Saved by Keely Adler and
The Myth of Objective Data
How is it even possible to maintain a data-driven approach to a world that simply does not make sense?
This is the future of work skill, which no one wants to talk about: Knowing when to look beyond the data, because only human intuition, leaps of logic, and sense-making can come to ‘an’ answer. (Note: Not ‘the’ answer... that’s another trapping of
... See moreMatt Klein • 3_TRENDS_Vol.27: Elliot Aronow: Sarapocial Relationships, Senseless Data + Boy Talk
alexi gunner added
Datasets aren’t simply raw materials to feed algorithms, but are political interventions. As such, much of the discussion around 'bias' in AI systems misses the mark: there is no 'neutral,' 'natural,' or 'apolitical' vantage point that training data can be built upon. There is no easy technical 'fix' by shifting demographics, deleting offensive ter... See more
L. M. Sacasas • The Uncanny Gaze of the Machine
Alex Wittenberg added
As scientific practice has matured, we’ve come to understand the value of interpretation, situated knowledge and trained judgement; but this fallacy has been perpetuated by our technologies, which flatten and lump together the myriad different expressions of the world.
James Bridle • Ways of Being: Animals, Plants, Machines: The Search for a Planetary Intelligence
Objectivity is an elusive and valuable product because what constitutes knowledge itself is always in flux. Modernity is defined by there being more to know than you can ever learn, and the fact that there is no final say on what is true. What you think is right and true has a lot to do with who you are and where you’re from. No matter how many fac... See more
Real Life Mag • OBJECTIVITY — Real Life
Darren LI and added
Why it’s a problem that AI-generated art assumes that data is the best/truest way to understand the world.
#704: Corporate cringe and ex-texts
Agalia Tan added
Darren LI added
There is a vast difference between understanding how answers are arrived at and simply being informed of their outcomes. As soon as digital computation was capable of the kind of complexities needed to model natural processes – to take on the tasks which were previously only possible in physical simulators, like the water computers – knowledge of t
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