Why I am not impressed by A.I.

I have thought a lot on it. The LLM per se would not know if the question is answerable or not, as it doesn't know if their output is good of bad.

So there's various approach to this issue:

1. The classic approach, and the one used for censoring: keywords. When the llm gets a certain key word or it can get certain keyword by digesting a text input then give back a hard coded answer. Problem is that while censoring issues are limited. Hard to answer questions are unlimited, hard to hard code all.

2. Self check answers. For everything question the llm could process it 10 times with different seeds. Then analyze the results and see if they are equivalent. If they are not then just answer that it's unsure about the answer. Problem: multiplication of resource usage. For some questions like the one in the post, it's possible than the multiple randomized answers give equivalent results, so it would still have a decent failure rate.

Sure buddy

Writing customer/company-wide emails is a good example. "Make this sound better: we're aware of the outage at Site A, we are working as quick as possible to get things back online"

Dumbing down technical information "word this so a non-technical person can understand: our DHCP scope filled up and there were no more addresses available for Site A, which caused the temporary outage for some users"

Another is feeding it an article and asking for a summary, https://hackingne.ws does that for its Bsky posts.

Coding is another good example, "write me a Python script that moves all files in /mydir to /newdir"

Asking for it to summarize a theory or protocol, "explain to me why RIP was replaced with RIPv2, and what problems people have had since with RIPv2"

i'm still not entirely sold on them but since i'm currently using one that the company subscribes to i can give a quick opinion:

i had an idea for a code snippet that could save be some headache (a mock for primitives in lua, to be specific) but i foresaw some issues with commutativity (aka how to make sure that a + b == b + a). so i asked about this, and the llm created some boilerplate to test this code. i've been chatting with it for about half an hour and testing the code it produces, and had it expand the idea to all possible metamethods available on primitive types, together with about 50 test cases with descriptive assertions. i've now run into an issue where the __eq metamethod isn't firing correctly when one of the operands is a primitive rather than a mock, and after having the llm link me to the relevant part of the docs, that seems to be a feature of the language rather than a bug.

so in 30 minutes i've gone from a loose idea to a well-documented proof-of-concept to a roadblock that can't really be overcome. complete exploration and feasibility study, fully tested, in less than an hour.

One thing which I find useful is to be able to turn installation/setup instructions into ansible roles and tasks. If you're unfamiliar, ansible is a tool for automated configuration for large scale server infrastructures. In my case I only manage two servers but it is useful to parse instructions and convert them to ansible, helping me learn and understand ansible at the same time.

Here is an example of instructions which I find interesting: how to setup docker for alpine Linux: https://wiki.alpinelinux.org/wiki/Docker

Results are actually quite good even for smaller 14B self-hosted models like the distilled versions of DeepSeek, though I'm sure there are other usable models too.

To assist you in programming (both to execute and learn) I find it helpful too.

I would not rely on it for factual information, but usually it does a decent job at pointing in the right direction. Another use i have is helpint with spell-checking in a foreign language.

Here’s a bit of code that’s supposed to do stuff. I got this error message. Any ideas what could cause this error and how to fix it? Also, add this new feature to the code.

Works reasonably well as long as you have some idea how to write the code yourself. GPT can do it in a few seconds, debugging it would take like 5-10 minutes, but that’s still faster than my best. Besides, GPT is also fairly fluent in many functions I have never used before. My approach would be clunky and convoluted, while the code generated by GPT is a lot shorter.

If you’re well familiar with the code you’ve working on, GPT code will be convoluted by comparison. If so, you can ask GPT for the rough alpha version, and you can do the debugging and refining in a few minutes.

Ask it for a second opinion on medical conditions.

Sounds insane but they are leaps and bounds better than blindly Googling and self prescribe every condition there is under the sun when the symptoms only vaguely match.

Once the LLM helps you narrow in on a couple of possible conditions based on the symptoms, then you can dig deeper into those specific ones, learn more about them, and have a slightly more informed conversation with your medical practitioner.

They’re not a replacement for your actual doctor, but they can help you learn and have better discussions with your actual doctor.

I mean, I would argue that the answer in the OP is a good one. No human asking that question honestly wants to know the sum total of Rs in the word, they either want to know how many in "berry" or they're trying to trip up the model.

This but actually. Don't use an LLM to do things LLMs are known to not be good at. As tools various companies would do good to list out specifically what they're bad at to eliminate requiring background knowledge before even using them, not unlike needing to somehow know that one corner of those old iPhones was an antenna and to not bridge it.

I think there's a fundamental difference between someone saying "you're holding your phone wrong, of course you're not getting a signal" to millions of people and someone saying "LLMs aren't good at that task you're asking it to perform, but they are good for XYZ."

If someone is using a hammer to cut down a tree, they're going to have a bad time. A hammer is not a useful tool for that job.

Yes

y do you ask?

What situations are you thinking of that requires reasoning?

I've used LLMs to create software i needed but couldn't find online.

Maybe if you focus on pro- or anti-AI sources, but if you talk to actual professionals or hobbyists solving actual problems, you'll see very different applications. If you go into it looking for problems, you'll find them, likewise if you go into it for use cases, you'll find them.

Exactly. The naming of the technology would make you assume it's intelligent. It's not.

Thats because it wasnt originally called AI. It was called an LLM. Techbros trying to sell it and articles wanting to fan the flames started called it AI and eventually it became common dialect. No one in the field seriously calls it AI, they generally save that terms to refer to general AI or at least narrow ai. Of which an llm is neither.

Artificial sugar is still sugar.

Because it contains sucrose, fructose or glucose? Because it metabolises the same and matches the glycemic index of sugar?

Because those are all wrong. What's your criteria?

Something that pretends or looks like intelligence, but actually isn't at all is a perfectly valid interpretation of the word artificial - fake intelligence.

The typo in "strawbery" leads to a conversation like "hey you spelt this wrong there's two r's (after the e) not one"

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".

It wasn't focusing on anything. It was generating text per its training data. There's no logical thought process whatsoever.