This is just my layman theory. Maybe it’s obvious to experts, probably has flaws. But it seems to make sense to me, perhaps will give you some ideas. I would love to hear your thoughts/feedback!
Consume input
The data you need from the world(like video), and useful metrics we want to optimize for, like number of paperclips in the world.
Make predictions and take action
Like deep learning does.
How do human brains convert their structure into action?
Maybe like:
- Take the current picture of the world as an input.
- Come up with random action.
- “Imagine” what will happen.
Take the current world + action, and run it through the ANN. Predict the outcome of the action applied to the world.
- Does the output increase the metrics we want? If yes — send out the signals to take action. If no — come up with another random action and repeat.
Update beliefs
Look at the outcome of the action. Does the picture of the world correspond to the picture we’ve imagined? Did this action increase the good metrics? Did the number of paperclips in the world increase? If it did — positive reinforcement. Backpropagation, and reinforce the weights.
Repeat
Take current picture of the world=> Imagine applying an action to it => Take action => Positive/Negative reinforcement to improve our model => Repeat until the metrics we want equal to the goal we have set.
Consciousness
Consciousness is neurons observing/recognizing patterns of other neurons.
When you see the word “cat”— photons from the page come to your retina and are converted to neural signal. A network of cells recognizes the shape of letters C, A, and T. And then a higher level, more abstract network recognizes that these letters together form the concept of a cat.
You can also recognize signals coming from the nerve cells within your body, like feeling a pain when stabbing a toe.
The same way, neurons in the brain recognize the signals coming from the other neurons within the brain. So the brain “observes/feels/experiences” itself. Builds a model of itself, just like it builds a map of the world around, “mirrors” itself(GEB).
Sentient and self-improving
So the structure of the network itself is fed as one of it’s inputs, along with the video and metrics we want to optimize for. It can see itself as a part of the state of the world it bases predictions on. That’s what being sentient means.
And then one of the possible actions it can take is to modify it’s own structure. “Imagine” modifyng the structure a certain way, if you predict that it leads to the better predictions/outcomes —modify it. If it did lead to more paperclips — reinforce the weights to do more of that. So it keeps continually self improving.
Friendly
We don’t want this to lead to the infinite amount of paperclips, and we don’t know how to quantify the things we value as humans. We can’t turn the “amount of happiness” in the world into a concrete metrics without the unintended consequences(like all human brains being hooked up to wires that stimulate our pleasure centers).
That’s why instead of trying to encode the abstract values to maximize for, we encode very specific goals.
- Make 100 paperclips (utility function is “Did I make 100 paperclips?”)
- Build 1000 cars
- Write a paper on how to cure cancer
Humans remain in charge, determine the goals we want, and let AI figure out how to accomplish them. Still could go wrong, but less likely.
(originally published on my main blog)
If this is supposed to be a description of how actual human brains work, I guess we naturally don't have any "useful metrics we want to optimize for". Instead we are driven by various impulses, which historically appeared by random mutations, and if they happened to contribute to human survival and reproduction, they were preserved and promoted by natural selection. At this moment, the impulses that sometimes make us (want to) optimize for some useful metrics are a part of that set. But they are just one among many desires, not some essential building block of the human brain.
There is some problem even with having seemingly finite goals. For example, if the machine has a probabilistic model of the world, and you ask it to make 100 paperclips, there is a potential risk -- depending on the specific architecture -- that the machine would recognize that it doesn't have literally 100% certainty of having already created 100 paperclips, and will try to optimize for making this certainty as high as possible (destroying humanity as a side effect). For example, the machine may think "maybe humans are messing with my memory and visual output to make me falsely believe that I have 100 paperclips, when in reality maybe I have none; I guess it would be safer to kill them all". So maybe the goal should instead be something like "make 100 paperclips with probability at least 99%", but... you know, the general idea is that there may be some unnoticed way how the supposedly finite goal might spawn an infinite subtask.
Otherwise... this seems like a nice high-level view of the things, but the devil is in the details. You could write thousands of scientific papers merely on how to correctly implement things like "picture of the world", "concept of a cat", etc. That is, the heavy work is hidden behind these seemingly innocent words.
Thank you for your reply!
For a long time, the way ANNs work kinda made sense to me, and seemed to map nicely onto my (shallow) understanding of how human brain works. But I could never imagine how could the values/drives/desires be implemented in terms of ANN.
The idea that you can just quantify something you want as a metric, feed it as an input, and see if the output is closer to what we want is new to me. It was a little epiphany, that seems to make sense, so it prompted me to write this post.
Evolutionary, I guess human/animal utility function would be s... (read more)