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Those are great points! Another related law is from queuing theory: waiting time goes to infinity when utilization approaches 100%. You need your processes/machines/engineers to have some slack otherwise some tasks will wait forever.
Yep, I used to work in a factory. Target utilization at planning time was 80%. If you over-predict your utilization, you waste money. If you under-predict, a giant queue of “not important” stuff starts to develop
Eg at Google (this was ten years ago or so), we could always spend leftover networking capacity on syncing a tiny bit faster and more often between our data centres. And that would improve users' experience slightly, but it also not something that builds up a backlog.
At a factory, you could always have some idle workers swipe the floor a bit more often. (Just a silly example, but there are probably some tasks like that?)
The subtle difference between the two being exactly what the author describes: Goodhart's law states that metrics eventually don't work, Campbell's law states that, worse still, eventually they tend to backfire.
And that's leaving out Jevon's paradox, where increasing efficiency in the use of some scarce resource sometimes/often increases its consumption, by making the unit price of the dependent thing affordable and increasing its demand. For example, gasoline has limited demand if it requires ten liters to go one km, but very high demand at 1 L/10km, even at the same price per liter.
When people know the answer is always “no” they save their energy to plea for stuff they really can’t do without. You start saying yes and they’ll ask for more. The trick is as always to find out the XY problem. What they really need may be way easier for you to implement than what they actually asked for.
If you are in the business of selling any product or service, then it's great that finding a way to make it cheaper also generates more demand for you.
Earlier quoted context omitted.
When people know the answer is always “no” they save their energy to plea for stuff they really can’t do without. You start saying yes and they’ll ask for more. The trick is as always to find out the XY problem. What they really need may be way easier for you to implement than what they actually asked for.
Sometimes you can just embrace it, instead of looking for tricks. If you are in the business of selling any product or service, then it's great that finding a way to make it cheaper also generates more demand for you.
But you do not get good art by early stopping, you do not get it by injecting noise, you do not get it by regularization. All these do help and are essential to our modeling processes, but we are still quite far. We have better proxies than FID but they all have major problems and none even come close (even when combined).
We've gotten very good at AI art but we've still got a long way to go. Everyone can take a photo, but not everyone is a photographer and it takes great skill and expertise to take such masterpieces. Yet there are masters of the craft. Sure, AI might be better than you at art but that doesn't mean it's close to a master. As unintuitive as this sounds. This is because skill isn't linear. The details start to dominate as you become an expert. A few things might be necessary to be good, but a million things need be considered in mastery. Because mastery is the art of subtly. But this article, it sounds like everything is a nail. We don't have the methods yet and my fear is that we don't want to look (there are of course many pursuing this course, but it is very unpopular and not well received. Scale is all you need is quite exciting, but lacking sufficient complexity, which even Sutton admits to be necessary). It's my fear that we get too caught up in excitement that we become blind to our limitations. Because it's knowing those limitations that is what gives us direction to improve upon. When every critique is seen as spoiling the fun of the party, we'll never be able to have anything better. I'm not trying to stop the party, in fact, I'm worried it'll stop.
Those are great points! Another related law is from queuing theory: waiting time goes to infinity when utilization approaches 100%. You need your processes/machines/engineers to have some slack otherwise some tasks will wait forever.
I’m remembering reading once that cities are incredibly efficient in how they use resources (compared to the suburbs and rural areas, I guess), and, in light of your comment about waiting time, I’m realizing why now why they’re so unpleasant: constant resource contention.
Yes, it’s inefficient. Yes, some people want that!
Earlier quoted context omitted.
I’m remembering reading once that cities are incredibly efficient in how they use resources (compared to the suburbs and rural areas, I guess), and, in light of your comment about waiting time, I’m realizing why now why they’re so unpleasant: constant resource contention.
Amusingly this is something that I see as being a huge divide in rural and urban politics. Yes, it’s inefficient. Yes, some people want that!
Those are great points! Another related law is from queuing theory: waiting time goes to infinity when utilization approaches 100%. You need your processes/machines/engineers to have some slack otherwise some tasks will wait forever.
There’s no room to absorb shocks. We saw a drastic version of this during COVID-19 induced supply chain collapse. Car manufacturers had built near 100% just in time manufacturing that they couldn’t absorb chip shortages and it took them years to get back up.
It also leaves no room for experimentation. Whatever experiment can only happen outside a system not from within it.
I recognize the author Jascha as an incredibly brilliant ML researcher, formerly at Google Brain and now at Anthropic. Among his notable accomplishments, he and coauthors mathematically characterized the propagation of signals through deep neural networks via techniques from physics and statistics (mean field and free probability theory). Leading to arguably some of the most profound yet under-appreciated theoretical…
Translation to laymen: ML is being analogized to the mathematical structure of signaling between entities and institutions in society.
Mathematician proposes problem that plagues one (overfitting in ML, the phenomena by which a neural network's ability to generalize is negatively impacted by overtraining so the functions it can emulate are tightly coupled to the training data), must plague the other.
In short, there must be a breakdown point at which overdevelopment of societal systems or signaling between them makes things simply worse.
I personally think all one need do is look at what would happen if every system were perfectly complied with to see we may already be well beyond that breakpoint in several industrial verticals.