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marți, 24 decembrie 2013
The (Gingerbread) White House
Moz Holiday Traditions
Moz Holiday Traditions |
| Posted: 23 Dec 2013 03:15 PM PST Posted by ssnsestak Here at Moz, we often feel like a big family. Just like any other family, over the years we've developed an eclectic set of traditions to celebrate the holiday season. We'd like to share a few of our favorites and welcome you to join us in these most joyous of festivities. ;) Seasons Greetings!
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A Very Special West Wing Week
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Seth's Blog : When you're offered precisely what you were hoping for...
When you're offered precisely what you were hoping for...
say, "yes."
If you're a musician, that means that when the internet says you can play what you want, record the way you want to, release it when you like, at the length you prefer, to the fans you'd like to share it with...
If you're an actor, that means that when the internet says you can perform what you'd like, film it with the team you've chosen and distribute it far and wide...
If you're a writer, that means that when the internet says you can write what you want, when you want to, at any length and subject matter and intensity you prefer, and then send it to five or ten or a million friends and followers...
You get the idea. Now, for the first time, you can choose yourself. You can be responsible for what you do and how you do it. You have to do the hard work of finding and pleasing an audience.
But you do have to say, "yes."
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- "In an abundance of caution"
- "Am I supposed to like this?"
- Noise-tolerant media
- Pick three
- Which charity?
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luni, 23 decembrie 2013
Mish's Global Economic Trend Analysis
Mish's Global Economic Trend Analysis |
- Obamacare Support at Record Low 35%; Obamacare Named "Lie of the Year"; Bait and Switch; Obamacare Roundup; Meltdown Coming?
- Wall Street Is My Landlord; Blackstone's Home Rental Bonds Yet Another Sign of Renewed Credit Bubble
- China Interest Rate Crisis Continues: 7-Day Interest Rate Doubles to 10% in One Week; China Bans Words "Cash Crunch"
| Posted: 23 Dec 2013 07:14 PM PST It's been a while since I commented on Obamacare. Given news comes out every day, nearly all of it negative, I have shown restraint. Tonight, here's a recap of what you may have missed. Obamacare Support at Record Low 35% Today, December 23, a CNN poll finds Obamacare support at all-time low. Poll Details
I have gathered a huge number of Obamacare links in the past few weeks that I did not have time to comment on. Here are a few of them. This is by no means a complete list. Obamacare Roundup, Last Two Weeks
The final link above is interesting. The URL title is "obama-administration-secretly-extends-health-care-enrollment-deadline". I guess it's not much of a secret. Please note the political spectrum in the above links encompasses everything between the Huffington Post and Fox News. That's quite an accomplishment! Meltdown Coming? Let's finish up with a highlight from the Washington Post: Sen. Joe Manchin (D-W.Va.) says Obamacare could suffer 'complete meltdown' Sen. Joe Manchin (D-W.Va.) said Sunday that Obamacare could be headed for a "complete meltdown" if costs rise too fast and people are unhappy with their coverage.Even Democrats are distancing themselves from this disaster. Mike "Mish" Shedlock http://globaleconomicanalysis.blogspot.com | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Wall Street Is My Landlord; Blackstone's Home Rental Bonds Yet Another Sign of Renewed Credit Bubble Posted: 23 Dec 2013 10:11 AM PST Blackstone Group LP, the world's largest private equity firm, became the largest owner of rental homes in the U.S. , acquiring 41,000 homes in the past two years. In October, Blackstone offered the first-ever "rental-home-backed" security on Wall Street. The bond is backed by just a fraction — 3,207 — of the rental properties owned by Blackstone. Monthly rent checks from the properties will be used to service the $479.1 million security. See Bloomberg's Blackstone's Big Bet on Rental Homes for a giant infographic. Inquiring minds may also be interested in a related Bloomberg article, Wall Street Is My Landlord. Let's focus on the credit aspect of what's in the Blackstone/Deutsche "Invitation Homes", first-ever rent-based bond offering. Here is a snip from the gigantic infographic in the first link above. Invitation Homes Bond Offering  What do investors get for their money? A Bond Credit Rating Table courtesy of Wikipedia will help explain.
