Yes, I doubt it. The scale of the problem is massively bigger than anybody will admit right now. To get to the point where an automated car will kill fewer people than human drivers, the car must have an understanding of its environment. Machine learning isn't going to get us there.
It's like questioning the existence of their God.
Humans are not as hard to beat as you think they are. They have lots of limitations:
- Can only see in a single direction at a time.
- Vision-only, no radar
- Prone to becoming emotional
- Drive in unfamiliar places where they do not have a full understanding of the environment or local laws
- Can be inebriated, easily distracted, fall asleep
- Selfish: time to destination is often more important than the safety of others
- Majority of people do not follow all traffic laws (speed limits, turn signals, lane-usage)
Are you sure about that?
> Machine learning isn't going to get us there
Do you mean ANNs aren't going to get us there?
Evolving FPGA's (by ignoring the digital abstraction) is a window into why.
Let's look at the scale. A brain neuron is 4 microns to 100 microns in size. Whereas a transistor sizes are now pushing 3nm. That's a lot of free space to work with, why can't we easily simulate many neurons within the space of a biological neuron?
Consider the 3 body problem. Without a math breakthrough, we can only approximate, and even with a math breakthrough, we can still only approximate (because real systems are not closed). This idea that we can simulate something misses the point. It's always going to be a guess, on bounded memory, because we cant copy real states.
Letting go of the digital abstraction provides a window into why this is hard.
https://www.damninteresting.com/on-the-origin-of-circuits/ http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50....
These circuits evolve to become hyper-specific to their enviroment. We have no hope of repeating those results with software, it's the physical attributes of the hardware that are being exploited.
We were able to solve the n-body problem with sufficient accuracy for Voyager to successfully encounter and gravity-slingshot around Jupiter twice and every other outer planet, with only the computing power available in 1977.
The mechanism of action of a neuron is not very complex. It follows the all-or-none principle along with an activation potential which is easily emulated in a binary circuit. It does have additional chemical inputs which can raise or lower activation potential. The only difficult part is sufficient connectivity, but we can simulate that, no need to use an FPGA to get actual connectivity. Modern transistors are super-fast and super-small compared to neurons so you have quite a bit of room to sum inputs, check activation energy, check chemical levels, and lookup + signal the 'connected' neurons.