A really excellent piece from Derek on the history of community management online.
You have to decide what your platform is for and what it’s not for. And, yeah, that means deciding who it’s for and who it’s not for (hint: it’s not bots, nor nazis). That’s not a job you can outsource. The tech won’t do it for you. Not just because it’s your job, but because outsourcing it won’t work. It never does.
- Tech is dominated by “true believers” and those who tag along to make money.
- Politicians seem to be forever gullible to the promises of tech.
- Management loves promises of automation and profitable layoffs.
But it seems that the sentiment might be shifting, even among those predisposed to believe in “AI”, at least in part.
From smart dust and spimes, through to online journaling and social media, to machine learning, big data and digital preservation…
Is the archive where information goes to live forever, or where data goes to die?
Machine learning is amazing if … the value of a correct answer is much higher than the cost of an incorrect answer.
Related to Laissez-faire Cognitive Debt:
And that’s where I start to get really annoyed by a lot of the LLM hype. It’s pushing machine-learning approaches into places where there are significant harms for sometimes giving the wrong answer. And it’s doing so while trying to outsource the liability to the customers who are using these machines in ways in which they are advertised as working. It’s great for translation! Unless a mistranslated word could kill a business deal or start a war. It’s great for summarisation! Unless missing a key point could cost you a load of money. It’s great for writing code! Unless a security vulnerability would cost you lost revenue or a copyright infringement lawsuit from having accidentally put something from the training set directly in your codebase in contravention of its license would kill your business. And so on. Lots of risks that are outsourced and liabilities that are passed directly to the user.
I think of Cognitive Debt as ‘where we have the answers, but not the thinking that went into producing those answers’.
Lately, I have started noticing examples of not just where the debt is being accrued, but who then has the responsibility to pick it up and repay it.
Too often, an LLM doesn’t replace the need for thinking in a group setting, but simply creates more work for others.
An even-handed assessment of the benefits and dangers of machine learning.