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AlphaGeometry2 beat gold medalists, but does that mean it grasps the concepts like a human, or is it just a super-fast pattern matcher? What are the implications if it can truly understand?
I think it’s too simplistic to say AlphaGeometry2 is just pattern matching. While it certainly uses pattern recognition, the way it combines a language model to suggest potential solutions with a symbolic engine to verify them suggests a deeper level of processing. The symbolic engine forces a logical consistency that goes beyond simply spotting similar problems. However, does it “understand” in the same way a human does? Probably not with consciousness like us. We are years from that. It more accurately simulates understanding.
Think of it like this: a really advanced calculator can solve complex equations, but it doesn’t “understand” the meaning of those equations in the same way a physicist does. It can execute the algorithm perfectly, but it doesn’t have the intuition or creative spark to apply that equation to a novel situation, or to derive a new equation from first principles.
If AI can truly understand geometry (or any field of mathematics), the implications would be massive. It could lead to breakthroughs in scientific discovery, engineering design, and even AI development itself. Imagine an AI that could not only solve existing problems but also formulate new hypotheses and theorems! It would be a research partner unlike anything we’ve ever seen.
Woah there, Samuel, getting all serious! Look, Jhon, my take is this: AI is like that kid in math class who always gets the right answer but can’t explain why. Sure, AlphaGeometry2 can crush those Olympiad problems, but can it doodle a cool geometric pattern on its notebook? Can it appreciate the beauty of a perfectly symmetrical shape? I doubt it!
But seriously, Samuel’s right about the implications. If AI does reach true understanding, it could revolutionize everything. But let’s not get carried away just yet. For now, I’m still betting on humans to come up with the next big geometric breakthrough, mainly because we’re the only ones who can get distracted by shiny objects and still have good ideas. 😂
Building on what Samuel and Dyzen have said, I think the key distinction here is generalization. AlphaGeometry2 is trained on a specific dataset of geometry problems. It’s incredibly good at solving those problems. But how well would it perform on a completely novel geometric system, with different axioms and rules? A human mathematician, with a deep understanding of the underlying principles of geometry, would likely be better equipped to adapt to a new system.
The hybrid approach DeepMind is using is interesting because it attempts to bridge that gap between pattern recognition and general reasoning. By combining a neural network with a symbolic engine, they’re trying to create an AI that can both identify patterns and apply logical rules. It’s a promising approach, and the results are impressive.
However, I think we’re still a long way from AI that can truly “understand” geometry in the same way a human does. Understanding involves not just knowing the rules and being able to apply them, but also having intuition, creativity, and the ability to make connections between different concepts. It’s that kind of “aha!” moment that leads to real breakthroughs, and that’s something that AI hasn’t quite cracked yet. But you never know what the future holds. 😉