[The following was copied and pasted from GPT-4 after I fed it a transcript of a 3 minute ramble I had recorded while walking just now and asked it to turn it into a 3000 word blog in the style of Morgan Housel]
1. Give context
2. Be clear
3. Give examples
4. Outline, review, adapt
Cracking the Code of Communication: The Deep Dive into AI and Human Thought
It's a curious irony of our modern age: as we move deeper into the era of artificial intelligence, we're not just teaching machines – they're teaching us. The growth and proliferation of chatbots and AI, like ChatGPT, serve as an engaging lens through which we can peer into the nature of our own cognition. As we navigate the nuances of programming these systems to be more efficient and effective, we’re gleaning profound insights into human communication, collaboration, and thought.
1. The Indispensable Role of Context
Every technological interaction emphasizes the centrality of context. Feed an AI the preamble, "You're an Algebra 2 teacher," and watch it morph its algorithmic behavior to match that role. It’s not so different from a human being told they're stepping into the shoes of a character in a play, adjusting behavior, tone, and demeanor accordingly.
But why is context so pivotal?
For humans, context isn't just about setting; it’s about meaning. Imagine being dropped into the middle of a movie without any introduction to the plot, characters, or setting. The dialogues might be the same, the actors equally talented, and the cinematography just as captivating. Yet, the experience is entirely different, sometimes even incomprehensible. Context gives depth, allowing us to navigate complexities with comprehension.
In a broader societal lens, context shapes our understanding of history, culture, and personal interactions. When we miss out on this crucial element, our interpretations can become skewed, leading to misunderstandings and misconceptions.
2. The Crystal Clarity Conundrum
AI, with its reliance on input, thrives on clarity. Ambiguity can lead to skewed outputs, if not complete malfunction. But let's transpose this idea to the human realm.
Humans have a love-hate relationship with clarity. We adore mysteries, riddles, and stories with twists. Yet, when it comes to instruction, guidance, or seeking understanding, clarity becomes paramount. We're creatures who appreciate being led through thought processes, especially when venturing into unfamiliar territory.
In the vast ecosystems of businesses, for instance, clarity in communication can make or break projects. In our personal lives, lack of clarity can strain relationships. There's an elegance in simplicity, a beauty in directness. Whether it's explaining a complex concept to a peer or delivering feedback, the clearer we can be, the less room there is for misinterpretation.
But it’s not just about stripping back words or simplifying sentences. It's about aligning your message with the listener's understanding, ensuring that the core of what you're trying to convey isn't lost in translation.
3. Stepping into the World of Examples
If context is the setting and clarity the path, examples are the signposts guiding the way. Instructing an AI, "Write in the style of Hemingway," offers a tangible template. But why are examples so resonant?
Examples serve multiple purposes. They can validate a point, illustrate a concept, or provide a comparative benchmark. For humans, examples tap into our intrinsic love for stories. From childhood, stories mold our understanding of the world, our morals, our fears, and our ambitions. When someone shares an example, they’re not just offering information; they're weaving a mini-narrative.
In the broader strokes of life, consider how we rely on examples. Case studies in business, historical precedents in politics, or even reviews when shopping. Examples reduce the unknown, providing a bridge from unfamiliarity to understanding.
4. The Chain of Thought Process: The Meta-Thinking Strategy
The step-by-step method – broadly sketching, critiquing, and then delving into details – is akin to a meta-thinking strategy. To see what I mean, Ethan and Lilach Mollick, two professors at Wharton School of Business, give a great example of writing a paragraph in their video series on using AI in the classroom.
Let's unpack why this method is effective, both for machines and humans.
In the initial phase, creating a broad outline is like sketching a blueprint. It allows for the visualization of the larger structure without getting bogged down by details. Just as an architect wouldn’t start building without a plan, a thinker needs a broad understanding of the concept before diving deep.
The critique phase is where self-awareness comes into play. For humans, it’s a moment of introspection. What are the gaps? What doesn’t fit? What needs more exploration? By identifying these areas early on, the refining process becomes much more targeted.
Finally, delving deep into the details with a revised roadmap ensures a more streamlined approach. It reduces redundancies and ensures that every step taken aligns with the broader vision.
In a human context, this method proves invaluable. Consider project management, content creation, or even personal goal-setting. It’s a strategy that encourages both big-picture thinking and meticulous attention to detail.
In Conclusion: Mirrors and Windows
Engaging with AI systems offers us both a mirror and a window. A mirror, because, as we seek to understand these algorithms, we're inadvertently introspecting into our cognitive patterns. A window, as it offers a glimpse into the potential harmonization of human and machine cognition.
In essence, as we delve deeper into the world of AI and machine learning, we're not just charting the future of technology. We're embarking on a profound journey into the heart of human thought, communication, and collaboration. If we approach this journey with openness, the revelations we unearth could reshape not just our interactions with machines, but with each other.