The latest AI news, in focus.

ChatGPT Plus for veterans and servicemembers

More than 20,000 veterans and transitioning service members have claimed a free year of ChatGPT Plus. See how they use it to prepare for civilian careers.

Performance Benchmarking of Federated Learning Frameworks for Medical Image Classification

arXiv:2611.10688v1 Announce Type: new Abstract: Selecting a Federated Learning (FL) framework requires understanding the resource demands of its training and evaluation workflow. This work compares Flower, NVIDIA FLARE, and FedBioMed using two-dimensional MedNIST classification with ResNet-10 in a containerized, single-host environment. Following a dated review of repository activity and documented medical applications, we evaluated nine configurations per framework, with three random seeds, for a total of 81 executions. The configurations varied federation size, communication rounds, and batch size. We monitored execution time, CPU and RAM consumption, host-level GPU utilization, server network traffic rates, and model accuracy at the end of each execution. The implementations shared nominal learning settings while retaining differences in data splitting and federated evaluation. Configuration-blocked non-parametric analysis identified differences in execution time, client and server memory, GPU utilization, and network traffic rates. NVIDIA FLARE exhibited the lowest server-memory consumption and the highest host-level GPU utilization, Flower exhibited the highest average server network traffic rate, and FedBioMed required more client memory and longer execution time. No statistically significant difference in final accuracy was detected within the evaluated short training schedules; this does not establish convergence or equivalent predictive performance. The results characterize the configured workflows under shared hardware resources. They provide a concrete case study of system behavior, with conclusions bounded by workflow asymmetries, local networking, and the lightweight MedNIST workload.

Closing the AI guidance gap in education

Lessons from OpenAI’s EMEA education partnerships and new ThinkYoung research. Supporting teachers, AI literacy and independent learning.

El Agente F\'armaco: A Scalable AI Scientist for Drug Discovery

arXiv:2611.03832v1 Announce Type: new Abstract: Computational drug discovery offers a growing range of tools for proposing and evaluating molecules, but combining them and deciding what to do next still requires substantial expert effort. Here, we introduce El Agente F\'armaco, an AI scientist that uses modern large language model (LLM) technology to turn discovery goals expressed in everyday language into complex, scalable computational screening workflows. The agentic harness selects scientific tools, reviews their results, and adjusts subsequent steps to the target and the user's objectives. Our approach centers on a shared scientific memory: F\'armaco Atlas, which stores molecules, structures, and results together with their provenance -- a record of how they were obtained. The agent uses targeted database queries to retrieve the evidence needed for each decision. A single request can launch calculations for an entire collection of molecules, without requiring a separate LLM decision for every compound. This design limits the information the model must process as screening collections grow, while keeping the underlying results available for inspection and further analysis. The integrated tools support molecule generation, prediction of binding and molecular properties, and searches for compounds available for purchase. In a retrospective comparison with an expert-led campaign, the agent proposed chemically distinct candidates with competitive docking scores and higher calculated drug-likeness under explicitly imposed selection criteria. In a 22-task benchmark against plain tool calls and Codex, the agent achieved the highest observed mean model-judged score, with lower median cumulative token use and peak context. F\'armaco provides a foundation for flexible, scalable drug discovery workflows that connect scientific reasoning with an enduring, traceable record of computational work.

Convex Potential Learning for Optimal Transport Between Medical Images

arXiv:2610.37844v1 Announce Type: new Abstract: Computing optimal transport maps between medical images plays a fundamental role in morphological analysis. However, existing methods are either difficult to scale to large 3D medical images or rely on regularized formulations that provide only approximate transport maps. To improve the accuracy of downstream medical image analysis, we propose a novel approach for estimating optimal transport maps by parameterizing the convex potential function. By construction, the resulting transport map is guaranteed to be curl-free, consistent with Brenier's theorem. We evaluate the proposed method on both 2D and 3D medical imaging datasets. Experimental results demonstrate that our approach achieves lower transport estimation errors and produces higher-q

