I read that Bing won’t write a cover letter if asked. I love the idea of a set
of files sitting on a filesystem at Microsoft that represent human ethics. It
reminds me of people that complain about being characterized by their skill
sets. I think we are going to learn just how little we are.
> On Feb 8, 2023, at 10:14 AM, glen <geprope...@gmail.com> wrote:
>
> I've recently developed a taste for judging people by the content of their
> character, something I used to and kindasorta still do denigrate as hubris
> (because our models of others' character are models, always wrong, rarely
> useful). And one of the best measures of character is how someone responds
> when presented with a "learning opportunity". ChatGPT is an extraordinary
> mansplainer. And even though, when you show it facts that contradict it's
> prior opinion, it gives lip-service with words like "sorry", it will continue
> to *confidently* spout half-truths and rhetorical bullsh¡t (even if you ask
> it to, say, write a LCG pRNG in C). Just like the tendency of apps like
> Stable Diffusion to "pornify" women, ChatGPT encodes the culture of its
> input. So if ChatGPT is a mansplainer or evil, in any sense, it's *because*
> the culture from which it draws its input is that. I.e. It's a mirror. We are
> evil. We are dreadworthy.
>
> BTW, I did a back of the envelope calculation and the cost of operating one
> (small) 777 per day seems to be about the same as operating the one ChatGPT
> per day (~$100k). Presumably, if/when OAI begins distributing the model, such
> that there are several of them out there (like the fleets of 777s), the costs
> will be lower. At that point, the semantic content of one 777 might exceed
> that of one ChatGPT instance.
>
>> On 2/8/23 09:08, Santafe wrote:
>> It’s funny. I was reading some commentary on this last week (can’t even
>> remember where now; that was _last week_!), and I remember thinking that the
>> description reminded me of Williams Syndrome in people. They have a
>> grammatical sense that is at the stronger end of the human range, but their
>> train of meaning has come to be characterized (again, a now-tropish
>> short-hand) as “word salad”.
>> That there should be several somewhat-autonomous processes running in
>> parallel in people, and coupled by some kind of message-passing, as Ray
>> Jackendoff proposes, seems quite reasonable and in keeping with brain
>> biology, and if there is, it would be a compact way to account for the
>> seeming independence in refinement of grammatical sense and whatever other
>> part of sentence-coherence we have come to term “semantics”.
>> Last year, too, someone (I think my boss at the time, which would make it
>> two years ago) told me about some nature paper saying that a comparative
>> genome analysis of domestic dogs and wolves had shown a mutation in the dogs
>> at the cognate locus to the one that results in Williams Syndrome in people.
>> That would be an easy indulgent interpretation: the greater
>> affectionateness preserved into adulthood, and the increased verbal-or-other
>> communicativeness. Though Barry Lopez, I think it was, argues that wolves
>> have higher social intelligence, which I guess would be making some claim
>> about a “semantics”.
>> The chatbot has, however, a knd of pure authentic evil that Philip K. Dick
>> tried to mimic (the argument with the door), and came close enough to be
>> laughing-through-tears, but could not truly simulate as it shines through in
>> the Ginsparg exchange. Or dealing with the maddening, horrifying computer
>> interfaces that every company puts up to its customers, after they have
>> fired all the human problem-solvers. Few things put me in a real dread,
>> because I am now fairly old, and getting older as fast as I can. But the
>> prospect of still being alive in a world where that interface is all that is
>> left to any of us, is dreadworthy.
>> Eric
>>>> On Feb 8, 2023, at 11:51 AM, glen <geprope...@gmail.com> wrote:
>>>
>>> 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 <mar...@snoutfarm.com>
>>>>>> Date: 2/6/23 9:39 PM (GMT+01:00)
>>>>>> To: The Friday Morning Applied Complexity Coffee Group
>>>>>> <friam@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-boun...@redfish.com> *On Behalf Of *Jochen Fromm
>>>>>> *Sent:* Sunday, February 5, 2023 4:38 AM
>>>>>> *To:* The Friday Morning Applied Complexity Coffee Group
>>>>>> <friam@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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>
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