Saskatchewan Food Industry Development Centre Inc.’s Post

Can I eat this food or not? Does it have ingredients I'm allergic to? How can I make this more nutritious or vegan or keto-free? Does this recipe conform to diabetes guidelines? These are simple yet powerful questions that we ask about our food. To answer these questions and more, we introduce: 𝙉𝙤𝙪𝙧𝙞𝙘𝙝: 𝘼 𝘾𝙪𝙨𝙩𝙤𝙢, 𝘾𝙤𝙢𝙥𝙖𝙘𝙩 𝙖𝙣𝙙 𝙉𝙚𝙪𝙧𝙤𝙨𝙮𝙢𝙗𝙤𝙡𝙞𝙘 𝘿𝙞𝙚𝙩 𝘼𝙄 𝙢𝙤𝙙𝙚𝙡 https://lnkd.in/eBJhiBDJ That analyses the suitability of a given recipe by analyzing ingredients and cooking actions in multiple contexts. The system also provides explanations in the form of reasoning to the users. Given a recipe is not suitable, the system aims to provide alternative recipes or ingredient substitutions. 𝘾𝙪𝙨𝙩𝙤𝙢: Tailored to analyze and reason over the suitability of a recipe for diabetes and provide alternative recipes or ingredient and cooking action substitutions. 𝘾𝙤𝙢𝙥𝙖𝙘𝙩: Lightweight and cost-effective model, optimized for real-time deployment on consumer-grade hardware 𝙉𝙚𝙪𝙧𝙤𝙨𝙮𝙢𝙗𝙤𝙡𝙞𝙘: An explainable food recommendation framework that adapts semantic, perceptual, and cognitive framework to ground data with semantic knowledge (semantics), mapping grounded data to disease context (perceptual) and provides reasoning for the recommendation(cognitive) to the users along with the source of knowledge utilized for recommendations. More on - https://lnkd.in/eFNpjCec This work is part of the NSF-sponsored grant project #Neurosymbolic AI project (https://lnkd.in/eT7SU6d9) from Artificial Intelligence Institute of South Carolina (#AIISC). This was one of the three applications showcased in my advisor Dr. Amit Sheth keynote on “Forging Trust in Tomorrow’s AI: A Roadmap for Reliable, Explainable, and Safe NeuroSymbolic AI Systems” at the Knowledge-infused Learning Workshop at ACM KDD 2024. Slides and links to three real-world applications: https://bit.ly/KDD-KiL Industry Collaborators: Edamam for data and knowledge and Hewlett Packard Enterprise for Common Meatada Framework (#CMF) 𝙏𝙚𝙖𝙢 𝙈𝙚𝙢𝙗𝙚𝙧𝙨: Renjith Prasad(He/Him) , Kanak Raj , Aditya Luthra , Kaushik Roy , Venkatesan Nadimuthu , Deepa Tilwani , Ritvik G

