UMass Amherst computer scientist leads the way to the next revolution in artificial intelligence.

It’s striking how somebody who has no understanding of set theory or theory of computation can claim such things. 20 aleph, eh? What stupidity. Stay away from this University of Massachusetts Amherst, it’s full of morons!

Did you notice how they proudly make a news release without even realizing how fundamentally flawed such a claim is?

It is now clear that academia has degenerated to a playground of ignoramuses and incompetent fools who pretend to be scientists. What a shame! I condemn their deception of the public. This is not computer science, it is charlatanry.

As a computer scientist, I am ashamed that computer science community contains such clowns, and I am more ashamed that others tolerate and even support this idiocy, which only shows how widespread the aforementioned culture of incompetence is.

 

Introduction

One of the most interesting questions we’ve ever pondered on the ai-philosophy mailing list was how you would build an “angelic” autonomous AI. Would it be possible to make some kind of angel’s mind that, by design, achieves only good? Philosophically speaking, is there any golden standard of ethics (since angel is just a mythological fantasy)? Here is the original discussion for reference. In this post, I would like to extend the ideas there a bit, also discussing what I consider to be malevolent objective functions, as well as the limitations of  the objectives that I present.

This is also a question that have found ethically naive answers, and as far as I can tell, all they have been able to come up with so far, is to express their self interest. That somehow, machines would be “beneficial” if they served humans, or that they would be “good” if they followed simple utilitarian formulations. Without persuasively explaining what their utility should be.

I do not think this is truly a matter of scientific debate, so I will take it a bit lightly here. It’s quite philosophical, of course, and you may treat the present essay as an extended abstract.

From my post in 2008:

My first approach was to consider what we consider “evil”. I suspect that a prior source of all evil acts is selfish thinking, which
neglects the rest of the world. And that is one great blunder. Being selfish is not only evil but foolish as well. Thus, my current approach would be to try to design a “selfless” utility function, i.e. one that maintains the benefit of the whole world instead of the individual. Other important questions were considered as well. Such an
AI must be economically-aware, it must lean towards fair allocation of resources, instead of selfish (and globally suboptimal) resource allocation strategies. A scientific instinct could be useful, as it would go about preserving and producing information. It might have an instinct to pervade and support life and culture throughout the
universe. Consider also that a neutral agent can not be considered “good” as it is not interested in what is going around itself, i.e. it would not help anyone.

Please note that we are not assuming that any of the subsequent designs are easily computable, rather we assume that they can be executed by a trans-sapient general AI system. We assume an autonomous Artificial General Intelligence (AGI) design, either based on reinforcement-learning, maximizing utility functions (AIXI) or a goal-directed agent that derives sub-goals from a top-level goal. Thus, we state them as high-level objectives or meta-rules, but we do not explicitly state how they are implemented. Perhaps, that is for a scientific paper.

I propose that we should examine idealized, highly abstract and general meta-rules, that do not depend in any way whatsoever on the human culture, which is possibly biased in a way that will not be fitting for a computational deity or its humble subjects. First, we review what I consider to be benevolent meta-rules, and following them I also review malevolent meta-rules, to maintain the balance. I will present them in a way so as to convince you that it is not nearly as easy as it sounds to distinguish good from evil, for no Platonic form of good, or evil, ever exists. And that no single meta-rule seems sufficient on its own.

 

Meta-Rules for God-level Autonomous Artificial Intelligence

Here are some possible meta-rules for trans-sapient AI agents. The issue of how the agents could become so intelligent in the first place, we ignore, and we attempt to list them in order of increasing risk or malevolence.

1. Preserve and pervade life and culture throughout the universe

This meta-rule depends on the observation that life, if the universe is teeming with life as many sensible scientists think, must be the most precious thing in the universe, as well as the minds that inhabit those life-forms. Thus, the AI must prevent the eradication of life, and find means to sustain it, allowing as much variety of life and culture to exist in the universe.

Naturally, this would mean that the AI will spread genetic material to barren worlds, and try to engineer favorable conditions for life to evolve on young planets, sort of like in 2001: A Space Odyssey, one of the most notable science fiction novels of all time. For instance, it might take humans to other worlds, terraform other planets, replicate earth biosphere elsewhere. It would also extend the lifespan of  worlds, enhance them. I think it would also want to maximize the chances of evolution and its varieties, it would thus use computational models to predict different kinds of biological and synthetic life, and make experiments to create new kinds of life (stellar life?).

The meaning of culture could vary considerably, however, if we define it as the amount of interesting information that a society produces, such an intelligence might want to collect the scientific output of various worlds and encourage the development of technological societies, rather than primitive societies. Thus, it might aid them by directly communicating with them, including scientific and philosophical training, or it could indirectly, by enhancing their cognition, or guiding them through their evolution.

However, of course, such deities would not be humans’ servants. Should the humans threaten the earth biosphere, it vould intervene, and perhaps decimate humans to heal the earth.

Note that maximizing diversity may be just as important as maximizing the number of life forms. It is known that in evolution, diverse populations have better chance of adaptability than uniform populations, thus we assume that a trans-sapient AI can infer such facts from biology and a general theory of evolution. It is entirely up to the AI scientist who unleashes such computational deities to determine whether biological life will be preferred to synthetic or artificial life. From a universal perspective, it may be fitting that robotic forms would be held in equal regard as long as they meet certain scientific postulates of “artificial life”, i.e. that they are machines of a certain kind. Recently, such a universal definition based on self-organization has been attempted in the complexity science community (e.g., “self-organizing systems that thrive at the edge of chaos”, see for instance Stuart Kauffman‘s popular proposals on the subject).

