Barrett Sheridan and Brendan Greeley answer a question many folks asked about the students: how many actually finished? Answer: 23,000 finished all the assignments.

Second, they note that professor Thrun is also at the forefront of another potentially transformative technology: self-driving cars:

Last fall, Stanford took the idea further and conducted two CS courses entirely online. These included not just instructional videos but also opportunities to ask questions of the professors, get homework graded, and take midterms—all for free and available to the public.

Sebastian Thrun, a computer science professor and a Google fellow overseeing the search company’s project to build driverless cars, co-taught one of the courses, on artificial intelligence. It wasn’t meant for everyone; students were expected to get up to speed with topics like probability theory and linear algebra. Thrun’s co-teacher, Peter Norvig, estimated that 1,000 people would sign up. “I’m known as a crazy optimist, so I said 10,000 students,” says Thrun. “We had 160,000 sign up, and then we got frightened and closed enrollment. It would have been 250,000 if we had kept it open.” Many dropped out, but 23,000 students finished all 11 weeks’ worth of assignments. Stanford is continuing the project with an expanded list of classes this year. Thrun, however, has given up his tenured position to focus on his work at Google and to build Udacity, a startup that, like Codecademy, will offer free computer science courses on the Web.

I wish Thrun success in both endeavors. Perhaps one day soon, commuters will settle in for an hour of online learning while their car drives them to work.

P.S. In case you missed it, Tom Vanderbilt has a fun article on self-driving cars in the latest Wired.

Zanran helps you to find ‘semi-structured’ data on the web. This is the numerical data that people have presented as graphs and tables and charts. For example, the data could be a graph in a PDF report, or a table in an Excel spreadsheet, or a barchart shown as an image in an HTML page. This huge amount of information can be difficult to find using conventional search engines, which are focused primarily on finding text rather than graphs, tables and bar charts.

Put more simply: Zanran is Google for data.

This is a stellar idea. The web holds phenomenal amounts of data that are hard to find buried inside documents. And Zanran offers a fast way to find and scan through documents that may have relevant material. Particularly helpful is the ability to hover your cursor over each document to see the chart Zanran’s thinks you are interested in before you click through to the document.

Zanran is clearly in beta, however, and has some major challenges ahead. Perhaps most important are determining which results should rank high and identifying recent data. If you type “united states GDP” into Zanran, for example, the top results are rather idiosyncratic and there’s nothing on the first few pages that directs you to the latest data from the Bureau of Economic Analysis. Google, in contrast, has the BEA as its third result. And its first result is a graphical display of GDP data via Google’s Public Data project. Too bad, though, it goes up only to 2009. For some reason, both Google and Zanran think the CIA is the best place to get U.S. GDP data. It is a good source for international comparisons, but it falls out of date.

Here’s wishing Zanran good luck in strengthening its search results as its competes with Google, Wolfram Alpha, and others in the data search.

Several of my Facebook friends are also remarkable curators, as are many of the blogs that I follow (e.g., Marginal Revolution and Infectious Greed, to name just two).  So curation turns out to be perhaps the most important service I consume on the web. In the wilderness of information, skilled guides are essential.

Of course, I also use Google dozens of times each day. Curation is great, but sometimes what you need is a good search engine. But as Paul Kedrosky over at Infectious Greed notes, search sometimes doesn’t work. That’s one reason that Paul sees curation gaining on search, at least for now:

Instead, the re-rise of curation is partly about crowd curation — not one people, but lots of people, whether consciously (lists, etc.) or unconsciously (tweets, etc) — and partly about hand curation (JetSetter, etc.). We are going to increasingly see nichey services that sell curation as a primary feature, with the primary advantage of being mostly unsullied by content farms, SEO spam, and nonsensical Q&A sites intended to create low-rent versions of Borges’ Library of Babylon. The result will be a subset of curated sites that will re-seed a new generation of algorithmic search sites, and the cycle will continue, over and over.

In the humorous glitch department, I tried out the names of two Internet services I use everyday, Google and Wikipedia. For some reason, the Ngram viewer defaults to the timeperiod 1800 to 2000 (rather than 2008), and this was the chart I got (click to see a larger version):

It’s amazing to see references to Wikipedia as far back as the 1820s. Impressive foresight. Google overtook Wikipedia in the late 1800s and, with the exception of a brief period in the 1970s, has led ever since.

