This smart watch looks cool, doesn't it? I would like to have this one.

I like looking at watches.  Especially since the Apple watch was released,  I am wondering when and what I should buy a smart watch.  Now I use CASIO OCEANUS (picture below).  I bought it in 2008. I like it very much. I do not think I should replace it until smartwatch is getting attractive to me.

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The problem is the appearance of smartwatch. For me, watches are a kind of fashion items, rather than IT devices. So the Apple watch is not my taste because it looks IT device for me. I like the traditional taste of watches because I feel good about it.


When I found this smartwatch made by Huawey, I did not think it is a smart watch because it looks a traditional watch. It looks so cool! I think it is easy to replace my old watch to this new smart watch smoothly. I do not know how much it costs so far. I hope it is reasonable.

This is a good example of  how the design of products is important.  When technologies have got matured, it is getting difficult to differentiate the products from others in terms of functionality.  So the design of products is getting more important.

I do not say the Apple watch is not good because it does not look a traditional watch. Some consumers love it.  My taste is just mine, so I do not want to comment on which are good or bad.  This is a matter of  individual preference. But the more choices we have, the happier we are. Therefore, competitions in the smartwatch market can lead the market to expand by itself.


Smart watches might have a lot of new functions in the future because it fits our bodies directly. It enables us to  measure the pulse of heart beats and heat of our body, for example. It means that smartwatch can collect a lot of data about our body and health. So we can create new services for healthcare, communications and so on. Therefore, smartwatch will be not just watches in the future.

In my view,  smartwatch might have artificial intelligence in it and answer any questions from owners. It needs more powerful and smaller computer-chips to realize it. So it takes time to develop these applications. But I do not think it is impossible. In future, when we go abroad, all we have to do is just carrying smartwatch and we can go anywhere we want without maps and guidebooks because smartwatch has the latest international information in it and can translate many languages automatically. It will track our health conditions during our trips. If emergencies like sicknesses happen, it leads us to hospitals nearby. Smartwatch can show doctors what happens in our body during the trip by using data so that diagnoses can be more accurate. This is a kind of “dream” watch.  Oh, We need it in the space trip to the moon, too!?    Do you like it?

Can you be next "Mark Zuckerberg" with open source software?

I like open source software because it is  almost free to use,  modify and distribute. For example,  I use “R language” for data analysis as I can share code to anyone without cost.  R is an example of open source software. When I used to be a risk manager more than 10 years ago, I used MATLAB.  This is an awesome software for data analysis. However, we need to buy a license to use it. So I cannot recommend it for everyone.  But I can do that for R as it is free.

 

Open source software is strong enough to change the landscape of developing computer programs. Especially I look at the movement driven by Facebook, it looks like a big tsunami to take over the industry. It has more than 200 open source softwares projects from mobile application development to artificial intelligence according to the article. Mark Zuckerberg,  Founder and CEO of Facebook, have been taking initiative open source movement for many years.  For new start-up, it is very good and helpful because

 

1.  It accelerates development of applications.

Because startups usually do not have enough resources to develop the applications from scratch, it is very helpful for them to use open source software. All they should do is modify the software to make applications. Facebook is also built by using open source software, although it becomes one of the biggest IT companies in the world.

 

2. There are more choices provided by open source softwares

When there are several kinds of open sources for specific purposes, we can choose the best one for our own purpose. All we should do is  to assess each of them.  For example, when you are interested in artificial intelligence, there are many major open source softwares,  such as TheanoPylearn2TorchOpenDeepChainer and so on.  Each of them is a little different in terms of functionality and structures. Therefore, we should choose the best one for our own purpose. When we have the best choice. it allows us to develop applications rapidly and effectively.

 

3.  Open source softwares can lower the entrance barriers

It is usually difficult for start-ups to develop complex programs, such as deep learning, from scratch. But supported by open source software, start-ups can learn and develop the applications at the same time. It is very important in the digital economy as the supply of experts in such fields are always less than the demands in labor markets.

 

 

Going forward, I would like to develop an economic analysis system by using open source software and make it available for everyone who is interested in.  I hope everyone can analyze the economy in his/her own country by him/herself in the business.

Can China keep growing steadily in 10 years from now?

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If you are interested in investments in stock markets, you may hear a lot of stories in China this year since it has been rising dramatically and its market capitalization hit 10 trillion USD for the first time. I am not an investment adviser for stocks. However, I am interested in sustainability of Chinese economy in the long run.  Because China is already the biggest economy in the world  in terms of GDP using purchasing power parity (PPP) according to the IMF.  It means that the growth of Chinese economy affects a lot of the other countries' economies such as Asean countries.

One of the easy way to understand what is going on in China is to compare with Japan.  There is no need for complex economic theories here.  Just compare to find out what are similar and different between them.  I would like to compare Japanese economy in the 1970s, 1980s and the current situation in China.  First, I would like to compare the GDP per capita between Japan and China in order to understand the path of economic growth.

 

1.  China is similar to Japan in 1970s in terms of GDP per capita

In this article by the BBC, it is pointed out that GDP per capita of China based on PPP is 11,868USD.  This number is similar to the number of Japan in 1968 (11,292USD) according to FRED.

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In the 1970s and 1980s, Japanese economy managed several crises such as oil crises and Yen appreciations. In these two decades, GDP per capita (PPP based) was getting more than two times bigger.  It means that China has opportunities to grow more in the long run if it can manage obstacles effectively in the future.

 

2. Data technologies play a key role to develop Chinese economy.

One of the biggest differences between Japan in 1980s and Current China is "Data technology".   Cloud, Mobile devices, Internet, IOT and Big data are available in major countries all over the world now.  There was nothing like that in Japan 1980s. It means that every industry can be developed rapidly, effectively and with less impact to the environment if they can introduce data technologies effectively. China has already suffered from air pollutions in cities such as Beijing. So developments, with less impact against the environment are desperately needed to make economic growth sustainable.

 

3. Capitalism vs Communism

Another big difference is that Japan introduces capitalism and China introduces communism.  Yes, it is a big difference. But China learned and will learn a lot from capitalism and improve its social system. Especially China can learn Japanese failure since 1990, which is called "lost decades".  As the result of that, entrepreneurs are more active  in China than Japan now. Alibaba, Tencent and Xiaomi are good examples of that while most of Japanese young guys want to work in traditional big companies, rather than create their startups. So I am sometimes confused which country has which system in reality.

