An -unequal- World of Data

Marc Puig, CTO

This is the second post from Marc’s series about Artificial Intelligence, you can read the first one ‘AI, an innovation from the 2nd century B.C.’ here.

The challenge is for AI to help reduce inequalities instead of increasing them.

Ever since the Dartmouth conference in 1956, artificial intelligence has gone through cycles of great enthusiasm and disappointment, important advancements and frustrating failures. Nevertheless, it has reached peaks that we thought unachievable in such a short time: beating humans in complex games (chess, go), understanding natural language, self-driving cars, etc. From now onwards, artificial intelligence could have a much greater role than the one it has had until now. AI is not only a research field for labs anymore, it does not have a handful of applications only. It is one of the keys for our future -and present-.

We’re living in an increasingly unequal world, which is why a big push for current scientific developments is being made in the AI field. In contrast with previous periods, research in AI does not belong solely to the sciences, it has extended beyond them. An impressive amount of money is invested in AI’s new applications and research industry. Politicians from all over the world mention it frequently and even compare it with nuclear technology.

AI will shake all business models. It is the next disruption and it’s already happening. For this reason, countries can now decide whether they want to be a part of this disruption, or to stay put, wait in line and see how their employment falls.

 

Some have started…

In the last few months several leading countries like China, UK, France or Canada have been developing national strategies around AI. Their goal is to lead or at least not to lose steam, not to miss the opportunity for development in such a strategic sector.

AI growth will be tied to strong economic growth, but it can also expedite the loss of certain types of jobs, transform others and, therefore, increase economic inequalities. New training procedures and job opportunities for those that lose their employment will be necessary. These are all reasons that reinforce the importance of the sector for countries worldwide.

Education should also be in the picture. New scientists will drive the sector in the future, but the experience of other industries too. Governments probably shouldn’t be entirely responsible for AI-expert’s education, but they should lead the efforts. Having specialized talent in Machine Learning (ML) will be key to winning the race. Today there are roughly around 1,000 people around the globe that can contribute to AI research and maybe 100,000 that can understand their work and participate actively in commercializing it. STEM talent is equally needed: skilled engineers, mathematicians and physicists that complement the experts.

When we speak about strategic sectors we usually think of healthcare, energy and environment, mobility, education and security;  but we should take into account that, in a world marked by inequality, artificial intelligence shouldn’t worsen exclusion problems or concentration of wealth. The goal of policies around AI should be twofold: guarantee that the development of this technology does not contribute to worsening social and economic inequalities; and making proper use of it to mitigate such problems.

The first priority for AI should be to help promote basic human rights, improving social relations and reinforce solidarity.