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AIhub monthly digest: April 2026 – machine learning for particle physics, AI Index Report, and table tennis

AIHub

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we meet PhD students and early-career researchers, find out how machine learning is used for particle physics discoveries, cast an eye over the latest AI Index Report, and watch a robot beating elite players at table tennis. In an article published in Nature this month, Sony AI introduced Ace, a table tennis robot that has beaten professional players in competitive matches. The system combines event-based vision sensors and a control system based on model-free reinforcement learning, as well as state-of-the-art high-speed robot hardware. The ninth edition of the Artificial Intelligence Index Report was published on 13 April 2026 .


2026 AI Index Report released

AIHub

The ninth edition of the Artificial Intelligence Index Report was published on 13 April 2026. Released on a yearly basis, the aim of the document is to provide readers with accurate, rigorously validated, and globally-sourced data to give insights into the progress of AI and its potential impact on society. The 2026 AI Index Report comprises nine chapters, covering: research and development, technical performance, responsible AI, economy, science, medicine, education, policy and governance, and public opinion. AI capability is accelerating and reaching more people than ever. Model performance continues to improve against benchmarks, and 80% of university students now use generative AI.


Identifying Imaging Follow-Up in Radiology Reports: A Comparative Analysis of Traditional ML and LLM Approaches

Park, Namu, Ramachandran, Giridhar Kaushik, Lybarger, Kevin, Xia, Fei, Uzuner, Ozlem, Yetisgen, Meliha, Gunn, Martin

arXiv.org Artificial Intelligence

Large language models (LLMs) have shown considerable promise in clinical natural language processing, yet few domain-specific datasets exist to rigorously evaluate their performance on radiology tasks. In this work, we introduce an annotated corpus of 6,393 radiology reports from 586 patients, each labeled for follow-up imaging status, to support the development and benchmarking of follow-up adherence detection systems. Using this corpus, we systematically compared traditional machine-learning classifiers, including logistic regression (LR), support vector machines (SVM), Longformer, and a fully fine-tuned Llama3-8B-Instruct, with recent generative LLMs. To evaluate generative LLMs, we tested GPT-4o and the open-source GPT-OSS-20B under two configurations: a baseline (Base) and a task-optimized (Advanced) setting that focused inputs on metadata, recommendation sentences, and their surrounding context. A refined prompt for GPT-OSS-20B further improved reasoning accuracy. Performance was assessed using precision, recall, and F1 scores with 95% confidence intervals estimated via non-parametric bootstrapping. Inter-annotator agreement was high (F1 = 0.846). GPT-4o (Advanced) achieved the best performance (F1 = 0.832), followed closely by GPT-OSS-20B (Advanced; F1 = 0.828). LR and SVM also performed strongly (F1 = 0.776 and 0.775), underscoring that while LLMs approach human-level agreement through prompt optimization, interpretable and resource-efficient models remain valuable baselines.


AI Index Report, HAI released the Artificial Intelligence report

#artificialintelligence

The annual report keeps track, collects e displays AI-related data, to support meaningful decisions, and advance AI responsibly and ethically. The AI Index Report supports many different organizations to track progress in artificial intelligence. These organizations include: the Center for Security and Emerging Technology at Georgetown University, LinkedIn, NetBase Quid, Lightcast, and McKinsey. The AI Index Report also expanded its tracking of global AI legislation from 25 countries in 2022 to 127 in 2023. The demand for AI-related job skills is increasing in virtually all industries (in the US).


2022 Artificial Intelligence Index Report published

AIHub

The 2022 AI Index Report has been published. Compiled by the Stanford Institute for Human-Centered Artificial Intelligence (HAI), it tracks, summarises and visualises data relating to artificial intelligence. The aim of the report is to provide unbiased, rigorously vetted, and globally sourced data for policymakers, researchers, executives, journalists, and the general public to develop intuitions about the complex field of AI. Find out more about the report here. You can access the full pdf version here.


Through Artificial Intelligence and Cutting-Edge Data Analytics Benefitfocus Helps HR Administrators Identify and Close Gaps in Employee Benefits Coverage

#artificialintelligence

Benefitfocus, Inc. (BNFT), a leading cloud-based benefits management platform and services provider, today announced it will present findings and recommendations from its recent Consumer Benefits Coverage Index report at the SHRM Annual Conference & Exposition. The conference is the largest gathering of human resources (HR) professionals, attracting over 18,000 attendees in Las Vegas from June 23-26, 2019. Based on a sample of one million consumers on the Benefitfocus platform which serves over 25 million individuals, the Consumer Benefits Coverage Index is a measure of how well the typical American's employee benefits portfolio aligns with their expected coverage needs. Co-presenting from Benefitfocus will be Cindi Van Meir, Director of Brand Marketing and Misty Guinn, Director of Benefits & Wellness. The session takes place on the opening day of the conference, Sunday, June 23, 2019, from 4:20-4:50 PM in the HR Technology Solutions Theater in the Las Vegas Convention Center.