That is exactly what it doesn’t. There is no “understanding” and that is exactly the problem. It generates some output that is similar to what it has already seen from the dataset it’s been fed with that might correlate to your input.
Ask it to write code that replaces every occurrence of “me” in every file name in a folder with “us”, but excluding occurrences that are part of a word (like medium should not be usdium) and it will give you code that does exactly that.
You can ask it to write code that does a heat simulation in a plate of aluminum given one side of heated and the other cooled. It will get there with some help. It works. That’s absolutely fucking crazy.
Maybe, that really depends on if that task or a very similar task exists in sufficient amounts in its training set. Basically, you could get essentially the same result by searching online for code examples, the LLM might just make it a little faster (and probably introduce some errors as well).
An LLM can only generate text that exists in its training data. That’s a pretty important limitation, which has all kinds of copyright-related issues associated with it (e.g. I can’t just copy a code example from GitHub in most cases).
No, it does not depend on preexisting tasks, which is why I told you those 2 random examples. You can come up with new, never before seen questions if you want to. How to stack a cable, car battery, beer bottle, welding machine, tea pot to get the highest tower. Whatever. It is not always right, but also much more capable than you think.
Ask it to finish writing the code to fetch a permission and it will make a request with a non-existent code. Ask it to implement an SNS API invocation and it’ll make up calls that don’t exist.
Regurgitating code that someone else wrote for an aluminum simulation isn’t the flex you think it is: that’s just an untrustworthy search engine, not a thinking machine
Not consistently and not across truly logical tests. They abjectly fail at abstract reasoning. They do well only in very specific cases.
IQ is an objectively awful measure of human intelligence. Why would it be useful for artificial intelligence?
For these tests that are so centered around specific facts: of course a model that has had the entirety of the Internet encoded into it has the answers. The shocking thing is that the model is so lossy that it doesn’t ace the test.
And global warming correlates with the decline in piracy rates. IQ is a garbage statistic invented by early 20th century eugenicists to prove that white people were the best.
You can’t boil down the nuance of the most complex object in the known universe to a single number.
It’s a computer that understands my words and can reply, even complete tasks upon request, nevermind the result. To me that’s pretty groundbreaking.
That is exactly what it doesn’t. There is no “understanding” and that is exactly the problem. It generates some output that is similar to what it has already seen from the dataset it’s been fed with that might correlate to your input.
It’s a probabilistic network that generates a response based on your input.
No understanding required.
Same
Ask it to write code that replaces every occurrence of “me” in every file name in a folder with “us”, but excluding occurrences that are part of a word (like medium should not be usdium) and it will give you code that does exactly that.
You can ask it to write code that does a heat simulation in a plate of aluminum given one side of heated and the other cooled. It will get there with some help. It works. That’s absolutely fucking crazy.
Maybe, that really depends on if that task or a very similar task exists in sufficient amounts in its training set. Basically, you could get essentially the same result by searching online for code examples, the LLM might just make it a little faster (and probably introduce some errors as well).
An LLM can only generate text that exists in its training data. That’s a pretty important limitation, which has all kinds of copyright-related issues associated with it (e.g. I can’t just copy a code example from GitHub in most cases).
No, it does not depend on preexisting tasks, which is why I told you those 2 random examples. You can come up with new, never before seen questions if you want to. How to stack a cable, car battery, beer bottle, welding machine, tea pot to get the highest tower. Whatever. It is not always right, but also much more capable than you think.
It is dependent on preexisting tasks, you’re just describing encoded latent space.
It’s not explicit but it’s implicitly encoded.
And you still can’t trust it because the encoding is intrinsically lossy.
It can come up with new solutions.
Ask it to finish writing the code to fetch a permission and it will make a request with a non-existent code. Ask it to implement an SNS API invocation and it’ll make up calls that don’t exist.
Regurgitating code that someone else wrote for an aluminum simulation isn’t the flex you think it is: that’s just an untrustworthy search engine, not a thinking machine
Yet it can outperform humans on some tests involving logic. It will never be perfect, but that implies you can test its IQ
IQ correlates with a good number of things though. It’a not perfect but it’s not meaningless either.
And global warming correlates with the decline in piracy rates. IQ is a garbage statistic invented by early 20th century eugenicists to prove that white people were the best.
You can’t boil down the nuance of the most complex object in the known universe to a single number.
Not perfectly you can’t. But similarly to how people’s SAT scores predict their future success, IQ tests in aggregate do have predictive power.
IQ is objectively a good measure of human intelligence. High IQ people have higher educational achievement, income, etc.
Don’t take the effect and make it the cause my guy
“Test it’s IQ”. The fact that you think IQ is a useful test for intelligence tells me everything I need to know
The fact you went out of your way to write it’s when I wrote the correct “its” tells me everything I need to know about your educational achievement