The original guys (McCarthy, minskey, etc) at least understand the gigantic complexity of the undertaking in so far as we understand how the mind works at all, and they never reduced things purely to classifiers. That’s like 0.1% of the job. The real researchers take philosophy of mind seriously — technologists want a quick and cheap solution that cannot exist.

Maybe you meant sapience or intelligence, but I wonder if people think computers won't be sentient (as in self aware) in our lifetime, even if they are still not able to match human cognition.

And yet all this complexity and purpose may potentially be simply the product of putting a large collection of matter in a heat bath for a few billion years.

Who says that this process can't be repeated via brute force simulation? That the emergence of complexity and life under thermal disequilibrium + sufficient number of degrees of freedom is actually a basic fact of the universe?

I don't see any rule saying why, with sufficient brute force computational power, we won't be able to set up better and better simulations that will allow for the arise of true artificial intelligences.

We think the brain has 100 billion neurons. Making those work is a very complex network, lots of chemical gradients, bio-electrical systems, cellular-level systems, and probably a host of other things we don’t know about. So it wouldn’t surprise me if for single neuron you’d need to model another billion things at minimum.

Now factor in all of the other input senses, and those 100B neurons have increased dramatically for I/O. Plus a multiplier to model everything going on inside a neuron. Then there’s also modeling the communication layers between, effects of myelin sheaths and all that.

So we’ve established that just to run a single “step in time” (whatever that means, as that’s a computing concept), we’re into probably billions of trillions of calculations.

Now what those model have to make sense and do something coherent. Every future step. So now you need some kind of meta model that is able to push this forward and continue in the right trajectory. Now complexity is dramatically even higher.

Now that we’ve somehow figured out how to model everything required for intelligence, we need to search that space through brute force, as you put it.

To me it seems like the time required will be astronomical — Sun might have blown up by then.

So why go down this route? Nature solved this in parallel relatively cheaply through billions of iterations and billions of permutations over tens of thousands of years.

Seems easier to simplify the problem down to more basic stuff that works well enough and iterate / combine systems. Either that or find a way to grow a “brain in a test tube” that you can hookup to a computer. There’s also always humans available at mass scale for pretty cheap across the globe — sophisticated intelligence built in.

It doesn't have to be. There are alternatives to that.

Methinks there is far further to go than we realise - especially with the current air of hubris around the entire endeavour.

>Catastrophic interference, also known as catastrophic forgetting, is the tendency of an artificial neural network to completely and abruptly forget previously learned information upon learning new information.

e.g. If an image (from the forward camera) returns the probability vector: {"stop_sign": .9, "green_light": .1} You stop the car 10/10 times.

Obviously you can set the RNG seed to be the same every time too, but even that only works if your system is wholly synchronous, which a car probably isn't.

Note that I doubt Monte Carlo methods are common in the autonomous vehicle space.

I wouldn’t be surprised if Monte Carlo techniques are useful for all manner of things related to ingesting sensor data on autonomous vehicles.

I'm not in the automotive space, but I'd be surprised if there were a viable self-driving car team not using Monte Carlo methods somewhere in the vehicle stack.