On Mon, Mar 20, 2023 at 10:15 AM Jason Resch <[email protected]> wrote:
Jason, that was a very interesting and insightful post, thanks for posting it. John K Clark See what's on my new list at Extropolis <https://groups.google.com/g/extropolis> i70 > > On Mon, Mar 20, 2023, 9:51 AM Telmo Menezes <[email protected]> > wrote: > >> >> >> Am Mo, 20. Mär 2023, um 14:28, schrieb Jason Resch: >> >> The video John shared is worth watching. This is significant. It is now >> solving complex math problems which requires a long sequence of steps. >> >> >> I agree that it is significant and extremely impressive. I never said the >> opposite. What baffles me is that John is now requiring religious reverence >> towards a scientific result, and criticizing when I ask questions that are >> part of the same standard machine learning methodology that got us here. >> > > I see, I appreciate that clarification. > > > >> Over-fitting is less of an issue here because it's trivial to write a >> sentence that's never before been written by any human in history. >> >> >> That is not enough. A small variation on a standard IQ test is still the >> same IQ test for a super powerful pattern detector such as GPT-4. >> >> I have no doubt that GPT-4 can generalize in its domain. It was >> rigorously designed and tested for that by people who know what they are >> doing. My doubt is that you can give it an IQ test and claim OMG GPT-4 IQ > >> 140. This is just silly and it is junk science. >> > > It's true that once one learns a way to solve problems it becomes easier > to reapply that method when you next encounter a related problem. > > But isn't that partly what intelligence is? If a system has read the whole > Internet and seen every type of problem we know how to solve, and it can > generalize to know what method to use in any situation, that's an > incredible level of intelligence which until now, we haven't had in machine > form before. > > > >> You can tweak the parameters of the problem to guarantee it's a problem >> it has never before been seen, and it can still solve it. >> >> >> Some yes, some no. Almost one century of computer science still applies. >> >> You can choose to wait for the academic write ups to come out a few >> months down the line but by then things will have advanced another few >> levels from where we are today. >> >> >> I am not wanting to wait for anything, I am asking questions that can be >> addressed right now: >> >> - Are there IQ tests in the training data of GPT-4. Yes or no? >> - Can we conceive of human-level intelligence without recurrent >> connections or some form of ongoing recursivity / Turing completeness? Yes >> or no? >> > > > I've been thinking about this a lot. > > > My friend with access to GPT-4 asked it: "Does your neural network contain > such reflexive loops, or is it strictly feed forward?", below is its reply: > > -------- > > "As an AI language model, my underlying architecture is based on the > Transformer model, which is primarily feedforward in nature but includes > some elements of recurrence or reflexivity, specifically in the > self-attention mechanism. > > The Transformer model consists of a multi-layer feedforward neural network > with self-attention mechanisms that allow the model to learn complex > relationships between input and output sequences. The self-attention > mechanism weighs the importance of different input elements relative to > each other, effectively capturing long-range dependencies and relationships > within the input data. This mechanism introduces a form of reflexivity or > recurrence, as it allows the model to consider the context of the entire > input sequence when generating outputs. > > However, it is important to note that the self-attention mechanism in the > Transformer model is not equivalent to traditional recurrent neural > networks (RNNs), which maintain a hidden state that is updated at each time > step. The Transformer model processes input sequences in parallel rather > than sequentially, which makes it fundamentally different from RNNs. > > In summary, while my neural network architecture is primarily feedforward, > it includes some elements of reflexivity in the form of self-attention > mechanisms that allow the model to capture complex relationships within > input sequences." > > -------- > > Is this enough to meet Hofstadter's requirements of recursion? I do not > have the expertise to say. But I do see recursion exist in a way no one > seems to ever mention: > > The output of the LLM is fed back in, as input to the LLM that produced > it. So all the high level processing and operation of the network at the > highest level, used to produce a few characters of output, then reaches > back down to the lowest level to effect the lowest level of the input > layers of the network. > > If you asked the network, where did that input that it sees come from, it > would have no other choice but to refer back to itself, as "I". "I > generated that text." > > Loops are needed to maintain and modify a persistent state or memory, to > create a strange loop of self-reference, and to achieve Turing > completeness. But a loop may not exist entirely in the "brain" of an > entity, it might offload part of the loop into the environment in which it > is operating. I think that is the case for things like thermostats, guided > missiles, AlphaGo, and perhaps even ourselves. > > We observe our own actions, they become part of our sensory awareness and > input. We cannot say exactly where they came from or how they were done, > aside from modeling an "I" who seems to intercede in physics itself, but > this is a consequence of being a strange loop. In a sense, our actions do > come in from "on high", a higher level of abstraction in the hierarchy of > processing, and this seems as if it is a dualistic interaction by a soul in > heaven as Descartes described. > > In the case of GPT-4, its own output buffer can act as a scratch pad > memory buffer, to which it continuously appends it's thoughts to. Is this > not a form of memory and recursion? > > For one of the problems in John's video, it looked like it solved the > Chinese remainder theorem in a series of discrete steps. Each step is > written to and saved in it's output buffer, which becomes readable as it's > input buffer. > > Given this, I am not sure we can say that GPT-4, in its current > architecture and implementation, is entirely devoid of a memory, or a > loop/recursion. > > I am anxious to hear your opinion though. > > Jason > > - > -- You received this message because you are subscribed to the Google Groups "Everything List" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/everything-list/CAJPayv3hypQ%3DwoCdNmNXMEeQRPfJwmfOc_2539xsG%2BESvr%2Bqvw%40mail.gmail.com.
