Though all the three technologies sound different, they are very much closely interconnected. We had already mentioned on one of our previous (link) articles that if you had to draw a Venn diagram on the three technologies, Artificial Intelligence (AI) would form the outermost generic circle that encompasses the other two self-encompassing technologies. The differences are subtle and often overlapping and this article is all about bringing the distinctions to light.
On a broader sense, you can define artificial intelligence as the process of giving the ability to machines and computer systems to think and behave like we humans do. It adds further as imbibing the abilities like speech recognition, visual perception, decision making, emotion comprehension and more into systems.
In simple words, you can say that machine learning is one of the preliminary means to attain the goal of artificially imbibing the machines with the above stated abilities by giving them the ability to learn autonomously. It is the development of computer algorithms and applications that allow the systems to access huge chunks of data and learn from them to complement specific visions.
It is again a subset of machine learning that gives the systems the ability to learn from data sets that are unlabeled or unstructured. One of the complexities that make deep learning stand out is the fact that the networks in this technology are unsupervised, meaning they are constantly learning without human intervention. The Facebook AI wing getting shut down was because of one such episode of unsupervised learning gone wrong.
One of the crucial concepts when it comes to the basis on which (in laymen terms) artificial intelligence functions is called Natural Language Processing (NLP). This means that with NLP, the computing systems and the machine not just learn from data sets but apply them like how humans would do. Your Siri or Cortana learning that Led Zeppelin is not as exciting as it sounds but when it plays the Immigrant song when you ask the virtual assistant to play a song for you is when Natural Language Processing comes into action.
Neural Networks are the building blocks of networks that power both machine learning and deep learning. One being the subset of the other makes the requirement of neural networks inevitable! Simply put, neural networks are the neurons of a computer system that transmit information from one connected device, software application and the likes. As far as machine learning is concerned, neural networks are capable of browsing through a series of text and understand to an extent the writer’s intentions. Or, it could listen to a soundtrack and zero in on the mood of a song. It can also detect your face or fingerprint and unlock your phone and know your sleeping pattern and mute notifications.
When it comes to deep learning, neural networks are still the powering sources of it. However, deep learning also works outside the neural networks and works with NLP to help AI become AI. The networks in deep learning are unsupervised and when done right, they can replace a worker who works on redundant tasks every single day.
If you still find this overwhelming and feel a visual aid might help, this video on the differences between the three technologies should help you out.
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