Whether or not one really believes the Aaa tranche truly deserves that rating, all those investors get is a coupon rate of 1.314%. Those buying the class "D" offering, rated Baa2, get a "lower medium grade" bond, two steps above junk (again assuming the class really deserves that rating). Those investors get a 2.314% return. Classes E and F, both "unrated" are highly likely to be pure garbage in my estimation. Blackstone Lures Investors to Home-Rental Bonds Feel warm and fuzzy with these bonds? If so please consider Blackstone Lures Investors to Home-Rental Bonds. Investors in Blackstone (BX) Group LP's debut sale of bonds backed by U.S. rental homes are agreeing to accept more risk than in traditional mortgage deals by at least two measures -- along with an unproven business.13-Point Deal Summary
What the biggest risk? Whalen says "The largest danger may be that Blackstone will be allowed to sell the Invitation Homes business or take it public before the securities mature". I disagree. I think the biggest deal-based risk is that individual properties can be sold over time, leaving increasing amounts of garbage in the loan portfolio. That thought alone makes me suspicious as to how Blackstone picked properties to go into this pool in the first place. Final Thoughts Without a doubt, Blackstone picked individual properties carefully, and every property placed in the pool was to maximize value for Blackstone, not investors. That is to be expected, but Blackstone went steps further, nearly to the point of detailing how investors are likely to be gored by this deal. Investors chasing this deal for paltry returns are picking up pennies in front of a steamroller. This is the way it is at the peak of every credit bubble. The only surprising thing is how quickly investors were willing to repeat their last mistake. Mike "Mish" Shedlock http://globaleconomicanalysis.blogspot.com | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Posted: 23 Dec 2013 01:21 AM PST A "cash crunch" is on in China. But don't call it that, because China banned use of the term last week. The New York Times reports China Rates Approach Crisis Levels Despite Central Bank Measures. An exceptional bid by China's central bank to curb soaring interest rates and relieve pressure on the financial system appeared to have come up short on Monday, as Chinese money market rates shrugged off the measure and continued to approach the crisis levels seen in June.China Bans Words "Cash Crunch" Last Friday, the Financial Times reported China presses media to tone down cash crunch story. Chinese propaganda officials have ordered financial journalists and some media outlets to tone down their coverage of a liquidity crunch in the interbank market, in a sign of how worried Beijing is that the turmoil will continue when markets reopen on Monday. Don't worry. There isn't a cash crunch because China says so. Heck, China even banned the words. That should be proof enough. Besides, a quick check shows the Shanghai composite index is up a bit today at the time of this writing. Clearly, central bankers everywhere have everything perfectly under control. For now. Mike "Mish" Shedlock http://globaleconomicanalysis.blogspot.com | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Machine Learning for SEOs
Machine Learning for SEOs |
| Posted: 22 Dec 2013 03:16 PM PST Posted by Tom-Anthony Since the Panda and Penguin updates, the SEO community has been talking more and more about machine learning, and yet often the term still isn't well understood. We know that it is the "magic" behind Panda and Penguin, but how does it work? Why didn't they use it earlier? What does it have to do with the periodic "data refreshes" we see for both of these algorithms? I think that machine learning is going to be playing a bigger and bigger role in SEO, and so I think it is important that we have a basic understanding of how it works. Disclaimer: Firstly, I'm no expert on machine learning. Secondly, I'm going to intentionally simplify aspects in places and brush over certain details that I don't feel are necessary. The goal of this post is not to give you a full or detailed understanding of machine learning, but instead to give you a high-level understanding that allows you to answer the questions in my opening paragraph should a client ask you about them. Lastly, Google is a black box, so obviously it is impossible to know for sure exactly how they are going about things, but this is my interpretation of the clues the SEO community has stitched together over time. Watermelon farmingMachine learning is appropriate to use when there is a problem that does not have an exact answer (i.e. there isn't a right or wrong answer) and/or one that does not have a method of solution that we can fully describe. Examples where machine learning is not appropriate would be a computer program that counts the words in a document, simply adds some numbers together, or counts the hyperlinks on a page. Examples where machine learning would be appropriate are optical character recognition, determining whether an email is spam, or identifying a face in a photo. In all of these cases it is almost impossible for a human (who is most