Blind-spot mass is a Good-Turing framework for quantifying deployment coverage risk in machine learning. In modern ML systems, operational state distributions are often heavy-tailed, implying that a long tail of valid but rare states is structurally under-supported in finite training and evaluation data. This creates a form of 'coverage blindness': models can appear accurate on standard test sets yet remain unreliable across large regions of the deployment state space. We propose blind-spot mass B_n(tau), a deployment metric estimating the total probability mass assigned to states whose empirical support falls below a threshold tau. B_n(tau) is computed using Good-Turing unseen-species estimation and yields a principled estimate of how much of the operational distribution lies in reliability-critical, under-supported regimes. We further derive a coverage-imposed accuracy ceiling, decomposing overall performance into supported and blind components and separating capacity limits from data limits. We validate the framework in wearable human activity recognition (HAR) using wrist-worn inertial data. We then replicate the same analysis in the MIMIC-IV hospital database with 275 admissions, where the blind-spot mass curve converges to the same 95% at tau = 5 across clinical state abstractions. This replication across structurally independent domains - differing in modality, feature space, label space, and application - shows that blind-spot mass is a general ML methodology for quantifying combinatorial coverage risk, not an application-specific artifact. Blind-spot decomposition identifies which activities or clinical regimes dominate risk, providing actionable guidance for industrial practitioners on targeted data collection, normalization/renormalization, and physics- or domain-informed constraints for safer deployment.

Microsoft has announced plans to invest 1.7bn in artificial intelligence and cloud services in Indonesia. Under the plans unveiled by Microsoft CEO Satya Nadella, the tech giant will train 840,000 people in Indonesia in the use of AI and provide support for the country's growing ranks of tech developers. The announcement marks the biggest investment by Microsoft in its nearly three-decade history in the Southeast Asian country. Nadella on Tuesday held talks with President Joko Widodo, popularly known as Jokowi, at Jakarta's presidential palace before delivering a keynote speech about AI in the Indonesian capital. "This new generation of AI is reshaping how people live and work everywhere, including in Indonesia," Nadella said on the first stop of a tour of Southeast Asia.

The core of the performance-driven computational design is to trace the sensitivity of variations of some performance indicators to the differences between design alternatives. Therefore any argument about the utility of AI for performancebased design must necessarily discuss the representation of such differences, as explicitly as possible. The existing data models and data representations in the field of Architecture, Engineering, and Construction (AEC), such as CAD and BIM are heavily focused on geometrically representing building elements and facilitating the process of construction management. Unfortunately, the field of AEC does not currently have a structured discourse based on an explicit representation of decision variables and outcomes of interest. Specifically, the notion of design representation and the idea of data modelling for representing "what needs to be attained from buildings" is rather absent in the literature.

This position requires a U.S. person or the ability to obtain an Export Authorization from the appropriate government agency for non-U.S. Raytheon BBN Technologies (BBN) is looking for creative, talented individuals to join our world-class Speech, Language, and Multimedia group and to help us advance the state-of-the-art in our areas of operation. Our work ranges from seminal research and development to advanced fielded solutions. Our research activities drive the development of industry-leading 24 7 solutions and their deployment into demanding user environments. At BBN, Staff Scientists work with a team of experienced staff to design and implement new techniques in a variety of technologies, including speech recognition, speaker ID, language ID, machine translation, information extraction, question-answering, machine learning, NLP, document image processing, and video analysis.

Meeting the challenges loved ideas, and loved to create the tools and environments of developing very large (for the day) systems on for people to solve problems. He made creative computers with minuscule main memory, he led them connections -- he was a true collaborator and to push the state of the art in virtual memory and operating friend. I was lucky to be one of the earliest beneficiaries systems, going from software to hardware paging, of the skill Terry Winograd has characterized as eventually producing TENEX.

The past few years have shown that U.S. government intelligence goes only so far. One of the biggest challenges is recognizing vital information in foreign languages -- and acting quickly on it. That's why the military would love software that can listen to TV broadcasts or phone conversations and read Web sites in Arabic and Chinese, translate them into English and summarize the key elements for humans. But each of those steps has long bedeviled computer scientists. Perfecting them and combining them -- well, that is "DARPA hard."

We address the problem of causal interpretation of the graphical structure of Bayesian belief networks (BBNs). We review the concept of causality explicated in the domain of structural equations models and show that it is applicable to BBNs. In this view, which we call mechanism-based, causality is defined within models and causal asymmetries arise when mechanisms are placed in the context of a system. We lay the link between structural equations models and BBNs models and formulate the conditions under which the latter can be given causal interpretation.

Bayesian approaches to learn the graphical structure of Bayesian Belief Networks (BBNs) from databases share the assumption that the database is complete, that is, no entry is reported as unknown. Attempts to relax this assumption involve the use of expensive iterative methods to discriminate among different structures. This paper introduces a deterministic method to learn the graphical structure of a BBN from a possibly incomplete database. Experimental evaluations show a significant robustness of this method and a remarkable independence of its execution time from the number of missing data.