Feed the world with AI, machine learning and the cloud
For example, in the public sector, maybe just to call out a couple. We 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 wanted to participate in that joint R&D. So there’s data we’ve just shared with the community, but we also care about data standards. So we’re on the board of AgGateway, which is a consortium of 200 or more food companies working on how we can actually advance digital agriculture? So we make sure the standards work for everyone, and we don’t end up with the exclusive ideas of each member of the food chain, but we can connect our data .
Again, the private sector is equally important. We are fortunate enough to have our headquarters in Basel, which is really a scientific cluster and especially chemical sciences. Lots of pharmaceutical companies around here. So we can also exchange a lot of what we learn between pharmaceuticals and agriculture, we can learn about chemistry, we can learn about practice, how we work, how we I work through my lab. We’re in close contact with colleagues around this area, but of course in other places as well, and it’s a completely natural cluster.
Perhaps last, and not least, one of the scenarios that was really interesting to me that I realized, I don’t know, just a few years ago, really not much, is how many if you look at it. industries. So I recently hired someone, a digital expert from Formula 1, and why is that? I mean, if you look at this technique, steering or handling, remotely understanding a Formula 1 racing car is not much different from driving a tractor. I mean, the vehicles will be wildly different, but the technology in a way has a lot of similarities. So understanding IoT in that case and understanding data transfer from the field to control centers, no matter what industry we are working in, we can learn it all.
We’re also working with a super-experienced partner in image recognition to better understand what’s going on in the field, where as Syngenta we can bring knowledge agronomist and that partner can bring technical knowledge about how to make most of the images. From a very different field that has nothing to do with agriculture, but skills are still transferable. So I’m really looking for talent across industries and literally anyone who supports our goals and not limited to people with life science experience.
laurel wreath: It’s interesting to think about the amount of data F1 processes on a race day, or generally the amount of input data from so many different places. I can see that would be very similar. You are dealing with databases of data and just trying to build better algorithms to come up with better conclusions. As you look around the larger community, you will inevitably see that Syngenta is definitely part of the ecosystem, so how do external factors like regulation and social pressure help Syngenta build products that better product to be a part of and not out of that inevitable agricultural revolution?
Thomas Jung: That’s a great point, because regulation in general is, of course, a real burden on some, or can be seen as a real burden. But for us in digital science, it’s a very welcome innovation driver. One of the key examples that we have at the moment is that our work with the Environmental Protection Agency in the United States, the EPA, has actually moved toward discontinuing support for chemical studies of mammals. breast in 2035. So, what does that mean? It sounds like a big threat, but what it is, it is the catalyst for digital science. So we very much welcome this request. We are currently researching data-driven science uses to demonstrate the safety of the products we devise. There are a number of great universities across the US that have received funding from the EPA to help find those ways to do our science, so we’re also getting involved to make sure we’re together. do this in the best possible way and we can really hit the target. Data-driven science is here, and we can stop doing all these real-world tests.
So it’s a great opportunity, but of course, it’s a long road ahead. I think 2035 is somewhat realistic. We are not close yet. What we can do today is, for example, we can model a cell. Organ-on-a-chip is a big trend, so we can model an entire organ, but there’s no way we can model a system or even an entire ecosystem. in this moment. So there’s a lot of space for us to explore and I’m really happy that the regulators are a partner in this, and even a driver. That was amazing. Another aspect you mentioned, social pressure is there too. I think it’s important for society to continue to promote causes like regenerative agriculture, because first of all, this is what lays the groundwork for us to help with that. Without demand, Syngenta could hardly promote alone.
So I think the need is important and realizes that we need to treat our planet in the best way possible, and we’re also working with, for example, The Nature Conservancy, where we I use their conservation science expertise to bring about sustainable agricultural practices in South America, for example, where we’re having a number of rainforest restoration projects, biodiversity restoration and see what we can do together there. So again, just like what we discussed before, we can only get better by collaborating across industries and that includes NGOs as well as institutions. management and society as a whole.