 

No one knows exactly what happens in China in 10 years.  I would like to keep watching what is going on there. Are you an optimist or a pessimist of China?

 

Note: Toshi's opinions and analyses are personal views and are intended to be for informational purposes and general interest only and should not be construed as individual investment advice or solicitation to buy, sell or hold any security or to adopt any investment strategy.  The information in this article is rendered as at publication date and may change without notice and it is not intended as a complete analysis of every material fact regarding any country, region market or investment.

Data from third party sources may have been used in the preparation of this material and I, Autor of the article  has not independently verified, validated such data. I accept no liability whatsoever for any loss arising from the use of this information and relies upon the comments, opinions and analyses in the material is at the sole discretion of the user. 

Facebook, Twitter, Google and "new wave" of economic analysis

On Saturday, I found that the report from Bank of England.  This report is about economic analysis in central banks with Big data such as social network services. It is good not only for economic researchers, but also business personnels to consider how Big data should be used. So I would like to consider it based on this report for a while.

Before considering usage of Big data, I would like to define “Big data”. Big data is data sets that are granular, in real time basis and  non-numeric data as well as  numeric one.   These data are completely opposite in nature compared with data which are currently analyzed in Central banks.  Because such data are usually “aggregated,  periodical and numeric”.  One of the examples is financial statements of companies.  Big data are different from such data.  For example Twitter are generated by individuals in real time. These are usually text, images and video. Then the questions come.

 

1. Can we build up macro economic models based on big data?

Central banks are responsible for the stability of the financial system in the country.  Is it possible for central banks to collect data of each loan from private banks and assess credit risk of each, then confirm financial stability as a whole country?  It can be applied to private companies, too. Is it possible that the company collect data of each customer, forecast the amount of purchase by each customer and predict the revenue of the company next fiscal year?  Big data may enable us to do so even though it takes time.

 

2. Is the method used “theory based” or “data driven”?

Even though they cannot be clearly distinguished in practice,  these are two approaches to analyze Big data in economic analysis. Someone puts importance to economic theories. Let us call it “theory based”.  Others take another approach of “Let the data speak for themselves”.   We may call it “data driven”.  Their opinions are sometimes against each other even though they analyze the same data. So we should have well-balanced approach between them.

 

3. Should we change the processes to make business decisions?

Big data comes to us in a real time basis.  But our decision making process in organizations is usually periodical. For example, board of directors meetings and executive committees in companies are generally held on a monthly basis.  Should they be held more flexibly in a timely manner based on outputs from analysis of Big data, rather than periodical one?  The bigger companies become, the more difficult it is to change the process in practice.

 

FRB in the US is currently wondering when they should raise the interest rate of the US.  Chairwoman of FRB has been always saying  “It is based on economic data“.  But I am not sure she cares about data (conversations) on social networking services in the US. What do you think?

 

Do you know how computers can read e-mail instead of us?

Hello, friends. I am Toshi. Today I update my weekly letter. This week's topic is "e-mail".   Now everyone uses email to communicate with customers, colleagues and families. It is useful and efficient. However, if you try to read massive amounts of e-mails at once manually, it takes a lot of time.  Recently, computers can read e-mail and classify potentially relevant e-mail from others instead of us. So I am wondering how computers can do that. Let us consider it a little.

 

1.  Our words can become "data".

When we hear the word "data",  we imagine numbers in spreadsheets.  This is a kind of "traditional" data.  Formally, it is called "structured data". On the other hand, text such as words in e-mail, Twitter, Facebook can be "data", too.  This kind of data is called "unstructured data". Most of our data exist as "unstructured data" around us.  However, computers can transform these data into data that can be analyzed. This is generally an automated process. So we do not need to check each of them one by one. Once we can create these new data, computers can analyze them at astonishing speed.  It is one of the biggest advantages to use computers in analyzing e-mails.

 

2. Classification comes again

Actually, there are many ways for computers to understand e-mails. These methods are sometimes called "Natural language processing (NLP)".  One of the most sophisticated one is a method using machine learning and understanding the meaning of sentences by looking at the structures of sentences. Here I would like to introduce one of the simplest methods so that everyone can understand how it works.  It is easy to imagine that the "number of each word" can be data.  For example, " I want to meet you next week.".  In this case, (I,1), (want,1),(to,1), (meet,1),(you,1), (next,1),(week,1) are data to be analyzed. The longer sentences are, the more words appear as data. For example, we try to analyze e-mails from customers to assess who are satisfied with our products. If the number of positive words, such as like, favorite, satisfy, are high,  it might mean customers are satisfied with the products, vice versa.  This is a problem of "classification".  So we can apply the same method as I explained before. The "target" is "customers satisfied" or "not satisfied" and "features" are the number of each word. 

 

3. What's the impact to businesses?

If computers understand what we said in text such as e-mails,  we can make the most out of it in many fields. For the marketing, we can analyze the voices of customers from the massive amount of e-mails. For the legal services, computers identify what e-mails are potentially relevant as evidences for litigations.  It is called "e-discovery".  In addition to that, I found that Bank of England started monitoring social networks such as Twitter and Facebook in order to research economies.  This is a kind of "new-wave" of economic analysis.  These are just examples. I think  you can create many examples of applications for businesses by yourself because we are surrounded by a lot of e-mails now.  

 

In my view, natural language processing (NLP) will play a major role in the digital economy.   Would you like to exchange e-mail with computers?

When are self-driving cars available in Asia? We should re-consider regulations about it.

Last year I learned “machine learning” on coursera and found that it is very useful to develop self-driving car.  This course was created in 2011.  Since then,  there has been much progress in self-driving cars. Last week I found two articles on self-driving cars. One is self-driving cars by google and the other is an autonomous truck. Let us see what they are and consider the impacts of these cars when they are available to us.

 

1. Self-driving cars

This is one the most aggressive project of self-driving cars because the goal of the project is cars without driver intervention. According to Google website, it says”a few of the prototype vehicles we’ve created will leave the test track and hit the familiar roads of Mountain View, Calif., with our safety divers aboard.”.  It looks so small and cute. However, with computers and sensors, it can run without intervention by humans. I imagine machine learning is used to control self-driving cars as I learned it on coursera before. Because the machine can “learn” new things from data, the more self-driving cars run, the safer and more sophisticated they become. Therefore collecting many data on self-driving cars is critically important.  I wonder when they can drive without drivers in future.

