High-Leverage AI/ML Opportunities in Alternative Proteins
(AI/ML Advisory Topic List)

Reducing the cost of raw ingredients for plant-based products using AI/ML techniques in agriculture, processing, and supply chain optimization.

The high cost of raw ingredients is a significant barrier to the widespread adoption of plant-based products. By leveraging AI/ML techniques, we seek proofs of concept that could optimize agriculture practices, streamline processing methods, and/or improve supply chain efficiency to reduce the overall cost of raw materials. Projects in this area could include using AI/ML for precision agriculture, developing predictive models for crop yields, or creating intelligent supply chain management systems that minimize waste and inefficiencies.

Developing AI/ML models to predict the cost and feasibility of scaling up alternative protein products, enabling startups to "fail fast" and minimize costly scale-up trials.

Scaling up production from bench to commercial scale is a critical challenge for many alternative protein startups. AI/ML models that accurately predict the cost and feasibility of scaling up a given product or process could help companies make informed decisions, prioritize resources, and avoid costly failures. Projects in this area could include proofs of concept for predictive models of equipment and facility costs, creating virtual scale-up simulations, or building decision support tools that integrate technical and economic feasibility analyses.

Creating a common task framework and benchmark dataset for alternative protein development, focusing on key aspects such as nutrition, taste, cost, and sustainability.

To accelerate innovation and comparative analysis in the alternative protein industry, there is a need for standardized benchmarks and datasets that enable researchers and companies to evaluate and optimize their products. A common task framework would define key metrics and evaluation criteria, such as nutritional content, sensory properties, production costs, and/or environmental impact. Projects in this area could include curating and annotating datasets of alternative protein ingredients and products, developing standardized testing protocols, or creating open-source tools for data analysis and visualization.

Leveraging large language models (LLMs) to generate appealing, nutritionally balanced, and cost-effective plant-based recipes tailored to individual preferences and constraints.

Engaging and personalized recipe suggestions can play a crucial role in promoting the adoption of plant-based diets. Large language models (LLMs) have the potential to generate novel, appetizing, and nutritionally optimized recipes based on user preferences, dietary restrictions, and available ingredients. Projects in this area could include fine-tuning LLMs on plant-based recipe databases, integrating nutritional constraints into the language generation process, or developing user-friendly interfaces for inputting preferences and displaying recipe recommendations.

Building AI-powered tools and platforms to educate and persuade consumers about the benefits of plant-based diets and alternative proteins, such as personalized nutrition planning apps or social media bots.

Effective communication and education are essential for driving consumer awareness and adoption of plant-based diets and alternative proteins. AI-powered tools and platforms can help deliver personalized, engaging, and science-based information to a wide audience. Projects in this area could include developing chatbots or virtual assistants that provide tailored nutrition advice, creating social media content generation tools that optimize for reach and engagement, or building gamified learning apps that teach users about the environmental and health benefits of plant-based eating.

Developing open-source datasets and benchmarks for customer preferences and tastes in alternative protein marketing and advertising to support more effective campaigns across the industry.

Understanding customer preferences and tastes is crucial for creating compelling marketing and advertising campaigns that drive the adoption of alternative proteins. However, many companies lack access to comprehensive and reliable data on consumer attitudes and behaviors. Developing open-source datasets and benchmarks in this area could help level the playing field and support more effective marketing strategies across the industry. Projects could include conducting large-scale surveys and choice experiments, analyzing social media sentiment and engagement data, or creating standardized metrics for evaluating the impact of different marketing approaches.

Exploring ways to ensure a balanced representation of plant-based and animal welfare perspectives in large language models (LLMs) that will increasingly mediate access to information.

As LLMs become more prevalent in mediating access to information, there is a risk that they may perpetuate biases against plant-based diets and animal welfare considerations. Ensuring a balanced representation of these perspectives in LLMs is crucial for promoting informed decision-making and avoiding the entrenchment of harmful narratives. Projects in this area could include developing techniques for detecting and mitigating biases in LLM training data, creating evaluation frameworks for assessing the fairness and inclusivity of LLM outputs, or engaging in public education and advocacy efforts to raise awareness about the importance of diverse perspectives in AI systems.