Responsible Quantum Machine Learning for Medical Imaging: A Fairness Audit Across Sensitive Subgroups

arXiv:2610.37819v1 Announce Type: new Abstract: Quantum machine learning (QML) is increasingly explored for medical classification, yet its behaviour under subgroup-aware fairness criteria remains understudied. We present an empirical fairness audit of classical and quantum classifiers for medical imaging, assuming QML is not inherently fair. Two scenarios are evaluated: dermatological lesions on DDI (sensitive attribute: Fitzpatrick skin type) and chest X-rays on CheXpert-derived data (sensitive attribute: age group). Using ResNet-18 embeddings, we train SVM, MLP, QSVM and QCNN under a baseline and three mitigation settings: differentiable Equalized Odds (EO) regularisation, Gradient Reversal Learning (GRL), and both combined. Foundation models (PanDerm, EVA-X) serve as external reference

Intuit rewires accounting education as AI pushes new hires toward client work

As AI automates routine finance work, the repetitive tasks that once defined entry-level accounting are disappearing, and advisory services are moving to the center of how firms create value. That shift leaves colleges racing to prepare graduates for a job that looks very different from the one in the textbooks. Closing the gap between classroom […] The post Intuit rewires accounting education as AI pushes new hires toward client work appeared first on SiliconANGLE.

Voice AI in vogue: Nvidia releases new open transcription model

Nvidia just released a free speech-to-text model that can transcribe up to 40 languages, but it only works on recordings, not live audio, so it’s a big step for meeting and medical transcription tools while still leaving real-time use to rivals like ElevenLabs.

Intuit rewires accounting education as AI pushes new hires toward client work

As AI automates routine finance work, the repetitive tasks that once defined entry-level accounting are disappearing, and advisory services are moving to the center of how firms create value. That shift leaves colleges racing to prepare graduates for a job that looks very different from the one in the textbooks. Closing the gap between classroom […] The post Intuit rewires accounting education as AI pushes new hires toward client work appeared first on SiliconANGLE.

MedJev: Evaluating Zero-Shot System-One Decision Making in Medicine

arXiv:2610.36006v1 Announce Type: new Abstract: System One decision models such as Jev answer structured questions in a single forward pass without generating text, offering fast and computationally efficient clinical decision support. We evaluate MedJev, an open System One model that runs locally on a laptop, across ten medical benchmarks spanning medical knowledge, diagnosis, and patient level prediction using MIMIC IV and MIMIC IV ED. We compare MedJev against zero shot LLM baselines, published GPT results, and supervised models. MedJev achieves accuracies of 72.5 percent on MedQA, 75.4 percent on USMLE, and 80.1 percent on NEJM, approaching published GPT 4 performance while maintaining well calibrated confidence estimates (ECE 0.028 to 0.063). On patient level tasks, MedJev achieves an

The sleep framework: A new way for LLMs to retain knowledge in continual learning

Education Innovation

Industrial Intelligence: The next growth opportunity for manufacturing

Every manufacturer has its own industrial intelligence. Some is documented, and some resides in the knowledge of experienced workers. Consider an experienced operator preparing to restart a line after a changeover. The machine settings matter, but so does knowing how a particular material behaves, what happened during the previous shift, and when an apparently minor…

Perturbation-Based Data Selection for Medical Question Answering

arXiv:2610.33440v1 Announce Type: new Abstract: Selecting a compact training subset is an important step in adapting medical language models from existing question-answering banks. We investigate perturbation-based data selection using two signals derived from Gaussian perturbations of the final-block representation: prediction-change frequency and non-flipping margin contraction. After a shared entropy prefilter, Representation-Sensitivity Prioritization (RSP) combines the signals using automatic root-mean-square scaling, while RSP-Drop ranks candidates by contraction alone. Both selectors use only questions and answer options during selection. We evaluate them with MedGemma-4B on five medical QA datasets at retained-set budgets of 64, 128, and 256 examples. At the primary budget of 128,

