As you read this, it's holding your phone or clicking your mouse with seemingly effortless grace. With over 20 degrees of freedom, human hands possess extraordinary dexterity, which can grip a heavy hammer, rotate a screwdriver, or instantly adjust when something slips. Executing complex tasks like key rotation, scissor use, and surgical procedures that are impossible with simple grippers. Their similarity to human hands makes them ideal for learning from vast human demonstration data. Despite this potential, most current robots still rely on simple "grippers" due to the difficulties of dexterous manipulation.

Ahead of the 34th International Joint Conference on Artificial Intelligence (IJCAI 2025), which will take place in Montréal, Canada, from 16 to 22 August 2025, the Local Arrangements Committee has launched a campaign to showcase the next generation of AI researchers in Canada. Through a series of 90-second videos, we meet students based in Canada and find out a bit about their work. Imane Chafi, PhD candidate at the Polytechnique Montréal, uses AI models to support dentists in designing dental preparations for dental crowns more efficiently and accurately. Liliane-Caroline Demers, Student Communication Coordinator for IJCAI 2025 Local Arrangement Committee and a recent master's graduate from Polytechnique Montréal, researches AI-generated music. Using a neurosymbolic approach that combines machine learning with constraint programming at inference time, she creates music that is both stylistically authentic and structurally coherent.

The MIT-Takeda Program, a collaboration between MIT's School of Engineering and Takeda Pharmaceuticals Company, fuels the development and application of artificial intelligence capabilities to benefit human health and drug development. Part of the Abdul Latif Jameel Clinic for Machine Learning in Health, the program coalesces disparate disciplines, merges theory and practical implementation, combines algorithm and hardware innovations, and creates multidimensional collaborations between academia and industry. With the aim of building a community dedicated to the next generation of AI and system-level breakthroughs, the MIT-Takeda Program is also creating educational opportunities. Every year Takeda funds fellowships to support graduate students pursuing research related to health and AI. This year's Takeda Fellows, described below, are working on projects ranging from electronic health record systems and robotic control to pandemic preparedness and traumatic brain injuries.

The 1999 cult classic film Office Space depicts Peter's dreary life as a cubicle-dwelling software engineer. Every Friday, Peter tries to avoid his boss and the dreaded words: "I'm going to need you to go ahead and come in tomorrow." This scene is still popular on the internet nearly 25 years later because it captures troubling aspects of the employment relationship -- the helplessness Peter feels, the fake sympathy his boss intones when issuing this directive, the never-ending demand for greater productivity. There is no shortage of pop culture depictions of horrible bosses. There is even a film with that title.

PhD Candidate in Machine Learning for Understanding Mental Health Interventions in Computer Science, Academic Posts with NORWEGIAN UNIVERSITY OF …

The 1999 cult classic film Office Space depicts Peter's dreary life as a cubicle-dwelling software engineer. Every Friday, Peter tries to avoid his boss and the dreaded words: "I'm going to need you to go ahead and come in tomorrow." This scene is still popular on the internet nearly 25 years later because it captures troubling aspects of the employment relationship – the helplessness Peter feels, the fake sympathy his boss intones when issuing this directive, the never-ending demand for greater productivity. There is no shortage of pop culture depictions of horrible bosses. There is even a film with that title.

Machine learning (ML) techniques are increasingly prevalent in education, from their use in predicting student dropout, to assisting in university admissions, and facilitating the rise of MOOCs. Given the rapid growth of these novel uses, there is a pressing need to investigate how ML techniques support long-standing education principles and goals. In this work, we shed light on this complex landscape drawing on qualitative insights from interviews with education experts. These interviews comprise in-depth evaluations of ML for education (ML4Ed) papers published in preeminent applied ML conferences over the past decade. Our central research goal is to critically examine how the stated or implied education and societal objectives of these papers are aligned with the ML problems they tackle. That is, to what extent does the technical problem formulation, objectives, approach, and interpretation of results align with the education problem at hand. We find that a cross-disciplinary gap exists and is particularly salient in two parts of the ML life cycle: the formulation of an ML problem from education goals and the translation of predictions to interventions. We use these insights to propose an extended ML life cycle, which may also apply to the use of ML in other domains. Our work joins a growing number of meta-analytical studies across education and ML research, as well as critical analyses of the societal impact of ML. Specifically, it fills a gap between the prevailing technical understanding of machine learning and the perspective of education researchers working with students and in policy.

An artificial intelligence (AI) model programmed using sequential health data extracted from electronic health records detected a subset of persons with a 25-fold danger of developing pancreatic cancer between 3 and 36 months, according to findings showcased at the 2022 Annual Meeting of the American Association for Cancer Research (AACR) that was held from April 8th to 13th. At the moment, there are no reliable biomarkers or screening tools that can detect pancreatic cancer early. The purpose of this study was to develop an artificial intelligence tool that can help clinicians identify people at high risk for pancreatic cancer so they can be enrolled in prevention or surveillance programs and hopefully benefit from early treatment. Bo Yuan presented the study. Pancreatic cancer is an aggressive form of cancer that is frequently diagnosed at later stages because of the absence of early symptoms and thus has a comparatively poor prognosis, said Davide Placido, a Ph.D. candidate at the University of Copenhagen and the study's co-first author.

Are you fascinated by the possibilities of machine learning systems and is it important to you that these technologies are used fairly? As a PhD Candidate, your research aims to answer the question how information retrieval systems based on machine learning can be used in a non-discriminatory and fair way. Information retrieval and recommender systems based on machine learning can be used to make decisions about people. Government agencies can use such systems to detect welfare fraud, insurers can use them to predict risks and to set insurance premiums, and companies can use them to select the best people from a list job applicants. Such systems can lead to more efficiency, and could improve our society in many ways.