Is this a real voice by human being? It is amazing as generated by computers

As I shared the article this week,  I found the exciting system to generate voices by computers. When I heard the voice I was very surprised as it sounds so real. I recommend you to listen to them in the website here.  There are versions of English and Mandarine. This is created by DeepMind, which is one of the best research arms of artificial intelligence in the world. What makes it happen?   Let us see it now.

 

1. Computers learns our voices deeper and deeper

According to the explanation of DeepMind, they use “WaveNet, a deep neural network for generating raw audio waveforms”.  They also explain”pixel RNN and pixel CNN”, which are invented by them earlier this year. (They have got one of best paper award atICML 2016, which are one of the biggest international conference about machine learning, based on the research). By applying pixel RNN and CNN to voice generation, computers can learn wave of voices far more details than previous methods. It enables computers generate more natural voices. It is how WaveNet is born this time.

As the result of learning raw audio waveforms, computer can generate voices that sound so real. Could you see the metrics below?  The score of WaveNet is not so different from the score of Human Speech (1). It is amazing!

 

2. Computers can generate man’s voice as well as woman’s voice at the same time

As computer can learn wave of our voices more details,  they can create both man’s voice and woman’s voice. You can also listen to each of them in the web. DeepMind says “Similarly, we could provide additional inputs to the model, such as emotions or accents”(2) . I would like to listen them, too!

 

3. Computers can generate not only voice but also music!

In addition to that,  WaveNet can create music, too.  I listen to the piano music by WaveNet and I like it very much as it sounds so real. You can try it in the web, too.  When we consider music and voice as just data of audio waveforms, it is natural that WaveNets can generate not only voices but also music.

 

If we can use WaveNet in digital marketing, it must be awesome! Every promotions, instructions and guidance to customers can be done by voice of  WaveNet!  Customers may not recognize “it is the voice by computers”.  Background music could be optimized to each customer by WaveNet, too!  In my view, this algorithm could be applied to many other problems such as detections of cyber security attack, anomaly detections of vibrations of engines, analysis of earthquake as long as data can form  of “wave”.  I want to try many things by myself!

Could you listen the voice by WaveNet? I believe that in near future, computers could learn how I speech and generate my voice just as I say.  It must be exciting!

 

 

1,2.  WaveNet:A generative model for Raw Audio

https://deepmind.com/blog/wavenet-generative-model-raw-audio/

 

 

Notice: TOSHI STATS SDN. BHD. and I 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

We might need less energy as artificial intelligence can enable us to do so

When I heard the news about the reduction of consumption energy in google data center (1), I was very surprised.  Because it has been optimized for a long time. It means that it is very difficult to improve the efficiency of the system more.

It is done by "Google DeepMind", which has been developing "General artificial intelligence". Google DeepMind is an expert on "deep learning'. It is one of major technologies of artificial intelligence. Their deep learning models can reduce the energy consumption in data center of google dramatically.  Many data are corrected in data center, -- data such as temperatures, power, pump speeds, etc. -- and the models provide more efficient control of energy consumption. This is amazing. If you are interested in the details, you can read their own blog from the link below.

 

It is easy to imagine that there are much room to get more efficiency outside google data centers. There are many huge systems such as factories, airport, power generators, hospitals, schools, shopping mall, etc.. But few systems could have the same control as Google DeepMind provides. I think they can be more effeicent based on the points below.

1.More data will be available from devices, sensors and social media

Most people  have their own mobile devices and use them everyday.  Sensors are getting cheaper and there are many sensors in factories, engines on airplanes and automobile, power generations,etc. People use social media and generate their own contents everyday. It means that massive amount of data are generating and volume of data are increasing dramatically. The more data are available, the more chances we can get to improve energy consumptions. 

 

2. Computing resources are available from anywhere and anytime

The data itself can say nothing without analyzing it.  When massive amount of data is available,  massive amount of computer resources are needed. But do not worry about that. Now we have cloud systems. Without buying our own computer resources, such as servers, we can start analyzing data with "cloud".  Cloud introduces "Pay as you go" system. It means that we do not need huge initial investments to start understanding data. Just start it today with cloud.  Cloud providers, such as Amazon web service, Microsoft Azure and Google Cloud Platform, prepare massive amount of computer resources which are available for us.  Fast computational resources, such as GPU (Graphics processing unit) are also available. So we can make most out of massive amount of data.

