How Should We Talk to AIs?—Stephen Wolfram Blog
samedi 21 novembre 2015 à 14:29palkeo - liens
Communication via algorithme au lieu de langage naturel. Pour permettre communication entre machines mais aussi entre humains.
Développe des idées de contrats lisibles par des machines et humains ==> ethereum.
------
Not many years ago, the idea of having a computer broadly answer questions asked in plain English seemed like science fiction. But when we released Wolfram|Alpha in 2009 one of the big surprises (not least to me!) was that we’d managed to make this actually work. And by now people routinely ask personal assistant systems—many powered by Wolfram|Alpha—zillions of questions in ordinary language every day.
Ask questions in ordinary language, get answers from Wolfram|Alpha
It all works fairly well for quick questions, or short commands (though we’re always trying to make it better!). But what about more sophisticated things? What’s the best way to communicate more seriously with AIs?
I’ve been thinking about this for quite a while, trying to fit together clues from philosophy, linguistics, neuroscience, computer science and other areas. And somewhat to my surprise, what I’ve realized recently is that a big part of the answer may actually be sitting right in front of me, in the form of what I’ve been building towards for the past 30 years: the Wolfram Language.
Maybe this is a case of having a hammer and then seeing everything as a nail. But I’m pretty sure there’s more to it. And at the very least, thinking through the issue is a way to understand more about AIs and their relation to humans.
Computation Is Powerful
The first key point—that I came to understand clearly only after a series of discoveries I made in basic science—is that computation is a very powerful thing, that lets even tiny programs (like cellular automata, or neural networks) behave in incredibly complicated ways. And it’s this kind of thing that an AI can harness.
A cellular automaton with a very simple rule set (shown in the lower left corner) that produces highly complex behavior
Looking at pictures like this we might be pessimistic: how are we humans going to communicate usefully about all that complexity? Ultimately, what we have to hope is that we can build some kind of bridge between what our brains can handle and what computation can do. And although I didn’t look at it quite this way, this turns out to be essentially just what I’ve been trying to do all these years in designing the Wolfram Language.
Language of Computational Thinking
I have seen my role as being to identify lumps of computation that people will understand and want to use, like FindShortestTour, ImageIdentify or Predict. Traditional computer languages have concentrated on low-level constructs close to the actual hardware of computers. But in the Wolfram Language I’ve instead started from what we humans understand, and then tried to capture as much of it as possible in the language.
In the early years, we were mostly dealing with fairly abstract concepts, about, say, mathematics or logic or abstract networks. But one of the big achievements of recent years—closely related to Wolfram|Alpha—has been that we’ve been able to extend the structure we built to cover countless real kinds of things in the world—like cities or movies or animals.
One might wonder: why invent a language for all this; why not just use, say, English? Well, for specific things, like “hot pink”, “new york city” or “moons of pluto”, English is good—and actually for such things the Wolfram Language lets people just use English. But when one’s trying to describe more complex things, plain English pretty quickly gets unwieldy.
Imagine for example trying to describe even a fairly simple algorithmic program. A back-and-forth dialog—“Turing-test style”—would rapidly get frustrating. And a straight piece of English would almost certainly end up with incredibly convoluted prose like one finds in complex legal documents.
The Wolfram Language specifies clearly and succinctly how to create this image. The equivalent natural-language specification is complicated and subject to misinterpretation.
But the Wolfram Language is built precisely to solve such problems. It’s set up to be readily understandable to humans, capturing the way humans describe and think about things. Yet it also has a structure that allows arbitrary complexity to be assembled and communicated. And, of course, it’s readily understandable not just by humans, but also by machines.
I realize I’ve actually been thinking and communicating in a mixture of English and Wolfram Language for years. When I give talks, for example, I’ll say something in English, then I’ll just start typing to communicate my next thought with a piece of Wolfram Language code that executes right there.
The Wolfram Language mixes well with English in documents and thought streams
Understanding AIs
But let’s get back to AI. For most of the history of computing, we’ve built programs by having human programmers explicitly write lines of code, understanding (apart from bugs!) what each line does. But achieving what can reasonably be called AI requires harnessing more of the power of computation. And to do this one has to go beyond programs that humans can directly write—and somehow automatically sample a broader swath of possible programs.
