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Innovative ASU Student Integrates AI and Deepfake Tech in Entomology Research A graduating student from Arizona State University is leading the way in entomology by applying artificial intelligence and deepfake technology to study insect behavior. By leveraging AI, the student analyzes large datasets to uncover patterns in insect movements and interactions. Deepfake technology is used to create realistic simulations, allowing for detailed visualization of complex behaviors that are otherwise challenging to observe. This innovative approach has resulted in significant findings published in reputable journals and sets the stage for future interdisciplinary research. The student's work not only advances the field of entomology but also demonstrates the transformative potential of integrating advanced technologies in biological research. Read more: https://lnkd.in/gXu7NVXd #ai #sustainability #esg #environmentalengineering #environmental #carbon #decarbonization #ArizonaStateUniversity #Entomology #AIResearch #DeepfakeTechnology #Biotech #StudentInnovation #AIGC

On 3rd July, I spent an inspiring and productive day at the Royal Entomological Society’s AI in Entomology conference! I had the opportunity to connect with some incredible professionals and leaders in entomology research and AI. As I am currently working on a pollinator monitoring project, the talks gave me a lot to think about. A few key takeaways for me include: 1. Artificial neural networks take inspiration from biological brains. For example, they mimic the skip connections in biological brains. 2. There is a need to optimize automated biodiversity identification in the field, and many successful attempts have been made using AI. While this research continues, the next step is to make the algorithms and model training data easily available. 3. The AMI (Autonomous Monitoring of Insects) System has shown great results for long-term monitoring of night-time insects, specifically moths. It has been trained to visually identify moth species and, in the future, it could be a good system for other insect families as well. I'm excited to implement these new ideas and continue the conversations sparked at the conference. If we didn't get a chance to meet, feel free to reach out—I’d love to connect and discuss further! #Networking #AIinEntomology #Conservation

🦤 The dodo bird, extinct for centuries, now speaks—thanks to AI! At the Cambridge Museum of Zoology, visitors can ask the dodo any question and receive real-time answers, offering a new way to interact with history and learn about this species' tragic extinction. This innovative use of AI not only brings history to life but also highlights the potential for AI to transform education. 🤔 What other industries or fields could benefit from this kind of AI technology? Video credit: DW News #AI #ArtificialIntelligence #CambridgeMuseum #AIinEducation #Innovation #TechInMuseums #AIImpact #FutureOfAI

The IALE-Europe Early Career working group with Beatriz Pierri Daunt have invited three guest speakers to the webinar #AI and Technology Applications in #LandscapeEcology Wednesday 11 December 2024 16:00-17:00 CET Join them for an engaging and accessible journey into the world of #ArtificialIntelligence (AI) and its transformative role in landscape ecology. This webinar is designed to captivate participants at all levels of expertise—whether you're an AI novice, an ecology enthusiast, or a seasoned professional seeking fresh insights. What to Expect: - Foundations of AI – We'll kick off with an easy-to-follow introduction to the core concepts of AI, breaking down complex ideas into digestible pieces. - Techniques That Shape the Field – Explore an overview of the most impactful AI techniques used in landscape ecology. - From Theory to Practice – Dive into applications, where we’ll showcase practical examples of how AI is transforming research, conservation, and decision-making in the realm of landscapes. Whether you’re a curious beginner or a seasoned expert, this webinar promises to inform, intrigue, and empower. Don’t miss the chance to be part of this dynamic discussion! Outline:  - Beatriz Pierri Daunt, IALE-Europe Early Career representative – Welcome - Carlos Santiago Carlos Santiago, Assistant Researcher at Institute for Systems and Robotics, LARSyS, Instituto Superior Técnico, ULisboa. - Stupariu Mihai-Sorin, Associate Professor at University of Bucharest, Faculty of Mathematics and Computer Science. - Dario Domingo Ruiz, Assistant professor at IuFOR, Cambium research group, University of Valladolid and GEOFOREST research group, University of Zaragoza - Q&A with involvement of participants If you are interested in joining the webinar, please, register here: https://lnkd.in/d8xVwFRP

📃Scientific paper: Species determination using AI machine-learning algorithms: Hebeloma as a case study Abstract: The genus Hebeloma is renowned as difficult when it comes to species determination. Historically, many dichotomous keys have been published and used with varying success rate. Over the last 20 years the authors have built a database of Hebeloma collections containing not only metadata but also parametrized morphological descriptions, where for about a third of the cases micromorphological characters have been analysed and are included, as well as DNA sequences for almost every collection. The database now has about 9000 collections including nearly every type collection worldwide and represents over 120 different taxa. Almost every collection has been analysed and identified to species using a combination of the available molecular and morphological data in addition to locality and habitat information. Based on these data an Artificial Intelligence (AI) machine-learning species identifier has been developed that takes as input locality data and a small number of the morphological parameters. Using a random test set of more than 600 collections from the database, not utilized within the set of collections used to train the identifier, the species identifier was able to identify 77% correctly with its highest probabilistic match, 96% within its three most likely determinations and over 99% of collections within its five most likely determinations. Continued on ES/IODE ➡️ https://etcse.fr/QKjWa ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

