Cognitive Flexibility in AI: To Flex or Not To Flex?

While rapidly advancing technologies in?machine learning?and artificial intelligence?(AI) have enabled superhuman performance in narrow domains, current AI lacks?higher-order cognitive abilities?such as cognitive flexibility that enable humans to adapt intelligence across contexts. Whether AI should strive for cognitive flexibility remains hotly debated among researchers, with disagreement over the potential benefits and risks.

Cognitive flexibility refers to an agent's ability to switch between tasks efficiently, generate multiple solutions to problems, and adapt thought and behavior to novel or changing situations (Gyselinck et al., 2019). AI researchers propose that equipping AI with cognitive flexibility could enable more useful,?general intelligence?that handles complex real-world tasks requiring?adaptive cognition?(Minton et al., 2019). However, others warn that too much flexibility could produce unpredictable,?opaque AI?behavior that's difficult for humans to evaluate and poses safety risks (Orseau & Armstrong, 2016).

Flexibility’s Promises

Proponents cite the benefits of cognitive flexibility in AI, including the ability to:

• Efficiently switch between tasks and handle multidimensional goals (Yao & Li, 2022).
• Adapt models by learning from new data without human input (Lake et al., 2017).
• Identify and remedy your knowledge gaps?through self-reflection (Clancey, 2021).

Flexibility’s Perils

Critics cite potential issues, such as:

• Unpredictable AI behavior due to logical flaws or incomplete specifications (Bostrom, 2014).
• Loss of intelligibility and opacity, potentially making systems ungovernable (Gunning, 2017).
• Difficulty evaluating flexible AI's behavior at scale (Orseau & Armstrong, 2016).

While cognitive flexibility could produce more capable AI, care must be taken to avoid risks. Researchers call for multi-disciplinary perspectives and?open debate?to guide the development of "safe" cognitive flexibility in systems deployed in the real world. Further research is needed to balance flexibility and control, exploring methods to rigorously verify flexible behavior while bolstering?AI systems' intrinsic motivation to act in humans' best interests. However, the debate shakes out; narrowing the gap between human and?machine intelligence?requires understanding flexibility's boundaries and potential.