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 <sasm...@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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