If someone missed that: it returned a wrong answer even in the demo video.
Cool, so the worst part of modern search engines has been made into its own standalone search engine. Very neat.
I don’t get the hype around LLM, it is a terrible way to search. It has never give me anything useful on any of my search, ever.
Most of the time asking chatgpt anything non-trivial, it will just spit out gibberish that doesn’t mean anything.
Who in their right mind would look at these terribly stupid thing and think: Yeah! This garbage is going to advance humanity.
Have you tried perplexity.ai? Using it to do some programming and it’s quite good so far. It’s basically LLM + Search Engines.
You can also use it to use different models (not just with ChatGPT).
Sometimes even run the code itself (Python for my case) and see if it’s valid.
Last time I tried ChatGPT it cannot even do a trie in Haskell, so I don’t see any way it is useful for me, unfortunately. IIRC, I was testing with some trivial modification of a trie, but I do not remember at this point.
Maybe it is useful for college homework, but I have yet to find any problem it can solve beyond college. But I would love to learn more, since you have more experience with it. :)
Edit: I tried a problem I encountered couple month ago on https://perplexity.ai. I want to implement a parser in Haskell that do not halt on error, but record the error and keeps going.
It should take 2 lines with mtl, and the AI gives me a more verbose answer that is also completely wrong.
So… I don’t see how they are helpful, honestly. Sorry.
I don’t get the hype around LLM, it is a terrible way to search
I’ll be playing devil’s advocate here just for a moment (despite the huge ecological, moral, political and economical costs) :
- what LLM does provide is a looser linguistic interface. That means instead of searching for exact words, one can approximately search for the “idea”. That means instead of hitting just the right keywords that an expert might know, one can describe a partial solution, a very rough guess of what the problem might be, and possibly get a realistic sounding answer. It might be wrong yet it might still be a step in the right direction.
So… yes I also don’t think the hype is justified but IMHO it’s quite clear that providing a solution that makes an interface easier to get some OK-looking result would appeal to masses. That means a LOT of people get their hopes up about potential empowerment and a few people ride that bubble making money on promises.
PS: for people interested in the topic but wanting to avoid the generative aspect I believe https://en.wikipedia.org/wiki/Semantic_search is a good starting point.
Can’t say I have the same experience. Other than for old niche content, the sources cited from asking perplexity.ai (I just use it since it’s free, no idea how it compares to others) tend to be exactly what I’m after.
That would be great if they just got the LLM AI out of real search engines.
Oh cool! A search engine that’ll give you fake URL’s!
Only a matter of time until someone genuinely puts glue on their pizza
I have kind of just been using ChatGPT 4o as my search engine, it’s been working pretty well.
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I used perplexity pretty exclusively for a while. Especially for work. Both have their place and use cases but when I’m looking for something truly specific or nuanced, it’s DDG and a manual search.
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No. ChatGPT pulls information out of its ass and how I read it SearchGPT actually links to sources (while also summarizing it and pulling information out of it’s ass, presumably). ChatGPT “knows” things and SearchGPT should actually look stuff up and present it to you.
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It’s not doing live queries at all, it just makes a statistically likely answer up from its training data
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I mean yeah it does include data scraped from the web but that is all three years old at this point. Hardly a search engine by any metric
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It literally doesn’t do that
This is like saying the library search engine and Bob the drunkard who looked at the shelf labels and swears up and down he knows where everything is are the same thing.
Look, ChatGPT is an averaging machine. Yes it has ingested a significant chunk of the text on the internet, but it does not reproduce text exactly as it found it, it produces an average of all the text it has seen, weighted towards what seems like it make sense for the situation. For really common information this is fine. For niche information, it is bullshitting without any indication.
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ChatGPT is not a search engine, it generates predictions on what is the most likely text completion to your prompt. It does not pull information from a database. It is a mathematical model. Its weights do not contain the training data. It is not indexing anything. You will not find any page from the internet in the model. It is all averaged out and any niche detail is lost, overpowered by more prevalent but less relevant training data. This is why it bullshits. When it bullshits it is not because it searched for something and came up empty, it is because in the training data there simply was not a sufficient number of occurrences of the answer to influence its response against the weight of all the other more prevalent training data. ChatGPT does not search anything.
From the train dataset that was frozen many years ago. It’s like you know something instead of looking it up. It doesn’t provide sources, it just makes shit up based on what was in the (old) dataset. That’s totally different than looking up the information based on what you know and then using the new information to create an informed answer backed up by sources
So their solution to a problem that their existing problem created is to use that problem to solve itself.
