8 ms·

I'm sorry but you completely fail at explaining what the difference is between GPT-3 and a human when generating sentences. And frankly I don't believe there is

by tinco 4y ago

I'm sorry but you completely fail at explaining what the difference is between GPT-3 and a human when generating sentences. And frankly I don't believe there is any significant difference. If you inform GPT-3 of what it has eaten then it can answer questions about it just as well as a human can, probably better. Why would you assume human thinking is different from generating the next N language tokens based on previous input? In my opinion the only difference between a human and GPT-3 is we have more intrinsic motivations and more hardwired/pretrained subsystems and sensors. Lamda is not a 7 year old child because it has no motivation other than to respond to queries.

upwardbound 4y ago
One plausible answer to your line of inquiry could be that GPT-3 is indeed broadly similar at least in outcomes to the Language Centers of the human brain (e.g. Broca's Area), but that the Language Centers are only a small portion of a complete human brain. An interesting thought experiment would be to imagine a cyborg who had sustained a stroke in their Language Centers and had those centers replaced with a GPT-3-like computer. Do you think this cyborg person would experience sentience in a different way after getting the GPT-3 implant? How do you think their subjective, 1st-person experience would compare in the three phases of their life? * Before the stroke * After the stroke * After the brain implant ?
- snowwrestler 4y ago
  It would be impossible to give someone a GPT-3-like computer to replace language processing in their brain because such a computer requires language as the input. If you can’t generate language, you cannot interact with a natural language processing ML system. This is one of the big clues that such systems are statistical engines and not actually thinking.
  - upwardbound 4y ago
    In this thought experiment, you would connect the computer to the brain by interfacing with the computer chip not at the token level like you are describing (tokens are words, subwords, and letters), but rather in the more abstract feature embedding space which represents abstract concepts. These concepts tend to be represented by patterns of neurons in the middle and late layers of NLP networks. Here's an intro about this: https://en.wikipedia.org/wiki/Word_embedding https://en.wikipedia.org/wiki/Word_embedding So imagine if you attach wires to the neural net neurons representing the concept of beer (not the word beer!); if you use those wires to increase the activation of those neurons, the network will produce sentences that are more likely to mention beer as well as related concepts such as wine or beer-pong. This is similar to how generative networks work. Basically the human would use a large language model in a generative mode in order to talk.
sweezyjeezy 4y ago
If I ask you what you ate for breakfast, I would expect you to answer with a truthful answer, if I asked you whether you enjoyed it and whether it improved your mood, again I would expect you to answer the question based on your own personal experience. GPT-3 obviously does not do this - it asks 'if the answer to the question was coming from text in the training data, what is a likely response'. Its answer is meaningless, it gives me no real data about the world. We are clearly not executing the same task.
- mobiuscog 4y ago
  What is the difference between 'training data' and 'memory' ?
  - retrac 4y ago
    One way to look at it: Everything you have ever seen, heard or otherwise perceived is your brain's "training data" to date.
- tinco 4y ago
  We are only not executing the same task because GPT-3 has no motivation to do anything else than memory recall. And precisely because it does so it does give you real data about the world. I think you maybe misunderstood what GPT-3 can do, maybe you haven't played with it yourself. It not only bases the answer on information in its training set, but also on information in the question. So if you in your question give it all the information that is relevant (interspersed with irrelevant information if you want) then it will respond with its best interpretation of the information it is given, just like a human would. Treat GPT-3 like someone who just awoke from a long sleep. You could give it today's newspaper to read, and then ask it questions about today. Tell it what its own personal experience was, and then it'll talk your ears off about it if you ask nicely. It is clearly executing the same task if you disregard humans tendency to prioritize other motivations than pure memory recall.
  - sweezyjeezy 4y ago
    > it will respond with its best interpretation of the information it is given, just like a human would The reason I can respond to this reply is not because I have read and memorised reams of text of people talking about AI and am simply regurgitating it mindlessly. I am able to do it because I can consider the points you are making, and what their actual 'meaning' is, try to come up with my own meaningful response and try to verbalise it back to you. There is a point where I am not just doing statistical language modelling. If you think that is wrong, and that what we are doing is closer to GPT-3, then could you explain why you think that? I am an AI researcher, and have spent plenty of time playing with GPT-3 btw.

