Brice Valentin Kok - Shun Department of Information Systems and Operations Management University of Auckland Auckland, New Zealand 0000 - 0001 - 9923 - 5042 Johnny Chan Department of Information Systems and Operations Management University of Auckland Auckland, New Zealand 0000 - 0002 - 3535 - 4533 Abstract -- This work - in - progress paper presents a novel approach to detecting sponsored advertisement segments in YouTube videos and comparing the advertisement with the main content. Our methodology involves the collect ion of 421 auto - generated and manual transcripts which are then fed into a prompt - engineered GPT - 4o for ad detection, a KeyBERT for keyword extraction, and another iteration of ChatGPT for ca tegory identification . The results revealed a significant prevalence of product - related ads across vari ous educational topics, with ad categories refined using GPT - 4 o into succinct 9 content and 4 advertisement categories . This approach provides a scalable and efficient alternative to traditional ad detection methods while offering new insights into the types and relevance of ads embedded within educational content. T his study highlights the potential of LLMs in transforming ad detection processes and improving our understanding of ad vertisement strategies in digital media. In recent years, video - sharing platforms like YouTube have become dominant sources of entertainment, education, and information [1] . YouTube is invaluable for content creators, marketers, and advertisers. One of the key features of YouTube's revenue model is the integration of sponsored advertisement (ad) segments, which allows content creators to monetize their videos while providing advertisers a direct route to target specific audiences [2] .

The ICRA conference is celebrating its $40^{th}$ anniversary in Rotterdam in September 2024, with as highlight the Happy Birthday ICRA Party at the iconic Holland America Line Cruise Terminal. One month later the IROS conference will take place, which will include the Earth Rover Challenge. In this challenge open-world autonomous navigation models are studied truly open-world settings. As part of the Earth Rover Challenge several real-world navigation sets in several cities world-wide, like Auckland, Australia and Wuhan, China. The only dataset recorded in the Netherlands is the small village Oudewater. The proposal is to record a dataset with the robot used in the Earth Rover Challenge in Rotterdam, in front of the Holland America Line Cruise Terminal, before the festivities of the Happy Birthday ICRA Party start.

This paper presents a comprehensive and quality collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics. Anatomical and functional MRI images have been used to understand the functional connectivity of the human brain and are particularly important in identifying underlying neurodegenerative conditions such as Alzheimer's, Parkinson's, and Autism. Recently, the study of the brain in the form of brain networks using machine learning and graph analytics has become increasingly popular, especially to predict the early onset of these conditions. A brain network, represented as a graph, retains rich structural and positional information that traditional examination methods are unable to capture. However, the lack of publicly accessible brain network data prevents researchers from data-driven explorations. One of the main difficulties lies in the complicated domain-specific preprocessing steps and the exhaustive computation required to convert the data from MRI images into brain networks. We bridge this gap by collecting a large amount of MRI images from public databases and a private source, working with domain experts to make sensible design choices, and preprocessing the MRI images to produce a collection of brain network datasets. The datasets originate from 6 different sources, cover 4 brain conditions, and consist of a total of 2,702 subjects. We test our graph datasets on 12 machine learning models to provide baselines and validate the data quality on a recent graph analysis model. To lower the barrier to entry and promote the research in this interdisciplinary field, we release our brain network data and complete preprocessing details including codes at https://doi.org/10.17608/k6.auckland.21397377 and https://github.com/brainnetuoa/data_driven_network_neuroscience.

ChatGPT has been a godsend for Joy. The New Zealand-based therapist has ADHD and often struggles with tasks such as drafting difficult emails, with procrastination kicking in when she feels overwhelmed. "Sitting down to compose a complicated email is something I absolutely hate. I would have to use a lot of strategies and accountability to get it done, and I would feel depleted afterward," says Joy, who is in her 30s and lives in Auckland. "But telling GPT'write an email apologising for a delay on an academic manuscript, blame family emergency, ask for consideration for next issue' feels completely doable."

