sherjilozair

A very useful addition to markdown would be the ability to put anchor links, and "open in new tab" links. Both of these are not link defaults, but are perfect examples of common cases that should work nicely.

Markdown is often used for one-page webpages due to its simplicity, and thus anchor links become important in this usecase.

Why are close-world assumptions bad for all cases? There might be cases limited to a domain where the computer does not need to know about the world too much. Consider, the language of geometry problems given in high school math books. Surely, to understand that, one doesn't need to have world knowledge, but only ideas about what intersect means, and so on. This way, SHRDLU can be modified to understand geometry problem statements. The same way, it could also work for other domain-limited applications. A general solution might have to go to probabilistic systems, as current research suggests.

move left.

Could you substantiate this with any references? I thought hard about this, and I find no reason to believe that equally-spaced points would give a biased estimator.

We don't necessarily have to use randomness. We can take equally-spaced points in the quarter as well, rather than sampling. This is called pseudo-Monte Carlo.

Any links on relevant material? I'm okay for delving into current research topic.

I've done something similar with Sphinx, which worked quite well too.

However, I feel the real challenge to productify such a technology would be to make this system recognise utterances spoken casually, over loud music, over friends talking in the background.

Also, how would the speech to text recognise the user's commands over some sound playing from its own speaker. Suppose I set up a text-to-speech system to read a book to me. I want to be able to make it repeat a paragraph (because my attention had drifted away). However, the input channel of the microphone would contain not only me shouting "stop", but also the output of the text-to-speech system.

We humans have a way of factoring out our own voice when we are listening to someone else. Is there a way to make machines do that?

Google.

https://github.com/rcorcs/NatI/blob/master/gapi.py#L26

None of these are what I'm looking for. These do have programming exercises. However, I'm looking for a very specific type of exercise: a problem is given, and an incomplete solution is also given, and the student is asked to fill in the blanks of the program to make it work.

simulated annealing. ;)

If you can solve something easily, then you can also verify its solutions easily.

It is a typo. It should say "but potentially extremely difficult for computers to solve."

Do you consider foreign applications? Asked this a few months back too.

You're just making a fool of yourself. This work is absurd, ridiculous and legit at the same time.

This module is not satire, and the existence of this module does not indicate anything about the quality of programmers of this generation.

For those who think so, try doing some machine learning, data handling code, or even web crawlers. Error tolerance is much needed.

For scientific experiments, sometimes the results of the experiment is still valuable even if there's a trivial bug (malformed row). A partially-correct execution is still more information than an unhandled exception causing an early exit.

> ethically

Double standards.

I don't understand. $50K being asked by professors and PhD students? They certainly enjoy funds orders of magnitude more. $50K is like two YC startups' seed money.

Why do they want it? Can't they self-fund it? Or do they just want other people to share the glory? Isn't the equipment shown in the video worth as much, or probably more?

For non-Hindi folks, "Aam Aadmi" means "Common Man".

Can a non-citizen apply?

If you use a time machine now, and travel to the past, you can collide with the past yourself, right? :)

I would pay to get a PDF version of this. I read PDF documents all the time.

    2) Need "speed" - write that part in C.)

That's not so easy. Interfacing Python and C code is also incredibly hard, and no one true way exists.

I must say, that the syntax and semantics are not intuitive at all. I've spent half an hour trying to learn how does this thing work, and have not been able to. I guess, a damn good tutorial is in order. I think one of the primary reasons Python took off really well, was because of very good tutorials available. The language of Factor's documentation is still only tuned for language designers, not language users.

This misses the most important line in the article: Acquiring Summly seems to have been an almost incidental side effect of a deal Yahoo made with SRI for a piece of "summarization technology".

Here[1], the density is said to be as low as 0.03 gm/cc which is the same as kg/liter. That gives a multiplicative factor of 33 over the above calculations, i.e. this could be 33% of the energy density of lithium ion batteries. Not bad.

[1]: http://www.strem.com/uploads/resources/documents/graphene_na...

Am I being down-voted because my opinions don't agree with yours?

So, one of the restrictions could be that my stuff would only be online if there is activity on it, subject to a maximum of say, a year.

I would like to try Posthaven and only if I feel that its worth 5$/month to me, then I can consider upgrading from the free plan to the paid one.

A free plan is beneficial to the company, since it would help in popularizing the app.

I think there should also be a free version, perhaps with restrictions.

No intro to profiling in Python is complete without any mention of line_profiler (http://pythonhosted.org/line_profiler/). As the name suggests, it gives you line-by-line profiling information. In a scripting language such as Python, I find this much more useful.

MNIST is not a good dataset to show any artificial intelligence on. The dataset is so simple, a good programmer can probably write 100 lines of python to write a classifier for it, based on no machine learning.

Neural Network techniques which work so well in small, easy and trivial datasets like MNIST do not generalize to more serious datasets, and that's where the "and this is where the magic happens" component is needed.

This is offtopic. I've noticed several websites with this theme. Is there a standard template available somewhere? I'm referring to the kudos widget, the static left sidebar.