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Artificial Superintelligence: Network of Intelligent Computers That Self-Improve

EasyChair Preprint no. 4315

7 pagesDate: October 3, 2020


Artificial Super Intelligence or ASI that is more potent and refined than human’s intelligence. ASI is based on the ideas that machines can imitate the human mind, their way of working to the extent that they can even supersede them. Today networking architectures is becoming smart and intelligent with the help of Internet Of Things ( IoT ) and Artificial Intelligence( AI ), one such applications is the integration of IoT and AI in a Smart City project where various cameras and sensors installed at different spots and connect them to data center servers that can make intelligent decisions based on the inputs from the cameras and sensors. In such structure, the IoT devices handle basic recognition and next level sophisticated inputs are sent to remote servers. In this paper, we implement object detection CNN-SVM with different SVM architectures running on different computers on a network and the camera ( IoT ) provide a stand-alone processing power for street object detection which is the core function of traffic systems and public safety in a city. A Convolutional Neural Network model works on sensory device and SVMs work on remote servers. The test results show good accuracy. Since sensory devices work round the clock in a city environment, there is no practical limit on how much CNN-SVM can learn and greater integration of AI into network architectures can help develop into cognitive networks that will show network-wide intelligent behavior with an ability for self-improvement.

Keyphrases: AI Enabled Networking, artificial superintelligence, CNN-SVM Model, Edge Computing

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Poondru Prithvinath Reddy},
  title = {Artificial Superintelligence: Network of Intelligent Computers That Self-Improve},
  howpublished = {EasyChair Preprint no. 4315},

  year = {EasyChair, 2020}}
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