AI can save businesses time and money. . . and it can save lives too
Artificial Intelligence is seemingly a technological Swiss Army Knife. Its applications range from locating important documents to guiding the preparation of a gourmet dinner. As useful as this may be, the real merit of AI arguably lies outside of developed economies. . . namely in addressing the life threatening problems that are part of everyday existence. Developing areas have to work with insufficient resources, hugely affecting quality of life. AI is already proving that it can alleviate these issues, but this relies on open collaboration. The Baobab Network was founded two and a half years ago to do just that, broking enriching global partnerships between big corporations and emerging tech startups to drive business development.
“When we can find businesses building big data sets and using AI, it’s pretty exciting. We enjoy getting involved with them because of the impact they can have,” says cofounder Tom Fairburn.
The Baobab Network now works across healthcare, financial services, agriculture, education and clean energy. But what problems is AI solving, and which companies are driving positive disruption?
1. Preventing the spread of disease
AI can pool together data from ecology, biology and the environment to form an accurate digital model of real world scenarios. Combined with the technology’s predictive capabilities, this information could be used to determine where viruses are most likely to break out. This technique isn’t just for humans, either. CROPTIX, a Penn State University spinoff, has developed a mobile spectrophotometer that can identify diseases in plants that appear healthy to the human eye. CROPTIX will deliver tailored SMS alerts to 350,000 Kenyan farmers by July, in the hope of reducing crop failures.
2. Preventing drought and famine
Much in the same way that machine learning algorithms could help to predict the spread and severity of disease, they could also forecast environmental hardships like famine and drought. Californian startup Harvesting uses machine learning to analyse satellite data of the earth’s surface. The aim is to flag up areas where resources like water, for instance, are low. Aid organisations and local farmers can then be warned about potential dry spells, allocating provisions and making preparations.
3. Accelerating agriculture
Developing countries rely on the efforts of local farmers. However, small-holders have to overcome a number of obstacles to provide sufficient output, and for many this seems an impossible task. Artificially intelligent tools like image recognition can make working in the unpredictable world of agriculture less of a gamble through weather forecasts, predicted crop yields, and by offering prescriptive analytics. Indian company Rallis, for example, uses drone technology fuelled by agricultural metrics to spread pesticides efficiently. According to Fairburn, The Baobab Network works with a number of companies that use AI for agriculture, collecting data from farms about soil quality and input requirements.
4. Streamlining medical diagnosis
Artificial intelligence doesn’t need to come in the form of a scarily realistic robot to be hugely effective. Ubenwa, a mobile app, uses an embedded machine learning model to analyse the soundwaves of babies’ cries to detect asphyxia. Making high quality software accessible in this way remedies the lack of medical resources in healthcare facilities, and is part of a wider research effort to enable people to track their own health. Stanford University has built an artificially intelligent programme that can diagnose a skin condition at the same level as a clinical screening. The end goal is to develop an app that can be used by global mobile users, which is especially important for those without access to reliable medical advice. . . and it’s not always about the illnesses that you can see.
“One of the startups we work with is using AI to help people suffering with mental health in Kenya, offering access to potential solutions like a network of psychiatrists,” says Fairburn, “They use it to assess social media, and to analyse the signs that can reveal poor mental health. We’re in the scoping phase at the moment and will be working with them in June this year.”
5. Resource provision
Artificial Intelligence can be used to work out what is likely to happen – say, for instance, an outbreak of a certain virus – but also to suggest a course of action in order to react effectively to any given situation. The ability to prescribe a response based on a range of metrics from different sources enables artificially intelligent systems to track demand, consider logistics and formulate a strategy. Prescriptive analytics is particularly helpful in that it also considers the consequences of any given action – so, for example, if an aid organisation sends medical supplies to one healthcare centre, it will mean cutting back in others.
AI may have grown up in the West, but its global application is already changing the lives of countless people across the globe. Before LEDCs can fully benefit from AI, though, the technology must be supported by infrastructure. There is still concern over the level of education and expertise needed to create the necessary conditions for adoption, as well as the negative impact of automation on largely low skilled workforces. Fairburn believes that encouraging technological business growth outside of the developed world presents a huge opportunity for Western companies.
“The private sector is a really powerful mechanism to make change in the developing world because often their governments move slower. There are big problems to solve and sometimes the best way to do that is to build a business around it, because you’re not relying on aid or NGO funding. It adds a lot to the world. These are high growth markets, and there are a lot of consumers buying new things,” he says.
It’s easy to assume that developing countries aren’t ready for an influx of technology, but blossoming tech hubs in traditionally under developed areas beg to differ. . . and the application of AI, says Fairburn, has seen particular expansion.
“I’d say it’s probably more common than people would think. If you can get good data sets, and you need them analysed, then using innovative technology to do that is great. We’re seeing it more and more.”
How else could AI address the challenges that face less economically developed countries? Should major tech companies invest in philanthropic projects outside of the western world? Can benevolent applications help AI to build an ethical ethos? Share your thoughts and opinions.
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