The AI landscape is littered with rabbit holes. Any one of them will be full of secrets and surprises - but enter at your own risk... most are cavernous. You may never emerge! Seriously, it's a challenge to stay focused when surrounded by so much creativity and innovation. So many bright shiny lights. We must choose wisely where to spend our valuable time. But from time to time, we should also roam freely... Today I stepped into the rabbit hole of 'AI for Ecology'. The paper, "A synergistic future for AI and ecology" (https://lnkd.in/e8jNPZC8) was especially enlightening. AI is at the very start of its journey to help us understand complex ecological systems. AI must evolve to incorporate diverse modes of expert knowledge in its reasoning and provide explainable outputs. I hope to be lucky and work down a rabbit hole like this. Making more money for Goldman Sachs doesn't matter. Sorting out our planet does. #AI #artificialintelligence #deeplearning #machinelearning #machinelearninginstitute #ecology #aiforecology #environment #earthscience

🌱 **Nature and Tech Intersect: Transforming Wildlife Research!** 🌱 A groundbreaking study from MIT's CSAIL reveals crucial insights into the effectiveness of multimodal vision language models (VLMs) in retrieving wildlife images. Key findings include: - Larger VLMs excel in identifying simple visual queries but struggle with complex, expert-level prompts. - The models lack the domain-specific knowledge required for nuanced scientific terms, indicating a need for more targeted training. - Efforts are underway to refine VLMs, potentially revolutionising how ecologists and conservationists access vital research data. How is your organisation adapting to leverage AI in your research efforts? Let’s discuss! #AIinResearch #Biodiversity #Ecology

Our article "Ultra-lightweight convolution-transformer network for early fire smoke detection" accepted for publication in the journal Fire Ecology is now available online. In this work, a hybrid of convolutional neural network and vision transformer blocks is utilized for fire-smoke detection in images captured in normal and foggy environmental conditions. The model shows remarkable performance and identifies even a tiny occurrence of smoke covering just 2% of the satellite image area. With only 0.6 million parameters and 0.4 billion floating point operations per second, the model is suitable for deployment in resource constrained devices for forest surveillance and early fire smoke detection. In this work, a satellite image dataset with 20,000+ images is collected, labelled and preprocessed. The dataset is publicly available at our github repository https:// github. com/ Image-and-Vision-Engineering-Group/IIITDMJ_ Smoke- Dataset Congratulations Shubhangi Chaturvedi, Shubham Chandravanshi, Poornima Singh Thakur and Prof. Pritee Khanna ! This is the first open access article by my student Shubhangi Chaturvedi, and I must thank the Editor in Chief, Fire Ecology, Dr Robert Keane and the Springer Nature Group Publication Team for their enormous support in making it happen. DOI: 10.1186/s42408-024-00304-9look Figure shows the t-distributed stochastic neighbor embedding (t-SNE) plots for (1) IIITDMJ_Smoke, (2) USTC_SmokeRS (Ba et al. 2019), (3) Khan et al. (2019), and (4) He et al. (2021) on (a) Tao et al. (2016), (b) Khan et al. (2019), (c) Muhammad et al. (2019), (d) Khan et al. (2021), (e) Majid et al. (2022), (f ) Almeida et al. (2022), (g) Ahmad et al. (2023), and (h) the proposed model

Bridging Ecology and AI - A Powerful Partnership In the quest to understand and predict the complexities of our ecosystems, the fields of ecology and artificial intelligence (AI) are more aligned than ever. Both disciplines strive to make sense of intricate systems characterised by non-linearities, multiple dimensions, and feedback loops across various scales. As an ecologist, Professor Jane Stout may not be a programming expert, but she recognises AI's immense potential in tackling ecological modeling challenges. The vast number of variables and the intricate nature of ecological systems make them difficult to analyse. However, AI has emerged as a powerful ally, capable of synthesising and analysing large datasets, identifying gaps, and making predictions. It's important to remember that the effectiveness of AI in ecology hinges on the quality of the data fed into these systems. As we continue to explore this synergy, we can unlock new insights that will help us address pressing environmental issues. To learn more about ECOLOGY and AI TUNE IN TO THIS VIDEO to learn more. https://bit.ly/4eZ7ttv #Ecology #ArtificialIntelligence #Sustainability #DataScience #EnvironmentalScience #Innovation

Breathing New Life into Museum Exhibits with AI In an exciting development at Cambridge University’s Museum of Zoology, artificial intelligence is giving voice to deceased animals, allowing them to share their stories and experiences. This innovative exhibition features a range of specimens, from an American cockroach to a fin whale skeleton, and invites visitors to engage in conversations through their mobile phones. Equipped with distinct personalities and accents, these exhibits aim to highlight the challenges faced by these creatures, fostering a deeper connection with biodiversity. Jack Ashby, the museum’s assistant director, notes that this project is unique in allowing objects to speak from their own perspective, potentially shifting public perceptions about species like the often-misunderstood cockroach. Developed by Nature Perspectives, the initiative uses AI to enrich visitor interactions, enabling conversations in over 20 languages and adapting to different age groups. As guests engage with the exhibits, they can explore more than just the labels—delving into meaningful dialogue that enhances understanding and appreciation of the natural world. This project exemplifies the potential of AI to transform our relationship with nature and inspire critical conversations about conservation. At Sentient Search, we believe in harnessing the power of AI to connect you with top talent in the industry. If you’re looking to enhance your team with elite AI experts, reach out to us today. Let’s unlock new possibilities for your business together! #AIRecruitment #TalentAcquisition #Biodiversity #Innovation #CambridgeUniversity #ArtificialIntelligence #MuseumExhibition #NaturePerspectives #Conservation #SentientSearch #TechForGood #FutureOfWork #EliteTalent #DataDriven #JoinUs https://lnkd.in/gAVAiP-P