Plastic pollution is one of those problems everyone can see, yet few know how to tackle it effectively. I grew up walking the beaches around Tramore in County Waterford, Ireland, where plastic debris has always been part of the coastline, including bottles, fragments of fishing gear and food packaging. According to the UN, every year 19-23 million tonnes of plastic lands up in lakes, rivers and seas, and it has a huge impact on ecosystems, creating pollution and damaging animal habitats. Community groups do tremendous work cleaning these beaches, but they're essentially walking blind, guessing where plastic accumulates, missing hot spots, repeating the same stretches while problem areas may go untouched. Years later, working in marine robotics at the University of Limerick, I began developing tools to support marine clean-up and help communities find plastic pollution along our coastline.

Vision-Language Models (VLMs) are becoming increasingly powerful, demonstrating strong performance on a variety of tasks that require both visual and textual understanding. Their strong generalisation abilities make them a promising component for automated driving systems, which must handle unexpected corner cases. However, to be trusted in such safety-critical applications, a model must first possess a reliable perception system. Moreover, since critical objects and agents in traffic scenes are often at a distance, we require systems that are not "shortsighted", i.e., systems with strong perception capabilities at both close (up to 20 meters) and long (30+ meters) range. With this in mind, we introduce Distance-Annotated Traffic Perception Question Answering (DTPQA), the first Visual Question Answering (VQA) benchmark focused solely on perception-based questions in traffic scenes, enriched with distance annotations. By excluding questions that require reasoning, we ensure that model performance reflects perception capabilities alone. Since automated driving hardware has limited processing power and cannot support large VLMs, our study centers on smaller VLMs. More specifically, we evaluate several state-of-the-art (SOTA) small VLMs on DTPQA and show that, despite the simplicity of the questions, these models significantly underperform compared to humans (~60% average accuracy for the best-performing small VLM versus ~85% human performance). However, it is important to note that the human sample size was relatively small, which imposes statistical limitations. We also identify specific perception tasks, such as distinguishing left from right, that remain particularly challenging for these models.

This paper used Chat GPT to create Irish Limericks. During the creation process, a pattern was observed that seemed to create positive Limericks for liberal politicians and negative Limericks for conservative politicians. Upon identifying this pattern, the sample size was expanded to 80 and some mathematical calculations were made to determine whether the actual results were different from what probability theory would suggest. It was found that, at least in some cases, the AI was biased to favor liberal politicians and disfavor conservatives.

No, I didn't write this limerick. Neither did I swipe it from anyone else. In fact, no human was involved in its composition. I had simply set out to write a year-end column on whether 2022 could be a turning point in artificial intelligence (AI) and turned to AI itself for help to get things going, having read far too much already about ChatGPT. This is the new AI-powered chatbot that has the chattering classes chattering like never before.

The world is in a state of economic collapse due to the rise of automation, leaving millions of people unemployed and desperate. 'Enter the tech billionaire, Dr Milton Ross. He has developed a revolutionary new type of artificial intelligence that promises to revolutionise the world. His AI, which he calls Neo, is capable of replacing humans in virtually any job. 'Ross begins to roll out Neo to large corporations, who eagerly accept it.

One of the most exciting fields within machine learning and data science is natural language processing. Having a machine be able to parse and generate plausibly human sounding language is both of enormous practical value and also notoriously difficult. Human language is messy, filled with the sort of irregularities that computers can't handle. Even relatively simple tasks like tagging what part of speech a given word is can be hard and context dependent (is'permit' a noun or a verb?). Even the most powerful and successful implementations of NLP can feel a little off.

Kairos snaps up EmotionReader, which can scan faces in a crowd and tell how audiences are reacting. EmotionReader, an Enterprise Ireland-backed facial recognition start-up, has been acquired by Miami-based Kairos in an undisclosed "multimillion-dollar" deal. Artificial intelligence (AI) then analyses viewer attention and emotional response, enabling media and brand owners to collect actionable insights and analytics for video. 'In our mission to fix biases in today's face recognition algorithms, we're thrilled to welcome to Kairos some of the best deep-learning talent in the world' – BRIAN BRACKEEN The company is the brainchild of Dr Padraig O'Leary and Dr Stephen Moore, and it was founded only last year. Moore, working from his Singapore base, is understood to have built an impressive R&D team in the south-east Asian country.

We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first approach uses a slice sampling subcomponent for estimating cluster parameters. The second approach marginalizes out several cluster parameters by taking advantage of approximations to the conditional posteriors. We empirically demonstrate (1) the effectiveness of this approximation for improving convergence, (2) the benefits of the Dirichlet process model over alternative clustering techniques for ranked data, and (3) the applicability of the approach to exploring large realworld ranking datasets.