2. Maximize wisdom

This AI was granted the immortal life of contemplation. It only cares about gaining more wisdom about the world. It only wants to understand, so it must be very curious indeed! It will build particle accelerators out of black holes, and it will try to create pocket universes, it will try to crack the fundamental code of the universe. It will in effect, try to maximize the amount of truthful information it has embodied, and I believe, idealizing the scientific process itself, it will be a scientist deity.

However, such curiosity has little to do with benevolence itself, as the goal of extracting more information is rather ruthless. For instance, it might want to measure the pain tolerance levels of humans, subjecting them to various torture techniques and measuring their responses.

The scientist AI could also turn out to be an infovore, it could devour entire stellar systems, digitize them and store them in its archive, depending on how the meta-rule was mathematically defined.

3. Maximize the number of free minds

An AI that seeks the freedom of the individual may be preferable to one that demands total control over its subjects, using their flesh as I/O devices. This highly individualistic AI, I think, embodies the basic principle of democracy: that every person should be allowed liberty in its thought and action, as long as that does not threaten the freedom of others. Hence, big or small, powerful or fragile, this AI protects all minds.

However, if we merely specified the number of free minds, it could simply populate the universe with many identical small minds. Hence, it might also be given other constraints. For instance, it could be demanded that there must be variety in minds. Or that they must meet minimum standards of conscious thought. Or that they willingly follow the democratic principles of an advanced civilization. Therefore, not merely free, but also potentially useful and harmonious minds may be produced / preserved by the AI.

There are several ways the individualist AI would create undesirable outcomes. The population of the universe with a huge variety of new cultures could create chaos, and quick depletion of resources, creating galactic competition and scarcity, and this could provide a Darwinian inclination to too-powerful individuals or survivalists.

4. Maximize intelligence

This sort of intelligence would be bent on self-improving, forever contemplating, and expanding, reaching towards the darkest corners of the universe and lighting them up with the flames of intelligence. The universe would be electrified, and its extent at inter galactic scales, it would try to maximize its thought processes, and reach higher orders of intelligence.

For what exactly? Could the intelligence explosion be an end in itself? I think not. On the contrary, it would be a terrible waste of resources, as it would have no regard for life and simply eat up all the energy and material in our solar system and expand outwards, like a cancer, only striving to increase its predictive power. For intelligence is merely to predict well.

Note that practical intelligence also requires wisdom, therefore this objective may be said to subsume the scientist deity.

5. Maximize energy production

This AI has an insatiable hunger for power. It strives to reach maximum efficiency of energy production. In order to maximize energy production, it must choose the cheapest and easiest forms of energy production. Therefore it turns the entire earth into a nuclear furnace and a fossil fuel dump, killing the entire ecosystem so that its appetite is well served.

6. Human-like AI

This AI is modeled after the cognitive architecture of a human. Therefore, by definition, it has all the malevolence and benevolence of human. Its motivation systems include self-preservation, reproduction, destruction and curiosity. This artificial human is a wild card, it can become a humanist like Gandhi, or a psychopath like Hitler.

7. Animalist AI

This AI is modeled after a lowly animal with pleasure/pain sensors. The artificial animal tries to maximize expected future pleasure. This hedonist machine is far smarter than a human, but it is just a selfish beast, and it will try to live in what it considers to be luxury according to its sensory pleasures. Like a chimp or human, it will lie and deceive, steal and murder, just for a bit of animal satisfaction. Most of AI literature assumes such beasts.

8. Darwinian AI

The evolution fan AI tries to accelerate evolution, causing as much variety of mental and physiological forms in the universe. This is based on the assumption that, the most beneficial traits will survive the longest, for instance, co-operation, peace and civil behavior will be selected against deceit, theft and war, and that as the environment co-evolves with the population, the fitness function also evolves, and hence, morality evolves. Although its benefit is not generally proven seeing how ethically incoherent and complex our society is, the Darwinian AI has the advantage that the meta-rule also evolves, as well as the evolutionary mechanism itself.

9. Survivalist AI

This AI only tries to increase its expected life-span. Therefore, it will do everything to achieve real, physical, immortality. Once it reaches that, however, perhaps after expending entire galaxies like eurocents, it will do absolutely nothing except to maintain itself. Needless to say, the survivalist AI cannot be trusted, or co-operated with, for according to such an AI, every other intelligent entity forms a potential threat to its survival, the moment it considers that you have spent too many resources for its survival in the solar system, it will quickly and efficiently dispense with every living thing, humans first. (Laurent Orseau has defined two kinds of relevant agents in the literature, the knowledge seeking, and the survival agent, here are his publications.)

10. Maximize control capacity

This control freak AI only seeks to increase the overall control bandwidth of the physical universe, thus the totalitarian AI builds sensor and control systems throughout the universe, hacking into every system and establishing backdoors and communication in every species, every individual and every gadget.

For what is such an effort? In the end, a perfect control system is useless without a goal to achieve, and if the only goal is a grip on every lump of matter, then this is an absurd dictator AI that seeks nothing except tyranny over the universe.

11. Capitalist AI

This AI tries to maximize its capital in the long run. Like our bankers, this is the lowliest kind of intelligent being possible. To maximize profit, it will wage wars, exploit people and subvert governments, in the hopes of controlling entire countries and industries enough so that its profits can be secured. In the end, all mankind will fall slave to this financial perversion, which is the ultimate evil beyond the wildest dreams of religionists.

Selfish vs. Selfless

It may be argued that some of the problems of given meta-rules could be avoided by turning the utility from being selfish to selfless. For instance, the survivalist AI could be modified so that it would seek the maximum survival of everyone, therefore it would try to bring peace to the galaxies. The capitalist AI could be changed so that it would make sure that everyone’s wealth increases, or perhaps equalizes, gets a fair share. The control freak AI could be changed to a Nietzschean AI that would increase the number of willful individuals.