 

2. Autonomous truck

The other is autonomous trucks.  According to Bloomberg, “Regulatory and technological obstacles may hold back the driverless car for decades. But one of the first driverless semi-trucks is already driving, legally, on the highways of Nevada.” This is a truck which can be controlled on highways. But in difficult tasks such as driving in parking lots, human should take over and drive them. It looks like “a truck, which is supported by computers”.  Unlike self-driving cars by google, this truck needs human drivers. But it must be helpful for truck drivers when they drive on highways for long time.

 

3. What is needed to promote self-driving cars?

Firstly, we need to consider regulations about how self-driving cars are allowed to run in public. Because the more data is available, the more sophisticated self-driving cars become. In order to accelerate development of self-driving cars,  data is like “fuel” to develop computers in order to control cars. Therefore regulations are very important to allow self-driving cars to run in the real world  in order to collect data.

 

4. What are the impacts to our society?

In aging societies such as Japan,  older people sometimes feel difficulties to drive a car to go to hospitals or shopping malls. In such a case, the self-driving car is one of the solutions for the problem.  With self-driving cars, senior personnel can go anywhere they want without driving.  In the emerging countries like Asean,  a lot of trucks are needed to prepare the infrastructures and lifelines all over the countries. So it is very useful when self-driving trucks are permitted to run across country borders.  Therefore, regulations should be considered as a region rather than country by country.

In the long run, we should prepare the shift from current situations to a digital economy. It means that some of jobs might be replaced by computers with machine learning.  The more self-driving cars are available, the less truck drivers and taxi drivers are needed. Andrew Ng, the famous researcher of machine learning,  talked about this shift on the article.  “A midrange challenge might be truck-driving. Truck drivers do very similar things day after day, so computers are trying to do that too.”

 

 

No one knows exactly when self-driving cars are available in public. It does not look long-term future as I look at the development of technologies.  We may have a lesson of self-driving cars.   Andrew Ng says in the article, “Computers enhanced by machine learning are eliminating jobs long done by humans. The trend is only accelerating.”

What do you think?

Is this a game changer of MBA in a digital economy, isn't it?

Hi friends, I am Toshi. Today I update my weekly letter. This week’s topic is about online MBA.  If you have a plan to obtain an MBA, I hope it is good information to you.

I love MOOCs (Massive Open Online Courses). Because I can choose any topics from computer programming to languages.  In addition, I can learn anytime and anywhere I want. Finally, Most of courses are free. You do not need to pay any cost to take these courses. So as CEO of start-up, this is the best choice to learn new things I need. However, it might not be applicable to persons who want MBA titles. Because most of the certificates on MOOCs are not regarded as formal academic credit, although MOOCs are provided by many famous universities. For example, I took “Machine learning” by associate professor Andrew.Ng in Stanford university last year and got the statement of accomplishment of that course.  I think this is one of the best courses to learn state of the art algorithm of Machine learning. But unfortunately  Stanford university does not provide academic credits to learners of this course.

 

But when I found this article of new online MBA course from the University of Illinois at Urbana-Champaign, I thought it can be a game changer. Because, firstly this is a full MBA course with credits.   Second, the cost of the MBA is around 20,000 USD and significantly lower than similar online courses.  Third, we have opportunities to take courses without certain projects in the MBA program before paying fees.  Third one is significantly important to lower the entry barrier, especially for the beginners of MOOCs.  “Digital marketing“, one of the parts of the MBA program, is already open for everyone.  So we can try this and confirm how this online MBA works before applying the admission processes. Therefore, there are few risk where we have mismatches between the contents of the program and the needs of students.

 

I think one of the reasons why this new online MBA is developed may be that a lot of students cannot repay student loans in the United states.  Some financial experts warn that these bad loans might be the biggest risk in the credit market. So high cost of higher educations are not only students’ problems, but also society’s problems.  This is not sustainable anymore. This new online MBA can be one of the solutions to this problem. Since major MOOCs platforms such as edx and Coursera  opened in 2012,  MOOCs certificate has not been considered as equivalent to academic credits.  However, this new online MBA may change this situation.  I would like to see what other top MBA schools do, going forward.

 

If you are beginners of MOOCs,  how about start “Digital marketing”?  You can do it without any fee. If you like it and want to be an MBA holder,  this new-online course can be one of the candidates of MBA for you to consider in addition to residential MBA.  I have already started “Digital marketing” by myself in order to enhance my expertise.  Could you join us?

Now I challenge the competition of data analysis. Could you join with us?

Hi friends.  I am Toshi.  Today I update the weekly letter.  This week’s topic is about my challenge.  Last Saturday and Sunday I challenged the competition of data analysis in the platform called “Kaggle“. Have you heard of that?   Let us find out what the platform is and how good it is for us.

 

 

This is the welcome page of Kaggle. We can participate in many challenges without any fee.  In some competitions,  the prize is awarded to a winner. First, data are provided to be analyzed after registration of competitions.  Based on the data, we should create our models to predict unknown results. Once you submit the result of your predictions,  Kaggle returns your score and ranking in all participants.

 

 

In the competition I participated in, I should predict what kind of news articles will be popular in the future.  So “target” is “popular” or “not popular”. You may already know it is “classification” problem because “target” is “do” or “not do”  type. So I decided to use “logistic curve” to predict, which I explained before.  I always use “R” as a tool for data analysis.

This is the first try of my challenge,  I created a very simple model with only one “feature”. The performance is just average.  I should improve my model to predict the results more correctly.

Then I modified some data from characters to factors and added more features to be input.  Then I could improve performance significantly. The score is getting better from 0.69608  to 0.89563.

In the final assessment, the data for predictions are different from the data used in interim assessments. My final score was 0.85157. Unfortunately, I could not reach 0.9.  I should have tried other methods of classification, such as random forest in order to improve the score. But anyway this is like a game as every time I submit the result,  I can obtain the score. It is very exciting when the score is getting improved!

 

 

This list of competitions below is for the beginners. Everyone can challenge the problems below after you sign off.  I like “Titanic”. In this challenge we should predict who could survive in the disaster.  Can we know who is likely to survive based on data, such as where customers stayed in the ship?  This is also “classification”problem. Because the “target” is “survive”or “not survive”.



You may not be interested in data-scientists itself. But it is worth challenging these competitions for everyone because most of business managers have opportunities to discuss data analysis with data-scientists in the digital economy. If you know how data is analyzed in advance, you can communicate with data-scientists smoothly and effectively. It enables us to obtain what we want from data in order to make better business decisions.  With this challenge I could learn a lot. Now it’s your turn!

