Recently, artificial intelligence has begun to gain ground in Africa in several areas, in several forms, and several countries.
Morocco, Senegal, and Madagascar are examples of countries where this new discipline has taken root.
In Morocco, two start-ups are harnessing AI for agriculture
In Morocco, for example, artificial intelligence could be a solution for making agriculture more productive, sustainable, and efficient. By 2050, 9.2 billion people will need to be fed, and arable land will only be able to increase by 4%. So we need to grow a little more, but above all we need to grow it better.
This is what two Moroccan start-ups specializing in agrotechnology are proposing. The system is based on the collection of data, either manually or via satellite information. The data is then analyzed and made available to farmers.
Nabil Ayoub, project manager of the Moroccan start-up Agridata, explained that the aim is to improve farm management by informing farmers of what is happening on their farms in real-time.
In Senegal, young graduates from the sector are looking for opportunities abroad
A large number of training courses have been set up in the field of Artificial Intelligence in Dakar in recent years, but once these young people have graduated, they do not necessarily find work, as Senegalese companies are still insufficiently aware of the potential that AI represents for their productivity and development.
This lack of jobs in the field is driving these graduates to seek professional opportunities abroad. These people contribute a great deal to the development of AI in Senegal. They are involved in all these training courses and are in direct contact with the latest technologies. All these people could return to Senegal when the companies are ready.
Madagascar: the little hands of outsourcing services
Many French start-ups in the field of artificial intelligence use companies in Madagascar to train algorithms, feeding huge amounts of information into the algorithm. Tedious tasks outsourced to the Big Island.
The work is done in groups. 40 in the morning, 40 in the afternoon, and 40 at night. Trainers monitor the progress of the work and if employees take too long to process a simple image, they receive a warning. If it happens a second time, they’re sent straight back.