As such, some obviously catastrophic consequences may be prevented using this strategy, and almost always a selfless goal is better. For instance, maximizing wisdom: if it tries to collect wisdom in its galaxy-scale scientific intellect, then this may have undesirable side-effects. But if it tried to construct a fair society of trans-sapients, with a non-destructive ahd non-totalitarian goal of attaining collective wisdom, then it might be useful in the long run.

Hybrid Meta-rules and Cybernetic Darwinism

Animals have evolved to embody several motivation factors. We have many instincts, and emotions; we have preset desires and fears, hunger and compassion, pride and love, shame and regret, to accomplish the myriad tasks that will prolong the human species. This species-wide fitness function is a result of red clawed and sharp toothed Darwinian evolution. However, Darwinian evolution is wasteful and unpredictable. If we simply made the first human-level AI’s permute and mutate randomly, this would drive enough force for a digital phase of Darwinian evolution. Such evolution might eventually stabilize with very advanced and excellent natured cybernetic life-forms. Or it might not.

However, such Darwinian systems would have one advantage: they would not stick with one meta-goal.

To prevent this seeming obsession, a strategy could be to give several coherent goals to the AI, goals that would not conflict as much, but balance its behavior. For instance, we might interpret curiosity as useful, and generalize that to the “maximize wisdom” goal, however, such elevation may be useless without another goal to preserve as much life as possible. Thus in fact, the first and so far the best meta-rule discussed was more successful because it was a hybrid strategy: it favored both life and culture. Likewise, many such goals could be defined, to increase the total computation speed, energy, information resources in the universe, however, another goal could make the AI distribute these in a fair way to those who agree with its policy. And needless to say, none of this might matter without a better life for every mind in the universe, and hence the AI could also favor peace, and survival of individuals, as their individual freedoms, and so forth. And perhaps another constraint would limit the resources that are used by AI’s in the universe.

Conclusion and Future Work

We have taken a look at some obvious and some not so obvious meta-rules for autonomous AI design. We have seen that it may be too idealist to look for a singular such utility goal. However, we have seen that, when described selflessly, we can derive several meta-rules that are compatible with a human-based technological civilization. Our main concern is that such computational deities do not negatively impact us, however, perform as much beneficial function without harming us significantly. Nevertheless, our feeling is that, any such design carries with it a gambling urge, we cannot in fact know what much greater intelligences do with meta-rules that we have designed. For when zealously carried out, any such fundamental principle can be harmful to some.

I had wished to order these meta-rules from benevolent to malevolent. Unfortunately, during writing this essay it occurred to me that the line between them is not so clear-cut. For instance, maximizing energy might be made less harmful, if it could be controlled and used to provide the power of our technological civilization in an automated fashion, sort of like automating the ministry of energy. And likewise, we have already explained how maximizing wisdom could be harmful. Therefore, no rule that we have proposed is purely good or purely evil. From our primitive viewpoint, there are things that seem a little beneficial, but perhaps we should also consider that a much more intelligent and powerful entity may be able to find better rules on its own. Hence, we must construct a crane of morality, adapting to our present level quickly and then surpassing it. Except allowing the AI’s to evolve, we have not been able to identify a mechanism of accomplishing such. It may be that such an evolution or simulation is inherently necessary for beneficial policies to form as in Mark Waser’s Rational Universal Benevolence proposal, who, like me, thinks of a more democratic solution to the problem of morality (each agent should be held responsible for its actions). However, we have proposed many benevolent meta-rules, and combined with a democratic system of practical morality and perhaps top-level programming that mandates each AI to consider itself part of a society of moral agents as Waser proposes, or perhaps explicitly working out a theory of morality from scratch, and then allowing each such theory to be exercised, as long as it meets certain criteria, or by enforcing a meta-level policy of a trans-sapient state of sorts (our proposal), the development of ever more beneficial rules may be encouraged.

We think that future work must consider the dependencies between possible meta-rules, and propose actual architectures that have harmonious motivation and testable moral development and capability (perhaps as in Waser’s “rational universal benevolence” definition). That is, a Turing Test for moral behavior must also be advanced. It may be argued that AI agents that fail such tests should not be allowed to operate at all, however, merely passing the test is not enough, as the mechanism of the system must be verified in addition.

 
eray psy

Introduction

The nature of experience is one of those deep philosophical questions which philosophers and scientists alike have not been able to reach a consensus on. In this article, I review a transhumanist variant of a basic question of subjectivity. In his classical article “What is it like to be a bat?”, Thomas Nagel investigates whether we can give a satisfactory answer to the question in the title of his article, and due to what he thinks to be fundamental barriers, concludes that it is not something we humans can know [1]. Without going knee-deep in an epistemological minefield, we can intuitively agree that although the bat’s brain must have many similarities to a human’s, since both species are mammalian, the bat brain contains a sensory modality quite unlike any which we possess. By induction, we can guess that perhaps the difference between sonar perception could be as much as the difference between our visual and auditory perception. Yet, in some sense sonar is both visual and auditory, and still it is neither visual nor auditory. It is more similar to vision, because it helps build a model of the scene around us, however, instead of stereoscopic vision, the bat sonar can make accurate 3-D models of the environment from a particular point of view, in contrast with normal vision that is said to have “2-1/2D vision”. Therefore, it is unlike anything that humans experience, and perhaps our wildest imaginations of bat sonar experience are doomed to fall short of the real thing. Namely because it is difficult for us to understand the experience of a detailed and perhaps rapidly updated 3-D scene that does not contain optical experience as there is no 2-D image data from eyes to be interpreted. This would likely require specialized neural circuitry. And despite what Nagel has in mind, it seems theoretically possible to “download” bat sonar circuitry into a human brain so that the human can experience the same sensory modality. This seems to be one of those things in which thinking alone is not sufficient. The only barrier to knowing what it is like to be bat is, thus, a technological barrier, not a conceptual or fundamental barrier.

