haidut

As far as I know, he owes almost no taxes. He is a resident of Monaco and it has no taxes on its residents AFAIK. The state of Maine actually invoked FATCA compliance as a legal backing for filing the form. I find that strange as FATCA is a federal law and if anybody would be enforcing it would be the IRS, but of course he is not going to argue with Main so he just ignored the form and left a month later. Probably not very smart on his part for ignoring it but he said there is no way he going to report all of his BTC assets to Uncle Sam simply for being a tourist visitor. However, for a US citizen/resident it could very well end up pretty badly as reporting assets that had not been taxed so far will get you state and federal tax (with interest) AND probably some fines. Not reporting it is even worse because the state (and IRS) now knows you have a BTC wallet and will assume you are hiding assets in it, which is both a state and federal felony. Anyways, it would be interesting to watch how this plays out.

Am I the only one who sees this as a move on Ohio's part to get its residents to "accidentally" disclose their Bitcoin assets by making a payment from their "hidden" stash invested in Bitcoins? You pay your taxes from your Bitcoin wallet? Oh how nice! Now here is your state subpoena to disclose the FULL value of your (possibly non-US or offline) Bitcoin wallet. I have a friend who lives in Portland (Maine) and used his BTC to pay some city fine. A month later got a formal order from the state's revenue collection service to file a non-US asset disclosure form and report ALL of his BTC in possession. And mind you, he is NOT a US citizen or even permanent resident. He is in the US on a tourist visa. A US citizen/resident could probably face criminal charges after disclosing he/she has Bitcoin stash they failed to previously report.

How is this different from the GENERAL employment reality in every late-stage capitalist society? What you just said is applicable to pretty much any job/occupation in any industry, unless a person is at the top of the food chain (CEO, tenured faculty, etc).

I think the equally important message, albeit not as shocking, is that 7 of the top 10 brands are shared between both political lines. This is not a small number. In other words, intensity of sentiment aside, there is not much fundamental difference in brand perception in regards to political allegiance.

The "ambient thinking" in the shower you are referring to has been studied extensively. Here is the New Yorker article on the studeis of insight and creativity: http://www.newyorker.com/reporting/2008/07/28/080728fa_fact_...

Research suggests (as you have discovered yourself as well) is that the most creative moments are 1) while showering 2) shortly before going to bed 3) shortly after waking up.

Can you please try our news recommendation service and let me know how it stacks up against your site in terms of (perceived) accuracy? The site is http://www.euraeka.com I can provide more info if you need it. Email me haidut (at) gmail (dot) com. I just need some feedback from someone who has built something similar/related.

My opinion is obviously biased b/c I am using that for my project but XLhost.com seem to offer dedicated servers that no other company can match in price/performance. Please note that I am NOT getting royalties from them and that you HAVE to do your own research before committing to them. Before signing up with them I read a few posts on various forums complaining about XLhost down time and poor customer service. I won't comment on those, please Google for the info and make your own judgement. My personal experience is that I can not find any other company were you can lease a 4-CPU machine with 8GB of RAM and 250GB or RAM in RAID-1 for $149. Again, many other factors come into play and XLhost seems to have taken the road of max hardware for less money but maybe lacking in the tech support area. My site is http://www.euraeka.com/faq, so feel free to check it out and judge/test on performance of the hosting provider mentioned above. If you want to discuss this more please send me an email. I don;t recommend any service specifically, but have been reading and accumulating a lot of reviews of various providers (i.e. VPS, dedicated, etc) from ppl who post on HN.

We have a number of options, with SVM being the fastest so far b/c the library is written in C. Pearson, NaiveBayes and a number of others have all been tried and "tested" for acceptance with several thousand users and the majority picked the results from SVM as the most accurate. The recs are computed offline but there is an option to do that on the fly given enough RAM to load the SVM models for all users. As far as asking users for too much - what exactly do you mean? All we are asking for is for the user to login and if they find article they like (on the front page or from the topics menu) then click on them. What more simple that that? The voting system up/down you talk about is exactly the same except that you explicitly ask the user to vote, while we implicitly do the same. TechnologyReview ran an article last year how asking the user to explicitly rate stuff is considered overburdening and how the system should quietly monitor the user without asking direct questions. Anyways, your up/down approach and our click/remove approach are the same in my opinion. But thanks for the comments.

Good point, will blog next time. Thx

Yes, this is what we were thinking of having eventually - separate sections based on score. The current design is something we threw together very quickly to get it out of the door. Btw, the article tags are clickable and run a search for that tag in the background. Also, you can filter news by domain. As we accumulate more data, we can start ranking domains based on the articles they have produced so far. Kinda like PageRank but not based on links. Finally, the algorithm can guess authoriship and cluster articles based on author as well. So again, we can rank authors/people after some time. This seems to be a much needed feature as this links discusses: http://threeminds.organic.com/2009/06/docs_are_old-school_we...

I think the above article came up on HN today.

The patent application is in the works. As far as the the data set - we definitely have it. In fact we were thinking of releasing it under some type of open license (i.e. creative commons) after the site gets some traction. In terms of search engines - we ARE in fact a search engine. Just type something in the box at the top and you can also use the available 4 score to filter rank search results. So the search works just like google but you get to sort the results by Controversial, Popular, Engaging, and Deceptive. Some pretty interesting combinations can be created using the scores. Like for instance, you can search for "paris hilton" but you are interested in her scandalous "achievements" rather than her community work. Well, then you search for "paris hilton" and sort by Controversial. Google can't give you that - i.e. sort results based on what impact are they likely to have on people. Thanks for suggestions!

