Feeding the world through AI, machine learning and the cloud

Feeding the world through AI, machine learning and the cloud

In the public sector, for example, just to call a couple maybe. We have worked with the Open Data Institute to release some of our data in a reusable format, essentially raw data, that scientists around the world can use, as we want to engage in this joint R&D practice. So there is data that we just share with the community, but we also care about data standards. So we’re on the board of AgGateway, which is a consortium of I think 200 or more food companies working on how we actually drive digital agriculture? So we make sure the standards work for everyone and we don’t end up with exclusive ideas from every member of the food chain, but we can connect our data to each other.

The private sector, again, is equally important. We are fortunate to have our headquarters in Basel, which is truly a scientific hub, and chemical sciences in particular. Many pharmaceutical companies are here. So we can also exchange a lot of what we learn between pharma and agriculture, we can learn chemistry, we can learn practices, how we work, how we work through our laboratories. We are in intense contact with our colleagues from the region here, but of course also from elsewhere, and it is quite a natural grouping.

Perhaps last but not least, one of the really exciting prospects for me that I realized, I don’t know, just a few years ago, not very many really, is how much there is if you look at across industries. So, I recently hired someone, a Formula 1 digital expert, and why is that? I mean, if you look at it technically, steering or controlling, understanding a Formula 1 race car from a distance isn’t much different than steering a tractor. I mean, the vehicles will be super different, but the technology kind of has a lot of similarities. So understanding the IoT in this case and understanding the transfer of data from the field to the control centers, no matter what industry we work in, we can learn anywhere.

We’re also working with a super-experienced partner in image recognition to better understand what’s happening in the field, where as Syngenta we can bring agronomic knowledge and that partner can bring technical knowledge on the way to get the most out of the images. From a very different field, nothing to do with farming, but still the skills are super transferable. So I’m really looking for talent across all industries, and literally anyone who supports our cause, not just people with a life science background.

Laurel Ruma: It’s really interesting to think about the amount of data that F1 processes in a single race day or, in general, the amount of input from so many different places. I can see how it would be very similar. You are dealing with databases of data and just trying to create better algorithms to come to better conclusions. Looking around the community as a whole, you certainly see that Syngenta is definitely part of an ecosystem, so how outside factors such as regulation and societal pressures help Syngenta create these best products to be part of and not out of of this inevitable agricultural revolution?

Thomas Jung: This is an excellent point, because regulation in general, of course, is a practical burden for some, or can be perceived as such. But for us in digital science, it’s a very welcome driver of innovation. One of the key examples we have right now is our work with the US Environmental Protection Agency, the EPA, which has decided to stop supporting mammalian chemical studies by 2035. So what does this mean? It sounds like a big threat, but in reality, it’s a catalyst for digital science. We therefore very much welcome this request. We are currently working on ways to use data-driven science to prove the safety of the products we invent. There are some great universities across the United States that have received funding from the EPA to help find these ways of doing our science, so we’re also committed to making sure we’re doing it in the best way possible together. and we can really land on data-based science here and we can stop doing all this real testing.

So, it’s a fantastic opportunity, but of course a long way to go. I think 2035 is somewhat realistic. We’re not close yet. What we can do today is, for example, model a cell. There’s organ-on-a-chip as a big trend, so we can model up to an entire organ, but there’s no way to model a system or even an ecosystem at this point. So we have a lot of room to explore, and I’m really happy that regulators are a partner in this, and even a driver. It is extremely useful. The other dimension you mentioned, societal pressure, is also present. I think it’s important that society continues to push for causes like regenerative agriculture, because that’s what, first of all, lays the groundwork for us to help with that. If there is no demand, it is difficult for Syngenta to push it forward on its own.

So I think the demand is great, and the awareness that we need to treat our planet in the best possible way, and we also work with, for example, The Nature Conservancy, where we use their scientists, their expertise in conservation to implement sustainable agricultural practices in South America, for example, where we have projects to restore rainforests, restore biodiversity, and see what we can do together there. So again, much like what we discussed earlier, we can only be better off collaborating across industries, and that includes NGOs as much as regulators and society as a whole.

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