Isn't affirming this capacity for knowing exactly GP's point?
Our own capacity for 'knowing' is contingent on real-world examples too, so I don't think that can be a disqualifier.
Jeremy Narby delivers a great talk on our tendency to discount 'intelligence' or 'knowledge' in non-human entities.[0]
It doesn't understand credit card rewards, it understands how to compose an elementary word problem into algebra
Probe it, go in and ask all sorts of questions to check if it understands credit card rewards, credit cards, rewards, their purpose, can solve math problems on this topic, etc.
Think of things it would get right only if it truly understood, not "common questions on reddit".
When I wrote code for the health care industry, if you had asked me anything deeper about the industry or how to do brain surgery, I couldn’t have answered your question.
Look, you're all over this thread misunderstanding LLMs and rejecting the relatively correct explanations people are giving you. The comment by joe_the_user upthread that you called an oversimplification was in fact a perfect description (randomly sampling from a space of appropriate inputs). That's exactly the intuition you should have.
Do you know the Wason test? The point is that people do not intuitively know how to correctly pick which experiments to do to falsify an assumption. My point is that you are not picking the right experiments to falsify your assumptions, instead you're confirming what you think is going on. You're exactly failing the Wason task here.
Really want to understand language models? Go build a few from scratch.
Don't have time for that? Read Wolfram's post or any of the other similar good recent breakdowns.
Only interested in understanding by playing with it? Great! An experimentalist in the true scientific tradition. Then you're going to have to do good experimental science. Don't be fooled by examples that confirm what you already think is going on! Try to understand how what people are telling you is different from that, and devise experiments to distinguish the two hypotheses.
If you think ChatGPT "understands" word problems, figure out what "understanding" means to you. Now try your best to falsify your hypothesis! Look for things that ChatGPT can't do, that it should be able to do if it really "understood" by your definition (whatever you decide that is). These are not hard to find (for most values of "understand"). Finding those failures is your task, that's how you do science. That's how you'll learn the difference between reality and what you're reading into it.
But to say it’s just a glorified search engine is minimizing the impact it and the likes of it can have on entire industries.
It’s already obviated the need for me to find a junior dev to farm off some scripts that I really didn’t have time to write
https://news.ycombinator.com/item?id=34811353
The relevant part
“However, keep in mind that the value of the rewards can vary depending on how you redeem them, and your personal spending habits and travel preferences may also play a role in determining which card is right for you.”
Does any of that change anything? Not really.
>It doesn't understand credit card rewards
Is this assertion based on anything but philosophical bias surrounding the word "understand"?
>it understands how to compose an elementary word problem into algebra
That's exactly how a human, who may or may not have understood rewards programs beforehand, would solve that word problem.
Dan Dennett is informative here.[0] Yes, consciousness (even your own!) can just be a bag of tricks.
My more complicated question comparing the Amex Hilton Aspire to the Amex Gold was inspired by this question asked over at r/creditcards
https://www.reddit.com/r/CreditCards/comments/112udp3/what_i...
——
To break even on the $250 annual fee, you need to earn Membership Reward points that are worth $250.
Since one Membership Reward point is worth 1.4 cents, we can calculate the number of points we need to earn by dividing $250 by 0.014:
$250 / 0.014 = 17857.14
So, we need to earn 17,857 Membership Reward points to offset the $250 annual fee.
Since we earn 4 Membership Reward points for every dollar we spend on groceries, we can calculate the amount we need to spend on groceries to earn 17,857 points by dividing 17,857 by 4:
17,857 / 4 = 4,464.25
Therefore, you would need to spend $4,464.25 on groceries in a year to earn enough Membership Reward points to break even on the $250 annual fee.
{ thunk: "(async function(query, dispatch){dispatch({type: 'compute'});const fee = 250;const pointValue = 0.014;const breakEven = fee / (pointValue * 4);dispatch({type: 'compute_response'});return {answer: breakEven, solvedProblems: [], computed: true, query: false};})(query, dispatch)", en: 'You need to spend ${answer} on groceries to break even.' }
Rules for thee but not for me?
I’ve been able to just throw my requirements into ChatGPT like I would give it to a junior dev and it came back with the correct answer 99% of the time with code quality and commenting I would expect from a junior dev. It has an “understanding” of the AWS SDK, Cloudformation, the CDK, etc.
Once it generated code that had duplicate code blocks that were only different by its input. I asked it “can you remove duplicated code” and it did the refactoring.
I’ve also I asked it what amounts to your standard middle school math problems and it solved the problem with explanations
And then all that's left is to solve for X