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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

#131

Post by lolikoisuru »

randomdata wrote: post #130
skgoa wrote: post #104

Earlier quoted context omitted.

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, represent…

>The definition of artificial intelligence is the ability to perform a task that would normally require human intelligence.

Do you have any specific sources that define it as that?

>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.

Responding to stimuli does not require any intelligence, let alone human intelligence.

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

#132

Post by cesarb »

magoon wrote: post #75

Unpopular question but here goes: What if some crashes are unavoidable? e.g. somebody darts out in front of a mobile vehicle. We accept that trains are not at fault for striking “trespassers” on their railways. Also, when we all drive cars with collision avoidance systems, who gets sued by whom? If my car e-brakes for no reason and I get rear-ended, is the guy who hits me still at fault like usual? I believe computer…

> What if some crashes are unavoidable? e.g. somebody darts out in front of a mobile vehicle.

It should brake as much as possible. Even if the collision is unavoidable, reducing the kinetic energy available for the collision is still a good idea.

> We accept that trains are not at fault for striking “trespassers” on their railways.

When a train "detects" an "obstacle" (the train driver sees the "trespasser"), it goes into emergency braking.

> Also, when we all drive cars with collision avoidance systems, who gets sued by whom? If my car e-brakes for no reason and I get rear-ended, is the guy who hits me still at fault like usual?

When we all drive cars with enough automation, the automation should keep enough distance that the following car can brake without colliding.

> but it’s interesting to see it implemented in cars as emergency help versus in modern commercial airliners (where autopilot and landing control systems are ubiquitous) when it is only relied on for the most routine and straight-shot ability.

Airplanes have the unfortunate property that they can't simply stop in an emergency; stopping would be an even bigger emergency. Cars (and trains) can simply stop, they won't fall from the sky in that case.

But even then, airplanes do have emergency help from their automation: the TCAS has a similar purpose to a car's automated emergency braking, that is, to prevent a collision.

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

#133

Post by Izkata »

glaberficken wrote: post #116
TTPrograms wrote: post #73

Earlier quoted context omitted.

But let's make sure that the definition of AI doesn't encompass this because...?

I guess author makes the distinction from deterministic Vs non-deterministic algorithms. As a layman in these matters I still don't fully understand how some people classify machine learning algorithms as non-deterministic... ...i started wondering about this when I was reading a few articles about the AlphaZero algorithm that learned to play chess entirely from self play and wondered if it would always play the same…

> As a layman in these matters I still don't fully understand how some people classify machine learning algorithms as non-deterministic...

Some algorithms start with random numbers for the model and converge towards a better model. After the model is generated the input->output will be deterministic, but since the model generation is non-deterministic the algorithm overall is considered non-deterministic.

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

#134

Post by randomdata »

lolikoisuru wrote: post #131
randomdata wrote: post #130

Earlier quoted context omitted.

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, represent…

>The definition of artificial intelligence is the ability to perform a task that would normally require human intelligence. Do you have any specific sources that define it as that? >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. Responding to stimuli does not require any intelligence, let alone human intelligence.

> Do you have any specific sources that define it as that?

Several definitions as provided by a Google Search.

> Responding to stimuli does not require any intelligence, let alone human intelligence.

Which is why we call it artificial intelligence instead of intelligence. If these systems were actually intelligent, there would be no reason to add the artificial moniker. We specifically call the types of systems artificial intelligence on recognition that it is not actually what we consider real intelligence.

I agree, there is nothing intelligent about an ABS system, and nobody is labeling it as intelligent.

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

#135

Post by jdmichal »

randomdata wrote: post #134
lolikoisuru wrote: post #131

Earlier quoted context omitted.

>The definition of artificial intelligence is the ability to perform a task that would normally require human intelligence. Do you have any specific sources that define it as that? >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. Responding to stimuli does not require any intelligence, let alone human intelligence.

