The second problem is that neural nets can't remember anything. Even if at one instant, the net sees the car, it must remember the pattern for the car at subsequent steps. This requires crazy "resonant circuits" which store the computational data. The resonant circuits means that the neurons excite other neurons and so on, in a loop, which stays active when you turn off the stimulus.
The loop idea leads to a serious problem--- neural nets with loops, as they are usually made, are unstable either to runaway activity, or to shutting off. If you activate neurons, and these activate others, the stable state is that all the neurons are either turned on or turned off. In order to get over this, you need a global control on neuron activity, which restricts the number which are turned on, and this global control is difficult to imagine.
In order to get around this, artificial neural nets just forbid loops. They make layers, where each neuron tells the next layer what to do. These layers are also observed in visual cortex, and they exist, but they are clearly impossible for storing memories. This only works for a quick run-through-once neural computation from input to output, not for steady-state thinking in closed loop.
The instability problem has not been satisfactorily addressed, although it is theoretically possible to do so. You can make complicated sum-rules for total firing, and try to get the computation to proceed naturally with these sum rules. But here, it is next to impossible to imagine how these resonant circuits recall distant memories, or do anything more than store the last immediate stimulus for a short time.
