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

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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?

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

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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?

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

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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.

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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.

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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!

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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”.

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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”?

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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 BIS, FSA 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 23 April.

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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.

 

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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.

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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.

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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.

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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.

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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.

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

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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!

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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!

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

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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.

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

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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 !

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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.