ajj

Come on folks, how is all this half-information bashing better than trial by media?

We don't know exactly what happened. There are various incentives for both parties to say things that may / may not have happened. AirBnB needs to be careful to make sure they are not the target of a lawsuit. The conversation that they had with EJ can be misrepresented. Or they may even be worse than what this shows.

Who knows? Why are we, a bunch who would normally need citations to believe that humans need water to survive, engaging in such ludicrous trial-by-media with hardly any validated information?

Until recently, I thought that the patents issue was a PITA, but not that big of a deal, and something that would eventually pass after a lot of damage is done.

Now, I really believe that unless things change, the US is going to have a lot of trouble attracting new businesses to start at home, or foreign companies providing services in the US. Spotify will run their own cost benefit analysis of fighting the lawsuit versus attracting future revenue. Many small services that cannot afford an upfront lawsuit in the hope of future American revenue will just not open shop or services for the US.

It hurts me to see how the patent system is screwing with every damn thing. As they say for startups, competition from other countries won't hurt America, but inefficiencies and bad decisions within would. And IMHO the risk is not hypothetical anymore.

You raise a great point. Definitely agree on the fact that legal / ethical frameworks are required.

However, it might be very hard to do without knowing what the emulated "being" looks like. Its hard to imagine placing legalities and ethics on something whose powers (and possibly, limitations) are absolutely unknown. Consequently, the law might be misplaced, restrictive, or downright abusive depending on what it turns out to be.

Having said that, I agree with your broad point - having that advanced technology without the social aspects to deal with it might be a nightmare!

I agree.

In terms of learning new things, the vote count helps tremendously! You can tell that a security-related suggestion earning 50 up votes is sound (of course considering context), technology-wise. I've learned a lot about passwords, plaintext, server-side hashing, salting and related best-practices solely from HN comments.

Displaying scores might make the other aspects troubling (since group-think is supported unnecessarily, often disregarding novel thoughts or disagreements). But I've learned to ignore such things, especially since most of the conflicts are "opinions" anyway, so the value addition is somewhat limited.

In terms of actual facts and expertise though, nothing can beat the vote count!

Having said that, I am still not sure which one I prefer given that there is inherently a tradeoff between the two aspects.

One very surprising example of someone playing with single eye vision. Mansur Ali Khan Pataudi (http://en.wikipedia.org/wiki/Mansoor_Ali_Khan_Pataudi) played in the Indian cricket team as a batter for many years. He lost one eye at the age of 20, but surprisingly still managed to bat.

This is particularly striking since batting involves judging the ball that comes at you with some speed. His previous judgement and muscle memory might be useful of course. But I'd love to read about any research on this, if anyone is aware...

I'm a PhD student working in machine learning, and I very highly recommend this library. I've used it for all sorts of problems within and outside my research, and it just works great. I've used it in C++, Python, as well as Matlab.

Their papers are excellent too if anyone is interested in reading about large-scale optimization problems for SVM.

We’re in the most ridiculous industry on earth. You can whip something up in a few hours and before you know it, people around the world will be using it.

Oh how I love this field! Its exhilarating!

In today's context, most work in AI is statistics-based. So if you are talking about conventional AI (logic-based for example), yep it would be relatively harder to find.

Lets not forget though that machine learning and data mining and their applications: natural language processing, a lot of computer vision, recommender systems, financial predictions, fraud monitoring, some types of games, and innumerable others are huge advancements made towards the same goal that AI was after, albeit with different means (statistics) than originally attacked with.

Statistical machine learning is AI. And it is bigger than ever before.

Its really bad that they've shut it down without any notice, at least for people to note down their data.

Thanks much for your detailed feedback.

We do plan on incorporating more events and activities from varied sources in the future. And yes, IE support is also on the way.

Yahoo Upcoming, although we plan to incorporate others in the near future.

I work in image processing / computer vision (albeit in completely different applications), and the technology is not very good at doing that, at least yet.

On the other hand, whenever you mention 'skin color' in a research proposal, that does not have an easy route either, even when it has nothing to do with racism.

There is no satisfaction like adding value to someone's life through your work.

Very important point. This will also help people to easily transition between online / offline working with tools that they already have. Especially, since such charts often include collaborative input.

Collaborate with that exec who uses MS Project, and work from your netbook with this web app on the go.

"My main motivation is to produce an original piece of work"

In that case, I suggest working on it right now, before a PhD. You can start doing a heavy literature review on a focused topic, and try to improve the state of the art by however small you can. The standards for publication are much lower than what many people outside academia believe.

You can do this and even get a publication without an advisor (may be not at a premier venue but a decent one nonetheless). That gives a great sense of accomplishment.

If you want to continue, the work will surely help you in your PhD (won't be wasted time). If you don't like it, you can move on to other things (and it won't feel like giving up on a long commitment).

The reason I say this: the beginning of a PhD is probably the hardest time to see results, and keep you motivated. So starting small would be helpful. Once you believe you need larger goals and are not satisfied by the small accomplishments, you can go for the long haul!

