- Meat and animal products are an important source of protein with a relevant nutritional value. Nonetheless, the environmental impact in terms of land use, water consumption and CO2 emissions have become of significant importance, also due to the continuous increasing consumption. By 2050 world meat production is projected to double, most of which is expected in developing countries. 
According to the Food and Agriculture Organization, the livestock sector generates more greenhouse gas emissions [Co2e] than transport, by 18%. 
It is also a major source of land and water degradation. Livestock’s requires vast tracts of land and a significant demand for feed crops, both contribute to biodiversity loss. Moreover, it’s among the most damaging sectors water resources, not only because of its water consumption, but also contributing to water pollution, eutrophication and the degeneration of coral reefs, due to the manure of livestock.
- Machine learning is a subset of AI, that generates algorithms that can learn from data and make predictions on it. In other words make machines learn from experience, experience coming in form of data and the more the data, the more it learns. Machine learning can be useful for making data-driven predictions or decisions.
To create healthier food, companies like Hamptons Creek are automating the extraction and analysis of plant proteins. This includes examining their molecular features and functional performance such as gelling, foaming, and emulsifying properties. Ultimately, the goal is to feed this research to an AI an through machine-learning algorithms identify the most-promising proteins for use in the creation of vegan food that tastes similar to animal products (mayonnaise, muffins, spreads, and other foods). Finally, we are applying generative design to food production. 
According to Lee Chae, Hampton Creek’s head of research and development, Hampton Creek applies deep machine learning to plant biological data to meet its objective of creating healthier food. 
- This is not really a new technology rather than a new technology application. To deploy this application, more enterprises in the food industry should be making research in this field, or applying the outcomes. More importantly, I personally don’t think that every enterprise should be conducting the same research rather than to have an open database with the results, would be a great way to expand the adoption of this knowledge. Furthermore, this new application raises the question of what other industries could be impacted by machine learning, through generative design? Let’s take for example the polymer industry, imagine to feed the AI data about the plant properties to replace plastics.
- Since deep learning and machine learning can be applied to several databases, to make predictions as well as generative design, many industries could benefit from this technology. In my opinion is a private sector driven technology, especially due to its large upfront capital investment. The first step to deploy it is to have a reliable database, this can be either from a primary or a secondary source, depending on the application.
Although it does require a group of people specialized in artificial intelligence, the results could be applied to the industry with no further disruption, this reduces the barriers to technology adoption.