Center for AI and Data Analytics for Business and Society’s Post

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

Proud Moment to Share (Kicking off new year with a better resolve) I’m elated to announce that my research journal, "Deep Insights with Exploring Plankton Communities through Artificial Neural Networks," has been published in the prestigious Dialogue Social Science Review (DSSR). This collaborative effort merges the power of Artificial Intelligence and marine ecology to address critical challenges in classifying marine species and preserving underwater biodiversity. As the lead author, this journey has been immensely rewarding—delving into AI models like CNNs and their potential for real-time monitoring of marine ecosystems. Through this research, we’ve showcased the transformative capabilities of deep learning architectures in ecological conservation, enabling scalability, efficiency, and accuracy. Special thanks to my co-authors for their invaluable contributions and collaboration in this interdisciplinary exploration. Together, we’re paving the way for innovative approaches to marine biodiversity research. Please read the research journal in my co-author's Syed Talal Musharraf post down below and share your thoughts on integrating AI in ecological studies and conservation! #ArtificialIntelligence #MarineScience #Ecology #Biodiversity #AIForGood #Research #Innovation #HECRecognized #Science #Conservation #NeuralNetworks #TechnologyForChange #DeepLearning #ScientificBreakthrough

🦤 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

🌿🔍 New Insights into Predator-Prey Dynamics! 🦊🐇 A recent study explores the intricate interactions in a three-species food chain model, highlighting the impact of odour and memory on predator-prey relationships. The research reveals how predator odour influences prey behaviour, leading to refuge-seeking strategies that can stabilize populations. Key findings include: - The role of prey odour in enhancing predator hunting efficiency. - The significance of memory effects on population dynamics. - Evidence of bifurcation phenomena, showcasing how small changes can lead to significant ecological shifts. This model not only deepens understanding but also underscores the importance of olfactory cues in ecosystem management! 🌍✨ #AI #Algorithms #ArtificialIntelligence #Biodiversity #DL #DS #DataScience #DeepLearning #Ecology #ML #MachineLearning #MathematicalModeling #PredatorPrey #ResearchInsights #Tech #Technology Source: https://lnkd.in/e2t37Yxt

📃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.

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

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

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

📃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.