Intelligent robots need to generate and execute plans. In order to deal with the complexity of real environments, planning makes some assumptions about the world. When executing plans, the assumptions are usually not met. Most works have focused on the negative impact of this fact and the use of replanning after execution failures. Instead, we focus on the positive impact, or opportunities to find better plans. When planning, the proposed technique finds and stores those opportunities. Later, during execution, the monitoring system can use them to focus perception and repair the plan, instead of replanning from scratch. Experiments in several paradigmatic robotic tasks show how the approach outperforms standard replanning strategies.

The paper describes the MetroPT data set, an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 that aimed to evaluate machine learning methods for online anomaly detection and failure prediction. By capturing several analogic sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed), we provide a dataset that can be easily used to evaluate online machine learning methods. This dataset contains some interesting characteristics and can be a good benchmark for predictive maintenance models.

We research and develop autonomous mobile service robots as Collaborative Robots, i.e., CoBots. For the last three years, our four CoBots have autonomously navigated in our multi-floor office buildings for more than 1,000km, as the result of the integration of multiple perceptual, cognitive, and actuations representations and algorithms. In this paper, we identify a few core aspects of our CoBots underlying their robust functionality. The reliable mobility in the varying indoor environments comes from a novel episodic non-Markov localization. Service tasks requested by users are the input to a scheduler that can consider different types of constraints, including transfers among multiple robots. With symbiotic autonomy, the CoBots proactively seek external sources of help to fill-in for their inevitable occasional limitations. We present sampled results from a deployment and conclude with a brief review of other features of our service robots.

AI and robotics researchers aim at having robots in our environments coexisting with humans, as artificial creatures that will help humans and collaborate with humans to improve our societies. There will be more than one robot.

Trailblazers Week celebrates the women who have pushed boundaries and paved the way for others in their industries. Manuela Veloso grew up in Portugal in the 1960s and '70s in a household where innovations, from the moon landing to the building of a huge bridge in Lisbon, were the subject of dinner-table discussion. In 1994, she moved to the U.S. to earn a master's degree in computer science, and she went on to get her Ph.D. at Carnegie Mellon. It was the golden era of artificial intelligence, the "years of deep thoughts, chess playing, hopping robots," she tells the Cut. Veloso spent more than two decades at the university, working her way up to become the head of its machine-learning department, and has been researching artificial intelligence ever since -- now as head of AI research at JPMorgan and professor emeritus at Carnegie Mellon.

J.P. Morgan envisions a chief role for artificial intelligence in putting an end to financial crimes, the company's head of AI research said Monday. Financial crimes that institutions like J.P. Morgan might have to reckon with include money laundering, sanctions violations, fraud and outright cyber theft. "There is this concept of values" for AI, said Manuela Veloso, who besides her work for J.P. Morgan has worked as a professor in Carnegie Mellon University's school of computer science. "First, we want AI to be able to eradicate financial crime." Veloso's comments came Monday during AI Week, an event produced by Scoop News Group.

Most large banks in the US are pursuing AI fairly assiduously, but JPMorgan Chase stands out for the depth of its commitment to the technology, the breadth of projects it has adopted, and the focus on driving actual business change from its AI initiatives. The Bank, the largest in the US and 6th largest in the world in terms of total assets, has AI projects or production applications in all the usual areas of banking: risk, fraud prevention, marketing, investment banking, wealth management advice, trading, back office automation, and customer engagement (particularly in the corporate banking area thus far). But JPMorgan Chase distinguishes itself from other banking firms in its level of investment, its hiring of AI academic stars, and its coordinated approach to the management of AI and analytics. JPMorgan Chase spends $11 billion a year on technology, and about half of that amount is devoted to research on new and emerging technologies. Its research investments cover a wide variety of domains, including investments in AI startups and AI-based hedge funds.

A year later, Barrick has parted ways with its chief innovation officer, chief digital officer and many of the team tasked with making this transformation a reality, according to people familiar with the matter. The revolution in machine learning, as predicted by Barrick Chairman John Thornton and other mining executives, has yet to come. Miners have said digital technologies like artificial intelligence, or AI, will revolutionize one of the world's oldest industries in the same way it has changed other businesses, from retail to hailing a cab. Some experts say the promise of AI in mining has been overhyped and progress has been slow. Companies, including Barrick and giants such as Rio Tinto PLC and BHP Group Ltd., are running some AI-led projects. But implementation at some companies has hit cultural hurdles.

Artificial intelligence is in our phones, beginning to control our money, and giving robots some decision-making powers. But Carnegie Mellon professor Manuela Veloso, incoming head of JPMorgan's AI research division, is frustrated that things aren't further along. "I came from the Boston airport last night and I didn't see a single mobile robot anywhere on my way here," she said. After her decades of work in AI research, Veloso is ready to see more mobile robots in our everyday lives. She envisions a future where everything with wheels, from suitcases to shopping carts, will automatically follow you at a single command.

J.P. Morgan announced that Dr. Manuela Veloso will be joining the Corporate & Investment Bank as the head of Artificial Intelligence (AI) Research. Veloso is currently the head of the Machine Learning Department at Carnegie Mellon University, widely considered the preeminent institution in the field. J.P. Morgan has already started to apply machine learning technology across its businesses and functions, and this expanded effort will be aimed at identifying further opportunities. A.I. is among the areas of investment within the bank's annual technology budget of $10.8 billion, with more than half earmarked for new investments. Veloso was named a Carnegie Mellon University Professor in 2014, the highest academic accolade bestowed by the university, and holds the Herbert A. Simon Chair in computer science.