The number one goal in United Nations’ Sustainable Development Goals for 2030 is: eliminate poverty. Today, around 1 billion people, that’s roughly one seventh of the world’s population, live in extreme poverty by earning less than 1.90$ per day. Though studies reveal that global poverty is reducing, we are still a long way from our goal.

To eradicate poverty, we first need the poverty distribution across the globe. The following diagram gives a rough estimate.

tree-map-of-extreme-poverty-distribution

But unfortunately, data availability is poor. Numerous countries have no survey conducted over the last 3 decades, and others, only a few. More importantly, in many African countries, a single survey has been conducted over the last decade, which makes the data inaccurate. Lastly, the surveys don’t yield a perfect result. Therefore, it is evident that a new method has to be conceived to obtain more precise information.

A STUDY FROM SPACE

A team of social and computer scientists at Stanford University in California, led by Marshall Burke, aim to map poverty from space with the help of artificial intelligence (AI). They collected a large amount of night-time satellite images of the planet, taken by high quality cameras. Studying the glow of lights on the starry map using machine learning algorithms, they aim to distinguish the poor regions from the rich, as higher intensity of light indicates better development. Unfortunately, it was hard to discern the moderately poor regions from the extremely poor, as the intensity between the two wasn’t considerably different.

earth_night

Therefore, they had to study daytime images and obtain key indicators such as: closest urban marketplace, distance from agriculture fields, nearest water sources and other such subtle signs.

They fed the computer large training datasets of images of regions where income per capita was previously known. The computer then used neural nets, a machine learning technique, to create links, discover relationships and find patterns. Then, they verified the accuracy of the algorithm on a validation set, and finally, implemented it on the test set. They focused on the African countries: Nigeria, Malawi, Rwanda, Tanzania and Uganda. Evidently, this technique doesn’t eradicate poverty, but provides reliable data to governments and NGOs.

But in all honesty, disregarding the hype about AI, is the new data really going to make a considerable difference? The World Bank may not have reliable information, but it is unlikely that the governments are completely unaware of the poverty spread in their country. Though a notable effort, this solution is more sensational than practical. And therefore we need another approach, one which hits the problem directly in the heart.

AI IN EDUCATION

“Education is not a way to escape poverty – It is a way of fighting it.”
– Julius Nyerere, former President of the United Republic of Tanzania

The primary step to alleviate poverty is: education. Simply put, if an underprivileged child can receive a decent education, the likelihood of him breaking away from the cycle of poverty increases. Therefore, education plays a crucial role in eradicating poverty.

The major difficulty with educating the poor is the lack of teachers. The reason is evident: helping the poor doesn’t pay, and so, there is no incentive for educators.

Therefore, taking inspiration from the Hole in the Wall experiment conducted by Sugata Mitra in 1999, we could bypass the problem. The study reveals that children can educate themselves only with the aid of a basic computer, requiring nearly no adult guidance. This form of education, known as Minimally Invasive Education (MIE), has significantly benefited over 300,000 underprivileged children from India and Africa.

hole-in-the-wall

Today, MIE can be substantially enhanced with AI and be made the future of education in the slums. With smart virtual bots installed in the systems, the machines would not only provide information, but could also “teach” the children. No external human guidance would be required, just the systems with the virtual “teachers” installed. Let us briefly look into how this can be achieved.

A GLIMPSE OF THE REQUIREMENTS OF A VIRTUAL “TEACHERS”

To interact with human beings, the machines would require advanced Natural Language Processing (NLP) algorithms, such as automatic speech recogniser (ASR), part of speech tagging (POS), syntactic/semantic parser, natural language generator, text-to-speech engine (TTS) etc. They should “understand” the language of the specific area so that children who don’t know English could communicate effortlessly. This would require accurate translation which again uses advanced NLP techniques.

Evidently, to create a solid dialogue system we need a huge database and therefore a centralised server to link all the systems globally might be a solution. But this would require an expensive infrastructure which beats the point of this endeavour.

Additionally, Machine Learning algorithms should make the systems learn from past mistakes, so that in the future, children find it easier to communicate. Furthermore, the interface should be simple, clean, and not cluttered with too many options. The courses could be designed specifically for the rural children or could simply be MOOCs. This would depend on the governments and their educational policies.

To conclude, this merely outlines the task ahead, gives a vision, a step to eradicate poverty. The work, the team required, the involvement needed, are enormous. The funding for the research is considerable, but if we can come together for this project, the entire world, we could succeed. It should be open source, so that anybody can contribute, from leading professors of AI and computer science, to students, to investors, to educators, to government officials, to NGOs…anybody. So are you willing to join hands in this endeavour? Are you willing to help your needy brothers? Does it bother you enough to make a change?