Mobile services will be enhanced by machine learning dramatically in 2015


Merry Christmas !  The end of 2014 is approaching.  It is a good time to consider what will happen in the fields of machine learning and mobile services in 2015.  This week we consider machine translation and image recognition,  next week recommender systems and internet of things as well as mobile services by machine leaning. I hope you can enjoy it !


1.  Machine translation / Text mining

Skype is a top innovator in this fields.   Microsoft already announced that machine translation between English and Spanish is available by Skype. So in 2015,  it would be possible to translate between English and other languages. Text translation is also available among 40 languages in its chat service.  So language barrier are getting lower and lower.  It is still difficult to answer to questions by computers automatically.  But it is also gradually improved.  Mizuho bank announced that it will use IBM Watson, one of the famous artificial intelligence to assist call center operators.  These technologies make global service to be developed more easily as manuscripts and frequent Q&A are translated from the language to another automatically.  I love that because my educational programs can be expanded to all over the world!


2. Image recognition

Since computers identified the image of cats automatically by deep learning, images recognition technology progresses dramatically.  Soft bank announced that Pepper, new robot for consumers, will be able to read human emotions. In my view, the most important factor to read emotions must be image recognition of  human facial expressions. Pepper could be very good at doing this therefore it can read human emotions.  Image recognition technology is very good for us as each smart phone has a nice camera and it is easy for people to take pictures and send them to clouds and social media.  Image recognition can enable us to analyze massive amount of images, which are sent through internet. That data must be a treasure for us.


These machine learning technologies must be connected to mobile phone of each customer in 2015. It means that mobile services are enhanced by machine learning dramatically. All information around us will be collected through internet and send to machine learning in real-time basis and machine learning will return the best answer for individuals. This will be standard model of mobile services as speed of calculation and communication are increasing rapidly.

Next week we consider recommender systems,  internet of things and investment technology.  See you next week!

Can Abenomics achieve its objects after winning the election?


Liberal Democratic party (LDP) won the election in Japan yesterday. Then Abenomics is going to fight against financial markets.  Financial market is tougher than other political parties as it reacts very quickly. If LDP can not convince the financial market that financial condition of Japan can be improved,  interest rate of Japanese government bond (JGB) will increase rapidly and price of JGB will be plunged. It means collapse of Japanese fiscal condition as Japanese government might not repay interest rare of debts. This is the race against time.

Moody’s Investors Service downgraded the Government of Japan’s debt rating by one notch to A1 from Aa3 at 1st December 2014.  The outlook is stable. This is the beginning of the story.  From now on,  market participants will focus on how Abenomics work in Japan after LDP won the election.  Rise of consumption tax is postponed until April 2017.  So only less than two and half years are left for Japan. Can Abenomics turn deflation to inflation during such a short period?  Can productivity of Japan be improved strong enough to rise the consumption tax?

Debt to GDP ratio already exceeds 200%.  Japan has no experience of such heavy burden except the time after world war 2. Ray Dalio, founder of Bridgewater Associates, explains that we need policy mix below to solve the heavy burden of national debt .

1. Wealth redistribution

2. Spending cut

3. Debt restructuring

4. Debt Monetization

In short,  tax rate will be  increased to wealthy people,  Japanese capital expenditure and social welfare will be cut and Bank of Japan will finance JGB.   This plocy mix is needed to avoid JGB default. LDP should convince Japanese people that this policy mix is needed and should be exercised in a timely manner.  This is the toughest task for LDP.  But it can not be postponed because it allows financial market to trigger the interest hike.

Productivity may increase gradually by Abenomics however it can not offset hike of consumption tax rate.  At the digital era,  knowledge of software engineering,  machine learning and artificial intelligence are critically important.  These are key in order to optimize the systems,  streamline the processes and improve the productivity. Unfortunately management of Japanese big companies are not familiar with fields of software . So Japanese companies are generally shy and away the innovations to improve productivity by big change.  I do see a few managements whose majors are software engineering and compter science in Japan.  In my view,  it is too late to change this situations because it takes ten years to train new managements within companies.  But we cannot wait such a long period anymore.

