4 ms·

> does anyone doubt that with massive, publicly-funded investment we could not achieve them? Yes, I doubt it. The scale of the problem is massively bigger tha

by mark-r 7y ago

> does anyone doubt that with massive, publicly-funded investment we could not achieve them? 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.

beerandt 7y ago
I say variations of this all the time. I don't get why people just assume AI will be better. It's like questioning the existence of their God.
- jakeogh 7y ago
  Sci-fi conditioning mixed with a hefty assumption that people are computers.
thfuran 7y ago
> 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. 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?
- jakeogh 7y ago
  Binary computing is never going to emulate wetware on a reasonable scale. A few neurons? Sure. It's going to take a different pardigm, likely biological. Evolving FPGA's (by ignoring the digital abstraction) is a window into why.
  - aeternum 7y ago
    Citation needed. 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?
    - jakeogh 7y ago
      Start with something easier, like simulating water. Our best ab-initio simulations, last time I checked, don't correctly predict it's phase change locations. Digital abstractions deliberately supress every single other interaction unless explicitely accounted for. Those interactions happen "for free" in real systems. We need to boil quite a bit of water to even approach simulating a small subset of atoms. 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/ https://www.damninteresting.com/on-the-origin-of-circuits/ http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.9691&rep=rep1&type=pdf 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.
      - aeternum 7y ago
        Sure we can only approximate, but why wouldn't those approximations be sufficient? 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.
        jakeogh 7y ago
        We will see! Our intuition that we understanding something is often incomplete in critical ways. Exciting times ahead either way.
aeternum 7y ago
Why is an understanding necessary, and how do you even define that? 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)

by mark-r 7y ago

beerandt 7y ago

I say variations of this all the time. I don't get why people just assume AI will be better. It's like questioning the existence of their God.

jakeogh 7y ago
Sci-fi conditioning mixed with a hefty assumption that people are computers.

thfuran 7y ago

> 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. 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?

jakeogh 7y ago
Binary computing is never going to emulate wetware on a reasonable scale. A few neurons? Sure. It's going to take a different pardigm, likely biological. Evolving FPGA's (by ignoring the digital abstraction) is a window into why.
- aeternum 7y ago
  Citation needed. 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?
  - jakeogh 7y ago
    Start with something easier, like simulating water. Our best ab-initio simulations, last time I checked, don't correctly predict it's phase change locations. Digital abstractions deliberately supress every single other interaction unless explicitely accounted for. Those interactions happen "for free" in real systems. We need to boil quite a bit of water to even approach simulating a small subset of atoms. 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/ https://www.damninteresting.com/on-the-origin-of-circuits/ http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.50.9691&rep=rep1&type=pdf 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.
    - aeternum 7y ago
      Sure we can only approximate, but why wouldn't those approximations be sufficient? 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.
      - jakeogh 7y ago
        We will see! Our intuition that we understanding something is often incomplete in critical ways. Exciting times ahead either way.

aeternum 7y ago

Why is an understanding necessary, and how do you even define that? 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)