IoT (Internet of Things), AI (artificial intelligence), and ML (machine learning). All of these words are changing every industry at a very rapid clip. If you read this column a few weeks ago, you learned that machine learning is an application of artificial intelligence, in which computer systems “learn” by making data-driven decisions.
The term “machine learning” was coined in the 1950s by Arthur Lee Samuel, who gave the world a successful early demonstration of self-learning and AI through his Samuel checkers-playing program. Fast forward to today, and there’s so much happening in the world of AI and machine learning.
In healthcare, for instance, researchers have developed a machine learning tool that will determine people who are at risk for an opioid use disorder in the next 12 months. We all know the opioid epidemic is at such a critical crisis that if it’s not addressed soon, who knows what will happen. The good news is that the tool analyzes diverse risk factors and applies predictive models using machine learning that can actually enhance healthcare decisionmaking.
Applying predictive algorithms based on machine learning and health data just might go a long way in reducing healthcare costs by helping us address potential issues before they even become problems.
Academia is going to play an important role in pushing the boundaries of machine learning and AI in industries like healthcare, infrastructure, and beyond.
In one example, The University of Manchester was recently awarded a pair of multi-million-pound research grants to work with industry partners Astra Zeneca and AkzoNobel. The AkzoNobel partnership in particular will apply cutting-edge machine learning technologies to help develop new coatings for medications that are more sustainable.
In infrastructure, NMGroup, which is a Trimble company, is undertaking an academic research project in the form of a “knowledge transfer partnership” with the U.K.’s Durham University. The project will explore how machine learning and deep neural networks can more accurately and reliably extract useful information from spatial data.
Spatial data can include things like the location of infrastructure, vegetation, buildings, power lines, and so on. Innovation in this arena just might go a long way in benefitting utilities looking for safer, more efficient, and more reliable power networks.
In academia, it’s not just the researchers who are making a difference. Students are also doing some really cool things with machine learning and AI. For instance, a 13-year-old boy in Portland, Ore., is looking for ways to apply AI and machine learning to the treatment of pancreatic cancer.
Rishab Jain is a finalist in the discovery education 3M young scientist challenge, which aims to foster scientific interest in American children grades 5-8. Specifically, this bright young man is looking to improve pancreatic cancer patient outcomes by applying AI and machine learning to inform MRI-guided radiation therapy.
Essentially, 13-year-old Jain aims to minimize radiation exposure to patients’ healthy tissues and maximize the treatment’s ability to kill cancer cells.
In another example, two PHD students studying at the Lawrence Berkeley National Lab are applying machine learning to take on data challenges associated with climate research.
The two students, Grzegorz Muszynski and Adam Rupe, are focusing their research on finding more effective ways to detect and characterize extreme weather events, and then develop more efficient methods for analyzing climate-related data. Both students are leveraging machine-learning-enabled automated pattern recognition to accomplish their goals.
All of these examples of machine learning’s applications vary tremendously, and our imaginations are the limit when it comes to the ways we can address key pain points in today’s society. So let’s awaken more innovators to uncover more amazing solutions.
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