We can do this through the kind of algorithm automation we’ve long used in Mathematica and the Wolfram Language, or we can do it through explicit machine learning, or through searching the computational universe of possible programs. But however we do it, one feature of the programs that come out is that they have no reason to be understandable by humans.
Engineered programs are written to be human-readable. Automatically created or discovered programs are not necessarily human-readable.
At some level it’s unsettling. We don’t know how the programs work inside, or what they might be capable of. But we know they’re doing elaborate computation that’s in a sense irreducibly complex to analyze.
There’s another, very familiar place where the same kind of thing happens: the natural world. Whether we look at fluid dynamics, or biology, or whatever, we see all sorts of complexity. And in fact the Principle of Computational Equivalence that emerged from the basic science I did implies that this complexity is in a sense exactly the same as the complexity that can occur in computational systems.
Over the centuries we’ve been able to identify aspects of the natural world that we can understand, and then harness them to create technology that’s useful to us. And our traditional engineering approach to programming works more or less the same way.
But for AI, we have to venture out into the broader computational universe, where—as in the natural world—we’re inevitably dealing with things we cannot readily understand.
What Will AIs Do?
Let’s imagine we have a perfect, complete AI, that’s able to do anything we might reasonably associate with intelligence. Maybe it’ll get input from lots of IoT sensors. And it has all sorts of computation going on inside. But what is it ultimately going to try to do? What is its purpose going to be?
This is about to dive into some fairly deep philosophy, involving issues that have been batted around for thousands of years—but which finally are going to really matter in dealing with AIs.
One might think that as an AI becomes more sophisticated, so would its purposes, and that eventually the AI would end up with some sort of ultimate abstract purpose. But this doesn’t make sense. Because there is really no such thing as abstractly defined absolute purpose, derivable in some purely formal mathematical or computational way. Purpose is something that’s defined only with respect to humans, and their particular history and culture.
An “abstract AI”, not connected to human purposes, will just go along doing computation. And as with most cellular automata and most systems in nature, we won’t be able to identify—or attribute—any particular “purpose” to that computation, or to the system that does it.
Giving Goals for an AI
Technology has always been about automating things so humans can define goals, and then those goals can automatically be achieved by the technology.
For most kinds of technology, those goals have been tightly constrained, and not too hard to describe. But for a general computational system they can be completely arbitrary. So then the challenge is how to describe them.
What do you say to an AI to tell it what you want it to do for you? You’re not going to be able to tell it exactly what to do in each and every circumstance. You’d only be able to do that if the computations the AI could do were tightly constrained, like in traditional software engineering. But for the AI to work properly, it’s going to have to make use of broader parts of the computational universe. And it’s then a consequence of a phenomenon I call computational irreducibility that you’ll never be able to determine everything it’ll do.
So what’s the best way to define goals for an AI? It’s complicated. If the AI can experience your life alongside you—seeing what you see, reading your email, and so on—then, just like with a person you know well, you might be able to tell the AI at least simple goals just by saying them in natural language.
But what if you want to define more complex goals, or goals that aren’t closely associated with what the AI has already experienced? Then small amounts of natural language wouldn’t be enough. Perhaps the AI could go through a whole education. But a better idea would be to leverage what we have in the Wolfram Language, which in effect already has lots of knowledge of the world built into it, in a way that both the human and the AI can use.
AIs Talking to AIs
Thinking about how humans communicate with AIs is one thing. But how will AIs communicate with one another? One might imagine they could do literal transfers of their underlying representations of knowledge. But that wouldn’t work, because as soon as two AIs have had different “experiences”, the representations they use will inevitably be at least somewhat different.
And so, just like humans, the AIs are going to end up needing to use some form of symbolic language that represents concepts abstractly, without specific reference to the underlying representations of those concepts.
One might then think the AIs should just communicate in English; at least that way we’d be able to understand them! But it wouldn’t work out. Because the AIs would inevitably need to progressively extend their language—so even if it started as English, it wouldn’t stay that way.
In human natural languages, new words get added when there are new concepts that are widespread enough to make representing them in the language useful. Sometimes a new concept is associated with something new in the world (“blog”, “emoji”, “smartphone”, “clickbait”, etc.); sometimes it’s associated with a new distinction among existing things (“road” vs. “freeway”, “pattern” vs. “fractal”).
