Demystifying & Explaining AI from different dimensions

When I wrote my blog post Becoming a Data Scientist — Curriculum via Metromap, little did I know that it will receive a rousing feedback. So a big THANK YOU first of all! Now here I’m after almost 4 years to write on a topic that is very close to my heart and yet again see a lot of confusing fluff floating around — Artificial Intelligence (A.I). I’m pretty sure many of you including me would say yes to the following,

  • Everyone in your LinkedIn connections list has AI in their title.
  • Are getting flooded with articles that talk about A.I transforming industries and doomsday articles that go hand in hand.
  • See articles that are not only confusing and misleading but also don’t tend to be comprehensive.
  • Hear from someone is working on “AI for X”, where X can be anywhere from treating cancer to ordering lunch.

Pardon me for overgeneralizing, but I also see of folks who very loosely use the word A.I and have absolutely no clue / idea about what they are talking about. If you try to avoid them and try to seek the answer for “What is AI?”, you are bound to get flooded with conflicting views and very obfuscated terms and definitions. Just because someone is using a deep learning library / package, that doesn’t mean their system is intelligent. There is more to it. So here is my yet another modest attempt to convey via a picture — “Demystifying AI”.

For a high resolution version if you want to poster print, please visit this link.

If I have to pick a great starting definition for AI, I would vote for John McCarthy’s. He probably gave the most profound and yet simple definition of A.I

”science and engineering of making intelligent machines, especially intelligent computer programs”

AI is a fascinating area and I personally feel it will not do justice to explain it without looking at it from multiple dimensions. I have provided my point of view on AI in the following dimensions,

  • Guard rails for AI
  • Core & essential building blocks
  • Types of data AI systems work on
  • Primary characteristics of an AI system
  • Different types of AI (yawn!)
  • Types of approaches to train / teach AI systems
  • Top Algorithms
  • Most common AI workloads / tasks
  • Common examples of AI systems at work
  • Dev Ops for AI — how are AI systems built?
  • Popular Platform, API’s, Libraries & Frameworks
  • Some of the absolute concepts and topics you need to take time in knowing
  • What’s next for AI?

My goal with this visual, is to provide you all with an ability to look at the big picture of AI and yet look at it from various dimensions. I have consciously not gone to great depth and detail, but stuck to a fairly high-level to convey the concepts clearly. Feel free to leave your comments and constructive feedback.

With Watson

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Swami Chandrasekaran

Written by

[Innovation @ KPMG. Ex-IBM Watson & IBM Distinguished Engineer]. [Ilaiyaraaja, Family Guy, Seinfeld]. [Music Mixer, Chronic Learner]

With Watson

Articles directly from the With Watson innovation & development team

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