|To prove its commitment to AI and helping farmers in Africa, Google inaugurated its own AI laboratory in Ghana / Photo by: The Pancake of Heaven via Wikimedia Commons|
Google is taking a step into AI very seriously, and a testament to that commitment is their recent foray into the technology that will be able to help farmers in Africa.
According to CNN, it’s thanks to Moustapha Cisse, Google’s research scientist with his AI efforts in Africa that the company is able to work toward providing “developers with the necessary research needed to build products that can solve problems that Africa faces today.”
Cisse and his team have been able to make an AI that can help farmers do really helpful, simple things for them. Cisse adds that the research centers had actually wanted to make the technology accessible for everyone, hence the use of open-source code.
It’s already helped farms identify crop diseases in produces like cassava, as is exemplified by the fact that some farmers are now able to recognize whether their crops are healthy or disease-ridden through the help of “TensorFlow,” a product of Google aligned with the company’s commitment “to help developers create solutions to real-world problems.”
Africa itself is interested in pushing for these technologies, as Cisse reveals that the center is actually investing in new minds that wish to contribute through giving them grants for when they express interests in working in artificial intelligence. In addition, Google also continues to support this push, too, as they also sponsor graduate programs in machine learning, which, at this point, works in tandem with AI.
Obviously, Google is doing fairly well in AI as of late, and working with communities and actual farmers in Africa who can greatly benefit from AI technology is a good sign, but there is still much to be done to improve representation and diversity, and therefore, make AI more far-reaching, says Nyalleng Moorosi, a software engineer working at a center focusing on diversity in AI.
Moorosi adds that diversity helps workers see the bigger picture, the extent to which they can build helpful products even for “foreign neighborhoods.”
She further emphasizes that “the best way to go about it is to have diverse teams working on these algorithms and then we will get somewhere.”