Often it’s science that gives us new distinctions between things, by identifying distinct clusters of behavior or structure. But the point is that AIs can do that on a much larger scale than humans. For example, our Image Identification Project is set up to recognize the 10,000 or so kinds of objects that we humans have everyday names for. But internally, as it’s trained on images from the world, it’s discovering all sorts of other distinctions that we don’t have names for, but that are successful at robustly separating things.
I’ve called these “post-linguistic emergent concepts” (or PLECs). And I think it’s inevitable that in a population of AIs, an ever-expanding hierarchy of PLECs will appear, forcing the language of the AIs to progressively expand.
But how could the framework of English support that? I suppose each new concept could be assigned a word formed from some hash-code-like collection of letters. But a structured symbolic language—as the Wolfram Language is—provides a much better framework. Because it doesn’t require the units of the language to be simple “words”, but allows them to be arbitrary lumps of symbolic information, such as collections of examples (so that, for example, a word can be represented by a symbolic structure that carries around its definitions).
So should AIs talk to each other in Wolfram Language? It seems to make a lot of sense—because it effectively starts from the understanding of the world that’s been developed through human knowledge, but then provides a framework for going further. It doesn’t matter how the syntax is encoded (input form, XML, JSON, binary, whatever). What matters is the structure and content that are built into the language.
Information Acquisition: The Billion-Year View
Over the course of the billions of years that life has existed on Earth, there’ve been a few different ways of transferring information. The most basic is genomics: passing information at the hardware level. But then there are neural systems, like brains. And these get information—like our Image Identification Project—by accumulating it from experiencing the world. This is the mechanism that organisms use to see, and to do many other “AI-ish” things.
But in a sense this mechanism is fundamentally limited, because every different organism—and every different brain—has to go through the whole process of learning for itself: none of the information obtained in one generation can readily be passed to the next.
But this is where our species made its great invention: natural language. Because with natural language it’s possible to take information that’s been learned, and communicate it in abstract form, say from one generation to the next. There’s still a problem however, because when natural language is received, it still has to be interpreted, in a separate way in each brain.
Information transfer: Level 0: genomics; Level 1: individual brains; Level 2: natural language; Level 3: computational knowledge language
And this is where the idea of a computational-knowledge language—like the Wolfram Language—is important: because it gives a way to communicate concepts and facts about the world, in a way that can immediately and reproducibly be executed, without requiring separate interpretation on the part of whatever receives it.
It’s probably not a stretch to say that the invention of human natural language was what led to civilization and our modern world. So then what are the implications of going to another level: of having a precise computational-knowledge language, that carries not just abstract concepts, but also a way to execute them?
One possibility is that it may define the civilization of the AIs, whatever that may turn out to be. And perhaps this may be far from what we humans—at least in our present state—can understand. But the good news is that at least in the case of the Wolfram Language, precise computational-knowledge language isn’t incomprehensible to humans; in fact, it was specifically constructed to be a bridge between what humans can understand, and what machines can readily deal with.
What If Everyone Could Code?
So let’s imagine a world in which in addition to natural language, it’s also common for communication to occur through a computational-knowledge language like the Wolfram Language. Certainly, a lot of the computational-knowledge-language communication will be between machines. But some of it will be between humans and machines, and quite possibly it would be the dominant form of communication here.
In today’s world, only a small fraction of people can write computer code—just as, 500 or so years ago, only a small fraction of people could write natural language. But what if a wave of computer literacy swept through, and the result was that most people could write knowledge-based code?
Natural language literacy enabled many features of modern society. What would knowledge-based code literacy enable? There are plenty of simple things. Today you might get a menu of choices at a restaurant. But if people could read code, there could be code for each choice, that you could readily modify to your liking. (And actually, something very much like this is soon going be possible—with Wolfram Language code—for biology and chemistry lab experiments.) Another implication of people being able to read code is for rules and contracts: instead of just writing prose to be interpreted, one can have code to be read by humans and machines alike.
But I suspect the implications of widespread knowledge-based code literacy will be much deeper—because it will not only give a wide range of people a new way to express things, but will also give them a new way to think about them.
Will It Actually Work?
