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Aka: Why hasn’t this burning money pit produced significant results for decades?
The future is here, and it looks nothing like what we expected. As we approach the 10th anniversary of Alexnet, we need to critically examine the successes and failures of machine learning.
We are looking 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 precision of our AI systems exceeds the wildest imaginations of yesteryear.
And yet it is not enough.
We are wrong about the future. All the predictions about self-driving cars have been wrong. We are not living in a future of autonomous cyborgs, and something else has become apparent.
Increase over automation.
Humans crave control. It is one of our deepest and most instinctive desires. There is no world in which we leave it. One of the biggest misunderstandings in the AI community today is that people get comfortable with automation over time. As the reliability of automated solutions proves itself, society’s microwave background comfort is constantly increasing.
This is false
The history of technology is not the history of automation. It is the story of control and abstraction. We are tool builders, so uncomfortable with experiences beyond our control that for thousands of years we have developed entire civilizations and myths around the movement of the heavens. So it is with all technology.
And so it is with AI.
From the earliest days, the problem with self-driving cars has been obvious: there is no control. When we look at successful autonomous car implementations, now several years old, we see lane assist and parallel parking. We see situations and use cases where the control panel between humans and machines is obvious. In all other situations, where the goal has been the quest for legendary 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 heavy traffic jam without a driver behind the wheel. At the 1939 World’s Fair, Norman Geddes’ Futurama exhibit outlined a plausible intelligent highway system that would effectively use magnetized spikes, such as electromagnetic fiducials, embedded in the road to guide cars. The predicted that self-driving cars would be the dominant form of transportation in the 1960s.
Of course, he was wrong too.
However, it is not about the technology. No, “smart highways” have been tremendously successful and easy where they have been implemented. Even without additional infrastructure, today we have self-driving cars that are more than capable of driving just as safely as humans. Yet even with more than $80 billion flowing into the field between 2014 and 2017, we don’t have driverless cars. For reference, the $108 billion the US federal government committed to public transportation 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 mount a train.
The problem, fundamentally, is that nobody has bothered to think about the new control panels that we are trying to enable. The question was never about driving automation. That is a short-sighted and narrow-minded way of thinking. The question is how to transform the transit experience.
They’re big, noisy, smelly, and basically the most inefficient form of transportation anyone can imagine. They are the most expensive thing a person owns after their house, but not create worth. It is not an asset that anyone wants possess, is an asset that people have own It is a regressive tax that destroys the planet and subsidizes the roads that devastate our cities. It’s an expensive and dangerous piece of metal that sits unused in an expensive garage almost 100% of the time.
And making them autonomous solves almost none of these problems. That is the problem. When we spend too much time focusing on the near-mythical state of full automation, we ignore the shocking problems in front of us. Uber was successful because he could hail a car with the push of a button. Leases are successful, despite the cost, because it’s a different dashboard 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 driver experience and critically designing a very novel interface for teleguidance, I think they have a real chance to deliver something useful out of the self-driving car frenzy. However, I think it’s important to realize that your teleguidance system is not a temporary bridge to get from here to there. Arguably, the remote guidance system and its supporting architecture is a more defensible advance for them than any algorithmic advantage. That, combined with a model that eliminates ownership, offers a compelling vision. From… you know… a bus.
Do not get distracted.
I have not used the Zoox remote guidance system. I’m not sure it’s more efficient than driving, but at least they point in the right direction. We have to stop thinking of self-driving cars as fully autonomous. When level 5 autonomy is always just around the corner, there’s no need to think about all the messy in-between states. The truth is that those messy intermediate states are the whole point.
This is the crux of the self-driving car issue.
If you’re an investor looking for the first company to “fix” driverless cars, you’re barking up the wrong tree. The winner is the company that can actually offer an improved economic unit in the operation of a vehicle. Until we solve that problem, all the closed track demos and all the vanity metrics in the world mean nothing. We are dreaming of the end of a career when we have not yet discovered how to take the first step.
and the barrier It is not machine learning.
It is the user experience.
Slater Victoroff is founder and CTO of Indico Data.
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