Ton Zijlstra commented a great post by Henrik Karlsson about the large language model GPT-3, which caused me to finally try it out.
My first impression is similar to theirs: “Just wow”, and it took me quite a while until I reached some limits (in particular when asking GPT to “Write a fictitious debate between xxx and yyy about zzz.”)
One undeniable affordance, however, of the machine’s responses is to get inspirations and stimulation for consideration. This is also the big topic of the note-takers and zettlekastlers crowd, for example using the autolinking of “unlinked references”. And I am noticing that it is probably a matter of taste and preferences, or perhaps even a matter of different working styles: If I am permanently working at my limits there is no room left for organic associations, and then I might be more impressed by an abundance of ideas and artificial creativity?
Perhaps I am too much of an ungrateful grumpy killjoy, but the abundance of artificial serendipitous stimulations makes me think of how onerous it will be to sift through them all to find out which ones are the most relevant ones for me.
Let’s contrast this sort of inspiration with the sort that comes through blog reactions. Karlsson explicitly compares blog posts to search queries and to the new kind of ‘conversations’ that we can have with GPT-3, and I think it is indeed very appropriate to see the interaction with these tools as a ‘communication’. Also Luhmann used this metaphor for his Zettelkasten, as Ton points out, and when we use GPT, the back and forth of ‘prompts’ and ‘completions’ is a dialog, too. So there are many beneficial similarities to blog comments and trackbacks.
However, blog respondents are not anonymous mass products. They have a background. They care about the topic I write about, and I care about theirs. I subscribe to people whose interests are not always the same as mine but often still close enough to be inspiring. And I trust that it is relevant what they are writing. (Formerly, we talked about bloggers as ‘fuzzy categories‘ and about ‘online resonance‘ and about the skill of ‘picking‘ from the abundance.) The grounding in a shared context and a known background, makes it easier for me to understand, and benefit from, their reactions, probably in a similar way as neural ‘priming’ works.
This is all missing when I process suggestions from a machine that does not know me and that I don’t know (I don’t even know what it knows and what it merely confabulates, and at what point its algorithm switches to the live web to look up more). It is unpersonal — even if it may impersonate Plato in a debate.
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You’re absolutely right. There’s communication here, wrt GPT-3, but the distributed conversations between bloggers across their blogs form a very different creature. What you share ends up in my RSS filters, what I share ends up in yours, and this multiplied across the many feeds we follow creates feedback loops that lifts signals above the noise. In those feedback loops we are active agents, like I described at https://www.zylstra.org/blog/2005/09/information_str/ a long time ago.
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Thanks for demonstrating what we are talking about, and for the link from 2005 which already then contained the caveat of echo-chambers. I wonder how ‘personal’ AIs will strike the balance of avoiding these echo-chambers, vs. learning from, and pleasing, the customer. (I should probably try Feedly Leo but I can’t convince myself to use a terrible web interface instead of my QuiteRSS desktop reader.) Perhaps we will need a slider switch like GPT’s ‘temperature’ to select a level of diversity?
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