[FRIAM] AI possibilities
Steve Smith
sasmyth at swcp.com
Fri Apr 7 13:14:43 EDT 2023
On 4/7/23 10:48 AM, Stephen Guerin wrote:
> As I wrote the prompt, I was wondering how it might deal with the
> ambiquity of color for these objects.
>
> Prompt: What might a fire engine, an apple and a rose have in common
> ChatGPT4:
> A fire engine, an apple, and a rose might not appear to have much in
> common at first glance, as they belong to different
> categories—vehicles, fruits, and flowers, respectively. However, one
> possible commonality among them is their color: red. Fire engines are
> typically red, apples can be red (among other colors), and roses can
> also be red. The color red is often associated with strong emotions
> such as love, passion, and urgency, which may be another connection
> among these seemingly unrelated object
After reading the article and dipping into the paper, I asked chatGPT4
about the differences of color perception, naming and organization
across cultures, the answer was pretty mundane and mealy-mouthed IMO.
I was hoping for a more subtle or extravagant treatment. I will be
trying to develop my "prompt engineer" skills more based on this topic...
>
> On Fri, Apr 7, 2023, 10:15 AM Steve Smith <sasmyth at swcp.com> wrote:
>
> I tripped over (in my Gnewsfeed) an article that seemed to speak
> more clearly
> <https://www.marktechpost.com/2023/04/06/8-potentially-surprising-things-to-know-about-large-language-models-llms/>
> to some of my maunderings:
>
>
> 8 Potentially Surprising Things To Know About Large Language
> Models LLMs
> <https://www.marktechpost.com/2023/04/06/8-potentially-surprising-things-to-know-about-large-language-models-llms/>
>
> And the paper it summarizes (with a similar title, more detail and
> references):
>
> 8 Things to know about Large Language Models - Samuel R
> Bowman <https://arxiv.org/pdf/2304.00612.pdf>
>
> And in particular this point made:
>
> 3. *LLMs frequently acquire and employ external-world
> representations.*
>
> More and more evidence suggests that LLMs build internal
> representations of the world, allowing them to reason at an
> abstract level insensitive to the specific language form of
> the text. The evidence for this phenomenon is strongest in the
> largest and most recent models, so it should be anticipated
> that it will grow more robust when systems are scaled up more.
> Nevertheless, current LLMs need to do this more effectively
> and effectively.
>
> The following findings, based on a wide variety of
> experimental techniques and theoretical models, support this
> assertion.
>
> * The internal color representations of models are highly
> consistent with empirical findings on how humans perceive
> color.
> * Models can conclude the author’s knowledge and beliefs to
> foretell the document’s future course.
> * Stories are used to inform models, which then change their
> internal representations of the features and locations of
> the objects represented in the stories.
> * Sometimes, models can provide information on how to depict
> strange things on paper.
> * Many commonsense reasoning tests are passed by models,
> even ones like the Winograd Schema Challenge, that are
> made to have no textual hints to the answer.
>
> These findings counter the conventional wisdom that LLMs are
> merely statistical next-word predictors and can’t generalize
> their learning or reasoning beyond text.
>
> On 4/6/23 8:27 AM, Steve Smith wrote:
>> I have been reading Jeff Hawkins' _1000 Brains_ which is roughly
>> *his* take on AI from the perspective of the Neuroscience *he*
>> has been doing for a few decades, including building models of
>> the neocortex.
>>
>> What struck me strongly was how much *I* expect anything I'd want
>> to call artificial *consciousness* to engage in "co-munnication"
>> in the strongest sense. Glen regularly admonishes us that
>> "communication" may be an illusion and something we don't
>> actually *do* or maybe more to the the point "it doesn't mean
>> what we think it means"?
>>
>> So for all the parlor tricks I've enjoyed playing with chatGPT
>> and DALL-E and maybe even more spectacularly the myriad examples
>> *others* have teased out of those systems, I am always looking
>> for what sort of "internal state" these systems are exposing to
>> me in their "utterances". And by extension, I am looking to see
>> if it is in any way apprehending *me* through my questions and
>> prompts.
>>
>> Dialog with chatGPT feels pretty familiar to me, as if I'm
>> conversing with an unusually polite and cooperative polymath.
>> It is freeing to feel I can ask "it" any question which I can
>> formulate and can expect back a pretty *straight* answer if not
>> always one I was hoping for. "It" seems pretty insightful and
>> usually picks up on the nuances of my questions. As often as
>> not, I need to follow up with refined questions which channel the
>> answers away from the "mundane or obvious" but when I do, it
>> rarely misses a trick or is evasive or harps on something from
>> it's own (apparent) agenda. It only does that when I ask it
>> questions about it's own nature, formulation, domain and then it
>> just seems blunted as if it has a lawyer or politician
>> intercepting some of those questions and answering them for it.
>>
>> I have learned to "frame" my questions by first asking it to
>> defer it's response until I've given it some ... "framing" for
>> the actual question. Otherwise I go through the other series of
>> steps where I have to re-ask the same question with more and more
>> context or ask a very long and convoluted question. At first it
>> was a pleasure to be able to unlimber my
>> convoluted-question-generator and have it (not mis) understand me
>> and even not seem to "miss a trick". As I learned to generate
>> several framing statements before asking my question, I have
>> found that I *can* give it too many constraints (apparently) such
>> that it respects some/most of my framing but then avoids or
>> ignores other parts. At that point I have to ask follow-up,
>> elaborating, contextualizing questions.
>>
>> I do not yet feel like I am actually seeing into chatGPT's soul
>> or in any way being seen by it. That will be for a future
>> generation I suspect. Otherwise it is one hella "research
>> assistant" and "spitball partner" on most any topic I've
>> considered that isn't too contemporary (training set ended 2021?).
>>
>> - Steve
>>
>> On 4/4/23 5:54 PM, Prof David West wrote:
>>> Based on the flood of stories about ChatAI, it appears:
>>> - they can 'do' math and 'reason' scientificdally
>>> - they can generate essays, term papers, etc.
>>> - they can engage in convincing dialog/conversations
>>> - as "therapists"
>>> - as "girlfriends" (I haven't seen any stories about women
>>> falling in love with their AI)
>>> - as kinksters
>>> - they can write code
>>>
>>> The writing code ability immediately made me wonder if, given a
>>> database of music instead of text, they could write music?
>>>
>>> The dialog /conversation ability makes me wonder about more
>>> real-time collaborative interaction, improv acting / comedy? Or,
>>> pair programming? The real-time aspect is critical to my
>>> question, as I believe there is something qualitatively
>>> different between two people doing improv or pair programming
>>> than simply engaging in dialog. I think I could make a much
>>> stronger argument in the case of improv music, especially jazz,
>>> but AIs aren't doing that yet.
>>>
>>> davew
>>>
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