Ratatouille: A tool for Novel Recipe Generation

Goel, Mansi; Christiani, David C.; Ponnaganti, Vijay; Khan, Minnet; Tatipamala, Sritanaya; Saini, Abhishek; Bagler, Ganesh

doi:10.1109/icdew55742.2022.00022

Cited by 5 publications

References 25 publications

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Monitoring Emerging Trends in LLM Research

Würsch,

David,

Mermoud

2024

Large Language Models in Cybersecurity

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Established methodologies for monitoring and forecasting trends in technological development fall short of capturing advancements in Large Language Models (LLMs). This chapter suggests a complementary and alternative approach to mitigate this concern. Traditional indicators, such as search volumes and citation frequencies, are demonstrated to inadequately reflect the rapid evolution of LLM-related technologies due to biases, semantic drifts, and inherent lags in data documentation. Our presented methodology analyzes the proximity of technological terms related to LLMs, leveraging the OpenAlex and arXiv databases, and focuses on extracting nouns from scientific papers to provide a nuanced portrayal of advancements in LLM technologies. The approach aims to counteract the inherent lags in data, accommodate semantic drift, and distinctly differentiate between various topics, offering both retrospective and prospective insights in their analytical purview. The insights derived underline the need for refined, robust, adaptable, and precise forecasting models as LLMs intersect with domains like cyber defense. At the same time, they are considering the limitations of singular ontologies and integrating advanced anticipatory measures for a nuanced understanding of evolving LLM technologies.

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Monitoring Emerging Trends in LLM Research

Würsch,

David,

Mermoud

2024

Large Language Models in Cybersecurity

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Meal Magic: An Image-Based Recipe-Generation System

Sravya,

Pariga,

Swetha

et al. 2024

Lecture Notes in Networks and Systems

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Computational gastronomy: capturing culinary creativity by making food computable

Bagler,

Goel

2024

npj Syst Biol Appl

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Cooking, a quintessential creative pursuit, holds profound significance for individuals, communities, and civilizations. Food and cooking transcend mere sensory pleasure to influence nutrition and public health outcomes. Inextricably linked to culinary and cultural heritage, food systems play a pivotal role in sustainability and the survival of life on our planet. Computational Gastronomy is a novel approach for investigating food through a data-driven paradigm. It offers a systematic, rule-based understanding of culinary arts by scrutinizing recipes for taste, nutritional value, health implications, and environmental sustainability. Probing the art of cooking through the lens of computation will open up a new realm of possibilities for culinary creativity. Amidst the ongoing quest for imitating creativity through artificial intelligence, an interesting question would be, ‘Can a machine think like a Chef?’ Capturing the experience and creativity of a chef in an AI algorithm presents an exciting opportunity for generating a galaxy of hitherto unseen recipes with desirable culinary, flavor, nutrition, health, and carbon footprint profiles.

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Ratatouille: A tool for Novel Recipe Generation

Cited by 5 publications

References 25 publications

Monitoring Emerging Trends in LLM Research

Monitoring Emerging Trends in LLM Research

Meal Magic: An Image-Based Recipe-Generation System

Computational gastronomy: capturing culinary creativity by making food computable

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