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Comment by msoad | Hacker News Reader

why can't the model do those things? As someone said, it's like Google-foo. It used to be the case where you had to give Google some context to disambiguate certain queries but now it is really good so I don't have to write anything crazy like site:wikipedia.com etc

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My experience with Google is the exact opposite. It is so poor at interpreting "what I really want" that for it to be really useful, I need to lean harder on google-fu than in the beforetimes. But Google has removed, weakened, or broken many of the operators that were used to do this, so google-fu isn't as possible as it used to be.

Because most people are so unbelievably boring, so statistically predictable and common, that taking off the edge of queries leads to higher click-through rates overall. It's the same pages all the time, and most people don't even bother to scroll to page 2. Of course, people who know what they want, and are best served with diversity and breadth - like you ;) - lose out.

I am absolutely not affiliated but you should seriously consider giving Kagi a try.

I was increasingly frustrated with all the NLPness and operator deprication in google which has been accelerating since at least the 2010s.

But with Kagi it reallys makes me feel like I am back at the wheel. I think for product search it still has some way to go but for technical queries it is just on a whole other level of SNR and actually respects my query keywords.

Huh, my recollection is the exact opposite. I remember the good old days when I could use inurl: link: and explore the website contents fully and drill down further if necessary, compared to now, where google seems to always think to know better than you what you are looking for. If you are not happy with the initial results it gave you, you are pretty much out of options, good luck trying to drill down to some specific thing.

Large language models will probably never be reliable computers. The solution is either providing translation examples (aka, in-context learning, few-shot) or fine tuning a model to return some symbolic method to tell an associated computing environment to evaluate a given bit of math or code.

In many ways this is sort of what humans do. They have something they don't yet know how to do and they go away and learn how to do it.

It definitely feels like I have to work to maintain the state of each algorithmic step when I'm multiplying two three digit numbers in my head. It's a lot easier if I can maintain that state on a piece of paper.

I'm definitely not calculating the path of a projectile in a similar manner when I catch a ball.

I'm definitely not "computing" a sentence when I read it in the same way that I compute the multiplication of those two three digit numbers!

>I'm definitely not calculating the path of a projectile in a similar manner when I catch a ball.

You're not calculating it in a traditional sense, but there's definitely some systems of partial differential equations being solved in real-time.

Because prompts are too general to solve most problems.

Prompt: "Calculate the average price of Milk"

This is far too vague to be useful.

Prompt: "Calculate the average Price of Milk between the years 2020 and 2022."

A little better but still vague

Prompt: "Calculate the average Price in US Dollars of 1 Gallon of Whole Milk in the US between the years 2020 and 2022."

Is pretty good.

For more complex tasks you obviously need much more complicated prompts and may even include one-shot learning examples to get the desired output.

> why can't the model do those things?

Isn't this just an intrinsic problem with the ambiguity of language?

Reminds me of this: https://i.imgur.com/PqHUASF.jpeg

edit: especially the 1st and last panels

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