Last week, I finished watching the amazing TV series Westworld. As a Sci-Fi fan, I kindly tired of the repeated laser gun and space ships, this TV series is really an open-minded setup with a solid storyline of Artificial Intelligence (Hosts) and Human (Makers/Vistors).
Imagine that technology was so advanced, you could step into a wholly immersive virtual reality world that was very nearly as real as our own. A theme park, where you can live out your fantasies. No awkward headsets or VR to wear. No fuzzy graphics, keyboard, and mice. Because it’s all real—for the most part.
You can buy drinks at the local saloon, or pay for sex with one of the AI prostitutes at the bar. You can get in gun fights with outlaws, or save the town from murderous highwaymen. Or, if you really want to, you can go to a farm house, kill the elderly parents there and rape their daughter. This is where the stories are written, tinkered with, and the microcosm of Westworld manipulated for the entertainment of its guests.
AI, Machine Learning and Deep Learning are transforming numerous industries. But building a machine learning system requires that you make practical decisions:
Should you collect more training data?
Should you use end-to-end deep learning?
How do you deal with your training set not matching your test set?
and many more.
Historically, the only way to learn how to make these “strategy” decisions has been a multi-year apprenticeship in a graduate program or company. I am writing a book to help you quickly gain this skill so that you can become better at building AI systems.
The book will be around 100 pages and contain many easy-to-read 1-2 page chapters. If you would like to receive a draft of each chapter as it is finished, please sign up for the mailing list.
The biggest mistake that you can make is to believe that you are working for somebody else. Job security is gone. The driving force of a career must come from the individual. Remember: Jobs are owned by the company, you own your career!
The learning problem and the principles before building a model.
This blog is mainly based on the book and lecture notes by Professor Yaser S. Abu-Mostafa from Caltech on Learning from data , you could benefit a lot from the lecture and videos.
“In God we trust, and others bring data”.
If you show a picture to a three-year-old and ask if there is a tree in it, you will likely get the correct answer. But if you ask a thirty-year-old what the definition of a tree is, you will likely get an inconclusive answer.
We didn’t learn what a tree is by studying the mathematical definition of a tree. We learned it by looking at trees. In other words, we learn from ‘Data’.