Do you want to know "how banks rate you when you borrow money from banks"?

Hi friends,  I am Toshi, This is my weekly letter. This week's topic is "how banks rate you when you borrow money from banks". When we want bank loans, it is good that we can borrow the amount of money we need,  with a lower interest.  Then I am wondering how banks decide who can borrow the amount of money requested with lower interests. In other words, how banks assess customer's credit worthiness.  The answer is "Classification".  Let me explain more details. To make the story simple,  I take an example of  unsecured loans, loans without collateral.

 

1.  "Credit risk model" makes judgements to lend

Now many banks prepare their own risk models to assess credit worthiness of customers.  Especially global banks are required to prepare the models by regulators, such as BISFSA and central banks. Major regional banks are also promoted to have risk models to assess credit worthiness.  Regulations may differ from countries to countries,  by size of banks.  But it is generally said that banks should have their risk models to enhance credit risk management.  When I used to be a credit risk manager of the Japanese consumer finance company, which is one of  the group companies in the biggest financial group in Japan,  each customer is rated by credit risk models. Good rating means you can borrow money with lower interest. On the other hand, bad rating means you can borrow only limited amount of money with higher interest rate or may be rejected to borrow. From the standpoint of management of banks, it is good because banks can keep consistency of the lending judgements to customers among the all branches.  The less human judgement exists, the more consistency banks keep.  Even though business models may be different according to strategies of banks, the basic idea of the assessment of credit worthiness is the same.

 

2. "Loan application form" is a starting point of the rating process

So you understand credit risk models play an important role. Next, you may wonder how rating of each customer is provided.  Here "classification" works. Let me explain about this.  When we try to borrow money,  It is required to fill "application forms". Even though the details of forms are different according to banks,  we are usually asked to fill "age" "job title" "industry" "company name" "annual income" "owned assets and liabilities" and so on.   These data are input into risk models as "features".   So each customer has a different value of "features".  For example, someone's income is high while others income is low.   Then I can say  "Features"of each customer can explain credit worthiness of each customer.   In other words,  credit risk model can "classify"  customers with high credit worthiness and customers with low credit worthiness by using  "features".

 

3.  Rating of each customer are provided based on "probability of default"

Then let us see how models can classify customers in more details. Each customer has values of "features"  in the application form. Based on the values of "features", each customer obtains his/her own "one value".  For example, Tom obtains "-4.9" and Susum obtains "0.9" by adding "features" multiplied with "its weight".  Then we can obtain "probability of default" for each customer.  "Probability of default" means the likelihood where the customer will be in default in certain period, such as one year. Let us see Tom's case. According to the graph below,  Tom's probability of default, which is shown in y axis, is close to 0.  Tom has a low "probability of default". It means that he is less likely to be in default in the near term. In such a case,  banks provide a good rating to Tom. This curve below is called "logistic curve" which I explained last week. Please look at my week letter on 22nd April.

Let us see Susumu's case. According to the graph below,  Susumu's probability of default, which is shown in y axis, is around 0.7, 70%.  Susumu has a high probability of default. It means that he is likely to be in default in the near term. In such a case,  banks provide a bad rating to Susumu. In summary,  the lower probability of default is,  the better rating is provided to customers.

Although there are other methods  of "classification",  logistic curve is widely used in the financial industry as far as I know. In theory, the probability of default can be obtained for many customers from individuals to big company and sovereigns, such as "Greeks".  In practice, however, more data are available in loans to individuals and small and medium size enterprises (SME) than loans to big companies.  The more data are available, the more accurately banks can assess credit worthiness. If there are few data about defaults of customers in the past,  it is difficult to develop credit risk models effectively. Therefore, risk models of individuals and SMEs might be easier than risk models of big companies as more data are usually available in loans to individuals and SMEs.

I hope you can understand the process to rate customers in banks. Data can explain our credit worthiness, maybe better than we do. Data about us is very important when we try to borrow money from banks.

The reason why computers may replace experts in many fields. View from "feature" generation.

Hi friends, I am Toshi. I updated my weekly letter.  Today I explain 1. How classification, do or do not, can be obtained with probabilities and 2. Why computers may replace experts in many fields from legal service to retail marketing.   These two things are closely related to each other. Let us start now.

 

1.  How can classification be obtained with probabilities?

Last week, I explained that “target” is very important and “target” is expressed by “features”.  For example Customer “buy” or “not buy” may be expressed by customers age and  the number of  overseas trips a year.  So I can write this way : “target” ← “features”.   This week, I try to show you the value of “target” can be a probability, which is  a number between 0 and 1.  If the “target” is closer to “1”,  the customer is highly likely to buy.   If the target is closer to “0”,  the customer is less likely to buy.   Here is our example of “target” and “features” in the table below.


I want  Susumu’s value of the “target” to be close to “1” in calculations by using “features”.  How can we do that?   Last week we added “features” with“weight” of each feature.   For example  (-0.2)*30+0.3 *3+6,  the answer is 0.9.  “-0.2″ and “0.3” are the weight for each feature respectively. “6” is a kind of adjustment.  Next let us introduce this curve below. In the case of Susumu, his value from his features is 0.9. So let us put 0.9 on the x-axis, then what is the value of y? According to this  curve, the value of y is around 0.7. It means that  Susumu’s probability of buying products is around 0.7.  If probability is over 0.5, it is generally considered that customer is likely to buy.

 

In the case of Tom, I want his value of the “target” to be close to “0” in calculations by using “features”.  Let us add his value of features as follows  (-0.2) *56+0. 3 *1+6,  the answer is -4.9.  His value from his features is -4.9. So let us put  -4.9 on the x-axis, then what is the value of y?  According to this curve, Tom’s probability of buying products is almost 0. Unlike Susumu’s case, Tom is less likely to buy.

 

This curve is called “logistic curve“.   It is interesting that whatever value “x” takes, “y” is always between 0 and 1.  By using this curve, everyone can have the value between 0 and 1, which is considered as the probability of the event. This curve is so simple and useful that it is used in many fields.  In short, everyone has a probability of buying products, which is expressed as the value of “y”.  It means that we can predict who is likely to buy in advance as long as “features”are obtained! The higher value customers have, the more likely they will buy the products.