Yes, we definitely have clustering. In fact, it's more extensive than simple word distances like cosine b/c we also take into account synonyms and word relationships (set membership, etc). For instance, in our system sentences like "Tiger chases antilope down the river" is very closely "related" to the sentence "Lion is pursuing a buffalo by the lake" b/c both sentences essentially say that a large cat is pursuing a prey of bovine origin near a water source. In terms of the "most viewed" and how we control for that - like I said we cross-track news on multiple news sites and weight the cross posted one more often. We also cross-validated the most important tags for an article by using Google Trends data. Basically we tracked topics on multiple sites and then performed some statistical analysis to see how those topics did over time based on their presence on the web (topic momentum and longevity). We also run a partial search engine in house that crawls a subset of the web so we can ensure that the numbers we get from Google/Yahoo are legit. Finally, there is linguistic theory of topic popularity and how memes propagate over time. We use some of that theory to control for the crowd effect - i.e. sometimes people pick up and spread topics that are of no real importance to the world. Example: Paris Hilton's latest escapades may be widely discussed online and appear important news but the latest report on the recession estimates and projections is of much higher "impact" to society. So we try to account/estimate some of that "impact". Combining all factors gives an article a composite score. No two article really have the same score but a lot of articles cluster close to each other in terms of their "importance" cores. We fed the articles in a machine learning algorithm that is a combination of Support Vector Machine, Neural Network, and Naive Bayes and when a new article is fetched by our crawler the model "preditc" its various scores (controversial, engaging, popular) based on the data set that it has already learned. Deception detection is much trickier and is almost entirely analysis based - i.e. no machine learning there. There is quite a bit of research on deception detection published online. Just search google for "deception detection ext:pdf" and it will come back with a lot of results.

We've nee collecting top rated news from Yahoo News, NYT, Washington Post, etc for the last 3 years. Almost all major sources have sections "Most viewed", "most read", "most email" etc. So as we have been collecting the top ranked news we also kept track of topics that have been top ranked at multiple sources. For instance, if a news article on Iran's riot gets to the top ranked in both NYT and Yahoo News it gets more points in our training set.

As far as your other point, Misleading is really a less legally loaded word than "deceptive". You are right, it's not very clear why the articles are misleading but the bottom line is that the language used in the article has high "deception markers" that other articles from the day. So when you sort by Misleading, it's not really that the article is beyond a doubt deceptive, it's just the ones with higher rate of deception markers (i.e. content and structural indicators associated with deception). The science behind is pretty solid and comes from forensic psychiatry - i.e. interviews/interrogations with criminals and analyzing their statements for deception hints. So for alack of a better term Euraeka essentially implements a linguistic polygraph. Thanks for the other comments, we'll work on fixing the issues.

This is in response to the submission "The good news is reality exists...". Read the whole article, the conclusion is towards the end. I don't understand how these teams can make such generic conclusions and then immediately contradict each other on such level. One team says "we are certain, reality doesn't exists", the other says "nope, it does exists". Either the articles interpreting the teams' results are exaggerating the claims, or quantum mechanics is mocking both teams making contradictory results possible.

I am interested as well. My email is haidut (-at-) gmail DOT com

thx

OK, there is a pretty "simple" way to do it but I can't divulge all the details b/c we are using it for my semantic news startup. However, I can give you a hint on what you need to do. You need to find a way to reduce different words to their "base" semantics and then compute a similarity measure of sorts between the different event descriptions. Traditionally, people have been breaking down the text into word vectors and then calculating a pearson or tanimoto, or whatnot similarity measures b/n the vectors. The problem with this approach is not the similarity measure but the fact that (as aristus points out) different people use different words to describe the same thing. So computing the tanimoto score of two sentences like "Bush visits Iraq to encourage soldiers" and "US. President arrives at Bagdad to raise troops morale" will result in very low similarity score using just the literal words used in both sentences. However, in reality both sentences are almost identical in meaning. So let's say that there was a way to reduce the semantics of both sentences down to their "base", then for sentence one you would have something like "US President present in Middle Eastern country to increase spirit of soldiers", and for sentence two you would have "US President present in capital of Middle Eastern country to increase spirit of soldiers". So NOW if you take the tanimoto measure of similarity or actually any other decent similarity measure b/n the semantic "base" of both sentences you would get a very high score, which is what is needed. So, like I said I can't divulge the method of how to do reduce text to its semantic "base" but there is definitely at least one way, which is what we are using. If you are interested, send me an email to haidut@gmail.com and when I launch my startup within the next couple of weeks I can hopefully divulge more. Sorry for my vagueness but my lawyers will make a minced-meat out of me if I say more:-) Here is something you can do and I am allowed to divulge. You can use Pointwise Mutual Information (PMI) to find out the semantic relatedness of words in both event descriptions and if the overall cross-word similarity is above a certain threshold then you can consider both event descriptions similar. To find out more on how to calculate PMI and some more info on how useful it is, read this paper: http://cogprints.org/1796/0/ECML2001.pdf Overall, what you are trying to do is simple in concept but not easy to implement. Basically, in order for semantic similarity to be computed automatically by a machine you'd need a mapping of EVERY word in EVERY language to its semantic "base" so you can create a table and compute this efficiently. Such mapping does not currently exists and one of the reasons is the constantly changing nature of English language itself - new words come out all the time and even if you could track them all - some of them don't have semantic "base". Using the example I gave above - the fact that Bush is currently a President won't be true in couple of months so that huge word-to-meaning mapping table would have to account for that, so mapping English language to a semantic base if a (very fast) moving target.

That's all I have for now. Good luck in your endeavor.