Saying “it’s a text generator” cannot be the full force of your criticism, if the text it generates is incredibly useful. What could AI be on a screen if not a text generator?
Your views have got to be formed by WHAT text is generated given that AI cannot express itself through any other channel than text.
If you can give AI a deeply complex code base and ask it to find the likely cause of an obscure bug, and the solution is right, then you’ll have to acknowledge that some form of reasoning is going on, no?
And given that no other system has previously enabled this form of reasoning, it seems a shame to judge AI on semantics, rather than usefulness.
If you can give AI a deeply complex code base and ask it to find the likely cause of an obscure bug, and the solution is right, then you’ll have to acknowledge that some form of reasoning is going on, no?
Absolutely not. LLMs are just prediction engines for words.
If a LLM has the right answer, it only means that the model had the correct training data for your issue. Nothing more.
ML is still very useful and used everyday. LLM could have been a nice tool in niche applications, but instead, big capitalists ruined that for us, and torching the environment and wrecking the economy with it instead.
I’m just a movement generator.
Saying “it’s a text generator” cannot be the full force of your criticism, if the text it generates is incredibly useful. What could AI be on a screen if not a text generator?
Your views have got to be formed by WHAT text is generated given that AI cannot express itself through any other channel than text.
If you can give AI a deeply complex code base and ask it to find the likely cause of an obscure bug, and the solution is right, then you’ll have to acknowledge that some form of reasoning is going on, no?
And given that no other system has previously enabled this form of reasoning, it seems a shame to judge AI on semantics, rather than usefulness.
Absolutely not. LLMs are just prediction engines for words.
If a LLM has the right answer, it only means that the model had the correct training data for your issue. Nothing more.
That is absolutely not how it works at all
What do you mean? That’s exactly how LLMs work. That’s why they “hallucinate”.
No they hallucinate because they lose context but are tasked to answer anyways. They also don’t really lose context as much as they did a year ago.
Their training data has nothing to do with it unless you’re using a local one without web access.
ML is my career and I’ve been doing it for decades. You’re wrong. LLMs hallucinate with full context.
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ML is still very useful and used everyday. LLM could have been a nice tool in niche applications, but instead, big capitalists ruined that for us, and torching the environment and wrecking the economy with it instead.
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