likely extremely good at these tasks) to write an exact set of rules for how to go about doing these things that they can feed into a computer program. Furthermore, there isn't always a right answer; one man's spam is another man's informative newsletter. Explaining Machine Learning with Will Critchlow at SearchLove 2013 in London. I like watermelons. The example I am going to use in this post is that of picking watermelons. Watermelons do not continue to ripen once they are picked, so it is important to pick them when they are perfectly ripe. Anyone who has been picking watermelons for years can look at a watermelon, give it a feel with their hands, and from its size, colour and from how firm it feels they can determine whether it is under-ripe, over-ripe or just right. They can do this with a high degree of accuracy. However, if you asked them to write down a list of rules or a flow chart that you or I could use to determined whether a specific watermelon was ripe, then they would almost certainly fail - the problem doesn't have a clean cut answer you can write into rules. Also note that there isn't necessarily a right or wrong answer - there may even be disagreement among the farmers. You can imagine that the same is true about how to identify whether a webpage is spammy or not; it is hard or impossible to write an exact set of rules that work well, and there is room for disagreement. Robo-farmersHowever, this doesn't mean that it is impossible to teach a computer to find ripe watermelons; it is absolutely possible. We simply need a method that is more akin to how humans would learn this skill: learning by experience. This is where machine learning comes in. Supervised learningWe can set up a computer (there are various methods, we don't need to know the details at this point, but the method you've likely heard of is artificial neural networks) such that we can feed it information about one melon after another (size, firmness, color, etc.), and we also tell the computer whether that melon is ripe or not. This collection of melons is our "training set," and depending the complexity of what is being learnt it needs to have a lot of "melons" (or webpages or whatever) in it. Over time, the computer will begin to construct a model of how it thinks the various attributes of the melon play into it being ripe or not. Machine learning can handle situations where these interactions could be relatively complex (e.g. the firmness of a ripe melon may change depending on the melon's color and the ambient temperature). We show each melon in the training set many times in a round robin fashion (imagine this was you; now that you've noticed something you didn't before you can go back to previous melons and learn even more from them). Once we're feeling confident that the computer is getting the hang of it, then we can give it a test by showing it melons from another collection it has not yet seen (we call this set of melons the "validation set"), but we don't share whether these melons are ripe or not. Now the computer tries to apply what it has learnt and predict whether the melons are ripe or not (or even how ripe they may or may not be). We can see from how many melons the computer accurately identifies how well it has learnt. If it didn't learn well we may need to show it more melons or we may need to tweak the algorithm (the "brain") behind the scenes and start again. This type of approach is called supervised learning, where we supply the learning algorithm with the details about whether the original melons are ripe or not. There do exist alternative methods, but supervised learning is the best starting point and likely covers a fair bit of what Google is doing. One thing to note here is that even after you've trained the computer to identify ripe melons well, it cannot write that exhaustive set of rules we wanted from the farmer any more than the farmer could. Caffeine infrastructure updateSo how does all this fit with search? First we need to rewind to 2010 and the rollout of the Caffeine infrastructure update. Little did we know it at the time, but Caffeine was the forefather of Panda and Penguin. It was Caffeine that allowed Panda and Penguin to come into existence. Caffeine allowed Google to update its index far faster than ever before, and update PageRank for parts of the web's link graph independently of the rest of the graph. Previously, you had to recalculate PageRank for all pages on the web at once; you couldn't do just one webpage. With Caffeine, we believe that changed and they could estimate, with good accuracy, updated PageRank for parts of the web (sub-graphs) to account for new (or removed) links. This meant a "live index" that is constantly updating, rather than having periodic updates. So, how does this tie in with machine learning, and how does it set the stage for Panda and Penguin? Lets put it all together... Panda and PenguinCaffeine allowed Google to update PageRank extremely quickly, far faster than ever before, and this is likely the step that allowed them finally apply machine learning at scale as a major part of the algorithm. The problem that Panda set out to solve is very similar to the problem of determining