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Aka: Why this burning money pit hasn’t produced meaningful results for decades.
The future is here and it doesn’t look like we expected. As we approach Alexnet’s 10th anniversary, we must critically examine the successes and failures of machine learning.
We look 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. In any case, the accuracy of our AI systems exceeds the wildest imaginations of times past.
And yet it is not enough.
We were wrong about the future. Every prediction about self-driving cars was wrong. We do not live in a future of autonomous cyborgs, and something else has come into focus.
Augmentation instead of automation.
People crave control. It’s one of our deepest, most instinctive desires. There is no world where we give it up. One of the biggest misconceptions of today’s AI community is that humans get used to automation over time. As the reliability of automated solutions is proven, society’s background microwave comfort is steadily increasing.
The history of technology is not the history of automation. It’s the story of control and abstraction. We are toolmakers so uncomfortable with experiences beyond our control that over thousands of years we have developed entire civilizations and myths centered around the movement of the heavens. It’s the same with all technology.
And so it is with AI.
The problem with self-driving cars has been obvious since the beginning: there is no control. If we look at the successful implementations of self-driving cars – which are now several years old – we see lane assist and parallel parking. We see situations and use cases where the control disc between man and machine is obvious. In all other situations where the pursuit of mythical Level 5 autonomy was the goal, 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 at the wheel. At the 1939 World’s Fair, Norman Geddes’ Futurama exhibit outlined a plausible intelligent highway system that effectively embedded magnetized spikes – like electromagnetic reference marks – into the road to guide cars. He predicted that autonomous cars would be the dominant mode of transportation in the 1960s.
Of course he was wrong too.
But not because of the technology. No, “smart highways” have been hugely successful and straightforward where implemented. Even without additional infrastructure, today we have self-driving cars that are more than capable of driving as safely as humans. Yet even with more than $80 billion poured into the field from 2014 to 2017, we don’t have self-driving cars. The $108 billion that the US federal government allocated to public transportation over 5 years was the largest investment the country has ever made in public transportation.
The difference, of course, is that I actually can journey Indent.
Basically, the problem is that no one has bothered to think about the new control windows we’re trying to enable. It was never about automating driving. This is short-sighted, narrow-minded thinking. The question is how the transit experience can be transformed.
They’re big, noisy, smelly and basically the most inefficient mode of transportation imaginable. They are the most expensive thing a person owns after their own home, but they don’t create Value. There is no benefit to anyone want owning it is an asset, man to have own. It’s a regressive tax that’s destroying the planet and subsidizing the freeways that are blighting our cities. It’s an expensive, dangerous piece of metal that sits unused in an expensive garage almost 100% of the time.
And making them self-propelled solves about none of those problems. That is the problem. If we spend too much time focusing on the quasi-mythical state of full automation, we ignore the serious problems that lie ahead. Uber was successful because you could call a car with the push of a button. Leasing is successful despite the cost because it is a different tax disc for the car. These are new transit experiences.
So where is the real opportunity?
I think companies like Zoox have an interesting and compelling thesis. By focusing on the driving experience and critically designing a whole new teleguidance interface, I think they have a real chance to squeeze something useful out of the self-driving car madness. I think it’s important to realize that their teleguidance system is not a 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. This, combined with a model that eliminates ownership, delivers a compelling vision. From… you know… a bus.
Don’t get distracted.
I have not used the Zoox teleguidance system. I’m not sure if it’s more efficient than driving a car, but at least they point you in the right direction. We need to stop thinking of self-driving cars as fully autonomous. With level 5 autonomy always right around the corner, you don’t have to think about all the messy in-between states. The truth is that these chaotic in-between states are the whole point.
This is the core of the problem with self-driving cars.
If you’re an investor looking for the first company that will “solve” self-driving cars, you’re barking up the wrong tree. The winner is the company that can actually deliver improved unit economy in operating a vehicle. Until we solve this problem, all the closed track demos and all the vanity metrics in the world mean nothing. We dream of finishing a race when we haven’t even figured out how to take the first step.
And the barrier is not machine learning.
It’s user experience.
Slater Victoroff is the founder and CTO of Indico Data.
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