We live in an age of rapid disruption and constant transformation. The public is accustomed to seeing the “next big thing” evolve from concept to use case in just a few years. Thanks to recent explosions in computing power and data availability, data science applications like machine learning are no exception to this trend. The quickening pace of discoveries encourages bold visions of highly personalized AI assistants and “digital twin” virtual realities in the future. But are these buzzy visions preventing us from seeing the innovation in data science that improves our world today?
Nearly every field and industry is now drawing on the data scientist’s toolbox, comprising technologies that span basic algorithms to cutting-edge neural networks. But the most groundbreaking work may not make its way to the front pages or your (algorithm-powered) newsfeed. That’s because they typically enhance, rather than replace, existing products and services. The latest or greatest algorithms don’t necessarily define data science innovations; instead, pairing applications like deep learning with current technologies push the boundaries of what’s possible in a given discipline.
For example, we’ve seen recent advances like algorithm-powered early detection systems for wildfires in California and other regions. Once humans had to shift through hundreds of surveillance photographs per day for signs of fire, AI systems can now flag possible outbreaks. And the more it’s used by dispatchers, the better the system becomes.
Here are four other examples of how data science is already driving technology forward in diverse fields today.
Optimizing agriculture
From the earliest civilizations, humans have used the latest technology to improve farming practices. In 300 BC, it was the iron plow, and in the 20th century, novel chemical fertilizers and pesticides brought industrialized agricultural techniques to nearly every corner of the world. Finding ways to grow more crops with greater efficiency appears to be an integral part of the human experience.
In today’s world, however, the most advanced farmers focus less on external inputs and more on a new kind of natural resource: data. By gathering extensive information on every aspect of their production, modern agribusinesses can make data-backed decisions to generate better outcomes. Data science applications for farming include convolutional neural networks to detect crop disease and deploy Internet of Things (IoT)-connected sensors to monitor soil health. Farmers can achieve even greater predictive power by combining site-specific data and third-party sources on weather and other factors.
Personalizing taste and nutrition
From delivery platforms to grocery logistics, data is already at the heart of the modern food system. Now, machine learning is quantifying our relationship with food at the individual level. Agri-food businesses like Kellog are turning to data science to navigate changing consumer preferences in a post-pandemic world, lining grocery stores with products whose textures, flavors, and packages were devised using AI. Even winemakers are updating their centuries-old practices with the power of analytics.
Data scientists aren’t just crafting tastier snacks, though. Their techniques are also helping solve complex nutrition and health challenges, such as pinpointing the dietary contribution to chronic diseases and creating targeted healthy eating campaigns for social media.
The new avant-garde: analytics art
Much of the dialogue around data science centers on the apparent synergies with STEM fields like life sciences. Without logical or rational goals, the thinking goes, it makes little sense to apply AI principles to creative endeavors. But this is a false dichotomy. Artists across disciplines are increasingly collaborating with (or becoming) data science practitioners.
For example, pioneering musicians have always used technology in the studio; now, artists are experimenting with applications that analyze and extrapolate from human-composed melodies. The application of data science in the arts isn’t limited to music, either; models are learning to create abstract paintings and even creative fiction.
Illuminating ancient and unknown languages
Most of us are familiar with Google Translate, today powered by the GNMT neural translation model. But algorithms are also being used to solve some of the field’s thorniest translation and linguistic challenges. For example, researchers at Boston University are deploying computer vision tools to decipher ancient texts and bridge the translation gap for “under-resourced” languages. Their approach uses deep learning to map languages like three-dimensional space by identifying keywords as “vectors” for bridging the gaps between languages, rather than having AI scour online sources for parallel words and phrases.
Analytics are even extending beyond human communication to the animal realm. The team at Project CETI is applying natural language processing, which assesses fragments of language in context, to thousands of hours of whale noises to identify if their communication features patterns recognizable as words or grammar.
The application of data science to a broad set of fields can improve innovation across the whole realm of human experience. Highlighting just a few ways data science has already contributed to these advances makes us even more optimistic for the eventual impact that ongoing breakthroughs in the data profession will have. At Anaconda, we’re excited to continue our support of this innovation. We can’t wait to see the unique and compelling ways data science will continue to improve our world.
