- Like Post
8 more ways you didn’t know data science was used
From pollinating plants to monitoring our digestive systems, here are eight more ways data science is changing the world around us.
A few weeks ago we published a blog about some of the lesser known uses of AI and data science. We talked about how data scientists are using analytics to translate lost languages, fight crime, and even find the secret to human happiness. But it occurred to me as I was writing that piece that we were still really only skimming the surface.
That’s what’s so great about the work we do – the possible applications of data science are endless, and the innovative ideas our colleagues and peers come up with are always capable of surprising us. With that in mind, here are eight more things you might not know data science is used for.
Pollinating the world’s crops
Due to the use of toxic pesticides, habitat loss and poor nutrition, the global bee population is dwindling. This is devastating news when you consider that around 70% of the crops that provide 90% of the world’s food rely on bees for pollination.
To address this problem, researchers have been busy developing robotic bees and drones that can take on the role of pollinating plants and flowers. Most notably, researchers at Harvard recently created a solar-powered robo-bee.
It doesn’t end there either. Right now, computer vision is being used to detect a common parasite in bee colonies, and smart sensors are being used to learn more about how bees interact with their environments.
With further research, these tiny drones and data-driven advances could help solve one of the modern world’s biggest natural problems.
Monitoring chat rooms
The world of online gaming is no stranger to toxic discourse.
With millions of gamers playing online – and a high percentage of them children – it’s important that steps are taken to stop in-game abuse. Thankfully, artificial intelligence might have the answer.
This year, an AI called Minerva was used to identify 7,000,000 toxic messages shared by players of Counter-Strike: Global Offensive. The AI issued 90,000 warnings and banned 20,000 players in a period of just six weeks – and this is just the first step.
Minerva uses machine learning to constantly improve, and its developers – the online gaming platform FACEIT – believe that in the future it will able to “detect and address all kinds of abusive behaviours in real-time”.
This could have a huge impact outside of gaming too, helping to address the problem of abuse on social media, for instance.
Examining our poop
Gastrointestinal issues affect tens of millions of people in the US alone. But one company in the US is fighting back – or perhaps more accurately, fighting bacteria, and using AI to combat them.
Seed Health is a collective of scientists, doctors, innovators, and entrepreneurs currently gathering tens of thousands of user-submitted pictures of faecal matter as part of an aptly titled ‘Give a S—t’ campaign.
Working with gut health startup Auggi, Seed is using these photos to build AI algorithms that combine with computer vision to help people track their own bowel movements and current state of health.
The companies believe that this ‘data dump’ could see tens of millions of people benefit.
Helping us sleep
Sleep is vital to human health. It’s during sleep that your body repairs itself, and sleep deficiency is linked to an increase in heart problems, kidney disease, high blood pressure, diabetes and strokes. However, this is an area of human biology where scientific knowledge still has a long way to go – for the time being, at least.
This year, scientists at Stanford’s Big Data in Precision Health conference held a discussion on data-based approaches to sleep science.
One of the possibilities raised was using a combination of wearable tech and smartphones to establish a ‘daytime score’ of freshness. The thinking behind this is that most sleep tracking takes place overnight, but by combining that overnight data with data about physical alertness the following morning, scientists may be able learn more about the nature of sleep than ever before.
The conference also looked at using genomic and molecular data to identify the causes of certain sleep disorders.
Informing healthcare policy
Informed healthcare policies are vital to the wellbeing of any country, and one lab in Boston is using data science to help make sure the best decisions are made for the citizens of the United States.
Founded in 2015, Harvard’s Health Policy Data Science Lab is run by two Associate Professors of Health Care Policy, Laura Hatfield, PhD. and Sherri Rose, PhD. The collaborative research space uses data science and analytics – namely machine learning and parametric Bayesian models – to provide actionable evidence that influences policy makers.
Past projects have seen the lab provide insights and recommendations into spending goals and explore intrinsic biases in US healthcare.
Driving political change
In recent years we’ve seen a change in the way political campaigns play out, with data-driven targeted ads and ‘fake news’ having a huge impact on voters.
However, this use of data and analytics in politics isn’t as new as you may think. Seven years ago, Barack Obama used data science to great effect in the run-up to his 2012 election victory.
The Obama team used analytics to see which emails had the best effect on potential voters (it was those from Michelle Obama) and analyse the impact different payment platforms have on donation habits.
The analysts found that people who joined the campaign’s ‘Quick Donate’ programme gave almost four times more than others. Armed with this information, Obama’s team was able to optimise its strategies and raise an enormous $1 billion to support the winning election campaign.
Improving public services
Have you ever heard the expression ‘no-collar work’?
In contrast to white-collar and blue-collar work, no-collar work refers to the routine and often mundane jobs that can be taken on by AI.
This can have a major time-saving effect in all sorts of industries, one of which is local government. In this context, AI can be used to answer common enquiries with chatbots, or to automate common processes, like checking official documents or helping citizens apply for permits and pay fines.
The benefits of this are two-fold. Firstly, AI rules out any chance of human error, which is often a problem with repetitive tasks. Secondly, it’s estimated that AI-based tools can save public sector workers upwards of an hour a day – time that can be redirected to help solve more challenging issues.
Breaking new ground in mathematics
Mathematical texts date back as far as 1900BC, so you’d be forgiven for thinking we’ve probably exhausted the possibilities for working out number problems by now. You wouldn’t be far from the truth, either. New mathematical formulas relating to fundamental constants are scarce. Or at least they were, before the introduction of The Ramanujan Machine.
The Ramanujan Machine presents a whole new way of doing maths, using computer power to search for unknown formulas and conjectures. So far it has met with astounding success, aiding the discovery of dozens of new algorithms.
If you’re interested in exploring new mathematic principles, and even having an algorithm named after you, keep up to date with the project’s progress by joining this mailing list.
Learn more about The Smart Cube’s data science capabilities
To learn more about what else is new in the world of data science and the projects we’re working on, explore our blog.
And read about how our solutions can help solve your business problems with data and analytics.
Prasad Kothari is an analytics and data science leader who has worked extensively building high-performing teams for various organizations and has provided consulting to many fortune 500 clients. As vice president of analytics and client solutions at The Smart Cube, he focuses on helping clients realize the value of data science to solve priority business problems, including customer analytics, marketing analytics, RWE/RWD, and supply chain analytics. Prasad has published healthcare data science research papers across leading journals, as well as books on AI. He has collaborated with several universities in the US and given guest lectures on Quantum Machine Learning, NLP/NLU, topological data analysis and computer vision research. He spends his weekends reading AI books and listening to Indian classical music.