111–120 of 145 posts
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.
Earlier quoted context omitted.
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.
Ok, what if the cardboard is flying over the road?
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…
> So if a normal Mercedez has a collision avoidance system that automatically brakes, there is no reason why an AI based system can't be built on top of that and the collision avoidance system automatically braking without intervention from the higher AI systems. In fact this 'bottom up' approach is exactly what all automoted driving efforts in the automotive industry follow. It's the biggest difference between how t…
Doing otherwise shows a worrying lack of systems engineering expertise.
Actually - the industry that really knows how to do this sort of thing is the defense industry.
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…
> So if a normal Mercedez has a collision avoidance system that automatically brakes, there is no reason why an AI based system can't be built on top of that and the collision avoidance system automatically braking without intervention from the higher AI systems. In fact this 'bottom up' approach is exactly what all automoted driving efforts in the automotive industry follow. It's the biggest difference between how t…
The car industry had 130 years to become the conservative, anti-agile, careful industry it is. They are in the business of costing human lives a lot longer.
The problem is "move fast and break things" works really extraordinarily well for software which is for entertainment and information purposes. Not for something serious.
Earlier quoted context omitted.
Unless it’s icy out, you have to swerve, or you’re being tailgated...but yes, stopping (or at least attempting to stop) is the best decision from a liability perspective.
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…
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.
Earlier quoted context omitted.
Yes. For new readers, suggestion for this article: start with the last sentence. Then skim the article re AI. Then respond. So we don't have to keep saying/hearing "Why not both?". The author's take is: both!
But let's make sure that the definition of AI doesn't encompass this because...?
...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 moves in response to a fixed set of opponent moves (assuming the opponent starts as white). My guess was that it wouldn't always respond in exactly the same way in case there's any MCTS like step somewhere in there blended with the Machine learning algorithm.
For a game like chess it would seem to make sense that the overall algorithm would still include a MCTS step (like AlphaGo did) but for an autonomous car it would seem crazy to any human to imagine that there would be any random search for a decision in a tree of possible interpretations of the input for example.
Does any one have any detailed knowledge about this? Would a non-deterministic algorithm ever be allowed in an autonomous car?
It should be obvious to anyone that you need to compose systems of different criticality to build a safe autonomous vehicle.
Of course the "AI" system needs to be complemented with a safety critical auto brake and other fail safes.
To a real degree, this is a downfall of machine learning. Every distribution has tails. If we learn purely from data, rather than from principle, we will necessarily make mistakes on the tails. For problems that can be effectively solved with 99% accuracy, this is fine, and we just deal with a few mistakes. With more data, our accuracy will improve anyhow.
If a datapoint costs a human life though, we can't afford to collect enough data. We must have a more sophisticated model of the world in order to operate on the tails without killing people.
I think that this might actually be a watershed moment for ML. Supervised learning is not adequate to this type of task. Either the computer does low level perception, and a human writes a high level algorithm to manage the risk, or datapoints have to contain a lot more information than just safe/unsafe. When you made a mistake as a child, your parents didn't just punish you, they explained what you did wrong and why, and a rule to follow to do better next time.
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.
My educated guess: The somewhat unusual shape of the combination of the woman and the bicycle, combined with the uneven lighting caused the vehicle to misclassify her as lightweight road debris. To a real degree, this is a downfall of machine learning. Every distribution has tails. If we learn purely from data, rather than from principle, we will necessarily make mistakes on the tails. For problems that can be effect…
Surely the classification has a confidence level, and a low confidence score should cause the vehicle to slow down if it's not confident in knowing what it's looking at? Also, the size of the "lightweight road debris" should have made the vehicle slow down slightly at least, because hitting a 6ft pile of paper wouldn't be great at even a low speed