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

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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

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Will the age of “Brain as a Service” come to us in near future?

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15 March 2016,  I found two things which may change the world in the future,  The former, artificial intelligence Go player “AlphaGo” and the latter is an automated marketing system “Google Analytics 360 Suite“. Both of them came from Google. Let me explain why I think the age of “Brain as a service” is coming  based on these two innovations.

1. AlphaGo

You may know what AlphaGo achieved on 15 March 2016.  At  Google DeepMind Challenge, where artificial intelligence Go player had five games against a top professional Go player. It beats Lee sedol, who is one of the strongest Go player in the world, 4 to 1.  Go is one of the oldest games, which are mainly played in China, Korea, Japan and Taiwan. At the beginning of the challenge, few people thought AlphaGo could win the games as it is always said that  Go is so complex that computers can not win professional Go players at least in 10 years. The result was, however, completely opposite. Therefore,  other professional Go players, artificial intelligence researchers and even people who do not play Go must be upset to hear the news. AlfaGo is strengthened by algorithms, which are called “deep learning” and “reinforcement learning“. It can learn the massive amount of Go patterns created by human being for a long time.  Therefore, we need not to program specifically, one by one as computers can learn by themselves. It looks like our brains. We are born without any knowledge and start learning many things as we grow.  Finally, we can be sophisticated enough to be “adult”. Yes, we can see “AlphaGo” as a brain.  It can learn by itself at an astonishing speed as it does not need to rest.  It is highly likely that Google will use this brain to improve many products in it in the future.

 

2. Google Analytics 360 Suite

Data is a king.  But it is very difficult to feed them into computers effectively.  Some data are stored in servers. Others are stored in local PCs. No one knows how we can well-organize data effectively to obtain the insights from data.  Google is strong for consumer use.  G-mails, Android and google search are initially very popular among consumers. But the situations are gradually changing.  Data and algorithms have no-boarders between consumers and enterprises. So it is natural for Google to try to obtain enterprise market more and more. One of the examples is  “Google analytics 360 Suites”. Although I never tried it yet, this is very interesting for me because it can work as a perfect interface to customers. Customers may request many things, ask questions and make complains to your services. It is very difficult to gather these data effectively when systems are not united seamlessly. But with “Google analytics 360 Suites”,  data of customers could be tracked in a timely manner effectively.  For example, the data from Google analytics 360 may be going to Google Audience Center 360,  which is a data management platform (DMP).  It means that the data is available to any analyses that marketers want.  “Google Audience Center 360” can collect data from other sources or third party data providers. It means that many kind of data could be ready to be fed into computers effectively.

 

3. Data is gasoline for “Artificial intelligence”

AlfaGo can be considered as “Artificial intelligence”. “Artificial intelligence” is like our brain.  There is no knowledge in it initially.  It has only structures to learn.  In order to be “intelligent”, it should learn a lot from data. It means that massive amount data should be fed into computers. Without data, “artificial intelligence” can do nothing. Now data management like “Google Audience Center 360” is in progress. It seems that data are getting well organized to be fed into computers.  The centralized data management system can collect data automatically from many systems. It becomes easier to feed massive amounts of data into computers. It enables to computers learn the massive amount of data. These things must be a trigger to change the landscape of our business, societies and lives. Because suddenly computers can be sophisticated enough to work just like our brain.  AlphaGo teaches us that it may happen when a few people think so. Yes, this is why I think that the age of “Brain as a Service” will come in near future.  How do you think of that?

 

 

Note: Toshifumi Kuga’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, Author of the article has not independently verified, validated such data. I and TOSHI STATS.SDN.BHD. 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. 

 

This is No.1 open-online course of “Deep Learning”. It is a new year present from Google!

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I am very happy to find this awesome course of “Deep Learning” now . It is the course which is provided by Google through Udacity(1), one of the biggest mooc platforms in the world. So I would like to share it to any person who are interested in “Deep Learning”.

It is the first course which explains Deep Learning from Logistic regression to Recurrent neural net (RNN) in the uniformed manner on mooc platform as far as I know. I looked at it and was very surprised how awesome the quality of the course is.  Let me explain more details.

 

1. We can learn everything from Logistic regression to RNN seamlessly

This course covers many important topics such as logistic regression, neural network,  regularization, dropout, convolutional net, RNN and Long short term memory (LSTM). These topics are seen in some articles independently before. It is however very rare to explain each of them at once in the same place.  This course looks like a story of development of Deep Learning. Therefore, even beginners of Deep Learning can follow the course. Please look at the path of the course. It is taken from the course video of L1 Machine Learning to Deep Learning .

DL path

Especially, explanations of RNN are very easy to understand. So if you do not have enough time to take a whole course, I just recommend to watch the videos of RNN and related topics in the course. I am sure it is worth doing that.

 

2. Math is a little required, but it is not an obstacle to take this course

This is one of the courses in computer science.  The more you understand math, the more you can obtain insights from the course. However, if you are not so familiar with mathematics, all you have to do is to overview basic knowledge of “vectors”, “matrices” and “derivatives”.  I do not think you need to give up the course because of the lack of knowledge of math. Just recall high school math, then you can start this awesome course!

