We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Register today!
Aka: Why this burning money pit has failed to produce meaningful results for decades.
The future is here, and it looks nothing like we expected. As we approach the 10-year anniversary of Alexnet, we have to critically examine the successes and failures of machine learning.
We are looking out from a higher plateau.
We have achieved things in computer vision, natural language processing and speech recognition that would have been unthinkable just a few years ago. By all accounts, the accuracy of our AI systems exceeds the wildest imaginations of yesteryear.
And yet, it’s not enough.
We were wrong about the future. Every prediction about self-driving cars has been wrong. We are not living in a future of autonomous cyborgs, and something else has come into focus.
Augmentation over automation.
Humans crave control. It is one of our deepest, most instinctual desires. There is no world where we give it up. One of the biggest misunderstandings of the AI community today is that people become comfortable with automation over time. As the reliability of automated solutions is proven, the microwave background comfort of society steadily rises.
This is false.
The history of technology is not the history of automation. It is the history of control and abstraction. We are tool-builders, so uncomfortable with experiences beyond our control that for thousands of years we developed entire civilizations and mythos around the movement of the heavens. So it is with all technology.
And so it is with AI.
Since the early days, the problem with self-driving cars has been obvious: there’s no control. When we look at the successful implementations of self-driving cars — now several years old — we see lane assist and parallel parking. We see situations and use cases where the control pane between human and machine is obvious. In all other situations, where the goal has been the pursuit of mythical level 5 autonomy, self-driving cars have failed miserably.
Technology is not the bottleneck.
In 1925 we had a radio-controlled car navigating the streets of New York City through a busy traffic jam without a driver behind the wheel. At the 1939 World’s Fair, Norman Geddes’ Futurama exhibit outlined a plausible smart highway system that would effectively use magnetized spikes — like electromagnetic fiducials — embedded in the road to guide cars. He predicted that autonomous cars would be the dominant form of transportation by the 1960s.
Of course, he was wrong too.
Not about the technology though. No, “smart highways” have been tremendously successful and straightforward where they’ve been implemented. Even without additional infrastructure, we’ve got self-driving cars today that are more than capable of driving as safely as humans. Yet, even with more than $80 billion flowing into the field from 2014 to 2017, we have no self-driving cars. For reference, the $108 billion the U.S. federal government committed to public transit over a 5-year period was the largest investment the country has ever made in public transportation.
The difference of course, is that I can actually ride a train.
The problem, fundamentally, is that nobody has bothered to think about the new control panes that we’re trying to enable. The question was never about automating driving. That’s a myopic, closed-minded way of thinking. The question is about how to transform the transit experience.
They’re big, loud, smelly and basically the most inefficient form of transportation someone could imagine. They’re the most expensive thing a person owns after their home, but they don’t create value. It’s not an asset that anybody wants to own, it’s an asset that people have to own. It’s a regressive tax that destroys the planet and subsidizes the highways that blight our cities. It’s an expensive, dangerous hunk of metal that sits unused in an expensive garage nearly 100% of the time.
And making them self-driving solves approximately none of these problems. That’s the problem. When we spend too much time focusing on the quasi-mythical state of full automation, we ignore the impactful problems that sit in front of us. Uber was successful because you could call a car with the press of a button. Leases are successful, despite the cost, because it’s a different control pane for the car. These are new transit experiences.
So, where’s the actual opportunity?
I think that companies like Zoox have an interesting and compelling thesis. By focusing on the rider experience, and critically by designing a highly novel interface for teleguidance, I think they have a real shot of delivering something useful out of the self-driving car frenzy. I think it’s important to realize, though, that their teleguidance system is not some temporary bridge to get from here to there. The teleguidance system and its supporting architecture is arguably a more defensible breakthrough for them than any algorithmic advantage. That, combined with a model that eliminates ownership delivers a compelling vision. Of … ya know … a bus.
Don’t be distracted.
I haven’t used Zoox’s teleguidance system. I don’t know for certain that it is more efficient than driving, but at least they’re pointed in the right direction. We have to stop thinking about self-driving cars as fully autonomous. When level 5 autonomy is always right around the corner, there’s no need to think about all the messy intermediate states. The truth is that those messy intermediate states are the whole point.
This is the crux of the problem with self-driving cars.
If you’re an investor looking for the first company that’s going to “solve” self-driving cars, you’re barking up the wrong tree. The winner is the company that can actually deliver improved unit economics on the operation of a vehicle. Until we solve that problem, all of the closed track demos and all of the vanity metrics in the world mean nothing. We’re dreaming about the end of a race when we haven’t even figured out how to take the first step.
And the barrier isn’t machine learning.
It’s user experience.
Slater Victoroff is founder and CTO of Indico Data.