So, OK, let’s say we want to use the Wolfram Language to communicate with AIs. Will it actually work? To some extent we know it already does. Because inside Wolfram|Alpha and the systems based on it, what’s happening is that natural language questions are being converted to Wolfram Language code.
But what about more elaborate applications of AI? Many places where the Wolfram Language is used are examples of AI, whether they’re computing with images or text or data or symbolic structures. Sometimes the computations involve algorithms whose goals we can precisely define, like FindShortestTour; sometimes they involve algorithms whose goals are less precise, like ImageIdentify. Sometimes the computations are couched in the form of “things to do”, sometimes as “things to look for” or “things to aim for”.
We’ve come a long way in representing the world in the Wolfram Language. But there’s still more to do. Back in the 1600s it was quite popular to try to create “philosophical languages” that would somehow symbolically capture the essence of everything one could think about. Now we need to really do this. And, for example, to capture in a symbolic way all the kinds of actions and processes that can happen, as well as things like peoples’ beliefs and mental states. As our AIs become more sophisticated and more integrated into our lives, representing these kinds of things will become more important.
For some tasks and activities we’ll no doubt be able to use pure machine learning, and never have to build up any kind of intermediate structure or language. But much as natural language was crucial in enabling our species to get where we have, so also having an abstract language will be important for the progress of AI.
I’m not sure what it would look like, but we could perhaps imagine using some kind of pure emergent language produced by the AIs. But if we do that, then we humans can expect to be left behind, and to have no chance of understanding what the AIs are doing. But with the Wolfram Language we have a bridge, because we have a language that’s suitable for both humans and AIs.
More to Say
There’s much to be said about the interplay between language and computation, humans and AIs. Perhaps I need to write a book about it. But my purpose here has been to describe a little of my current thinking, particularly my realizations about the Wolfram Language as a bridge between human understanding and AI.
With pure natural language or traditional computer language, we’ll be hard pressed to communicate much to our AIs. But what I’ve been realizing is that with Wolfram Language there’s a much richer alternative, readily extensible by the AIs, but built on a base that leverages human natural language and human knowledge to maintain a connection with what we humans can understand. We’re seeing early examples already… but there’s a lot further to go, and I’m looking forward to actually building what’s needed, as well as writing about it…
Communication via algorithme au lieu de langage naturel. Pour permettre communication entre machines mais aussi entre humains.
Développe des idées de contrats lisibles par des machines et humains ==> ethereum.
------
Not many years ago, the idea of having a computer broadly answer questions asked in plain English seemed like science fiction. But when we released Wolfram|Alpha in 2009 one of the big surprises (not least to me!) was that we’d managed to make this actually work. And by now people routinely ask personal assistant systems—many powered by Wolfram|Alpha—zillions of questions in ordinary language every day.
Ask questions in ordinary language, get answers from Wolfram|Alpha
It all works fairly well for quick questions, or short commands (though we’re always trying to make it better!). But what about more sophisticated things? What’s the best way to communicate more seriously with AIs?
I’ve been thinking about this for quite a while, trying to fit together clues from philosophy, linguistics, neuroscience, computer science and other areas. And somewhat to my surprise, what I’ve realized recently is that a big part of the answer may actually be sitting right in front of me, in the form of what I’ve been building towards for the past 30 years: the Wolfram Language.
Maybe this is a case of having a hammer and then seeing everything as a nail. But I’m pretty sure there’s more to it. And at the very least, thinking through the issue is a way to understand more about AIs and their relation to humans.
Computation Is Powerful
The first key point—that I came to understand clearly only after a series of discoveries I made in basic science—is that computation is a very powerful thing, that lets even tiny programs (like cellular automata, or neural networks) behave in incredibly complicated ways. And it’s this kind of thing that an AI can harness.
A cellular automaton with a very simple rule set (shown in the lower left corner) that produces highly complex behavior
Looking at pictures like this we might be pessimistic: how are we humans going to communicate usefully about all that complexity? Ultimately, what we have to hope is that we can build some kind of bridge between what our brains can handle and what computation can do. And although I didn’t look at it quite this way, this turns out to be essentially just what I’ve been trying to do all these years in designing the Wolfram Language.
