All the recent amazing achievements of artificial intelligence are categorized as narrow intelligence, but there is a lot of speculation about general and super intelligence. A recent, famous and impressive example of narrow intelligence is AlphaGo (and later AlphaGo Zero) by DeepMind, which can beat the top human players of the game of Go, considered for decades as very difficult for computers to play.
Eight ways AI applications are already making conventional EC smarter – from chatbots to creative AI
There’s no doubt that artificial intelligence (AI) will fundamentally change the world over the next few decades. What many do not realize however, is that in some fields, it has already become a large part of the status quo. One such example is e-commerce (EC).
New Approach to Innovation – A Collaborative Ecosystem
Data is one of the most important resources for the economy in the present age. And what’s more, AI is now bringing new value to society at a significant pace.
In relation to deep learning, the two-layer network Word2Vec developed by Google was revolutionary. If you input text, you can obtain a vector set as the output, i.e., the feature vector of words in the text. By grouping the words and creating vectors, you can make judgments on similarity, and infer the meaning. Since it uses figures, it is scalable by processing in parallel and this is one of the great advantages of Word2Vec. It is not only applicable to the field of text analysis, but to any kind of data. At Rakuten, we have developed an extended version called Category2Vec and have released it as OSS (ref: https://github.com/rakuten-nlp/category2vec). We are training it with various kinds of EC data and are beginning to see the possibility of application on a broad scale for product/user analysis and categorization, and estimation of loss data. While Word2Vec is not deep learning itself, it is worth understanding this as a technology in numerical form that can be used in a deep neural network.
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.