This is the focus of work by and, which won the best paper award at the recent RoboCup symposium . The symposium takes place alongside the annual RoboCup competition, which this year was held in Salvador, Brazil. We caught up with some of the authors to find out more about the work, how their method can be transferred to applications beyond RoboCup, and their future plans for the competition. Could you start by giving us a brief description of the problem that you were trying to solve in your paper "Self-supervised Feature Extraction for Enhanced Ball Detection on Soccer Robots"? The main challenge we faced was that deep learning generally requires a large amount of labeled data. This is not a major problem for common tasks that have already been studied, because you can usually find labeled datasets online.

Over the course of 2024, we had the pleasure of finding out more about a whole range of AI topics from researchers around the world. Here, we highlight some of our favourite interviews from the past 12 months. Please note: we have not included our interviews with AAAI/ACM SIGAI Doctoral Consortium participants – these are highlighted in this dedicated collection. Christopher Chandler tells us about model checking and how it is used in the context of autonomous robotic systems, specifically looking at creating multi-step plans for a differential-drive wheeled robot so that it can avoid immediate danger. Bo Li and colleagues won an outstanding datasets and benchmark track award at NeurIPS 2023 for their work DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models.

Scientific innovation is pivotal for humanity, and harnessing large language models (LLMs) to generate research ideas could transform discovery. However, existing LLMs often produce simplistic and repetitive suggestions due to their limited ability in acquiring external knowledge for innovation. To address this problem, we introduce an enhanced planning and search methodology designed to boost the creative potential of LLM-based systems. Our approach involves an iterative process to purposely plan the retrieval of external knowledge, progressively enriching the idea generation with broader and deeper insights. Validation through automated and human assessments indicates that our framework substantially elevates the quality of generated ideas, particularly in novelty and diversity. The number of unique novel ideas produced by our framework is 3.4 times higher than without it. Moreover, our method outperforms the current state-of-the-art, generating at least 2.5 times more top-rated ideas based on 170 seed papers in a Swiss Tournament evaluation.

Development of defenses against physical world attacks such as adversarial patches is gaining traction within the research community. We contribute to the field of adversarial patch detection by introducing an uncertainty-based adversarial patch localizer which localizes adversarial patch on an image, permitting post-processing patch-avoidance or patch-reconstruction. We quantify our prediction uncertainties with the development of \textit{\textbf{D}etection of \textbf{U}ncertainties in the \textbf{E}xceedance of \textbf{T}hreshold} (DUET) algorithm. This algorithm provides a framework to ascertain confidence in the adversarial patch localization, which is essential for safety-sensitive applications such as self-driving cars and medical imaging. We conducted experiments on localizing adversarial patches and found our proposed DUET model outperforms baseline models. We then conduct further analyses on our choice of model priors and the adoption of Bayesian Neural Networks in different layers within our model architecture. We found that isometric gaussian priors in Bayesian Neural Networks are suitable for patch localization tasks and the presence of Bayesian layers in the earlier neural network blocks facilitates top-end localization performance, while Bayesian layers added in the later neural network blocks contribute to better model generalization. We then propose two different well-performing models to tackle different use cases.

Over the course of 2022, we had the pleasure of finding out more about a whole range of AI topics from researchers around the world. Here, we highlight some of our favourite interviews from the past 12 months. Rose Nakasi and her colleagues have developed a machine-learning method to detect malaria parasites in blood samples. We spoke to Rose about the motivation for this project, the progress so far, and what they are planning next. Paula Arguello, Jhon Lopez, Carlos Hinojosa and Henry Arguello won the best paper award at the International Conference on Image Processing (ICIP) this year, for their work "Optics lens design for privacy-preserving scene captioning".

Paula Arguello, Jhon Lopez, Carlos Hinojosa and Henry Arguello won the best paper award at the International Conference on Image Processing (ICIP) this year, for their work Optics lens design for privacy-preserving scene captioning. In this interview, Carlos tells us more about privacy-preserving scene captioning, how they approached the problem, and the key contributions of their work. We have digital cameras everywhere. They are fundamental to a range of intelligent systems that recognize relevant events and assist us in our daily activities. We have them in our cars, homes, hospitals, etc. However, their ever-improving ability to imitate the human vision system and produce the highest-quality images has raised concerns about privacy and security.

Welcome to our November 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 researchers who've developed an AI system for live music accompaniment and improvisation. Amongst other things, we also find out more about counterfactual explanations for reinforcement learning, planning robust frictional multi-object grasps, and social bias in knowledge graphs. Olga Vechtomova and Gaurav Sahu envisioned and developed a system, LyricJam Sonic, that uses AI to create a real-time generative stream of music based on an artist's own catalogue of studio recordings. The purpose is to inspire the artist with potentially unexpected combinations of sounds.

Howie Choset is a Professor of Robotics where he serves as the co-director, along with Matt Travers, of the Biorobotics Lab. Choset's research program has made contributions to strategically significant problems in surgery, manufacturing, on-orbit maintenance, recycling and search and rescue. His work is most famous for its snake robots and other biologically inspired systems and recently his group has been contributing to robotic modularity, multi-agent planning, information-based search, and skill learning. Currently, Choset's projects include: medical support in the field, expeditionary robotics, on-orbit maintenance and construction of structures in space, rapidly carrying heavy objects up several flights of stairs, recycling of E-waste, food preparation, "edge"-sensing, and aerospace painting. Choset has led multi-PI projects centered on manufacturing: (1) automating the programming of robots for auto-body painting; (2) the development of mobile manipulators for agile and flexible fixture-free manufacturing of large structures in aerospace, and (3) the creation of a data-robot ecosystem for rapid manufacturing in the commercial electronics industry.

CVPR 2021 Best Paper Award Goes to Michael Niemeyer and Andreas Geiger from the Max Planck Institute for Intelligent Systems and the University of Tubingen for their paper called Giraffe, which looks at the task of controllable image synthesis. In other words, they look at generating new images and controlling what will appear, the objects and their positions and orientations, the background, etc. Using a modified GAN architecture, they can even move objects in the image without affecting the background or the other objects! CVPR is a yearly conference that happened just last week where a ton of new research papers in computer vision were out just for this event. As you already know, if you regularly read my articles, conventional GAN architectures work with an encoder and a decoder setup, just like this.

Welcome to our December 2021 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 cover, amongst other things, NeurIPS 2021, sustainable cities and communities, and the BBC Reith Lectures. One of the main events in the AI world this month was the 35th conference on Neural Information Processing Systems (NeurIPS2021). We were lucky enough to attend and only scratched the surface of the vast array of talks, panels, workshops and posters on offer. You can read the first of our round-ups of the invited talks, #NeurIPS2021 invited talks round-up: part one – Duolingo, the banality of scale and estimating the mean.