[ExI] LLM's cannot be concious

Thu Mar 23 23:48:25 UTC 2023

People make a big deal of referents because they think without direct
experiences of things like stones, trees or other things in the world an AI
cannot really understand, in particular NLMs. But GPT-4 can now understand
images anyway, you can easily combine understanding images and language,
images are a form of language anyway.
These arguments are trite, and they are all an excuse to give humans some
kind of priority over other intelligences, when we are just more
sophisticated NLMs ourselves (with other information processing
modules added to it).
It seems to me that we now have all the ingredients for a true AGI to
emerge soon, it is just a question of increasing their training parameters
and maybe a 10x or at most 100x higher computational power. That can be
achieved in 3-4 years max given the trend in parameter training and
computational power observed in the last few years.
Soon there will be no excuses for human intelligence exceptionalists.
Giovanni

On Thu, Mar 23, 2023 at 4:11 PM Jason Resch via extropy-chat <
extropy-chat at lists.extropy.org> wrote:

>
>
> On Thu, Mar 23, 2023, 6:39 PM Adrian Tymes via extropy-chat <
> extropy-chat at lists.extropy.org> wrote:
>
>> On Thu, Mar 23, 2023 at 1:02 PM Jason Resch via extropy-chat <
>> extropy-chat at lists.extropy.org> wrote:
>>
>>> Others had argued on this thread that it was impossible to extract
>>> meaning from something that lacked referents. it seems you and I agree that
>>> it is possible to extract meaning and understanding from a data set alone,
>>> by virtue of the patterns and correlations present within that data.
>>>
>>
>> With the caveat that referents are themselves data, so if we include
>> appropriate referents in that data set then yes.  Referents are often
>> referenced by their correlations and matching patterns.
>>
>
> I don't understand what you are saying here.
>
>
>
>>
>>>
>>> I am not convinced a massive brain is required to learn meaning. My AI
>>> bots start with completely randomly weighted neural networks of just a
>>> dozen or so neurons. In just a few generations they learn that "food is
>>> good" and "poison is bad". Survival fitness tests are all that is needed
>>> for them to learn that lesson. Do their trained neural nets reach some
>>> understanding that green means good and red means bad? They certainly
>>> behave as if they have that understanding, but the only data they are given
>>> is "meaningless numbers" representing inputs to their neurons.
>>>
>>>
>>>
>>>> Just because one type of AI could do a task does not mean that all AIs
>>>> are capable of that task.  You keep invoking the general case, where an AI
>>>> that is capable is part of a superset, then wondering why there is
>>>> disagreement about a specific case, discussing a more limited subset that
>>>> only contains other AIs.
>>>>
>>>
>>> There was a general claim that no intelligence, however great, could
>>> learn meaning from a dictionary (or other data set like Wikipedia or list
>>> of neural impulses timings) as these data "lack referents". If we agree
>>> that an appropriate intelligence can attain meaning and understanding then
>>> we can drop this point.
>>>
>>
>> I recall that the claim was about "no (pure) LLM", not "no (general)
>> intelligence".
>>
>
> My original claim was for an intelligent alien species.
>
>
>> Also there  is  a substantial distinction between a dictionary or
>> Wikipedia, and any list of neural impulses.  A pure LLM might only be able
>> to consult a dictionary or Wikipedia (pictures included); a general
>> intelligence might be able to process neural impulses.
>>
>
> In all cases it's a big file of 1s and 0s containing patterns and
> correlations which can be learned.
>
>
>>
>>> There is no task requiring intelligence that a sufficiently large LLM
>>> could not learn to do as part of learning symbol prediction. Accordingly,
>>> saying a LLM is a machine that could never learn to do X, or understand Y,
>>> is a bit like someone saying a particular Turing machine could never run
>>> the program Z.
>>>
>>
>> And indeed there are some programs that certain Turing machines are
>> unable to run.  For example, if a Turing machine contains no randomizer and
>> no way to access random data, it is unable to run a program where one of
>> the steps requires true randomness.
>>
>
> Randomness is uncomputable. And I would go so far to say say true
> randomness doesn't exist, there is only information which cannot be guessed
> or predicted by certain parties. This is because true randomness requires
> creation of information but creation of information violates the principal
> of conservation of information in quantum mechanics.
>
> In any case my point wasn't that everything is computable, it's that the
> universality of computation means any Turing machine can run any program
> that any other Turing machine can run. The universality of neural networks
> likewise implies not that every function can be learned, but any function
> that a neutral network can learn can be learned by any neural network of
> sufficient size. Our brains is fundamentally a neural network. If our
> brains can learn to understand meaning then this should be in the scope of
> possibility for other neural networks.
>
> Much has been written about the limits of psuedorandom generators; I defer
>> to that literature to establish that those are meaningfully distinct from
>> truly random things, at least under common circumstances of significance.
>>
>
> I am quite familiar with pseudorandom number generators. They are a bit of
> a fascination of mine.
>
>
>> One problem is defining when an AI has grown to be more than just a LLM.
>> What is just a LLM, however large, and what is not just a LLM (whether or
>> not it includes a LLM)?
>>
>
> That's a good question. I am not sure it can be so neatly defined. For
> example, is a LLM trained on some examples of ASCII art considered having
> been exposed to visual stimuli?
>
> Jason
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