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Comment by andyxor | Hacker News Reader

I like Andrej from his PhD research days and awesome blog posts but this is a series of disasters in the making, that is until FTC steps in after more people die from “self-driving” accidents under interesting and unexpected circumstances.

The whole vision vs. LIDAR stuff is a distraction as long as Tesla “AI” doesn’t have common sense.

It literally doesn’t know what it’s doing, and the tail of edge cases to "fit" the models is infinitely long. ANNs are fundamentally backwards looking and cannot adapt to unforeseen or even slightly unusual combination of circumstances. It will go fine for n miles and will dramatically fail at mile n+1 where a new situation requires understanding of ones surroundings, and n is arbitrary number.

It would be more honest to show the cases where it missed, thankfully there is no lack of them in “FSD beta“ videos on YouTube.

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> ANNs are fundamentally backwards looking and cannot adapt to unforeseen or even slightly unusual combination of circumstances. It will go fine for n miles and will dramatically fail at mile n+1 where a new situation requires understanding of ones surroundings, and n is arbitrary number.

And humans also fail after some arbitrary number of m+1 miles. And in many cases far more catastrophically then AI.

>common sense

What a ridiculously useless term.

exactly, who needs common sense when you can sell the bright vision of self-driving future.

Try using common sense to figure out what he meant by that.

a better phrase might be “ability to do higher order reasoning to decide what to do in a novel situation”

There are two fundamental reasons, in principle, vision alone can do it: 1) Humans do it with vision alone 2) You can actually predict lidar’s output with vision alone. So many systems out there actually use lidar to generate more labeled data to make lidar unnecessary

besides vision, humans also have this thing called brain, and reasoning, and instincts, and being able to tell if the object in front of them is e.g. a roof of an overturned truck vs. empty space, etc, etc.

the key word is "etc" which expands into infinite tail, which no big data training on farms of GPUs would ever help with.

Humans and other animals have an ability to understand the scene and generalize from prior experiences to infinite set of new and unexpected circumstances, the "common sense" these dumb curve-fitting models are fundamentally lacking.

> humans also have this thing called brain, and reasoning, and instincts, and being able to tell if the object in front of them is e.g. a roof of an overturned truck vs. empty space, etc, etc.

Assuming the car is reasonably good at this, it has the advantage that it can see in every direction at once.

I don't think self-driving cars will ever be perfect, but I think they will quickly become less-lethal than the average human.

For the average case, perhaps. But we are not doing too badly for the average case either.

Let’s see how well does a Tesla react to exceptional cases where actual decision making is required, few data sample is available, etc. Statistics can be misleading with rare events.

I'd hope they have a "panic" mode that at least "safes" the car.

It's also worth pointing out that humans are notoriously bad at "exceptional cases" sometimes. People full-stop on the freeway for small animals to cross sometimes or panic when adjusting their eyes out of tunnels.

I do think it would be interesting to compare the two (computers vs humans) and look at the venn diagram of crash cases.

There are many things humans do with vision alone that machines can't. For instance see if a person standing 50 ft away is looking at you or at the house behind you.

The human vision system operates at a bitrate equivalent of well beyond 500 gigabits per second. Resorting to "humans can do it with vision alone" is only a sufficient argument if your computer vision system can match that.

> The human vision system operates at a bitrate equivalent of well beyond 500 gigabits per second

Do you have a source for this claim? Academic sources suggest numbers 4-5 orders of magnitude smaller: 6Mbps [1], 10Mbps [2].

[1] The Oxford Companion to the Mind (1987)

[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1564115/

Alright, I should clarify, as my first post was a bit sloppy.

We know the eye has highly nonuniform resolution, with the high resolution region moving around all the time to cover areas of interest. I agree this system has something like 10 Mbps actual bitrate.

But we don't know how to make such a system for computer vision. Our CV systems all have uniform resolution. And thus a CV system needs to match the peak resolution of the human vision system. That requires a ~500 megapixel resolution at 24 bits per pixel and ~50 frames per second.

Thanks for the clarification. I still don't fully buy the argument.

An AI driver doesn't necessarily need to match or exceed a human driver's sensors in order to drive as well or better. The human eye does indeed have very impressive resolution and tracking, but the ability to attend to a certain area of interest also means that you miss things in other areas.

Instead of using the human as the reference point, we can turn the question around and ask instead: given a video feed from a current generation Tesla, would a human be able to drive safely? I would suggest yes, though it would take some adjustment from existing driving habits. If a human could do it with Tesla's current sensors then an AI almost certainly can eventually do better with the same sensors.

I actually think the bigger issue with Tesla's approach (and I say this as a generally happy Tesla owner... though the timing is terrible since it had to be towed to the service center today) is their insistence on minimal maps/local data for driving. I understand why they want to do that for scaling purposes, but there are lots of intersections that are confusing for your average human the first time they come to it. I expect figuring out the traffic pattern in a confusing intersection is the harder AI problem.

In principle. But we do have 3 billion neurons for that with ridiculous number of interconnections - even if an order of magnitude less connection would be enough, we are still far away from that amount of computations. And that is actually the easy part of the problem - mapping an image to a depth map. The hard part is interpreting it for which we have a complete inner universe built up, with context-dependent logic. FSD is simply a really hard problem that we are not even getting close to. We are at a fancy robot vacuum level.

With what kind of reliability can LIDAR data be predicted with vision nowadays?

> Humans do it with vision alone

And despite humans having legs and birds having articulated wings, ground vehicles use wheels and airplanes use fixed wings with propellers or jets. Why?

Just because biology solves a problem a certain way doesn't make that same approach optimal for machines. Typically with machines you need to work around other systemic deficiencies.

Humans may have only two cameras, but those cameras are connected to a vision, spatial understanding, and reasoning system that is light years beyond what current day AI is capable of. But the good news is that we can make up for AI deficiencies by giving vehicles super-human sensing ability which makes those problems easier.

Tesla's approach seems to be one in which they think the sensors are the expensive/hard part and solving general AI is the cheap/easy part. I think plenty of people would not agree.

Human higher-order reasoning is crucial to our ability to use vision to interpret the world. We have a model of the world and we use our vision to adjust it. We don't just draw bounding boxes, we label objects as cars or trees or shadows, and use our understanding of basic physics to predict their possible movement. We further label other data as humans or driving cars or animals, and use our understanding of other agents to predict their plausible movements.

Vision is only used to periodically adjust this model of the world - most of the time, we are living in our heads. This is so true that we normally don't even notice that we entirely lose our vision every time we blink or move our eyes, and we don't notice the extreme blurriness that we have in areas of our 'vision' where we are not currently focusing our eyes.

It's very educational to visit some areas built with non - common sense objects to understand just how limited human vision is if you remove our understanding of the world from it.

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