These experts on AI are here to help us understand important things about AI.
Who are these generous, helpful experts that the CBC found, you ask?
“Dr. Muhammad Mamdani, vice-president of data science and advanced analytics at Unity Health Toronto”, per LinkedIn a PharmD, who also serves in various AI-associated centres and institutes.
“(Jeff) Macpherson is a director and co-founder at Xagency.AI”, a tech startup which does, uh, lots of stuff with AI (see their wild services page) that appears to have been announced on LinkedIn two months ago. The founders section lists other details apart from J.M.'s “over 7 years in the tech sector” which are interesting to read in light of J.M.'s own LinkedIn page.
Other people making points in this article:
C. L. Polk, award-winning author (of Witchmark).
“Illustrator Martin Deschatelets” whose employment prospects are dimming this year (and who knows a bunch of people in this situation), who per LinkedIn has worked on some nifty things.
“Ottawa economist Armine Yalnizyan”, per LinkedIn a fellow at the Atkinson Foundation who used to work at the Canadian Centre for Policy Alternatives.
Could the CBC actually seriously not find anybody willing to discuss the actual technology and how it gets its results? This is archetypal hood-welded-shut sort of stuff.
Things I picked out, from article and round table (before the video stopped playing):
Does that Unity Health doctor go back later and check these emergency room intake predictions against actual cases appearing there?
Who is the “we” who have to adapt here?
AI is apparently “something that can tell you how many cows are in the world” (J.M.). Detecting a lack of results validation here again.
“At the end of the day that’s what it’s all for. The efficiency, the productivity, to put profit in all of our pockets”, from J.M.
“You now have the opportunity to become a Prompt Engineer”, from J.M. to the author and illustrator. (It’s worth watching the video to listen to this person.)
Me about the article:
I’m feeling that same underwhelming “is this it” bewilderment again.
Me about the video:
Critical thinking and ethics and “how software products work in practice” classes for everybody in this industry please.
It's worse that "copy-pasting from stack-overflow" because the LLM actually loses all the answer trustworthiness context (i.e. counts and ratios of upvotes and downvotes, other people's comments).
That thing is trying to find the text tokens of answer text nearest to the text tokens of your prompt question in its text token distribution n-dimensional space (I know it sound weird, but its roughly how NNs work) and maybe you're lucky and the highest probability combination of text-tokens was right there in the n-dimensional space "near" your prompt quest text-tokens (in which case straight googling it would probably have worked) or maybe you're not luck and it's picking up probabilistically close chains of text-tokens which are not logically related and maybe your're really unlucky and your prompt question text tokens are in a sparcelly populated zone of the n-dimensional text space and you're getting back something starting and a barelly related close cluster.
But that's not even the biggest problem.
The biggest problem is that there is no real error margin output - the thing will give you the most genuine, professional-looking piece of output just as likely for what might be a very highly correlated chain of text-tokens as for what is just an association of text tokens which is has a low relation with your prompt question text-token.