Language of Computational Thinking
I have seen my role as being to identify lumps of computation that people will understand and want to use, like FindShortestTour, ImageIdentify or Predict. Traditional computer languages have concentrated on low-level constructs close to the actual hardware of computers. But in the Wolfram Language I’ve instead started from what we humans understand, and then tried to capture as much of it as possible in the language.
In the early years, we were mostly dealing with fairly abstract concepts, about, say, mathematics or logic or abstract networks. But one of the big achievements of recent years—closely related to Wolfram|Alpha—has been that we’ve been able to extend the structure we built to cover countless real kinds of things in the world—like cities or movies or animals.
One might wonder: why invent a language for all this; why not just use, say, English? Well, for specific things, like “hot pink”, “new york city” or “moons of pluto”, English is good—and actually for such things the Wolfram Language lets people just use English. But when one’s trying to describe more complex things, plain English pretty quickly gets unwieldy.
Imagine for example trying to describe even a fairly simple algorithmic program. A back-and-forth dialog—“Turing-test style”—would rapidly get frustrating. And a straight piece of English would almost certainly end up with incredibly convoluted prose like one finds in complex legal documents.
The Wolfram Language specifies clearly and succinctly how to create this image. The equivalent natural-language specification is complicated and subject to misinterpretation.
But the Wolfram Language is built precisely to solve such problems. It’s set up to be readily understandable to humans, capturing the way humans describe and think about things. Yet it also has a structure that allows arbitrary complexity to be assembled and communicated. And, of course, it’s readily understandable not just by humans, but also by machines.
I realize I’ve actually been thinking and communicating in a mixture of English and Wolfram Language for years. When I give talks, for example, I’ll say something in English, then I’ll just start typing to communicate my next thought with a piece of Wolfram Language code that executes right there.
The Wolfram Language mixes well with English in documents and thought streams
Understanding AIs
But let’s get back to AI. For most of the history of computing, we’ve built programs by having human programmers explicitly write lines of code, understanding (apart from bugs!) what each line does. But achieving what can reasonably be called AI requires harnessing more of the power of computation. And to do this one has to go beyond programs that humans can directly write—and somehow automatically sample a broader swath of possible programs.
We can do this through the kind of algorithm automation we’ve long used in Mathematica and the Wolfram Language, or we can do it through explicit machine learning, or through searching the computational universe of possible programs. But however we do it, one feature of the programs that come out is that they have no reason to be understandable by humans.
Engineered programs are written to be human-readable. Automatically created or discovered programs are not necessarily human-readable.
At some level it’s unsettling. We don’t know how the programs work inside, or what they might be capable of. But we know they’re doing elaborate computation that’s in a sense irreducibly complex to analyze.
There’s another, very familiar place where the same kind of thing happens: the natural world. Whether we look at fluid dynamics, or biology, or whatever, we see all sorts of complexity. And in fact the Principle of Computational Equivalence that emerged from the basic science I did implies that this complexity is in a sense exactly the same as the complexity that can occur in computational systems.
Over the centuries we’ve been able to identify aspects of the natural world that we can understand, and then harness them to create technology that’s useful to us. And our traditional engineering approach to programming works more or less the same way.
But for AI, we have to venture out into the broader computational universe, where—as in the natural world—we’re inevitably dealing with things we cannot readily understand.
What Will AIs Do?
Let’s imagine we have a perfect, complete AI, that’s able to do anything we might reasonably associate with intelligence. Maybe it’ll get input from lots of IoT sensors. And it has all sorts of computation going on inside. But what is it ultimately going to try to do? What is its purpose going to be?
This is about to dive into some fairly deep philosophy, involving issues that have been batted around for thousands of years—but which finally are going to really matter in dealing with AIs.
One might think that as an AI becomes more sophisticated, so would its purposes, and that eventually the AI would end up with some sort of ultimate abstract purpose. But this doesn’t make sense. Because there is really no such thing as abstractly defined absolute purpose, derivable in some purely formal mathematical or computational way. Purpose is something that’s defined only with respect to humans, and their particular history and culture.
An “abstract AI”, not connected to human purposes, will just go along doing computation. And as with most cellular automata and most systems in nature, we won’t be able to identify—or attribute—any particular “purpose” to that computation, or to the system that does it.
Giving Goals for an AI
Technology has always been about automating things so humans can define goals, and then those goals can automatically be achieved by the technology.
