I don't believe Colab is real-time live in the way that Google Docs is. I'm using it to teach a class and would love that kind of functionality, but unless I'm missing some option, when I make changes to the notebook, students who have it open don't see the changes immediately, only when they reload.
With which option! I have used Colab myself and I don't see live editing. I would like to see the cursor of the collaborators in real time while they edit. Also, the option to share the runtime.
Not necessarily, it depends on the advisor, university, program and field.
The problem is that once you start you are committed for 4 years. Abandoning a PhD program and starting another one is quite more complicated than changing of job.
It's not only about the odds, but the expected return. That's the whole point of insurance, for instance. Having an accident under these circumstances is quite improbable, but in case it happened, the return would be massive. Actually, since it might be a matter of life or death, perhaps it doesn't even make sense to think about the expected utility, because it would be infinite.
I know this is a crazy thought: But why should every student need to buy their own calculator for testing? In a given class period, what, 10% of a school's population is in a math class? The school should be providing adequate calculators for test-taking, but of course, that would cut TI's income by a factor of 10.
(It's worth noting that "an X for every student" mentality really only is beneficial to whoever is selling X.)
This is the kind of ad-hoc PEP proposal that could be completely avoidable if there was a generic modifier or initialization/declaration construction for declaring a variable as constant. So incredibly simple, so incredibly useful that it's hard to believe that it hasn't been accepted yet (it was proposed).
There’s a common mindset at play when you seek interaction through a site.
On the street, there’s no rule that you can’t speak to anyone. Most people, however, find comfort in black-and-white answers in knowing if is appropriate to engage. It is this uncertainty that drives individuals not to engage.
When I was in college I was mystified by genetic algorithms, without knowing much about them. After taking 2 subjects on the matter and reading some books, I came to the conclusion that apart from being inherently inefficient (that's what you apply them when you have no alternative), they are actually outperformed by hill climbing (which can be seen as a particular case of the former if population = 1). Also, the crossover operator seems to make more harm than good, and it's not fully understood it's usefulness in nature, although there are some theories (this last point is taken from Pedro Domingos book).
Interesting. I want to ask this in a way that is respectful because I'm not grandstanding, but coming to that conclusion after two undergraduate courses, based on preceding mystification, did it not occur to you that it might be your understanding, rather than the whole field?
My personal story: I worked for just under a decade on EAs (PhD and professionally, late 90s and early 00s). I don't entirely disagree with you about pure GAs. But even back then, pure GAs were rarely used. But I do disagree about evolutionary algorithms in general.
The usefulness of EAs is highly problem dependent. And I will definitely concede that the space of problems in which they excel and are tractable is smaller than the space of problems for something like a neural network, in my experience.
As for crossover, when it works (and in engineering contexts the operator needs to be designed with the problem in mind) it does so by allowing individuals at different points on the landscape to share the optimisations they find. At the cost of increased genetic load. This, of course, is only useful if the fitness landscape is both heavily multimodal and self-similar. And it requires some effort to avoid the population converging around a single solution (where you do have a stochastic hill climb).
In my experience, EAs come into their own with non-fixed genotypes or complex genotype to phenotype expression. Multivariate optimisations with a fixed number of variables is probably best done with other tools.
> And I will definitely concede that the space of problems in which they excel and are tractable is smaller than the space of problems for something like a neural network, in my experience.
The only thing that EAs and neural networks have in common is the buzzword abuse. Neural networks are actually useful as they are at their core an efficient way to approximate multivariate functions, while EAs are computationally expensive heuristics abused by being passed off as optimization algorithms even in domains where deterministic algorithms (and even brute force approaches) actually perform better.
That depends on the particular problem you are trying to solve. If hill climbing works for your problem, then you are lucky and you should not use GAs.
I've also had the experience that hill climbing (with random restarts) can outperform GA's. In many problems you can fairly easily define a neighborhood concept for moves from the current solution. That tends to be easier than getting a meaningful crossover and mutation working. Then you just can hill climb by making neighborhood moves, with random restarts. Run 1000 random restarts and pick the best one, and you're going to have a really good solution.
Genetic algorithms aren't useless, evolutionary algorithms are, the crossover method is terrible at searching the space in any kind of dynamic way, but things like pep-g and covariance matrix adaptation are also genetic algorithms and they work significantly better because they have essentially dynamic learning rates, they are to genetic algorithms what Adam is to backpropagation.
