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Adaptive AI Use Cases

Adaptive AI was named by Gartner Research as among the top 10 strategic technologies for 2023  – a form of artificial intelligence that learns from the real-time data it ingests and can modify its response to that data.

“It’s built to learn almost autonomously on a regular basis,” said Erick Brethenoux, a VP Analyst at Gartner. These traits make adaptive AI a promising technology — one that’s expected to substantially improve a range of processes across multiple sectors. “ Adaptive AI is useful when change is rapid  – ie: in healthcare and finance, where you need real-time analytics based on changes happening in the data sets,” said Adnan Masood, chief architect of AI and machine learning (ML) at UST, an IT services and consulting company. ” This is where this technology really shines.”

Adaptive AI is powered by various techniques, including ML, reinforcement learning, neural networks, agent-based modeling and evolutionary algorithms. The fact that it’s a mix, or composite, of different AI technologies puts adaptive AI in the broader category of composite AI. This combination of technologies enables Adaptive AI to change its own code in response to changing circumstances and its experiences over time. As such, it can improve its own performance and accuracy as it operates. “Adaptive AI is where you’re working with systems that are capable of learning from the data and changing their behaviors over time,” Masood said. “It is constantly updating itself based on incoming data.”

That stands in contrast to other AI systems. For example, static AI systems feature algorithms that are trained and tested in a development phase and then, once proven accurate and efficient, are locked into place before deployment.

Other types of AI systems are trained on historical, or static, data and therefore do not incorporate information from after the data’s cutoff date. Even ChatGPT runs up against this limitation, with its maker OpenAI acknowledging that its GPT-4 model “generally lacks knowledge of events that have occurred after the vast majority of its data cuts off (September 2021), and does not learn from its experience.”

“The basic idea of Adaptive AI is that it tops off with more current knowledge — it is literally more adaptive to changes in its data set,” said Nicholas Napp, a senior member of IEEE and co-founder of consulting firm Xmark Labs. In addition, Adaptive AI combines its context awareness with other capabilities, such as risk scoring, making it a particularly powerful technology. Brethenoux called it “the most advanced form of AI implementations on the market today.”

AI, including Adaptive AI, can be useful for many business applications.

Adaptive AI is particularly well suited for use in dynamic situations that are chaotic or complex, where it is impossible to anticipate or plan for all possible scenarios. Areas where Adaptive AI is already in use, starting to make inroads or likely to be applied in the future include the following –

Energy companies, utilities and similar organizations are using Adaptive AI to monitor facilities that are difficult to reach and inspect, such as offshore wind turbines, Brethenoux said. “They’re adaptive systems because their behavior is based on what they observe. You can’t program that in advance.”  In this use case, Adaptive AI is used to direct activities and take actions based on varying circumstances. For example, the technology could direct drones to make inspections when it detects ideal weather conditions, process information from those initial inspections, and then direct drones to perform additional inspections or tests based on the results.

Similarly, Adaptive AI can help scan agricultural areas with drones and other equipment that can collect data (ie: images or soil samples) that Adaptive AI systems can analyze to make decisions about the best action to take next.

Adaptive AI is helpful for anomaly detection because it can adapt to make systems much more accurate when circumstances rapidly change, said Manjeet Rege, professor and chair of the software engineering and data science department at the University of St. Thomas. As an example, consider an Adaptive AI system designed to detect credit card fraud. At the start of the COVID-19 pandemic, it could have incorporated information about the lockdowns and recognized that a credit card user’s sudden shift from minimal online shopping to shopping exclusively online was not likely to be a signal of fraud.

Weiguo Patrick Fan, professor of business analytics at the Tippie College of Business at the University of Iowa, pointed to commercial finance as an area benefiting from Adaptive AI systems. Adaptive AI can receive and sort through myriad signals and data sets — from changing financial markets to U.S. Federal Reserve policy announcements to social media trends — and accurately react to unexpected and unanticipated developments.