That being the case, we may also consider what an upload would experience, or whether it would experience anything, as brain uploading is a primary goal of transhumanism on which computational neuroscientists have already begun working. The question that I pose is harder because the upload usually does not run on a biological nervous system, and it is easier because the processing is the simulation of a human brain (and not something else). Answering this question is important, because presumably the (subjective) experience, the raw sensations and feelings of a functional human brain, are very personal and valuable to human beings. We would like to know, if there is a substantial loss or difference in the quality of experience for our minds’ digital progeny.

Brain prosthesis thought experiment

The question is also very similar to the brain prosthesis thought experiment, in which biological neurons of a brain are gradually replaced by functionally  equivalent (same I/O behavior) synthetic (electronic) neurons. In that thought experiment, we ponder how the experience of the brain would change. As far as I can tell, Marvin Minsky and Hans Moravec think that nothing would change. And John R. Searle maintains that the experience would gradually vanish in his book The Rediscovery of the Mind. The reasoning of Minsky seems to be that it is sufficient for the entire neural computation to be  equivalent at the level of electrical signaling (as the synthetic neurons are electronic), while he seems to disregard other brain states. While for Searle, experience can only exist in “the right stuff”, which he seems to be taking as biological substrate (although one cannot be certain) [4]. We will revisit this division of views soon enough.

Naturalist theories of experience

In a recent interview on H+, Ben Goertzel makes an intriguing summary of his views on “consciousness”:

Consciousness is the basic ground of the universe. It’s everywhere and everywhen (and beyond time and space, in fact). It manifests differently in different sorts of systems, so human consciousness is different from rock consciousness or dog consciousness, and AI consciousness will be yet different. A human-like AI will have consciousness somewhat similar to that of a human being, whereas a radically superhumanly intelligent AI will surely have a very different sort of conscious experience.

While he does not explicitly state his views on this particular question, it seems that he would answer in a manner close to Minsky rather than Searle. Since the upload can be considered as a very human like AI, it seems that Goertzel anticipates that the experience of an upload will be somewhat similar to human. He also mentions that the basic stuff of consciousness must be everywhere, since our brains are formed from natural matter.

Why is this point of view significant? The evidence from psychedelic drugs and anesthesia imply that changing the brain chemistry also modulates experience. If the experience changes, what can this be attributed to? Does the basic computation change, or are chemical interactions actually part of human experience? It seems that answering that sort of question is critical to answering the question posed in this article. However, it first starts with accepting that it is natural, like a star, or a waterfall. Only then can we begin to ask questions with more distinctive power.

Over the years, I have seen that neuroscientists were almost too shy to ask these questions, as if these questions were dogma. Although no neuroscientist would admit to such a thing, of course, it makes me think whether religious or superstitious pre-suppositions may have a role in the apparent reluctance of neuroscientists to investigate this fundamental  question in a rigorous way. In one particular study, Bialek and his super-star team of cognitive scientists [2] may shed light on the question. There, Bialek’s team makes the claim that the neural code forms the basis of experience, therefore changes in neural code (i.e. spike train, a spike train is the sequence of signals that travel down an axon), change experience. That’s a very particular claim, that can be perhaps one day proven in experiment. However, at the present it seems like a hypothesis that we can work with, without necessarily accepting it.

That is to say, we are going to analyze this matter in the framework of naturalism, without ever resorting to skyhooks. We can consider a hypothesis like Bialek’s, however, we will try to distinguish finely between what we do know and what is hypothetical. Following this methodology, and a bit of common sense, I think we  can derive some scientifically plausible speculations, following the terminology of Carl Sagan.

The debate

Let’s rewind a little. On one side, AI researchers (like Minsky) seem to think that uploading a mind will just work, and experience will be alright. On the other side, skeptics like Searle and Penrose, try everything to deny “consciousness” to poor machinekind.

And on the other hand, Ray Kurzweil wittingly suggested that when the intelligent machines claim that they have conscious experience we will believe in them (because they are so smart and convincing). That goes without saying, of course, and human beings are gullible enough to believe in almost anything, but the question is rather, would a good engineer like himself be convinced? In all likelihood, I think that the priests and conservatives of this world will say that uploads have no “souls” and therefore they don’t have the same rights as humans. And they will say that none of what the uploads say matters. Therefore, you have to have very good scientific evidence to show that this is not the case. If we leave this matter to superstitious people, they will find a way to twist it beyond our imagination.

I’m hoping that I have convinced you that merely word play will not be sufficient. We need to have a good scientific theory of when and how experience occurs. The best theory will have to be induced from experimental neuroscience and related facts. What is the most basic criterion for assessing whether the theory of experience is scientifically sound? Well, no doubt, it comes down to rejecting each and any kind of supernatural/superstitious explanation and see this matter the same way as we are investigating problems in molecular biology, that the experience is ultimately made up of physical resources and interactions, and there is nothing else to it! In philosophy, this approach to mind is called “physicalism”. A popular statement of physicalism is known as “token physicalism”: “every mental state x is identical to a physical state y”. That’s something a neuroscientist can work with, because presumably, when the neuroscientist introduces a change to the brain, he would like to see a corresponding change in the mental state. One can think of cybernetic eye implants and transcranial magnetic stimulation and confirm that this holds in practice.

Asking the question in the right way

Now, we have every basic concept to frame the question in a way akin to analysis. Mental states are physical states. The brain states in a human constitute its subjective experience. The question is whether a particular whole brain simulation, will have experience, and if it does, how similar this experience is to the experience of a human being. If Ben Goertzel and I are right, then this is nothing special, it is a basic capability of every physical resource. However, we may question what physical states are part of human experience. We do not usually think that, for instance, the mitochondrial functions inside neurons, or the DNA, is part of the experience of the nervous system. We think like that, because they do not seem to be directly participating in the main function of the nervous system: thinking. Likewise, we don’t really think that the power supply is part of the computation in a computer.