If your objective is only to get focused and motivated, and not necessarily do research, a PhD might be too much of a time sink (I am a PhD student in CS). More so if you are thinking of taking it up part time, since most of your free time will be spent on it. Further, expect that the first couple years will not give you many positive returns to motivate you to go on: those are mainly spent building the foundation.

Having said that, if you truly enjoy the process of research, that might be just the thing to get you more focused and motivated. I definitely enjoy it at the moment.

In summary, don't go for it only to get motivated - if it doesn't interest you, it might end up taking too much energy for nothing.

The lesson I learn from Hacker Monthly: you can make something truly beautiful with content and ideas (articles / comments) out there.

You need to see the opportunity and execute it well. Great job, once again.

"I knew it (in the shower) was a good time to have ideas. Now I'd go further: now I'd say it's hard to do a really good job on anything you don't think about in the shower."

This is so very true. It is incredibly hard to get myself motivated about things I do not think about in the shower. On the other hand, it is impossible to stop myself from working towards things I do think about.

Sometimes this is scary -- its almost as if I don't have any control over what I will be passionately pursuing.

Agree completely.

The question (probably similar to patents,) is: How can we protect the spirit of why this law is in place (to recognize the contribution of those who may not be directly compensated and give them their appropriate share), without letting people completely exploit and abuse it.

Looked at it that way, its an extremely hard problem with a myriad of economic and social issues along with a bunch of vested interests. Sigh...

Unfortunately, this labeling and clinging onto the early hypothesis happens all too often in daily life. For medical diagnosis, we wish the standards would be higher, but at the very end, there is a human making a judgement.

When analyzing, I often realize that I give more weight to who said something, rather than what was said.

Sad but true?

"My measurement will be the parking lot: it should be substantially full at 7:30 AM and 6:30 PM"

In other words: I know its not a good measure (based on a previous statement), but I am still going to use that, because I suck so much that I am too mad to think of anything else. To stop getting fired, I will measure how many people die of hunger, and kill farmers to make sure people get more food.

Being a PhD student, that touches a sensitive nerve. They say a dissertation is never finished, its abandoned!

Sports - absolutely cannot live without. Enjoy it as much as my work.

Anything goes: soccer, volleyball, squash, or rarely some water sports. I follow a lot of sports too.

An absolutely refreshing read first thing Monday morning..

A welcome change from logical hair-splitting arguments (as much as I love them) into something that matters most - emotional support from a loved one.

"It simply takes too long to evaluate intelligence...". True. But even if intelligence is evaluated, it might not be in the interest of the female to go with the most intelligent mate.

What she prioritizes will be a combination of wealth, smartness, and other factors that will make her happy and her offspring "fitter", obviously often in a subconscious way.

Btw, I agree with all you are saying. I just thought I should support it, since even evaluating that someone is intelligent in our definition does not necessarily make them a good mate in the evolutionary sense.

I think that is right, since the freezing points / boiling points are also influenced by pressure. So when it tries to expand and cannot expand due to an unbreakable container, the pressure increasing substantially, and the respective points also change.

I'm sure if you try to boil water to steam, and keep it in a container that cannot expand, it won't convert completely to steam, since pressure increases its boiling point. With the conversion to ice, I am not exactly sure (something similar happens possibly) just because it is a little atypical in terms of volumes and liquid / solid states. Also, too tired to look this up :)

The Radial Basis Function kernel K(x,y) = exp(-(x-y)^2/gamma) is a very general kernel function that can get you this.

Finally, whether you actually get such a classifier depends on your data, and as I had mentioned setting the parameters (like gamma) is not very straightforward. Typically you may have to try with various values and see what works for your data.

Train your SVM (or any kernel method) and see what classifier it gives (run the classifier on data points near the boundary you want to detect to see where the exact boundary lies). This would only be to see how the classification function looks like.

This was exactly the thought I had while reading Patrick's post. What if the sign-up page on the website has a link saying "Ask my friends (on FB, twitter, whatever) if they can refer me."

When people look for jobs, this happens often. Someone you know well works somewhere and you ask him/her to refer you. This makes it much more palatable, I believe even more than the double-sided incentive.

Of course, the downside being only people who end up on the webpage will do this. The double-sided incentive actively seeks to find more customers.

It can in principle, and this is exactly where the kernel trick is useful.

The standard SVM formulation can only give linear classifiers. But, if you project your data into feature space (a higher, possibly infinite dimensional space), a linear separator in that space can be a circle in your original space. Since you can not do explicit computations in an infinite dimensional space, the kernel trick lets you get away without doing them at all. You can thus get an inner product value of two infinite dimensional vectors using a kernel function. So classifiers that only require inner product values and never the explicit vectors can exploit the kernel trick. i.e., SVM, logistic regression, etc.

That being said, choosing the appropriate kernel function is not always straightforward for your data.

Couldn't agree more. My mom can get stuff done (email attachments, etc.) if she is sitting alone and absolutely has to do it.

If at all I am available, she will ask help for the smallest stuff fearing she would break something.

Sometimes I think its not even about breaking anything, but more about not fancying herself to recover from "even more damage."