In today’s information age, the most abundant resource has quickly become information itself, more specifically data. By 2010, the world had created 1.2 zettabytes (1.3 trillion gigabytes), an equivalent to 75 million 16 GB iPads. By 2016, the world completely surpassed this, creating 90 percent of all data in just the last two years. 2.5 quintillion bytes of data are created every day.
These numbers are much too large for any single person to comprehend, but with the help of technology and machine learning the data can help optimize transportation in cities, predict the stock market and diagnose diseases, along with a vast amount of other tasks. Big data has largely been a tool for the developed world; however, there is plenty of potential for it to become a key factor in ending poverty.
In August, Stanford researchers published a paper on using satellite imaging and machine learning to track and measure poverty throughout Africa. Accurate measurement of poverty in Africa was extremely lacking as “39 out of 59 African countries conducted fewer than two surveys to measure poverty” between 2000 and 2010, according to The World Bank.
Previous strategies for measuring poverty also included tracking mobile phone usage and satellite photos of lights at night; however, phone data was not always available and nighttime light data could not differentiate between poverty and extreme poverty levels.
Instead, the Stanford researchers used daytime images of development, such as paved roads, farms, metal roofs, along with nighttime light intensity data to measure poverty. They input the data into a computer model to map out poverty levels throughout the test countries, Nigeria, Uganda, Tanzania, Malawi and Rwanda. Using methods such as these, African governments and NGOs will be better equipped to design policy and find areas most affected by poverty.
Furthermore, one of the most famous machine learning tools is IBM Watson, a supercomputer that uses advanced software to answer questions. In 2014, IBM launched Project Lucy, a mission to bring Watson to Africa and use the artificial intelligence to help solve the problems surrounding “health care, education, water and sanitation, human mobility and agriculture.”
More generally, scientists predict that machine learning has the potential of predicting the future and keeping watch over society. More specifically, the technology has the capability of forecasting underperforming crops in developing countries and situations that will call for an international convention.
Using biometric data, governments, especially that of India, hope to identify all citizens and ensure they can receive subsidies and benefits, helping to end inefficiency and corruption.
Machine learning is clearly a revolutionary technology, but its true potential is still unclear. So far, it acts as an aid to researchers, aggregating data and producing summaries.
However, machine learning could even advance to levels of innovating on its own. For example, instead of diagnosing a disease, machine learning could help find the cure to one. In the next decade or so, the world will wait and see where this amazing technology can take it.
– Henry Gao