A Great Introduction to ML and Its Roots
I have to admit the first I heard the title of Pedro Domingos‘ recent book, The Master Algorithm, I was off-put, similar to the way I react negatively to the singularity made famous by Ray Kurzweil. I don’t tend to buy into single answers or theories of everything.
But as a recent talk by Domingos at Google shows, he has much more insight to share about the roots and “tribes” associated with machine learning. If you are new to ML and want to learn more about the big picture underlying its main approaches and tenets, the hour spent watching this video will prove valuable:
The strength of the talk is to describe what Domingos calls the five “tribes” underlying machine learning and the lead researchers, premises and approaches underlying each:
- Symbolists — based in logic, this approach attempts to model the composition of knowledge by inverting the deductive process
- Connectionists — also known as neural networks or deep learning, this mindset is grounded most in trying to mimic how the brain actually works
- Evolutionists — the biological evolution of life of mixing genes through reproduction as altered by mutations and cross-overs guides these genetic algorithms
- Bayesians — since the world is uncertain, likely outcomes are guided by statistical probabilities, which also change as new evidence is constantly brought to bear
- Anagolizers — this tribe attempts to reason by analogy by looking for similarities to examples or closely related factors.
You can also see the slides here to Domingos’ talk.
As Domingos emphasizes, each of these approaches has its applications, strengths and weaknesses. He posits there are shared aspects and generalities underlying all of these methods that can help point the way to perhaps more universal approaches, the master algorithm.
I have argued elsewhere about the importance of knowledge bases to recent AI breakthroughs more than algorithms, but ultimately, of course, specific calculation methods need to underpin any learning approach. Though I’m not convinced there is a “master” algorithm, there is also great value in understanding the premises and mindsets behind these main approaches to machine learning.
