How the Quest for the Ultimate Learning Machine Will Remake Our World
I wouldn’t do Professor Pedro Domingos justice by trying to describe his entire book in this blog post, but I did want to share one core thought as I reflect on his book.
Domingos’ core argument is that machine learning needs an unifying theorem, not unlike the Standard Model in physics or the Central Dogma in biology. He takes readers through a historical tour of artificial intelligence and machine learning and breaks down the five main schools of machine learning (below). But he argues that each has its limitations and the main goal for current researchers should be to discover/create “The Master Algorithm” that has the ability to learn any concept aka act as a general purpose learner.
As with any great book, it leads to more questions than answers. My main question, as applied to startups, is this:
What’s the speed at which machine learning is improving?
Why is this an important question?
For the past several decades, the category defining companies from Intel to to Apple to Google to Facebook have benefited from 2 core unifying theories of technology.
First, Moore’s Law created the underlying framework for the speed at which computing power increases (doubling every two years or so) that has directly enabled a generation of products. Products that were at first bulky and expensive, such as room-sized mainframes, were able to ride Moore’s Law and become smaller and cheap, leading to mass products like phones and smart watches.
Second, Metcalfe’s Law governed the value of a network of users (n(n − 1)/2) that has enabled a generation of Internet services to effectively serve the majority of the world’s Internet population. As more users join a network, their value grow exponentially while costs generally grow linearly. This incentivizes even more users and the flywheel is set in motion.
So now the question is…is there a third “Law” that governs the speed of improvement of machine learning.
In Lee Kai-Fu’s (李开复) commencement speech at Columbia, he gives hints at this.
In speaking about his investments in now publicly listed Meitu and two other AI investments, he notes that in all three cases, the AI technology underlying the startups went from essentially not useful to indispensable.
The three software companies I mentioned earlier, when they were first launched: often made people uglier, lost millions in bad loans, and thought I was some talk show celebrity. But given time and much more data, their self-learning made them dramatically better than people. Not only are they better, they don’t get tired nor emotional. They don’t go on strike, and they are infinitely scalable. With hardware, software, and networking costs coming down, all they cost is electricity.
In god we trust, all others bring data…
To put some data behind it, if we look at the ImageNet Challenge, AI image recognition technology has improved 10X from 2010 to 2016, catalyzed by the introduction of deep learning methods in 2012.
On the backs of this “Law” of machine learning improvement, we’ll see a Cambrian explosion of new products and services that fits Kai-Fu’s description of products that are at first flawed, but with time and data, become essential and, for all practical purposes, perfect.
Questions, not answers
The ImageNet Challenge and image recognition is just one application of AI so it doesn’t give us enough to say what the “Law” of machine learning improvement is. I can’t say AI is doubling in intelligence every 18 to 24 months or that AI gets exponentially better by a factor of n(n − 1)/2 with each data point.
But I do think a particular “Law” governing the rate at which AI is improving exists and I can’t wait for someone in the field to articulate (or solve) it.
Because understanding the speed at which artificial intelligence is getting more intelligent will allow us to understand the third major foundational wave, in addition to Moore’s and Metcalfe’s Law, that will bring us the dominant companies and the brilliant products of the Age of AI.
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