Decoding Human Brain Evolution to Understand AI Common Sense

AI’s common sense has been drawn by generative chatbots like ChatGPT. They’re climbing the ranks, reaching jobs once deemed the sole purview of human grey matter. Yet, for all their linguistic gymnastics, these digital dynamos stumble where even the most average of adolescents would soar—making the logical leaps and deducing the kinds of solutions that seem, to us, as natural as breathing.

Max Bennett, AI entrepreneur, gave the world a tour of this peculiar landscape in “A Brief History of Intelligence: Evolution, AI, and the Five Breakthroughs That Made Our Brains.” 

His quest? To uncover the mystery of AI’s missteps by looking into the organic wonder they’re patterned after: our brain.

Through the saga, with its false starts and misfires, he traces five pivotal moments that shaped our cerebral prowess. Bennett posits that the slow-brewed genius of human evolution could be a template to refine the AI of tomorrow.

In a slice of his work, he zeroes in on how AI, like GPT-3, attempts to shadow the predictive powers of our neocortex. Yet, it fumbles when faced with the elusive nuances of human chit-chat.

Imagine the human brain, sprawling words etched over it, a poetic map of thoughts unspoken.

Now, picture GPT-3 in training, gobbling up words, sentences, whole paragraphs, hungry to guess what comes next. With every guess, its neural net tweaks a tad closer to the bullseye. The repetition is relentless, and through it, GPT-3 emerges, seemingly savvy enough to guess the next word in unfamiliar territory—a trick that mirrors a shade of our cerebral lingua franca.

Max Bennett isn’t just poking fun at AI’s failed moments. He’s spotlighting a well-known mishap in our digital friends’ reasoning—common sense is their Achilles’ heel.

But humans aren’t without their quirks.

Take Tom W., our hypothetical friend; you might peg him as a librarian over a construction worker based on his demeanor and glasses. It’s a classic brain hiccup: overlooking the bigger picture, like the sheer number of construction workers out there.

This hiccup springs from our penchant for mental simulations—we picture a shy Tom and slot him into the stereotype of a librarian. It’s the stage where unconscious biases creep in, where our imaginations paint scenes that miss the mark of reality.

In Contrast to GPT-3

Humans navigate the world of math and language by anchoring symbols to the vivid simulations in their minds, not by rote sequence learning. Think of the mental rehearsals we perform when tallying digits or contemplating a question—this is where the human touch outstrips the current AI.

Yes, GPT-3 can crack some math puzzles, but when humans truly ponder 1 + 1, we’re not just parroting facts; we’re validating them in the theater of the mind—a capability GPT-3 has yet to master.

Our brains boast a dual arsenal: a word-predicting autopilot and a rich, simulating engine. Tests that pit instinct against introspection—like the cognitive reflection test—underscore this duality. Most will flub the “bat and ball cost” riddle or the “machines and widgets”puzzle on autopilot, just like GPT-3.

Whenit comes to language, its power isn’t just in the structure but in how it empowers us to conjure and share entire worlds within our heads—something AI, at its core, isn’t wired to understand.

AI’s struggles with tasks demanding flexibility, imagination, and common sense offer a sober reminder: there’s a profound difference between predicting the next word and understanding a story. The gap isn’t a mere technical glitch; it’s the chasm between simulating knowledge and embodying wisdom.

AI is still a fledgling, echoing our speech, yet to dream our dreams.

Bennett urges patience and humility as we shepherd these digital offspring, hoping one day they’ll inherit not just our words, but the very essence of our thought. For now, though, in the cosmic-like world of language and logic, humans remain the sole guardians.

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