whether a water melon is ripe. Anyone reading this blog post could take a short look at a webpage, and in most cases tell me how spammy that page is with a high degree of accuracy. However, very few people could write me an exact list of rules to judge that characteristic for pages you've not yet seen ("if there are more than x links, and there are y ads taking up z% of the screen above the fold..."). You could give some broad rules, but nothing that would be effective for all the pages where it matters. Consider also that if you (or Google) could construct such a list of strict rules, it would become easier to circumvent them. So, Google couldn't write specific sets of rules to judge these spammy pages, which is why for years many of us would groan when we looked at a page that was clearly (in our minds) spammy but which was ranking well in the Google SERPs. The exact same logic applies for Penguin. The problems Google was facing were similar to the problem of watermelon farming. So why weren't they using machine learning from day one? TrainingGoogle likely created a training set by having their teams of human quality assessors give webpages a score for how spammy that page was. They would have had hundreds or thousands of assessors all review hundreds or thousands of pages to produce a huge list of webpages with associated spam scores (averaged from multiple assessors). I'm not 100% sure on exactly what format this process would have taken, but we can get a general understanding using the above explanation. Now, recall that to learn how ripe the watermelons are we have to have a lot of melons and we have to look at each of them multiple times. This is a lot of work and takes time, especially given that we have to learn and update our understanding (we call that the "model") of how to determine ripeness. After that step we need to try our model out on the validation set (the melons we've not seen before) to assess whether it is working well or not. In Google's case, this process is taking place across its whole index of the web. I'm not clear on the exact approach they would be using here, of course, but it seems clear that applying the above "learn and test" approach across the whole index is immensely resource intensive. The types of breakthroughs that Caffeine enabled with a live index and faster computation on just parts of the graph are what made Machine Learning finally viable. You can imagine that previously if it took hours (or even minutes) to recompute values (be it PageRank or a spam metric) then doing this the thousands of times necessary to apply Machine Learning simply was not possible. Once Caffeine allowed them to begin, the timeline to Panda and subsequently Penguin was pretty quick, demonstrating that once they were able they were keen to utilise machine learning as part of the algorithm (and it is clear why). What next?Each "roll out" of subsequent Panda and Penguin updates was when a new (and presumably improved) model had been calculated, tested, and could now be applied as a signal to the live index. Then, earlier this year, it was announced that Panda would be continuously updating and rolling out over periods of around 10 days, so the signs indicate that they are improving the speed and efficiency with which they can apply Machine Learning to the index. Hummingbird seems to be setting the stage for additional updates. I fully expect we will see more machine learning being applied to all areas of Google over the coming year. In fact, I think we are already seeing the next iterations of it with Hummingbird, and at Distilled we are viewing the Hummingbird update in a similar fashion to Caffeine. Whilst Hummingbird was an algorithm update rather than an infrastructure update, we can't shake the feeling that it is setting the foundations for something yet to come. Wrap-upI'm excited by the possibilities of machine learning being applied at this sort of scale, and I think we're going to see a lot more of it. This post set out to give a basic understanding of what is involved, but I'm afraid to tell you I'm not sure the watermelon science is 100% accurate. However, I think understanding the concept of Machine Learning can really help when trying to comprehend algorithms such as Panda and Penguin. Sign up for The Moz Top 10, a semimonthly mailer updating you on the top ten hottest pieces of SEO news, tips, and rad links uncovered by the Moz team. Think of it as your exclusive digest of stuff you don't have time to hunt down but want to read! |
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Seth's Blog : "In an abundance of caution"
"In an abundance of caution"
Do we have a caution shortage?
Is it necessary to have caution in abundance?
When a lawyer or a doctor tells you to do something in an abundance of caution, what they're actually doing is playing on your fear. Perhaps we could instead for an abundance of joy or an abundance of artistic risk or an abundance of connection. Those are far more productive (and fun).
Also: The things we have the most abundance of caution about are rarely the things that are actual risks. They merely feel like risks.
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