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> Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed mo

> Progress in AI is not coming from abstract reasoning. It is coming from an increase in raw power, driven by GPUs, and mathematical models that are designed more to harness large numbers and brute force searches for formulas, rather than a high-minded algorithmic embodiment of abstraction. The dead horse that I like to beat is that there has been no progress in AI so far, the past 5-10 years of successes are all about mere perception. If you like the Kahneman "fast and slow" framework, almost without exception the systems we're seeing today are fast, "system 1" responders, which take input and need to make an instant decision. That's freaking awesome, and was way out of reach even 15 years ago, so I'm throwing no shade at all on that achievement! It's astounding how much turns out to be possible in systems like this - a priori, I never would have thought that transformers could do as much as they've proven to be able to, because frankly the architecture is super limited and it's super unlikely that humans are able to extract anywhere near as much meaning from an instant, non-reflective pass over a paragraph of text as GPT-3 does. But there's a lot about those systems that makes them much easier to design and train, not the least of which is that descent via backprop works great as a training strategy when the input and target output are available at the same time. Real "system 2" thought can be spread over minutes, hour, or days, and I don't care how far you unroll backprop-through-time, you're not going to train that effectively without some other innovation by simply following the obvious error metrics. If we can get there we will almost certainly see big data lose its pedestal: it's great to have, but humans don't need to read the entire Internet to understand a web page, that's an artifact of forcing this to be done by a model that doesn't have dynamics. I disagree with Hofstadter's view (at least when he wrote GEB and some of his other classics) that explicit abstract reasoning is the right way to solve any of this; my gut tells me that in the end we're going to end up still using some sort of neural architecture and abstract reasoning rules will be implicitly learned mostly locally based on internal "error" signals that guide state transitions. "Learning how to learn" is probably going to be a big part of that, because current systems don't learn, they are merely trained to perceive by brute forcing weights down the stack. But some serious shifts in research focus will be required in order to break through the weaknesses of today's systems, and they all point more in the direction of reasoning, which is woefully underprioritized in all but a very small handful of niche AI labs today.

jrumbut 6y ago
I wonder frequently about what would happen if we stopped searching for a network architecture that would learn intelligence from training data amd treated it more as an engineering problem, taking these very successful components (GPT-3, an object recognition model, one of the strategy game playing reinforcement learning networks) and then putting enormous human effort into the plumbing between them and an interface between the combined result and the world. At the least, you would learn which domains really do need new architectures and which are basically good enough when used as part of a larger system that can help compensate for the shortcomings.
- jkhdigital 6y ago
  One of my big takeaways from reading GEB was that while higher-level semantics can emerge from any low-level symbolic substrate, the details of how that semantics emerges are not at all simple or obvious or “likely to happen by random chance”. Dawkins’ book The Selfish Gene, published just a few years earlier, is the clearest exposition that I have read of how this process probably played out in terrestrial life: the “semantics” encoded by amino acid sequences correspond to a molecule/cell/organism’s likelihood of surviving and replicating. For all but the simplest and most ephemeral replicators, this generally means accurately predicting environmental conditions. General intelligence, then, would conceivably arise simply due to selection pressures pushing organisms to live in the broadest range of environments. In some sense, this process does sound more like an engineering problem, as the embodiment which “contains” the intelligence is probably not an optional component.
rubatuga 6y ago
Yeah. This kind of mirrors my thought that our visual cortex is excellent at split-second image recognition, for things like identifying predators, dangers, etc, and this lack of "thinking" is why our current generation of neural networks has matched the performance of humans on image recognition tasks. I agree that there is a "time" component necessary for improved thinking or intelligence, and since our current neural networks don't have these dynamics, they are lacking. Extremely deep neural networks, especially with residual connections, IMO are better approximations of "thinking". Models such as ALBERT demonstrate that duplication of the same layer can still perform extremely well on NLP tasks. I'm still intrigued by the Neural Ordinary Differential Equations paper [0] and the research in that direction. I also remember reading about differentiable neural computers, but I haven't followed the progress of that at all. 0: https://arxiv.org/abs/1806.07366 https://arxiv.org/abs/1806.07366

jrumbut 6y ago

I wonder frequently about what would happen if we stopped searching for a network architecture that would learn intelligence from training data amd treated it more as an engineering problem, taking these very successful components (GPT-3, an object recognition model, one of the strategy game playing reinforcement learning networks) and then putting enormous human effort into the plumbing between them and an interface between the combined result and the world. At the least, you would learn which domains really do need new architectures and which are basically good enough when used as part of a larger system that can help compensate for the shortcomings.

jkhdigital 6y ago
One of my big takeaways from reading GEB was that while higher-level semantics can emerge from any low-level symbolic substrate, the details of how that semantics emerges are not at all simple or obvious or “likely to happen by random chance”. Dawkins’ book The Selfish Gene, published just a few years earlier, is the clearest exposition that I have read of how this process probably played out in terrestrial life: the “semantics” encoded by amino acid sequences correspond to a molecule/cell/organism’s likelihood of surviving and replicating. For all but the simplest and most ephemeral replicators, this generally means accurately predicting environmental conditions. General intelligence, then, would conceivably arise simply due to selection pressures pushing organisms to live in the broadest range of environments. In some sense, this process does sound more like an engineering problem, as the embodiment which “contains” the intelligence is probably not an optional component.

rubatuga 6y ago

Yeah. This kind of mirrors my thought that our visual cortex is excellent at split-second image recognition, for things like identifying predators, dangers, etc, and this lack of "thinking" is why our current generation of neural networks has matched the performance of humans on image recognition tasks. I agree that there is a "time" component necessary for improved thinking or intelligence, and since our current neural networks don't have these dynamics, they are lacking. Extremely deep neural networks, especially with residual connections, IMO are better approximations of "thinking". Models such as ALBERT demonstrate that duplication of the same layer can still perform extremely well on NLP tasks. I'm still intrigued by the Neural Ordinary Differential Equations paper [0] and the research in that direction. I also remember reading about differentiable neural computers, but I haven't followed the progress of that at all. 0: https://arxiv.org/abs/1806.07366 https://arxiv.org/abs/1806.07366