Returning nearer to the current day, we discover industrial growth of AI beholden to “The Bitter Lesson.” After Nvidia’s CUDA enabled environment friendly tensor operations on GPUs and deep networks like AlexNet drove unprecedented progress in different fields, the beforehand various strategies competing for dominance in machine studying benchmarks homogenized to solely throwing extra compute at deep studying.
There’s maybe no higher instance of the bitter lesson than massive language fashions, which displayed unbelievable emergent capabilities with scaling over the previous decade. May we actually attain synthetic normal intelligence (AGI), that’s, methods amounting to the archetypal depictions of AI seen in Blade Runner or 2001: A House Odyssey, just by including extra parameters to those LLMs and extra GPUs to the clusters they’re educated on?
My work at UCSD was predicated on the idea that this scaling wouldn’t result in true intelligence. And, as we’ve seen in current reporting from prime AI labs like OpenAI and luminaries like François Chollet, the way in which we’ve been approaching deep studying has hit a wall. “Now everyone is trying to find the subsequent massive factor,” Sutskever aptly places it. Is it attainable that, with methods like making use of reinforcement studying to LLMs à la OpenAI’s o3, we’re ignoring the knowledge of the bitter lesson (although these methods are undoubtedly computationally intensive)? What if we sought to know a “idea of every little thing” for studying, after which double down on that?
Now we have to deconstruct, then reconstruct, how AI fashions are educated
Quite than black-box approximations, at UCSD we developed breakthrough expertise that understands how neural networks truly be taught. Deep studying fashions characteristic synthetic neurons vaguely much like ours, filtering information by way of them after which backpropagating them again as much as be taught options within the information (the latter step is alien to biology). It’s this characteristic studying mechanism that drives the success of AI in fields as disparate as finance and healthcare.