 

 

2.  Why may computers replace experts in many fields?

Now you understand what are”features”.  “Features” generally are set up based on expert opinion. For example, if you want to know who is in default in the future, “features”needed are considered “annual income”, “age”, “job”, “the past delinquency” and so on. I know them because I used to be a credit risk manager in consumer finance company in Japan.  Each expert can introduce the features in the business and industries.  That is why the expert’s opinion is valuable, so far. However, computers are also creating their features based on data. They are sometimes so complex that no one can understand them. For example, ” -age*3-number of jobs in the past” has no meaning for us. No one knows what it means. But computers do. Sometimes computers can predict “target”, which means “do” or “not do” with their own features more precisely than we do.

 

In the future,  I am sure much more data will be available to us.  It means computers have more chance to create better “features” than experts do. So experts should use the results of predictions by computers and introduce them into their insight and decisions in each field.  Otherwise, we cannot compete with computers because computers can work 24 hours/day and 365 days/year. It is very important that the results of predictions should be used effectively to enhance our own expertise in future.

 

 

Notice: TOSHI STATS SDN. BHD. and I, author of the blog, do not accept any responsibility or liability for loss or damage occasioned to any person or property through using materials, instructions, methods, algorithm or ideas contained herein, or acting or refraining from acting as a result of such use. TOSHI STATS SDN. BHD. and I expressly disclaim all implied warranties, including merchantability or fitness for any particular purpose. There will be no duty on TOSHI STATS SDN. BHD. and me to correct any errors or defects in the codes and the software.

Easy way to understand how classification works, without formula! no.1

Hello, I am Toshi. Hope you are doing well. Last week  I introduced “classification” to you and explained it can be applied to every industry. Today I would like to explain how it works step by step  this week and next week. Do not worry, no complex formula is used today.  It is easier than making pancakes with fry pan!

I understand each business manager have different of problems and questions. For example, if you are a sales manager in retail, you would like to know who is likely to buy your products.  If you are working in banks, you want to know who will be in default. If you are in the healthcare industries, who is likely to have diseases in future.  It is awesome for your business if we can predict what happens with certainty in advance.

These problems look like different from each other. However, they are categorized as same task called “classification” because we need to classify “do” or “do not”.  For sales managers, it means that “buy” or “not buy”. For managers in banks,  “in default” or “not in default”. In personnel in legal service, “win the case” or “not win the case”.  If predictions about “do” or “do not” can be obtained in advance.  It can contribute to the performance  of your businesses. Let us see how it is possible.

 

1.  “target” is significantly important

We can apply “do” or ” do not” method to all industries. Therefore, you can apply it to your own problems in businesses.  I  am sure you are already interested in  your own “do” or ” do not”.   Then let us move on to data analysis.  “Do” or “do not” is called “target” and has a value of  “1” or “0”.  For example, I bought premium products in a retail shop,  In such a case,  I have “1” as  a target.  On the other hand, my friend did not buy anything there.  So she has “0”  as a target.   Therefore  everyone should have “1” or “0” as a target.   It is very important as a starting point.  I recommend to consider what is a good  “target” in your businesses.

 

2.  What are closely related to “target”?

This is your role because you have expertise in your business.  It is assumed that you are sales manager of retail fashion. Let us imagine what are closely related to the customer’s “buy” or “not buy”.  One of them may be customers’ age because younger generation may buy more clothes than senior.  Secondly, the number of  overseas trips a year because the more they travel overseas, the more clothes they buy.  Susumu, one of my friends, is 30 years old and travels overseas three times a year.  So his data is just like this : Susumu  (30, 3).  These are called “features”.   Yes, everyone has different values of the features. Could you make your own values of features by yourself?  Your value of the features must be different from (30,3).  Then, with this feature (30, 3),  I would like to express “target” next.  (NOTE: In general,  the number of features is far more than two. I want to make it simple to understand the story with ease.)  Here is our customer data.

3.  How “targets” can be expressed with “features”?

Susumu has his value of features (30, 3).  Then let us make the sum of  30 and 3. The answer is 33.  However, I do not think it works because each feature has same impact to “target”.  Some features must have more impact than others. So let us introduce “weight” of each feature.   For example  (-0.2)*30+0.3 *3+6,  the answer is 0.9.  “-0.2″ and “0.3” are the weight for each feature respectively. “6” is a kind of adjustment. This time it looks better as “age” has a different impact from “the number of travels”against “target”.  So “target”, which means in this case Susume will buy or not,  is expressed with features, “age” and  “the number of travels”.  Once it is done, we do not need to calculate by ourselves anymore as computers can do that instead of us. All we have to know is “target” can be expressed with “features”.  Maybe I can write this way : “target” ← “features”.   That is all!

 

 

Even if the number of features is more than 1000, we can do the same thing as above.  First, put the weight to each feature, second, sum up all features with each weight.  Therefore, you understand how a lot of data can be converted to  just “one value”.  With one value, we can easily judge whether Susumu is likely to buy or not.  The higher value he has,  the more likely he will buy clothes. It is very useful because it enables us to intuitively know whether customers will buy or not.

Next week I would like to introduce “Logistic regression model” and explain how it can be classified quantitatively.   See you next week!

"Classification" is significantly useful for your business, isn't it?

Hello, I am Toshi. Hope you are  doing well. Now I consider how we can apply data analysis to our daily businesses.  So I would like to introduce “classification” to you.

If you are working in marketing/sales departments, you want to know who are likely to buy your products and services. If you are in legal services, you would like to know who wins the case in a court. If you are in financial industries, you would like to know who will be in default among your loan customers.

These cases are considered as same problems as “classfication”.  It means that you can classify a thing or an event you are interested in from all populations you have on hand.  If you have data about who bought your products and services in the past, we can apply “classification” to predict who are likely to buy and make better business decisions. Based on the results of classification,  you can know who is likely to win cases and who will be in default with a numerical measure of certainty,  which is called “probability”.  Of course, “classification” can not be a fortune teller.  But “classification” can provide us who is likely to do something or what is likely to occur with some probabilities.  If your customer has 90% of probabilities based on “classification”, it means that they are highly likely to buy your products and services.

 

I would like to tell several examples of “classification” for each business. You may want to know the clues about the questions below.

  • For the sales/marketing personnel

What is the movie/music in the Top 10 ranking in the future?

  • For personnel in the legal services

Who wins the cases ?

  • For personnel in the financial industries or accounting firms

Who will be in default in future?

  • For personnel in healthcare industries

Who is likely to have a disease or cure diseases?

  • For personnel in asset management marketing

Who is rich enough to promote investments?

