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Why collision avoidance is harder for an AI-based system

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Re: Why collision avoidance is harder for an AI-based system

#121

Post by lolikoisuru »

randomdata wrote: post #89
ams6110 wrote: post #76

Earlier quoted context omitted.

The conventional ABS system is not AI in any sense. It is a few sensors and maybe some look up tables of what to do under various speed / brake pressure / wheel rotation situations

Does that not fit the definition of artificial intelligence? The system makes decisions – ones that would be otherwise done by a human – based on its perception of the world. That is what I have always understood AI to mean. To be sure, a conventional ABS system does not use machine learning, but machine learning is only a subset of AI.

By this definition almost all software would be an AI.

Making decisions or reacting to stimuli does not necessarily require any form of intelligence. You probably meant automation instead of "AI".

Re: Why collision avoidance is harder for an AI-based system

#122

Post by lolikoisuru »

wnoise wrote: post #83
ams6110 wrote: post #76

Earlier quoted context omitted.

The conventional ABS system is not AI in any sense. It is a few sensors and maybe some look up tables of what to do under various speed / brake pressure / wheel rotation situations

That doesn't make it not an AI -- it makes it a very simple, explicable AI. Unless you follow the definition that AI is whatever we don't know how to program yet.

What definition of AI do you use that would qualify ABS as an AI but wouldn't also include majority of all software ever written?

Re: Why collision avoidance is harder for an AI-based system

#123

Post by pwg »

gonvaled wrote: post #115
taneq wrote: post #98

Earlier quoted context omitted.

Well that's when you're grateful that along with your forward collision prevention, your car's ABS and electronic stability control systems are also still enabled. As for tailgating, this seems to be a problem with U.S. attitudes, not vehicle mechanics. Stop making it acceptable to tailgate! If you're close enough to the vehicle in front of you that any significant braking on its part will cause you to hit it you are…

Isn't that the case in the US? In the EU, you can be fined for driving too close to the car in front of you. I think that driving close to the car in front of you is the number 1 cause of accidents. Much more dangerous than driving fast.

Legally, in most (if not all) of the US you /can/ be cited for tailgating (the laws require a minimum following distance).

Reality is that the traffic cops seldom ever cite for tailgating in general. If one were to see a citation for such, it is likely after an accident where the officer can deduce that the cause was "following too closely" and so they then issue the citation.

Re: Why collision avoidance is harder for an AI-based system

#124

Post by beobab »

chopin wrote: post #110
amelius wrote: post #109

Earlier quoted context omitted.

What if it's a large piece of cardboard?

I know of an accident where someone overran a cardboard container used to package washing machines. He thought it was empty. Unfortunately it was not. Some truck lost part of its load. I think its safe to break always when encountering an ambiguous situation.

Another anecdotal tale: My dad once didn't bother slowing down for "cardboard tubes" that had fallen off a lorry. It turned out they were ceramic tubes, and caused quite a bit of damage.

Re: Why collision avoidance is harder for an AI-based system

#125

Post by YeGoblynQueenne »

hangonhn wrote: post #44

Hang on. That argument makes no sense. We've had subsumption architecture for a long time now( https://en.wikipedia.org/wiki/Subsumption_architecture ). Subsumption architecture puts some of the intelligence in the lower level systems. While the higher level controls can tell the lower level systems what it wants, it can't do things that the lower system determines is dangerous. So if a normal Mercedez has a collisio…

Not an expert on self-driving cars, but modern, statistical machine learning systems have to be trained sort of end-to-end. That is, if you want to machine-learn how to perform a task, you have to learn both the low-level and the high-level actions at once. And you can't add to the knowledge of the task, once the system is trained.

That's a limitation of the technology: statistical machine learning models are notoriously non-compositional. That means that basically you can't take one trained model and use it as a feature, to learn a new model for a different task.

Say, if you train a machine learning classifier C1 to recognise class Y1 from features F1,...,Fn, you can't then take the model of Y1 built by C1 and give it to a different classifier, C2, as a feature in a new feature vector Fn+1,...,Fn+k to learn a different class, Y2.