For most kinds of technology, those goals have been tightly constrained, and not too hard to describe. But for a general computational system they can be completely arbitrary. So then the challenge is how to describe them.
What do you say to an AI to tell it what you want it to do for you? You’re not going to be able to tell it exactly what to do in each and every circumstance. You’d only be able to do that if the computations the AI could do were tightly constrained, like in traditional software engineering. But for the AI to work properly, it’s going to have to make use of broader parts of the computational universe. And it’s then a consequence of a phenomenon I call computational irreducibility that you’ll never be able to determine everything it’ll do.
So what’s the best way to define goals for an AI? It’s complicated. If the AI can experience your life alongside you—seeing what you see, reading your email, and so on—then, just like with a person you know well, you might be able to tell the AI at least simple goals just by saying them in natural language.
But what if you want to define more complex goals, or goals that aren’t closely associated with what the AI has already experienced? Then small amounts of natural language wouldn’t be enough. Perhaps the AI could go through a whole education. But a better idea would be to leverage what we have in the Wolfram Language, which in effect already has lots of knowledge of the world built into it, in a way that both the human and the AI can use.
AIs Talking to AIs
Thinking about how humans communicate with AIs is one thing. But how will AIs communicate with one another? One might imagine they could do literal transfers of their underlying representations of knowledge. But that wouldn’t work, because as soon as two AIs have had different “experiences”, the representations they use will inevitably be at least somewhat different.
And so, just like humans, the AIs are going to end up needing to use some form of symbolic language that represents concepts abstractly, without specific reference to the underlying representations of those concepts.
One might then think the AIs should just communicate in English; at least that way we’d be able to understand them! But it wouldn’t work out. Because the AIs would inevitably need to progressively extend their language—so even if it started as English, it wouldn’t stay that way.
In human natural languages, new words get added when there are new concepts that are widespread enough to make representing them in the language useful. Sometimes a new concept is associated with something new in the world (“blog”, “emoji”, “smartphone”, “clickbait”, etc.); sometimes it’s associated with a new distinction among existing things (“road” vs. “freeway”, “pattern” vs. “fractal”).
Often it’s science that gives us new distinctions between things, by identifying distinct clusters of behavior or structure. But the point is that AIs can do that on a much larger scale than humans. For example, our Image Identification Project is set up to recognize the 10,000 or so kinds of objects that we humans have everyday names for. But internally, as it’s trained on images from the world, it’s discovering all sorts of other distinctions that we don’t have names for, but that are successful at robustly separating things.
I’ve called these “post-linguistic emergent concepts” (or PLECs). And I think it’s inevitable that in a population of AIs, an ever-expanding hierarchy of PLECs will appear, forcing the language of the AIs to progressively expand.
But how could the framework of English support that? I suppose each new concept could be assigned a word formed from some hash-code-like collection of letters. But a structured symbolic language—as the Wolfram Language is—provides a much better framework. Because it doesn’t require the units of the language to be simple “words”, but allows them to be arbitrary lumps of symbolic information, such as collections of examples (so that, for example, a word can be represented by a symbolic structure that carries around its definitions).
So should AIs talk to each other in Wolfram Language? It seems to make a lot of sense—because it effectively starts from the understanding of the world that’s been developed through human knowledge, but then provides a framework for going further. It doesn’t matter how the syntax is encoded (input form, XML, JSON, binary, whatever). What matters is the structure and content that are built into the language.
Information Acquisition: The Billion-Year View
Over the course of the billions of years that life has existed on Earth, there’ve been a few different ways of transferring information. The most basic is genomics: passing information at the hardware level. But then there are neural systems, like brains. And these get information—like our Image Identification Project—by accumulating it from experiencing the world. This is the mechanism that organisms use to see, and to do many other “AI-ish” things.
But in a sense this mechanism is fundamentally limited, because every different organism—and every different brain—has to go through the whole process of learning for itself: none of the information obtained in one generation can readily be passed to the next.
But this is where our species made its great invention: natural language. Because with natural language it’s possible to take information that’s been learned, and communicate it in abstract form, say from one generation to the next. There’s still a problem however, because when natural language is received, it still has to be interpreted, in a separate way in each brain.
