Why I am not impressed by A.I.

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From a linguistic perspective, this is why I am impressed by (or at least, astonished by) LLMs!

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Yup, the problem with that iPhone (4?) wasn't that it sucked, but that it had limitations. You could just put a case on it and the problem goes away.

LLMs are pretty good at a number of tasks, and they're also pretty bad at a number of tasks. They're pretty good at summarizing, but don't trust the summary to be accurate, just to give you a decent idea of what something is about. They're pretty good at generating code, just don't trust the code to be perfect.

You wouldn't use a chainsaw to build a table, but it's pretty good at making big things into small things, and cleaning up the details later with a more refined tool is the way to go.

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I can already see it...

Ad:
CAN YOU SOLVE THIS IMPOSSIBLE RIDDLE THAT AI CAN'T SOLVE?!

With OP's image. And then it will have the following once you solve it: "congratz, send us your personal details and you'll be added to the hall of fame at CERN Headquarters"

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We didn’t stop trying to make faster, safer and more fuel efficient cars after Model T, even though it can get us from place A to place B just fine. We didn’t stop pushing for digital access to published content, even though we have physical libraries. Just because something satisfies a use case doesn’t mean we should stop advancing technology.

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Has the number of "r"s changed over that time?

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There's also a "r" in the first half of the word, "straw", so it was completely skipping over that r and just focusing on the r's in the word "berry"

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I asked mistral/brave AI and got this response:

How Many Rs in Strawberry

The word "strawberry" contains three "r"s. This simple question has highlighted a limitation in large language models (LLMs), such as GPT-4 and Claude, which often incorrectly count the number of "r"s as two. The error stems from the way these models process text through a process called tokenization, where text is broken down into smaller units called tokens. These tokens do not always correspond directly to individual letters, leading to errors in counting specific letters within words.

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sounds like a perfectly sane idea https://freethoughtblogs.com/pharyngula/2025/02/05/ai-anatomy-is-weird/

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I think these are actually valid examples, albeit ones that come with a really big caveat; you're using AI in place of a skill that you really should be learning for yourself. As an autistic IT person, I get the struggle of communicating with non-technical and neurotypical people, especially clients who you have to be extra careful with. But the reality is, you can't always do all your communication by email. If you always rely on the AI to correct your tone or simplify your language, you're choosing not to build an essential skill that is every bit as important to doing your job well as it is to know how to correctly configure an ACL on a Cisco managed switch.

That said, I can also see how relying on the AI at first can be a helpful learning tool as you build those skills. There's certainly an argument that by using tools, but paying attention to the output of those tools, you build those skills for yourself. Learning by example works. I think used in that way, there's potentially real value there.

Which is kind of the broader story with Gen AI overall. It's not that it can never be useful; it's that, at best, it can only ever aspire to "useful." No one, yet, has demonstrated any ability to make AI "essential" and the idea that we should be investing hundreds of billions of dollars into a technology that is, on its best days, mildly useful, is sheer fucking lunacy.

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The issue is that AI is being invested in as if it can replace jobs. That's not an issue for anyone who wants to use it as a spellchecker, but it is an issue for the economy, for society, and for the planet, because billions of dollars of computer hardware are being built and run on the assumption that trillions of dollars of payoff will be generated.

And correcting someone's tone in an email is not, and will never be, a trillion dollar industry.

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Sure, but for what purpose would you ever ask about the total number of a specific letter in a word? This isn't the gotcha that so many think it is. The LLM answers like it does because it makes perfect sense for someone to ask if a word is spelled with a single or double "r".

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The dumbed down text is basically as long as the prompt. Plus you have to double check it to make sure it didn't have outrage instead of outage just like if you wrote it yourself.

Are you really saving time?

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If you always rely on the AI to correct your tone or simplify your language, you’re choosing not to build an essential skill that is every bit as important to doing your job well as it is to know how to correctly configure an ACL on a Cisco managed switch.

This is such a good example of how it AI/LLMs/whatever are being used as a crutch that is far more impactful than using a spellchecker. A spell checker catches typos or helps with unfamiliar words, but doesn't replace the underlying skill of communicating to your audience.

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Dumbed down doesn't mean shorter.

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Yes, I'm saving time. As I mentioned in my other comment:

Yeah, normally my "Make this sound better" or "summarize this for me" is a longer wall of text that I want to simplify, I was trying to keep my examples short.

And

and helps correct my shitty grammar at times.

And

Hallucinations are a thing, so validating what it spits out is definitely needed.

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It works well. For example, we had a work exercise where we had to write a press release based on an example, then write a Shark Tank pitch to promote the product we came up with in the release.

I gave AI the link to the example and a brief description of our product, and it spit out an almost perfect press release. I only had to tweak a few words because there were specific requirements I didn't feed the AI.

Then I told it to take the press release and write the pitch based on it.

Again, very nearly perfect with only having to change the wording in one spot.

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If the amount of time it takes to create the prompt is the same as it would have taken to write the dumbed down text, then the only time you saved was not learning how to write dumbed down text. Plus you need to know what dumbed down text should look at to know if the output is dumbed down but still accurate.

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AI is slower and less efficient than the older search algorithms and is less accurate.

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They’re pretty good at summarizing, but don’t trust the summary to be accurate, just to give you a decent idea of what something is about.

That is called being terrible at summarizing.

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y do you ask?

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Why I am not impressed by A.I.