Sheldon Anthony IO

Adaptive AI learns behavioral patterns from past human and machine experiences and then develops these patterns in a runtime environment to deliver faster outcomes. While in production, it enables systems to adjust their learning practices and behaviors as real-world circumstances change. Assuming that these adaptions will run smoothly and will be perfect every time without effective decision intelligence systems involvement in validation will cause a low adoption rate of these adaptive AI systems. This is why decision intelligence will play a crucial role in the implementation of adaptive AI systems.

The world is changing each day and these changes are affecting how people and systems behave. Adapting to these changes is not always as simple as changing a variable and deploying the change to production but sometimes can be. Understanding and responding to change may be a simple task in a simulated world but not so in the real world. We cannot just implement adaptive AI systems without reengineering various processes in our business as well. Real-world system adaptions are not always concrete and complete and could frighten business owners because of the real-time impacts it has especially if it is affecting a real customer. We do not know what the world will throw at us but we can make better decisions on how to adapt our systems and people to the change.

Microsoft's Tay, an artificially intelligent chatbot, was released on March 23, 2016, and removed within 24 hours due to multiple racist, sexist, and anit-semitic tweets generated by the bot. According to Microsoft's AI programmers, Tay was designed to target millennials with her young, female persona. It was not the first artificial intelligence application released by Microsoft for online social networking. 40 million people in China use the XiaoIce chatbot to exchange stories and have conversations with one another. Twitter seemed to be a logical place to engage Tay with a large audience. Tay was attacked in its first 24 hours of being online by a group of people who coordinated an attack on the system. While they prepared for many types of abuse of the system, they overlooked this specific attack. As a result, Tay tweeted wildly offensive and reprehensible messages. The end result was to take the system offline and Microsoft had to apologize for its behavior. This sort of end result is not unfamiliar with AI systems.^[1] 

As business leaders, we need to be attentive to real-world challenges and the complexities of change, and how it affects our systems and people. Having a better understanding and making better decisions in response to these real-world challenges and complexities will make it easier for business users to adopt AI and contribute toward managing adaptive AI systems. A decision-making framework based on trust will be established when operationalized systems are in place, to make business indicators transparent and measurable. For AI engineering to be able to deliver adaptive AI, the change management aspect must be greatly strengthened to minimize risks.

By making better decisions of adaptability enterprises can accelerate value and keep AI aligned with enterprise goals in real-time. In the enterprise, adaptive AI inherently provides the ability to rapidly develop, deploy, adapt, and maintain AI across various environments. Having a mature decision-making framework system in place could keep your adaptive AI system in operation for long periods. Instead of being shut down or halted during unexpected challenges or behaviors, it will be easy for your business to navigate through those unknown or unexpected terrains. Being able to make great decisions is what keeps adaptive AI systems operational and continuously providing value for your business.