6. But I’d still
like to buy an
elephant gun,
please.
I am NOT an
#Organism-Whole,
I am a
7.
8.
9.
10. Well, as I’m
conscious, my
actions – and my
symbols for those
actions – are
grounded in the
OK, so we’re NOT
grounded directly …
… but we ARE
grounded indirectly,
since we learn to use
symbols from the
texts arising from
YOUR grounded uses.
11.
12.
13.
14.
15.
16. My words have
never carried so
much meaning now
that I can directly
ground them via
actions in the
18. @everycolorbot is a minimalist
data-only Twitterbot.
The bot generates a random six-
digit color hex-code (for an R-G-B
Red/Green/Blue mix) and a
swatch of corresponding color.
Though very simple, can we say that this bot’s use of
RGB symbols is grounded in external visual reality?
19. Dulux uses pretentious names with positive effect, but a
bot might call this one “cow urine” or “rusty battleship”.
This bot would exhibit humor and visual appreciation,
while grounding its use of color symbols in real stimuli.
The RGB symbol-codes in
@everycolorbot are not used as
linguistic symbols, and are not
used to convey semantics.
What if we build a bot that
assigns meaningful color names
to these arbitrary RGB symbols?
20. First, let’s ground the
meaning of color
words in actual RGB
codes that a computer
can render on screen.
When a bot combines
color words, it can
also combine their
RGB color codes.
A compositional semantics for linguistic symbols is paired
to a compositional semantics for RGB codes,
so that we can also ground the meaning of complex phrases.
21.
22. We can use Web n-grams to
suggest attested combinations of
our color stereotypes, such as
“paper tiger” and “rose garden”.
Readymade combinations of
words make much more sense
than purely random ones.
23. The lower the n-gram frequency, the
less conventional the readymade ...
… so the more striking and unusual
the color name that can be derived.
