The four types of AI — and which one could help food safety
In 2017, Google X called Elliot about a secret project on AI and agriculture. At the time, he said, image-recognition AI couldn’t reliably tell a Chihuahua from a blueberry muffin. Seven years later, that small internal project had become Mineral, an Alphabet company building AI tools for agriculture and the food supply chain — and Eliot was running it.
Most food safety professionals have already used at least one form of AI: generative chatbots like ChatGPT or Claude. But that’s only one of four fundamentally different categories, and each matters differently for prevention.
Predictive AI recognizes images, transcribes speech, and forecasts yields — likely the category already running in many food and ag operations. Agentic AI learns from real-world experience and takes action on its own, from self-driving cars to DeepMind’s AlphaGo. Domain-specific AI goes further still, being trainable in scientific domains such as biology and genetics.
“We're now training AI beyond text to understand the real world and to really unpack domains like biology and genetics, which are super important to the food industry and food safety.” — Dr. Elliot Grant
The most ambitious version of this fourth category, Eliot said, is what he calls a world model: an AI with a deep enough grasp of pathogen biology to anticipate risks before conventional monitoring would detect them. None of that exists yet, and — wary of predicting an exponential technology — he won’t guess when it might.
Data decides what AI can see
Food safety data on paper sits beyond the reach of any AI model. Getting it into digital form is the starting point — but the diversity of that data matters as much as the volume. A model trained only on processes that ran smoothly has no examples of failure to learn from.
“Without good quality, diverse training data, the AI models are rubbish. But with good, thoughtful, diverse data, a model can create new knowledge about a process and discover hidden patterns that no human could have seen.” — Dr. Elliot Grant
Elliot likes to illustrate the trade-off with bananas. A toddler learns what one is from seeing it once, whereas a computer needs thousands of images before it reliably recognizes one. But the advantage flips with scale. Humans struggle to process more than three dimensions of data, yet an AI model can process millions of dimensions, which would lend itself well to the multi-dimensional problems food safety presents, shaped at once by weather, supply chains, farm practices, and pathogen behavior.
He calls this the ART of AI — augment, replace, transform. Augmentation and replacement improve how existing tasks get done. Transformation surfaces risks that were previously invisible, and it’s the category the industry has engaged with least.
Where AI is already being tested
At Mineral, Elliot’s team trained AI models to detect 20 different defects on a single type of produce. Compared with human inspectors, the models performed at least as well — and far more consistently, which is what makes long-term trend tracking possible.
“By performing at least as well as a human, I could do it in 10 times more places for a fraction of the cost.” — Dr. Elliot Grant
That consistency also opened up places quality checks couldn’t reach before — further upstream in the field, downstream in distribution, and at the front of the store. A fifty-dollar camera mounted above a hand-washing station, continuously monitoring anonymized workers, can do something no human supervisor could sustain for an eight-hour shift.
Mineral’s team also showed that remote sensing via satellite imaging could identify a field’s crop type (even distinguishing spinach from romaine), tell organic farming apart from conventional, and flag a field’s proximity to feedlots or flood-prone waterways.
Frank’s own pilot at the FDA applied machine learning to seafood imports — a category where roughly 94% of what Americans eat comes from abroad — and increased the agency’s hit rate for violative shipments, shifting sampling toward what the data shows in real time.
Two structural problems are slowing adoption
The first is interpretability. The strongest models often reach conclusions through paths that humans — and sometimes the models themselves — can’t easily trace. That’s a problem for regulators who need to certify a tool and for operators who need enough confidence in it to act on what it flags.
The second is data. Recalls are mercifully rare, so no single company accumulates enough examples of failure to train a model to recognize one — and those edge cases are exactly what would make a food safety AI genuinely powerful.
“Unless we pool all this data together, we're all going to be worse off. There's just not enough information being collected to train a model to become very smart.” — Dr. Elliot Grant
Elliot pointed to a few ways to share data without exposing it. One is an anonymized data trust, similar to what the United Kingdom’s National Health Service has built for medical research. Another is confidential computing, where a model trains on encrypted data that no one, including the model’s operator, ever sees directly. AI agents could also someday negotiate on behalf of companies and regulators, each bound by its own disclosure rules.
The technology for these examples exists today — the only thing missing is the legal and regulatory agreements to use them.
A practical first step for using AI in food safety
Elliot's advice is to begin with data. Start collecting more of it, in digital form, now. The best time to start was ten years ago. The second best, as he puts it, is tomorrow. Sensor, camera, and storage costs have all dropped sharply, and every year without that data leaves future models weaker.
“Do little projects in low-risk applications. It's a great way to help the organization learn the strengths and weaknesses of AI systems and build some in-house knowledge.” — Dr. Elliot Grant
A vision system that checks whether best-by dates are printed legibly on packaging is the kind of small pilot that builds knowledge without major risk. Nothing breaks if it doesn't work, and the company walks away knowing more about where AI helps and where it doesn't.
The harder part is culture. A company that doesn't question what the AI tells it, and hasn't built data quality into daily practice, won't get what AI can deliver, no matter how good the tool is. Tools change faster than organizations do, and for most companies, that cultural shift takes longer than any technical rollout.
This episode of Smarter Food Safety is available now, wherever you get your podcasts.
To learn more about the host, Frank Yiannas, and why Ecolab is partnering on this show, read our profile here.
Episode Field Notes
Terms and resources worth bookmarking for food and beverage operators this year.
PREDICT: The FDA's import screening system Frank references for seafood — an automated risk-scoring tool that directs sampling toward higher-risk shipments: https://www.fda.gov/industry/fda-import-process/entry-screening-systems-and-tools
Seafood AI Learning Pilot: The machine learning program built on PREDICT, shifting sampling toward what real-time data flags rather than a fixed schedule: https://www.fda.gov/food/new-era-smarter-food-safety/smarter-tools-and-approaches-prevention-and-outbreak-response-core-element-2-new-era-smarter-food
Risk-based inspection frequency: The high-risk/non-high-risk facility classification Elliot calls a "blunt instrument" in this episode, every three years versus every five: https://www.fda.gov/food/inspections-protect-food-supply/how-does-fda-prioritize-domestic-human-food-facility-inspections
Remote Regulatory Assessments: FDA's framework for evaluating facility compliance without an on-site inspection, finalized in 2025, the kind of digital oversight tool this episode's "tools change faster than organizations" discussion points toward: https://www.fda.gov/regulatory-information/search-fda-guidance-documents/conducting-remote-regulatory-assessments-questions-and-answers
FSMA Food Traceability Rule (Section 204): The regulation underlying this episode's "get your data into digital form" theme. Compliance date extended to July 20, 2028: https://www.fda.gov/food/food-safety-modernization-act-fsma/fsma-final-rule-requirements-additional-traceability-records-certain-foods
