When studying machine learning, you will sometimes come across the term “online learning”. For example, in supervised learning such as SVM, first you train with all of the sample data given. Budepending on the volume of sample data or the application, it may not be appropriate to learn all the data at once. As mentioned above, all businesses have time restrictions. There are also system limitations on the volume of data that can be handled, such as the computer processing capacity and memory capacity. There are also cases where the sample data is provided in stages. In these cases, it can be convenient to optimize the parameters for each data set provided, and then revise it and retrain. This kind of method is called online learning. You could also call it “consecutive leaning”. Typical online learning methods are perceptron, CW, AROW and SCW. There is also a method called Streaming Random Forests, which is Random Forest by online learning.
Omotenashi (Hospitality) & AI
This blog is based on my speech at IBM Watson Summit on April 28th, 2017 following to joint press release of Rakuten AI Platform by Rakuten and IBM. ( https://global.rakuten.com/corp/news/press/2017/0426_01.html)
The Rakuten AI Platform has intended to facilitate various AI applications such as automated chat bot for customer services to be utilized in wide spectrum of Rakuten services including E-commerce, FinTech, Digital content and etc.
On the other hand, unsupervised learning is a method in which actual data is analyzed without receiving any prior sample data so that the intrinsic structure and characteristics existing in the data are extracted. For example, in recommendation, which is an important function in EC, we often use data clustering, which is an unsupervised learning method that categorizes the customers and products to recommend. As well as supervised learning, unsupervised learning is also used for security purposes. When detecting log-in attacks, we deploy clustering to determine what kind of attack patterns there are.
As an introduction to AI, let’s look at the different applications of machine learning in e-commerce
As a first step to explaining AI, which will without a doubt hugely transform our society over the next few years, let’s walk through an overview of machine learning. The way that machine learning (ML) is used in practice in Rakuten’s e-commerce (EC) platform Rakuten Ichiba, for example, is not very well known, so here I will cover the use of ML methodically section by section.
[Beginning of Artificial Intelligence]
The term “Artificial Intelligence” is considered to be used by John McCarthy, computer scientist for the first time at Dartmouth conference in 1956. Since then it has been passed 60 years. For ordinary people the definitions and understandings of Artificial Intelligence vary, so it is often a topic of discussions what should we call as the Artificial Intelligence. However, I would like to avoid sticking to its definition but I would like to broadly capture it as a computing processing that can do the things only human can do in the past, or a computing processing that can do better than human.
[Artificial Intelligence also challenged to Quiz Champion]
Artificial Intelligence had challenged Quiz Champion. AI fought against on the famous TV quiz program “Jeopardy” in United States. The AI fight was “Watson” created by IBM. This quiz show has a style to answer as quickly as possible and type of question is known like “The capital of the country is Tokyo”. Every round there are about six topics and the difficulty of the quizzes increase as the reward amount becomes higher.
[Artificial Intelligence as a good partner]
With very rapid penetration of internet, computers around the world are being connected. When I first used internet in 1980s, there was no search engine existed. Since then, search engine was invented and we can instantly display the information in the computer around the globe.
Rakuten has been running an internal Platform-as-a-Service (PaaS) for over 4 years. Rakuten application teams use our PaaS not only for testing but also for running production scale services. Because of the power of PaaS, we’ve been enabling them great productivity. For example, they can release their application and scale them out horizontally when needed using a single command.