Honestly I feel people are using them completely wrong.
Their real power is their ability to understand language and context.
Turning natural language input into commands that can be executed by a traditional software system is a huge deal.
Microsoft released an AI powered auto complete text box and it’s genius.
Currently you have to type an exact text match in an auto complete box. So if you type cats but the item is called pets you’ll get no results. Now the ai can find context based matches in the auto complete list.
This is their real power.
Also they’re amazing at generating non factual based things. Stories, poems etc.
They’re really, really bad at context. The main failure case isn’t making things up, it’s having text or image in part of the result not work right with text or image in another part because they can’t even manage context across their own replies.
See images with three hands, where bow strings mysteriously vanish etc.
New models are like really good at context, the amount of input that can be given to them has exploded (fairly) recently… So you can give whole datasets or books as context and ask questions about them.
So if you type cats but the item is called pets get no results. Now the ai can find context based matches in the auto complete list.
Google added context search to Gmail and it’s infuriating. I’m looking for an exact phrase that I even put in quotes but Gmail returns a long list of emails that are vaguely related to the search word.
Searching with synonym matching is almost.decades old at this point. I worked on it as an undergrad in the early 2000s.and it wasn’t new then, just complicated. Google’s version improved over other search algorithms for a long time.and then trashed it by letting AI take over.
Exactly. The big problem with LLMs is that they’re so good at mimicking understanding that people forget that they don’t actually have understanding of anything beyond language itself.
The thing they excel at, and should be used for, is exactly what you say - a natural language interface between humans and software.
Like in your example, an LLM doesn’t know what a cat is, but it knows what words describe a cat based on training data - and for a search engine, that’s all you need.
Honestly I feel people are using them completely wrong.
Their real power is their ability to understand language and context.
Turning natural language input into commands that can be executed by a traditional software system is a huge deal.
Microsoft released an AI powered auto complete text box and it’s genius.
Currently you have to type an exact text match in an auto complete box. So if you type cats but the item is called pets you’ll get no results. Now the ai can find context based matches in the auto complete list.
This is their real power.
Also they’re amazing at generating non factual based things. Stories, poems etc.
…they do exactly none of that.
No, but they approximate it. Which is fine for most use cases the person you’re responding to described.
They’re really, really bad at context. The main failure case isn’t making things up, it’s having text or image in part of the result not work right with text or image in another part because they can’t even manage context across their own replies.
See images with three hands, where bow strings mysteriously vanish etc.
New models are like really good at context, the amount of input that can be given to them has exploded (fairly) recently… So you can give whole datasets or books as context and ask questions about them.
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Google added context search to Gmail and it’s infuriating. I’m looking for an exact phrase that I even put in quotes but Gmail returns a long list of emails that are vaguely related to the search word.
Searching with synonym matching is almost.decades old at this point. I worked on it as an undergrad in the early 2000s.and it wasn’t new then, just complicated. Google’s version improved over other search algorithms for a long time.and then trashed it by letting AI take over.
Exactly. The big problem with LLMs is that they’re so good at mimicking understanding that people forget that they don’t actually have understanding of anything beyond language itself.
The thing they excel at, and should be used for, is exactly what you say - a natural language interface between humans and software.
Like in your example, an LLM doesn’t know what a cat is, but it knows what words describe a cat based on training data - and for a search engine, that’s all you need.
That’s called “fuzzy” matching, it’s existed for a long, long time. We didn’t need “AI” to do that.