Bit coins purse company has financed 270 million

Bitchain wallet company Blockchain announced Thursday that it has raised $ 40 million ($ 270 million), venture capital firm Lakestar, Google Ventures and billionaire Richard Branson.

This investment coincided with the growing interest in encrypted currencies, especially bitcoin, whose prices have recently hit a record high since the beginning of the year.

Blockchain has developed a special currency wallet, which is actually a software that can store this digital currency and handle transactions between users.

According to Blockchain CEO Pitt Smith, $ 40 million in financing will be used to further expand the team and invest in R & D. In addition, the start-up company also hopes to open a new office in different countries to expand.

Smith said the recent rise in Bitcoin prices has caused demand for Blockchain wallet products to rise, and this round of financing will help the company meet these needs.

“We focus on expanding the scale to meet the market’s record needs,” Smith said to CNBC.

He said Blockchain has nearly 15 million registered users, “the monthly wallet activities worth billions of dollars”, including deposits and trading activities.

Smith also said the company’s total revenue this year is expected to grow by more than 1000%, but he did not disclose the specific figures.

Blockchain was founded in 2011 and now has 140 users worldwide. Its last round of financing in 2014, the scale of 30.5 million US dollars. Other investors in the latest round of financing include Nokota Management and Digital Money Group.

Google can handle a variety of tasks for voice and images

Recently, Google released a low-key academic papers, in the paper depicts the machine learning blueprint. Google has developed a new machine learning system, which is called “a multifunctional model for learning all tasks.” This model provides a template for later research on how to create a machine that can handle multiple tasks well model.
    
As Google researchers call, the “multifunctional model” accepts a variety of task training, including translation, language analysis, speech recognition, image recognition and target detection. While its results do not show a fundamental improvement in the existing approach, it is at least that training the machine learning system on different tasks helps to improve its overall performance.
    
The accuracy of the “multifunctional model” in machine translation, speech and grammar analysis has been improved compared to training on a single computing machine.
    
Google’s paper can provide a case for the future development of machine learning systems that can be applied more widely and may be more accurate than most of the narrow solutions on the market today. More importantly, these techniques (or their derivatives) can help reduce the amount of training data required to train a viable machine learning algorithm.