I guess you can say the same thing for the Turing Test. Simple chat bots beat it ages ago in specific settings, but the bar is much higher now that the average person is familiar with their limitations.
If/once we have an AGI, it will probably take weeks to months to really convince ourselves that it is one.
Also, it depends a great deal on what we define as AGI and whether they need to be a strict superset of typical human intelligence. o3's intelligence is probably superhuman in some aspects but inferior in others. We can find many humans who exhibit such tendencies as well. We'd probably say they think differently but would still call them generally intelligent.
Personally, I think it's fair to call them "very easy". If a person I otherwise thought was intelligent was unable to solve these, I'd be quite surprised.
I believe this sort of core knowledge is learnable through movement and interaction data in a simulated world and it will not present a very difficult barrier to cross.
(OpenAI purchased a company behind a Minecraft clone a while ago. I've wondered if this is the purpose.)
Maybe! I suppose time will tell. That said, spatial intelligence (connection/movement included) is the whole game in this evaluation set. I think it's revealing that they can't handle these particular examples, and problematic for claims of AGI.
I'm starting to really see no limits on intelligence in these models.
That's not a fact
I had the same take at first, but thinking about it again, I'm not quite sure?
Take the "blue dots make a cross" example (the second one). The inputs only has four blue dots, which makes it very easy to see a pattern even in text data: two of them have the same x coordinate, two of them have the same y (or the same first-tuple-element and second-tuple-element if you want to taboo any spatial concepts).
Then if you look into the output, you can notice that all the input coordinates are also in the output set, just not always with the same color. If you separate them into "input-and-output" and "output-only", you quickly notice that all of the output-only squares are blue and share a coordinate (tuple-element) with the blue inputs. If you split the "input-and-output" set into "same color" and "color changed", you can notice that the changes only go from red to blue, and that the coordinates that changed are clustered, and at least one element of the cluster shares a coordinate with a blue input.
Of course, it's easy to build this chain of reasoning in retrospect, but it doesn't seem like a complete stretch: each step only requires noticing patterns in the data, and it's how a reasonably puzzle-savvy person might solve this if you didn't let them draw the squares on papers. There are a lot of escape games with chains of reasoning much more complex and random office workers solve them all the time.
The visual aspect makes the patterns jump to us more, but the fact that o3 couldn't find them at all with thousands of dollars of compute budget still seems meaningful to me.
EDIT: Actually, looking at Twitter discussions[1], o3 did find those patterns, but was stumped by ambiguity in the test input that the examples didn't cover. Its failures on the "cascading rectangles" example[2] looks much more interesting.
[1]: https://x.com/bio_bootloader/status/1870339297594786064
Just playing devils' advocate or nitpicking the language a bit...
I’m not even going that far, I’m talking about performance on similar tasks. Something many people have noticed about modern AI is it can go from genius to baby-level performance seemingly at random.
Take self driving cars for example, a reasonably intelligent human of sound mind and body would never accidentally mistake a concrete pillar for a road. Yet that happens with self-driving cars, and seemingly here with ARC-AGI problems which all have a similar flavor.
Also not knowing something is hardly a criteria , skilled humans focus on their areas of interest above most other knowledge and can be unaware of other subjects.
Fields medal winners for example may not be aware of most pop culture things doesn’t make them not able to do so, just not interested
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[1] most doctors including surgeons and many respected specialists, some doctors however do need that skills but those are specialized few and generally do know how to use email
A PHD learnt their field. If they learnt that field, reasoning through everything to understand their material, then - given enough time - they are capable of learning email and street smarts.
Which is why a reasoning LLM, should be able to do all of those things.
Its not learnt a subject, its learnt reasoning.