As Dave knows, I invited him to speak to Congressional digital directors based on his Analytics Activism book, it was the first book I read that I felt understood how data could fit into an organization like Congress that wasn't entirely concerned with profit.
I think he is underselling how useful it was after 2016. I found it plenty useful and I know a lot of the digital directors appreciated the talk.
The greater concern was exactly the problem he is describing now: data is both not understood and when it was used it was used in a way to try to remove decision makers from hard choices. Also the lack of goals that congressional offices had for themselves contributed to massive confusion about what analytics digital directors are supposed to be collecting.
At the time reading it, I was struck by how much time was dedicated to talking about Google's ranking of web pages and the race that ensued to game the pagerank system. This was before my time doing digital work (we were mostly focused on social media platforms and email) and it reminded me that very little of the dynamics have changed in what is supposed to be a cutting edge space.
Big companies control information spaces on their own terms. You can try to squeeze out the utility that is given either intentionally or accidentally but the problem isn't fundamentally a tech problem, it's a power problem.
Thinking of predictive AI as "just a rebrand of the Big Data hype bubble from 10-15 years ago" seems right, and the habit of substituting "data for strategy" nicely sums up the real risks of many forms of AI, including the kind that generates cultural artifacts.
Deflating AI hype requires pointing out all the ways AI doesn't work, but it also requires pointing out how truly terrible using it can be when it works as designed to make some types of management decisions. Davies is a great example of how to think about this problem in terms of organizations, as is The Ordinal Society. Analytical Activism is now on my list.
The recent chemistry Nobel was for the prediction of protein structure. Describing it as being for โthe study of chemical compoundsโ is like saying โthe Nobel Prize in chemistry was given for the study of chemistry.โ
I was wondering what had become of "Big Data" as the Next Big Thing. In the 2016 time frame my old company was going through another bout of reinventing itself. Part of this was supposed to involve the retraining of large numbers of employees to save their jobs by going into Big Data, Computer Security, or VMware. Like all of these programs in my experience, a year or two later it was all forgotten except the out-sourcing part. Almost none of the folks who retrained ever found inside work in the new specialties. Big Data just disappeared as a term along with the groups doing it. Maybe they reinvented themselves as something else. Computer Security groups existed but never hired any of the trainees that I heard of. The same with the VMware group. Within 2-3 years VMware was replaced by "The Cloud" as the Next Big Thing and is today a shell of what it was for a brief period. It makes me wonder after NFTs and Crypto if AI as a thing will pass or if it will be applied to whatever follows LLMs. It's nice to learn that other folks still remember the Siren Song of Big Data!
As Dave knows, I invited him to speak to Congressional digital directors based on his Analytics Activism book, it was the first book I read that I felt understood how data could fit into an organization like Congress that wasn't entirely concerned with profit.
I think he is underselling how useful it was after 2016. I found it plenty useful and I know a lot of the digital directors appreciated the talk.
The greater concern was exactly the problem he is describing now: data is both not understood and when it was used it was used in a way to try to remove decision makers from hard choices. Also the lack of goals that congressional offices had for themselves contributed to massive confusion about what analytics digital directors are supposed to be collecting.
At the time reading it, I was struck by how much time was dedicated to talking about Google's ranking of web pages and the race that ensued to game the pagerank system. This was before my time doing digital work (we were mostly focused on social media platforms and email) and it reminded me that very little of the dynamics have changed in what is supposed to be a cutting edge space.
Big companies control information spaces on their own terms. You can try to squeeze out the utility that is given either intentionally or accidentally but the problem isn't fundamentally a tech problem, it's a power problem.
Thinking of predictive AI as "just a rebrand of the Big Data hype bubble from 10-15 years ago" seems right, and the habit of substituting "data for strategy" nicely sums up the real risks of many forms of AI, including the kind that generates cultural artifacts.
They don't use the term in the book, but last year the Snake Oil guys put out a paper with a few others on what they call "predictive optimization," which I think helps frame this danger. Here is a link to post about it: https://www.aisnakeoil.com/p/ai-cannot-predict-the-future-but?utm_source=publication-search
Deflating AI hype requires pointing out all the ways AI doesn't work, but it also requires pointing out how truly terrible using it can be when it works as designed to make some types of management decisions. Davies is a great example of how to think about this problem in terms of organizations, as is The Ordinal Society. Analytical Activism is now on my list.
Thanks mentioning my review essay!
The recent chemistry Nobel was for the prediction of protein structure. Describing it as being for โthe study of chemical compoundsโ is like saying โthe Nobel Prize in chemistry was given for the study of chemistry.โ
Good note, thanks.
I was wondering what had become of "Big Data" as the Next Big Thing. In the 2016 time frame my old company was going through another bout of reinventing itself. Part of this was supposed to involve the retraining of large numbers of employees to save their jobs by going into Big Data, Computer Security, or VMware. Like all of these programs in my experience, a year or two later it was all forgotten except the out-sourcing part. Almost none of the folks who retrained ever found inside work in the new specialties. Big Data just disappeared as a term along with the groups doing it. Maybe they reinvented themselves as something else. Computer Security groups existed but never hired any of the trainees that I heard of. The same with the VMware group. Within 2-3 years VMware was replaced by "The Cloud" as the Next Big Thing and is today a shell of what it was for a brief period. It makes me wonder after NFTs and Crypto if AI as a thing will pass or if it will be applied to whatever follows LLMs. It's nice to learn that other folks still remember the Siren Song of Big Data!
"You donโt have to make hard strategic choices anymore. Just trust the augments. The AI might be a black box, but it has a genius inside."
The ol' implausible deniability trick.
I'll join Rob in thanking you for mentioning my review of Amodei's manifesto!
This is a big 'ol subtweet of Future Forward lol -- testing ourselves to meaninglessness
You have a "know" that should be a "no"
Great post, and AI Snake Oil is my next read.