DELVING ‘DEEPER’ INTO AI: Key Differences between Artificial Intelligence, Machine Learning and Deep Learning

Blogs > DELVING ‘DEEPER’ INTO AI: Key Differences between Artificial Intelligence, Machine Learning and Deep Learning

The human race has been constantly challenging the notion of “impossible” for centuries. With unbelievable inventions and discoveries, we have emerged as a supreme species. We have carved an almost invincible niche for ourselves in the planet. And our successor-of-sorts is AI.

.AI is being used by individuals and groups across the globe. But there is an acute lack of awareness among many users about the precise difference between a machine performing a particular task mechanically and technology becoming independent to carry out these tasks. We’re back with a blog! Today, let’s look at AI at the macro as well as micro levels – with its two major offshoots: Machine Learning and Deep Learning.

1.Meet AI. Again. In All Its Glory.

While AI is, more often than not, an integral part of the modern world at various levels, it is often taken for granted in the sense that one rarely stops to ponder about the mechanisms involved in the working of AI. And when you open the door to the processes involved in AI, you unearth a Pandora’s box.

AI is a one-stop term for referring to the ability of machines to observe and understand certain rule-based tasks typically performed by humans, and then emulate these tasks in order to relieve humans of monotony, and pave the way for us to perform more intellectually stimulating tasks. Self-driving cars; video games; the names of your friends that your keyboard-prediction completes for you; your conversations with Siri and Alexa; and Amazon’s suggestions for you based on your previous online shopping spree – it’s time to thank AI for all of these supercool phenomena.

But…wait. Is a human secretly helping Siri follow my instructions every single time?
Have you ever wondered with amazement at how (at least most of the times) Siri is not only able to follow your instructions efficiently, but also converse with you spontaneously when you inquire about its personal life and well-being? And also come up with different, dynamic, even witty responses each time?
Obviously, the development of virtual assistants like Siri, Google Assistant and Alexa would be quite futile if humans were expected to function behind the scenes every single time, instructing these assistants to provide satisfactory answers to the users.

2.Enter, machine learning.

“The greatest sign of success for a teacher…is to be able to say, ‘The children are now working as if I did not exist,’” says Maria Montessori. And Machine Learning is technology’s teacher. ML uses the mechanisms of AI, but goes a step further. It ‘empowers’ technological equipment by making your machines not only intelligent, but also independent.

ML involves enabling our technological equipment to not only work efficiently on its own, but also to constantly adapt itself to any new information or experience over time. If your device is able to suggest those cool Versace leather-jackets for you based on your previous overcoat purchases on Amazon, you need to thank ML for it!

3.But hang on. Your machines are capable of EVEN ‘deeper’ stuff!

Try to think of the first time you, as a toddler, learnt to label a chair as ‘chair’. Now try and recollect the various other chairs you may have been accustomed to ever since. Were all these chairs identical to the one you had first labelled as a child? Most likely, not at all. But did you still find a broad similarity between all these chairs and label all of them under the umbrella-term of ‘chair’? That’s exactly what DL teaches machines to do.

While ML teaches technology to perform AI-based tasks independently, DL goes even beyond the ‘independence’ phase. It teaches your machines to also be smart enough to carry out abstract connections and form prototypical conceptualizations. It consists of certain algorithms that process and store every relevant input, based on which they come up with a statistical model. This becomes the prototypical output.

This process continues until a precise prototypical image of the output has been obtained. In our day-to-day lives, DL is able to predict and process extensive data online, and typically provide accurate solutions to the same. If ML enables online retail websites to suggest a certain brand of clothes based on your previous shopping records, DL takes online shopping to another level. If you have been repeatedly purchasing products whose target audience is women, DL not only provides you suggestions based on that classy pair of heels you bought the last time, but also some trending tops and accessories based on its prediction of your gender-identity. That’s the brilliance of DL.


And so, while AI is the broadest category that one can think of for machines being able to mimic humans in their mundane tasks, AI is not complete in itself, without its subsets of Machine Learning and Deep Learning. And together, AI, ML and DL constantly work in tandem in order to make our lives a little more efficient and productive, every single day.

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