Connect with us

Tech

Feeding the world by AI, machine learning and the cloud

Published

on

Feeding the world by AI, machine learning and the cloud


In the public sector, for example, just to call out a couple maybe. We’ve been working with the Open Data Institute to publish some of our data in a reusable format, raw data essentially, that scientists across the world can use, because we want to engage in that 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 a board member of AgGateway, that is a consortium of I think what 200 or more food sector companies working on how do we actually drive digital agriculture? So we’re making sure that the standards work for all and we don’t end up with proprietary ideas by each member of the food chain, but we can connect our data across.

The private sector, again, it’s just as important. We’re lucky enough to be headquartered in Basel, which is a cluster of science really, and of chemical sciences in particular. A lot of pharma companies are around here. So, we can also exchange 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 through our labs. We’re intensely in touch with our colleagues around the region here, but of course also elsewhere, and it’s quite a natural cluster.

Maybe last, not least, one of the real exciting perspectives for me that I realized, I don’t know, just couple of years ago, not many really, is how much there is if you look across industries. So, recently I hired somebody, a digital expert from Formula 1, and why that? I mean, if you look at this technically, steering or controlling, understanding a Formula 1 race car remotely isn’t much different from steering a tractor. I mean, the vehicles will be super 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, it doesn’t matter what industry we’re working on, we can learn all across.

We’re also working with a super experienced partner in the image recognition space to understand better what happens on the field, where as Syngenta, we can bring agronomic knowledge and that partner can bring technical knowledge on how to make most of the images. From a very different field, nothing to do with agriculture, but still the skills are super transferable. So, I’m really looking for talent across industries, and literally anybody who’s up for our cause, and not limited to people with life science experience.

Laurel Ruma: That’s really interesting thinking about how much data F1 processes on a single race day or just in general, the amount of inputs from so many different places. I can see how that would be very similar. You’re dealing with databases of data and just trying to build better algorithms to get to better conclusions. As you look around the larger community, you’re certainly seeing Syngenta is definitely part of an ecosystem, so how do outside factors like regulation and societal pressures help Syngenta build those better products to be part of and not outside of that unavoidable agricultural revolution?

Thomas Jung: It’s a great point, because regulatory in general, of course, is a practical burden to some, or may be perceived as one actually. But for us in digital science, it’s a very welcome driver of innovation. One of the key examples that we have at the moment is our work with the Environmental Protection Agency in the US, the EPA, which has stepped forward actually to stop supporting chemical studies on mammals by the year 2035. So, what does that mean? It sounds like a big threat, but really what it is, it’s a catalyst for digital science. So we very much welcome this request. We’re now working on ways to use data-based science to prove the safety of products that we invent. There’s couple of major universities across the US that have received funding from the EPA to help with finding those ways to do our science, so we are also engaging to make sure that we do this in the best possible way together and we can really land at a data-driven science here and we can stop doing all these real-life tests.

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, we can model a cell. There’s organ-on-a-chip as a big trend, so we can model up to a whole organ, but there’s no way we can model a system or even an ecosystem at this point. So, a lot of space for us to explore, and I’m really happy that regulators are a partner in this, and actually even a driver. That’s superbly helpful. The other dimension that you mentioned, societal pressure is also there. I think it is important that society keeps pushing for causes like regenerative agriculture, because this is what, first of all, creates the grounds for us to help with that. If there is no demand, it’s hard for Syngenta to push it forward alone.

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

Tech

The hunter-gatherer groups at the heart of a microbiome gold rush

Published

on

The hunter-gatherer groups at the heart of a microbiome gold rush


The first step to finding out is to catalogue what microbes we might have lost. To get as close to ancient microbiomes as possible, microbiologists have begun studying multiple Indigenous groups. Two have received the most attention: the Yanomami of the Amazon rainforest and the Hadza, in northern Tanzania. 

Researchers have made some startling discoveries already. A study by Sonnenburg and his colleagues, published in July, found that the gut microbiomes of the Hadza appear to include bugs that aren’t seen elsewhere—around 20% of the microbe genomes identified had not been recorded in a global catalogue of over 200,000 such genomes. The researchers found 8.4 million protein families in the guts of the 167 Hadza people they studied. Over half of them had not previously been identified in the human gut.

Plenty of other studies published in the last decade or so have helped build a picture of how the diets and lifestyles of hunter-gatherer societies influence the microbiome, and scientists have speculated on what this means for those living in more industrialized societies. But these revelations have come at a price.

A changing way of life

The Hadza people hunt wild animals and forage for fruit and honey. “We still live the ancient way of life, with arrows and old knives,” says Mangola, who works with the Olanakwe Community Fund to support education and economic projects for the Hadza. Hunters seek out food in the bush, which might include baboons, vervet monkeys, guinea fowl, kudu, porcupines, or dik-dik. Gatherers collect fruits, vegetables, and honey.

Mangola, who has met with multiple scientists over the years and participated in many research projects, has witnessed firsthand the impact of such research on his community. Much of it has been positive. But not all researchers act thoughtfully and ethically, he says, and some have exploited or harmed the community.

