IEEE Technology and Society Magazine - September 2023 - 8

Editorial
Instead, we seem to be having mostly a distractive
conversation about singularities, which is arguably
a domain of sheer speculation, rather than a more
pressing conversation about " the system " that produced
the technology and what it is doing right now
to people, society, and processes, in particular, the
educational process. Therefore, starting from a
viewpoint on education as the democratization of
knowledge, this article presents six talking points as
a contribution to the conversation about those considerations,
especially with respect to education.
Democratization of knowledge
The history of education, together with the construction
of infrastructure and the application of technology
to support that education, from schools and
libraries for the former, and the printing press and
the internet for the latter, has arguably been about
the democratization of knowledge, where in this context
" democratization " means providing comprehensive
access to knowledge that is relevant, accurate,
age-appropriate, up-to-date and meaningful (whether
this has been a key driver in the history of education
is arguably a different matter). The outcomes of education
include the transformation of an individual
from uninitiated to initiated in democratic citizenship
[1], civic participation and living lives together
(better) [2], and as human beings exercising the fullness
of their capacities (e.g., Aristotle's eudaemonia).
This process of democratizing knowledge has, of
course, been accelerated in the years since the creation
of the internet and mobile communications, and
their global penetration in terms of time, geography,
and population. Indeed, in the 2020s, there is a generation
for some of whom the distinction between
" online " and " offline " is almost meaningless, and
distinctions of space (between physical and digital)
and personhood (between body and avatar, or avatars)
are increasingly blurred. For such people, the
sequence of technological developments that leads
from gopher site to search engine to voice-activated
virtual assistant to large language model (LLM) is
almost entirely natural and inevitable: each is simply
providing increasingly convenient and broader
access to a near-complete archive of human knowledge
and creativity (i.e., complete to the extent of
what knowledge has been digitalized up to the time
of the model's training).
In terms of the democratization of knowledge,
LLM, as used in generative AI chatbots, can offer
8
a remarkably powerful, effective, and convenient
way of accessing knowledge and solving problems
that would otherwise have required consulting a
" guru, " a Q&A website (such as stackexchange),
or, in pre-internet/online manual days, holding four
different pages of a programming language manual
open with one hand and trying to type code with the
other. At the same time, chatbots such as ChatGPT
(and its successors) and text-to-image generators
(such as DALL-E) have caught popular public attention.
It can seem that almost everyone has " had a
go " and also has an anecdote about what " humorous "
output has been generated by their particular
interactive session with what turns out to be, in this
usage, a rather naff conversation piece, albeit one
that does answer back in a reasonably recognizable
form of the user's own language.
Quite why this
large-scale attention-grabbing
should have occurred is not entirely clear: perhaps,
generative AI will prove to be as iconic to the " information
revolution " as the steam engine is to the
Industrial Revolution; or, perhaps, the technology
reached a tipping point that created a pressing social
need to have chatbot experience to participate in
social interaction and alleviate the FOMO; or, alternatively,
it was the typically overexaggerated existential
threats to humankind which, as it turns out, are
rather good for share price. But, in the best spirit of
Marshall McCluhan, while it is probably for the better
that there is some conversation about the medium,
there really needs to be a broader and deeper conversation
about the message, that is, the impact that
technology has on people, society, and processes.1
There are, of course, several insightful commentaries
on the message, for example, Malik [3], Benson
[4], and Klein [5] to pick just a few, although
their voices are seemingly drowned out by more
sensationalist commentary on the medium. Klein
[5] makes a particularly cogent critique of hallucinations.
A curious outcome of generative AI has
been called an (artificial) hallucination, whereby the
AI/LLM produces a plausible output that, however,
does not seem to be warranted by the training data
(in which sense it differs from a human hallucination
in that it generates a false exterior output rather than
an interior experience based on a false perception).
1To be fair, OpenAI's technical report on ChatGPT-4 does say: " GPT-4's capabilities and
limitations create significant and novel safety challenges, and we believe careful
study of these challenges is an important area of research given the potential
societal impact " [7, p. 2]. This article is a dialectical contribution to this study (others
can be the judge of its carefulness).
IEEE Technology and Society Magazine
IEEE Technology and Society Magazine - September 2023

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