The article also describes an unusual battle between book publishers and Amazon about the pricing of electronic books:

Amazon had been buying many e-books from publishers for about thirteen dollars and selling them for $9.99, taking a loss on each book in order to gain market share and encourage sales of its electronic reading device, the Kindle. By the end of last year, Amazon accounted for an estimated eighty per cent of all electronic-book sales, and $9.99 seemed to be established as the price of an e-book. Publishers were panicked. David Young, the chairman and C.E.O. of Hachette Book Group USA, said, “The big concern—and it’s a massive concern—is the $9.99 pricing point. If it’s allowed to take hold in the consumer’s mind that a book is worth ten bucks, to my mind it’s game over for this business.”

As an alternative, several publishers decided to push for

an “agency model” for e-books. Under such a model, the publisher would be considered the seller, and an online vender like Amazon would act as an “agent,” in exchange for a thirty-per-cent fee.

That way, the publishers would be able to set the retail price themselves, presumably at a higher level that the $9.99 favored by Amazon.

Ponder that for a moment. Under the original system, Amazon paid the publishers $13.00 for each e-book. Under the new system, publishers would receive 70% of the retail price of an e-book. To net $13.00 per book, the publishers would thus have to set a price of about $18.50 per e-book, well above the norm for electronic books. Indeed, so far above the norm that it generally doesn’t happen:

“I’m not sure the ‘agency model’ is best,” the head of one major publishing house told me. Publishers would collect less money this way, about nine dollars a book, rather than thirteen; the unattractive tradeoff was to cede some profit in order to set a minimum price.

The publisher could also have noted a second problem with this strategy: publishers will sell fewer e-books because of the increase in retail prices.

Through keen negotiating, the publishers have thus forced Amazon to (a) pay them less per book and (b) sell fewer of their books. Not something you see everyday.

All of which yields a great topic for a microeconomics or business strategy class: Can the long-term benefit (to publishers) of higher minimum prices justify the near-term costs of lower sales and lower margins?

(For those of you reading this via email, Google Reader, etc., here are some example links: German and Spanish.)

P.S. Kudos to the WordPress member who wrote the code for this.

As an example, I made a flash chart of state unemployment rates from 1990 to the present. Puerto Rico (which counts as a state for these purposes), Michigan, Nevada, and Rhode Island currently have the highest unemployment rates, so I thought it would be interesting to see how they stacked up against the other states over the past twenty years.

WordPress doesn’t allow me to embed Flash, but if you click on the image above and then click play, you will see the evolution of state unemployment rates over time. (Spoiler alert: All those colored bars move sharply upward toward the end of the “movie”.)

Long-time readers may recall my series of posts criticizing Google for directing its users to unemployment data that have not been seasonally adjusted. Happily, Google now allows the user to use either seasonally adjusted or non adjusted data. Two cheers for Google.

Why only two cheers rather than three? Because Google still directs unsuspecting users to unadjusted data–without the ability to switch to seasonally adjusted–if they do a Google search on “unemployment rate United States“. That’s a big deal, particularly for February 2010 when the official unemployment rate was 9.7%, but the unadjusted figure reported by Google was 10.4%.

Clearly, the two parts of Public Data need to integrate a bit more.

This is probably of interest only to other bloggers, but for the record: When I first started this blog, it took about six weeks for it to appear regularly in Google search results. After several months, the blog inexplicably (to me, at least) disappeared from Google’s results. As in *really* disappeared; as one friend pointed out, you couldn’t even find it if you searched for “Donald Marron blog”.  About eight weeks elapsed before it reappeared regularly in the first few pages of Google’s results.

My eight-week exile provided a nice natural experiment for evaluating Google’s importance. Not surprisingly, Google drives a good amount of traffic; readership is larger when Google knows about the blog. The more interesting impact, though, is a version of the Long Tail: with Google’s help, more posts find readers on any given day.

