Stress, arising from the dynamic interaction between external stressors, individual appraisals, and physiological or psychological responses, significantly impacts health yet is often underreported and inconsistently documented, typically captured as unstructured free-text in electronic health records. Ambient AI technologies offer promise in reducing documentation burden, but predominantly generate unstructured narratives, limiting downstream clinical utility. This study aimed to develop an ontology for mental stress and evaluate the feasibility of using a Large Language Model (LLM) to extract ontology-guided stress-related information from narrative text. The Mental Stress Ontology (MeSO) was developed by integrating theoretical models like the Transactional Model of Stress with concepts from 11 validated stress assessment tools. MeSO's structure and content were refined using Ontology Pitfall Scanner! and expert validation. Using MeSO, six categories of stress-related information--stressor, stress response, coping strategy, duration, onset, and temporal profile--were extracted from 35 Reddit posts using Claude Sonnet 4. Human reviewers evaluated accuracy and ontology coverage. The final ontology included 181 concepts across eight top-level classes. Of 220 extractable stress-related items, the LLM correctly identified 172 (78.2%), misclassified 27 (12.3%), and missed 21 (9.5%). All correctly extracted items were accurately mapped to MeSO, although 24 relevant concepts were not yet represented in the ontology. This study demonstrates the feasibility of using an ontology-guided LLM for structured extraction of stress-related information, offering potential to enhance the consistency and utility of stress documentation in ambient AI systems. Future work should involve clinical dialogue data and comparison across LLMs.

Less than a decade ago, neuroscientist Amir Khosrowshahi was drilling holes and sticking needles into skulls to learn about the human brain. He turned his knowledge of neuroscience into entrepreneurial success, cofounding Nervana, a startup that helped large companies run neural networks, the technology now driving explosive results in AI. Intel, the world's largest maker of computer chips, acquired Nervana for more than $350 million just two years later, in 2016. Since then, Harvard and UC Berkeley-trained Khosrowshahi has emerged as a key AI thinker within Intel, where AI and chips are colliding in new and profound ways. In an interview with VentureBeat, Khosrowshahi, now CTO of AI, said he is staying at Intel because of a group of researchers there. This team is building a new kind of integrated circuit (IC), filled with transistors that could one day operate at minuscule amounts of energy -- as low as 100 millivolts.

Intel is working on a new transistor called MESO that could be 10 to 30 times more efficient than existing transistors, a potential game-changer for the industry (see our main article here). It could help solve many of the world's biggest problems, spurring AI efforts that could help everything from fighting climate change to improving waste management. We interviewed Intel's Amir Khosrowshahi, CTO of AI, and Ian Young, Senior Fellow and circuit designer and lead researcher on the MESO project. Khosrowshahi, who is supposed to be focused on product development and thus on projects with impact within the next 2 to 5 years, says he's more excited about MESO than any other project right now -- even though it could take 10 years to get to market. Young's team wrote a paper about MESO for Nature, published in December.

This is a developer-centric meetup focused on Apache Spark, Apache Flink, Apache Kafka, Apache Mesos, related Typesafe and Twitter OSS stacks, and broader distributed Data Science and Machine Learning. How it may be complementary to the original Spark Users, now Bay Area Spark Meetup: Spark in its end-to-end ecosystem -- Mesos, Akka, Kafka, Cassandra, etc., with focus on what works for the final goals of the whole pipeline. We will teach you how to use Scala for Spark to make you more effective, and consider devops options so you can get to production faster. We'll invite projects relevant to or inspired by Apache Spark, such as Apache Storm, Apache Flink, and others, and will be focused on putting together useful OSS as a system.

The three cutting-edge frameworks showcased in these talks from MesosCon North America demonstrate the amazing power and flexibility of Apache Mesos for solving large-scale problems. Perhaps you have noticed, in our Apache Mesos series, the importance of frameworks. Mesos frameworks are the essential glue that make everything work in a Mesos cluster, the layer between Mesos and your applications. They perform a multitude of tasks, including launching and scaling applications, monitoring and health checks, configuration management, and scheduling. In these talks, you'll learn how: Netflix uses Mesos to power their recommendation engines.