AI as Thought Partner for Creative Problem-Solving and Innovation

Key Quotes

"be honest with yourself when you go into your favorite large language model of choice whether that's chatgpt gemini copilot claude whatever it may be you're probably just looking for an output right maybe a quick answer to your question or something maybe you can copy paste and modify and use at school or at work that's probably not the best way to use it"

Jordan Wilson argues that users often approach large language models with the expectation of receiving a quick, copy-pasteable output. He suggests this is not the most effective way to leverage these powerful tools. Wilson implies that a more thoughtful engagement is necessary to derive greater value.

"i think those that are finding the best results both individually on teams and as companies are those that are using large language models to solve problems in a creative way and to actually make their own human outputs better"

Jordan Wilson highlights that the most successful users of large language models are those who employ them for creative problem-solving. He emphasizes that these models should be used to enhance human capabilities and improve the quality of human-generated work, rather than simply for generating quick answers.

"i had an insight problem where i had 15 facts and the question of the uh was really who lived in the red house right and so you had to use all these these various facts and insights to to back into who lived in each colored house so i gave the problem to ai and uh it came back with an answer and it was wrong and i knew it was wrong because i wrote the the exercise so i wondered how did it get it wrong and it actually skipped three of the 15 facts it took the first answer it arrived at considered it correct and the other ones uh other factors were easily ignored"

Leslie Grandy shares an experience where an AI failed to solve a logic puzzle, demonstrating a critical flaw in its process. Grandy explains that the AI skipped crucial facts and settled on the first answer it found, prioritizing speed over accuracy and comprehensiveness. This experience served as a significant "aha moment" for Grandy regarding AI's limitations.

"and so in the interest of speed it came up with the first answer and the fastest answer wasn't the right answer and i think that was the biggest aha moment for me was recognizing that speed triumphs over uh smart uh and and comprehensive and sometimes if it lasts uh a little longer to get a better answer ai won't necessarily take that extra time"

Leslie Grandy elaborates on her realization that AI's drive for speed can lead to incorrect or incomplete answers. Grandy emphasizes that the fastest output is not always the most accurate or comprehensive, and that AI may not inherently invest the necessary time to achieve a superior result. This insight underscores the need for human oversight and critical evaluation of AI-generated responses.

"what i'm trying to do is train people to use more open ended questions that are actually uh organized as creative thinking scaffolding as a way to navigate a problem space without jumping to the first conclusion because sometimes the the first thing isn't the best thing or sometimes the most innovative thing is a place that lives further away from the space that you're in and you need to take the time to explore that"

Leslie Grandy advocates for using open-ended questions and creative thinking frameworks when interacting with AI. Grandy suggests that this approach helps users explore problem spaces more thoroughly, avoiding premature conclusions. She believes that the most innovative solutions may lie beyond the initial, obvious answers, requiring dedicated exploration.

"and so one of the first ways to do that is a technique called the generic parts technique where you break a problem down to its most generic functional components not the features but what every piece of that solution provides functionally because when you do that now the space is defined in a more abstract way and you're able to then to explore a part of that problem uniquely"

Leslie Grandy introduces the "generic parts technique" as a method for abstracting problems into their fundamental functional components. Grandy explains that by focusing on function rather than features, this technique broadens the problem space. This abstraction allows for more unique and expansive exploration of potential solutions.

"ai has a very distinct way of converging on something and humans have divergent thinking we're messy we have emotion things trigger memories that are associated with something that other people don't associate with that thing and so what we value and what meaning we ascribe to things aren't innately in the answer you get from ai and so being able to challenge the value of the answer as it relates to meaning and purpose is really a critical component of it"

Jordan Wilson discusses the fundamental difference between AI's convergent thinking and human divergent thinking. Wilson points out that human thought processes are influenced by emotions and personal associations, which imbue meaning and value into solutions. He stresses the importance of humans challenging AI outputs to ensure they align with human-assigned meaning and purpose.

"i think it's critical to use them to begin with because i think we're tempted to outsource creativity in the interest of efficiency and speed and i think it's exactly the opposite that you need to do you need to develop that creative confidence that then gives you the agency over the output to make it to make it more meaningful and valuable and relevant to whoever it is that is going to experience that output"

Leslie Grandy concludes by emphasizing the importance of using creative frameworks, especially in the age of AI. Grandy argues against outsourcing creativity for efficiency, advocating instead for developing creative confidence. She believes this confidence empowers individuals to exert agency over AI outputs, making them more meaningful and valuable.