Differentiating between AI, Machine Learning, Deep Learning, and NLP – the simple way!
Blog: Technology and Learning
Artificial intelligence, also popularly known as AI is gaining solid ground in the world of computers and technology – and contradicting stereotypical belief systems, it is not turning on us, yet. In fact, experts believe that for artificial intelligence to master human is a far off phenomenon that none of us alive today might experience in their lifetime.
However, it is being used regularly to solve tasks and what it basically allows is for computers to think on their own. Programmers code algorithms that help these software take decisions and make the call – it is pretty sci-fi novel-ish. But what are the other terms you can see on the heading?
One must remember that all of these terms are in fact parts of Artificial Intelligence itself, which in this case is the broader Venn diagram. Let’s take a look –
• Machine Learning
It is the process in which artificial intelligence is fed with statistical management and data calculation procedures instead having an explicit code for each type of data it handles. In this process, the AI starts teaching itself by recognizing patterns, following trends, and the likes. It is from this knowledge that machine learning can produce predictions without being monitored, and also bring new insights which will be useful for users.
• Deep Learning
In the broader Venn diagram of machine learning, deep learning forms a more specific part and it employs hierarchical interpretation of data. This creates a neural network in its process, much like our brain and it is not only able to use its algorithms for text, but also for images. Deep learning algorithms are used to differentiate between pictures of different kinds, like that cats and dogs, cars and pavements, etc.
• Natural Language Processing
Another process of machine learning, it equips the AI to detect and retrieve information from normal text format rather than code language, which is easier to understand for it. Natural language processing is a part of machine learning and the ideas of both often overlap in many cases.
However, NLP is probably the most subjective of them all and needs to learn to understand the mechanisms of rhetoric, which is never just black and white. It is this heavy dependence of NLP on linguistics that makes it extremely advanced – trying to understand everything from puns, sarcasm, hyperboles, and the likes. To understand all of this better, here’s a lucid example for you –
If you had to make a presentation on something that has a humongous amount of data involved, but you do not have the time to analyze all of it – something like a scientific research paper that has been developed by scientists from different parts of the world progressively for years, here’s how each of these AI processes could help you –
1. You would have to feed all the data to your AI machine and develop the necessary algorithms that it would use to analyze the data.
2. With the help of machine learning algorithms, it would take into account the patterns and different progressions made by the scientists, and analyze the volume of data statistically to mete out future trends and predictions.
3. The deep learning algorithms could be designed to help with the research images and identifying, analyzing those trends, and also with translation of language in said images, with its hierarchical data analysis processes.
4. Finally, the natural language processing would extract data from all the papers and journals that the scientists have written during their experimentation and present you with the extracted information.
With the help of these AI processes, one could basically reduce a process that would take years to a matter of days or even less. And in all of these cases, the AI feeds itself too, gaining knowledge in the process and only getting better. The performance of artificial intelligence is always ever changing and dynamic, giving it an edge over everything we have used yet.
All of these three methods are used as a mechanism to develop AI better so that data processing can be carried forward inexhaustibly. Humans have acquired and are still acquiring enormous amounts of information, and to retrieve the vitals from it is impossible for minds that need rest, and can be affected by emotions, sentiments, fatigue, and the likes. This is where we need AI and its different components like machine learning, natural language processing, and deep learning.