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fwubglubbel t1_j56y0lo wrote

Reply to comment by Surur in How close are we to singularity? Data from MT says very close! by sigul77

>believe that an AI that can translate as well as a human will be presumably know as much about the world as a human.

This sounds like nonsense. Just because a machine can translate doesn't mean in "knows" anything. (see Searle's Chinese Room)

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currentpattern t1_j575ja4 wrote

It would be nonsense to posit that "AGI" means it's a system that "understands/knows" language, in this case. What these projections seem to be saying is that around 2027, we're likely to have systems that are just as capable as humans at utilizing language. I.e., Chinese Rooms that are indistinguishable from humans in regards to language use.

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BitterAd9531 t1_j57z7zd wrote

Chinese Room is once again one of these experiments that sound really good in theory but has no practical use whatsoever. It doesn't matter if the AI "understands" or not if you can no longer tell the difference.

It's similar to the "feeling emotions vs emulating emotions" or "being conscious vs acting conscious" discussion. As long as we don't have a proper definition for them, much less a way to test them, the difference doesn't matter in practice.

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Surur t1_j57gd78 wrote

> Just because a machine can translate doesn't mean in "knows" anything

You could say the same thing of a translator then. Do they really "know" a language or are they just parroting the rules and vocabulary they learnt?

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songstar13 t1_j57nunw wrote

You can ask a translator a question about the world and if they have knowledge on that topic then they can answer you with certainty.

Current GPT models are basically a super-powered predictive text bot that answers questions. It would be like trying to answer a question using the suggested words on your phone keyboard but far more sophisticated.

They are fully capable of lying to you or giving inconsistent answers to the question because they don't "know" anything other than patterns of word association and grammar rules.

At least, this was my understanding of them fairly recently. Please correct me if that has changed.

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Surur t1_j57w5tj wrote

I imagine you understand that LLM are a bit more sophisticated than Markov chains, and that GPT-3 for example has 175 billion parameters, which corresponds to the connections between neurons in the brain, and that the weights of these connections influences which word the system outputs.

These weights allows the LLM to see the connections between words and understand the concepts much like you do. Sure, they do not have a visual or intrinsic physical understanding but they do have clusters of 'neurons' which activate for both animal and cat for example.

In short, Markov chains use a look-up table to predict the next word, while LLM use a multi-layer (96 layer) neural network with 175 billion connections tuned on nearly all the text on the internet to choose its next word.

Just because it confabulates sometimes does not mean its all smoke and mirrors.

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songstar13 t1_j58shuh wrote

Thank you for the more detailed explanation! I was definitely underestimating how much more complex some of these AI models have become.

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