Entefy recently covered AI disruptions already underway in several industries, like utilities and manufacturing. These are industries where you would normally not expect to find advanced AI systems in place and having a disruptive impact. But as the capabilities of algorithmic systems develop rapidly, the surprise these days is finding an industry not experiencing the impact of artificial intelligence.
The story is no different in traditional industries like agriculture and banking. AI is hard at work untangling difficult, longstanding challenges; automating labor- and time-intensive work; and creating entirely new products and markets.
Here are some of the most impactful uses of artificial intelligence technologies in traditional industries:
Automation systems are already relieving humans of dangerous farming jobs like picking lettuce, which can expose workers to potentially toxic chemicals. AI may also hold the key to the use of automated farming to solve the global food crises by ensuring better crop yields through targeted farming strategies. Drones can already collect data from vast swaths of farmland to identify which areas are thriving and which are at risk of failing. Some researchers are even attempting to teach drones to cooperate with one another, converging on areas with significant weed problems so they can unleash pesticides on the afflicted sections.
Advances in natural language processing (NLP) have made financial industry self-service systems capable of increasingly complex functions, such as onboarding new customers and assisting them with major loan decisions. Also, machine learning and optical character recognition are further simplifying banking by allowing people to submit financial documents through their smartphones. A customer can snap photos of the documents, and the system will automatically upload the images and extract the relevant information.
Blended learning, in which teachers use technology to enhance traditional classroom environments, is gaining prominence in American schools, thanks in large part to AI. Technologies such as machine learning and NLP create the potential for AI-based lifelong learning companions. These programs would tailor their content to individual students based on the subject areas a child struggles with and which lessons are most effective. AI already serves as a kind of digital teaching assistant, taking over tasks such as grading homework and papers so that teachers can focus more deeply on lesson planning and student engagement.
A powerhouse combination of machine learning and DNA sequencing could lead to food products that help people manage chronic disease. We’re not talking kale and blueberries here, either – these superfoods would be developed around specific peptides and how they impact diseases such as high blood pressure and Type-2 diabetes. The speed of AI-powered analysis could advance a field of study that has long grappled with slow results and extremely high costs, and could lead to breakthroughs in nutrition.
AI is augmenting government work across the spectrum, from data entry to disease outbreak responses. Cognitive applications based on neural networks now analyze data anomalies that impact terrorist threat levels or signal shifts in the markets, events that require urgent government attention. Real-time tracking is also helping the government improve medical outcomes by identifying clusters of serious disease outbreaks. The military is developing technologies that can assess soldiers’ wounds based on data collected through wearable technology, enabling medics to prioritize treatments and treat urgent cases more swiftly. In more ordinary cases, sensors on street lights collect real-time data about traffic and maintenance needs and give citizens a heads up when their parking meters are about to expire.
Machine learning is helping doctors make faster, more precise diagnoses by studying medical records and contrasting images of healthy versus diseased organs. This technology could be used to solve the global caregiver shortfalls with better medical diagnosis and healthcare. In 2015 alone, China’s 80,000 radiologists saw 700,000 new cases of lung cancer. Fortunately, AI programs that can identify lesions and other disease markers are helping radiologists and doctors make earlier diagnoses and therefore prescribe treatment sooner.
The use of AI in the legal discovery process is becoming more mainstream. Technology is expanding into other areas as well, including predictive analysis and contract reviews. The former could prove especially valuable to companies as they determine whether to go to trial and assess their risks. Knowing the likely outcome of a case could save significant resources and shape better policies down the road.
Although lawyers must be involved in contract reviews, legal industry machine learning platforms can decrease the time lawyers spend on those tasks by 20% to 60%, allowing them to focus on high-level tasks only humans can perform. Litigation strategist James Yoon said clients are still willing to pay a premium for complex, high-stakes legal services. “For the time being, experience like mine is something people are willing to pay for. What clients don’t want to pay for is any routine work.”
AI is literally saving lives in the nonprofit world. One suicide prevention hotline uses machine learning for the greater good to identify the phrases most often associated with emergency cases so it can prioritize those messages and respond faster to people in need. Another nonprofit, this one aimed at improving students’ writing, uses natural language processing to address users’ problems with sentence fragmentation. The organization had its system analyze 100,000 grammatically correct sentences, then used an NLP platform to break those down. Once the program learned to distinguish sentence fragments from complete thoughts, it showed an 84% accuracy rate on picking out fragments in students’ writing.
How much privacy would you trade for cheaper insurance? Artificial intelligence is powered by data. And when it comes to data, often more is better. One distinctive aspect of the insurance industry’s adoption of AI is how these companies intend to collect their data. Insurers are turning to sensors that collect data directly from individuals, including technologies like in-home monitors, automobile transponders, and wearables. These new data sources open the doors to new products and pricing models, but whenever data collection intersects with a real person’s life, privacy questions emerge.
As you’ve seen from the variety and scope of this list, AI is being used to tackle challenges large and small—creating new opportunities for innovation at companies around the world.