 

3. Algorithms will be improved at astonishing speed.

I have heard that there are more than 1000 research papers to submit and apply to one major machine learning international conference. It means that many researchers are developing their own models to improve the algorithms everyday. There are many international conferences on machine learning every year. I can not imagine how many innovations of algorithms will appear in future.

 

At the end of their blog, Google DeepMind says

"We are planning to roll out this system more broadly and will share how we did it in an upcoming publication, so that other data centre and industrial system operators -- and ultimately the environment -- can benefit from this major step forward."

 

So let us see what they say in next publication.  Then we can discuss how to apply their technology to our own problems. It must be exciting!

 

 

(1) DeepMind AI Reduces Google data center cooling bill by 40%,  21st July 2016

https://deepmind.com/blog

 

 

Notice: TOSHI STATS SDN. BHD. and I 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

How can we track our mobile-e-commerce? Google analytics academy is good to start learning!

Last week, I found that Alibaba, the biggest e-commerce in China, announced the financial result of Q2 2016.  One of things that were attracting me is 75% sales are coming from mobile device, rather than PC.

This is amazing. This is much bigger than I expected.  When we consider many younger people use mobile devices as their main devices. This rate is expected to increase steadily going forward.

Then I wonder how we can track customer behaviors on mobile-e-commerce with ease. Because it is getting more important as many customers come to your e-commerce shop from mobile devices. What do you think?

 

I found that Google analytics academy, which teaches how to use Google analytics, provides awesome online courses for free.  Although you may not be users of Google analytics, it is very beneficial because it shares the idea and concept of mobile-e-commerce. If you want to know which marketing generates the most valuable users, it is worth learning it. Let me explain several take aways

 

1. "High-value user" vs "Low-value user"

When we have many users at our mobile-e-commerce shop,  we find that some users buy many products or subscriptions than other users. They are "High-value users". On the other hand, some users rarely buy them. They are "Low-value users". This idea is good and useful to prepare target lists of new campaigns in order to put priority among  many customers. So our goal is to increase the number of  "High-value user" effectively.

 

2. Segmentation of customer is critically important

Segmentation means prepare the correct subset data to get insights form data. It is popular and widely-used across industries. When we analyze data, creating appropriate user segments are critically important. You may want create the segment of "buy-users and not-buy-users" and get the insights of what factors influence people to buy. There are many segmentations you can imagine.  You can create your own segmentations on Google analytics! 

 

3. How to measure behavior of customers

 It is also important to track behavior of each customer. There are many data to be obtained.  Ex : What screen each customer visit and what actions they take. How many minutes they stay on each screen and how much they spend to buy products. The former data is formed as "categorical" and the latter as "numerical".  It is noted that these data should be relevant to identify and increase the number of "high-value user" as it is our goal. When you identify good candidates of data to use,  you can add them to your own segmentations and analyze them deeper in order to get insights from these data.

 

In addition to the on-line courses,  Google analytics makes real data of their e-commerce shop "Google Merchandise Store " available to everyone who wants to learn it for free. It is called "Google analytics demo account". This is also an amazing service as e-commerce data in real-world are rarely available to us before.  I would like to go deeper and get insights from them in near future.  Of course I will share it here with you as it is beneficial to everyone. Please see the one of awesome reports on Google analytics demo account.

 

 

Do you like it?  I recommend you to start learning with Google analytics academy. When you are getting familiar with data of mobile-e-commerce, it is more easier to learn more advanced data analytics, such as machine learning. Anyway, this course is free so you can access many awesome contents without paying any fee. Let us try and enjoy it!

 

 

 

Notice: TOSHI STATS SDN. BHD. and I 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.

What is the marketing strategy at the age of "everything digital"?

presentation-1311169_640.jpg

In July,  I have researched TensorFlow, which is a deep learning library by Google, and performed several classification tasks.  Although it is open-source software and free for everyone, its performance is incredible as I said in my last article.

When I perform image classification task with TensorFlow,  I found that computers can see our world better and better as deep learning algorithms are improved dramatically. Especially it is getting better to extract "features", what we need to classify images.

Images are just a sequence of numbers for computers. So some features are difficult for us to understand what they are. However computers can do that. It means that computers might see what we cannot see in images. This is amazing!

