Abstract--The risks associated with adopting large language model (LLM) chatbots in software organizations highlight the need for clear policies. We examine how 11 companies create these policies and the factors that influence them, aiming to help managers safely integrate chatbots into development workflows. In software organizations, the software product is gradually evolving to AI-powered software (AIware) with the use of AI, more specifically, large language models (LLMs) in the development process [2]. LLMs are increasingly seen as valuable tools for improving productivity, which motivated enterprises to adopt them [3]. However, these models have introduced risks and concerns that impact the organization, the software engineers, and the product. Integrating LLMs into software development raises challenges related to the quality and ownership of generated content [4], which complicates accountability and can affect product reliability . In addition, interactions with LLMs (e.g., through external APIs) may expose organizations to liability where developers unintentionally transmit sensitive data, resulting in legal repercussions [5].

We study active correlation clustering where pairwise similarities are not provided upfront and must be queried in a cost-efficient manner through active learning. Specifically, we focus on the cold-start scenario, where no true initial pairwise similarities are available for active learning. To address this challenge, we propose a coverage-aware method that encourages diversity early in the process. We demonstrate the effectiveness of our approach through several synthetic and real-world experiments.

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.

This post contains a list of the AI-related seminars that are scheduled to take place between 2 September and 31 October 2024. All events detailed here are free and open for anyone to attend virtually. Vienna Manifesto on Digital Humanism – The first five years Panellists: Veronica Kaup-Hasler, Jens Schneider, Noshir Contractor, George Metakides, Christiane Wendehorst Organised by: The Digital Humanism (DIGHUM) Initiative The talk will be livestreamed on YouTube here. Title to be confirmed Speaker: Jiajun Wu (Stanford University) Organised by: Vanderbilt University Check the Google group for Zoom instructions. Exploiting artificial intelligence in synthesis planning Speaker: Samuel Genheden (AstraZeneca) Organised by: Chalmers University of Technology Zoom link is here.

We use different methods to examine and explore the data and find differences in what students write about courses depending on gender of the examiner. Data from English and Swedish courses are evaluated and compared, in order to capture more nuance in the gender bias that might be found. Here we present the results from the work so far, but this is an ongoing project and there is more work to do.

The Lovász ϑ function of a graph, a fundamental tool in combinatorial optimization and approximation algorithms, is computed by solving a SDP. In this paper we establish that the Lovász ϑ function is equivalent to a kernel learning problem related to one class SVM. This interesting connection opens up many opportunities bridging graph theoretic algorithms and machine learning. We show that there exist graphs, which we call SVM ϑ graphs, on which the Lovász ϑ function can be approximated well by a one-class SVM. This leads to novel use of SVM techniques for solving algorithmic problems in large graphs e.g.

The explosion of content generated by Artificial Intelligence models has initiated a cultural shift in arts, music, and media, where roles are changing, values are shifting, and conventions are challenged. The readily available, vast dataset of the internet has created an environment for AI models to be trained on any content on the web. With AI models shared openly, and used by many, globally, how does this new paradigm shift challenge the status quo in artistic practices? What kind of changes will AI technology bring into music, arts, and new media?

The thought of humans having wings, tentacles or an extra arm may all seem rather unlikely. But these scenarios could actually become reality in the next few decades, thanks to leaps in human augmentation. Researchers have already designed a'Third Thumb' controlled by foot movements, which allows the wearer to unscrew a bottle, peel a banana or thread a needle using just one hand. Now, experts believe the thumb is just a first step towards larger, more dramatic additions to the human body. Tamar Makin, a professor of cognitive neuroscience at Cambridge University, said the brain's ability to adapt to an extra limb was'extraordinary'.

We are a group of researchers from Sweden, Netherlands, and Germany and kindly invite you to our survey on "Machine Learning Experiment Management Tools." Such tools support practitioners performing machine learning (ML) or deep learning (DL) experiments, systematically managing all involved artifacts (scripts, datasets, hyperparameters, models, …). As a machine learning practitioner, we kindly invite you to participate. We also invite you to forward this invitation to other colleagues who might be interested in this survey as well. Our survey elicits information on the management tools practitioners adopt, their perceived benefits, challenges, and limitations.

Can robots adapt their own working methods to solve complex tasks? Researchers at Chalmers University of Technology, Sweden, have developed a new form of AI, which, by observing human behavior, can adapt to perform its tasks in a changeable environment. The hope is that robots that can be flexible in this way will be able to work alongside humans to a much greater degree. "Robots that work in human environments need to be adaptable to the fact that humans are unique, and that we might all solve the same task in a different way. An important area in robot development, therefore, is to teach robots how to work alongside humans in dynamic environments," says Maximilian Diehl, Doctoral Student at the Department of Electrical Engineering at Chalmers University of Technology and main researcher behind the project.