An autonomous vessel (AV) is a complex cyber-physical system (CPS) with software enabling many key functionalities, e.g., navigation software enables an AV to autonomously or semi-autonomously follow a path to its destination. Digital twins of such AVs enable advanced functionalities such as running what-if scenarios, performing predictive maintenance, and enabling fault diagnosis. Due to technological improvements, real-time analyses using continuous data from vessels' real-time operations have become increasingly possible. However, the literature has little explored developing advanced analyses in real-time data in AVs with digital twins built with machine learning techniques. To this end, we present a novel digital twin-based approach (ODDIT) to detect future out-of-distribution (OOD) states of an AV before reaching them, enabling proactive intervention. Such states may indicate anomalies requiring attention (e.g., manual correction by the ship master) and assist testers in scenario-centered testing. The digital twin consists of two machine-learning models predicting future vessel states and whether the predicted state will be OOD. We evaluated ODDIT with five vessels across waypoint and zigzag maneuvering under simulated conditions, including sensor and actuator noise and environmental disturbances i.e., ocean current. ODDIT achieved high accuracy in detecting OOD states, with AUROC and TNR@TPR95 scores reaching 99\% across multiple vessels.

Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task that aims to identify and classify entity mentions in texts across different categories. While languages such as English possess a large number of high-quality resources for this task, Brazilian Portuguese still lacks in quantity of gold-standard NER datasets, especially when considering specific domains. Particularly, this paper considers the importance of NER for analyzing historical texts in the context of digital humanities. To address this gap, this work outlines the construction of MariNER: \textit{Mapeamento e Anotações de Registros hIstóricos para NER} (Mapping and Annotation of Historical Records for NER), the first gold-standard dataset for early 20th-century Brazilian Portuguese, with more than 9,000 manually annotated sentences. We also assess and compare the performance of state-of-the-art NER models for the dataset.

Demis Hassabis, CEO of Google DeepMind, says that reaching artificial general intelligence or AGI--a fuzzy term typically used to describe machines with human-like cleverness--will mean honing some of the nascent abilities found in Google's flagship Gemini models. Google announced a slew of AI upgrades and new products at its annual I/O event today in Mountain View, California. The search giant revealed upgraded versions of Gemini Flash and Gemini Pro, Google's fastest and most capable models, respectively. Hassabis said that Gemini Pro outscores other models on LMArena, a widely used benchmark for measuring the abilities of AI models. Hassabis showed off some experimental AI offerings that reflect a vision for artificial intelligence that goes far beyond the chat window.

Earlier today, Google debuted Gemini 2.0. The company says its new machine learning model won't just enhance its existing products and services. It will also power entirely new experiences. To that point, Google previewed Project Mariner, an AI agent that can navigate within a web browser. Mariner is an experimental Chrome extension that is currently available to select "trusted testers."

The capability to navigate safely in an unstructured environment is crucial when deploying robotic systems in real-world scenarios. Recently, control barrier function (CBF) based approaches have been highly effective in synthesizing safety-critical controllers. In this work, we propose a novel CBF-based local planner comprised of two components: Vessel and Mariner. The Vessel is a novel scaling factor based CBF formulation that synthesizes CBFs using only point cloud data. The Mariner is a CBF-based preview control framework that is used to mitigate getting stuck in spurious equilibria during navigation. To demonstrate the efficacy of our proposed approach, we first compare the proposed point cloud based CBF formulation with other point cloud based CBF formulations. Then, we demonstrate the performance of our proposed approach and its integration with global planners using experimental studies on the Unitree B1 and Unitree Go2 quadruped robots in various environments.

This post contains major spoilers for season two, episode seven of'Star Trek: Lower Decks.' Artificial intelligence has been baked into the Star Trek universe since the original series. Kirk and his crew occasionally faced off against computers gone amok, including Nomad, Landru and the M-5. The only way to defeat these digital villains was to outwit them using logic, which caused them to self-destruct. But in The Next Generation, the franchise became more interested in exploring the personhood of artificial beings like Data and his family, Voyager's holographic doctor or the exocomps. This week, Lower Decks dredges up the old-style megalomaniacal AI and asks, are you really sure about those rights?

The reputation and bottom line of a company can be adversely affected if defective products are released. If a defect is not detected, and the flawed product is not removed early in the production process, the damage can be costly – and the higher the unit value, the higher those costs will be. And worst of all, dissatisfied customers can demand returns. To mitigate these costs, many manufacturers install cameras to monitor their products as they move along their production lines. However, the data obtained may not always be useful – or more appropriately said, the data is useful, but existing machine vision systems may not be able to accurately assess it at full production speeds.

Amazon's voice technology has made its way into Major League Baseball. Earlier this month, the Seattle Mariners became the first professional sports franchise to place Amazon Echo devices inside stadium suites. At Safeco Field, fans at each of the 59 suites can now use their voice to order food, change TV channels, play music, and even have Alexa -- the AI powered voice assistant built into the Echo -- sing "Take Me Out To The Ballgame." GeekWire had a chance to test out the new technology this week before the Mariners took on the Oakland Athletics. Fans can use Alexa for its normal capabilities -- asking about the weather, playing music, providing news updates, etc.

It's real and it's here to stay. Whether you are in marketing, operations or finance, machine learning data can help you do what you do better. In this blog post, I outline 7 reasons why Machine Learning is here to stay: Customer Churn, Customer Segmentation, Buyer Behavior, Asset Monitoring, Demand Forecasting, Fraud Detection and Anomaly Detection. We often say "You have 1,000 customers on January 1. You have 1,000 customers on December 31. How many customers did you lose?"