The AAAI/SIGAI Doctoral Consortium provides an opportunity for a group of PhD students to discuss and explore their research interests and career objectives in an interdisciplinary workshop together with a panel of established researchers. This year, 30 students have been selected for this programme, and we'll be hearing from them over the course of the next few months. In this interview, Célian Ringwald, tells us about his work on natural language processing and knowledge graphs. I am a PhD student at the Université Côte d'Azur in Inria, the French Institute in Research in AI. I am part of the Wimmics team, a research group bridging formal semantics and social semantics on the web.

This post contains a list of the AI-related seminars that are scheduled to take place between 11 July and 31 August 2023. All events detailed here are free and open for anyone to attend virtually. APOLLO: an AI driven national platform for CT coronary angiography for clinical and industrial applications Speaker: Lee Hwee Kuan Organised by: Cambridge Centre for AI in Medicine Sign up here. Title to be confirmed Speaker: To be confirmed Organised by: I can't believe it's not better (ICBINB) Check the website nearer the time for instructions on how to join. Distributed communication-constrained learning Speakers: Alexander Jung (Aalto University), Danijela Cabric (UCLA), Stefan Vlaski (Imperial College London), Lara Dolecek (UCLA), Yonina Eldar (Weizmann Institute of Science) Organised by: One World Signal Processing To receive the link to attend, sign up to the mailing list.

How can I contribute to the list? What contents can I add? Do you provide funding/scholarships for the students? Does the list include the same summer schools as available on awesome-mlss repo?

Welcome to our December 2022 monthly digest, where you can catch up with any AIhub stories you may have missed, get the low-down on recent events, and much more. This month, we hear from best paper award winners at ICIP and NeurIPS, and find out more about teleoperation, multilingual translation, and quality-diversity algorithms. We also have exciting news, in the form of a new focus series. We're delighted to announce the launch of our new focus series on AI around the world, where we cover exciting applications of AI across the globe. To kick off the series, we spoke with Rose Nakasi.

We are pleased to announce that this summer AI4SD will be running a hybrid residential summer school from the 20th-24th June 2022 at the University of Southampton. This summer school will introduce you to basic python programming, different areas of machine learning including mathematical foundations for ML, classification and clustering, kernel methods, introduction to deep learning and case studies in chemistry including reinforcement learning in chemistry. There will also be talks to upskill scientists in other relevant areas including Group Management, Presentation Skills, Research Data Management, Referencing, LaTeX, GitHub and Ethics. The summer school will include a hackathon where students can compete in teams to solve the same problem in the best way. Group presentations will take place on the friday and prizes will be given to the winning team.

Equipping machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing goal of AI. Over the last decade, large-scale knowledge bases, also known as knowledge graphs, have been automatically constructed from web contents and text sources, and have become a key asset for search engines. This machine knowledge can be harnessed to semantically interpret textual phrases in news, social media and web tables, and contributes to question answering, natural language processing and data analytics. This article surveys fundamental concepts and practical methods for creating and curating large knowledge bases. It covers models and methods for discovering and canonicalizing entities and their semantic types and organizing them into clean taxonomies. On top of this, the article discusses the automatic extraction of entity-centric properties. To support the long-term life-cycle and the quality assurance of machine knowledge, the article presents methods for constructing open schemas and for knowledge curation. Case studies on academic projects and industrial knowledge graphs complement the survey of concepts and methods.

Data Science Africa summer school is aimed at equipping participants with Machine Leaning, Data Science and Artificial Intelligence skills. This will be organised in sesions and after each session there will be exercises to assess the learning. To this end, we require that participants are well versed with the basics of the technologies and languages that will be used in the summer school. Particularly, we want to make sure participants have sufficient base skills in Python programming, Data science and Machine learning. You are required to download the notebook from the link below (by clicking it), complete the notebook and then fill out the registration form below that requires you to upload the completed notebook.

The event includes talks by CREATE DAV faculty and industry experts on current research topics in big data science, as well as hands-on experience in York and OCAD U laboratories. The curriculum reflects the wide range of research areas at CREATE DAV, which includes research on machine learning, data mining, signal processing, computer vision, image processing, computer graphics, virtual human modeling, serious games, natural language processing, human perception & cognition, visualization & design.

On Monday, September 2, 2019, the summer school "IoT meets AI" started very successfully in Werk 1 in Munich. The team around Marc-Oliver Pahl had invested a significant portion of their weekend on setting up a cozy work environment on the one hand, and preparing everything for the livestream (https://future-iot.org/stream/) on the other hand. Marc-Oliver Pahl (TUM) opened the summer school by introducing his co-organizers Arne Bröring (Siemens) and Nicolas Montavont (IMT). He credited the organizing team that did a wonderful job in helping to setup the event for the 32 participants and about 30 support staff people. In particular he mentioned Fabian Rhein (Siemens), Catharina Vos (TUM International), his team consisting of Lars Wüstrich, Erkin Kirdan, Stefan Liebald, and Christian Lübben, and Olivia Pahl (FGW), who helped almost the entire weekend, and Marlene Eder (Werk1).

The PRAIRIE AI summer school comprises lectures conducted by renowned experts in different areas of artificial intelligence. The second edition of this summer school will be held in Paris from the 3rd to the 5th October 2019. It will include presentations on several topics, including, computer vision, machine learning, natural language processing, robotics, healthcare. Details of the previous edition in 2018 can be found here. Supporting Organizations The AI Summer School is organized by Inria and the institutes PRAIRIE and MIAI.