Information transfer: Level 0: genomics; Level 1: individual brains; Level 2: natural language; Level 3: computational knowledge language
And this is where the idea of a computational-knowledge language—like the Wolfram Language—is important: because it gives a way to communicate concepts and facts about the world, in a way that can immediately and reproducibly be executed, without requiring separate interpretation on the part of whatever receives it.
It’s probably not a stretch to say that the invention of human natural language was what led to civilization and our modern world. So then what are the implications of going to another level: of having a precise computational-knowledge language, that carries not just abstract concepts, but also a way to execute them?
One possibility is that it may define the civilization of the AIs, whatever that may turn out to be. And perhaps this may be far from what we humans—at least in our present state—can understand. But the good news is that at least in the case of the Wolfram Language, precise computational-knowledge language isn’t incomprehensible to humans; in fact, it was specifically constructed to be a bridge between what humans can understand, and what machines can readily deal with.
What If Everyone Could Code?
So let’s imagine a world in which in addition to natural language, it’s also common for communication to occur through a computational-knowledge language like the Wolfram Language. Certainly, a lot of the computational-knowledge-language communication will be between machines. But some of it will be between humans and machines, and quite possibly it would be the dominant form of communication here.
In today’s world, only a small fraction of people can write computer code—just as, 500 or so years ago, only a small fraction of people could write natural language. But what if a wave of computer literacy swept through, and the result was that most people could write knowledge-based code?
Natural language literacy enabled many features of modern society. What would knowledge-based code literacy enable? There are plenty of simple things. Today you might get a menu of choices at a restaurant. But if people could read code, there could be code for each choice, that you could readily modify to your liking. (And actually, something very much like this is soon going be possible—with Wolfram Language code—for biology and chemistry lab experiments.) Another implication of people being able to read code is for rules and contracts: instead of just writing prose to be interpreted, one can have code to be read by humans and machines alike.
But I suspect the implications of widespread knowledge-based code literacy will be much deeper—because it will not only give a wide range of people a new way to express things, but will also give them a new way to think about them.
Will It Actually Work?
So, OK, let’s say we want to use the Wolfram Language to communicate with AIs. Will it actually work? To some extent we know it already does. Because inside Wolfram|Alpha and the systems based on it, what’s happening is that natural language questions are being converted to Wolfram Language code.
But what about more elaborate applications of AI? Many places where the Wolfram Language is used are examples of AI, whether they’re computing with images or text or data or symbolic structures. Sometimes the computations involve algorithms whose goals we can precisely define, like FindShortestTour; sometimes they involve algorithms whose goals are less precise, like ImageIdentify. Sometimes the computations are couched in the form of “things to do”, sometimes as “things to look for” or “things to aim for”.
We’ve come a long way in representing the world in the Wolfram Language. But there’s still more to do. Back in the 1600s it was quite popular to try to create “philosophical languages” that would somehow symbolically capture the essence of everything one could think about. Now we need to really do this. And, for example, to capture in a symbolic way all the kinds of actions and processes that can happen, as well as things like peoples’ beliefs and mental states. As our AIs become more sophisticated and more integrated into our lives, representing these kinds of things will become more important.
For some tasks and activities we’ll no doubt be able to use pure machine learning, and never have to build up any kind of intermediate structure or language. But much as natural language was crucial in enabling our species to get where we have, so also having an abstract language will be important for the progress of AI.
I’m not sure what it would look like, but we could perhaps imagine using some kind of pure emergent language produced by the AIs. But if we do that, then we humans can expect to be left behind, and to have no chance of understanding what the AIs are doing. But with the Wolfram Language we have a bridge, because we have a language that’s suitable for both humans and AIs.
More to Say
There’s much to be said about the interplay between language and computation, humans and AIs. Perhaps I need to write a book about it. But my purpose here has been to describe a little of my current thinking, particularly my realizations about the Wolfram Language as a bridge between human understanding and AI.
With pure natural language or traditional computer language, we’ll be hard pressed to communicate much to our AIs. But what I’ve been realizing is that with Wolfram Language there’s a much richer alternative, readily extensible by the AIs, but built on a base that leverages human natural language and human knowledge to maintain a connection with what we humans can understand. We’re seeing early examples already… but there’s a lot further to go, and I’m looking forward to actually building what’s needed, as well as writing about it…