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Three Big Problems Companies Are Trying to Solve With AI

February 16, 2018
Artificial intelligence computer brain

Among the many topics discussed at this year’s World Economic Forum in Davos, artificial intelligence (AI) was pervasive throughout the multi-day meeting of some of the most powerful business people and political leaders in the world. The head of Google, Sundar Pichai, even went as far as saying that it was more important to humanity than fire or electricity.

That’s a bold claim, but Pichar may end up being right. Three of the big problems that companies are using AI to help address are disease and medical errors, feeding a growing population and access to clean water.

Disease and Medical Errors

The top three killers in the U.S. are heart disease, cancer and… medical errors? After analyzing eight years of medical death rate data, researchers from Johns Hopkins extrapolated that 251,454 deaths were a result of medical error in 2013, out of a total of 35,416,020 hospitalizations.

Inaccurate or incomplete diagnosis and treatment is one cause of medical errors and some big-name companies are turning to AI to help solve that problem. For instance, IBM (IBM) is offering a range of options using IBM Watson, including one that is focusing on oncology. Physicians and researchers utilize this technology to analyze patient files and Watson then provides evidence-based treatment options.

IBM is also working on a service called Medical Sieve, which is a long-term project to develop an AI-powered assistant that can aid clinical decision-making in radiology and cardiology.  The eventual goal is for Medical Sieve to be able to analyze radiology images to detect disease more reliably and faster than doctors.

Another major tech company, Google-parent Alphabet (GOOG, GOOGL), is applying DeepMind, its AI division, to find solutions for a variety of healthcare problems. Some of its projects are in still in the early stages of research. However, DeepMind eventually sees AI systems playing a major role in healthcare by interpreting a wide range of test results.

“The system is learning how to identify potential issues within these images, and how to recommend the right course of action to a clinician,” according to DeepMind. “As the algorithm processes more images, it refines its understanding and interpretation of the information. It then provides increasingly useful feedback, and segmentation, of the data for the clinicians to use for better diagnoses and treatment.” 

Feeding a Growing Population: Farming and Food Supply

By 2050, the United Nations projects that the world population will hit 9.7 billion, up from roughly 7.8 billion today. Feeding that population will require improved efficiency in farming and food supply chains, and AI may be able to help.

Deere (DE) recently bought Blue River Technology, a company that develops smart agriculture equipment that uses a combination of robotics and AI. So far, the technology is largely being used in lettuce and cotton crops. Instead of mass-spraying crops with herbicides, Blue River’s machines are pulled by tractors and use computer vision to precisely target individual weeds and unwanted plants.

They are also using this technology to thin crops, which helps to maximize yield because farmers typically overplant. Eliminating weaker plants, or ones that are growing too closely together, improves the growth of the remaining crops. DE said its strategic rationale behind acquiring Blue River was that it “accelerates implementation of machine learning technology that enables farm management decisions at the plant level.”

Microsoft (MSFT) is another company that is looking to implement tech solutions for farmers to help them gather data and improve yields. As part of its FarmBeats project, it has been working on low-cost sensors, drones, and machine learning algorithms that are useful for agricultural applications. MSFT also has an alliance with Ernst & Young that is focused on combining their capabilities to develop data-driven solutions for farmers that leverage advances in AI and the Internet of Things (IoT).

Improving yields is one way to help feed a growing population; reducing food waste is another. In the U.S. alone, the Department of Agriculture estimated in 2010 that 30% to 40% of our entire food supply is wasted—this equated to a loss of 133 billion pounds of food, or $161 billion worth that year. Roughly 10% of that food waste is attributed to grocery stores.

Arming grocery stores with technology that predicts product demand may help minimize waste. Kroger (KR) has announced plans to invest an estimated $9 billion on its “Restock Kroger” initiative. Part of that plan calls for increased investment in IoT sensors, machine learning and artificial intelligence to make its operations more efficient and also help reduce waste. KR has a company goal to achieve zero food waste by 2025.

Clean Water: Filtering and Infrastructure

In the U.S., we often take for granted our access to clean water, a problem that is unfortunately still plaguing many parts of the world.

Our access to clean water is something that’s dependent on proper filtering to ensure safety, and infrastructure that works to deliver the water to your faucet.  The U.S.’s water infrastructure is aging and the challenge of replacing, and expanding, is large and pricey. The American Water Works Association estimated in 2011 that it would cost approximately $1.7 trillion through 2050.

The challenge with aging water infrastructure is there isn’t exactly an easy way to check on all of the underground pipes that pump clean water throughout the U.S. That’s something California-based startup Fracta hopes to change. To help water utilities prioritize the pipes that need to be replaced first, Fracta uses machine learning to analyze weather records, soil types and other data to identify pipes that are most likely to fail.

Water Planet, a manufacturer of industrial water filtration systems and control technology, has also developed AI to improve access to clean water. The company has created software that analyzes data from a large number of sensors. They combine this data with historical information to determine protocols for keeping the filters clear and clean. When the water filtration system is not running optimally, it alerts the operators and provides instructions on what steps need to be taken to improve operations.

Takeaways for Investors

These are just three of the many problems that companies are trying to solve using AI capabilities. Over the years, there have been many investment opportunities in companies that are solving problems and providing solutions to the world’s, and consumer’s, challenges. However, that doesn’t necessarily mean that every company that solves a problem is a great investment.

As you can see in the examples above, quite frequently there are a great number of companies, both large and small, public and private, that are working on solutions to the same problems. That can create competitive dynamics where multiple companies are racing to be leaders in a particular niche, whether that’s using AI to diagnose disease, improve crop yields or one of the countless other challenges different industries face.  

In addition to individual companies, investors that are looking to get exposure to companies that are developing AI could look into exchange-traded funds (ETFs) as well. There are quite a few different ones that are focused on artificial intelligence and this would also spread the investment out over a larger number of companies instead of a single one.

When looking for potential investments, companies that are working to solve problems is one place to start. But it’s just that, a place to start. Then comes the work of digging deeper and researching the company.

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