[nopinsight]

Thanks! I've analyzed some easy problems that o3 failed at. They involve spatial intelligence including connection and movement. This skill is very hard to learn from textual and still image data.

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.)

[lswainemoore]

> 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.

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.

[MVissers]

Probably just not trained on this kind of data. We could create a benchmark about it, and they'd shatter it within a year or so.

I'm starting to really see no limits on intelligence in these models.

[sungho_]

Doesn't the fact that it can only accomplish tasks with benchmarks imply that it has limitations in intelligence?

[qup]

> Doesn't the fact that it can only accomplish tasks with benchmarks

That's not a fact

[PoignardAzur]

> This skill is very hard to learn from textual and still image data.

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

[2]: https://x.com/_AI30_/status/1870407853871419806