[FRIAM] Datasets as Experience

[glen]

I wrote and deleted a much longer response. But all I really want to say is that these *models* are heavily engineered. TANSTAAFL. They are as engineered, to intentional purpose, as a Boeing 777. We have this tendency to think that because these boxes are opaque (more so to some than others), they're magical or "semantic-less". They simulate a human language user pretty well. So even if there's little structural analogy, there's good behavioral analogy. Rather than posit that these models don't have semantics, I'd posit *we* don't have semantics.

The problem with communication is the illusion that it exists.

On 2/7/23 14:16, Steve Smith wrote:
> DaveW -
> 
> I really don't know much of/if anything really about these modern AIs, beyond what pops up on the myriad popular science/tech feeds that are part of *my* training set/source.   I studied some AI in the 70s/80s and then "Learning Classifier Systems" and (other) Machine Learning techniques in the late 90s, and then worked with folks who did Neural Nets during the early 00s, including trying to help them find patterns *in* the NN structures to correlate with the function of their NNs and training sets, etc.
> 
> The one thing I would say about what I hear you saying here is that I don't think these modern learning models, by definition, have neither syntax *nor* semantics built into them..   they are what I colloquially (because I'm sure there is a very precise term of art by the same name) think of or call "model-less" models. At most I think the only models of language they have explicit in them might be the Alphabet and conventions about white-space and perhaps punctuation?   And very likely they span *many* languages, not just English or maybe even "Indo European".
> 
> I wonder what others know about these things or if there are known good references?
> 
> Perhaps we should just feed thesemaunderings into ChatGPT and it will sort us out forthwith?!
> 
> - SteveS
> 
> 
> On 2/7/23 2:57 PM, Prof David West wrote:
>> I am curious, but not enough to do some hard research to confirm or deny, but ...
>>
>> Surface appearances suggest, to me, that the large language model AIs seem to focus on syntax and statistical word usage derived from those large datasets.
>>
>> I do not see any evidence in same of semantics (probably because I am but a "bear of little brain.")
>>
>> In contrast, the Cyc project (Douglas Lenat, 1984 - and still out there as an expensive AI) was all about semantics. The last time I was, briefly, at MCC, they were just switching from teaching Cyc how to read newspapers and engage in meaningful conversation about the news of the day, to teaching it how to read the National Enquirer, etc. and differentiate between syntactically and literally 'true' news and the false semantics behind same.
>>
>> davew
>>
>>
>> On Tue, Feb 7, 2023, at 11:35 AM, Jochen Fromm wrote:
>>> I was just wondering if our prefrontal cortex areas in the brain contain a large language model too - but each of them trained on slightly different datasets. Similar enough to understand each other, but different enough so that everyone has a unique experience and point of view o_O
>>>
>>> -J.
>>>
>>>
>>> -------- Original message --------
>>> From: Marcus Daniels <marcus at snoutfarm.com>
>>> Date: 2/6/23 9:39 PM (GMT+01:00)
>>> To: The Friday Morning Applied Complexity Coffee Group <friam at redfish.com>
>>> Subject: Re: [FRIAM] Datasets as Experience
>>>
>>> It depends if it is given boundaries between the datasets.   Is it learning one distribution or two?
>>>
>>>
>>> *From:* Friam <friam-bounces at redfish.com> *On Behalf Of *Jochen Fromm
>>> *Sent:* Sunday, February 5, 2023 4:38 AM
>>> *To:* The Friday Morning Applied Complexity Coffee Group <friam at redfish.com>
>>> *Subject:* [FRIAM] Datasets as Experience
>>>
>>>
>>> Would a CV of a large language model contain all the datasets it has seen? As adaptive agents of our selfish genes we are all trained on slightly different datasets. A Spanish speaker is a person trained on a Spanish dataset. An Italian speaker is a trained on an Italian dataset, etc. Speakers of different languages are trained on different datasets, therefore the same sentence is easy for a native speaker but impossible to understand for those who do not know the language.
>>>
>>>
>>> Do all large language models need to be trained on the same datasets? Or could many large language models be combined to a society of mind as Marvin Minsky describes it in his book "The society of mind"? Now that they are able to understand language it seems to be possible that one large language model replies to the questions from another. And we would even be able to understand the conversations.
>>>


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