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?
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.
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!
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.
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...
Their papers are excellent too if anyone is interested in reading about large-scale optimization problems for SVM.
Oh how I love this field! Its exhilarating!
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.
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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.
Collaborate with that exec who uses MS Project, and work from your netbook with this web app on the go.
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!
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.
You need to see the opportunity and execute it well. Great job, once again.
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.
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...
When analyzing, I often realize that I give more weight to who said something, rather than what was said.
Sad but true?
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.
Anything goes: soccer, volleyball, squash, or rarely some water sports. I follow a lot of sports too.
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.
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'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 :)
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.
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.
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.