Hello friends! If you would like to read some engrossing longform about Israel/Palestine this weekend (maybe with a good coffee or tea?)
I strongly recommend my newly-published article on the causes of the Oct 7 crisis in @newlinesmag:
Hello friends! If you would like to read some engrossing longform about Israel/Palestine this weekend (maybe with a good coffee or tea?)
I strongly recommend my newly-published article on the causes of the Oct 7 crisis in @newlinesmag:
Hello friends! If you would like to read some engrossing longform about Israel/Palestine this weekend (maybe with a good coffee or tea?)
I strongly recommend my newly-published article on the causes of the Oct 7 crisis in @newlinesmag:
over the past few days I have found my little corner of the bird app completely taken over by people who think an evil superintelligent AI is going to take over the world and destroy humanity
I do not know where all these people came from
it is fundamentally different from the fear of computers taking our jobs
and it is coming from inside the tech community
and challenging that narrative results in incredibly vitriolic responses
my most curmudgeonly current opinion is that
people who think a machine learning algorithm trained on internet language is on the verge of doing hard intellectual work
have no idea what hard intellectual work is actually like
the slow, painful, migraine-inducing, comprehensively-researched, logically-contiguous labour of love that goes into every great work of art, science, and literature
the deep skill that takes years to hone like a fine blade
Because quality work isn’t just about sourcing information or assembling words. It’s about having something you desperately want to say and struggle to first understand, then communicate, and fight over and over and over again to clarify enough to get onto a page.
And if you’ve never experienced that beautiful, infuriating agony, it’s easy to think a machine can just generate ideas and solve hard problems.
Because you don’t see the work that goes in, and think it’s a simple matter or serendipity, “intelligence”, or pattern-matching.
The following needs to be stated clearly:
People in tech are not afraid of AI.
They are afraid of an incumbent monopoly instantly rolling out a net-new innovation to every enterprise business before startups can even play around with it.
Which is a terrifying new dynamic!
An entire community who believed in their heart of hearts that “startups innovate, incumbents catch up” just saw that narrative undeniably violated on a world-changing technology.
And that panic is more a byproduct of worldview collapse than anything else.
I think in dependency trees a lot. Not just in terms of engineering or resource planning, but structured innovation, too.
“What does success look like and what would we need to know to get there” is as valid a question as any about personnel, capital, time, or infrastructure.
there’s a flavour of tech thinking that starts from the assumption that everything is already known. There is no place for science or discovery in this worldview; anything unknown can merely be derived from sets of arbitrary assumptions called “first principles”.
This thinking results in problems being redefined to fit available solutions, instead of being solved by deeply studying a problem and solving the unknowns until a proper solution becomes possible.
Which makes it a worldview devoid of real innovation.
This, she says, is because of history. In the 1970s, news orgs began providing continuous FOREX updates to banks. The move was hotly debated at the time, as people worried it would make journalists more like stock traders (but for information) than public interest reporters.
While there was already competition between newsrooms, the addition of continuous data and an algorithmic trading component transformed news orgs from reporters to newsmakers
similar to how hedge funds went from investors to marketmakers
so the real innovation is as much about how to redesign the role of the human-in-the-loop to add value
as it is to employ new technological advancements to keep up with the increasing pace of information
we are already drowning in content; just producing more reduces its value