Beyond Heuristics and Commercial Navigation: An AI-Augmented Decision-Support for Ambulance Travel Time Estimation

arXiv:2610.31968v1 Announce Type: new Abstract: Accurate ambulance travel time estimation is critical for emergency medical dispatch, yet existing decision-support practices often rely on static lookup tables or simplified assumptions regarding how ambulance travel time differs from ordinary passenger-car travel. For instance, they assume ambulances simply travel a fixed percentage (e.g., 5%) faster than ordinary passenger cars. However, such assumptions are rarely validated using real ambulance operational data and may fail to capture the effects of trip priority, temporal context, and origin-destination. We address these limitations using historical ambulance trips from the Sk{\aa}ne region in Sweden. Our analysis reveals that ambulance travel times vary substantially by hour, day, and t

TutorCaseBench Released: The First LLM Tutoring Evaluation Benchmark for Special Education Scenarios

On October 24, 2026, researchers published TutorCaseBench on arXiv (ID 2610.30828v1)—an LLM tutoring evaluation benchmark built on real-world cases from pedagogy, educational psychology, and special education, focusing on the ability to support students with learning and behavioral challenges. The benchmark comprises 923 dialogue segments, covering five categories of challenging behaviors, and employs a dual-track assessment approach: open-ended generation (1,560 questions) and multiple-choice questions (2,537 questions). Among 11 state-of-the-art large models, the top performer met the evaluation criteria in only 55.8% of the open-ended items.

Why it mattersThis study fills a critical gap in existing AI‑based educational assessment, which has overlooked the complexity of students with special needs. Its methodology emphasizes evidence‑based tracing—combining qualitative case studies with source‑grounded rubrics—and all data are openly available, ensuring reproducibility and relevance to educational practice. The findings clearly reveal significant shortcomings of current LLMs in generating contextually appropriate responses, offering important warnings and guidance for the practical implementation of educational AI.

Google open-sources a medical foundation model to help implement global tuberculosis screening.

Google, together with partners such as Nexus AI, has brought a chest‑X‑ray–based AI tool for tuberculosis screening from the laboratory to primary‑care settings in six countries, and has open‑sourced multimodal foundation models like MedGemma and MedSigLIP, supporting offline deployment, localized adaptation, and data sovereignty.

Black October: A Comprehensive Overview of the AI-Driven Cybersecurity Crisis—From Its Peak to Defensive Response

Around October 21–22, 2026, over a span of approximately 48 hours, more than a dozen high‑severity AI‑related vulnerabilities and intrusion incidents were reported worldwide, including PHP2Shell, GitLab RCE (CVSS 10), NGINX/HTTP/3 DoS, LiteLLM password theft, eMudhra CA key leakage, and Quest Diagnostics medical data exposure. State actors such as Storm‑0940 leveraged open‑source intelligent penetration‑testing frameworks like ARTEX to launch large‑scale attacks. Concurrently, AI security defense systems were rapidly being developed, encompassing formal verification, open‑source patching agents, standardization of agent capabilities, and hardware‑level optimizations.

Microsoft claims its AI diagnostic tool achieves accuracy up to four times that of human doctors, with the related research published in the journal Science.

Microsoft AI CEO Mustafa Suleyman told The Verge that the company has developed an AI diagnostic tool whose accuracy in testing for multiple diseases is up to four times that of human doctors; the findings were published in the top-tier journal Science, based on a comparative evaluation of real-world medical cases and emergency‑room data against Microsoft’s proprietary benchmark (name not fully disclosed).

Amazon Quick has obtained HIPAA qualification, but compliance responsibility still rests with the customer.

Amazon Quick is an HIPAA‑compliant service offered by AWS that enables healthcare organizations to process Protected Health Information (PHI) upon signing a Business Associate Agreement (BAA); however, compliance with HIPAA does not equate to full regulatory compliance. Customers must rigorously manage its PHI processing workflows, encryption mechanisms, logging policies, and irreversible configuration requirements.

Ukraine has, for the first time in combat, used drones to evacuate wounded personnel, marking a new phase in battlefield first aid.

Ukrainian forces successfully carried out a globally rare aerial evacuation of wounded personnel using a heavy six-rotor drone at the front line, validating the unmanned battlefield medical evacuation concept that has long been in the experimental phase. The time and location of the operation were not disclosed, but related video footage was released by the Ukrainian military on October 19, 2026.