How thinking styles affect Generative AI

AI is in every conversation I am having now! People are talking about how to harness the power of AI to be more productive, efficient, and effective. There's evidence all around us that these systems are augmenting all aspects of the work we do. We will all be affected by generative AI and there are some big questions to be asked that are as deep and contemplative as humanity itself.

Discovering what it means to be human is an ancient question. The richness of what it means to be human has many sides, curiosity, mystery, exploration, our thoughts and emotions, our physical body language, the list is expansive. If we think about our ability to think consciously to navigate the human condition, we strike a field of study that has baffled the best minds. It's an ongoing discovery filled with more questions than answers.

Brain scientists try to understand the brain through a range of techniques including behavioral neuroscience, molecular and cellular neuroscience.  Beyond neuroscience we delve deeply into psychology and other disciplines, such as philosophy or anthropology. Different fields approach the study of human cognition from a range of thinking perspectives, and they all offer insights into thinking processes that will feed AI.

Some thinking models use metaphors to describe and map our thinking. The Whole Brain® Framework and the HBDI® is one such way tool. Developed in neuroscience it allows us to understand our thinking preferences and then explore that in an inclusive way that incorporates other people's thinking. It explores left and right brained thinking and modes and clusters that provide a map to the mind. This is important because as we become more self-aware of our own thinking and limitations, we generate awareness of thinking that is different to ours. We can then look to connect with other styles and use our thinking to enhance our decision making, problem solving, choice creation and communication.

These examples are heavily tested by Herrmann, the originators of the HBDI® and The Whole Brain® Framework. As an accredited practitioner and thinking coach of these programs I have been fascinated by the scope of this method since the day I was introduced to it. One thing I have been contemplating is the relationship between thinking styles and the development of Generative AI and pondering the question, “how does your thinking style impact on the creation of generative AI models?”

We all have our own lens and ergo when we ask AI questions or prompts, the AI algorithm will provide data that has been prompted by that style of thinking. So analytical people will ask analytical prompts, empathic people will explore from a feeling's perspective and so on. Different thinking styles might influence the design and architecture of AI algorithms.

For instance, an algorithm designed by someone with a preference for structured thinking might be more rule-based, while one developed by someone with a preference for holistic thinking might be more adaptive and nuanced.

Edwards De Bono’s 6 thinking hats techniques has taught us to explore problems and decisions using a range of perspectives and the Whole Brain® Framework does the same. In fact, we must employ many diverse types of thinking models to produce human centered AI design that considers employees, stakeholders, customers, suppliers, and users. Placing people at the core of the context, we consider their desires and capabilities then when we are asking AI questions there is less bias, its more transparent, ethical, adaptable, and diverse and inclusive.

It's important to consider the difference between traditional AI and generative AI. Traditional AI are programmed to perform specific tasks on predefined rules and logic. Generative AI models can generate novel and realistic outputs that mimic human creativity.

Generative AI often uses techniques such as deep learning, neural networks, and probabilistic modeling to learn patterns and relationships from large datasets. These models can then generate added content by extrapolating from the learned patterns or by combining existing data in novel ways.

The future is GenAI, and the future direction is multifaceted and as unique as humans. As we feed in more data and think about AI models, the more they evolve and learn. There are still many questions, how might we contribute to the development of more ethical and inclusive AI systems? How might advances in neuroscience or cognitive science shape the development of AI in the coming years? What new challenges and opportunities might arise as a result?

What does this mean for thinking?

To my mind it means that understanding our own mind and thoughts, critical thinking and interrogation of those thoughts will still play an incredibly important part in forming smart informed AI regardless of the creators thinking style. There's an old saying with data and information systems, “garbage in, garbage out”; AI will be as smart, ethical, diverse, informed and inclusive as we make it.

p.s. This article was all my own thinking and did not use GenAI to create it! #thethinkingcoach