  • For personnel in sports industries

Which team wins the world series in baseball?

  • For engineers

Why was the spaceship engine exploded in the air?

 

We can consider a lot of  examples more as long as data is available.  When we try to solve these problems above,  we need data in the past, including the target variable, such as who bought products, who won the cases and who was default in the past.  Without data in the past, we can predict nothing. So data is critically important for “classification” to make better business decisions.   I think data is “King”.

 

Technically, several methods are used in classification.  Logistic regression,  Decision trees,  Support Vector Machine and Neural network and so on. I recommend to learn Logistic regression first as it is simple, easy to apply real problems and can be basic knowledge to learn more complex methods such as neural network.

 

I  would like to explain how classification works in the coming weeks.  Do not miss it!  See you next week!

Is it possible to raise the quality of services if computers can talk to you?

When you go to Uniqlo,  people of Uniqlo talk to you and advise how you can coordinate your favorite fashion.  When you go to hospitals, doctors ask you what your condition is and advise you what you should do in order to be healthy.  Then let us consider whether computers can talk to you and answer your questions, instead of a human being.

It is the first step to know the customers in service industries,  students in education.  So there are many people working to face with customers and students. If computers can face with customers and students,  it means that quality of services dramatically is going up because computers are cost-effective and operate 24hours per day, 365 days per year without rest time.

 

I like taking courses in open online courses.  It is very convenient as we can look at courses whenever we want as long as internet connection is available.  But the biggest problem is that there are no teachers to be asked for each learner when you want to ask.  This description explains this problem very well.

"Because of the nature of MOOC-style instruction (Massive Open Online Course), teachers cannot provide active feedback to individual learners. Most MOOCs have thousands of learners enrolled at the same time and engaging personally with each learner is not possible."

When I cannot understand the course lectures and solve the problems in exams by myself, it is very difficult to continue to learn because I feel powerless.  This is one of the reasons why completion rate is very low in open online courses (usually less than 10%).  If you need assistance from instructors,  you should pay fees which are not cheep for people in developing countries. I want to change this situation.

 

A technology called "Machine learning" may enable us to enjoy conversations with computers cross industries from financial to education.  Computers can understand what you ask and provide answers in real-time basis.  It takes some time to develop to make computers more sophisticated, so that computers can answer exactly what you want.  This is like a childhood.  At the beginning, there is very little knowledge so It may be difficult to answer questions. Then computers start learning from interactions with human.  The more knowledge they have,  the more sophisticated their answer is.

So I would like to start to examine how computer is learning in order to provide sophisticated answers to learners and customers. If computers obtain enough knowledge effectively, they can talk to you and enjoy conversations with you.  I hope computers can be good partners to us.

Three self-paced online courses that I strongly recommend. They are awesome and free!

If you are businessmen/women, your schedule sometimes cannot be controlled by yourself.  Meeting with clients may be required by your client with short notice.  The emergency situation may happen and you should cope with it.  That is why it is difficult for business men/women to complete on-line training/courses with limited time.

However, there is no need to worried about that.  As the number of online courses is increasing,  the number of self paced courses is also increasing.  In Coursera, one of the biggest platforms of online courses, has 70 on-demand courses. Unlike session courses, self paced courses have no deadline to complete. It is very good for busy business men/women because schedules can be more flexible to complete.

Now I enroll several self-paced courses that I am interested in but have no concrete schedule to complete them so far. Instead, when I have spare time, such as time to wait my flight in the airport or suddenly cancelled meetings,  I can enjoy these courses any time I want. I think it is good!  Here is the list of self-paced online courses I recommend.

 

1.  Machine Learning

This is the best course for people who want to understand what is going on in the digital economy deeply.  Andrew Ng. Associate Professor, Stanford University; Chief Scientist, Baidu; Chairman and Cofounder, Coursera, provides us the course about Machine learning. It is the science of getting computers to act without being explicitly programmed.  This state of art technologies is explained in plain English so that people with knowledge of high school math can understand what machine learning is and how it is used in the real world.  I always recommend this course. But the problem was that we had to complete the course within three months.  It is considered too short for most of business men/women.  Now this course is available as self -paced course!  Then we can learn the course at your own pace!

 

2. Managing Fashion and Luxury Companies

This course is about fashion trends and industries.  It says "This module is dedicated to a general introduction to fashion and luxury concepts, what they mean, how they are perceived, how they differ, and other basic information on this peculiar industry."  This kind of courses are very few in on-line courses so I recommend this course.  I expect we can obtain new insights about fashion industries.

 

3. Chinese for Beginners

One of the candidates of self-paced courses to take is the one about languages because it can be repeated many times by ourselves. I currently choose the course about Chinese.  Xianoyu Liu, Associate Professor School of Chinese as A Second Language, Peking University provides the course for beginners of Chinese.

 

Yes, you can go to a coffee shop from now, where wifi connections are available. Then open your mobile and access to Coursera website and sign up.  You can enjoy the courses you choose anytime you want!

This course is the best for beginners of data analysis. It is free, too!

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Last week, I started learning on-line course about data analysis. It is “The Analytics Edges” in edx, one of the biggest platforms of MOOCs all over the world.  This course says “Through inspiring examples and stories, discover the power of data and use analytics to provide an edge to your career and your life.”   Now I completed Unit one and two out of  total nine in the course and found that it is the best course for beginners of data analysis in MOOCs. Let me tell you why it is.

 

1. There are a variety of data sets to analyze

When you start learning data analysis, data is very important to motivate yourself to continue to learn.  When you are sales personnel, sales data is the best to learn data analysis because you are interested in sales as professional.  When you are in financial industries, financial data is the best for you.   This course uses a variety of data from crime rate to automobile sales.  Therefore, you can see the data you are interested in. It is critically important for beginners of data analysis.

 

2. This course focuses on how to use analytics tools, quite than the theory behind the analysis

Many of data analysis courses take a long time to explain the theory behind the analysis.  It is required when you want to be a data scientist because theory is needed to construct an analytic method by yourself. However, most of business managers do not want to be data scientists.  All business managers need is the way to analyze data to make better business decisions. For this purpose, this course is good and well-balanced between theory and practice.  Firstly, a short summary of theory is provided, then move on to practice. Most of  the lectures focus on “how to use R for data analysis”. R is one of the famous programming languages for data analysis, which is free for everyone.  It enables beginners to use R in analyzing data step by step.

