Why do not copy concepts how children learn ethical codes?
Inherited is: fear of death, blood, disintegration and harm generated by overexcitation of any of the five senses.
Aggressive actions of a young child against others will be sanctioned. The learning effect is “I am not alone in this world—whatever I do it can turn against me”. A short term benefit might cause overreaction and long term disadvantages. Simplified ethical codes can be instilled although a young child cannot yet reason about it.
Children between the ages of 7 and 12 years appear to be naturally inclined to feel empathy for others in pain. [Decety et al 2008]
After this major development process parents can explain ethical codes to their child. If a child kills an animal or destroys something—intentionally or not—and receives negative feedback: this even gives opportunity for further understanding of social codes. To learn law is even more complex and humans need years until they reach excellence.
Many AI researchers have a mathematical background and try to cast this complexity into the framework of today’s mathematics. I do not know how many dozens of pages with silly stories I read about AIs misinterpreting human commands. Example of silly mathematical interpretation: The human yell “Get my mother out [of the burning house]! Fast!” lets the AI explode the house to get her out very fast[Yudkowsky2007]. Instead this human yell has to be interpreted by an AI using all unspoken rescuing context: Do it fast, try to minimize harm to everybody and everything: you, my mother, other humans and things. An experienced firefighter with years of training will think instantaneously what are the options, what are the risks, will subconsciously evaluate all options and will act directly in a low complexity low risk situation. Higher risks and higher complexity will make him consult with colleagues and solve the rescue task in team action.
If we speak about AGI we can expect that an AGI will understand what “Get my mother out!” implies. Silly mathematical understanding of human communication is leading to nowhere. AIs being incapable of adding hidden complex content are not ripe for real life tasks. It is not enough that the AGI had learned all theoretical content of firefighting and first aid. The robot embodiment has to be equipped with proper sensory equipment to navigate (early stages are found at Robocup rescue challenges). Furthermore many real life training situations are neccessary for an AI to solve this complex task. It has to learn to cooperate with humans using brief emergency instructions. “The axe!” together with a hand sign can mean “Get the fire axe from the truck and follow me!”
Learning social values, laws, taboos, cannot be “crafted into detailed [mathematical] rules and value functions”. Our mathematics is not capable of this kind of complexity. We have to program into our AIs some few existential fears. All other social values and concepts have to be instilled. The open challenge is to find an infrastructure that makes learning fears and values easy and long time stable.
Why do not copy concepts how children learn ethical codes?
Because the AI is not a child, so doing the same thing would probably give different results.
I do not know how many dozens of pages with silly stories I read about AIs misinterpreting human commands.
The essence of the problem is that the difference between “interpreting” and “misinterpreting” only exists in the mind of the human.
If I as a computer programmer say to a machine “add 10 to X”—while I really meant “add 100 to X”, but made a mistake—and the machine adds 10 to X, would you call that “misinterpreting” my command? Because such things happen every day with the existing programming languages, so there is nothing strange about expecting a similar thing happening in the future.
From the machine point of view, it was asked to “add 10 to X”, it added 10 to X, so it works correctly. If the human is frustrated because that’s not what they meant, that’s bad for the human, but the machine worked correctly according to its inputs.
You may be assuming a machine with a magical source of wisdom which could look at command “add 10 to X” and somehow realize that the human would actually want to add 100, and would fix its own program (unless it is passively aggressive and decides to follow the letter of the program anyway). But that’s not how machines work.
Let us try to free our mind from associating AGIs with machines. They are totally different from automata. AGIs will be creative, will learn to understand sarcasm, will understand that women in some situations say no and mean yes.
On your command to add 10 to x an AGI would reply: “I love to work for you! At least once a day you try to fool me—I am not asleep and I know that + 100 would be correct. ShalI I add 100?”
We have to start somewhere, and “we do not know what to do” is not starting.
Also, this whole thing about “what I really meant-” I thing that we can break down these into specific failure modes, and address them individually.
-One of the failure modes is poor contextual reasoning. In order to discern what a person really means, you have to reason about the context of their communication.
-Another failure mode involves not checking activities against norms and standards. There are a number of ways to arrive at the conclusion that Mom is be to rescued from the house alive and hopefully uninjured.
-The machines in these examples do not seem to forecast or simulate potential outcomes, and judege them against external standards.
“Magical source of wisdom?” No. What we are talking about is whether is it possible to design a certain kind of AGI-one that is safe and friendly.
We have shown this to be a complicated task. However, we have not fleshed out all the possible ways, and therefore we cannot falsify the claims of people who will insist that it can be done.
Poor contextual reasoning happens many times a day among humans. Our threads are full of it. In many cases consequences are neglectable. If the context is unclear and a phrase can be interpreted one way or the other, no magical wisdom is there:
Clarification is existential: ASK
Clarification is nice to have: Say something that does not reveal that you have no idea what is meant and try to stimulate that the other reveals contextual information.
Clarification unnecessary or even unintended: stay in the blind or keep the other in the blind.
