If you're 50 years old, your personality is a product of 50-ish years. Another way to say this is that humans have a very large context window (that can span multiple decades) for solving the problem of presenting a "face" to the world (socializing, which is something humans in general are specifically good at).

It's a very large context window, but it is compressed down a lot. I don't know every line of insert your PL of choice's standard library, but I do know a lot of it with many different excerpts from the documentation, relevant experiences where I used this over that, or edge cases/bugs that one might fall into. Add to it all the domain knowledge for the given project, with explicit knowledge of how the clients will use the product, etc, but even stuff like what might your colleague react to to this approach vs another.

And all this can be novelly combined and reasoned with to come up with new stuff to put into the "context window", and it can be dynamically extended at any point (e.g. you recall something similar during a thought train and "bring it into context").

And all this was only the current task-specific window, which lives inside the sum total of your human experience window.

> Do they? I certainly don't. I don't know if it's my memory deficiency, but I frequently hit my "context window" when solving problems of sufficient complexity.

Human context windows are not linear. They have "holes" in them which are quickly filled with extrapolation that is frequently correct.

It's why you can give a human an entire novel, say "Christine" by Stephen King, then ask them questions about some other novel until their "context window" is filled, then switch to questions about "Christine" and they'll "remember" that they read the book (even if they get some of the details wrong).

> Can you provide some examples of problems where humans have such large context windows?

See above.

The reason is because humans don't just have a "context window", they have a working memory that is also their primary source of information.

IOW, if we change LLMs so that each query modifies the weights (i.e. each query is also another training data-point), then you wouldn't need a context window.

With humans, each new problem effectively retrains the weights to incorporate the new information. With current LLMs the architecture does not allow this.