Artificial Intelligence (AI) and Machine Learning (ML) for Beginners – Part 1 of 3!

AI is the new electricity. Just as electricity transformed everything as people knew then, AI is going to transform everything as we know now – proclaimed Andrew Ng, thought leader in AI and a professor at Stanford University.

And yes, AI is very much here. Almost everyone, including my retired father, is using it without even realizing it. AI has already been silently infused into the products and services that we all use on a regular basis. Do you use email? Well, you are already using AI via the spam filtering feature of your email provider. Do you buy on Amazon? The recommendations regarding what products you should buy are powered by AI. You like Amazon’s quick delivery? Well, the Kiva robots in Amazon’s warehouses enable that quick delivery. What about entertainment? Netflix, Spotify? The movie and song recommendations are powered by AI. Are you on Facebook? The face recognition feature is enabled via AI. Alexa, Siri? Again, it is all powered by AI. As you can see, AI is already integral to the many services that you use on a daily basis and now that usage is going to further explode to almost all industries, products and services.

I want to provide a simple mental model, a framework for beginners, by which they could start understanding the various technologies within AI. Differentiating between related technologies and use cases instead of using them interchangeably, is key to understanding AI in the most efficient way.

So, let’s dive into it – what is a very simplistic definition of AI? AI is a set of technologies that enable machines to perceive, think and act, just like humans do. Click To Tweet

As you will notice from the above figure – Computer Vision and NLP (natural language processing) are the technologies that enable computer/machines to perceive things whereas Machine learning enables computers to learn, think, reason and Autonomous Vehicle, Robotics, Chatbots and Virtual assistant are action enabling technologies. Over the series of articles, let’s cover Machine Learning since people use the term interchangeably with AI.

What is Machine learning (ML)?
Machine learning is a subset of AI. It is the science of getting computers to learn from the data presented to them, from the experiences, without being explicitly programmed. ML is one approach of AI, an approach that is based on statistics; hence it is also called the statistical approach or probabilistic approach.

So, If ML is one approach, what is the other approach? The other approach is the deterministic approach or rules-based approach. In Deterministic or rules-based approach, machines are programmed directly by the experts with a company’s best practices in mind. The early part of AI history was all about deterministic approach. The rise of expert systems in the early 1980s was a direct result of this deterministic approach being at the forefront of AI development. However, the current explosion of AI is not due to advances in ML, but primarily Deep Learning.

So, then, what is Deep Learning?
Deep learning (DL) is a type of ML that consists of inter-connected layers of ‘neurons’ (software-based calculators) that form a ‘neural network’. The network ingests more data, processes the data through multiple layers, learns complex features of the data, uses what it has learned to make determinations about new data and provide more accurate results. Here is the timeline and the hierarchy of AI/ML/DL.

Source: Data Science Central

With above frameworks/mental models as a starting point, let us dig deeper into ML in the next two articles of this series.

By – VS Joshi.
(Head of Product Marketing | Digitate)

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