This discrepancy arises because Google directs users to data that haven’t been adjusted for seasonal variations. Almost all discussions of the national economy, however, use data that have been seasonally-adjusted. Why? Because seasonally-adjusted data (usually) make it easier to figure out what’s actually happening in the economy. The unemployment rate always spikes up in January, for example, because retailers lay off their Christmas help. But that doesn’t mean that we should get concerned about the economy every January. Instead, we should ask how the January increase in the unemployment rate compares to a typical year. That’s what seasonal adjustment does.

My concern about Google’s approach is that many (if not most) data users know nothing about seasonal adjustment. They simply want to know what the unemployment rate is and how it has changed over time. Directing those users to the non-seasonally-adjusted data thus seems like a form of search malpractice.

I’ve wondered why Google has chosen this approach, and thus was thrilled when reader Jonathan Biggar provided the answer in a recent comment. Jonathan writes:

I reported the unemployment data issue internally at Google a month or so ago. I’m still waiting for definitive action on it, although one response I got was that an advantage to seeing the non-seasonally-adjusted data was that it can be compared against state and local unemployment data, which is not available in seasonally-adjusted form.

But even so, what Google has now needs a prominent explanation and a pointer to the seasonally-adjusted data, if nothing else.

This is a good sign, not least because it indicates some logic to Google’s decision thus far. In short, Google wants to satisfy another crucial goal in providing data to its users: whenever possible, the data should be comparable. Suppose, for example, that a user is looking at the unemployment rate for Anchorage, Alaska and wants to compare it to the national unemployment rate. The Anchorage rate is available only without seasonal adjustment and so, as Google argues, it makes the most sense to compare it to the national unemployment rate without adjustment as well:

(Random factoid: the not-seasonally-adjusted unemployment rate in Anchorage spikes in February, not January.)

Google is absolutely right to worry about such comparability. However, their way of implementing it has an unfortunate side-effect. In order to ensure comparability, Google reports all unemployment rates without seasonal adjustment, even though seasonally-adjusted figures are often available.

Specifically, seasonally-adjusted data are available for the U.S. as a whole and (contrary to what Jonathan was told) for each of the individual states. For smaller areas, however, only the non-adjusted data are available.

Under Google’s current approach, all data users have to make do under the limitations that apply only to data for the smaller areas.

Here’s my advice for Google:

• I realize it’s more work, but your data base should include both seasonally-adjusted and non-seasonally adjusted data, whenever both are available.

• Users should be directed to seasonally-adjusted data whenever they are available (e.g., in searches for the U.S. as a whole or for individual states).

• Users should be directed to non-adjusted data when they are the only data available.

• Users should be able to switch between the two types of data at will.

• One way to do this would be to have a check box at the top of the list of places on the left hand side of the screen that allow users to toggle between adjusted and non-adjusted.

• As Jonathan suggests, there should be some sort of prominent link to a short explanation of the difference between the series.

]]> http://dmarron.com/2009/09/14/insight-on-google-and-unemployment/feed/ 2 Donald UE for US and Anchorage http://dmarron.com/2009/09/12/google-and-me/ http://dmarron.com/2009/09/12/google-and-me/#comments Sat, 12 Sep 2009 20:21:24 +0000 Donald Marron http://dmarron.com/?p=1692 ]]> A strange this happened last week: Google misplaced my blog.

I’ve run all the usual diagnostics, and I can confirm that Google still knows that my blog exists. But it no longer appears in any of the searches – e.g., “natural gas price”, “unemployment”, “budget deficit”, or “brooke boemio” – that used to help new readers find posts on my site.

Things are so bad, in fact, that my blog doesn’t even come up when you search for “donald marron”. I feel an existential crisis coming on.

I presume this is just the result of some obscure algorithm tweak and that, over time, my posts will reappear in the ranks of the Google-worthy. But it’s fun to imagine that Google is mad at me for my posts criticizing the way it reports unemployment data.

I just checked and, no surprise, Google is still reporting the wrong data. If you search for “unemployment rate”, Google will tell you that the U.S. unemployment rate was 9.6% in August, when in fact it was 9.7%. Why the difference? Because Google is reporting an obscure measure of unemployment, not the one used by 99% of the world.