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What you say has just enough technical detail to seem entirely plausible to someone who doesn't understand technology. It is also entirely wrong. The problem

by btilly 8y ago

What you say has just enough technical detail to seem entirely plausible to someone who doesn't understand technology. It is also entirely wrong. The problem that I am describing is not the Halting Problem. A theoretically perfect NN is also able to do a lot better than any person could. The ability to have binocular vision in multiple directions, and mix that with radar awareness of the local environment and electronic communication with other cars to be aware of obstacles that are out of sight. This technology can drive more quickly and safely than humans ever could. This sort of thing is admittedly some time off. But it is doable, and it is almost certain to happpen within our lifetimes.

salawat 8y ago
>It's also entirely wrong. It's not. You're asking for a Neural network capable of making generic inferences about the output of another neural network, in real time. That's Halting Problem. Will the data I'm feeding in cause the program I'm running to encounter an edge case (return/not return). You can't generify a neural network beyond it's training set. Cheekyness of the previous post aside, I stand by my statement. Comments that hail the almighty neural network as being anything more than an interesting exercise in feature extraction/input output mapping/information synthesis woefully underestimate the fundamental limitations of the technology. With current technology, short of everyone participating in your network of street cars (hint: many won't) and is a good agent (they won't be), you'll be at the mercy of the same forces that make "driving" such an interesting task today, just in different forms, with more processors involved,and when the NN rewrites happen to need beefier hardware, everybody's vehicle gets recalled. Throw in second and third order social effects (I.e. implementation of multi-spectrum panoptic surveillance networks for exploitation, possibility of remote exploitation of the driving software, decay of the actual skills required to drive safely, and the sudden stranglehold position that the self-driving vehicle manufacturer gets when their customer base gets large enough) leads me to the conclusion that there are a lot more problems to be solved before self-driving anything becomes a no-brainer turn key solution. Consider that now, with thermal cameras being ubiquitous, there is debate over whether your thermal signature is considered "public information" that can be collected and analyzed by law enforcement sans warrant. Next we'll get LIDAR on cars that will have a software application made to tap into the LIDAR feed which would then be capable of reading vibrations off glass with a bit of setup. Does everything you say in your home become public information too just because the cars we drive become mobile labs equipmentwise? That sound cool? Not to me. People need to think beyond first-order outcomes. As programmer or system developer or User alike.
- btilly 8y ago
  A neural network capable of making generic inferences about the output of another neural network, in real time, just needs to be able to run a simulation of that other network. While we do not currently have the resources to do it with a human brain, it is in principle quite doable. The critical difference between this and the Halting problem is that solving the Halting problem by running a simulation would require simulating in finite time what another system does in infinite time. This requires simulating in finite time what the other system also does in finite time. Which requires a better system, but not an impossibly better system. Moving on, you are over-estimating the requirements of the system that I describe. Today, with humans, a system like Waze can provide warnings about the road ahead which provides a useful assist to human drivers. This with a very small fraction of humans using the program, and even fewer actively registering hazards like "object on road". And yet, it is useful. Automated driving systems can participate in a similar system. Only they will be more likely to provide information, and their information can be more detailed. Such as what lane the foreign object is in. It doesn't take a lot of data about what is out of view ahead to improve driving by a lot. But humans are built to only pay attention to one thing at that speeds. Automated systems can integrate information from multiple. Which means that they can be better. Yes, what they are doing is simply feature extraction/input output mapping/information synthesis - but in principle nothing more is needed to drive better than is possible for humans. At the moment, humans are better. But it is not impossible that computers can become better. It is, in fact, inevitable.

by btilly 8y ago

salawat 8y ago

>It's also entirely wrong. It's not. You're asking for a Neural network capable of making generic inferences about the output of another neural network, in real time. That's Halting Problem. Will the data I'm feeding in cause the program I'm running to encounter an edge case (return/not return). You can't generify a neural network beyond it's training set. Cheekyness of the previous post aside, I stand by my statement. Comments that hail the almighty neural network as being anything more than an interesting exercise in feature extraction/input output mapping/information synthesis woefully underestimate the fundamental limitations of the technology. With current technology, short of everyone participating in your network of street cars (hint: many won't) and is a good agent (they won't be), you'll be at the mercy of the same forces that make "driving" such an interesting task today, just in different forms, with more processors involved,and when the NN rewrites happen to need beefier hardware, everybody's vehicle gets recalled. Throw in second and third order social effects (I.e. implementation of multi-spectrum panoptic surveillance networks for exploitation, possibility of remote exploitation of the driving software, decay of the actual skills required to drive safely, and the sudden stranglehold position that the self-driving vehicle manufacturer gets when their customer base gets large enough) leads me to the conclusion that there are a lot more problems to be solved before self-driving anything becomes a no-brainer turn key solution. Consider that now, with thermal cameras being ubiquitous, there is debate over whether your thermal signature is considered "public information" that can be collected and analyzed by law enforcement sans warrant. Next we'll get LIDAR on cars that will have a software application made to tap into the LIDAR feed which would then be capable of reading vibrations off glass with a bit of setup. Does everything you say in your home become public information too just because the cars we drive become mobile labs equipmentwise? That sound cool? Not to me. People need to think beyond first-order outcomes. As programmer or system developer or User alike.

btilly 8y ago
A neural network capable of making generic inferences about the output of another neural network, in real time, just needs to be able to run a simulation of that other network. While we do not currently have the resources to do it with a human brain, it is in principle quite doable. The critical difference between this and the Halting problem is that solving the Halting problem by running a simulation would require simulating in finite time what another system does in infinite time. This requires simulating in finite time what the other system also does in finite time. Which requires a better system, but not an impossibly better system. Moving on, you are over-estimating the requirements of the system that I describe. Today, with humans, a system like Waze can provide warnings about the road ahead which provides a useful assist to human drivers. This with a very small fraction of humans using the program, and even fewer actively registering hazards like "object on road". And yet, it is useful. Automated driving systems can participate in a similar system. Only they will be more likely to provide information, and their information can be more detailed. Such as what lane the foreign object is in. It doesn't take a lot of data about what is out of view ahead to improve driving by a lot. But humans are built to only pay attention to one thing at that speeds. Automated systems can integrate information from multiple. Which means that they can be better. Yes, what they are doing is simply feature extraction/input output mapping/information synthesis - but in principle nothing more is needed to drive better than is possible for humans. At the moment, humans are better. But it is not impossible that computers can become better. It is, in fact, inevitable.