My soothsayer friend BG told me last year that “deep learning is the next big thing”. I didn’t know what that meant. A few days ago, I attended the AIFrontiers conference in Santa Clara, California. Now I have a glimpse of what he meant :-)
In this context, by “intelligence”, I interpret it as “smart”. Yes, we have smart phones, smart TVs, and smart speakers. But imagine way more smarter software and devices… like self-driving cars!
Note that artificial Intelligence is about understanding intelligence. Machine Learning is a “brute force” data-driven approach to simulating intelligence., they are related but not the same thing. There are many areas that will lead to Artificial General Intelligence (AGI) which means “a software that can do any task”, as opposed to Machine Learning which creates software that can do specific tasks. This conference was about Machine Learning, and specifically Deep Learning.
To summarize the scope of the areas, Artificial Intelligence > Machine Learning > Deep Learning.
The mantra at this conference was that we will move from a software stack to an intelligence stack to solve future engineering challenges.
This was best explained by the legendary Jeff Dean in his keynote speech, talking about how many products at Google use deep learning:
Deep Learning at Google
Machine learning is one technique to achieve intelligence.
What is machine learning? My understanding is: it is about making computer programs whose behavior is learned from data instead of solely based on lines of code written by humans. Think spam filters – whenever we click on “Spam” or “Not Spam” buttons, the spam filtering system learns from this and the behavior changes over time to reflect that, without somebody explicitly writing code for every single email. On top of this idea, design the system to learn by itself, and it can learn and improve orders of magnitude faster.
What makes Machine Learning special? Because the system is now learning behaviors that is more accurate for the task and can handle more situations than the algorithms we humans could have imagined! Think converting sentences from one human language to another, self-driving cars, etc. Think of all the situations that such systems need to handle. We could have not written code to handle every situation.
Why now? Because machine learning requires: