data science
  • Admin
  • July 30, 2019

Artificial Intelligence or AI can be categorized as the inter-disciplinary field of Computer science and Cognitive science which focuses on the intelligence demonstrated by machines in analogy with the natural intelligence demonstrated by human beings. It emphasizes on the creation of intelligent machines which can react and learn like human beings.

Artificial Intelligence can be classified into three major types of systems:
  1. Analytical Systems

  2. Human-inspired Systems

  3. Humanized Artificial Intelligence

  4. In 1956, Artificial intelligence was recognized as an academic discipline but faced disappointment and loss of funding in the 1970s which is also known as the AI Winter.

    The dawn of 21st Century saw an upward curve of this field and within a half a decade, AI technology became an integral part and was embedded in the structure of every industry

    The goals of the research on AI includes: Reasoning, Knowledge representation, Planning, Natural language processing and perception. The long term goals include: Statistical methods, Computational Intelligence and Traditional symbolic. All the approaches are also used in the following related fields: Computer science, cognitive science, Optimization, Robotics, Intelligent systems, Neural networks and methods based on statistics, probability and economics.

    Why is AI important?

    Artificial Intelligence is important because organizations extract vale from data by automating and optimizing processes and also to construct an actionable insight.

    1. Global spend on AI would reach up to $57.6 billion by 2021.

    2. Companies driven by AI insight will take $1.2 trillion year in comparison to companies which are not driven by AI      insight.

    3. The net gain in jobs resulting from AI adoption will be over 5 million.

      Hot topics to work under Artificial Intelligence:

    4. Large Scale machine learning: Machine learning is concerned about developing systems that improve their performance with experience.

    5. Deep Learning: It is re-branding of neural networks, a class of models inspired by biological neurons in our brain.

    6. Reinforcement learning: It is a closed form of learning in a way human being learns. It consists of an intelligent agent that interacts with the environment smartly to reap a numerical reward.

    7. Robotics: It is separate branch of its own but AI has made robot navigation in dynamic environment possible.

    8. Computer Vision: AI has a huge application in computer vision and digital image processing.

    9. Natural Language Processing: It is concerned with systems that are able to perceive and understand spoken human language.

    10. Game theory and computational design: It considers systems with multiple agents from economics and social science to see how these agents make choices in an incentive based environment.

    11. Internet of things: It is a concept that daily use physical devices are connected to the internet and can communicate with each other via exchange of data.

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