Correct associations with few contextual hints is what AGI is about. Narrow AI translation software is even today quite good to figure out context by brute force statistical similarity analysis.
Why do not copy concepts how children learn ethical codes?
Inherited is: fear of death, blood, disintegration and harm generated by overexcitation of any of the five senses. Aggressive actions of a young child against others will be sanctioned. The learning effect is “I am not alone in this world—whatever I do it can turn against me”. A short term benefit might cause overreaction and long term disadvantages. Simplified ethical codes can be instilled although a young child cannot yet reason about it.
After this major development process parents can explain ethical codes to their child. If a child kills an animal or destroys something—intentionally or not—and receives negative feedback: this even gives opportunity for further understanding of social codes. To learn law is even more complex and humans need years until they reach excellence.
Many AI researchers have a mathematical background and try to cast this complexity into the framework of today’s mathematics. I do not know how many dozens of pages with silly stories I read about AIs misinterpreting human commands.
Example of silly mathematical interpretation: The human yell “Get my mother out [of the burning house]! Fast!” lets the AI explode the house to get her out very fast [Yudkowsky2007].
Instead this human yell has to be interpreted by an AI using all unspoken rescuing context: Do it fast, try to minimize harm to everybody and everything: you, my mother, other humans and things. An experienced firefighter with years of training will think instantaneously what are the options, what are the risks, will subconsciously evaluate all options and will act directly in a low complexity low risk situation. Higher risks and higher complexity will make him consult with colleagues and solve the rescue task in team action.
If we speak about AGI we can expect that an AGI will understand what “Get my mother out!” implies. Silly mathematical understanding of human communication is leading to nowhere. AIs being incapable of adding hidden complex content are not ripe for real life tasks. It is not enough that the AGI had learned all theoretical content of firefighting and first aid. The robot embodiment has to be equipped with proper sensory equipment to navigate (early stages are found at Robocup rescue challenges). Furthermore many real life training situations are neccessary for an AI to solve this complex task. It has to learn to cooperate with humans using brief emergency instructions. “The axe!” together with a hand sign can mean “Get the fire axe from the truck and follow me!”
Learning social values, laws, taboos, cannot be “crafted into detailed [mathematical] rules and value functions”. Our mathematics is not capable of this kind of complexity. We have to program into our AIs some few existential fears. All other social values and concepts have to be instilled. The open challenge is to find an infrastructure that makes learning fears and values easy and long time stable.
Because the AI is not a child, so doing the same thing would probably give different results.
The essence of the problem is that the difference between “interpreting” and “misinterpreting” only exists in the mind of the human.
If I as a computer programmer say to a machine “add 10 to X”—while I really meant “add 100 to X”, but made a mistake—and the machine adds 10 to X, would you call that “misinterpreting” my command? Because such things happen every day with the existing programming languages, so there is nothing strange about expecting a similar thing happening in the future.
From the machine point of view, it was asked to “add 10 to X”, it added 10 to X, so it works correctly. If the human is frustrated because that’s not what they meant, that’s bad for the human, but the machine worked correctly according to its inputs.
You may be assuming a machine with a magical source of wisdom which could look at command “add 10 to X” and somehow realize that the human would actually want to add 100, and would fix its own program (unless it is passively aggressive and decides to follow the letter of the program anyway). But that’s not how machines work.
Let us try to free our mind from associating AGIs with machines. They are totally different from automata. AGIs will be creative, will learn to understand sarcasm, will understand that women in some situations say no and mean yes.
On your command to add 10 to x an AGI would reply: “I love to work for you! At least once a day you try to fool me—I am not asleep and I know that + 100 would be correct. ShalI I add 100?”
Very good!
But be honest! Aren’t we (sometimes?) more machines which serve to genes/instincts than spiritual beings with free will?
We have to start somewhere, and “we do not know what to do” is not starting.
Also, this whole thing about “what I really meant-” I thing that we can break down these into specific failure modes, and address them individually.
-One of the failure modes is poor contextual reasoning. In order to discern what a person really means, you have to reason about the context of their communication.
-Another failure mode involves not checking activities against norms and standards. There are a number of ways to arrive at the conclusion that Mom is be to rescued from the house alive and hopefully uninjured.
-The machines in these examples do not seem to forecast or simulate potential outcomes, and judege them against external standards.
“Magical source of wisdom?” No. What we are talking about is whether is it possible to design a certain kind of AGI-one that is safe and friendly.
We have shown this to be a complicated task. However, we have not fleshed out all the possible ways, and therefore we cannot falsify the claims of people who will insist that it can be done.
Poor contextual reasoning happens many times a day among humans. Our threads are full of it. In many cases consequences are neglectable. If the context is unclear and a phrase can be interpreted one way or the other, no magical wisdom is there:
Clarification is existential: ASK
Clarification is nice to have: Say something that does not reveal that you have no idea what is meant and try to stimulate that the other reveals contextual information.
Clarification unnecessary or even unintended: stay in the blind or keep the other in the blind.
Correct associations with few contextual hints is what AGI is about. Narrow AI translation software is even today quite good to figure out context by brute force statistical similarity analysis.