producing novel discoveries. PROTEUS uses separate modules to simulate different stages of the scientific process, from open-ended data exploration to specific statistical analysis and hypothesis proposal. It formulates research directions, tools, and results in terms of relationships between biological entities, using unified graph structures to manage complex research processes. We applied PROTEUS to 10 clinical multiomics datasets from published research, arriving at 360 total hypotheses. Results were evaluated through external data validation and automatic open-ended scoring. Through exploratory and iterative research, the system can navigate high-throughput and heterogeneous multiomics data to arrive at hypotheses that balance reliability and novelty. In addition to accelerating multiomic analysis, PROTEUS represents a path towards tailoring general autonomous systems to specialized scientific domains to achieve open-ended hypothesis generation from data.

With the development of artificial intelligence, its contribution to science is evolving from simulating a complex problem to automating entire research processes and producing novel discoveries. Achieving this advancement requires both specialized general models grounded in real-world scientific data and iterative, exploratory frameworks that mirror human scientific methodologies. In this paper, we present PROTEUS, a fully automated system for scientific discovery from raw proteomics data. PROTEUS uses large language models (LLMs) to perform hierarchical planning, execute specialized bioinformatics tools, and iteratively refine analysis workflows to generate high-quality scientific hypotheses. The system takes proteomics datasets as input and produces a comprehensive set of research objectives, analysis results, and novel biological hypotheses without human intervention. We evaluated PROTEUS on 12 proteomics datasets collected from various biological samples (e.g. immune cells, tumors) and different sample types (single-cell and bulk), generating 191 scientific hypotheses. These were assessed using both automatic LLM-based scoring on 5 metrics and detailed reviews from human experts. Results demonstrate that PROTEUS consistently produces reliable, logically coherent results that align well with existing literature while also proposing novel, evaluable hypotheses. The system's flexible architecture facilitates seamless integration of diverse analysis tools and adaptation to different proteomics data types. By automating complex proteomics analysis workflows and hypothesis generation, PROTEUS has the potential to considerably accelerate the pace of scientific discovery in proteomics research, enabling researchers to efficiently explore large-scale datasets and uncover biological insights.

Vision foundation models are renowned for their generalization ability due to massive training data. Nevertheless, they demand tremendous training resources, and the training data is often inaccessible, e.g., CLIP, DINOv2, posing great challenges to developing derivatives that could advance research in this field. In this work, we offer a very simple and general solution, named Proteus, to distill foundation models into smaller equivalents on ImageNet-1K without access to the original training data. Specifically, we remove the designs from conventional knowledge distillation settings that result in dataset bias and present three levels of training objectives, i.e., token, patch, and feature, to maximize the efficacy of knowledge transfer. In this manner, Proteus is trained at ImageNet-level costs with surprising ability, facilitating the accessibility of training foundation models for the broader research community. Leveraging DINOv2-g/14 as the teacher, Proteus-L/14 matches the performance of the Oracle method DINOv2-L/14 (142M training data) across 15 benchmarks and outperforms other vision foundation models including CLIP-L/14 (400M), OpenCLIP-L/14 (400M/2B) and SynCLR-L/14 (600M). Code is available at here.

In a giant warehouse in Reading, Massachusetts, I meet a pair of robots that look like goofy green footstools from the future. Their round eyes and satisfied grins are rendered with light emitting diodes. They sport small lidar sensors like tiny hats that scan nearby objects and people in 3D. Suddenly, one of them plays a chipper little tune, its mouth starts flashing, and its eyes morph into heart shapes. This means, I am told, that the robot is happy.

For decades, one of the hardest problems for robot developers to crack has been something seemingly mundane: how to replicate the human hand's ability to pick up stuff. The tech giant last month unveiled a collection of new robots, one of which is suited to replacing humans in the most common job at Amazon – picking up items and placing them elsewhere. The linchpin of this new kind of automation is a robot arm – appropriately named Sparrow after the tenacious, pervasive bird – that combines advanced artificial intelligence, a variety of grippers, and the speed and precision that is now standard in off-the-shelf industrial robotic arms. The announcement was easy to miss, coming as it did amid a run of news that, in part, illustrated some of the challenges Amazon is trying to tackle with its automation effort. The company began layoffs of corporate employees in mid-November, part of a sweeping cost-cutting effort to deal with the aftereffects of its rapid expansion during the pandemic. The company's workforce more than doubled during that period, to exceed 1.6 million as of early this year.

Since Amazon began bringing robots to its warehouses in 2014, company executives have repeatedly claimed that they improve worker safety. But company records obtained by Reveal showed that between 2016 and 2019 serious injuries occurred more often in Amazon warehouses with robots than those without them, suggesting that robots made employees less safe by causing managers to raise performance quotas. Analysis of filings with the US Occupational Safety and Health Administration (OSHA) by The Washington Post found that in 2020, serious injuries were roughly twice as likely to occur in Amazon warehouses than those run by other companies. A separate analysis of OSHA data by labor union coalition the Strategic Organizing Center found the same pattern for 2021. Amazon didn't mention that track record late last month when it announced a machine called Proteus, which company officials call their first fully mobile and collaborative robot.

For the last decade, Amazon has been building an army of robot employees to sort packages and move products safely around its warehouses. Now the company has unveiled its latest robot called Proteus, which it describes as its'first fully autonomous mobile robot'. Proteus is designed to work alongside humans, moving large trolleys full of packages around the warehouse floor. The robot uses Amazon's own safety, perception, and navigation technology to move around autonomously and avoid bumping into human workers. 'Historically, it's been difficult to safely incorporate robotics in the same physical space as people,' the company said in a blog post.

In a post looking back over the past 10 years since it purchased robotics company Kiva, Amazon has revealed its new machines, including its first fully autonomous warehouse robot. It's called Proteus, and it was designed to be able to move around Amazon's facilities on its own while carrying carts fulls of packages. The company said the robot uses an "advanced safety, perception and navigation technology" it developed to be able to do its work without hindering human employees. In the video Amazon posted, you can see Proteus moving under the carts and transporting them to other locations. It emits a green beam ahead of it while it moves, and it stops if a human worker steps in front of the beam.