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One of the really insidious parts of illegal logging is that the same criminal enterprises
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are also engaged in trafficking drugs, weapons, and typically human trafficking as well. It's
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not just damage to the environment where the trees were cut down, and it's not just the
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source country losing those resources, it's also the fact then that those profits go on
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to support these other kinds of international crime.
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We're at a point in time where the
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world's forests need all hands on deck, if we are not actively engaged in some form of
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protection and some form of combating illegal logging, if we don't do it now, we may
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not have a chance. We lose habitat that maybe we can never get back.
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Internationally, we see the bulk of illegal logging happening in the tropics.
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So we talk about deforestation,
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illegal logging, land conversion in places like Congo, in sub-Saharan Africa, or the
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Amazon in South America. The role that the US plays in the global illegal logging trade
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is that we're a demand side, we're a consumer side of this problem. We know from research
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and investigations that have been going on now quite actively for about 10 years, that
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there's actually a lot more fraud and misrepresentation of forest products than people realize and
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some of that material is doubtless of illegal origin. So when we're involved in the fight
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against illegal logging, our role is really on this forensic timber identification piece.
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This is the Center For Wood Anatomy Research. We're the home of the world's largest scientific
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research wood collection. Everyone, I think, is familiar with forensics nowadays. One area
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of forensics that people often overlook is botanical forensics. We'll work on whatever
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cases that we need to. We've done plane crashes, we've done murder cases, we've worked on arson
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cases, attempted car bombings. Since about 1999, one of the places that we've really
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focused on heavily is using forensic wood anatomy to combat illegal logging. We develop
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the technology and the tools for field deploying this kind of information so that field agents
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can look at a paper and it says, "This is supposed to be species X. This is supposed
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to be species Y." They need some kind of knowledge or technology to let them look at the wood
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and see whether it lines up with the paperwork. And traditionally that's done by someone like
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me, or I would train someone. And training human beings to do this takes a lot of time,
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it takes a lot of effort. I really started saying, "How can we solve this problem of
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needing to build this capacity and needing to get this information out, because I don't
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think we can train human beings fast enough". And that actually led us down a pretty interesting
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road of trying to come up with a technological solution to this problem.
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It is essential to have an interdisciplinary team that knows about law enforcement, AI,
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wood anatomy, and all these fields come together here. Alex is the wood anatomist, and he's
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learned a little bit of machine learning from me. I'm a machine learner and I've learned
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a little bit of wood anatomy from Alex. The Xylotron itself is this handheld, portable
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device connected to a laptop to do wood identifications. The XyloTron is built from off the shelf components
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and uses all open source technologies.
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It has two primary components. It has the imaging device itself, which we call the xyloscope,
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and then it has the laptop where we actually do the AI portion of it where the actual identification is made.
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The biggest advantage is you can carry this thing in two hands and take it anywhere in the world.
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That is not true of any of the other forensic technologies. They're really restricted to the laboratory.
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The artificial intelligence model that we use in the XyloTron to identify wood is a neural network.
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A neural network is basically
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a mathematical computation, which has some numbers or knobs and parameters that can be
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tweaked and changed in a data driven manner. In our case, the input is the image of a wood
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and the output is going to be what species or what category this wood belongs to.
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To operate this, all we have to do is have a specimen of wood and then place this flush
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on the top surface there. Turning on the light, pressing this onto the surface of the specimen,
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and then I have to orient the Xyloscope so that a certain botanical feature called "rays"
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are running vertically in the image. It captures an image, it feeds that image into the system,
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it breaks it down according to the machine learning algorithm, and then it gives me my
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output. It's basically the exact same thing I see when I look with my hand lens at a block of wood.
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There's always going to be this cat and mouse game between what the criminals
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are doing and technology catching up with their misdeeds. Illegal logging is a global
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problem, and to address it at a global scale, what we need is access to technology and expertise
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that is scalable, affordable, and easy to adopt and adapt. The XyloTron project ticks
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boxes on all these levels and it can be deployed at many different points in the wood supply chain.
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In an ideal world, we would combat illegal logging in the forest. Right?
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We would stop the people with the chainsaws.
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And there are people out there trying to do that, but that's not a place where the science that we do here
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can contribute much. If a shipment comes into the United States and we do the identifications
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and it's illegal because they mis-declared what it was or because it's an endangered
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species, we have the legal authority to seize that shipment. And if you can do that enough,
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you're going to reduce the demand, you're going to reduce the degree
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to which it's desirable to be out there illegally logging.
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Because if they can't make money, they're not going to do it as frequently.
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It's just that simple.