In The Intelligence Tsunami (https://www.amazon.com/dp/B0CXH5DDRP), I mention this amazing experience I had writing the book. I would open ChatGPT and Google Gemini, write the word “Accurate?” in the prompt box, and then cut and paste a section of text from the book. Both large language models (LLMs) would analyze the text, tell me whether they thought it was accurate, provide bullet points describing how they came to that conclusion, and provide suggestions if they felt something wasn’t as accurate as it could be. Try that yourself with a piece of text you wrote. You’ll be impressed.
Once upon a time, I was the point person on Wall Street as an executive at the public company KEMET, which had been a subsidiary of Union Carbide. In 1984, a massive gas leak occurred at a Union Carbide pesticide plant in Bhopal, India, exposing over 500,000 people to highly toxic gas and killing over 2,000. The story I heard from the KEMET CEO and repeated to Wall Street analysts many times was that Union Carbide went bankrupt and sold off its non-chemical-related businesses.
I wrote a story for the book about how KEMET's executives all made small fortunes leading the company’s buyout from Union Carbide. I wrote the word “Accurate?” in the ChatGPT and Google Gemini prompt boxes and cut and pasted the text of that story into the LLMs. Amazingly, ChatGPT answered that the story was not quite accurate. Union Carbide had been restructured, but it had not gone bankrupt. I was stunned. That’s not the story I heard from an authority, KEMET’s CEO. That led me to research the issue more. ChatGPT was right! Think about that fantastic ability of ChatGPT to pull that esoteric piece of information from its immense memory at precisely the right time.
Recently, I talked with a design engineer at a global engineering firm. When designs are complete, she and her colleagues write memos documenting their work. Imagine an intelligent agent trained on public building code data and proprietary data from thousands of memos written by this firm's engineers over time. As a design engineer is working, the agent, in real-time, assesses whether changes to the design comply with building codes. This is similar to how spelling and grammar checkers operate as you write text. The agent could also suggest design alternatives, for example, noting that a designer in Portland had dealt with a similar problem five years ago. How powerful would that be?
Mike Switzer and I discussed this amazing capability of intelligent agents on the South Carolina Business Review this week.
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