WELCOME TO DIGITAL REALITY: MAIN DIFFERENCES BETWEEN AI AND ML EVERYONE SHOULD KNOW

in #terminology6 years ago

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Everyday we are facing with such terminology as Artificial Intelligence (AI) and Machine Learning (ML). They are already deeply implemented in our lives, in web-platforms we use, they are forming the wearable technologies basis and help Sri and Alexa recognising voice and give us correct answers.

But!
The question is: do you know how these two technologies differ from each other? It is not far the same, so let’s check out the most spread questions like:

  • Which technology is applied in what spheres?
  • How we can recognise them?
  • Are we using any of them in our smart devices?
  • What is the future of digital technologies?

So let’s have a fast look at AI and ML, how scientists define them.

ARTIFICIAL INTELLIGENCE FEATURES

Artificial Intelligence is a broad science brach related to Computer Sciences, which makes computer systems behave like human beings, being “smart”. Basically scientists are working on AI to make it do all the job autonomously – so after uploading all required data and algorithms, AI should work without any human help.

The technologies, which can be mentioned as AI based, are these:

  • Voice recognition
  • Decision making
  • Providing optimal solutions
  • Automatic driving

As one can see, AI is very diverse and refers almost to any Computer Science Sphere.

MACHINE LEARNING FEATURES

Machine Learning is basically a subdivision of AI being a study about certain machine algorithms, which help to improve data and create new commands. The main idea is to use these smart algorithms that can use huge volumes of data, learning it and making complex operations.

Unlike AI, ML requires the human assistance from time to time, uploading data, re-newing algorithms, and so on.

The technologies, working with ML research are these:

  • Data analysis
  • Work with big data
  • Predicting technologies
  • Learning data

So basically, Artificial Intelligence gives the way to Machine Learning, Deep Learning and many other more specific technologies, which relate to certain spheres. Such diversification helps to make a more detailed view on each scientific field. It is very likely that we will have more AI subdivisions in future, working on very specific aspects. Let’s see how it will be, for sure, we are close to meet new realities.