 

3. “Deep learning” can be implemented with “TensorFlow“, which is open source provided by Google

This is the most exciting part of the course if you are developers or programmers.  TensorFlow is a  python-based language. So many developers and programmers can be familiar with TensorFlow easily.  In the program assignments, participants can learn from simple neural net to sequence to sequence net with TensorFlow. It must be good! While I have not tried TensorFlow programming yet, I would like to do that in the near future. It is worth doing that even though you are not programmers. Let us challenge it!

 

 

In my view,  Deep Learning for sequence data is getting more important as time series data are frequently used in economic analysis,  customer management and internet of things.   Therefore, not only data-scientists, but also business personnel, company executives can benefit from this course.  It is free and self-paced when you watch the videos. If you need a credential, small fee is required. Why don’t you try  this awesome course?

 

 

(1) Deep Learning on Udacity

https://www.udacity.com//course/viewer#!/c-ud730/l-6370362152/m-6379811815

 

 

 

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.

 

 

Revolution or evolution? Who wins, Google or Toyota in 2025?

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Google is a big IT company and Toyota is a giant of automobile industries. They may be categorized in different industries.  However, they must be in tight competition in future because both of them will provide automobiles with artificial intelligence (AI).  Let us consider this competition here.  On 4 Sep, 2015,  Toyota announced that it  collaborates with MIT and Stanford University to develop artificial intelligence to be used in automobiles  and robotics(1).  Toyota thinks drivers can be supported by AI effectively. On the other hand, Google has been developing “self-driving car”, which needs no human  interventions. It seems that Google hired John Krafcik, a car industry veteran who previously led Hyundai’s business in the U.S  to work at Google self-driving car project (2).  It seems that the competition between Google and Toyota has already started.

 

1  Definition of “automobile”

Although both of them use the term of “car”,  each “car” is a little different from each other. According to Toyota, drivers should control their car while AI can support and assist drivers.  On the other hand,  Google promotes self-driving cars, which can drive without human interventions.  The purpose of each “automobile” is the same. It is the transportation from place A to place B.  However, each automobile looks completely different. Toyota AI automobiles might look the same as ordinal automobiles.  They have a steering wheel, accel and brake pedal. On the other hand, Google self-driving cars have no steering wheel, no accel and brake pedal. They may be more compact than ordinal automobiles.

 

2  Self-driving car technologies and human centric technologies

Technologies of each “automobile” are similar.  Both use state of the art technology “Artificial intelligence”.  But the aim of each is a little different.  It means that “Support” vs. “Control”.  I remembered Toyota used “Fun to drive” as a corporate statement in the latter half of 1980s.   Driving is fun because drivers can control cars by themselves. And Toyota uses the terms of ” human centric technologies”.  In my view,  Toyota thinks AI exists to support drivers and it is important to keep a good relationship between human and AI while human should play a major role in driving.  On the other hand,  Google thinking is simple. AI can control automobiles better than human. That is it!

 

3  Revolution or evolution?

The self-driving car is completely new for us in our daily lives. So it sounds revolutionary.  On the other hand, Toyota AI car still needs driver and AI can assist drivers. So it sounds an evolution to me.  Evolution, however, is not always easier than revolution. Because Toyota “human centric technologies” should include “human being” as a major part of the system. According to the video on Toyota websites, Toyota focus on collaboration between human and artificial intelligence. Therefore, human behavior should be analyzed and predicted so that AI knows when and how AI intervenes control of the automobile. If it is not accurate,  this system does not work effectively.  It seems to be more difficult than the system without human intervention is.  AI should learn not only automobile behavior, but drivers’ behavior.  As long as drivers take control automobiles, it is necessary and critically important for automobiles with AI.

 

 

Which automobile do you like better?  The self-driving car is OK for you?  Consumers may have different opinions by country. For example, ASEAN countries, including Malaysia are in the time of motorizations.  Therefore, many consumers want to own and drive their cars by themselves. In addition to that,  the train systems are not so convenient yet in most of the regions so they need their own cars anyway.  On the other hand,  in Japan, consumers are not so enthusiastic in owning cars anymore, especially for younger generations.  For example, in 1980s, Japanese automobile companies produced many sports cars, which were stylish and reasonable for young consumers. They were very popular at that time. Now there are some because sports car is not so popular for younger generations in Japan anymore. In additions to that, in the big cities of Japan such as Tokyo and Osaka,  there are a lot of train networks so there is no need to own cars in daily lives.  Therefore Japanese consumers may be more likely to accept self-driving cars. I am sure each country should consider regulations about automobiles with AI carefully based on the needs and  preferences of the people.

No one knows who wins Google or Toyota In 2025. But I am sure we need a lot of discussions about regulations, insurance, public transportation planning, jobs, and so on. I would like to keep watching it going forward.

 

 

 

Source

1. Toyota Establishes Collaborative Research Centers with MIT and Stanford to Accelerate Artificial Intelligence Research,  website of Totoya motor, 4, September 2015

http://newsroom.toyota.co.jp/en/detail/9233109/

 

2.  Yes, true: I’m joining the Google Self-Driving Car project in late September. 13, September 2015, Twitter of John Krafcik

https://twitter.com/johnkrafcik?lang=en

 

3. Google Self-Driving Car Project

http://www.google.com/selfdrivingcar/

 

Note: Toshifumi Kuga’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, Author 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.