Machine Screws and AI

 

The attraction of AI is that it is learns by experience. All learning requires feedback, whether its an animal, a human or a computer doing the learning, The learning-entity needs to explore its environment in order to try different behaviors, create experiences and then learn from them.

For computers to learn about computer-based environments is relatively easy. Based on a set of instructions, a computer can be trained to learn about code that it is executing or is being executed by another computer. The backbone of the internet uses something to similar to ensure data gets from point A to point B.

For humans to learn about human-environments, this is also easier. It is what we have been doing for tens of thousands of years.

For humans to learn about computer-based environments is hard. We need a system to translate from one domain into another. Then we need a separate system to interpret what we have translated. We call this computer programming, and because we designed the computer, we have a bounded-system. There is still a lot to understand, but it is finite and we know the edges of the system, since we created it.

It is much harder for computers to learn about human environments. The computer must translate real-world (human) environment data into its own environment, and then the computer needs to decode this information and interpret what it means. Because the computer didn’t design our world it doesn’t have the advantage that humans do when we learning about computers. It also doesn’t know if it is bounded-system or not. For all the computer knows, the human-world is infinite and unbounded – which it could well be.

In the short term, to make this learning feasible we use human input. Human’s help train computers to learn about the real-world environments. I think of the reasons that driver-less car technology is being focused on, is that the road system is a finite system (essentially its 2D) that is governed by a set of rules.

•Don’t drive into anything, except a parking spot.

•Be considerate to other drivers, e.g. take turns at 4-way stop signs.

•Be considerate to pedestrians and cyclists.

•etc

This combination of elements and rules makes it a perfect environment to train computers to learn to drive, not so much Artificial Intelligence but Human-Assisted Intelligence. Once we have trained a computer to decode the signals from this real-world environment and make sensible decisions with good outcomes, we can then apply this learning to different domains that have more variability in them, such as delivering mail and parcels.

This is very similar to the role of the machine screw in the industrial revolution. Once we had produced the first screw, we could then make a machine that could produce more accurate screws. The more accurate the screw, the more precise the machine, the smaller tolerance of components it could produce, the better the end-machine. Without the machine screw, there would have been no machine age.

This could open the doors to more advanced AI, it is some way off though because time required to train computers to learn about different domains.

Driver-less Cars: Will They Really Help Uber and Lyft?

I have been thinking about why Uber is so keen to develop driver-less technology.

In their current business model, they recruit drivers who have cars (most of the time). These drivers show up with their cars on the app when there are riders to pick up.  As the rider volume ebbs and flows throughout the day so does the driver volume. It’s near-perfect scale-able resource.

Once Uber adopts driver-less car technology, it will no longer need drivers. No more HR and employment headaches. However, it will still need cars.  To survive, Uber will become a fleet taxi operation, albeit a fleet of driver-less taxis. If Uber goes all-out driver-less a key question needs to be answered:

Just how many driver-less taxis will Uber need to have in its fleet?

Does Uber design around peak usage? And, as a result recreate the scarcity problem that it originally solved, a ride when you want it. Or does it design around total usage? In which case it will have costly excess capacity sitting idle.

At the moment Uber owns very few cars. If it buys 100,000 cars worldwide, the cost to the business over 5 years could be $300 million a year (Assuming it pays $15,000 per car fitted with driver-less technology) plus running expenses and insurance. These additional costs could be as $5000 a year or $500mn for a fleet this size, giving rise to annual costs of $800mn to operate this fleet. However, a fleet of 100,000 cars wouldn’t give Uber sufficient density across the ~500 cities they operate in worldwide to maintain their market position.

The only scenario that is viable is if Uber were to replace all of its driver-owned fleet with driver-less cars. In this scenario it would need to purchase 1 million driver-less vehicles (one for each of the drivers who reportedly work for Uber). Using the same assumptions as before, this would cost Uber $3bn a year plus annual running costs and insurance of $5bn.  All in all, $8bn a year. This is quite a drag on the business that ride fees will need to cover.

Using publicly available data, Uber’s gross bookings in 2016 were $20bn derived from 700mn rides. Each ride averaging at $28.73 – this might be slightly inflated because of UberEats revenue.  On this basis, the revenue from ~280mn rides each year would cover the ownership and running costs of the 1mn driver-less fleet, this represents 40% of gross booking revenues.

At this rate Uber could still be very profitable, but their business-model will shift from a gig-economy business to one with high capital costs baked-in, that is less insulated to changes in technology and culture, as well as being more exposed to threats from direct competitors.

In many ways, the strength of Uber, and similar companies, is their ability to recruit and manage a scale-able resource to meet demand and grow their businesses long term.  Although, as many gig economy businesses are realizing, it is hard to build a company using freelancers, so while the adoption of a driver-less fleet will change the nature of ride-hailing companies’ economics, it might be the best next step for Uber, et al. in order to maintain growth.