Neural Networks and Artificial Intelligence Btech Seminar

The Research Abstract Neural Networks and Artificial Intelligence Btech Seminar on Neural Networks and Artificial Intelligence talks about the human brain as an amazing processor. The research paper talks about the multiple challenges the human brain throws. The research abstract talks about the various applications that can be undertaken by Neural Networks not replacing the traditional standards of Artificial Intelligence. The paper talks about Neural Networks as an application used for processing large amounts of data. A considerable light is cast on Artificial Neural Networks as an emerging domain.

What is  Artificial Neural Network : An  artificial  neural  network  is, in  essence, an  attempt  to  simulate  the  brain. Neural  network  theory revolves  around  the  idea  that  certain  key  properties  of  biological  neurons  can  be  extracted  and  applied  to  simulations, thus  creating  a  simulated (and  very  much  simplified) brain. A neural network as defined Robert Hecht-Nielsen: “A neural network is a computing system which is made up of a number of simple, highly inter connected processing elements and which  processes  information  by  its  dynamical  state  response  to  external  inputs”.

Scope of Neural Networks:

Neural  networks  appear  to  be  able  to  solve  “monster”  problems  of  AI  that  traditional systems  have   found   difficulty  with. These  include ,  but  are  not  limited  to ,  speech recognition  and  synthesis ,  vision ,  and  pattern  recognition. Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques.

Advantages of Neural Networks:

  1. Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
  2. Self-Organization: An ANN can create its own organization or representation of the information it receives during learning time.
  3. Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.
  4. Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage

Conclusion :

The research abstract concludes positing that, artificial  neural  networks  are  one  of  the  promises  for  the  future  in computing. They  offer  an  ability  to   perform  tasks  outside  the  scope  of  traditional  processors. Neural  networks  can  now  pick  stocks ,  cull  marketing  prospects,  approve  loans,  deny  credit  cards,  tweak  control  systems, grade  coins, and  inspect  work. Though  neural  networks  have  a  huge  potential  we  will  only  get  the  best  of  them  when  they  are  integrated  with  computing ,  AI , fuzzy  logic  and related  subjects.

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