TITLE: A Spectator to Phase Change
AUTHOR: Eugene Wallingford
DATE: March 12, 2023 9:00 AM
DESC:
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BODY:
Robin Sloan speculates that language-learning models like ChatGPT
have gone through
a phase change
in what they can accomplish.
AI at this moment feels like a mash-up of programming and biology.
The programming part is obvious; the biology part becomes apparent
when you see AI engineers probing their own creations the way
scientists might probe a mouse in a lab.
Like so many people, I find my social media and blog feeds filled
with ChatGPT and LLMs and DALL-E and ... speculation about what
these tools mean for (1) the production of text and code, and (2)
learning to write and program. A lot of that speculation is
tinged with fear.
I admire Sloan's effort to be constructive in his approach to the
uncertainty:
I've found it helpful, these past few years, to frame my anxieties
and dissatisfactions as questions. For example, fed up with the
state of social media, I asked: what do I want from the internet,
anyway?
It turns out I had an answer to that question.
Where the GPT-alikes are concerned, a question that's emerging for
me is:
What could I do with a universal function — a tool for
turning just about any X into just about any Y with plain language
instructions?
I admit that I am reacting to these developments slowly compared
to many people. That's my style in most things: I am more likely
to under-react to a change than to over-react, especially at the
onset of the change. In this case, there is no chance of
immediate peril, so waiting to see what happens as people use
these tools seems like a reasonable reasonable. I haven't made
any effort to use these tools actively (or even been moved to),
so any speculating I do would be uninformed by personal experience.
Instead, I read as people whose work I respect experiment with
these tools and try to make sense of them. Occasionally, I draw
a small, tentative conclusion, such as that
prompting these generators is a lot like prompting students.
After a few months of reading and a little reflection, I still
think the biggest risk we face is probably that
we tend to change the world around us to accommodate our technologies.
If we put these tools to work for us in ways that enhance what we
do, then the accommodation will pay off. If not, then we may, as
Daniel Steinberg wrote in one of his newsletters, stop asking the
questions we want to ask and start asking only the questions these
tools can answer.
Professionally, I think most about the effect that ChatGPT and its
ilk will have on programming and CS education. In these regards,
I've been paying special attention to reports from David Humphrey,
such as
this blog post
on his university's attempt to grapple the widespread availability
of these tools. David has approached OpenAI with an open mind and
written thoughtfully about the promise and the risk. For example,
he has
written a lot of code
with an LLM assistant and found it improving his ability both to
write code and to think about problems. Advanced CS students can
benefit from this kind of assistance, too, but David wonders how
such tools might interfere
with students first learning to program.
What do we educators want from generative programming tools anyway?
What do I as a programmer and educator want from them?
So, at this point, my personal interaction with the phase change
that Sloan describes has been mostly passive: I read about what
others are doing and think about the results of their exploration.
Perhaps this post is a guilty conscience asserting that I should be
doing more. Really, though, I think of it more as an active form
of inaction: an effort to collect some of my thinking as I slowly
respond to the changes that are coming. Perhaps some day soon I
will feel moved to use of these tools as I write code of my own.
For now, though, I am content to watch from the sidelines. You can
learn a lot just by watching.
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