How was this assembled? From a meta point of view, how much AI was used to curate and highlite the goals; how much was used to assemble the site itself? Or deploy it?
6,367 karma · joined April 27, 2018
I have not worked at Google, but rather at 4 different companies that built automation machinery (ie multi discipline execution between mechanical, electrical, and software engineers). In none of those cases was it ever “the hacker ethos”. It was mid to upper level management whose incentive loops tended to be associated more with either shareholder perception or HR head counts. All of the engineers were usually begging the company to do a better job (allocate resources) to last mile customer support. Engineers build things, and are usually more satisfied if the products they build are well received because of the “last mile” bit where someone helps a customer appreciate their creation.
Hmmm… this is giving me thought actually. Given the choice between that and the current administration where the goals of self destruction are strongly in evidence, it’s actually worth thinking about. At least. Let me get back to you :)
On a tangential note, I’m curious if researchers have started running virtual simulations, where sandboxed AIs are used as decision makers of key political and business positions?
But I get your meaning. What should they be called instead? AI Sausage Factories maybe (cue Upton Sinclair?)?
Is it the case that the numbers always appear more optimistic, and then are revised to be more pessimistic values? Or is it just that the original numbers are off and are revised? Does that make sense?
Where my angst comes, is worrying that no one will ever get that experience anymore. They might have had some eventual success, who knows what monstrosity a much less guided LLM would have done, but experential learning may be mostly a thing of the past. And it creates a real tension between the person with experience and the person without.
The amount of times humanity has said this and time itself was not enough of an ingredient to achieve some anticipated outcome are legion. But we filter those out and go back to making more predictions based on the current linear derivative we’re observing.
Thanks for your paper btw. I did 20 years of Smalltalk. I came to view Smalltalk programming more like biological culture manipulation (you had a dish of live objects that you morphed into the result you wanted), whereas the conventional file->syntax->executable employed by most development systems is more akin to the industrial factory pipeline of assembling products from raw goods.
During those younger years, money mattered. Tools were sold on the premise that they reduced or mitigated the various costs inherint with employing programmers. And then... around the late 90s, that just kind of dropped from the discussion. As quantitave easing made money available to speculate with and hitting it big with the software that was "eating the world", things like web programming burgeoned with very little thought about "does this make it cheaper".
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Same for AI.
Where I struggle and find my ego self defensively screaming “But…!” is in work relationships. Product managers, where their wrongness makes my downstream life more miserable. Basically any relationship where I have a (self perceived) need for the outcome to be a certain way to protect/enhance my well being. Asymmetric relationships.
A: Copying Garbage Collector (semi space). Chapter 4!
Great book. I was always fascinated by bakers treadmill. Always wanted a real world case where I could implement one with Fibonacci sized mills.
Isn't that kind of Elon's thing?
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> "We're moving towards this 'one true answer' world. Before, Google would give you 10 blue links and you would kind of do your own research. But AI just gives you one answer. It becomes so easy to just take things at face value. You need to be careful."
In a world of insecurities, and a world where we crave out-facting or out-proving our fellow discussioners, this “one right answer” is like synthetic drugs to the social experience. And we suspect “it’s not good for us” but it’s just so damned addictive.
I work with two pretty green developers. The rate that they can make a mess is now phenomenal. And the sense of confidence the tools give them with early successes, means any experience I might have to offer means less now. Which is ok, I’m not going to be that “my experience has to be useful to you so I still fell relevant” old guy. But I do find myself curious how “lessons are learned” that lead to greater and greater tool exploitation in this brave new world.