In 2015, all 193 member countries of the United Nations ratified the 2030 “Sustainable Development Goals” (SDG): a call to action to “end poverty, protect the planet and ensure that all people enjoy peace and prosperity.” The 17 goals – shown in the chart below – are measured against 169 targets, set on a purposefully aggressive timeline. The first of these targets, for example, is: “by 2030, [to] eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day”.
The UN emphasizes that Science, Technology and Innovation (STI) will be critical in the pursuit of these ambitious targets. Rapid advances in technologies which have only really emerged in the past decade – such as the internet of things (IoT), blockchain, and advanced network connectivity – have exciting SDG applications.
No innovation is expected to be more pervasive and transformative, however, than artificial intelligence (AI) and machine learning (ML). A recent study by the McKinsey Global Institute found that AI could add around 16 per cent to global output by 2030 – or about $13 trillion. McKinsey calculates that the annual increase in productivity growth it engenders could substantially surpass the impact of earlier technologies that have fundamentally transformed our world – including the steam engine, computers, and broadband internet.
AI/ML is not only revolutionary in its own right, but also increasingly central to the foundation upon which the next generation of technologies are being built. But the pace and scale of the change it will bring about also creates risks that humanity must take very seriously.
Our research has led us to conclude that AI/ML will directly contribute to at least 12 of the 17 SDGs – likely more than any other emerging technology. In this piece, we explore potential use cases in three areas which are central to the Global Goals: financial inclusion, healthcare and disaster relief, and transportation.
FINANCIAL INCLUSION
Access to basic financial services – including tools to store savings, make and receive payments, and obtain credit and insurance – are often a prerequisite to alleviating poverty. Around 2 billion people around the world have limited or no access to these services.
AI/ML is increasingly helping financial institutions create business models to serve the unbanked. For example, one of the biggest barriers to issuing loans is that many individuals and micro businesses have no formal credit history. Start-ups are increasingly running ML algorithms on non-traditional sources of data to establish their creditworthiness – from shopkeepers’ orders and payments history to psychometric testing. Analysis of data on crop yields and climate patterns can be used to help farmers use their land more effectively – reducing risks for lenders and insurance providers.
AI/ML is also being used to help service providers keep their costs down in markets where revenue per customer is often very small. These include automated personal finance management, customer service chat-bots, and fraud detection mechanisms.
HEALTHCARE AND DISASTER RELIEF
The inequality between urban and rural healthcare services is an urgent problem in many developing countries.
Rural areas with poor infrastructure often suffer from severe shortages of qualified medical professionals and facilities. Smart phones and portable health devices with biometric sensors bring the tools of a doctor’s office to patients’ homes – or a communal location in a village center for shared use. AI then automates much of the diagnostic and prescriptive work traditionally performed by doctors. This can reduce costs, enable faster and more accurate diagnoses, and ease the burden on overworked healthcare workers.
AI is also being used to get medical supplies where they are needed. A start-up called Zipline, for example, is using AI to schedule and coordinate drones to deliver blood and equipment to rural areas in Rwanda (and soon other countries in Africa) which are difficult to access by road. Doctors order what they need via a text messaging system, and AI handles delivery. This dramatically reduces the time it takes to obtain blood in an emergency and eliminates wastage.
When it comes to disaster relief, predictive models – based on data from news sources, social media, etc. – can help streamline crisis operations and humanitarian assistance. For example, AI-powered real-time predictions about where earthquakes or floods will cause the most damage can help emergency crews decide where to focus their efforts.
Read the source article at KDNuggets.
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