The technology enabling Alibaba to sell $30.8 billion in Double 11 goods

The technology enabling Alibaba to sell $30.8 billion in Double 11 goods

  • November 17, 2018
Table of Contents

The technology enabling Alibaba to sell $30.8 billion in Double 11 goods

In order for everything to stay up, Tmall.com and its ecosystem relied entirely on Alibaba’s technology portfolio, including the use of artificial intelligence (AI) to improve the shopping experience for buyers and sellers, as well as pushing Alibaba’s cloud infrastructure to the limit to process a high volume of transactions. Alibaba used an intelligent operating platform, DC Brain, to optimise the performance of the 200-plus global internet datacentres (IDCs) hosting its online stores in areas including energy consumption, temperature, energy efficiency, and reliability. Through machine learning, DC Brain can predict the electric consumption and Power Usage Effectiveness of each IDC in real time, allocating to each to reduce energy consumption.

For this year’s festival, Alibaba also made available its ‘hyper-scale green datacentre’ located in Zhangbei, northern China. Alibaba said that through the facility’s use of wind and liquid immersion cooling, energy consumption was reduced by as much as 59 percent.

Source: zdnet.com

Tags :
Share :
comments powered by Disqus

Related Posts

Decision Tree in Machine Learning

Decision Tree in Machine Learning

A decision tree is a flowchart-like structure in which each internal node represents a test on a feature (e.g. whether a coin flip comes up heads or tails), each leaf node represents a class label (decision taken after computing all features) and branches represent conjunctions of features that lead to those class labels. The paths from root to leaf represent classification rules. Below diagram illustrate the basic flow of decision tree for decision making with labels (Rain(Yes), No Rain(No)).

Read More
Accurate Online Speaker Diarization with Supervised Learning

Accurate Online Speaker Diarization with Supervised Learning

Speaker diarization, the process of partitioning an audio stream with multiple people into homogeneous segments associated with each individual, is an important part of speech recognition systems. By solving the problem of “who spoke when”, speaker diarization has applications in many important scenarios, such as understanding medical conversations, video captioning and more. However, training these systems with supervised learning methods is challenging — unlike standard supervised classification tasks, a robust diarization model requires the ability to associate new individuals with distinct speech segments that weren’t involved in training.

Read More
EPO Issues First Guidelines on AI Patents

EPO Issues First Guidelines on AI Patents

The European Patent Office (EPO) has issued official guidelines on the patenting of artificial intelligence and machine learning technologies. The guidelines became valid on November 1st, 2018. When determining whether the claimed subject-matter satisfies this condition, the guidelines note that expressions such as “support vector machine,” “reasoning engine” or “neural network” may not qualify, as these are regarded as terms for mathematical methods which do not have a unique technical character of their own.

Read More