Ill add this:

If it is possible for GPT to do 1+1, it can do a large number of them incrementally. It's not smart enough to do it all the time by planning ahead, (you'll have more success if you encourage GPT to have a train of thought reasoning here and here) but it's possible.

Indeed, it just made them more efficient so we need less of them and/or less pay for them.

Why? Each of those tokens is O(1) and it is predicting each one incrementally, taking into account the ones it has just generated. So the full answer has taken O(m) where m is the number of tokens.

If it is possible for GPT to do 1+1, it can do a large number of them incrementally. It's not smart enough to do it all the time (you'll have more success if you encourage GPT to have a train of thought reasoning) but it's possible.

> Moreover, I doubt any human programmer will have troubles with the "Beginner" problems, regardless of their specialization.

Have you not heard about how many fail to pass FizzBuzz interview questions?

While GPT-4 is autoregressive, it takes into account the tokens it has chosen to generate incrementally. So it is only limited to O(1) if it attempts to answer with the correct answer immediately. It can in theory take O(m) steps, where m is the number of intermediate tokens it predicts.

While GPT-4 is autoregressive, it takes into account the tokens it has chosen to generate incrementally. So it is only limited to O(1) if it attempts to answer with the correct answer immediately. It can in theory take O(m) steps, where m is the number of intermediate tokens it predicts.

How do you square that with these observations on fresh problems from Codeforces (further discussion here

A couple options off the top of my head

• Add orientation prediction to the bounding box
• Add keypoints for the 4 actual corners as a prediction
• Postprocess boxes with classical techniques, looking for the outermost corners that fit certain properties
• Do everything classically, and deal with the difficulties you have mentioned in your comment.

The first two require annotations of attributes for each box, and will be predicted directly by the model. Though note that you don't have to do this for every label, you can just not train parts of the model when certain attributes are unlabeled.

Both will require some care in modeling, e.g. orientation can have a loss condition at 360 degrees that you'll want to handle, and regressing keypoints can be done well and not well, reference how corners are modeled. And then of course you'll need to postprocess the model's outputs to align/visualize on an image.

> https://fullstackdeeplearning.com/ ; They have a public discord now, join that

Did you have a link? I'm struggling to find it.