by tinco 4y ago

upwardbound 4y ago

One plausible answer to your line of inquiry could be that GPT-3 is indeed broadly similar at least in outcomes to the Language Centers of the human brain (e.g. Broca's Area), but that the Language Centers are only a small portion of a complete human brain. An interesting thought experiment would be to imagine a cyborg who had sustained a stroke in their Language Centers and had those centers replaced with a GPT-3-like computer. Do you think this cyborg person would experience sentience in a different way after getting the GPT-3 implant? How do you think their subjective, 1st-person experience would compare in the three phases of their life? * Before the stroke * After the stroke * After the brain implant ?

snowwrestler 4y ago
It would be impossible to give someone a GPT-3-like computer to replace language processing in their brain because such a computer requires language as the input. If you can’t generate language, you cannot interact with a natural language processing ML system. This is one of the big clues that such systems are statistical engines and not actually thinking.
- upwardbound 4y ago
  In this thought experiment, you would connect the computer to the brain by interfacing with the computer chip not at the token level like you are describing (tokens are words, subwords, and letters), but rather in the more abstract feature embedding space which represents abstract concepts. These concepts tend to be represented by patterns of neurons in the middle and late layers of NLP networks. Here's an intro about this: https://en.wikipedia.org/wiki/Word_embedding https://en.wikipedia.org/wiki/Word_embedding So imagine if you attach wires to the neural net neurons representing the concept of beer (not the word beer!); if you use those wires to increase the activation of those neurons, the network will produce sentences that are more likely to mention beer as well as related concepts such as wine or beer-pong. This is similar to how generative networks work. Basically the human would use a large language model in a generative mode in order to talk.

sweezyjeezy 4y ago

If I ask you what you ate for breakfast, I would expect you to answer with a truthful answer, if I asked you whether you enjoyed it and whether it improved your mood, again I would expect you to answer the question based on your own personal experience. GPT-3 obviously does not do this - it asks 'if the answer to the question was coming from text in the training data, what is a likely response'. Its answer is meaningless, it gives me no real data about the world. We are clearly not executing the same task.

mobiuscog 4y ago
What is the difference between 'training data' and 'memory' ?
- retrac 4y ago
  One way to look at it: Everything you have ever seen, heard or otherwise perceived is your brain's "training data" to date.
tinco 4y ago
We are only not executing the same task because GPT-3 has no motivation to do anything else than memory recall. And precisely because it does so it does give you real data about the world. I think you maybe misunderstood what GPT-3 can do, maybe you haven't played with it yourself. It not only bases the answer on information in its training set, but also on information in the question. So if you in your question give it all the information that is relevant (interspersed with irrelevant information if you want) then it will respond with its best interpretation of the information it is given, just like a human would. Treat GPT-3 like someone who just awoke from a long sleep. You could give it today's newspaper to read, and then ask it questions about today. Tell it what its own personal experience was, and then it'll talk your ears off about it if you ask nicely. It is clearly executing the same task if you disregard humans tendency to prioritize other motivations than pure memory recall.
- sweezyjeezy 4y ago
  > it will respond with its best interpretation of the information it is given, just like a human would The reason I can respond to this reply is not because I have read and memorised reams of text of people talking about AI and am simply regurgitating it mindlessly. I am able to do it because I can consider the points you are making, and what their actual 'meaning' is, try to come up with my own meaningful response and try to verbalise it back to you. There is a point where I am not just doing statistical language modelling. If you think that is wrong, and that what we are doing is closer to GPT-3, then could you explain why you think that? I am an AI researcher, and have spent plenty of time playing with GPT-3 btw.