rng-concern
Joined 2 years ago
Joined 2 years ago
Not my idea though, greater minds than I have talked about this a lot: Neil Postman, Lewis Mumford, etc..
My point was, their ethics WOULD have prevented them from doing the thing. But they chose not to think about it too hard. Perhaps subconsciously. I'm not above doing this sort of thing either. We all do it for various things.
I've added code that is bad for the user (overbearing telemetry for instance) because my salary depended on it. At the time I tried not to think about it too much, as it would cause cognitive dissonance.
It reminds me of the saying: "It Is Difficult to Get a Man to Understand Something When His Salary Depends Upon His Not Understanding It".
If these people thought about it for a few minutes, they would understand, but they choose not to, as ignoring it is too advantageous.
I admit I was tempted, as the price of all streaming services goes up, and services become more and more fragmented. During the same period where I have not had a raise.
Gamification is not a new word for an old idea. It's a new thing, at least in many contexts.
#1 seems a lot more human to me.
> Shipping first time code is like going into debt. A little debt speeds development so long as it is paid back promptly with a rewrite.... The danger occurs when the debt is not repaid. Every minute spent on not-quite-right code counts as interest on that debt. Entire engineering organizations can be brought to a stand-still under the debt load of an unconsolidated implementation, object-oriented or otherwise.
I own and drive 2 cars.
I'll just stop there. I don't feel this conversation can continue in a positive way.
It seems the fact I knew some paltry french and I was trying was enough. A strange but nice experience.
As a personal anecdote, when I used to pirate things, I still bought things in the same category, ie: I would pirate movies and I still bought movies. I would pirate games and I still bought games.
I don't think it affected how much of each thing I purchased by much, but I don't really know.
But I mean: they appear to be results. They look real, but are not. There are subtle errors hidden that the casual observer will not, or even cannot detect.
Obviously there are exceptions to this. Many exceptions. AI right now can probably write most if not all algorithms on par or better than I could. It can put together working prototypes for many ideas I've had and not had time to implement.
But the danger is in assuming that because it can do A, it can do B, because a human that does A can do B.
Recently I commented that: Artificial intelligence produces artificial results.
I liked the double-artificial but I wasn't happy with the meaning. Perhaps Simulacra is more accurate? I will see :)
However, it's no longer a good metric, as "drawing svg pelicans" is now showing up too much in the training data, so is not proof of generalization.