Flames burn and smoke asphyxiates, perfectly highlighting why relying on fire is a bad idea.
I keep having to repeat this, but the conversation does keep going on a loop: LLMs aren’t entirely useless and they’re not search engines. You shouldn’t ask it any questions you don’t already know the answer to (or have the tools to verify, at least).
LLMs are good for some searches or clarification that the original website doesn’t say. Ex the “BY” attribute in creative commons being acronymed to “BY” (by John Doe) and not “AT” (attributed to John Doe)
Or if you’re fine with non-factual answers. I’ve used chatgpt various times for different kinds of writing, and it’s great for that. It can give you ideas, it can rephrase, it can generate lists, it can help you find the word you’re trying to think of (usually).
But it’s not magic. It’s a text generator on steroids.
Sure! Used as… you know, what it is, there’s a lot of fun/useful stuff you can do. It’s just both AIbro shills and people who have decided to make hating on this tech a core part of their personality have misrepresented that.
It’s indeed very, very good text generation/text parsing. It is not a search engine, the signularity, Skynet or a replacement for human labor in the vast majority of use cases.
I had to tell DDG to not give me an AI summary of my search, so its clearly intended to be used as a search engine.
“Intended” is a weird choice there. Certainly the people selling them are selling them as search engines, even though they aren’t one.
On DDG’s implementation, though, you’re just wrong. The search engine is still the search engine. They are using an LLM as a summary of the results. Which is also a bad implementation, because it will do a bad job at something you can do by just… looking down. But, crucially, the LLM is neither doing the searching nor generating the results themselves.
What do you mean its not generating the results? If the summation isn’t generated, wheres it come from?
I dont want to speak for OP but I think they meant its not generating the search results using an LLM
Maybe I just don’t know what “generating results” means. You query a search engine, and it generates results as a page of links. I don’t understand how generating a page of links is fundamentally different from generating a summation of the results?
Its a very different process. Having work on search engines before, I can tell you that the word generate means something different in this context. It means, in simple terms, to match your search query with a bunch of results, gather links to said results, and then send them to the user to be displayed
then send them to the user to be displayed
This is where my understanding breaks. Why would displaying it as a summary mean the backend process is no longer a search engine?
Yeah. Everyone forgot the second half of “Trust, but Verify”. If I ask an LLM a question, I’m only doing it because I’m not 100% sure how to look up the info. Once it gives me the answer, I’m checking that answer with sources because it has given me a better ability to find what I was looking for. Trusting an LLM blindly is just as bad as going on Facebook for healthcare advice.
I thought it was “butt verify” whoops
Trust butt, verify
✅ Verified
I find LLMs very useful for setting up tech stuff. “How do I xyz in docker?” It does a great job of boiling together several disjointed How Tos that don’t quite get me there into one actually usable one. I use it when googling and following articles isn’t getting me anywhere, and it’s often saved so much time.
They are also amazing at generating configuration that’s subtly wrong.
For example, if the bad LLM generated configurations I caught during pull requests reviews are any example, there are plenty of people with less experienced teams running broken kubernetes deployments.
Now, to be fair, inexperienced people would make similar mistakes, but inexperienced people are capable of learning with their mistakes.
Yep. Or because you can recognize the answer but can’t remember it off the top of my head. Or to check for errors on a piece of text or code or a translation, or…
It’s not “trust but verify”, which I hate as a concept. It’s just what the tech can and cannot do. It’s not a search engine finding matches to a query inside a large set of content. It’s a stochastic text generator giving you the most likely follow up based on its training dataset. It’s very good autocorrect, not mediocre search.
honestly LLMs are about a thousand times more useful than Google at this point. Every week i try googling and get nothing but spam results.
for example just yesterday i was searching for how to reclaim some wasted space on one of my devices. so i searched on Google and tried 8 different pages that were ad-riddled hell holes.
i gave up and spent 10 seconds with an LLM and got the answer i needed. i will admit that i had to tell it to quit bullshitting me at one point but i got what i needed. and no ads.
