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How a Portland nonprofit is using artificial intelligence to help save whales, giraffes, zebras
But each animal’s black and white markings are like a fingerprint, distinct — and invaluable for scientists who need to track the animals and information about them, including their births, deaths, health and migration patterns.
Portland, Oregon-based nonprofit Wild Me has developed AI to pick out identifying markers — the stripes on a zebra, the spots on a giraffe, the contours of a sperm whale’s fin — and catalog animals much faster than a human can.
Photo surveys are increasingly used as the backbone for population estimates, and Wild Me’s Wildbooks, which catalog various species, are giving conservation groups, governments and citizen scientists a faster way to monitor animals around the world.
He wanted to find a different way to track the animals other than invasive tagging, so he teamed up with a biologist and a NASA astronomer, adapting the algorithm for the Hubble telescope to match the shark’s spot patterns.
Thousands of photos of the species must be manually annotated so that the algorithm learns what a given animal is, what the distinguishing characteristics are and what’s just background noise.
She said she watched Facebook implement facial recognition and wanted to use similar technology to help identify whales within the endangered species (there are only about 400 North Atlantic right whales left).
The Wildbook for whales, called Flukebook, encourages collaboration, which is particularly useful for whales that travel long distances because it can be difficult for one research group to effectively monitor one area.
“By doing the matching themselves, by contributing their own data, not only do they get to know the animals, but it creates a locally motivated community of people that can react when conservation actions come up.”
One of Wild Me’s more recent innovations is an AI-driven feature that datamines YouTube videos of whale sharks and sea turtles, using user-generated videos (often taken by tourists) to get a better sense of the populations.
“The point of going to a fully automated system is to shorten that cycle so we can take all of the data over the past week or two weeks and have a continuous prediction of population size.
How artificial intelligence outsmarted the superbugs
One of the seminal texts for anyone interested in technology and society is Melvin Kranzberg’s Six Laws of Technology, the first of which says that “technology is neither good nor bad;
By this, Kranzberg meant that technology’s interaction with society is such “that technical developments frequently have environmental, social and human consequences that go far beyond the immediate purposes of the technical devices and practices themselves, and the same technology can have quite different results when introduced into different contexts or under different circumstances”.
The researchers used the technology to tackle the problem of bacterial resistance to conventional antibiotics – a problem that is rising dramatically worldwide, with predictions that, without a solution, resistant infections could kill 10 million people a year by 2050.
The team of MIT and Harvard researchers built a neural network (an algorithm inspired by the brain’s architecture) and trained it to spot molecules that inhibit the growth of the Escherichia coli bacterium using a dataset of 2,335 molecules for which the antibacterial activity was known – including a library of 300 existing approved antibiotics and 800 natural products from plant, animal and microbial sources.
This produced a hundred candidates for physical testing and led to one (which they named “halicin” after the HAL 9000 computer from 2001: A Space Odyssey) that was active against a wide spectrum of pathogens – notably including two that are totally resistant to current antibiotics and are therefore a looming nightmare for hospitals worldwide.
So while the main beneficiaries of machine learning for, say, a toxic technology like facial recognition are mostly authoritarian political regimes and a range of untrustworthy or unsavoury private companies, the beneficiaries of the technology as an aid to scientific discovery could be humanity as a species.
Our customers have achieved success across a range of hardware: massive inference deployment on CPUs across multiple data centers for content recommendation engines, VPU-based cameras that protect endangered species from poachers in real-time, and FPGAs that provide acceleration for fast search results.
We’re working closely with the AI community to accelerate discoveries and make meaningful progress in how we use artificial intelligence to add value to our work and lives, whether it’s finding water on the moon, speeding critical medical diagnoses, or seeing product defects faster.
- On 25. oktober 2021
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