cory on ai

cory doctorow on ai

via this thread:

My latest @locusmag column is “Past Performance is Not Indicative of Future Results,” an essay about the limits of machine learning and the reason that statistical inference will not lead to consciousness.

https://t.co/IJJfeDnPR4

1/ https://t.co/scVuVPApBb

Original Tweet: https://twitter.com/doctorow/status/1323460840947765248

@doctorow: At its core, machine-learning is *”theory free correlation-detection” – that is, it takes training data and finds things that appear together in it. Two things labeled an eye and one thing labeled a nose and one thing labeled a mouth all add up to a face. 2/

*this is exactly what we need tech to be.. not artificial intelligence but rather.. tech w/o judgment.. to match dot com us via the daily curiosities of 8b people

imagine if we just focused on listening to the itch-in-8b-souls.. first thing.. everyday.. and used that data to augment our interconnectedness.. we might just get to a more antifragile, healthy, thriving world.. the ecosystem we keep longing for..

what the world needs most is the energy of 8b alive people.. not intellect ness

mufleh humanity lawwe have seen advances in every aspect of our lives except our humanity– Luma Mufleh

@doctorow: But the classifier doesn’t know what a noses, eyes, or mouths are. It doesn’t know what a face is. Your doorbell camera doesn’t know that the face-like thing in the melting snow on your walk CAN’T be a face, so it repeatedly warns you about a stranger on your doorstep. 3/

this is perfect for us.. in order to find/maintain our fittingness

@doctorow: That theory-free-ness, combined with the abstruse mathematics of statistics, is what gets “AI” into so much *trouble. Give machine learning classifiers of all the successful people at your company and it will tell you to hire people like them. 4/

this *trouble is because we’re not using tech as it could be

we’re expecting it to do things it can (as you say) never do.. and then overcoding (over biasing) it (the algos) to make up for our misuse of it

@doctorow: But if you’ve been missing great people due to bias, that is terrible advice – and it’s got the veneer of empiricism. Remember when AOC got tons of shit from far-right assholes for calling an algorithm racist? How can math be racist? https://livescience.com/64621-how-algorithms-can-be-racist.html… 5/

@doctorow: Theory-free isn’t good enough. To understand what’s happening in a complex situation, you haven’t to be an anthropologist, not just a statistician. You need what Clifford Geertz called “thick description” – the qualitative accounts of the quantitative phenomenon. 6/

ie: self-talk as data.. the itch-in-8b-souls .. everyday.. a new..

let’s quantify/multiply that expo ness

@doctorow: Quantitative researchers are infamous for screwing this up. The qualitative elements are hard to do math on, so they incinerate them and leave behind a *quantitative residue and do math on that, assuming it will be sufficient. It’s (usually) not. 7/

*not only usually not.. but it’s killing us

we need to let go of any form of measuring/accounting

@doctorow: That’s why exposure notification isn’t contact tracing: knowing that two Bluetooth radios were close to each other for 15 minutes doesn’t tell you if they were swapping spit or stuck in adjacent, sealed automobiles in a traffic jam. 8/

@doctorow: Using theory-free inference to understand the world doesn’t and can’t lead to comprehension. “Theory-free” is the opposite of comprehension. We may not have a universal, agreed-upon definition of “artificial intelligence” but “understanding” is definitely a part of it. 9/

i think that’s the wrong focus for tech.. for what we could be using it for (ie: to free people)

ai humanity needs: augmenting interconnectedness

www ness et al

@doctorow: Machine-learning classifiers have done amazing things to automate away a ton of drudgery, just as smiths did amazing things to shape metal. But smiths couldn’t make reliable internal combustion engines. Incremental improvements in metal-beating don’t evolve into machining. 10/

@doctorow: Reliably turning out the precision components that produced engines needed casting and machining. Getting there required a shift in approaches, not improvement in the existing approach. 11/

@doctorow: Theory-free statistical inference does a lot of good stuff – and produces a lot of bad outcomes – but the idea that if we do enough of it we’ll get artificial intelligence is fundamentally wrong. eof/

and i think us focusing on intellect ness (augmenting human intellect; augmenting collective intelligence; et al).. is not boding us well..

decision making is unmooring us law

we need to let go of all that.. and instead .. focus on undoing our hierarchical listening to self/others/nature

our findings:

1\ undisturbed ecosystem (common\ing) can happen

2\ if we create a way to ground the chaos of 8b free people

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