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I suspect that there is something else going on than intelligence which will become obvious over the next few years. There was a horse, "Clever Hans" who appea

I suspect that there is something else going on than intelligence which will become obvious over the next few years. There was a horse, "Clever Hans" who appeared to have the ability to answer surprisingly complicated mathematical questions. Did "Clever Hans" have mathematical intelligence. Not at all. He was responding to a cue unknowingly being given by his trainer. I suspect the same thing is happening with ChatGPT. What if all that is happening is that the text is being formulated to very complicated cues that are implicit in the very complicated, statistical analysis? https://en.wikipedia.org/wiki/Clever_Hans https://en.wikipedia.org/wiki/Clever_Hans

H8crilA 3y ago
Clever Hans is just a proxy. ChatGPT and other LLMs obviously can process information on their own. These two have nothing in common, even GPT-3 would have noticed this.
- larryfreeman 3y ago
  Let us disagree on what is "obvious". Given an input and an output, you believe that the complexity of the output proves that intelligence takes place. I agree that ChatGPT is more than a proxy. Unlike Clever Hans, it is processing the content of the question asked. But it is like Clever Hans in that the query is processed by looking for a signal in the content of the data used to train ChatGPT. The real question is where this intelligent behavior comes from? Why does statistical processing lead to these insights? I believe that the processing is not intelligent primarily because I see that holes in the data available leads to holes in reasoning. The processing is only as good as the dynamics of the content that it being processed. This is the part that I believe will become obvious over time.
  - H8crilA 3y ago
    I just said that they process information on their own, and this is indeed obvious - you can download and run LLaMA on an airgapped machine.
    - larryfreeman 3y ago
      Agreed. LLMs process information on their own. I thought you were saying it was "obvious" that the processing demonstrated intelligence. My point was the level of intelligence shown is relative the quality and quantity of the data used for training. The data is where the intelligence is and the model is a compression of that latent intelligence.
gwd 3y ago
A month or so ago I was doing some analysis on our mailing list traffic. I had a complex SQL query (involving tables mapping variations of email addresses to names, and then names to companies they worked for within specific date ranges), that I'd last modified a year previously (the last time I was doing the same sort of analysis), and didn't feel like wrapping my head around the SQL again; so I pasted it into GPT-4 and asked it, "Can you modify this query to group all individuals with less than 1% total contributions into a single 'Other' category?" The query it spat out worked out of the box. Whatever it's doing, at least for code, it's not a glorified Markov chain -- there's some sort of a model in there.
- larryfreeman 3y ago
  I agree. The model is where the intelligence is which is the compressed intelligence latent in the training data. I am arguing similar to John Searle that the processing is not intelligent. The model is a Searlean rulebook. https://en.wikipedia.org/wiki/Chinese_room https://en.wikipedia.org/wiki/Chinese_room
  - gwd 3y ago
    I've always disagreed w/ Searle re the Chinese Room. My guess is that Searle never built an adder circuit from logic gates: combining irrational elements together into something rational is the core magic of computer science. If you want to see someone asking humans questions where they consistently fail to be rational, to the extent that they sometimes seem to approximate a stochastic parrot, read Thinking Fast and Slow by Daniel Kahneman. (It might actually be interesting to give GPT-4 some of the questions in that book, to see how similar or different they are.)
    - larryfreeman 3y ago
      I'm not sure why you disagree with the Chinese Room argument. I would be interested. I agree that Searle was solely a philosopher who did not take an engineering viewpoint. Searle's main point is that if I have a book that tells me how to respond and I never learn Chinese, then I do not understand Chinese. If you see a flaw in this reasoning, I am very interested. My point is just that LLM models are a compression of the content available on the internet equivalent to a rule book. It is definitely fascinating how powerful LLMs are as far as summarization and forming coherent responses to input. I am a big fan of Kahneman and agree with you that it is will be very interesting to ask GPT-4 the questions in that book.

H8crilA 3y ago

Clever Hans is just a proxy. ChatGPT and other LLMs obviously can process information on their own. These two have nothing in common, even GPT-3 would have noticed this.

larryfreeman 3y ago
Let us disagree on what is "obvious". Given an input and an output, you believe that the complexity of the output proves that intelligence takes place. I agree that ChatGPT is more than a proxy. Unlike Clever Hans, it is processing the content of the question asked. But it is like Clever Hans in that the query is processed by looking for a signal in the content of the data used to train ChatGPT. The real question is where this intelligent behavior comes from? Why does statistical processing lead to these insights? I believe that the processing is not intelligent primarily because I see that holes in the data available leads to holes in reasoning. The processing is only as good as the dynamics of the content that it being processed. This is the part that I believe will become obvious over time.
- H8crilA 3y ago
  I just said that they process information on their own, and this is indeed obvious - you can download and run LLaMA on an airgapped machine.
  - larryfreeman 3y ago
    Agreed. LLMs process information on their own. I thought you were saying it was "obvious" that the processing demonstrated intelligence. My point was the level of intelligence shown is relative the quality and quantity of the data used for training. The data is where the intelligence is and the model is a compression of that latent intelligence.

gwd 3y ago

A month or so ago I was doing some analysis on our mailing list traffic. I had a complex SQL query (involving tables mapping variations of email addresses to names, and then names to companies they worked for within specific date ranges), that I'd last modified a year previously (the last time I was doing the same sort of analysis), and didn't feel like wrapping my head around the SQL again; so I pasted it into GPT-4 and asked it, "Can you modify this query to group all individuals with less than 1% total contributions into a single 'Other' category?" The query it spat out worked out of the box. Whatever it's doing, at least for code, it's not a glorified Markov chain -- there's some sort of a model in there.

larryfreeman 3y ago
I agree. The model is where the intelligence is which is the compressed intelligence latent in the training data. I am arguing similar to John Searle that the processing is not intelligent. The model is a Searlean rulebook. https://en.wikipedia.org/wiki/Chinese_room https://en.wikipedia.org/wiki/Chinese_room
- gwd 3y ago
  I've always disagreed w/ Searle re the Chinese Room. My guess is that Searle never built an adder circuit from logic gates: combining irrational elements together into something rational is the core magic of computer science. If you want to see someone asking humans questions where they consistently fail to be rational, to the extent that they sometimes seem to approximate a stochastic parrot, read Thinking Fast and Slow by Daniel Kahneman. (It might actually be interesting to give GPT-4 some of the questions in that book, to see how similar or different they are.)
  - larryfreeman 3y ago
    I'm not sure why you disagree with the Chinese Room argument. I would be interested. I agree that Searle was solely a philosopher who did not take an engineering viewpoint. Searle's main point is that if I have a book that tells me how to respond and I never learn Chinese, then I do not understand Chinese. If you see a flaw in this reasoning, I am very interested. My point is just that LLM models are a compression of the content available on the internet equivalent to a rule book. It is definitely fascinating how powerful LLMs are as far as summarization and forming coherent responses to input. I am a big fan of Kahneman and agree with you that it is will be very interesting to ask GPT-4 the questions in that book.