zlacker

>>vforgi+(OP)
Imagine thinking that regression based function approximators are capable of anything other than fitting the data you give it. Then imagine willfully hyping up and scaring people who don't understand, and because it can predict words you take advantage of the human tendency to anthropomorphize, so it follows that it is something capable of generalized and adaptable intelligence.

Shame on all of the people involved in this: the people in these companies, the journalists who shovel shit (hope they get replaced real soon), researchers who should know better, and dementia ridden legislators.

So utterly predictable and slimy. All of those who are so gravely concerned about "alignment" in this context, give yourselves a pat on the back for hyping up science fiction stories and enabling regulatory capture.

>>srslac+I7
What do you think about the papers showing mathematical proofs that GNNs (i.e. GATs/transformers) are dynamic programmers and therefore perform algorithmic reasoning?

The fact that these systems can extrapolate well beyond their training data by learning algorithms is quite different than what has come before, and anyone stating that they "simply" predict next token is severely shortsighted. Things don't have to be 'brain-like' to be useful, or to have capabilities of reasoning, but we have evidence that these systems have aligned well with reasoning tasks, perform well at causal reasoning, and we also have mathematical proofs that show how.

So I don't understand your sentiment.

>>chaxor+hB
To be fair LLMs are predicting the next token. It's just that to get better and better predictions it needs to understand some level of reasoning and math. However it feels to me that a lot of this reasoning is brute forced from the training data. Like chatgpt gets some things wrong when adding two very large numbers. If it really knew the algorithm for adding two numbers it shouldn't be making them in the first place. I guess same goes for issues like hallucinations. We can keep pushing the envelope using this technique but I'm sure we will hit a limit somewhere

>>rdedev+3F
> If it really knew the algorithm for adding two numbers it shouldn't be making them in the first place.

You're using it wrong. If you asked a human to do the same operation in under 2 seconds without paper, would the human be more accurate?

On the other hand if you ask for a step by step execution, the LLM can solve it.

>>visarg+yR
am i bad at authoring inputs?

no, it’s the LLMs that are wrong.

>>catchn+f81
Create two random 10 digit numbers and sit down and add them up on paper. Write down every bit of inner monologue that you have while doing this or just speak it out loud and record it.

ChatGPT needs to do the same process to solve the same problem. It hasn’t memorized the addition table up to 10 digits and neither have you.

>>throwu+Vd1
No, but I can use a calculator to find the correct answer. It's quite easy in software because I can copy-and-paste the digits so I don't make any mistakes.

I just asked ChatGPT to do the calculation both by using a calculator and by using the algorithm step-by-step. In both cases it got the answer wrong, with different results each time.

More concerning, though, is that the answer was visually close to correct (it transposed some digits). This makes it especially hard to rely on because it's essentially lying about the fact it's using an algorithm and actually just predicting the number as a token.

>>chongl+MB1
You asked it to use a calculator plugin and it didn’t work? Or did you just say “use a calculator”? Which it doesn’t have access to so how would you expect that to work? With a minimal amount of experimentation I can get correct answers up to 7 digit numbers so far even with 3.5. You just have to give it a good example, the one I used was to add each column and then add the results one at a time to a running total. It does make mistakes and we had to build up to that by doing 3 digit then 4 digit the 5 etc but it was working pretty well and 3.5 isn’t the sharpest tool in the shed.

Anyways, criticizing its math abilities is a bit silly considering it’s a language model, not a math model. The fact I can teach it how to do math in plain English is still incredible to me.

>>throwu+to2
It’s not that incredible to me given the sheer amount of math that goes into its construction.

I digress. The critique I have for it is much more broad than just its math abilities. It makes loads of mistakes in every single nontrivial thing it does. It’s not reliable for anything. But the real problem is that it doesn’t signal its unreliability the way an unreliable human worker does.

Humans we can’t rely on are don’t show up to work, or come in drunk/stoned, steal stuff, or whatever other obvious bad behaviour. ChatGPT, on the other hand, mimics the model employee who is tireless and punctual. Who always gets work done early and more elaborately than expected. But unfortunately, it also fills the elaborate result with countless errors and outright fabrications, disguised as best as it can like real work.

If a human worker did this we’d call it a highly sophisticated fraud. It’s like the kind of thing Saul Goodman would do to try to destroy the reputation of his brother. It’s not the kind of thing we should celebrate at all.