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Daily news and analysis about the food tech revolution

DeepMind

Google Takes on Food Waste With Food Tech Innovations Built by X

X, the “moonshot factory” of Google parent company Alphabet, announced today that two prototypes developed as part of a project called Project Delta have graduated and are now being transferred to Google for scaling and commercialization.

Project Delta, which has been incubating within X for the past two and a half years, was led by Emily Ma, who announced the transition to Google today in a blog post.

From the post:

Our team’s mission was to create a smarter food system — one that knows where the food is, what state it’s in, and where best to direct it to ensure it doesn’t end up in a landfill and instead goes to the people who need it most. After two and a half years of prototyping and testing a range of technologies to help reduce food waste and food insecurity, I’m pleased to share that some of our prototypes and team are moving to Google so we can scale up our work.

In her post, Ma highlights two prototypes developed as part of Project Delta. The first is an “intelligent food distribution network” nicknamed “dana-bot.” To build dana-bot, the X team took a dataset donated by the Southwest Produce Cooperative, categorized and standardized each entry, and then used it to match food in food banks and pantries based on “real-time needs in the Feeding America network.” Grocery chain Kroger also leveraged dana-bot to manage excess deli products, which allowed it to open up “millions more meals to communities that need it.”

Above: Project Delta’s prototype food identification and categorization system uses machine learning to automatically identify different types of food.

The second prototype utilized computer vision and machine learning to capture images of food thrown out in Alphabet kitchens. After running it in 20 different units across Alphabet cafes over a period of six months, the system was “able to automatically collect two times as much information about the kitchen’s food waste as the manual system.”

And now these two systems are graduating to Google proper, where Ma says her team hopes to “start tackling food waste and food insecurity on a larger scale.” The team plans to roll out its computer vision system to more Alphabet kitchens and utilize Google’s resources to expand the food distribution network and eventually offer it to other organizations.

As more and more venture funding pours into the future of food, it looks like big tech is starting to also wake up to the possibilities of applying their technology innovation to creating a new food system. Google is no exception. This news is just the latest development from Google and its parent company Alphabet that could have larger-scale implications on the broader food system.

Last week Alphabet announced that its DeepMind group had used AI to help solve a grand challenge around protein structure prediction that the scientific community had been working on for half a century. In August, Google Lookout added food label reading to help the visually impaired, and back in 2017 the company unveiled that its Lens visual discovery technology could serve up recipe suggestions based on images of food.

How Alphabet’s AI-Powered Leap in Protein Structure Prediction Could Accelerate New Food Development

This past week Alphabet, the parent company of Google, announced that its DeepMind group has solved a long-standing grand challenge in the scientific community around protein structure prediction.

In 1972, scientist Christian Anfinsen predicted that a protein’s structure could be determined by its amino acid sequence. However, figuring out that sequence is immensely difficult since there are near-infinite ways in which a protein can fold. This led Cyrus Levinthal to postulate that calculating all the known configurations would take longer than the age of the known universe if our only way there was brute force calculation (a problem often referred to as Levinthal’s Paradox).

Thankfully, now we won’t have to wait forever (literally) since DeepMind’s DeepFold AI can predict protein structure to the width of an atom within days. My former Gigaom colleague, Darrell Etherington, explained the feat in a post on Techcrunch:

The test that AlphaFold passed essentially shows that the AI can correctly figure out, to a very high degree of accuracy (accurate to within the width of an atom, in fact), the structure of proteins in just days — a very complex task that is crucial to figuring out how diseases can be best treated, as well as solving other big problems like working out how best to break down ecologically dangerous material like toxic waste.

As many in the world of future food development know, AI is becoming an increasingly important tool to accelerate the development of new proteins and other food-building blocks. Companies like Climax Foods are embracing machine learning to help them develop new approaches to making food like plant-based cheese. And now, with DeepMind’s advances in protein folding prediction paths, we can expect AI to become an even more important tool in new food development.

To get a feel for what the impact of this milestone might be, I asked Sudeep Agarwala, a synthetic microbiologist for Gingko, what he thought DeepMind’s work could possibly mean for food:

“There’s so much that can happen with this,” Agarwala told me. “Think about different textures or mouth feels with food proteins that the AI can design. Or even different amino acid contents for the proteins. And that’s just for the end proteins (if you want the protein as the end product).”

“If we’re engineering proteins inside the cell to produce a small molecule (a flavor, a fragrance, a fat) we can think about making completely new enzymes with multiple functionalities. So something like a 5-step process might be condensed. This has the potential to simplify so much of the metabolic engineering we do to produce these products.”

Agarwala also believes that using technology like that of DeepMind’s will also be way more effecient in terms of resources and have a much smaller ecological footprint:

“Even if the proteins aren’t ultimately going to be consumed, being able to rationally design enzymes that are involved in producing small molecules (think fats, fragrances, or flavors) is really exciting to think about,” said Agarwala. “This isn’t small potatoes: think about how much energy and resources goes into making vanilla or saffron, for example. Being able to do this more efficiently will provide a less ecologically expensive way to produce these materials.”

Just as disease and virology experts are buzzing about what this new milestone could mean for their work, it’s clear food scientists like Agarwala are equally as excited.

“It’s such an exciting time to be a biologist!” said Agarwala.

Yes, it is.