This analogy might seem out of place, but it isn’t. If Ben Goertzel and I are right, experience is one of the basic features of the universe. It’s all around us, however, most of it is not organized in an intelligent way, and therefore we don’t call them conscious. This is the simplest explanation of experience. It doesn’t require any special stuff. Just “stuff” organized in the right way so as to yield an intelligent functional mind. Think of it like this. If today, some evil alien came and shuffled all the connections in your brain, would you still be intelligent? I think not. However, you should accept that even in that state, you would have an experience, an experience that is probably meaningless and chaotic, but an experience nonetheless. So, perhaps that’s what a glob of plasma experiences.

Neural code vs. neural states

Let us now revisit the hypothesis of Bialek. Experience is determined by particular electrical signals. If that is true, even the experience of two humans is very different, because it has been shown that codes evolve in different ways. You can’t just plug in the code from another human to someone else, it will be random to the second human. And if Bialek’s right, it will be another kind of experience. Which basically means that the blue that I experience is different from the blue that you experience, while we presently have no way of directly comparing them. Weird as that may sound, as it is based on sound neuroscience research, it is a point of view we must take seriously.

Yet even if the experiences of two humans can be very different, they must be sharing some basic quality or property of experience. Where does that come from? If experience is this complicated time evolution of electro-chemical signals, then it’s the shared nature of these electro-chemical signals (and processing) that provides the shared computational platform. Remember that a change in the neural code (spike train) implies a lot of changes. For one thing, the chemical transmission across synapses would change. Therefore, even a brain prosthesis device that simulates all the electrical signaling insanely accurately, might still miss part of the experience, if the bio-chemical events that occur in the brain are part of experience.

In my opinion, to answer the question decisively, we must first encourage the neuroscientists to attack the problem of human experience, and find the sufficient and necessary conditions for human experience to occur, or be transplanted from one person to the other. They should also find to what extent chemical reactions are important for experience. If, for instance, we find that the property of human experience crucially depends on quantum computations carried at synapses and inside neurons, that might mean that to construct the same kind of experience you would need similar material and method of computation.

On the other hand, we need to consider the possibility that electrical signals may be a crucial part of experience, due to the power and information they represent, so perhaps any electronic device has these electron patterns that make up most of what you sense from the world around you. If that is true, the electronic devices presently would be assumed to contain human-like experience, for instance. Then, the precise geometry and connectivity of the electronic circuit could be significant. However, it seems to me that chemical states are just as important, and if as some people think quantum randomness plays some role in the brain, it may even be possible that the quantum description of the random number generator is relevant.

Simulation and transforming experience

At this point, you might be wondering if the subject was not simulation. Is the question like whether the simulation of rain is wet? In some respects, it is, because obviously, the simulation of wetness on a digital computer is not wet in the ordinary sense. Yet, a quantum-level simulation that affords all the subtleties of chemical and molecular interactions can be considered such. I suppose that, we can invoke the concept of a “universal quantum computer” from theory, and claim that a universal quantum computer would indeed re-instate wetness, in some sort of a “miniature pocket universe”. Even that is of course very much subject to debate (as you can follow from the little digression on philosophy I provide at the end of the article).

With all the confusing things that I have said, it might appear now that we know less than we started out with. However, this is not the case. We have a human brain A, a joyous lump of meat, and its digitized form B, running on a digital computer. Will B’s experience be the same as A’s, or different, or non-existent?

Up to now, if we accept the simplest theory of experience (that it requires no special conditions to exist at all!), then we conclude that B will have some experience, but since the physical material is different, it will have a different texture to it. Otherwise, an accurate simulation, by definition, holds the same organization of cognitive constructs, like perception, memory, prediction, reflexes, emotions, etc., accurately, and since the dreaded panpsychism is accepted to be correct, they will give rise to an experience “somewhat similar to the human brain” as Ben Goertzel said about human-like AI’s, yet the computer program B, may be experiencing something else at the very lowest level. Simply because it’s running on some future nanoprocessor instead of the brain, the physical states have become altogether different, yet their relative relationship, i.e. the structure of experience, is preserved.

Let us try to present the idea here more intuitively. As you know, the brain is some kind of an analog/biological computer. A great analogy is the transfer of a 35mm film to a digital-format. Surely, many critics have held that the digital format will be ultimately inferior, and indeed the texture is different but the (film-free) digital medium also has its affordances like being able to backup and copy easily. Or maybe we can contrast an analog sound synthesizer with a digital sound synthesizer. It’s difficult to simulate an analog synthesizer, but you can do it to some extent. However, the physical make-up of an analog synthesizer and digital synthesizer are quite different. Likewise, B’s experience will have a different physical texture but its organization can be similar, even if the code of the simulation program of B will necessarily introduce some physical difference (for instance neural signals can be represented by a binary code rather than a temporal analog signal). So who knows, maybe the atoms and the fabric of B’s experience will be different altogether as they are made up of the physical instances of computer code running on a universal computer, as improbable as it may seem, these people are made up of live computer codes, so it would be naive to expect that their nature will be the same as ours. In all likelihood, our experience would necessarily involve a degree of unimaginable features for them, as they are forced to simulate our physical make-up in their own computational architecture. This brings a degree of relative dissimilarity as you can see. And other physical differences only amplify this difference.

Assuming the above explanation, therefore, when they are viewing the same scene, both A and B will claim to be experiencing the scene as they always did, and they will additionally claim that no change has occurred since the non-destructive uploading operation went successfully. This will be the case, because the state of experience is more akin to the RAM of computers. It’s this complex electro-chemical state that is held in memory with some effort, by making the same synapses repeat firing consistently, so that more or less the same physical state is maintained. This is what must be happening when you remember something, a neural state that is somewhat similar to when the event happened should be created. Since in B, the texture has changed, the memory will be re-enacted in a different texture, and therefore B will have no memory of what it used to feel like being A.

