The adoption of machine learning (ML) and deep learning methods has revolutionized molecular medicine by driving breakthroughs in genomics, transcriptomics, drug discovery, and biological systems modeling. The increasing quantity, multimodality, and heterogeneity of biological datasets demand automated methods that can produce generalizable predictive models. Recent developments in large language model-based agents have shown promise for automating end-to-end ML experimentation on structured benchmarks. However, when applied to heterogeneous computational biology datasets, these methods struggle with generalization and success rates. Here, we introduce Agentomics-ML, a fully autonomous agent-based system designed to produce a classification model and the necessary files for reproducible training and inference. Our method follows predefined steps of an ML experimentation process, repeatedly interacting with the file system through Bash to complete individual steps. Once an ML model is produced, training and validation metrics provide scalar feedback to a reflection step to identify issues such as overfitting. This step then creates verbal feedback for future iterations, suggesting adjustments to steps such as data representation, model architecture, and hyperparameter choices. We have evaluated Agentomics-ML on several established genomic and transcriptomic benchmark datasets and show that it outperforms existing state-of-the-art agent-based methods in both generalization and success rates. While state-of-the-art models built by domain experts still lead in absolute performance on the majority of the computational biology datasets used in this work, Agentomics-ML narrows the gap for fully autonomous systems and achieves state-of-the-art performance on one of the used benchmark datasets. The code is available at https://github.com/BioGeMT/Agentomics-ML.

It features a unique combination of properties that distinguish it from other such games, including continuous action and state spaces, uncertainty in execution, a unique turntaking structure, and of course an adversarial nature. This article discusses some of the work done to date, focusing on the software side of the pool-playing problem. We discuss in some depth CueCard, the program that won the 2008 computational pool tournament. Research questions and ideas spawned by work on this problem are also discussed. We close by announcing the 2011 computational pool tournament, which will take place in conjunction with the 25th AAAI Conference.

Alex Karpenko hands me a camera and tells me to run. We're standing on a pier in San Francisco, and the device in Karpenko's hand is an unreleased prototype of a new, software-driven video camera called Rylo. Karpenko wants me to see what he and co-founder Chris Cunningham show recruits and investors when they ask why they should get involved. Karpenko says I don't have to worry about where to point the camera, or try to hold it still. So I grab the camera--a small, oblong 360-degree shooter with a lens on either side--and start running.

Apple's new Control Center lets you mix and match your favorite shortcuts in iOS11 (Photo: Apple) MANHATTAN BEACH, Calif -- You don't have to go out and spend $700 on a swanky new iPhone. The latest mobile operating system upgrade, iOS 11, was released Tuesday, and it has "hundreds" of new features, according to Apple. If you go to Settings, Control Center, you can choose what shortcuts you'd like to be in there. The iOS11 operating system upgrade lets you mark up screen shots.