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




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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