Partnerships in AI Drive Conservation Efforts: WWF’s Dave Thau

**Sam Ransbotham** (0:02)
From satellite imaging to marine acoustics, wildlife conservationists can use artificial intelligence to advance their vital work. Find out more on today's episode.

**Dave Thau** (0:12)
I'm Dave Thau from the World Wildlife Fund, and you're listening to Me, Myself, and AI.

**Sam Ransbotham** (0:18)
Welcome to Me, Myself, and AI, a podcast on artificial intelligence and business. Each episode, we introduce you to someone innovating with AI.
I'm Sam Ransbotham, Professor of Analytics at Boston College. I'm also the AI and Business Strategy guest editor at MIT Sloan Management Review.

**Shervin Khodabandeh** (0:37)
And I'm Shervin Khodabandeh, senior partner with BCG and one of the leaders of our AI business. Together, MIT SMR and BCG have been researching and publishing on AI since 2017, interviewing hundreds of practitioners and surveying thousands of companies on what it takes to build and to deploy and scale AI capabilities and really transform the way organizations operate.

**Sam Ransbotham** (1:03)
Welcome. Today, Shervin and I are excited to be joined by Dave Thau, Global Data and Technology Lead Scientist at the World Wildlife Fund.
Dave, thanks for taking the time to talk with us. Welcome.

**Dave Thau** (1:13)
My pleasure. Thanks for having me.

**Sam Ransbotham** (1:15)
Let's start with the World Wildlife Fund.
My first blush is that's a little bit of an unusual organization that you wouldn't normally pair with artificial intelligence. How are you using artificial intelligence in your job?

**Dave Thau** (1:29)
WWF is very interesting. It's a federation. We're active in about 100 different countries.
Many of those offices are run completely independently. But cutting across all of them, we have a network of analysts. The team I'm on is called the Global Science Team, and we span the network.
We have scientists on our team who focus on forest, food, climate, and they work with the people throughout the network who focus on those areas. I manage the data and technology team, and I'm working with these scientists. We work across the organization, helping with projects that are starting up in all the local offices. We have our own set of work within the Global Science Team. A lot of that is focused on impact monitoring. And then I'm out in the world talking to other conservation organizations that are doing data management and artificial intelligence and coordinating with them. And conservation organizations have been using AI for a long time. One of the first applications of artificial intelligence has been in land cover monitoring. So there are satellites surrounding the earth monitoring the environment, but the signals from the satellites are very noisy. And so artificial intelligence has long been used to do things like identify, am I looking at a forest? Am I looking at a grassland?
What sort of land cover am I looking at? So that's one of the initial applications of machine learning and artificial intelligence and conservation.

**Sam Ransbotham** (3:05)
So you said initial.
How far back is initial? Is initial last week, last month, last year, last decade? When did you start doing all this?

**Dave Thau** (3:14)
The use of machine learning to analyze satellite data in general dates back to the 70s.
I'm not sure when WWF started using it for conservation applications, but it's been quite a while. I've been at WWF for four years, but the application of machine learning on satellite data proceeded that. It really broke through, though, sort of around 2008 when NASA made the Landsat satellite data archives freely available. This is a series of satellites that have been collecting Earth data since the early 70s, but up until 2008, you had to buy the imagery, and so you were limited to what kinds of analyses you could do. Around 2008, the US government decided to make those publicly available and free, and so then you saw a real explosion of the use of that kind of information.

**Shervin Khodabandeh** (4:13)
Yeah, that's quite fascinating.
We do a fair amount of satellite imagery work at BCG. It's not my area of expertise, but I have to imagine that with both this proliferation of data, as you're talking about, as well as the higher and higher resolution that's becoming available, as well as this massive jump in computing and complicated neural nets and machine learning models, that the state of the art has changed a lot. So maybe if you can contrast, like, what's the cutting edge of this stuff today versus maybe what it was a decade ago or two decades ago?

**Dave Thau** (4:50)
Yeah, the changes in machine learning in particular over the past five years even have been enormous. And it goes hand in hand with access to computational resources and data. In the past, you could do a Ph.D. on one Landsat scene, which is about, I don't know, about 100 kilometers by 100 kilometers. That was, you know, cutting edge.