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Can I eat this food or not? Does it have ingredients I'm allergic to? How can I make this more nutritious or vegan or keto-free? Does this recipe conform to diabetes guidelines? These are simple yet powerful questions that we ask about our food. To answer these questions and more, we introduce: 𝙉𝙤𝙪𝙧𝙞𝙘𝙝: 𝘼 𝘾𝙪𝙨𝙩𝙤𝙢, 𝘾𝙤𝙢𝙥𝙖𝙘𝙩 𝙖𝙣𝙙 𝙉𝙚𝙪𝙧𝙤𝙨𝙮𝙢𝙗𝙤𝙡𝙞𝙘 𝘿𝙞𝙚𝙩 𝘼𝙄 𝙢𝙤𝙙𝙚𝙡 https://lnkd.in/eBJhiBDJ That analyses the suitability of a given recipe by analyzing ingredients and cooking actions in multiple contexts. The system also provides explanations in the form of reasoning to the users. Given a recipe is not suitable, the system aims to provide alternative recipes or ingredient substitutions. 𝘾𝙪𝙨𝙩𝙤𝙢: Tailored to analyze and reason over the suitability of a recipe for diabetes and provide alternative recipes or ingredient and cooking action substitutions. 𝘾𝙤𝙢𝙥𝙖𝙘𝙩: Lightweight and cost-effective model, optimized for real-time deployment on consumer-grade hardware 𝙉𝙚𝙪𝙧𝙤𝙨𝙮𝙢𝙗𝙤𝙡𝙞𝙘: An explainable food recommendation framework that adapts semantic, perceptual, and cognitive framework to ground data with semantic knowledge (semantics), mapping grounded data to disease context (perceptual) and provides reasoning for the recommendation(cognitive) to the users along with the source of knowledge utilized for recommendations. More on - https://lnkd.in/eFNpjCec This work is part of the NSF-sponsored grant project #Neurosymbolic AI project (https://lnkd.in/eT7SU6d9) from Artificial Intelligence Institute of South Carolina (#AIISC). This was one of the three applications showcased in my advisor Dr. Amit Sheth keynote on “Forging Trust in Tomorrow’s AI: A Roadmap for Reliable, Explainable, and Safe NeuroSymbolic AI Systems” at the Knowledge-infused Learning Workshop at ACM KDD 2024. Slides and links to three real-world applications: https://bit.ly/KDD-KiL Industry Collaborators: Edamam for data and knowledge and Hewlett Packard Enterprise for Common Meatada Framework (#CMF) 𝙏𝙚𝙖𝙢 𝙈𝙚𝙢𝙗𝙚𝙧𝙨: Renjith Prasad(He/Him) , Kanak Raj , Aditya Luthra , Kaushik Roy , Venkatesan Nadimuthu , Deepa Tilwani , Ritvik G

Can I eat this food or not? Does it have ingredients I'm allergic to? How can I make this more nutritious or vegan or keto-free? Does this recipe conform to diabetes guidelines? These are simple yet powerful questions that we ask about our food. To answer these questions and more, we introduce: 𝙉𝙤𝙪𝙧𝙞𝙘𝙝: 𝘼 𝘾𝙪𝙨𝙩𝙤𝙢, 𝘾𝙤𝙢𝙥𝙖𝙘𝙩 𝙖𝙣𝙙 𝙉𝙚𝙪𝙧𝙤𝙨𝙮𝙢𝙗𝙤𝙡𝙞𝙘 𝘿𝙞𝙚𝙩 𝘼𝙄 𝙢𝙤𝙙𝙚𝙡 https://lnkd.in/eBJhiBDJ That analyses the suitability of a given recipe by analyzing ingredients and cooking actions in multiple contexts. The system also provides explanations in the form of reasoning to the users. Given a recipe is not suitable, the system aims to provide alternative recipes or ingredient substitutions. 𝘾𝙪𝙨𝙩𝙤𝙢: Tailored to analyze and reason over the suitability of a recipe for diabetes and provide alternative recipes or ingredient and cooking action substitutions. 𝘾𝙤𝙢𝙥𝙖𝙘𝙩: Lightweight and cost-effective model, optimized for real-time deployment on consumer-grade hardware 𝙉𝙚𝙪𝙧𝙤𝙨𝙮𝙢𝙗𝙤𝙡𝙞𝙘: An explainable food recommendation framework that adapts semantic, perceptual, and cognitive framework to ground data with semantic knowledge (semantics), mapping grounded data to disease context (perceptual) and provides reasoning for the recommendation(cognitive) to the users along with the source of knowledge utilized for recommendations. More on - https://lnkd.in/eFNpjCec This work is part of the NSF-sponsored grant project #Neurosymbolic AI project (https://lnkd.in/eT7SU6d9) from Artificial Intelligence Institute of South Carolina (#AIISC). This was one of the three applications showcased in my advisor Dr. Amit Sheth keynote on “Forging Trust in Tomorrow’s AI: A Roadmap for Reliable, Explainable, and Safe NeuroSymbolic AI Systems” at the Knowledge-infused Learning Workshop at ACM KDD 2024. Slides and links to three real-world applications: https://bit.ly/KDD-KiL Industry Collaborators: Edamam for data and knowledge and Hewlett Packard Enterprise for Common Meatada Framework (#CMF) 𝙏𝙚𝙖𝙢 𝙈𝙚𝙢𝙗𝙚𝙧𝙨: Renjith Prasad(He/Him) , Kanak Raj , Aditya Luthra , Kaushik Roy , Venkatesan Nadimuthu , Deepa Tilwani , Ritvik G