Over a billion people worldwide have livers with excess fat, which can lead to a host of medical problems. Researchers think AI tools can spot the condition--and help stop it--early enough to save lives. All over the world, a slow, insidious change is taking place in the composition of the livers of more than a billion people. While the presence of fat in a normal, healthy liver is negligible, many adults and even children have livers where fat exceeds 5 percent or even 10 percent of the organ's total weight. Its unnatural presence causes inflammation, cell damage, and the formation of scar tissue known as fibrosis, all hallmarks of fatty liver disease, a condition that now impacts approximately 30 percent of adults worldwide .

Intrusion Tolerant Systems (ITSs) have become increasingly critical due to the rise of multi-domain adversaries exploiting diverse attack surfaces. ITS architectures aim to tolerate intrusions, ensuring system compromise is prevented or mitigated even with adversary presence. Existing ITS solutions often employ Risk Managers leveraging public security intelligence to adjust system defenses dynamically against emerging threats. However, these approaches rely heavily on databases like NVD and ExploitDB, which require manual analysis for newly discovered vulnerabilities. This dependency limits the system's responsiveness to rapidly evolving threats. HAL 9000, an ITS Risk Manager introduced in our prior work, addressed these challenges through machine learning. By analyzing descriptions of known vulnerabilities, HAL 9000 predicts and assesses new vulnerabilities automatically. To calculate the risk of a system, it also incorporates the Exploitability Probability Scoring system to estimate the likelihood of exploitation within 30 days, enhancing proactive defense capabilities. Despite its success, HAL 9000's reliance on NVD and ExploitDB knowledge is a limitation, considering the availability of other sources of information. This extended work introduces a custom-built scraper that continuously mines diverse threat sources, including security advisories, research forums, and real-time exploit proofs-of-concept. This significantly expands HAL 9000's intelligence base, enabling earlier detection and assessment of unverified vulnerabilities. Our evaluation demonstrates that integrating scraper-derived intelligence with HAL 9000's risk management framework substantially improves its ability to address emerging threats. This paper details the scraper's integration into the architecture, its role in providing additional information on new threats, and the effects on HAL 9000's management.

Emotional Intelligence (EI) is a critical yet underexplored dimension in the development of human-aligned LLMs. To address this gap, we introduce a unified, psychologically grounded four-layer taxonomy of EI tailored for large language models (LLMs), encompassing emotional tracking, cause inference, appraisal, and emotionally appropriate response generation. Building on this framework, we present EICAP-Bench, a novel MCQ style multi-turn benchmark designed to evaluate EI capabilities in open-source LLMs across diverse linguistic and cultural contexts. We evaluate six LLMs: LLaMA3 (8B), LLaMA3-Instruct, Gemma (9B), Gemma-Instruct, Qwen2.5 (7B), and Qwen2.5-Instruct on EmoCap-Bench, identifying Qwen2.5-Instruct as the strongest baseline. To assess the potential for enhancing EI capabilities, we fine-tune both Qwen2.5-Base and Qwen2.5-Instruct using LoRA adapters on UltraChat (UC), a large-scale, instruction-tuned dialogue dataset, in both English and Arabic. Our statistical analysis reveals that among the five EI layers, only the Appraisal layer shows significant improvement through UC-based fine-tuning. These findings highlight the limitations of existing pretraining and instruction-tuning paradigms in equipping LLMs with deeper emotional reasoning and underscore the need for targeted data and modeling strategies for comprehensive EI alignment.

Sparsely-activated Mixture-of-Experts (MoE) architecture has increasingly been adopted to further scale large language models (LLMs) due to its sub-linear scaling for computation costs. However, frequent failures still pose significant challenges as training scales. The cost of even a single failure is significant, as all GPUs need to wait idle until the failure is resolved, potentially losing considerable training progress as training has to restart from checkpoints. Existing solutions for efficient fault-tolerant training either lack elasticity or rely on building resiliency into pipeline parallelism, which cannot be applied to MoE models due to the expert parallelism strategy adopted by the MoE architecture. We present Lazarus, a system for resilient and elastic training of MoE models. Lazarus adaptively allocates expert replicas to address the inherent imbalance in expert workload and speeds-up training, while a provably optimal expert placement algorithm is developed to maximize the probability of recovery upon failures. Through adaptive expert placement and a flexible token dispatcher, Lazarus can also fully utilize all available nodes after failures, leaving no GPU idle. Our evaluation shows that Lazarus outperforms existing MoE training systems by up to 5.7x under frequent node failures and 3.4x on a real spot instance trace.

This is the first in a three-part series. In the already fast-changing world of HR, the ongoing COVID-19 pandemic is creating unimagined twists and turns as 2020 progresses, leading to unprecedented attention on HR technology to help employers manage these new challenges. No emerging technology arguably has had more impact on the evolution and refinement of the pandemic workplace than artificial intelligence--which is expected to continue in the months and years ahead. One HR area that has benefited the most from AI-based solutions is workforce management, mainly in recruiting for employers whose business sectors continued to thrive, or in managing challenges such as furloughs and layoffs for the sectors hit hardest by COVID-19. According to Greg Moran, CEO at OutMatch, a SaaS-based talent intelligence platform, the movement toward HR digitization, with the use of AI and machine learning, was already well underway at the start of the year.

Little Theater has a tiny orchestra pit, with room for only a handful of players, and a modest budget. So when it mounts a big musical like "Beauty and the Beast," it brings in an electronic ringer. A laptop computer, loaded with a program called OrchEXTRA, serves as a "virtual orchestra," from strings to woodwinds, drums to horns, giving the music such a rich sound that audience members may wonder how a full Broadway orchestra fits into the tiny pit. "As far as sound quality, these things are great," says Dorian Boyd, the sound designer/technician for Little Theater, referring to OrchEXTRA. Virtual orchestras are much better than the early systems of just a few years ago, he says, which could sound like "video game music."