Our purpose is to put our energy where it matters, to decarbonise the New Zealand energy sector and promote #changematters. We are passionate about our mission and proud to have a tribe of people behind us working towards a common purpose. With such an ambitious goal, you might ask yourself – how does this opportunity help support a better, cleaner NZ? Contact is transforming its business with a data-first focus on operational excellence, enabling our team to do their best. Kōrero mō te tūranga - About the role We are on a journey to lift our organisational data capability to enable our people to do what they do best – deliver amazing customer experiences, create growth, and increase the value of our business. You'll be part of a data team working with business stakeholders to deliver these outcomes.

At Wander cafe in Auckland's Wynyard Quarter, someone at the next table is listening to Sean Gourley while he is being interviewed about artificial intelligence. After eavesdropping on the chat they get up, walk over to Gourley's table and tell him how scared they are. Gourley says most people think there is a 1% chance of war between China and the United States, but in his universe it is looking more like 50%. US defence and intelligence clients account for a large portion of the business Gourley's San Francisco-based artificial intelligence (AI) company, PrimerAI, does – and right now business is booming. READ MORE: * Keeping up with the machines, new supercomputer will be NZ's most powerful for AI * Kiwis need to think about what they want from the age of AI, report says * 'Google should not be in the business of war', says employee after it drops Pentagon AI contract * While artificial intelligence is tipped to be'as significant as electricity', it's not coming for your job, yet ...

Researchers at Carnegie Mellon University's School of Computer Science and the University of California have designed a system that enables a small, low-cost robot to climb and descend stairs, traverse uneven and varied terrain, walk across gaps, and even operate in the dark. The research could be a step toward solving existing challenges facing legged robots and bringing them into people's homes, say researchers. A paper supporting the research - Legged Locomotion in Challenging Terrains Using Egocentric Vision - will be presented at the upcoming Conference on Robot Learning in Auckland, New Zealand. "Empowering small robots to climb stairs and handle a variety of environments is crucial to developing robots that will be useful in people's homes as well as search-and-rescue operations," says Deepak Pathak, an Assistant Professor at Carnegie Mellon's Robotics Institute. "This system creates a robust and adaptable robot that could perform many everyday tasks."

Analysis While the legal and ethical implications of assistive AI models like GitHub's Copilot continue to be sorted out, computer scientists continue to find uses for large language models and urge educators to adapt. Brett A. Becker, assistant professor at University College Dublin in Ireland, provided The Register with pre-publication copies of two research papers exploring the educational risks and opportunities of AI tools for generating programming code. The papers have been accepted at the 2023 SIGCSE Technical Symposium on Computer Science Education, to be held March 15 to 18 in Toronto, Canada. In June, GitHub Copilot, a machine learning tool that automatically suggests programming code in response to contextual prompts, emerged from a year long technical preview, just as concerns about the way its OpenAI Codex model was trained and the implications of AI models for society coalesced into focused opposition. In "Programming Is Hard – Or at Least It Used to Be: Educational Opportunities And Challenges of AI Code Generation" [PDF], Becker and co-authors Paul Denny (University of Auckland, New Zealand), James Finnie-Ansley (University of Auckland), Andrew Luxton-Reilly (University of Auckland), James Prather (Abilene Christian University, USA), and Eddie Antonio Santos (University College Dublin) argue that the educational community needs to deal with the immediate opportunities and challenges presented by AI-driven code generation tools.

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Dr. Reinhard Klette, Fellow of the Royal Society of New Zealand, is a Professor at the Auckland University of Technology (AUT). His numerous publications include the books "Computer Vision for Driver Assistance" (co-authored by Mahdi Rezaei), "Multi-target Tracking" (co-authored by Junli Tao), "Concise Computer Vision", "Euclidean Shortest Paths" (co-authored by Fajie Li), "Panoramic Imaging" (co-authored by Fay Huang and Karsten Scheibe), "Digital Geometry" (co-authored by the late Azriel Rosenfeld), "Computer Vision - Three-Dimensional Data from Images" (co-authored by Karsten Schluens and Andreas Koschan), "The Handbook of Image Processing Operators" (co-authored by the late Piero Zamperoni), and "Fast Algorithms and their Implementation on Specialized Parallel Computers" (co-authored by Jozef Miklosko, Marian Vajtersic, and Imre Vrto)