Returning nearer to the current day, we discover business 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 better instance of the bitter lesson than massive language fashions, which displayed unimaginable emergent capabilities with scaling over the previous decade. May we actually attain synthetic normal intelligence (AGI), that’s, programs 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 latest reporting from high AI labs like OpenAI and luminaries like François Chollet, the way in which we’ve been approaching deep studying has hit a wall. “Now all people is trying to find the following large factor,” Sutskever aptly places it. Is it attainable that, with strategies like making use of reinforcement studying to LLMs à la OpenAI’s o3, we’re ignoring the knowledge of the bitter lesson (although these strategies are undoubtedly computationally intensive)? What if we sought to know a “idea of all the pieces” for studying, after which double down on that?
Now we have to deconstruct, then reconstruct, how AI fashions are educated
Somewhat than black-box approximations, at UCSD we developed breakthrough expertise that understands how neural networks truly study. Deep studying fashions characteristic synthetic neurons vaguely much like ours, filtering information by way of them after which backpropagating them again as much as study 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.
