Recent advancements of large language models (LLMs) have led to claims of AI surpassing humans in natural language processing (NLP) tasks such as textual understanding and reasoning. This work investigates these assertions by introducing CAIMIRA, a novel framework rooted in item response theory (IRT) that enables quantitative assessment and comparison of problem-solving abilities of question-answering (QA) agents: humans and AI systems. Through analysis of over 300,000 responses from ~70 AI systems and 155 humans across thousands of quiz questions, CAIMIRA uncovers distinct proficiency patterns in knowledge domains and reasoning skills. Humans outperform AI systems in knowledge-grounded abductive and conceptual reasoning, while state-of-the-art LLMs like GPT-4 and LLaMA show superior performance on targeted information retrieval and fact-based reasoning, particularly when information gaps are well-defined and addressable through pattern matching or data retrieval. These findings highlight the need for future QA tasks to focus on questions that challenge not only higher-order reasoning and scientific thinking, but also demand nuanced linguistic interpretation and cross-contextual knowledge application, helping advance AI developments that better emulate or complement human cognitive abilities in real-world problem-solving.

Human respiratory rate and its pattern convey essential information about the physical and psychological states of the subject. Abnormal breathing can indicate fatal health issues leading to further diagnosis and treatment. Wireless light-wave sensing (LWS) using incoherent infrared light shows promise in safe, discreet, efficient, and non-invasive human breathing monitoring without raising privacy concerns. The respiration monitoring system needs to be trained on different types of breathing patterns to identify breathing anomalies.The system must also validate the collected data as a breathing waveform, discarding any faulty data caused by external interruption, user movement, or system malfunction. To address these needs, this study simulated normal and different types of abnormal respiration using a robot that mimics human breathing patterns. Then, time-series respiration data were collected using infrared light-wave sensing technology. Three machine learning algorithms, decision tree, random forest and XGBoost, were applied to detect breathing anomalies and faulty data. Model performances were evaluated through cross-validation, assessing classification accuracy, precision and recall scores. The random forest model achieved the highest classification accuracy of 96.75% with data collected at a 0.5m distance. In general, ensemble models like random forest and XGBoost performed better than a single model in classifying the data collected at multiple distances from the light-wave sensing setup.

The law has been relatively slow to regulate artificial intelligence, but the rules are evolving. An important question is whether an AI company can be held liable for malfunctioning AI. Ryan E. Long writes that a company's liability for its AI depends on whether a defect was present upon the AI release and whether, in the EU at least, the application is "high-risk." Artificial intelligence (AI) use has blossomed. The AI market was valued at $27.3 billion in 2019 and is projected to grow to $266.92 billion by 2026.

The story of how the FBI finally tracked down notorious fugitive Lynn Cozart, using its brand-new, 1 billion facial recognition system, seems tailor-made to disarm even the staunchest of skeptics. Cozart, a former security guard in Beaver County, Pennsylvania, was convicted of deviant sexual intercourse in 1996. According to court filings, he had molested his three juvenile children, two girls and one boy, from 1984 through 1994. It wasn't until May 11, 1995, that the children's mother came forward and told the Pennsylvania State Police what Cozart had been doing. He was convicted, but he failed to show up for his sentencing hearing in April 1996. Federal agents raided his home, interviewed family members and released photos of the man to the general public. In August 2006, the Cozart case was featured in "America's Most Wanted," the national television program, under a segment titled "Ten Years of Hell for Three Children."