Jan Marco, niet bij een ‘level playing field’ toch? Als je de onderliggende wetgeving waterpas houdt komt het overal even hoog te staan en niet hier en daar een stuwmeer:
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Google’s Very Clever Trick With Their Offshore $30 Billion Stash And Acquisitions
As we all know the guys over at Google are pretty smart. And their accountants are pretty savvy about how to hang on to the cash that the company has earned as well. They manage to irritate just about every government in Europe by managing to not pay much tax to any of them and then they also don’t pay it to Uncle Sam by not bringing it back into the US.
I tend to support all of this of course as I tend to think that the bright guys at Google will do more to make life better with such piles of cash than whatever loon has managed to get himself into office this electoral cycle would. And while that’s sometimes an unpopular view it becomes rather more popular when stated in that blunt manner.
Inmiddels is de waterspiegel weer gestegen, van Google werd vorig jaar gezegd dat het gestalde bedrag is opgelopen tot 47 miljard dollar.
Verwijzing van een correspondent van De Correspondent, wat doen ze eigenlijk met al dat geld:
Cheap cab ride? You must have missed Uber’s true cost
A recent article in The Information, a tech news site, suggests that during the first three quarters of 2015 Uber lost $1.7bn while booking $1.2bn in revenue. The company has so much money that, in at least some North American locations, it has been offering rides at rates so low that they didn’t even cover the combined cost of fuel and vehicle depreciation.
Uber’s game plan is simple: it wants to drive the rates so low as to increase demand – by luring some of the customers who would otherwise have used their own car or public transport. And to do that, it is willing to burn a lot of cash, while rapidly expanding into adjacent industries, from food to package delivery.
An obvious but rarely asked question is: whose cash is Uber burning? With investors like Google, Amazon’s Jeff Bezos and Goldman Sachs behind it, Uber is a perfect example of a company whose global expansion has been facilitated by the inability of governments to tax profits made by hi-tech and financial giants.
To put it bluntly: the reason why Uber has so much cash is because, well, governments no longer do. Instead, this money is parked in the offshore accounts of Silicon Valley and Wall Street firms. Look at Apple, which has recently announced that it sits on $200bn of potentially taxable overseas cash, or Facebook, which has just posted record profits of $3.69bn for 2015.
The Guardian - Opinion - Evgeny Morozov - Sunday 31 January 2016
[quote=“alkema_jm, post:197, topic:354, full:true”]
Gebruikers op Facebook zijn gemiddeld 3,57 stappen verwijderd van alle andere gebruikers op het sociale netwerk. Hierdoor is nagenoeg iedereen op Facebook te vinden via een ‘vriend van een vriend van een vriend van een vriend’. Eigenlijk zeggen ze dat ze een totaal netwerk hebben, iedereen is met iedereen verbonden, ze hebben ‘goud in handen’.[/quote]
Facebook daarover:
Calculating degrees-of-separation at scale
More accurately, for each number of hops we estimate the number of distinct people you can reach from every source. This estimation can be done efficiently using the Flajolet-Martin algorithm [9].
How does it work? Imagine you have a set of people and you want to count how many are unique. First you assign each person a random integer; let’s call it hash. Approximately 1/2 of the people will have an even hash: the binary representation of the hash will end with 0. Approximately 1/4 of the people will have a hash divisible by 4; that is, the binary representation ends with 00. In general, 1/2n people will have the binary representation of their hash end with n zeros.
Now, we can reverse this and try to count how many different people we have by reading their hash values one by one. To do that, we track the biggest number of zeroes we’ve seen. Intuitively, if there were n zeroes, we can expect set to have c*2n unique numbers, where c is some constant. For better accuracy we can do this computation multiple times with different hash values.
Zoeken op ‘Flajolet-Martin’ verwijst onder andere naar dit handboek:
Chapter 8
Advertising on the Web
One of the big surprises of the 21st century has been the ability of all sorts of interesting Web applications to support themselves through advertising, rather than subscription. While radio and television have managed to use advertising as their primary revenue source, most media – newspapers and magazines, for example – have had to use a hybrid approach, combining revenue from advertising and subscriptions.
By far the most lucrative venue for on-line advertising has been search, and much of the effectiveness of search advertising comes from the “adwords” model of matching search queries to advertisements. We shall therefore devote much of this chapter to algorithms for optimizing the way this assignment is done.
The algorithms used are of an unusual type; they are greedy and they are “online” in a particular technical sense to be discussed. We shall therefore digress to discuss these two algorithmic issues – greediness and on-line algorithms – in general, before tackling the adwords problem.
A second interesting on-line advertising problem involves selecting items to advertise at an on-line store. This problem involves “collaborative filtering,” where we try to find customers with similar behavior in order to suggest they buy things that similar customers have bought.
Wetenschappers schrijven het nog een keer op:
Search ads are placed among the results of a search query. Advertisers bid for the right to have their ad shown in response to certain queries, but they pay only if the ad is clicked on. The particular ads to be shown are selected by a complex process, to be discussed in this chapter, involving the search terms that the advertiser has bid for, the amount of their bid, the observed probability that the ad will be clicked on, and the total budget that the advertiser has offered for the service.
Voor degene die nadien de kans krijgt om in dienst te treden bij Facebook:
However, the Web offers an opportunity to tailor display ads in a way that hardcopy media cannot: it is possible to use information about the user to determine which ad they should be shown, regardless of what page they are looking at. If it is known that Sally likes golf, then it makes sense to show her an ad for golf clubs, regardless of what page she is looking at. We could determine Sally’s love for golf in various ways:
- She may belong to a golf-related group on Facebook.
- She may mention “golf” frequently in emails posted on her gmail account.
- She may spend a lot of time on the Yahoo! golf page.
- She may issue search queries with golf-related terms frequently.
- She may bookmark the Web sites of one or more golf courses.
Each of these methods, and many others like these, raise enormous privacy issues. It is not the purpose of this book to try to resolve those issues, which in practice probably have no solution that will satisfy all concerns.
On the one hand, people like the free services that have recently become advertisingsupported, and these services depend on advertising being much more effective than conventional ads. There is a general agreement that, if there must be ads, it is better to see things you might actually use than to have what pages you view cluttered with irrelevancies.
On the other hand, there is great potential for misuse if the information leaves the realm of the machines that execute advertising algorithms and get into the hands of real people.