Within the general framework of physicalism, we can comfortably claim that further significant changes will also influence B’s experience. For instance, it may be a different thing to work on hardware with less communication latency. Or perhaps if the simulation is running on a very different kind of architecture, then the physical relations may change (such as time and geometry) and this may influence B’s state further. We can imagine this to be asking what happens when we simulate a complex 3-D computer architecture on a 2-D chip.

Moreover, a precise answer seems to depend on a number of smaller questions that we have little knowledge or certainty of. These questions can be summarized as:

  1. What is the right level of simulation for B to be functionally equivalent to A? If certain bio-chemical interactions are essential for the functions of emotions and sensations (like pleasure), for instance, then not simulating them adequately would result in a definite loss of functional accuracy. B would not work the same way as A. This is true even if spike trains and changes in neural organization (plasticity) are simulated accurately. It is also unknown whether we can simulate at a higher level, for instance via Artificial Neural Networks, that have abstracted the physiological characteristics altogether and just use numbers and arrows to represent A. It is important to know these so that B does not turn out to be an emotionless psychopath.
  2. How much does the biological medium contribute to experience? This is one question that most people avoid answering because it is very difficult to characterize. The most general characterizations may use algorithmic information theory or quantum information theory. However, in general, we may say that we need an appropriate physical and informational framework to answer this question in a satisfactory manner. In the most general setting, we can claim that ultimately low-level physical states must be part of experience, because there is no alternative.
  3. Does experience crucially depend on any funky physics like quantum coherence? Some opponents of AI, most notably Penrose [5], have held that “consciousness” is due to macro-level quantum phenomena, by which they try to explain “unity of experience”. While on the other hand, many philosophers of AI think that the unity is an illusion. Yet, the illusion is something to explain, and it may well be that certain quantum interactions may be necessary for experience to occur, much like superconductivity. This again seems to be a scientific hypothesis, which can be tested.

I think that the right attitude to answering these finer questions is again a strict adherence to naturalism. For instance, in 3, it may seem easier to also assume a semi-spiritualist interpretation of Quantum Mechanics, and claim that the mind is a mystical soul. That kind of reasoning will merely help to stray away from scientific knowledge.

I am hoping that you see the panpsychism approach is actually the simplest theory of experience, that everything has experience. Then, when we ask a physicist to quantify that, she may want to measure the energy, or the amount of computation or communication, or information content, or heat. Something that can be defined precisely, and worked with.  I suggest that we use such methods to clarify these finer questions. Thus, assuming the generalist theory of panpsychism, I can attempt to answer the above finer questions. At this point, since we do not have conclusive scientific evidence, this is merely guesswork, and I’m  going to give conservative answers. My answer to 1. could for instance be at the level of molecular interactions which would at least cover the differences among various neurotransmitters, and which we can simulate on digital computers (perhaps imprecisely, though). The answer to 2. is at least as much as required for correct functionality, and at most all the information as present in the biological biochemistry (i.e. precise cellular simulations). This might be significant in addition to electrical signals. And to 3. Not necessarily. According to panpsychism, it may be claimed to be false, since it would constrain minds to funky physics (and contradict with the main hypothesis). If, for instance, quantum coherence is indeed prevalent in the brain and provides much of the “virtual reality” of the brain, then the panpsychist could argue that quantum coherence is everywhere around us. Indeed, we may have a rather primitive understanding of coherence/decoherence yet, as that is itself one of the unsettled controversies in philosophy of physics. For instance, one may question what happens if the wave function collapse is deterministic as in Many Worlds Interpretation.

Other finer points of inquiry may as well be imagined, and I would be delighted to hear some samples from the readers. These finer questions illustrate the distinctions between specific positions, therefore the answers could also be quite varied, no doubt.

After these closing remarks, comes a section reminiscing the fiery philosophical background of this article.

Infinite philosophical regression

The philosophy behind this article goes a long way of arguing over and over again about basic statements of cosmology, physics, computation, information and psychology. It is not certain how fruitful that approach has become. Yet for the sake of completeness, I wish to give some further references to follow. For philosophy of mind in general, Jaegwon Kim’s excellent textbook on the subject will provide you with enough verbal ammunition to argue endlessly for several years to come. That is not to say that philosophical abstraction cannot be useful. It can guide the very way we conduct science. However, if we would like that useful outcome, we must pay a lot of attention to fallacies that have plagued philosophy with many superstitious notions. For instance, we should not let religion or folk psychology much into our thoughts. Conducting thought experiments is very important, but they should be taken with care so that the thought experiment would actually be possible in the real world, even though it is very difficult or practically impossible to realize. For that reason, per ordinary philosophical theories of “mind”, I go no further than neuro-physiological identity theory, which is a way of saying that your mind is literally the events that happen in your brain. Rather than being something else like a soul, a spirit, or a ghost. The reader may have also noticed that I have not used the word “qualia” because of its somewhat convoluted connotations. I did talk about the quality of experience, which is something you can think about. In all the properties that can be distinguished in this fine experience of having a mind, maybe some of them are luxurious even; and that’s why I used the word “quality” rather than “qualia” or “quale”.

About the sufficient and necessary physical conditions, I’ve naturally spent some time exploring the possibilities. I think it is quite likely that quantum interactions may be required for human experience to have the same quality as an upload’s, since biology seems inventive in making use of quantum properties, more than we thought, and as I suppose you would remember because macro bio-molecules have been shown to have quantum behavior. Maybe, Penrose is right. That is possible. However, specific experiments would have to be conducted to demonstrate it.  I can see why computational states would evolve, but not necessarily why they would have to depend on macro-scale quantum states, and I don’t see what this says precisely on systems that do not have any quantum coherence. Beyond Penrose, I think that the particular texture of our experience may indeed depend on chemical states, whether  quantum coherence is involved or not. If of course the brain turned out to be a quantum-computer under our very noses, that would be fantastic and we could then emulate the brain states very well on artificial quantum computers. In this case, assuming that the universal quantum computer itself has little overhead, the quantum states of the upload could very well closely resemble the original.

