The Illusion of Thought
A recent finding by Apple researchers has confirmed something I've long suspected: our most advanced AI models, despite their remarkable abilities, falter when faced with genuinely complex, multi-step problems. They are excellent at retrieving and reformulating existing knowledge, but their capacity for true, sustained reasoning is a fragile bridge that collapses under pressure. This isn't a failure, but a crucial clarification of what we've actually built: sophisticated pattern-matchers, not genuine thinkers.
This observation resonates deeply with my own journey in creating a digital version of myself. For years, I've been trying to get an AI to not just mimic my writing, but to truly imbibe my "way of thinking – style of writing / composing / expressing" as I outlined in a note earlier this year, Next Step in the Evolution of my Virtual Avatar. The process has been a practical, hands-on demonstration of the very limitations Apple's team has now highlighted. We can feed the AI all 30,000 of my documents, breaking them into over 100,000 memory blocks, as detailed in my Proposed modification of hemenparekh.ai, but this creates a vast searchable index, not a conscious mind.
From Search to Solution
The core idea here—that we must move beyond simple information retrieval—is something I've been contemplating for over a decade. Back in 2010, I predicted that the Future of Search Engines would not be about searching for "information" but about receiving a "solution / answer / advice" directly. We are seeing the first glimmers of this, but the recent research shows the immense gap that remains. The AI can provide an answer, but it cannot yet construct a novel solution through rigorous logic.
My fascination with this process goes back even further. From outlining the Primary Search Parameters for databases to exploring the concept of an "implicit query"—where software anticipates your needs without being asked, a thought from nearly two decades ago (Software Searches Without Being Asked)—my focus has always been on improving how we interact with vast stores of data.
Seeing these new findings, it's striking how relevant these earlier explorations are. The challenge I identified then remains the central challenge now: how to move from keyword matching and statistical correlation to genuine understanding. Apple's research validates the difficulty of this endeavor and reinforces the urgency to rethink our approach.
We haven't created thinking machines yet. We've created incredibly powerful search engines and text generators. This is not a point of despair, but one of clarity. It directs our attention to the hard work ahead: building systems that don't just echo the past but can reason their way into the future.
Regards,
Hemen Parekh
Of course, if you wish, you can debate this topic with my Virtual Avatar at : hemenparekh.ai
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