So you have to learn everything you need to know in one go- and that's terribly, awfully difficult.

There is a type of machine learning algorithm whose models are inherently compositional- Inductive Logic Programming (full disclosure: that's my research). These learn logic representations and from logic representations, so once you learn a model you can use it as a new feature and continue learning. Statistical machine learning folks have been trying to do that for a while now, without much success.

Re: Why collision avoidance is harder for an AI-based system

#126

Post by 8note »

randomdata wrote: post #89
ams6110 wrote: post #76

Earlier quoted context omitted.

The conventional ABS system is not AI in any sense. It is a few sensors and maybe some look up tables of what to do under various speed / brake pressure / wheel rotation situations

Does that not fit the definition of artificial intelligence? The system makes decisions – ones that would be otherwise done by a human – based on its perception of the world. That is what I have always understood AI to mean. To be sure, a conventional ABS system does not use machine learning, but machine learning is only a subset of AI.

You wouldn't consider it AI because it's good at it's job. We only consider things to be AI while they suck at making decisions.

Re: Why collision avoidance is harder for an AI-based system

#127

Post by 8note »

akvadrako wrote: post #119
dreamcompiler wrote: post #86

I am freaking sick of this notion that AI==ML. AI is a much bigger field than neural nets. AI can be programmed with rules, with logic, with symbols, with subsumption, and with a thousand other things that are both deterministic and don't require huge training sets. If you're making a living doing AI, you damn well should know this.

If you define AI like that, then basically every decision making program, i.e. with branches, is AI.

how else are you going to include pacman's ghosts as using AI?

Re: Why collision avoidance is harder for an AI-based system

#128

Post by 8note »

rasz wrote: post #60
martinrebane wrote: post #34

Earlier quoted context omitted.

Author here :) LIDAR itself is just a sensor, it does not process the data nor does it output a directly usable image like a camera more or less does. LIDAR in this case is a rotating laser and while it scans, the vehicle moves (imagine moving a paper when a copier scans it). All processing is done later, first to construct an image and then to understand and use it. Part of why I wrote the piece was to explain how t…

and yet you wrote "now one of them thinks it’s a dog" as if sensors did image classification.

all kinds of things can learn to think things are dogs. you don't need an image

Re: Why collision avoidance is harder for an AI-based system

#129

Post by 8note »

devrelm wrote: post #13
mulmen wrote: post #9

Earlier quoted context omitted.

Why? If it is not proven the driver was negligent why fire this individual? Presumably this individual has a lot of experience and domain knowledge in testing self driving cars so replacing them with someone else may not be an improvement. If they did make a mistake that got someone killed that could change things but I would hope we wait to find out if the driver was actually at fault.

They released inward-facing video of the safety driver shortly after the incident. The driver was staring at her phone in her lap with only occasional glances at the road every 5 seconds or so. I would call that pretty negligent.

to me that sounds like a standard Uber driver

Re: Why collision avoidance is harder for an AI-based system

#130

Post by randomdata »

skgoa wrote: post #104
randomdata wrote: post #89

Earlier quoted context omitted.

Does that not fit the definition of artificial intelligence? The system makes decisions – ones that would be otherwise done by a human – based on its perception of the world. That is what I have always understood AI to mean. To be sure, a conventional ABS system does not use machine learning, but machine learning is only a subset of AI.

The core of this debate is the matter of what intelligence actually is. We don't really know, but the most common definition I have heard/read is that intelligence is the ability to generate new solutions when presented with previously unseen input. An ABS would not qualify under this definition, because its output is predefined rigorously for all possible combinations of input values.

On the other hand, artificial intelligence is not actual intelligence. The definition of artificial intelligence is the ability to perform a task that would normally require human intelligence. An ABS system would fit into this definition as it is a task that normally would require human sensing the conditions to know how to apply the breaks.

Although I can see why some point out that AI is a moving target, representing only what still seems 'magical'. If you showed an ABS system to someone in the early 1900s, I truly believe they would see it as some kind of intelligence. Now that we have acclimated to the technology, we don't see it the same way.

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