> Do you have any specific sources that define it as that? Several definitions as provided by a Google Search. > Responding to stimuli does not require any intelligence, let alone human intelligence. Which is why we call it artificial intelligence instead of intelligence. If these systems were actually intelligent, there would be no reason to add the artificial moniker. We specifically call the types of systems artif…

"Artificial" is opposed to "natural", not "genuine". It's a synonym of "machine intelligence". It's a comment on the fact that the intelligence has been artificially created in a machine. It is not in any way about the current capabilities of artificial intelligence.

https://en.wikipedia.org/wiki/Artificial_intelligence

> Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals.

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

#136

Post by wodenokoto »

YeGoblynQueenne wrote: post #125
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 notorio…

Actually they are extremely compassable. A feed forward neural network is often called a multiple layer perceptron, because it is multiple perceptron classifiers that feed into each other.

If you want to build a domain specific image classifier, you take the bottom layers of a generic classifier and train it on your otherwise too small dataset.

You can absolutely add predictions from one classifier as a feature to the next. It is quite common to have a pipeline where you augment text with Part of Speech, Named Entity Recognition, etc, before performing the final task.

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

#137

Post by lolikoisuru »

randomdata wrote: post #134
lolikoisuru wrote: post #131

Earlier quoted context omitted.

>The definition of artificial intelligence is the ability to perform a task that would normally require human intelligence. Do you have any specific sources that define it as that? >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. Responding to stimuli does not require any intelligence, let alone human intelligence.

> Do you have any specific sources that define it as that? Several definitions as provided by a Google Search. > Responding to stimuli does not require any intelligence, let alone human intelligence. Which is why we call it artificial intelligence instead of intelligence. If these systems were actually intelligent, there would be no reason to add the artificial moniker. We specifically call the types of systems artif…

>I agree, there is nothing intelligent about an ABS system, and nobody is labeling it as intelligent.

Artificial intelligence is intelligence, just made artificially instead of organically. You clearly have no understanding of the term so you should probably shut up.

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

#138

Post by akvadrako »

8note wrote: post #127
akvadrako wrote: post #119

Earlier quoted context omitted.

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?

You shouldn't because those ghosts are stupid.

If intelligence just means "can make decisions based on inputs" then everything is intelligent. That's a useless definition.

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

#139

Post by wnoise »

lolikoisuru wrote: post #122
wnoise wrote: post #83

Earlier quoted context omitted.

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?

There is no sharp line, just as there is no sharp line for "intelligence". Lot of software can indeed be classified as (very weak) AI, because it makes decisions in service of a goal.

Because there is no sharp line, where to draw the line depends on context, and the point being made. I have found that "AI" is a good term to apply when it is more useful to take the intentional stance[+] to explain an artificial system's behavior than to trace the actual low-level implementation.

At the very lowest end I could include some closed-loop control as AI (a simple proportional controller wouldn't count, a PID might). The more complex the model it has of the environment, and it's own effect on the environment, the more useful it is to call it an AI.

A slightly more complex example are computer games, whose simple AIs are generally readily explainable from their code. In an RTS game with good AI, it's not useful to look at how computer opponents (or my own units) do pathing, only where they're trying to go, and the obstacles they'll encounter.

ABS is lower than I would normally consider AI to be a useful descriptor because of how little information processing occurs (basically just wheel speed and brake pedal trajectory). In this context it's worth not excluding precisely to emphasize that AI is not just machine learning, but just about anything that autonomously makes intelligent decisions in response to changing environments.

[+] https://en.wikipedia.org/wiki/Intentional_stance

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

#140

Post by taneq »

pwg wrote: post #123
gonvaled wrote: post #115

Earlier quoted context omitted.

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.

In Australia, if you are the rear vehicle in a rear-end accident you are automatically at fault in almost all circumstances. The only exceptions I know of are if the front car pulled out immediately in front of the rear car, or if the front car was reversing.
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