nepstein

I'm not sure I agree that STL that containers are explicitly not OO.

That said, I don't disagree that C++ can support functional programming. I would just argue there are languages better suited for this.

(edited for clarity)

Nice article and effective demonstration of the FunctionalPlus library.

That said, this makes me a bit nervous about shoe-horning functional programming into contexts where it is inappropriate. FunctionalPlus aside, C++ is very clearly designed for an object oriented style (even the name, C++, refers to "C plus objects").

Going too far down the path of working against the intent of the standards committee seems like a recipe for trouble (unless you're just talking about hobby projects).

Just to clarify, there is no direct cost associated with RC. It is free to attend for all participants. Grants, for those who qualify, are for living expenses.

That said, 3 months of wages is still a prohibitive cost for many would-be participants.

It's a charitable donation - https://www.ycombinator.com/nonprofits/

If you're finding part-time study too slow, there are a number of non-academic contexts in which you can study code full time.

Despite the reputation, there are some web-dev bootcamps that have good placement rates.

A few in the US: 1) http://www.fullstackacademy.com/ 2) http://www.hackreactor.com/ 3) http://flatironschool.com/

If you're a woman, there's the deferred tuition (no charge until you have a job) option:

http://www.gracehopper.com/

Or if you're not interested in a bootcamp, but rather a self directed environment surrounded by a community of other developers (and want a free option) there's Recurse Center (https://www.recurse.com/)

Worth noting that selective schools also tend to be more expensive. Even if the ROI isn't markedly higher, high price tag means a "larger investment" into the high ROI option of college education.

That's a substantive advantage (to those that can afford it) given that 12% return opportunities are scarce.

AI => automation => unemployment => basic income

That basic argument makes sense to me but the leap to say that AlphaGo proves automation is accelerating doesn't.

Go is one of many task-specific AI's. It's not clear that it implies anything about generalized AI (which most experts agree is still very far off) or the rate at which task-specific AI's are developed.

True - though this means only that the signal (outperformance by a group that is the target of discrimination) might not be present (if discrimination continues past initial selection as you point out).

However, the test may still useful to help confirm bias. If outperformance is observed, you can infer one of 3 things is true:

1) there is bias at initial selection but not after (or at least reduced bias)

2) members of the outperforming group are simply stronger performers (different but still interesting)

3) there is no bias at selection but there are affirmative action effects after the initial selection (not obvious why this would be the case)

Ah, should've given it a closer look! Apologies for the mixup.

UPDATE: the price increase has been rescinded

http://www.nytimes.com/2015/09/22/business/big-price-increas...

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The README links to a blog post (http://nepste.in/jekyll/update/2015/02/22/MDP.html) which details how the library is implemented from the definition of a MDP.

For a more rigorous treatment, Andrew Ng's notes (http://cs229.stanford.edu/notes/cs229-notes12.pdf) are an excellent resource.

I can definitely understand wanting to classify Pavlov as supervised learning. I think it's a murky issue because supervised and reinforcement are very closely related (and it is frequently possible to reframe problems of one type as problems of the other depending on what type of model one uses).

My two main reasons for going with this name are as follows (if people see issues in my logic, I'm happy to be convinced):

1) Reinforcement learning gets its name from the behaviorist psychology concept of reinforcement in which an agent's actions are met with rewards in order to shape that agent's future behavior. This is precisely the kind of response conditioning that Pavlov is well known for.

2) The key difference is what the training data look like.

In a supervised learning problem, the training data are input/output pairs (a stimulus and an appropriate action).

In reinforcement learning, the training data are input and reward pairs (an action and the reward applied to that action).

I would argue that Pavlov's experiments more like the latter case - the dogs are not shown 'this is the correct action for this stimulus', they are shown 'this is the reward for this stimulus'.