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""Are we really still having this conversation in 2024 ?! :-( "" </Sarcasm> Are we in 2024 and people are still just saying "duh its statistics, nothing to se

""Are we really still having this conversation in 2024 ?! :-( "" </Sarcasm> Are we in 2024 and people are still just saying "duh its statistics, nothing to see here".

octonion137 2y ago
As opposed to what?
- FrustratedMonky 2y ago
  added sarcasm note. it was referring to the parent.
asdasdsddd 2y ago
I dont get it, what's the unique insight in the article, isn't it just stats and bad data?
- FrustratedMonky 2y ago
  It's just being a bit reductive to just toss AI out as un-interesting because it is just based on statistics. And, think there is question of 'bad data' so study is bad/invalid, versus studying impact of 'bad data' on responses is the result.
  - asdasdsddd 2y ago
    I meant, the answer to the question of why does AI hallucinate is the same answer to the question "why does any statistical system ever produce an incorrect result" which I thought was well established.
    - FrustratedMonky 2y ago
      How can it be well established when the LLMs are still actively changing and improving. Don't think we can say, statistical systems can have incorrect results, hence we no longer need to study how statistical system produce incorrect results, because we already know they can have incorrect results.
fnordpiglet 2y ago
I am impressed that with multimodal modals in the hands of everyone people still say this. I mean it’s true on the surface but the ability to take a picture of a data sheet and wiring diagram and get it to give me step by step instructions to wire two components together and an example of the UART protocol in cpp that while not directly functional captures all the essential information I need. That’s amazing and it’s not just word prediction, the source is images of documents with diagrams! Anyone who does this and comes away jaded has lost the ability to dream.
- imiric 2y ago
  It's not just true on the surface—it's true at its core. All those features you mention are based on statistical relationships. This doesn't mean that these systems can't be very useful, but it also doesn't mean that they are _intelligent_, as much as we like to call them that. They have no understanding of their input or output, beyond being able to pattern match and mechanically decide what the next token should be based on their training data.
  - bongodongobob 2y ago
    Then you need to explain why statistics isn't enough to be intelligent. We are at the point where that isn't an obvious argument anymore and I'll point to the best models as to why you might be wrong.
    - imiric 2y ago
      Because being able to find patterns in large amounts of data will never make the system intuitively understand math[1], or history[2], or any other field. It will always depend on the data and biases we feed it, at least with the current approaches. You might say that this is how humans learn and demonstrate intelligence, but it's not the same thing. We have the ability to advance our understanding of the universe without being explicitly trained on every topic. Until we can build systems that can do this, I wouldn't label them as intelligent. But, again, this doesn't mean that they can't be useful. [1]: https://towardsdatascience.com/9-11-or-9-9-which-one-is-higher-6efbdbd6a025 https://towardsdatascience.com/9-11-or-9-9-which-one-is-high... [2]: https://www.theguardian.com/technology/2024/mar/08/we-definitely-messed-up-why-did-google-ai-tool-make-offensive-historical-images https://www.theguardian.com/technology/2024/mar/08/we-defini...

""Are we really still having this conversation in 2024 ?! :-( "" </Sarcasm> Are we in 2024 and people are still just saying "duh its statistics, nothing to see here".

octonion137 2y ago

As opposed to what?

FrustratedMonky 2y ago
added sarcasm note. it was referring to the parent.

asdasdsddd 2y ago

I dont get it, what's the unique insight in the article, isn't it just stats and bad data?

FrustratedMonky 2y ago
It's just being a bit reductive to just toss AI out as un-interesting because it is just based on statistics. And, think there is question of 'bad data' so study is bad/invalid, versus studying impact of 'bad data' on responses is the result.
- asdasdsddd 2y ago
  I meant, the answer to the question of why does AI hallucinate is the same answer to the question "why does any statistical system ever produce an incorrect result" which I thought was well established.
  - FrustratedMonky 2y ago
    How can it be well established when the LLMs are still actively changing and improving. Don't think we can say, statistical systems can have incorrect results, hence we no longer need to study how statistical system produce incorrect results, because we already know they can have incorrect results.

fnordpiglet 2y ago

I am impressed that with multimodal modals in the hands of everyone people still say this. I mean it’s true on the surface but the ability to take a picture of a data sheet and wiring diagram and get it to give me step by step instructions to wire two components together and an example of the UART protocol in cpp that while not directly functional captures all the essential information I need. That’s amazing and it’s not just word prediction, the source is images of documents with diagrams! Anyone who does this and comes away jaded has lost the ability to dream.

imiric 2y ago
It's not just true on the surface—it's true at its core. All those features you mention are based on statistical relationships. This doesn't mean that these systems can't be very useful, but it also doesn't mean that they are _intelligent_, as much as we like to call them that. They have no understanding of their input or output, beyond being able to pattern match and mechanically decide what the next token should be based on their training data.
- bongodongobob 2y ago
  Then you need to explain why statistics isn't enough to be intelligent. We are at the point where that isn't an obvious argument anymore and I'll point to the best models as to why you might be wrong.
  - imiric 2y ago
    Because being able to find patterns in large amounts of data will never make the system intuitively understand math[1], or history[2], or any other field. It will always depend on the data and biases we feed it, at least with the current approaches. You might say that this is how humans learn and demonstrate intelligence, but it's not the same thing. We have the ability to advance our understanding of the universe without being explicitly trained on every topic. Until we can build systems that can do this, I wouldn't label them as intelligent. But, again, this doesn't mean that they can't be useful. [1]: https://towardsdatascience.com/9-11-or-9-9-which-one-is-higher-6efbdbd6a025 https://towardsdatascience.com/9-11-or-9-9-which-one-is-high... [2]: https://www.theguardian.com/technology/2024/mar/08/we-definitely-messed-up-why-did-google-ai-tool-make-offensive-historical-images https://www.theguardian.com/technology/2024/mar/08/we-defini...