When people think about the benefits of A.I. and its impact in our world, they usually think of self-driving cars, advanced automations, deep learning systems, clever chatbots, etc. Those particularly infatuated with the idea of A.I. tend to go even further and fantasize about super-intelligent machines that will magically solve all our problems without any effort from us (pretty much like a deus ex machina figure in some ancient theater play). However, the more pragmatic A.I. thinkers focus more on particular applications of A.I. that can be implemented fairly easily, and that target specific issues that would be impractical to solve in conventional ways. One such case is that of detecting how contaminated beehives are by a particular parasite.
Why should we care about this matter? Don’t we have larger problems to deal with? Perhaps. After all, there are more evident problems out there that require unconventional ways of tackling them, problems that could benefit a lot by a narrow A.I. designed for them. However, the issue of infested beehives is not a minor one, as it represents a real danger for the whole species of these buzzing insects. It’s worth noting that bees are not useful for just the honey they produce; they are key in plant polination, and as such they play an important role in our planet’s fragile ecosystem, that’s on the wane lately. So, it may be a big deal after all.
Developing an A.I. to tackle the beehive infestation problem is a project disproportionate to its impact, as it is fairly manageable with the existing technology, at least for a particular parasite, called the Varroa mite. These organisms can cause serious issues to the bees, issues that are observable with the naked eye. However, assessing the infestation may not be so straight-forward, making it difficult to take intelligent action against it (e.g. how can you tell which beehives are in imminent danger and prioritize accordingly?). That’s where Computer Vision comes in handy, an automated way for a computer system to evaluate what a camera attached to it observes. The images from the camera feed, when coupled with some deep learning network, can help measure the magnitude of the issue in a very small amount of time (check out a demo of an app by TopLab, that does just that). Will this be enough? Possibly, if this process is coupled with an effort to eliminate the parasites once identified. However, knowing about the infestation issue in an objective and practical manner, can definitely speed things up.
Perhaps A.I. is not as futuristic as it is often perceived, nor as high-level as it comes across. After all, just like any other applied science, it aims to solve real-world problems right here and now, in an efficient and effective manner. The question is, are we willing to apply it to more strategic problems, like the case of an impaired ecosystem, or are we going to use it only to make our urban lives more convenient? Hopefully that’s a question we can answer with just our natural intelligence...
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests.