“The best way to predict your future is to create it.” — Abraham Lincoln
Artificial Intelligence (AI) has opened up so many new opportunities from new markets, to progress in critical areas such as education, health, environment, energy, production, sales to the way we work, or live our lives today. Public-private investments in basic and applied research has been accelerating all this.
AI has already surpassed human tasks, such as in some aspects of image recognition, and it’s still expected to advance, to reach and exceed human performance in more and more tasks.
But as ever before in other revolutions, we can see that gains stay in the richest sections of society. The labor force polarisation has been on the news for quite some time now and as the progress towards less inequalities falls behind, we can observe it not only between social classes in society, but even between nations. Introducing new technologies will have less of a negative impact on developed countries than emerging countries, which lag behind.
It is crucial that AI education is more accessible to everyone, in order to prevent that it increases inequalities and divides, leading to unfair advantages for only a few organisations, countries and nations.
Emerging countries VS. emerging technologies
The US and China are already predicted to take 70 % of the estimated US $15.7 trillion that AI will generate in wealth by 2030.
In emerging markets artificial intelligence will reduce the competitive advantage of their cheap labour and not enough skills are being adapted to this new shift.
At only around 90.000 people trained in AI worldwide with the right skill set, (last year Element AI estimated that there were fewer than 10.000 people in the world with the expertise needed to create machine learning systems*) and automation causing a dramatic wealth gap, we’re setting ourselves up for more inequalities, and a less distributed understanding to act on what we’re creating, not to mention adapting to job losses. We’re widening the inequality gap and we seem to miss the most important, and critical question of all:
(How) Can we bridge the gap between rich nations, emerging markets, and less developed countries with AI & new technologies?
It seems right now that the major regions of Africa, Central Asia, and most of Latin America have been left behind. The developing countries have the raw materials needed to fuel AI, but they appear to lack the know-how to engineer their own AI developments.
Africa has been undergoing its digital revolution for over 10 years and is gradually achieving the developed world standards. However, connectivity lingers behind at 21% (compared to 43%), the majority of its SMEs are lacking the basic skills that can enable them to take part in the digital economy.
Latin America is also taking AI very seriously and as a result, global corporations are setting up innovation centres, where AI solutions, including machine learning, hybrid intelligence and augmented reality, play a growing role in solving real-life client problems.
How do we make sure we don’t increase the chasm between the technology elites and the rest of the population in access to these new technologies, with un-biased algorithms and accurate job shifts?.. How can we unbiasedly measure the impact of these technologies, the economical, social or even ethical?
.. And how do we make sure we don’t leave anyone behind?..
As the youth from emerging countries is becoming innovative in finding locally relevant solutions to daily challenges like in health, agriculture and education, among other areas, governments and corporations need to understand how to get involved and tap into this innovative spirit and help compete globally, to ensure faster and quicker business growth.
Research reveals structural deficiencies in emerging countries that minimise their ability to integrate new technologies, as some we can think of like quality of the education systems, lack of scientific research institutions, as well as weak research and innovation ecosystems at national, regional and global levels. The main challenging constraint being low levels of trust and a lack of collaborative mindsets.
The next wave of emerging economies must chart a new course. If AI is to be a boon and not a global burden, its benefits will need to be shared.— Kai-Fu Lee
An AI-driven world needs a data-literate citizen community, that understands the principals of data to interpret and participate to the debate of the AI-enabled world we are creating. In this Fourth Revolution, data is what cotton was in the First Industrial Revolution. In order for AI to continue to reap benefits and improve people’s lives and help us solve the world’s most challenging inefficiencies, we need to actively understand and participate to the world we want to live in.
All countries, emerging and developed need to harness new technologies. The slow ones will be left behind to serve the fast, first movers. As they embrace these technologies, they must come to terms with the consequences on employment and income distribution, and adapt strategies.
Towards a more distributed, inclusive revolution
Every country has the right to participate in this global “revolution”, get access to tools to prepare and reap the benefits of job creation, innovation, and economic growth... however, too many places are currently excluded from this revolution and continue to consume developed countries technologies, or even less fruitful, participate in building developed countries technologies, without taking any advantage of these innovations.
What AI do we want for tomorrow?
With the increase in algorithme use, we’re increasing and letting in AI in our daily lives, but we don’t necessarily know it. Are we ready for it? How are governments approaching this transformation?
It is important to address the potential pitfalls of competition towards transformative AI, where:
- Key stakeholders, including the developers, may ignore/underestimate safety procedures for of faster utilisation
- The fruits of the technology won’t be shared by the majority of people to benefit humanity, but only by a selected few
- Creating even more inequalities by not acknowledging bias (the social credit system that is tested right now in China), or not to mention racial bias (like the criminal justice process, COMPAS that falsely flagged black defendants as future criminals at nearly twice the rate as white defendants)
The key for emerging countries is to adopt an innovative mindset and focus on skills development to ensure that digital transformation opportunities can be filled and led locally, by these countries. It also means that they must shift from consumers, to creators of technology.
Enter Global Startup Weekend AI
More people and organisations are joining this growing global community that seeks to tackle today’s challenges with technology to better understand and answer to them.
These events around the world are a way to learn, train and democratise Artificial Intelligence, a way for anyone, anywhere to start their journeys and empower, not only their startup ecosystems, but themselves. Data scientist, robotics specialist, developer, machine learning expert, researchers, entrepreneurs or amateurs are creating the future we all want to live in.
We need to be bold and progress not only in AI, but in society, using technology as a tool to bring us closer, move us forward faster, but more responsible. Progress cannot be made in isolation, so we thrive to bring as many regions as possible together, to build the future we ALL want to live in. — Laura Calmore, Initiator of Global SW AI
With this Global AI initiative we want avoid to bring together with technology, not divide. Therefore, we encourage emerging countries to develop their communities, promote AI education & accessibility, and connect them with the global community to exchange, share, learn from each other and move forward together.
Do we want to stand besides and take the risk of getting left behind or do you want be part and influence the change?
- note from tex: Tencent, however published its own estimate of global AI talent, putting the figure at a far higher 200.000 to 300.000 people who were either AI researchers or industry practitioners. The 2 numbers differ because of companies trying to decide whether to build their own AI and data science teams or contract with consulting firms and third parties, impacting salaries. According to Element AI, there are about 22.000 PhD-educated researchers, of which about 3.000 are currently seeking work.