May 2017

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  • Opencv tutorial people detection
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  • Microsoft and cognitive service computer vision is one of the most visible on build 2017 

    Looks pretty cool, Microsoft machine learning for safety in work environment. Recognizes people identity, real time tracking, evaluate their role in respect with position  against to safety. We will see in near future this technology more often. Lets have a look to strategy little bit closer. All who play with computer vision just know, that the demonstration is one think and the general service available to the Employers is something totally different. Cool, interesting as amazon GO. Lets compare with Amazon Go much closer.
    Microsoft Build conference 2017


    Microsoft cognitive vs Amazon Go

    Both are huge co-leaders on the market with cloud computing. Obviously, Who have this computational power should entered the future of everything, starting from small IOT devices to large scale distributed intelligent platforms driven by machine learning. There is maybe question, why microsoft do not follow the Amazon Go concept in general form, open to everybody. Analysis of video stream in retail statistics and marketing. I know it is conference demonstration, mentioned everywhere. Maybe I can add some original idea, why to slightly change the focus. 

    Employees monitoring system 

    This is related to mentality, human resources and people comfort in working environment. Sure as a employer you can control how effectively your money are spend covered by all by safety of the employees buzzwords. In some environment with strict rules question of life or serious health problems is necessary to go this way. Hard stop and sensors are already part of this kind of environments. Can we expect that this environments send video to be analysed somewhere as a service? Is this really the best market? Is this what everybody want. Is this dangerous or provide benefit with safety or making just people leaving the companies that push privacy questions so hard behind the borders.

    Do you know details about the architecture ? Let me comment.. As a service to process video streaming in cloud, There is several problem and much more critical one, when we are talking about heath monitoring of anyone. 

    Amazon go do right think 

    Advantage of the Amazon go concept is more features. They are count not only to computer vision but also to sensor fusion from different sources to provide better features processed by deep learning. Main advantage against Microsoft is that Amazon focus on their own environment but Microsoft to general one. Problem is general one not in retail but in environment, where the human health is concerned. This should be critical problem. Where Amazon has same situation in same environment and lights conditions handled hundred times. Microsoft should adapt on every possible light, environment and other variables related to deployment in different places. What is worse in much more critical applications and situations.

    This could be hard and slow down to go with this on market..

    Microsoft go to harder segment for computer vision

    Again, Just a comparison Microsoft concept and situation of and Amazon go. Why the microsoft position is just a little bit uncomfortable. 

    Amazon go can easily solve customers problem, refund the money for customers complains and the application is  basically not so critical. In the other hand. Microsoft need in such a cases like hospital and security critical environment count whit certification problems, law issues and more. In Microsoft segment is much more responsibility. This is why amazon can speed up the development based on experience in real application instead of segment when is necessary be perfect.
    On the other hands, AI is able control cars itself without human. This segment is also little bit risky from that point of view.


    I really think that they should start competition with amazon Go as general service for all retails. Somehow bound the requirements for the stores environment and use sensor fusion. For example in medical application like hospital is maybe good idea to use thermo cameras combined which normal one..

     Provide benefits for customer and high valuable information about the customers behavior to boost the retail and advertisement impact. This is just a save. No problem issue. 

    Video stream delivery 

    I think that microsoft mostly provide all AI in form of service. You deliver stream or image request and We give you a results back. The limitation here is bandwidth, Video stream quality and time delays. What if the network go down, resolution or frame rate and something happened. Cars has brain in their body.. This service has brain somewhere else. I expect, do not know for sure. 
    Microsoft can guaranty availability of the service and accuracy, but who will be responsible in case that the system is not able to connect and something happened. Just a case. You always need to count with the worse one and hope that never happened. 

    Delay, Video stream delays of real time broad casting. Delay is here. Delay in video will be here also in future. When some situation occurred the second play roles in microsoft case. If the alert response come back with delay. This service will be replace by something else.   Maybe is better to deploy something like in form of IoT devices than services. Maybe this kind of service should provide pretrained parameters of deep neural network for IoT devices, compute only forward pass without learning. But not transfer, analyze the whole content and response somewhere..  Who know what will be final solution. Power of cloud, power of machine learning in this case needs to be response available on time. That mean calculation directly in camera devices or on local network.


    Good luck to Microsoft. Here is a fan. Let me test your solution ! :) . Be careful

    Do you like this post ?? Feel free to share. This keep me doing this kind of content. Hopefully I have got time to also some new tutorial post.. Hopefully 

    Future of Machine learning in 2017 from the dark side

    Machine intelligence is our future. It is almost everywhere in some form right over the web. Machine learning started to be part of the small devices, distributed systems, cars, cameras and many others. Widely connected, distributed and able to do incredible thinks. Every technology has its plus and minus. I will try to focus on one strange minus. So powerful to destroy future of individual peoples, governments and institutions.
    Machine learning future



    Machine learning generated FAKE news

    This will be serious problem in near future. Even now, is a problem realize, what is true and what is not. It is hard to find from the heap of resources the good one and believe what trusted media brings on board. Most probably, you can heard about PewDiePie vs Wall Street Journal. Whole thinks is just obscure. Call the joking YouTuber the racist and so on. Is little bit to much.  Sure everyone have different sense of humor. Difference is also in each countries. To be visible, famous and do not piss anybody in this world is almost impossible. Still this is only internet, this is only humor and it is hard to find true in what is written. 

    What people believe

    People maybe in near future stop trust to written media at all. What about the others media. Can we expect the same ? We trust to most of the thinks which are visible on the screen and even better with sound. Probably you know following video.. If not. Go on. 


    Project Page: http://www.graphics.stanford.edu/~nie...


    Scary. The future of machine learning is almost like any breaking through technology. Lost of positives. Tons of negatives.

    Trusted media

    Behavior of trusted media which fighting for every our advertisement per click rating. Basically money are follow the strange rules. In better cases, They publish almost everything what is caught on camera or audio as a true. In worse example they are just speculating over the pictures. Even worse, brings the fake pictures online.   This could be worse problem than the media stand side by side the owners. 

    Fake on real faces

    On the video above, I can not recognize the difference between real behavior and acting behavior. We can expect the whole scenes generated fake news. Whole, situation. Recurrent neural network now successfully generate music but they are also generate trustful tone and character of the voice of concrete person.

    We have fake behavior in video. We can generate speech and follow the real template of concrete person. 

    Challenge for machine learning of 2017 

    Now we successfully doing interesting staff against us. We need to also find the way how to use this technology to defense us against fake news, fake actors, fake speech. There is the real power to destroy lot of the thinks over us.. 


    Machine learning vs machine learning  To understand the truth

    Fight already begin  


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