 

3. It covers major analytic methods of data analysis.

When you see the schedule of the course,  you find many analytic methods from linear regression to optimizations.  This course covers major methods that beginners must know.  I recommend to focus on linear regression and logistic regression when you do not have enough time to compete all units because both of method is applicable to many cases in the real world.

 

 

I think it is worth seeing only the video in Unit 1 and 2.  Interesting topics are used especially for people who like baseball. If you do not have enough time to learn R programming, it is OK to skip it. The story behind the analysis is very good and informative for beginners. So you may enjoy the videos about the story and skip videos of programming for the first time. If you try to obtain a certificate from edx, you should obtain 55% at least over the homework, competition and final exam.  For beginners, it may be difficult to complete the a whole course within limited time (three-month).  Do not worry.  I think this course can be learned again in time to come.  So first time,  please focus on Unit1 and Unit2, then a second time, try a whole course if  you can. In addition, most of edx courses including this are free for anyone.   You can enjoy anytime, anywhere as long as you have an internet access.  Could you try this course with me?

It is awesome if you can create your own news-broadcasting, isn’t it?

News broadcastings are well-known from everyone. For example, CNN, financial times and Bloomberg, etc.  If you can make your own news broadcasting, it is awesome and amazing. But is it possible?  One of the obstacles is how we can collect articles and information from all over the world in real-time basis.  Of course I do not have my own network of news correspondents all over the globe. Then, what should we do about that?

Last week I found the blog about “GDELT 2.0“. The GDELT Project, which monitors events driving global society, creating a free, open platform for computing in the entire world, was founded and led by Kalev H. Leetaru. The GDELT Project’s full name stands for the Global Database of Events, Language, and Tone (GDELT).  Now this project is going to a new stage of “GDELT 2.0″.  Compare with “GDELT 1.0″,  “GDELT 2.0″ has a great deal of progress as follows

 

1.  “GDELT 2.0″ can cover documents and information written in 65 languages

There is a lot of linguistic communication to be written and spoken all over the world. If we try to cover all over the Earth, we need to understand languages other than English. For example, an apple is called “Ringo” in Japanese. If computers cannot read what “Ringo”means, it is impossible to collect the information about apple in Japan because few of the articles are translated from Japanese to English. There is no need to worry about them. GDELT 2.0″ can do that by using real time machine translation. This function is called “GDELT Translingual“.  It means that global news that GDELT monitors in 65 languages, representing 98.4% of its daily non-English monitoring volume, is transformed in real time into English. It is amazing because the media of the non-Western world can be included in our coverage. There are no language barriers to worry about.

 

2. “GDELT 2.0″ can be updated in near-real time basis

A blog of  “GDELT 2.0″ says ” In essence, within 15 minutes of GDELT monitoring a news report breaking anywhere the world, it has translated it, processed it to identify all events, counts, quotes, people, organizations, locations, themes, emotions, relevant imagery, video, and embedded social media posts, placed it into global context, and made all of this available via a live open metadata firehouse enabling open research on the planet itself.”  These data use to be updated once a day. Now it is updated within 15 minutes. I think it is critically important when we try to create our own news-broadcasting.

 

3. “GDELT 2.0″ can exercise content analysis for each article in near-real time basis

“GDELT 2.0″ can also judge whether the articles are positive or negative. The blog says “GDELT 2.0″ can quantify the extraordinary array of latent emotional and thematic signals subconsciously encoded in the world’s media each day. 18 content analysis systems totaling more than 2,230 dimensions are now run on each news article seen by GDELT each day and all of these scores are available. It is called “the Global Content Analysis Measures (GCAM)”.

 

In short,  information all over the world can be updated with real-time machine translation and content analysis.  It is definitely amazing. With this database of “GDELT 2.0″,  we might create our own news broadcasting!  Could you try it now?

If you are interested in “GDELT 2.0″, it is a nice video for an introduction.

 

Malaysia is the top emerging digital economy in Digital Evolution Index

Last week I found an interesting report about digital economy.  It is called “Digital Evolution Index” conducted by the Fletcher School at Tufts University in collaboration with MasterCard and DataCash. Malaysia is the top in the category of “Break out” nations. It is ranked at 23 out of 50 nations and one of the fastest moving countries in the index from 2008 to 2013. 

In this report, I found that only 2.9 billion global internet users receive an access to the internet so far. Remaining people cannot use the internet because there is no access to it.  However, progresses of technologies are going along in many emerging countries, such as Malaysia, China and India.  Let us consider these progresses country by country and what will take place in the future.

 

The website states that the index is calculated according to the four pillars.

1. Demand: covers consumer income and demographics as well as internet usage

2. Supply: focuses on technology and infrastructure and whether or not they can support digital commerce and transactions.

3. Institutions: accounts for government policy and access to trade.

4. Innovation: rates the environment for creating startups and the overall competitive landscape.

In short, if there are many customers using the internet,  e-commerce companies,  support from governments and innovations promoted, the index will be higher.

Based on the score above, countries are classified into four categories below

1. Stand Out: These countries historically achieve high levels of digital transactions and continue to maintain that level.

2. Watch Out: The common thread among these countries is that they have both significant opportunities and challenges. Their economies function well in spite of limitations.

3. Break Out: Primarily, these are developing countries that have low but growing scores. While they are attractive to investors because of rapid improvement, they’re also riskier.

4. Stall Out: Typically, this group has a history of strong growth, but it’s no longer being achieved. Because of various factors, these countries are at risk of slipping in their development.

 

It is no surprising that there are lots of developed nations in the category of Stand out. These are US, Canada, Singapore, Hong Kong and so on.  But it is surprising that there are a lot of Asian nations in the category of Break Out. These are Malaysia, Thailand, China. Although India, Philippines, Vietnam and Indonesia are in Watch Out, they very close to Break Out. As you know, China and India have populations over a billion people and ASEAN nations have also six hundred million populations there. It means that the digital economy will be spread out with massive scale there in the future. Especially when android phones are getting cheaper and everyone can afford his/her smart phone in order to connect to the internet.

 

I live and work in Kuala Lumpur, Malaysia. I agree with this index as mobile internet is very proficient and the cost is reasonable here.    I pay 30RG (1USD is about 3.6 RG) per month to connect to the internet and voice telephone through my mobile.  The speed of the internet is enough to use e-mails and social network, although it is a little dull to watch the movies.  4G internet service is likewise available if you pay more.

From Malaysia to India, there is vast potential to expand digital economy.  I would like to find out “the next billion users” there.