Well, you shouldn’t be using Google Search, but that’s a completely different conversation and the answer shouldn’t (can’t) be “let’s just use LLMs, then”.
bing or duck duck go, too. i just say googling because it sounds stupid as shit to say anything else. DDG is my default search engine. kagi isn’t much better, and comes with its own issues
So we’re talking about SEO and the content being generated in the first place? Yeah, it’s worse than it used to be when the main application online was websites, but I still want/need a reliable way to parse results across… you know, Wikipedia and Reddit, mostly. IMDB sometimes. It may have looped around to the old days of Altavista directory search, but it’s still a valuable tool. And crucially not replaced by an LLM, especially for the kind of non-obvious queries where you don´t just go to the site you know will have the answer directly.
LLM is a random person in the internet, or the first link on a search.
If you wouldn’t blandly trust them, don’t trust it.
LLM is a LLM. LLM is a transformer model generating likely output from a dataset.
I hate all this analogy stuff people keep resorting to. The thing does what it does, and trying to understand what it does by analogy is being used disingenuously to push all sort of misinformation-filled agendas.
It’s not about “trust”, it’s about how the output you’re being given is generated, and so what types of outputs are useful on what applications.
The answer is fairly narrow, particularly compared to how it’s being marketed. It absolutely, 100% isn’t a search engine, though. And even when plugged into a search engine and acting as a summarization engine it’s actually pretty terrible and very likely to distort an output that anybody who has been near a computer in the past thirty years can parse faster at a glance.
That is exactly the point, LLM aim to simulate the chaotic best guess flow of the human mind, to be conscious and at least present the appearance of thinking and from that to access and process facts but not be a repository of facts in themselves. The accusation here that the model constructed a fact and then built on it is missing the point, this is exactly the way organic minds work. Human memory is constantly reworked and altered based on fresh information and simple musings and the new memory taken as factual even while it is in large part fabricated, and to an increasing extent over time. Many of our memories of past events bear only cursory fidelity to the actual details of the events themselves to the point that they could be defined as imagined. We still take these imagined memories as real and act upon them exactly as has been done here by the AI model.
As below, stop with the analogies. No, that’s not “the chaotic best guess flow of a human mind”, that’s a whole bunch of tensor math generating likely chains of tokens. Those two things aren’t the same thing.
They aren’t the same thing in the strict sense, but they’re also not the same thing in practical terms at the end user level. If I ask a friend if they remember some half-forgotten factoid they can tell me not just if they do remember, but also how well they remember, how sure they are and why they know it. No LLM can do that, because LLMs know as little about themselves as about anything else. Which is nothing, because they’re LLMs, not people.
This is like the dozenth time Google put hallucinations in their AI presentation/AD. They just don‘t care.
Especially considering that the “pointing out of said hallucinations” comes much later than when they’re shared. And NEVER made it as far and wide as the initial bullshit.
The weirdness came partway through, when the ad actually showed Google Gemini in action. It told the cheese vendor that Gouda accounts for “50 to 60 percent of the world’s cheese consumption.” Now, Gouda’s hardly a hardcore real head pick like Roquefort or BellaVitano, but there’s also no way it’s pulling in cheddar or mozzarella numbers. Travel blogger Nate Hake and Google-focused Twitter account Goog Enough documented the erroneous initial version of the ad, but Google responded by quietly swapping in a more accurate Gemini-suggested blurb in all live versions of the ad, including the one that aired during the Super Bowl.
They should have kept quiet and let Google show how shit they are on live TV
begs the question
Not it doesn’t. Did an Ai slop this story too?
It’s an obsolete usage of “beg” that’s now preserved only in that particular set phrase. One of English’s many linguistic fossils, which you should learn more about before trying to critique anyone’s language use.
Not it doesn’t. Did an Ai slop this story too?
No it doesn’t. Did an AI slop this story too?
Why post the same comment?
Fair question.
That user goes around issuing weird and pointless corrections to other people’s comments, even sometimes to the point of personally insulting people who make grammatical or spelling errors – often common ones that non-native speakers make, so I thought it’d be funny to do the same in turn, since their comment history is filled with much of the same.
I wouldn’t usually do it, it’s a pointless exercise IMO.
Can take the user off reddit, but the reddit never leaves the user
Slightly off topic, but the writing on this article is horrible. Optimizing for Google engagement, it seems. Ironically, an AI would probably have produced something vastly more readable.
Stop calling gpt ai