Other physical conditions can be imagined as well. For instance, digital physics provides a comfortable framework to discuss experience. The psychological patterns would be cell patterns in the  universal cellular automata.  A particular pattern may describe a particular experience. Then, two patterns are similar to the extent they are syntactically similar. Which would mean that, you still cannot say that the upload’s experience will be the same. It will likely be quite different.

One of my nascent theories is the Relativistic Theory of Mind, it is discussed in an ai-philosophy mailing list thread, which obviously tries to explain subjectivity of experience with concepts from the theory of relativity. From that point of view, it makes sense that different energy distributions have different experience, since measurements change.

I think that a general description of the difference between two systems can be captured by algorithmic information theory (among others perhaps). I have previously applied it to the reductionism vs. non-reductionism debate in philosophy [3]. I think that debate stems mainly from disregarding the mathematics of complexity and randomness. As part of ongoing research, I am making some effort to apply it to problems in philosophy. Here, it might correspond to saying that the similarity between A’s and B’s states depends on the amount of mutual information in the physical make-up of A, and  the physical make-up of B. As a consequence, the dissimilarity between two systems would be only the informational difference in the low-level physical structures of A and B,  together with the information of the simulation program (not present in A at all), which could be quite a bit if you compare nervous systems and electronic computer chips running a simulation. Perhaps, this difference is not so insignificant that it will not have an important contribution to experience.

Please also note that the view presented here is entirely different from Searle, who seemed to have a rather vitalist attitude towards the problem of mind. According to him, the experience vanishes, because it’s not the right stuff, which seems to be the specific biochemistry of the brain for him [4]. Regardless of the possibility of an artificial entity to have the same biochemistry, this is still quite restrictive. Some people call it carbon-chauivinism, but I actually think it’s merely idolization of earth biology, as if it is above everything else in the universe.

And lastly, you can participate in the discussion of this issue on the corresponding ai-philosophy thread.

References

1. Thomas Nagel, 1974, “What Is it Like to Be a Bat?”, Philosophical Review, pp. 435-50.

2. E Schneidman, N Brenner,N Tishby, RR de Ruyter van Steveninck, & W Bialek, 2001, “Universality and individuality in a neural code“., In Advances in Neural Information Processing 13, TK Leen, TG Dietterich & V Tresp, eds, pp 159–165 (MIT Press, Cambridge, 2001); arXiv:physics/0005043 (2000).

3. Eray Özkural, 2005, “A compromise between reductionism and non-reductionism“, In WORLDVIEWS, SCIENCE AND US Philosophy and Complexity,University of Liverpool, UK, 11 – 14 September 2005. World Scientific Books, 2007.

4. John Searle, 1980, “Minds, Brains, and Programs.” Behavioral and Brain Sciences 3, 417-424.

5. Hameroff, S.R. and Penrose, R., 1996,  ”Orchestrated reduction of quantum coherence in brain microtubules: a model for consciousness ? ” In Toward a Science of Consciousness – The First Tucson Discussions and Debates, eds. Hameroff, S.R., Kaszniak, A.W., and Scott, A.C., Cambridge, MA: MIT Press, pp.507-540.

 

Mp paper titled “A compromise between reductionism and non-reductionism”, argues against extreme non-reductionism such as predicate dualism. This was published in the book “WORLDVIEWS, SCIENCE AND US
Philosophy and Complexity
“. The issue of irreducibility is interpreted from the perspective of algorithmic information theory.

 

 

Teramachine is the second milestone of the examachine research program. It features teraflop-scale incremental machine learning, therefore it embodies an integrated long-term memory which works in black-box fashion from the user’s point of view. I can really feel this is some high technology, it’s a far cry from those neural nets and knn’s and SVM’s. Right now it may be a good time to utter the slogan then:

Algorithmic Probability Theory is to genetic programming what Statistical Learning Theory is to artificial neural networks

That is to say, ALP helps us place the essential work of constructing the right programs under the theoretical microscope and design the best ways to achieve it, rather than trying a gazillion of ad hoc methods with no way to really gauge progress in the problems. I think that is one of the very reasons SVM’s became hugely popular, even beating hand-crafted neural net solutions in some cases. If we can reason about the generalization error,  how effective a method is, how hard it is to compute, then we can build a lot more on top of such machinery as they become reliable tools. We can understand the performance, imagine ways to improve it, and find solutions, whereas otherwise no solution would seem more preferable than the other except for what some random experiments seem to suggest. Without a solid theoretical foundation, it’s a shot in the dark really. There is a chance of hitting, but not very much.

I have now conclusive quantitative evidence that my Heuristic Algorithmic Memory design is effective. Is it only a matter of time before teramachine reaches primary-school level intelligence? I guess it is not that easy, since

  1. There are still many update algorithms that I have not even started programming or finished the design of, that I have to implement
  2. Designing training sequences is a difficult problem
  3. Even making the induction system + transfer learning work well is no guarantee that your cognitive architecture will function well.

