This post contains a list of the AI-related seminars that are scheduled to take place between 4 November and 31 December 2024. All events detailed here are free and open for anyone to attend virtually. Young stars session: 1) K-anonymous counterfactual explanations, 2) Neur2BiLO: Neural Bilevel Optimization, 3) On constrained mixed-integer DR-submodular minimization Speakers: Sofie Goethals (University of Antwerp), Esther Julien (TU Delft), Qimeng (Kim) Yu (Université de Montréal) Organised by: Association of European Operational Research Societies To receive the seminar link, sign up to the mailing list. Learning accurate and interpretable decision trees Speaker: Dravyansh Sharma (TTIC) Organised by: Carnegie Mellon University Zoom link is here. Title to be confirmed Speaker: Elena Celledoni (Norwegian University of Science and Technology) Organised by: One World Machine Learning Register for the mailing list to receive Zoom joining instructions.

These championships were the first time the technology, formally known as the Judging Support System, or JSS, had been used on every apparatus in a gymnastics competition--and its first use in a competition that could make or break an athlete's Olympic dreams. While the AI judging system did not replace human judges--rather, it was available to help judges review routines in case of an inquiry or a "blocked score"--it still marked a watershed moment for the sport that was years in the making. The International Gymnastics Federation (known as FIG, its initials in French) first used JSS to judge pommel horse, rings, and vault back at the 2019 World Championships before adding more events at different competitions each year. There are obvious upsides to using this kind of technology in competition. Human gymnastics judges must have an eye for quick, tiny movements: the point of a toe, the angle of a split (did she hit 180?), the slightest bend at the hip. AI could help take the guesswork out of the technicalities.

A new Rembrandt painting – by a computer. Photos of people who don’t exist: when it comes to realizing art world fantasies, Artificial Intelligence is delivering the goods. A great opportunity - or are there dangers? How AI is changing creativity.

Electronic address: mike.landry@hvl.no. 2 Western Norway University of Applied Sciences, Norway.

Night after night, Fien de Meulder sat in front of her Linux computer flagging names of people, places, and organizations in sentences pulled from Reuters newswire articles. De Meulder and her colleague, Erik Tjong Kim Sang, worked in language technology at the University of Antwerp. It was 2003, and a 60-hour workweek was typical in academic circles. She chugged Coke to stay awake. The goal: develop an open source dataset to help machine learning (ML) models learn to identify and categorize entities in text.

MUNICH, Germany and ANTWERP, Belgium, Dec 14, 2020 – The Nemetschek Group, one of the world's leading software providers for the architecture, engineering, construction, and building operations (AECO) industry, announced that its subsidiary Spacewell – headquartered in Antwerp, Belgium – has acquired 100% of DEXMA. Based in Barcelona, Spain, DEXMA is a fast-growing provider of innovative SaaS solutions with artificial intelligence and machine learning capabilities for energy data management. The company enables over 4,000 customers in 30 countries worldwide to effectively measure, monitor, and manage their energy consumption and costs. "Buildings account for 30 percent of our total energy use and 28 percent of global carbon emissions. This acquisition is a huge benefit for our customers who are aiming to become more sustainable in their operations. Energy management is an important element in creating truly autonomous buildings that automatically adapt their behaviors to the occupants and stakeholders," says Koen Matthijs, Chief Division Officer, Operate & Manage Division at the Nemetschek Group.

Antwerp University Hospital (UZA) in Belgium is using an AI-powered robot to greet patients, check temperatures, and ensure masks are worn correctly. By removing initial human-to-human contact, the multilingual robot makes life a little easier and safer for hospital staff. The robot works pretty intuitively. As patients enter the hospital, they collect a barcode and feed it into the robot. This then uses temperature measurement cameras and ultrasonic sensors to check that the patient is safe.

--Fingerprinting techniques, which are a common method for indoor localization, have been recently applied with success into outdoor settings. Particularly, the communication signals of Low Power Wide Area Networks (LPW AN) such as Sigfox, have been used for localization. In this rather recent field of study, not many publicly available datasets, which would facilitate the consistent comparison of different positioning systems, exist so far . In the current study, a published dataset of RSSI measurements on a Sigfox network deployed in Antwerp, Belgium is used to analyse the appropriate selection of preprocessing steps and to tune the hyperparameters of a kNN fingerprinting method. Initially, the tuning of hyperparameter k for a variety of distance metrics, and the selection of efficient data transformation schemes, proposed by relevant works, is presented. In addition, accuracy improvements are achieved in this study, by a detailed examination of the appropriate adjustment of the parameters of the data transformation schemes tested, and of the handling of out of range values. With the appropriate tuning of these factors, the achieved mean localization error was 298 meters, and the median error was 109 meters. T o facilitate the reproducibility of tests and comparability of results, the code and train/validation/test split used in this study are available. The recent emergence of Internet of Things (IoT) technologies has made so that a plethora of low power devices make their appearance worldwide, in people's everyday life. The concept of smart cities becomes familiar to the broad public, and numerous applications are being proposed, implemented and deployed in domains such as massive gathering of sensor measurements, automatic control, asset tracking, etc.

BenevolentAI, founded in 2013, creates and applies AI technologies to transform the way medicines are discovered and developed. BenevolentAI seeks to improve patient's lives by applying technology designed to generate better data decision making and in doing so lower drug development costs, decrease failure rates and increase the speed at which medicines are generated. The company has developed the Benevolent Platform - a discovery platform used by BenevolentAI scientists to find new ways to treat disease and personalise drugs to patients. BenevolentAI is HQ'd in London with a research facility in Cambridge (UK) and further offices in New York and Antwerp. BenevolentAI has active R&D drug programmes from discovery to PhaseIIb in disease areas such as ALS, Parkinson's, Ulcerative Colitis and Sarcopenia.

BenevolentAI, founded in 2013, creates and applies AI technologies to transform the way medicines are discovered and developed. BenevolentAI seeks to improve patient's lives by applying technology designed to generate better data decision making and in doing so lower drug development costs, decrease failure rates and increase the speed at which medicines are generated. The company has developed the Benevolent Platform - a discovery platform used by BenevolentAI scientists to find new ways to treat disease and personalise drugs to patients. BenevolentAI is HQ'd in London with a research facility in Cambridge (UK) and further offices in New York and Antwerp. BenevolentAI has active R&D drug programmes from discovery to PhaseIIb in disease areas such as ALS, Parkinson's, Ulcerative Colitis and Sarcopenia.