Feeding the world through AI, machine learning and the cloud

In the public sector for example, maybe just to call a few. We’ve worked with the Open Data Institute to publish some of our data in a reusable format, essentially raw data, that scientists around the world can use because we want to be part of this shared R&D practice. So there is data that we only share with the community, but we also care about data standards. So we’re on the board of AgGateway, a consortium of, I think, 200 or more food industry companies working on how we’re actually going to drive digital farming? So we make sure the standards work for everyone and we don’t end up with proprietary ideas from every member of the food chain, but can connect our data across the board.

The private sector, in turn, is just as important. We are fortunate to have our headquarters in Basel, which is really a cluster of sciences and chemical sciences in particular. Many pharmaceutical companies are located here. So we can also share a lot of what we learn between pharma and agriculture, we can learn about chemistry, we can learn about practices, how we work, how we work in our labs. We are here, but of course elsewhere, in close contact with our colleagues in the region, and it is a very natural cluster.

Maybe, last but not least, one of the really exciting prospects for me that I realized is, I don’t know, just a few years ago, really not many, how much there is when you look across industries. So, I recently hired someone, a digital expert from Formula 1, and why is that? I mean, technically speaking, steering or steering, understanding a Formula 1 race car from a distance isn’t much different than steering a tractor. I mean, the vehicles will be very different, but the technology has a lot of similarities in a way. So if we understand the IoT in this case and understand the data transmission from the field to the control centers, it doesn’t matter what industry we work in, we can learn anywhere.

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

Laurel Ruma: It’s really interesting to think about how much data F1 processes in a single race day or more generally how much input comes from so many different places. I imagine that would be very similar. They are dealing with databases of data and are simply trying to develop better algorithms to draw better conclusions. If you look around the larger community, you will surely see that Syngenta is definitely part of an ecosystem. So how are external factors such as regulation and societal pressures helping Syngenta make these better products to be part of, and not outside of, this inevitable agricultural revolution?

Thomas Young: That’s a great point because regulation in general is of course a practical burden for some, or actually 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 collaboration with the US Environmental Protection Agency, the EPA, which has stepped forward to end support for mammalian chemical studies by 2035. So what does that mean? It sounds like a big threat, but what it really is, it’s a catalyst for digital science. We therefore very much welcome this request. We are now working on ways to use data-based science to prove the safety of products we invent. There are some big universities in the US that have been funded by the EPA to help us find these avenues for our science, so we’re also committed to making sure we can do this together in the best way possible and really land a data driven science here and we can stop doing all this real world testing.

So it’s a fantastic opportunity, but of course there’s still a long way to go. I think 2035 is reasonably realistic. We’re not close yet. For example, what we can do today is we can model a cell. There’s organ-on-a-chip as a big trend, so we can model a whole organ, but we can’t model a system or even an ecosystem at this point. So plenty of room for us to explore, and I’m really happy that regulators are a partner and even a driver in this. This is super helpful. The other dimension you mentioned, societal pressures, is also there. I think it’s important that society continues to push for issues like regenerative agriculture, because that creates the basis for us to be able to help. If there is no demand, it is difficult for Syngenta to drive it alone.

So I think demand is important and awareness that we need to treat our planet in the best possible way and we also work with The Nature Conservancy for example where we use their scientific expertise and their expertise in conservation for example sustainable to bring up agricultural practices in South America, where we are running some projects to restore rainforests and biodiversity, and see what we can do there together. So again, a bit like what we discussed before, we can only get better if we work together across industries and that includes NGOs as well as regulators and society as a whole.

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