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AKA: Why this burning money pit has failed to produce meaningful 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 take a serious look at the successes and failures of machine learning.
We are looking out from a high plateau.
We have achieved things in computer vision, natural language processing and speech recognition that were unimaginable until a few years ago. By all accounts, the accuracy of our AI systems exceeds the wildest imaginations of the past.
And yet, it is not enough.
We were wrong about the future. Every prediction about self-driving cars has been wrong. We’re not living in the future of autonomous cyborgs, and something else has come to mind.
Increase on automation.
Man wants control. It is one of our deepest, most innate desires. There is no world where we leave it. One of the biggest misconceptions of the AI community today is that people get comfortable with automation over time. As the reliability of automated solutions is proven, the microwave background comfort of society continues to grow.
this is wrong.
The history of technology is not the history of automation. This is a history of control and abstraction. We are tool-makers, so uncomfortable with experiences beyond our control that for thousands of years we have developed entire civilizations and myths around the movement of heaven. So it is with all technology.
And so is it with AI.
Since the early days, the problem with self-driving cars has been clear: It has no controls. When we look at the successful implementation of self-driving cars – which is now several years old – we see lane assist and parallel parking. We look at situations and use cases where the control panel between human and machine is clear. In all other situations, where the goal has been the pursuit of legendary level 5 autonomy, self-driving cars have failed miserably.
Technology is not a hindrance.
In 1925 we had a radio-controlled car navigating through a busy traffic jam on the streets of New York City without a driver behind the wheel. At the 1939 World’s Fair, Norman Geddes’s Futurama exhibit outlined a laudable smart highway system that would effectively use electromagnetic fiducials such as magnetic spikes embedded in the road to guide cars. He Autonomous cars were predicted to be the dominant form of transportation by the 1960s.
Of course, he was wrong too.
Not about technology though. No, the “smart highways” where they have been implemented have been quite successful and straightforward. Even without additional infrastructure, today we have self-driving cars capable of driving as safely as humans. Yet, despite over $80 billion flowing into the region from 2014 to 2017, we have no self-driving cars. For context, the US federal government committed $108 billion to public transportation over a 5-year period, the nation’s largest ever investment in public transportation.
Of course, the difference is that I can actually Ride a train.
Basically the problem is that no one has bothered to think of the new Control Pane that we’re trying to enable. The question was never about automating driving. This is a short-sighted, closed-minded way of thinking. The question is how to change the experience of transit.
Cars suck.
They are big, fast, smelly and basically the most inefficient form of transportation one can imagine. They’re the Most Expensive Thing After Your Home, But They Don’t to create Value. It is not a property that any Wants to themselves, it is an asset that people have to own. It is a regressive tax that subsidizes the highways that destroy the planet and ravages our cities. It’s an expensive, dangerous hunk of metal that goes unused in an expensive garage almost 100% of the time.
cars suck.
And making them self-driving doesn’t solve any of these problems. he is problem. When we spend too much time focusing on the semi-legendary state of full automation, we tend to neglect the impressive problems sitting before us. Uber was successful because you could call a car with the press of a button. Leases are successful regardless of cost, as it is a separate control panel 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. Seriously, by focusing on the rider experience, and designing a highly novel interface for teleguidance, I think they have a real shot at delivering something useful out of the self-driving car craze. However, I think it’s important to realize that their teleguidance system is no temporary bridge to get from here to there. The teleguidance system and its supporting architecture are arguably more defensive success for them than any algorithmic advantage. It offers a compelling vision with an end-to-end model of ownership. Of… know… a bus.
Don’t get distracted.
I haven’t used Zoox’s teleguidance system. I don’t know for sure if it’s more efficient than driving, but at least they’re pointing in the right direction. We need to stop thinking about 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 intermediate states. In fact, 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 provide better unit economics on the operation of the vehicle. All the closed track demos in the world and all the vanity metrics don’t make sense until we address that problem. We are dreaming about the end of a race when we haven’t even thought about how to make the first move.
and hindrance Not there machine learning.
This is user experience.
Slater Viktoroff is the founder and CTO of Indico Data.
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