Anyway,  we should listen carefully to what the financial  market says.  I am not sure whether LDP wins over the financial market or not. All I can say is that Japan should be changed by herself, rather than being forced to change  by the market.

Financial industry and artificial intelligence


UBS announced that it will deliver personalized advice to the bank’s wealthy clients by using artificial intelligence (AI). UBS plans to roll out a digital service in Asia next April.

I think this is one of the example for financial institutions to go to “digital personalized marketing”  by artificial intelligence.  In future  personalized services by AI are one of the key strategic technologies in the financial industry. Let us consider how artificial intelligence are implemented and used in marketing of  financial industries more details.


1. data

This is a basis for the analysis to predict what financial products customers want.  According to this article about UBS, in the presentation by founders of Sqreem, they said that they crawl through a wide range of openly available, unstructured data. I would like to explain unstructured data. It means the data is not organized in a database as we usually see. So I assume massive amount data could be gathered automatically.  Data might be gathered in real-time basis so final outputs such as recommendations also might be provided in real-time basis. It is a dynamic process, rather than a static process.


2. algorithm

There is no disclosure about how calculations are done in details as far as I know. So this is my assumptions based on the article.  This might be one of the recommender systems. As the article says, this focuses on the behavior of customers.  Behavior of customers could be identified in deeper level and precise recommendations to individual customers could be  provided effectively.  In my thought,  this system might be on-line learning system, too. It means that algorithm could learn new things by themselves, could be updated based on stream data in real-time basis and adjust the change of customers’ preferences.


3. output

This is also my assumptions based on the article. The articles mentioned mobile phones and other digital devices.   I think recommendations might be mainly provided to individual customers through their mobile phones. Mobile phones could be personal interface against banks and financial institutions.  One of the biggest advantage of mobile phones is that customers preference could be gathered through interaction between customers and banks without any official inquiry to the customers.


This is not the end of story but the beginning of it.  As technology is progressed,  a lot of industries will try to introduce such kind of personalized recommender systems. This is marketing of digital era so that everyone can obtain the best products and services among a lot of choices. How wonderful it is !

What is singular value decomposition?


Last week I introduced inner product as a simple model in recommender systems. This week I would like to introduce more advanced model for recommender systems. It is called singular value decomposition.


According to Mining Massive datasets in Coursera, one of the best on-line courses about machine learning and big data,  singular value decomposition or SVD is defined as follows.

Matrix A=UΣV’

U : left singular matrix

Σ : singular matrix

V : right singular matrix

Row vectors and column vectors of matrix A can be transformed into lower dimensional space. This space is called “concept”. In other words row vectors and column vectors can be mapped to concept space, which has smaller dimensions than row and column vectors of matrix A. Strength of each concept is defined in singular matrix where diagonal values are positive. When SVD is applied to recommeder systems,  row vectors of matrix A can be customers’ preference and column vectors can be items features.  For example, movies can be classified as a SF movie or a romance movie, which are “concept”.   Each customer may like SF movies or romance movies. We can predict unknown rating for customers and items by using SVD.


SVD is also used for dimensionality reduction and advantages of  SVP are as follows.

1.  find hidden correlations

2.  make visualization of data easier

3.  reduce the amount of data


Therefore SVD can be applied to not only recommender system but other kinds of business applications.


Let us see R to analyze data by singular value decomposition. R has a function of  singular value decomposition, SVD. Therefore we can execute singular value decomposition by just inputting data into function of svd() in R. IDE below is RStudio.


In this case,  matrix ss is decomposed into $d,$u and $v.

$u : left singular matrix

$d:  singular matrix

$v : right singular matrix

When we look at $d,  value of the first and second column are large, therefore we focus on the first concept and second concept.  In $u, the row vectors of ss are mapped to concept space.  In $v, the column vectors of ss are also mapped to same concept space.  Red rectangular and blue rectangular show similarity based on “concept”. I recommend you to try svd() to analyze data in R as it is very easy and effective.

SVD is a little complicated than inner product but it is very useful when there are a lot of data which has large dimensions. Let us be familiar with SVD because we would like to use this model going forward.