Harrison Durland

1894 karmaJoined


Sorted by New


Topic contributions

I almost clarified that I know some models technically are multi-modal, but my impression is that the visual reasoning abilities of the current models are very limited, so I’m not at all surprised they’re limited. Among other illustrations of this impression, occasionally I’ve found they struggle to properly describe what is happening in an image beyond a relatively general level.

Again, I'd be interested to actually see humans attempt the test by viewing the raw JSON, without being allowed to see/generate any kind of visualization of the JSON. I suspect that most people will solve it by visualizing and manipulating it in their head, as one typically does with these kinds of problems. Perhaps you (a person with syntax in their username) would find this challenge quite easy! Personally, I don't think I could reliably do it without substantial practice, especially if I'm prohibited from visualizing it.

Just because an LLM can convert something to a grid representation/visualization does not mean it can itself actually "visualize" the thing. A pure-text model will lack the ability to observe anything visually. Just because a blind human can write out some mathematical function that they can input into a graphing calculator, that does not mean that the human necessarily can visualize what the function's shape will take, even if the resulting graph is shown to everyone else.

I wouldn't be surprised if that's correct (though I haven't seen the tests), but that wasn't my complaint. A moderately smart/trained human can also probably convert from JSON to a description of the grid, but there's a substantial difference in experience from seeing even a list of grid square-color labels vs. actually visualizing it and identifying the patterns. I would strike a guess that humans who are only given a list of square color labels (not just the raw JSON) would perform significantly worse if they are not allowed to then draw out the grids.

And I would guess that even if some people do it well, they are doing it well because they convert from text to visualization.

Can anyone point me to a good analysis of the ARC test's legitimacy/value? I was a bit surprised when I listened to the podcast, as they made it seem like a high-quality, general-purpose test, but then I was very disappointed to see it's just a glorified visual pattern abstraction test. Maybe I missed some discussion of it in the podcasts I listened to, but it just doesn't seem like people pushed back hard enough on the legitimacy of comparing "language model that is trying to identify abstract geometric patterns through a JSON file" vs. "humans that are just visually observing/predicting the patterns."

Like, is it wrong to demand that humans should have to do this test purely by interpreting the JSON (with no visual aide)?

I've been advocating for something like this for a while (more recently, here and here), but have only ever received lukewarm feedback at best. I'd still be excited to see this take off, and would probably like to hear what other work is happening in this space!

I spent way too much time organizing my thoughts on AI loss-of-control ("x-risk") debates without any feedback today, so I'm publishing perhaps one of my favorite snippets/threads:

A lot of debates seem to boil down to under-acknowledged and poorly-framed disagreements about questions like “who bears the burden of proof.” For example, some skeptics say “extraordinary claims require extraordinary evidence” when dismissing claims that the risk is merely “above 1%”, whereas safetyists argue that having >99% confidence that things won’t go wrong is the “extraordinary claim that requires extraordinary evidence.” 

I think that talking about “burdens” might be unproductive. Instead, it may be better to frame the question more like “what should we assume by default, in the absence of definitive ‘evidence’ or arguments, and why?” “Burden” language is super fuzzy (and seems a bit morally charged), whereas this framing at least forces people to acknowledge that some default assumptions are being made and consider why. 

To address that framing, I think it’s better to ask/answer questions like “What reference class does ‘building AGI’ belong to, and what are the base rates of danger for that reference class?” This framing at least pushes people to make explicit claims about what reference class building AGI belongs to, which should make it clearer that it doesn’t belong in your “all technologies ever” reference class. 

In my view, the "default" estimate should not be “roughly zero until proven otherwise,” especially given that there isn’t consensus among experts and the overarching narrative of “intelligence proved really powerful in humans, misalignment even among humans is quite common (and is already often observed in existing models), and we often don’t get technologies right on the first few tries.”

I definitely think beware is too strong. I would recommend “discount” or “be skeptical” or something similar.

Venus is an extreme example of an Earth-like planet with a very different climate. There is nothing in physics or chemistry that says Earth's temperature could not one day exceed 100 C. 
[Regarding ice melting -- ] That will take time, but very little time on a cosmic scale, maybe a couple of thousand years.

I'll be blunt, remarks like these undermine your credibility. But regardless, I just don't have any experience or contributions to make on climate change, other than re-emphasizing my general impression that, as a person who cares a lot about existential risk and has talked to various other people who also care a lot about existential risk, there seems to be very strong scientific evidence suggesting that extinction is unlikely.

Everything is going more or less as the scientists predicted, if anything, it's worse.

I'm not that focused on climate science, but my understanding is that this is a bit misleading in your context—that there were some scientists in the (90s/2000s?) who forecasted doom or at least major disaster within a few decades due to feedback loops or other dynamics which never materialized. More broadly, my understanding is that forecasting climate has proven very difficult, even if some broad conclusions (e.g., "the climate is changing," "humans contribute to climate change") have held up. Additionally, it seems that many engineers/scientists underestimated the pace of alternative energy technology (e.g., solar).


That aside, I would be excited to see someone work on this project, and I still have not discovered any such database.

Load more