AI is the new electricity, says Andrew Ng. He continues to say that the only competitive advantage of companies that are big enough (1B market cap), is their use of AI.
People listen. The prevalent specialization today is AI and Machine Learning.
These facts are true but people’s understanding of them is incorrect. Think of this: electricity powers your home, etc. But who brings it to you? — The electrician!! You usually can only change the bulb, but for putting in a socket you invite a certified electrician.
What does this mean to us? That we need to change the focus. You can study AI and Machine Learning, but to make something practical out of it, you need to become an AI electrician, or a practicing engineer.
For each “data scientist” there are usually at least three “data engineers.” If AI goes into the depth of knowledge, then data engineering is the breadth. And it is not easy or obvious. Soon, we will have enough quickly baked data scientists but they will be fundamentally lacking in the knowledge of computer science and data engineering.
Our course on Practical AI has collected hundreds of students. My AI students frequently ask the same question, “Now what do I do with all this mathematical knowledge?” And the answer is that now you go back to the computer science and data engineering drawing board. In the next installment, I will show you what you really need to study and practice. It will not be easy but it will be right.
