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Comment by cheeselip420 | Hacker News Reader

These articles are all the same. It's like complaining how the Ford Model T couldn't drive you to England in the early 20th century while ignoring how it would revolutionize cities and life within America.

Whether you believe it's 'real' or not is irrelevant. It's here, it's useful and the world will not be the same as it was.

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Language models have been around for a very long time. However, they were much smaller than today and were mainly used to check the fluency of a text, basically if a text was a problable text in English or in French for instance. These models were trained over words and had a huge problem with unknown words or spelling errors. They were used in translation models to check the quality of what was produced. In this sense, a language model might seem something ancient. However, if we take your analogy with a car, modern LM are to ancient LM what a plane is to a car. You still travel with it but it is a complete different beast. To say that these models are nothing new is a total travesti of reality. Transformer architectures have been around since 2017, a mere 6 years, but they have revolutionized the way LLM are trained and implemented. If you speak about language models without talking about transformer or attention, you are basically saying that a plane is the same as a car.

And if anything the LLMs are better than the average person realises. Everyone I have demoed it to assumed it was like a slightly better Siri or cleverbot. None of them expected it to be capable of synthesising full long form text works with coherent and largely accurate content.

What about the fact that it's not reliable? Seems unusable to me if you are not a domain expert that just gets help from the system and who is able to judge whether its advice is true or made up.

It’s reliable for many many use cases (e.g. anything that transforms text into a different representation), but not for a few others (e.g. anything that requires logical reasoning) - a F1 car is great on race tracks, we don’t see people taking them on gravel roads and then complain they’re unreliable …

I'd say it's unreliable if you can't decide apriori for which use cases it's reliable. For instance take this example where it makes contradictory assertions

https://news.ycombinator.com/item?id=34288209

How reliable was it a few years back? Oh yeah, it didn't exist! AI is improving at breakneck speeds and will only get better

Humans aren't reliable either. This is why we write tests, have code reviews, fact-checking/editing, etc.

How would LLM revolutionize everyday life?

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