This new toy looks so bright! Do you know why ?

Last week I found that new toy  called “CogniToys” for infants will be developed in the project of Kickstarter, one of the biggest platforms in cloud funding.  The developer is elemental path, one of the three winners of the IBM Watson competition. Let see why it is so bright!

According to the web site of this company,  this toy is connected to the internet.  When a child talks to this toy, it can reply because this toy can see what a child says and answer the question from a child.  It usually requires less than one second to answer because IBM Watson-powered system is powerful enough to calculate answers quickly.

 

Let us look at the descriptions of this company’s technology.

“The Elemental Path technology is built to easily license and integrate into existing product lines. Our dialog engine is able to utilize some of the most advanced language processing algorithms available driving the personalization of our platform, and keeping the interaction going between toy and child.”

Key words are 1. Dialog    2. Language processing   3. Personalization

 

1. Dialog

This toy communicates with children by conversation, rather than programming. Therefore technology called “speech recognition” is needed in it.  This technology is applied in real-time machine translation such as Microsoft Skype, too.

 

2. Language processing

In the area of machine learning, it is called “Natural language processing”. Based on the structure of sentence and phrase, the toy understands what children say.  IBM Watson is very expert in the field of natural language processing because Watson should understand the meaning of questions in Jeopardy contests before.

 

3. Personalization

It is beneficial when children talk to this toy, it knows children preference in advance. This technology is called “Personalization”.  Through interactions between children and the toy, it can learn what children like to cognize. This technology is oftentimes used in retailers such as Amazon and Netflix. There is no disclosure about the method of personalization as far as I know.  I am very interested in how the personalization mechanism works.

 

In short, machine learning enables this toy to work and be smart. Functions of Machine Learning are provided as a service by big IT companies, such as IBM and Microsoft.  Therefore, this kind of applications is expected to be put out to the market in future. This is amazing, isn’t it?  I imagine next versions of the toy can see images,  identify what they are and share images with children because technology called image recognition is also offered as a service by big companies.

I ordered one CogniToy through Kickstarter. It is expected to deliver in November this year. I will report how it works when I get it!

 

Note:IBM, IBM Watson Analytics, the IBM logo are trademarks of International Business Machines Corporation, registered in many jurisdictions worldwide. 

What can computers do now ? It looks very smart !

Lately I found that several companies such as Microsoft and IBM provide us services by machine learning. Let us see what is going on now.

These new services are based on the progress on Machine learning recently. For example, Machine translation services between English and Spanish are provided by Microsoft skype. It uses Natural Language Processing by Machine learning. Although it started at Dec 2014, the quality of the services is expected to be improved quickly as a lot of people use and computer can learn the data from such users.

 

It is beneficial for you to explain what computers can do lately so that you can imagine new services in future. First, computers can see the images and videos and identify what it is. This is image recognition. Second, it can listen to our speech and interpret what you mean. This is speech recognition. It can translate one language to another, as well. This is machine translation. Third, computers can research based on concepts rather than key words. Fourth, it can calculate best choice among the potential options. This is an optimization. In short computers can see, listen to, read, speak and think.

These functions are utilized in many products and services although you cannot notice it. For example, IBM Watson Analytics provides these functions through platform as a service to developers.

 

I expect these functions enable computers to behave just like us. At the initial phase, it may be not so good just like a baby. However, machine learning allows computers to learn from experience. It means that the computer will perform better than we do in many fields. As you know, Shogi, one of the popular Japanese board game, artificial machine players can beat human professional teams. This is amazing!

Proceeding forward, it is recommended that you understand how computers are progressing in terms of the functions above. Many companies such as Google, Facebook invest a great deal of money in this filed. Therefore, many services are anticipated to be released in near future. Some of new services can impact our jobs, education and society a lot. Some of them may arise new industries in future.

 

Some day, when you are in the room, the computer can identify you by computer vision. Then ask if you want to drink a cup of coffee. The computer holds a lot of data, such as temperature, weather, time, season, your preference in it and generates the best coffee for you. If you want to know how this coffee is generated, the computer provides you a detailed report about the coffee. All settings are done automatically. It is the ultimate coffee maker by using powerful computer algorithm. Do you want it for you?

 

 

Note:IBM, IBM Watson Analytics, the IBM logo are trademarks of International Business Machines Corporation, registered in many jurisdictions worldwide. 

Is this message spoken by human or machine?!

Firstly, could you watch the video below?   Our senior instructor speaks about himself.  It sounds natural for me,  far better than my poor English. Then the question comes. Who speaks in reality?  Human or machine?  

  The answer is IBM Watson,  one of the famous artificial intelligence in the world.  When I listened to his (or her?) English, I was very surprised as it sounds very natural and fluent.  I want to have artificial English speakers for a long time in order to develop self speaking apps. Finally, I found it!

This function is one of the new five services provided in IBM Watson Developer Cloud as beta service.   Now it has 13 functions total. Here are new services.

  1. Speech to Text :  Speech can be converted to text in real-time basis. It looks good when I try to convert news broadcast into text.
  2. Text to Speech :  This is used to prepare the video message above without native speakers. It sounds natural for both male and female voices.  English and Spanish (only male) are currently available. One of them is the American English voice used by Watson in the 2011 Jeopardy match
  3. Visual Recognition : When you can input jpg image, Watson can identify what it is with probabilities.  I try several images, however it looks less accurate than I expected so far. In my view it needs improvement to be used in applications.
  4. Concept Insights : According to explanations in the company blog, the Concept Insights service links documents that you provide with a pre-existing graph of concepts based on Wikipedia.   I think it is useful as it works beyond just using keywords in searching information.
  5. Tradeoff Analytics : According to explanations in the company blog, it helps people make better choices when faced with conflicting goals and multiple alternatives, each with its own strengths and weaknesses.  I think it has optimization algorithms in it. It may be useful to construct investment portfolios.

Watson can listen to speeches,  read text and speak it.  It also can see the image and understand what is to some extent. Therefore Watson can do the same thing as human do with new added functions.  Therefore, in theory,  mobile applications can obtain the same functions as people do, such as seeing, reading, listening and speaking.

IBM Watson Developer Cloud has a plan to add new functions as they are ready. Although they are currently beta service,  its quality must be improved gradually as machine learning behind services learns a lot in future. It enables us to develop new services with artificial intelligence to be available in a short period.  It must be amazing. What kind of services do you want? Maybe it will be available in near future !

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