The U.S. military is already using Adaptive AI to train personnel. As well, the technology is expected to make inroads in other education and training programs because of the important benefits. This includes – assessing student responses, learning what’s most effective for different types of students and use that information to fine-tune the lesson delivered.

Autonomous vehicles, also known as self-driving cars, are already using Adaptive AI to navigate unexpected or unpredictable environments and instantaneously react to changing circumstances — even those they weren’t initially programmed to anticipate. Perhaps more importantly, Adaptive AI learns from those actions so that it can continuously improve its navigation capabilities and responses to ever-changing driving conditions. “It analyzes what it observes, and then can make a decision very, very quickly and then, based on the feedback, can adjust,” Fan said.

In robotics, Adaptive AI can deliver similar capabilities as in self-driving cars: It uses current and historical data to figure out how to best respond in the moment, and then uses its responses to improve its reactions even under changing circumstances. As an example, companies are building companion robots to help support older adults or people with various health issues. Adaptive AI can also enable those robots to tailor their interactions to each individual based on that person’s interests or preferences – then adjust their behavior in future interactions to create an optimal or better experience.

Adaptive AI is being used to create hyper-personalized services for Customers, Brethenoux said. It can also help with content recommendations, such as those offered by streaming services, and product recommendations from retailers by incorporating the most up-to-date data about a Customer’s preferences and circumstances. “It’s always learning about what you like,” said Nicolas Avila, CTO for North America at digital consulting firm Globant.

Adaptive AI is expected to improve the effectiveness of customer service chatbots, which have historically shown mixed results when it comes to providing helpful and appropriate responses to Customer queries. Adaptive AI, however, enables bots to continuously learn from their interactions with Customers. This enables the bots to become more accurate in their assessments of what Customers want, even if Customers pose an awkward or unusually phrased question. That increased accuracy makes it more likely that a bot’s response will meet the Customer’s needs.

Adaptive AI improves supply chain resilience by monitoring for changes in variables, more quickly identifying possible disruptions and other changing market conditions, and adjusting to compensate for those factors, Rege said. Furthermore, Adaptive AI can analyze all those variables to move the right products to the right place at the right time and in the right quantity with the optimal prices to meet Customer demand. Taken together, “this all means minimizing costs in the supply chain and better matching supply and demand,” Rege said.

Adaptive AI can improve diagnoses in healthcare with additional capabilities to identify changes in patients over time and learning how those changes are indicative of diseases in their earliest stages. Likewise, Adaptive AI can improve patient monitoring, advising on preventive care, doing drug discovery, etc.

Masood said he sees potential for Adaptive AI to process healthcare claims more efficiently while simultaneously becoming better at rooting out fraudulent claims. He explained that the technology’s ability to make sense of and learn from complex circumstances could eliminate the manual work needed to review claims that don’t fit within predefined rules.

With extensive knowledge and a lot of siloed information difficult to access or not available in companies, Adaptive AI can better position enterprises to leverage and get more benefits from all information – internal and external.

Organizations can utilize Adaptive AI with digital twins to more accurately understand how changing scenarios could affect the system or person represented by the digital twin. “When you open a digital twin to a wide range of information, you can’t predict everything,” Brethenoux said. “Because it can be confusing or chaotic, that’s where the Adaptive AI can help.”

Interest in Adaptive AI is growing as it proves its value in various areas. Further, as decision makers and influencers learn more about generative AI, the opportunities and use cases grow. Already, Adaptive AI is used in finance, energy and utilities, agriculture, aerospace, the military, traffic management and many other industries. Brethenoux said he expects the types of industries using Adaptive AI will quickly expand because of the technology’s ability to bring order to complex, unpredictable and even chaotic situations uniquely positions to help in many areas. With organizations sitting on a lot of data that they struggle with leveraging, monetizing or activating, AI can extract the value of their data and make Innovation more rewarding.

Dec 13, 2023  by  Mary K. Pratt / CAIL Innovation commentary                905-940-9000