Neues Kryptogeld Libra: Facebook plant die Weltwährung | heise online
Facebook will die Finanzwelt umkrempeln: Das Online-Netzwerk hat eine neue globale Währung erfunden. Das Digitalgeld mit dem Namen Libra basiert ähnlich wie der Bitcoin auf der sogenannten Blockchain-Technologie, soll aber ohne Kursschwankungen auskommen. Facebook werde dabei auch auf Datenschutz achten, versicherte der für das Projekt zuständige Manager David Marcus.

In der Anfangszeit dürfte das Digitalgeld vor allem für Überweisungen zwischen verschiedenen Währungen eingesetzt werden, sagte Marcus der dpa. Damit würde Libra mit Diensten wie Western Union oder Moneygram konkurrieren, die für internationale Überweisungen hohe Gebühren verlangen. Die Vision sei aber, Libra schließlich zu einem vollwertigen Zahlungsmittel für alle Situationen zu machen.
Blockchain  Facebook  Libra  Libra_Association  VISA  Mastercard  PayPal  Stripe  Banken  Finanzen  Vodafone  Ebay  Spotify  Uber  Lyft  Smart_Contracts  LibraBFT  Kryptowährung 
4 weeks ago by snearch
Airbnb Rival Booking Is Having an Identity Crisis — The Information
Internet travel conglomerate Booking Holdings has been a dependable company for investors, generating consistent profits and returns in line with the S&P 500 over the last five years. But in an industry radically altered by hot brands ranging from Airbnb to Uber to Google, Booking has struggled ...  ctrip  ota 
9 weeks ago by JINYONG86
Lucas Javier Bernardi | Diagnosing Machine Learning Models - YouTube
A Machine Learning Models is never perfect. If it completely fails, it must be fixed, if it performs well, we want to improve it.
In this talk, several techniques to diagnose the source of errors a model makes will be presented.

Your model performs worse than a random model. What do you do? Your model has 99.9% ROC AUC, should you just celebrate? Every time you add a new feature the interpretation of the model's parameters changes completely. What 's wrong? Your model has 75% ROC AUC. Should you add more data? More features? Use a more complex model? You are out of new features, no matter what you do, the model performance is the same? What is happening?

Many of these questions appear once and again when working with Machine Learning,, answering them takes time and has a huge impact on the final outcome of a Machine Learning project. Understanding the current condition of a model is the key to decide what to do next.

In this talk I will describe several techniques to diagnose algorithms and models, some of them are: Bias and Variance Decomposition Calibration Curves Response Distribution Chart Residual Plots etc.
ML  multicollinearity  response  distribution  analysis 
march 2018 by foodbaby
Learning to Match TSMO_2018_paper_10.pdf - Google Drive is a virtual two-sided marketplace where guests and
accommodation providers are the two distinct stakeholders. They
meet to satisfy their respective and different goals. Guests want to

be able to choose accommodations from a huge and diverse inven-
tory, fast and reliably within their requirements and constraints.

Accommodation providers desire to reach a reliable and large mar-
ket that maximizes their revenue. Finding the best accommodation

for the guests, a problem typically addressed by the recommender

systems community, and finding the best audience for the accom-
modation providers, are key pieces of a good platform. This work

describes how extends such approach, enabling the
guests themselves to find the best accommodation by helping them

to discover their needs and restrictions, what the market can actu-
ally offer, reinforcing good decisions, discouraging bad ones, etc.

turning the platform into a decision process advisor, as opposed to
a decision maker. implements this idea with hundreds

of Machine Learned Models, all of them validated through rigor-
ous Randomized Controlled Experiments. We further elaborate on

model types, techniques, methodological issues and challenges that
we have faced.
ML  guide  rails  response  distribution  analysis 
march 2018 by foodbaby
Truth about | BookingEmployee []
>> Booking is destroying my career because I am not allowed to do anything new. I am not allowed to use new technologies. I'm not allowed to "design" anything big. I am not allowed to write tests. I am allowed to copy that 500 line subroutine into another module. If people have done that several times before, maybe it should be refactored instead of duplicated? If you do that, you get in trouble. One developer talks about "unforking" code is he refactor because he will get in trouble if he is caught refactoring. If you are not willing to cut and paste the same routine over and over, you are not "Booking Blue" and management will start talking to you about your attitude problem. Why? Cutting and pasting code is faster than refactoring code. They cannot measure how much work you save the next developer. They can measure how fast you make features. Booking believes that if they do not know how to measure something, it does not exist.  perl 
october 2017 by po
[1709.05820] Toward a full-scale neural machine translation in production: the use case
While some remarkable progress has been made in neural machine translation (NMT) research, there have not been many reports on its development and evaluation in practice. This paper tries to fill this gap by presenting some of our findings from building an in-house travel domain NMT system in a large scale E-commerce setting. The three major topics that we cover are optimization and training (including different optimization strategies and corpus sizes), handling real-world content and evaluating results.
papers  nmt  case-study 
september 2017 by arsyed
How manipulates you
It strikes me that someone could write a userscript or browser extension to make the site usable.
analysis  design  algorithms  dark-patterns 
september 2017 by pw201
Uber’s New CEO – Stratechery by Ben Thompson
To that end, Uber’s strength — and its sky-high valuation — comes from the company’s ability to acquire customers cheaply thanks to a combination of the service’s usefulness and the effects of aggregation theory: as the company acquires users (and as users increases their usage) Uber attracts more drivers, which makes the service better, which makes it easier to acquire marginal users (not by lowering the price but rather by offering a better service for the same price). The single biggest factor that differentiates multi-billion dollar companies is a scalable advantage in customer acquisition costs; Uber has that.
Uber  management  DaraKhosrowshahi  strategy  aggregationtheory  accommodation  Expedia  comparison  Stratechery  2017 
august 2017 by inspiral

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