orange_tee
Joined 5 years ago
Joined 5 years ago
There's also something to be said about the kind of innovation that disrupts a whole market, and the kind of innovation that incrementally improves on some process or tool. Maybe another question would be, is there enough of the latter kind of innovation to make up (economically) for the lack of unicorns?
It's all about putting the price in context.
The challenge with automating seemingly monotonous human tasks is that often when the human is doing the task, they may be doing it without thinking 99% of the time, but if they have to, they can resort to their human intellect. No deep learning model is going to be able to do that because it does not have any higher intellect to resort to. And more importantly it cannot even know when it is failing.
It's a joke that they are trying to push this for self serving reasons, but cannot even come up with one good reason for people to wear them.
What a fucking joke. There is clearly no practical upside to this for the wearer, or the rest of society.
The author is assuming a certain "natural order of things" and placing the UK a few ranks higher than it actually stands.
1. often when I read read software written by profession programmers I find it very hard to read because it is too abstract, almost every time I try to figure out how something works, it turns out I need to learn a new framework and api, by contrast research code tends to be very self contained
2. when I first wrote research software I applied all the programming best practices and was told these weren't any good; turns out using lots of abstraction to increase modularity makes the code much slower, this is language dependent of course
3. you will find it much harder to read research code if you don't understand the math+science behind it
> many of those writing software know very little about how to do it
This is just not true. I found in my experience that people writing research software have a very specific skillset that very very few industry programmers are likely to have. They know how to write good numerics code, and they know how to write fast code for super computers. Not to mention, interpreting the numerics theory correctly in the first place is not a trivial matter either.