[FRIAM] Datasets as Experience

[Prof David West]

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.
>  
> -J.
>  
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