Centuries ago people didn’t know how to read and write. Only certain religious clerks could and society believed that this was enough. Maybe back then it was. But not today. I think it is similar to coding and understanding AI nowadays – you can go without it, but if you want to be part of all the exciting new discoveries, you’d better have some technical knowledge. It helps to make sense of this world. As literature, geography and maths do..  

You might have noticed the strong AI hype since the launch of ChatGPT at the end of 2022. Everyone talks and writes about Artificial Intelligence today: Is it going to replace or even outsmart😬 humans? How will it affect industries ? How will people and companies adapt? 

Millions , even billions of dollars are now invested in AI apps. In the last months all big tech companies released AI products – Google’s Bard, Microsoft’s Bing, Amazon’s Bedrock.

To keep out of the hysteria and distinguish between hype and informed concerns, learn the basics of AI, its strengths and limitations. That is the conviction of Andrew Ng – a machine learning pioneer and expert.

I always enjoy listening to Andrew and I would recommend watching his courses. Recently, I watched an introductory one to AI – “AI for Everyone” hosted on Coursera – and it was lovely! Honestly, the content felt a bit outdated given the latest breakthrough in the realm of AI (Unfortunately, Coursera has this limitation that it doesn’t show the date a course has been created.) Anyway, the course was still helpful and inspiring. I will summarise it for you in the article. 

The course explains:

  • What is AI
  • How to work on AI projects.
  • How to transform into an AI company

Part 1: What is AI

AI, Machine Learning, Deep Learning, Data Science – we hear these terms everywhere but what do they mean? Here’s a visual by Andrew Ng showing how they are interconnected: 

In simple words – machine learning is a subfield of AI, deep learning is a subfield of machine learning. When we talk about AI today, we talk about deep learning mostly. Data science is in part DL, ML, AI and something else. This is not a formal division of the fields, as formal definitions are missing as well. This is Andrew’s Ng interpretation and I share his one.