191–200 of 1001 posts
> ARC-AGI serves as a critical benchmark for detecting such breakthroughs, highlighting generalization power in a way that saturated or less demanding benchmarks cannot. However, it is important to note that ARC-AGI is not an acid test for AGI
I’m super curious as to whether this technology completely destroys the middle class, or if everyone becomes better off because productivity is going to skyrocket.
Is anyone here aware of the latest research that tries to predict the outcome? Please share - super curious as well
Earlier quoted context omitted.
It doesn't need to be general intelligence or perfectly map to human intelligence. All it needs to be is useful. Reading constant comments about LLMs can't be general intelligence or lack reasoning etc, to me seems like people witnessing the airplane and complaining that it isn't "real flying" because it isn't a bird flapping its wings (a large portion of the population held that point of view back then). It doesn't…
> Reading constant comments about LLMs can't be general intelligence or lack reasoning etc, to me seems like people witnessing the airplane and complaining that it isn't "real flying" because it isn't a bird flapping its wings (a large portion of the population held that point of view back then). That is a natural reaction to the incessant techbro, AIbro, marketing, and corporate lies that "AI" (or worse AGI) is a re…
I've been doing AI things for about 20+ years and llms are wild. We've gone from specialized things being pretty bad as those jobs to general purpose things better at that and everything else. The idea you could make and API call with "is this sarcasm?" and get a better than chance guess is incredible.
Efficiency is now key. ~=$3400 per single task to meet human performance on this benchmark is a lot. Also it shows the bullets as "ARC-AGI-TUNED", which makes me think they did some undisclosed amount of fine-tuning (eg. via the API they showed off last week), so even more compute went into this task. We can compare this roughly to a human doing ARC-AGI puzzles, where a human will take (high variance in my subjective…
> ~=$3400 per single task report says it is $17 per task, and $6k for whole dataset of 400 tasks.
The programming task they gave o3-mini high (creating Python server that allows chatting with OpenAI API and run some code in terminal) didn't seem very hard? Strange choice of example for something that's claimed to be a big step forwards. YT timestamped link: https://www.youtube.com/watch?v=SKBG1sqdyIU&t=768s (thanks for the fixed link @photonboom) Updated: I gave the task to Claude 3.5 Sonnet and it worked first s…
Let me go against some skeptics and explain why I think full o3 is pretty much AGI or at least embodies most essential aspects of AGI. What has been lacking so far in frontier LLMs is the ability to reliably deal with the right level of abstraction for a given problem. Reasoning is useful but often comes out lacking if one cannot reason at the right level of abstraction. (Note that many humans can't either when they…
What's your explanation for why it can only get ~70% on SWE-bench Verified? I believe about 90% of the tasks were estimated by humans to take less than one hour to solve, so we aren't talking about very complex problems, and to boot, the contamination factor is huge: o3 (or any big model) will have in-depth knowledge of the internals of these projects, and often even know about the individual issues themselves (e.g.…
For one, I speculate OpenAI is using a very basic agent harness to get the results they've published on SWEBench. I believe there is a fair amount of headroom to improve results above what they published, using the same models.
For two, some of the instances, even in SWEBench-Verified, require a bit of "going above and beyond" to get right. One example is an instance where the user states that a TypeError isn't properly handled. The developer who fixed it handled the TypeError but also handled a ValueError, and the golden test checks for both. I don't know how many instances fall in this category, but I suspect its more than on a simpler benchmark like MATH.
Earlier quoted context omitted.
It doesn't need to be general intelligence or perfectly map to human intelligence. All it needs to be is useful. Reading constant comments about LLMs can't be general intelligence or lack reasoning etc, to me seems like people witnessing the airplane and complaining that it isn't "real flying" because it isn't a bird flapping its wings (a large portion of the population held that point of view back then). It doesn't…
And look at the airplanes, they really can’t just land on a mountain slope or a tree without heavy maintenance afterwards. Those people weren’t all stupid, they questioned the promise of flying servicemen delivering mail or milk to their window and flying on a personal aircar to their workplace. Just like todays promises about whatever the CEOs telltales are. Imagining bullshit isn’t unique to this century. Aerospace…
O3 High (tuned) model scored an 88% at what looks like $6,000/task haha I think soon we'll be pricing any kind of tasks by their compute costs. So basically, human = $50/task, AI = $6,000/task, use human. If AI beats human, use AI? Ofc that's considering both get 100% scores on the task
Let me go against some skeptics and explain why I think full o3 is pretty much AGI or at least embodies most essential aspects of AGI. What has been lacking so far in frontier LLMs is the ability to reliably deal with the right level of abstraction for a given problem. Reasoning is useful but often comes out lacking if one cannot reason at the right level of abstraction. (Note that many humans can't either when they…
What's your explanation for why it can only get ~70% on SWE-bench Verified? I believe about 90% of the tasks were estimated by humans to take less than one hour to solve, so we aren't talking about very complex problems, and to boot, the contamination factor is huge: o3 (or any big model) will have in-depth knowledge of the internals of these projects, and often even know about the individual issues themselves (e.g.…