This is an example "how images are represented as a sequence of numbers. You can see many numbers above (These are just a small part of all numbers). These numbers can be converted to the image above which we can see. But computers cannot see the image directly.  It can only see the image through numbers above. On the other hand, we can  not understand the sequence of numbers above at all as they are too complicated. It is interesting.

In marketing,  when images of products are provided,  computers might see what are needed to improve the products and to be sold more. Because computers can understand these products more in a deferent way as we do. It might give us new way to consider marketing strategy.  Let us take T shirts as an example. We usually consider things like  color, shape,  texture,  drawings on it,  price. Yes, they are examples of "features" of T shirts because T-shirts can be represented by them. But computers might think more from the images of T shirts than we do. Computers might create their own features of T-shirts.

 

Then, I would like to point out three things to consider new marketing strategy.

1.Computers might extract more information that we do from same images.

As I explained, computers can see the images in a different way as we do. We can say same things for other data, such as text or voice mail as they are also just a sequence of numbers for computers. Therefore computers might understand our customers behavior more based on customer related data than we do when deep learning algorithms are much improved. We sometimes might not understand how computers can understand many data because computers can understand text/speech as a sequence of numbers and provide many features that are difficult to explain for us.

 

2.Computers might see many kind of data as massive amount data generated by costomers

Not only images but also other data, such as text or voice mail are available for computers as they are also just a sequence of numbers for computers. Now everything from images to voice massages is going to digital.  I would like to make computers understand all of them with deep learning. We cannot say what features are used when computers see images or text in advance. But I believe some useful and beneficial things must be found.

 

3. Computers can work in real-time basis

As you know, computers can work 24 hours a day, 365 days a year. Therefore it can operate in real-time basis. When new data is input, answer can be obtained in real-time basis. This answer can be triggered next actions by customers. These actions also can be recorded as digital and fed to into computers again. Therefore many digital data will be generated when computers are operated without stop /rest time and the interactions with customers might trigger chain-reactions. I would like to call it "digital on digital"

 

Images, social media, e-mails from customers, voice mail,  sentences in promotions, sensor data from customers are also "digital". So there are many things that computers can see. Computers may find many features to understand customer behaviors and preferences in real-time basis. We need to have system infrastructures to enable computers to see them and tell the insight from them. Do you agree with that?

 

 

 

Notice: TOSHI STATS SDN. BHD. and I 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.

 

This is our new platform provided by Google. It is amazing as it is so accurate!

In Deep learning project for digital marketing,  we need superior tools to perform data analysis and deep learning.  I have watched "TensorFlow", which is an open source software provided by Google since it was published on Nov 2015.   According to one of the latest surveys by  KDnuggets, "TensorFlow" is the top ranked tool for deep learning (H2O, which our company uses as main AI engine, is also getting popular)(1).

I try to perform an image recognition task with TensorFlow and ensure how it works. These are results of my experiment. MNIST, which is hand written digits from 0 to 9, is used for the experiment. I choose convolutional network to perform it.  How can TensorFlow can classify them correctly?

 

I set the program of TensorFlow in jupyter like this. This comes from tutorials of TensorFlow.

This is the result . It is obtained after 80-minute training. My machine is MAC air 11 (1.4 GHz Intel Core i5, 4GB memory)

Could you see the accuracy rate?  Accuracy rate is 0.9929. So error rate is just 0.71%!  It is amazing!

 

Based on my experiment, TensorFlow is an awesome tool for deep learning.  I found that many other algorithms, such as LSTM and Reinforcement learning, are available in TensorFlow. The more algorithms we have,  the more flexible our strategy for solutions of digital marketing can be.

 

We obtain this awesome tool to perform deep learning. From now we can analyze many data with TensorFlow.  I will provide good insights from data in the project to promote digital marketing. As I said before "TensorFlow" is open source software. It is free to use in our businesses.  No fees is required to pay. This is a big advantage for us!

I can not say TensorFlow is a tool for beginners as it is a computer language for deep leaning. (H2O can be operated without programming by GUI). If you are familiar with Python or similar languages, It is for you!  You can download and use it without paying any fees. So you can try it by yourself. This is my strong recommendation!

 

TensorFlow: Large-scale machine learning on heterogeneous systems

1 : R, Python Duel As Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results

http://www.kdnuggets.com/2016/06/r-python-top-analytics-data-mining-data-science-software.html

 

Notice: TOSHI STATS SDN. BHD. and I 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.