One enduring problem, says Mangola, is that scientists have tended to come and study the Hadza without properly explaining their research or their results. They arrive from Europe or the US, accompanied by guides, and collect feces, blood, hair, and other biological samples. Often, the people giving up these samples don’t know what they will be used for, says Mangola. Scientists get their results and publish them without returning to share them. “You tell the world [what you’ve discovered]—why can’t you come back to Tanzania to tell the Hadza?” asks Mangola. “It would bring meaning and excitement to the community,” he says.

Some scientists have talked about the Hadza as if they were living fossils, says Alyssa Crittenden, a nutritional anthropologist and biologist at the University of Nevada in Las Vegas, who has been studying and working with the Hadza for the last two decades.

The Hadza have been described as being “locked in time,” she adds, but characterizations like that don’t reflect reality. She has made many trips to Tanzania and seen for herself how life has changed. Tourists flock to the region. Roads have been built. Charities have helped the Hadza secure land rights. Mangola went abroad for his education: he has a law degree and a master’s from the Indigenous Peoples Law and Policy program at the University of Arizona.

Continue Reading

Tech

The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan

Published

on

The Download: a microbiome gold rush, and Eric Schmidt’s election misinformation plan


Over the last couple of decades, scientists have come to realize just how important the microbes that crawl all over us are to our health. But some believe our microbiomes are in crisis—casualties of an increasingly sanitized way of life. Disturbances in the collections of microbes we host have been associated with a whole host of diseases, ranging from arthritis to Alzheimer’s.

Some might not be completely gone, though. Scientists believe many might still be hiding inside the intestines of people who don’t live in the polluted, processed environment that most of the rest of us share. They’ve been studying the feces of people like the Yanomami, an Indigenous group in the Amazon, who appear to still have some of the microbes that other people have lost. 

But there is a major catch: we don’t know whether those in hunter-gatherer societies really do have “healthier” microbiomes—and if they do, whether the benefits could be shared with others. At the same time, members of the communities being studied are concerned about the risk of what’s called biopiracy—taking natural resources from poorer countries for the benefit of wealthier ones. Read the full story.

—Jessica Hamzelou

Eric Schmidt has a 6-point plan for fighting election misinformation

—by Eric Schmidt, formerly the CEO of Google, and current cofounder of philanthropic initiative Schmidt Futures

The coming year will be one of seismic political shifts. Over 4 billion people will head to the polls in countries including the United States, Taiwan, India, and Indonesia, making 2024 the biggest election year in history.

Continue Reading

Tech

Navigating a shifting customer-engagement landscape with generative AI

Published

on

Navigating a shifting customer-engagement landscape with generative AI


A strategic imperative

Generative AI’s ability to harness customer data in a highly sophisticated manner means enterprises are accelerating plans to invest in and leverage the technology’s capabilities. In a study titled “The Future of Enterprise Data & AI,” Corinium Intelligence and WNS Triange surveyed 100 global C-suite leaders and decision-makers specializing in AI, analytics, and data. Seventy-six percent of the respondents said that their organizations are already using or planning to use generative AI.

According to McKinsey, while generative AI will affect most business functions, “four of them will likely account for 75% of the total annual value it can deliver.” Among these are marketing and sales and customer operations. Yet, despite the technology’s benefits, many leaders are unsure about the right approach to take and mindful of the risks associated with large investments.

Mapping out a generative AI pathway

One of the first challenges organizations need to overcome is senior leadership alignment. “You need the necessary strategy; you need the ability to have the necessary buy-in of people,” says Ayer. “You need to make sure that you’ve got the right use case and business case for each one of them.” In other words, a clearly defined roadmap and precise business objectives are as crucial as understanding whether a process is amenable to the use of generative AI.

The implementation of a generative AI strategy can take time. According to Ayer, business leaders should maintain a realistic perspective on the duration required for formulating a strategy, conduct necessary training across various teams and functions, and identify the areas of value addition. And for any generative AI deployment to work seamlessly, the right data ecosystems must be in place.

Ayer cites WNS Triange’s collaboration with an insurer to create a claims process by leveraging generative AI. Thanks to the new technology, the insurer can immediately assess the severity of a vehicle’s damage from an accident and make a claims recommendation based on the unstructured data provided by the client. “Because this can be immediately assessed by a surveyor and they can reach a recommendation quickly, this instantly improves the insurer’s ability to satisfy their policyholders and reduce the claims processing time,” Ayer explains.

All that, however, would not be possible without data on past claims history, repair costs, transaction data, and other necessary data sets to extract clear value from generative AI analysis. “Be very clear about data sufficiency. Don’t jump into a program where eventually you realize you don’t have the necessary data,” Ayer says.

The benefits of third-party experience

Enterprises are increasingly aware that they must embrace generative AI, but knowing where to begin is another thing. “You start off wanting to make sure you don’t repeat mistakes other people have made,” says Ayer. An external provider can help organizations avoid those mistakes and leverage best practices and frameworks for testing and defining explainability and benchmarks for return on investment (ROI).

Using pre-built solutions by external partners can expedite time to market and increase a generative AI program’s value. These solutions can harness pre-built industry-specific generative AI platforms to accelerate deployment. “Generative AI programs can be extremely complicated,” Ayer points out. “There are a lot of infrastructure requirements, touch points with customers, and internal regulations. Organizations will also have to consider using pre-built solutions to accelerate speed to value. Third-party service providers bring the expertise of having an integrated approach to all these elements.”

Continue Reading

Copyright © 2021 Seminole Press.