Applications




There are many applications for machine learning, including:

In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million. Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly. In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis. In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software. In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists. In 2019 Springer Nature published the first research book created using machine learning.


Machine Learning based Mobile Applications:

Mobile applications based on machine learning are reshaping and affecting many aspects of our lives.

  • Challenges
    • computational power
    • energy
    • latency
    • low memory
    • privacy risks
  • Application Architectures
    • Cloud inference without training The mobile application sends a request to the cloud through an application programming interface (API) together with the new data, and the service returns a prediction.
    • Both inference and training in the cloud
    • On-device inference with pre-trained models
    • Both inference and training on device
    • Hybrid Architecture

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