For me the salvation currently lies in 3. Whenever I suffer from loss of motivation I think about what I actually want to code, and usually there is something. Dig deep in memory and ask why you choose to become a dev. For me it was the tiny code projects where some interesting phenomenon could be explored. There are always new things to learn and fun ideas to try. Doing it during work hours is ok under the pretext of "training".
They tell a story of the quality of the software. A good way to decide whether to use some library is to look at how well it throws exceptions. If it's buggy and poorly documented then don't use it.
How do you define "low status"? Is elderly care "low status" according to your definition? It's not according to mine. I consider myself a proud member of a "normal status profession" where my skills can be adequately utilized.
I think one also has to be a little bit "crazy"/"idealistic" as a person to be a real entrepreneur. As a realist (coder) in a tiny startup I know I could never run it like my CEO. He may not be the smartest guy but man does he have belief in the idea. If I were in his shoes I would have given up 1 year ago and bankrupted, but he kept investing his own money and now he might actually get it sold for big cash.
80-95% of "ML" has always been "boring" and any data scientist/ML-engineer worth their salt know this. This also concerns what you refer to as "pure ML job". It only takes fresh grads and juniors a couple of projects to realize the true meaning of "data cleaning", "outlier detection" "robustness" and their likes - it's painstaking work.
I also think FB, contrary to many other massive enterprises, suffers extra much from this bcs it doesn't even make sense as a massive enterprise. It's a social network platform and when it grew too large it mutated into something that in its worst days looks like a scene out of 1984. Hence Lego recruits are needed not just bcs its massive but because they shouldn't be asking the difficult questions.
The main problem is not backpropagation though, but the fixation of resources on DL projects (that's what I call local minimum!). In my department, for example, they don't seem to care about the application, integration, deployment etc, as long as it's DL or DRL.
Is this related to the "build things that don't scale" motto? That one was conceived for startups in general if I remember correct. Is this same thing but in more detail?
> Taking on the responsibility of pushing back hard on poorly conceived new features is one of the important hidden skills of being an effective software developer.
example from my current project: 1. Inherit half finished large software with lots of features. 2. It contains bugs and is impossible to effectively maintain/develop for the allocated manpower. 3. Management still wants all features. 4. Be brave and don't work on anything except essentials until they're sorted out. Lie to management if you have to e.g. that you found serious bugs that must be fixed (which is kind of true but they wouldn't understand)
The big problem is that the populace do not understand statistics. As soon as a self driving car kills someone the witch hunt begins and the whole fleet needs to be retracted until investigation is over. Can your image network solve this problem?
There is a push to upgrade road infrastructure with sensors so might be sooner. You're totally right about regulation though - engineers are only solving a "technical" problem, but what about the non-technical ones? Where is the brigade of lobbyists needed to get something like this through legal?
The "key" is a total abuse of the term "AI". The dictionary definitions basically say "machines that appear intelligent", and "intelligence" is "ability to acquire knowledge [/skills through experience]". The bar is extremely low for what one can call AI. QuickSort qualifies. A* qualifies.
Interesting point of view. So as long as a paper is "non-tabloid" it should be more trusted. A Chinese or Russian state owned non-tabloid is then more trustworthy than NYP? Propaganda is everywhere and the definition of what and when something is sensationalist is fuzzy.
Although the "data problem" is already well known by all ML and RL engineers/researchers (with varying degree depending on what one works on), it's an innovative and concise post that really boils it down. It's particularly important for modeling and when you have to generate all the data on your own.
Disagree on the "playground" historical note students hundreds of years ago would probably not agree at all, basically since life back then was tougher for everyone including elite compared to tosay. Somewhat agree with 2d and 3d paragraphs though
Doesn't this just mean that the "reward" definition is poor in this case i.e. the agent does not care about maximizing it because there are additional components left out of it (e.g. survival)?
The correct answer in this case is of course YES, it's justified. It's a hedge fund after all. Motivate with evolutionary theory starting with Darwin and ending with Nietzsche, maybe even bring in Sun Tzu depending on interviewer.