- Fitness functions (see the quote below)
- On Amazon’s use of robots for business
- About Cognitively Augmented workers
- The law of conservation of attractive profits
- On Intelligence (human and artificial)
- How to pair humans with machines
- Step changes and their impact
- On anticipating and countering the worst fears
- The Robustness Principle
- Agreement Protocols
- On Platforms and Eco-systems
- On doing meaningful work
I will pick a few of the topics (1-5) and take the liberty of quoting from the transcript. My goal is to kindle your interest enough to read the article and then the book.
On Fitness functions and how they focus companies on delivering value with technology (including AI).
If you look at Google; their fitness function on both the search and the advertising side is relevance. You look at Facebook; loosely it could be described as engagement.
On Amazon’s use of robots in their business:
an analysis of Amazon. In the same 3 years which they added 45,000 robots to their factories, they’ve added hundreds of thousands of human workers.
About Cognitively Augmented workers.
Then, Sidecar and Lyft figured out the other piece of the equation, because Uber was just black cars. They figured out that in order to have enough drivers to really fill out the marketplace, other than a small segment of well-off people, you’d get ordinary people to supply their cars. And you could do that because those drivers are cognitively augmented. It used to be that you had to be a professional driver, because [when] somebody says, “I want to go to such and such an address,” you’d need to really know the city. You [would] need to have a lot of experience to know the best routes. Well, guess what, with these apps [like] Google Maps and Waze, anybody can do it. So I started looking at that and [saw that] we have a marketplace of small businesses managed by algorithms to help them match up with customers.
Law of conservation of attractive profits:
Clay Christensen, back in 2004 talked about “the law of conservation of attractive profits,” and that’s what helped me get from open source to web 2.0—[is] when one thing becomes a commodity, something else becomes valuable. So if self-driving cars commoditize driving, you have to ask yourself, what becomes valuable. And I think it’s going to be new kinds of augmentation for humans, new kinds of services that you’ll put on top of driving.
On Intelligence (artificial):
And so what would be something that we have today that would qualify or come close to qualifying as that in your mind?
You mean, in terms of machines?
And why do you think that?
I’m with Gary Marcus on this, you know. He kind of talked about how the frontier of AI right now is deep learning, and it’s great, but you still have to train it by showing it a gazillion examples of something, and after you show it a gazillion examples, it can figure stuff out. That’s great, but it can’t figure that out without being exposed to those examples. So, we’re a long way from kind of just flicking the switch, having a machine take in its experience of the world, and basically come to conclusions about it.
Tim ends with this appeal and an inspiring message.
“Hey, we have a lot of things to worry about, we have enormous new powers, let’s put them to work, in the right, way, tackling the hard problems.”
I have been a big fan of Tim’s “Work on stuff that matters” and quoted him. It framed some of my decisions on what to spend time on.
I have been following Tim for a couple of decades. More about that in a later post. I recently started listening to Byron’s One Minute AI podcast and the GigaOm AI podcast.