[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 500 word blog in the style of Morgan Housel]
Title: Voice to Text: Unleashing Ideas Trapped in the Mind
It seems I've stumbled upon a new method of capturing and nurturing my fleeting thoughts, one that may just revolutionize my approach to idea generation and sharing. This new tool in my arsenal? A humble walk, a recording app, and Google's cutting-edge AI, GPT.
Let me elaborate.
My mind often bustles with thoughts, insights, ideas - fragments of innovation begging to be explored. In the past, I would diligently jot them down in Evernote or Google Drive, bullet point outlines that held the seeds of future blog posts. The intention was always to revisit these thought-germs, to expand them into coherent, engaging stories that I could share with my readers.
Yet, more often than not, these ideas remained confined to their bullet-pointed prisons, unshared, unexplored, unrealized. The connection between conception and fruition seemed to be lost somewhere in my meticulous note-taking and drafting process.
To tackle this, I turned to an unlikely ally: Google's AI. I began recording my thoughts during walks, converting these voice notes to text using Google's transcription services, and pasting this raw, unstructured thought-data into GPT.
My request to the AI? "Take this and turn it into a 500 word blog post in the style of Morgan Housel."
If you're unfamiliar with Housel, he's an acclaimed author and blogger who seamlessly blends finance and psychology. His clear, engaging style of storytelling is a masterclass in making complex ideas digestible and, importantly, memorable. Borrowing his writing style might seem ambitious, but remember, our ultimate aim is improvement, and to improve, one must have a model to strive towards.
So, how does this process of idea translation via AI work out?
Interestingly, GPT uses something called "cosine similarity" to decide what comes next. In other words, it measures the cosine of the angle between two vectors in a multi-dimensional space to predict the next word or idea. It's a fascinating melding of linguistics and mathematics.
Is the result perfect? Certainly not. The output is essentially an average of all possible responses based on the AI's estimation of word or idea probabilities. But it is something tangible, a completed work, even if it’s a rough version of what was initially in my mind.
And herein lies the beauty of this process. It bridges the gap between intention and action, between idea and creation. These AI-assisted posts may not be flawless, but they're a significant step up from the dormant bullet points languishing in my digital notes.
This approach has breathed new life into my ideas that were previously resigned to obscurity. I'm unearthing these once-forgotten thoughts and giving them a chance to be seen, read, and perhaps even inspire. I'm taking my implicit ideas, making them explicit, and in the process, creating a trove of thought pieces that, while perhaps not perfect, are undeniably my own.
Because in the end, isn't it better to have something imperfect than nothing at all?