On Mon, Mar 20, 2023, 9:51 AM Telmo Menezes <te...@telmomenezes.net> 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

I am anxious to hear your opinion though.


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