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[author here] It's true that I didn't present a lot of concrete evidence of GPT-4's limitations. This is a blog post, not an academic paper. To my mind, the m
by snewman 4y ago
[author here]
It's true that I didn't present a lot of concrete evidence of GPT-4's limitations. This is a blog post, not an academic paper.
To my mind, the most concerning thing about GPT-4's performance on these two examples is not that it got the wrong answer, but that it utterly, utterly failed to understand that it was having difficulty. Even after repeated hints and prompts, it continues to make exactly the same mistakes, with no attempt to reason more carefully. There are plenty of other examples around of GPT-4 (to say nothing of earlier releases) having similar troubles.
If you scroll down to the Further Reading section at the very end of the post, you'll find a reference to an extensive paper from Microsoft Research that comes to similar conclusions regarding GPT-4's limitations (I found this only after writing the initial draft of the post). For instance:
> These examples illustrate some of the limitations of the next-word prediction paradigm, which manifest as the model’s lack of planning, working memory, ability to backtrack, and reasoning abilities. The model relies on a local and greedy process of generating the next word, without any global or deep understanding of the task or the output. Thus, the model is good at producing fluent and coherent texts, but has limitations with regards to solving complex or creative problems which cannot be approached in a sequential manner.
The main point I tried to make is that GPT-4's "nearly superhuman" performance on a wide variety of tasks is somewhat illusory, and leans heavily on memorization. I spelled out some reasons why I think it looks more intelligent than it is. Relative to past work in AI, it is extremely impressive. Relative to the threshold required to perform economically useful work, it's... mixed; we're already seeing useful applications, but I think the majority of "information worker" tasks are still beyond it, and I'll go ahead and predict that the same statement will hold for GPT-5.
- nicpottier 4y agoI think your article is a worthy read and has some fun examples. I'm not so sure I agree with your conclusion though. The limitations you pose both in your article and here are real but already have answers in some cases. For one equipping GPT with upgrades for various tasks helps tremendously, IE, Wolfram Alpha for math, wikipedia to refresh its memory on facts etc.. I think you pointed out a really interesting edge case for the goat puzzle but also showed that with different words it CAN reason correctly but trusts it's memory more (and I correctly). I think you are setting far too high a bar with "majority of information worker tasks". My wild ass guess is that with some thoughtful design and focus as it stands today GPT-4 could do 25% of tasks (or make workers 25% more efficient). The economic impacts of that are vast and I'm not particularly optimistic about who will benefit most from them. That's what the fuss is about IMO, not that we have some kind of AGI on our hands, which is far too high a bar.
- danaris 4y ago> For one equipping GPT with upgrades for various tasks helps tremendously, IE, Wolfram Alpha for math, wikipedia to refresh its memory on facts etc.. Which is interesting in terms of what we can do practically for now, but I don't think it says anything about GPT's capability as an AI agent. All it says is that humans are good at hooking together individual specialist agents to pass off tasks to each other (which only works as long as those individual specialist agents continue to work the same way—not a guarantee when they're all owned by different organizations!).