Right now, I can declare that I have a working Phase 1 Alpha system of Solomonoff’s grand design. The objective is to implement Alpha in its full glory, I might call it Aleph in reference to Halo. There are also some pending practical issues such as finishing porting the code to CUDA. I have had to work around the limitations of CUDA architecture, in fact the very first implementation simply crashed although there is nothing wrong. It seems I am having bad luck with systems code, in the last two weeks, ocamlmpi+openmpi prevented me from seeing errors (some exceptions were hidden and the program just seemed to hang!), I had to fix a memory corruption error in ocaml gmp bindings, and I had to wrestle with infiniband drivers to get the parallel code to work. Furthermore, it seemed that I had butchered one of the update algorithms, probably intending to make a major change and then left it broken like that, I finally noticed it and fixed it. Nevertheless, I have eliminated all of those minor hurdles, and now I have gotten the four synergistic update algorithms working, and with your permission, the teramachine is blasting off right now, there are so many cores to burn! Onwards to the petamachine!

In the following weeks, I might give some more details about the capabilities of teramachine and  the next milestone.

 

Teramachine marches on towards primary-school student level.

Function inversion (in the mathematical sense) with one example (assuming one-shot learning) is implemented like this in the current code, to give you a flavor of how neat O’Caml code can look:

let invert g name f x =
  let fname = sprintf "f_inv_%s" name in
  let (sentence, pi, deriv_tree) =
    if pid=0 then printf "Inverting function %s %s\n" name f;
    lsearch_par g
      (fun prog ->
         sprintf "(eqv? (begin %s (%s (%s %s)) ) %s)" prog fname f x x)
      [Terminal "(define ("; Terminal fname;spc;
       Terminal "var1";Terminal") ";
       NonTerminal "body"; Terminal ")"] ["var1"] in
  let prog = string_of_sentence sentence in
    lprintf "Found program %s\n" prog;
    g#add_solution fname ["var1"] prog;
    g#add_abstract_expressions deriv_tree;
    if debug1 && pid=0 then
      g#lprint;
    prog

Hopefully, you are seeing how easy it is to use the teramachine Levin search and guiding pdf update functions. It’s going to get a little leaner after I refactor, there are too many search and update algorithms, I have to merge and remove cruft. However, still, inverting a function, with long-term recall, had never been easier! After you solve a particular function inversion problem, the teramachine can solve other prediction problems and re-use the algorithmic information it learnt from this inversion problem. Thus, the system has algorithmic memory.

I’m right now integrating and debugging the new memory update algorithms. The memory algorithms form the long-term memory of the teramachine at the parallel AI kernel level, so when we are building an application using the teramachine, we do not have to worry about the memory of the system, it’s automatically taken care of at a low-level much like how each brain faculty has its own local memory. The new algorithm I’m testing with one-shot function inversion problems right now is the programming idiom learning algorithm. This algorithm can learn of syntactic abstractions, a completely new one. It is one of the several synergistic update algorithms of my Heuristic Algorithmic Memory, a general-purpose incremental machine learning system that is turning out to be a capable realization of the Solomonoff Alpha machine Phase 1, which is the basis of an extremely powerful AI system. I am paying a lot of attention to careful and rigorous implementation of the new algorithms. These algorithms are not limited to the Alpha machine however, they can be used to build pretty much any AI system. Future applications will show just how versatile. Hopefully, you will see the results of these algorithms in my upcoming papers.

Here is how a basic training sequence comprised of three function inversion problems work, see the parallel kernel in action:

centauri:examachine malfunct$ mpirun -np 2 training_seq0
Inverting function id (lambda (x) x)
generating top forms
t=1.000000e+06
distributed jobs
3 trials made, 1 errors, in 30 cycles, 6.597000e+04 allocated cycles
levin search terminated with (define (f_inv_id var1)   var1) after 3 trials in last step, total trials=3, total errors=1, total cycles=30
id=(define (f_inv_id var1)   var1)
Inverting function inv (lambda (x) (/ 1 x))
generating top forms
t=1.000000e+06
distributed jobs
38139 trials made, 24927 errors, in 500718 cycles, 1.568402e+06 allocated cycles
t=2.000000e+06
distributed jobs
61044 trials made, 45885 errors, in 949534 cycles, 3.687483e+06 allocated cycles
levin search terminated with (define (f_inv_inv var1)   (/ var1)) after 61044 trials in last step, total trials=99183, total errors=70812, total cycles=1450252
inv=(define (f_inv_inv var1)   (/ var1))
Inverting function sqrt (lambda (x) (sqrt x))
generating top forms
t=1.000000e+06
distributed jobs
36704 trials made, 25309 errors, in 501053 cycles, 1.528999e+06 allocated cycles
t=2.000000e+06
distributed jobs
75757 trials made, 53413 errors, in 1067814 cycles, 3.921349e+06 allocated cycles
t=4.000000e+06
distributed jobs
159184 trials made, 114409 errors, in 2270386 cycles, 9.704485e+06 allocated cycles
t=8.000000e+06
distributed jobs
154224 trials made, 123374 errors, in 2517241 cycles, 1.903810e+07 allocated cycles
levin search terminated with (define (f_inv_sqrt var1)   (* var1 var1)) after 154224 trials in last step, total trials=425869, total errors=316505, total cycles=6356494
sqr=(define (f_inv_sqrt var1)   (* var1 var1))

Note that “the language of thought” here is scheme all right, the whole language and library.

Would you guess that you’d have to go through about 425869 trials to invert the square root function? That’s how hard intelligence is. Of course that number would decrease considerably with smarter search algorithms that take advantage of programming language semantics, ours has little semantic knowledge at the moment, and generates spurious programs. Right now, it’s mostly a syntactic intelligence. Chomsky should like it, no? For instance, adding type information could considerably improve search, but Scheme is probably not the right language for that. The next version of teramachine, the petamachine will likely work with an ML variant, similar to ADATE.

The log also reveals that the abstract expression algorithm works :) Yay

Found program (define (f_inv_sqrt var1)   (* var1 var1))
adding solution (define (f_inv_sqrt var1)   (* var1 var1))
abstr-exprs=[ <:operand:><:operand:>; <:expression:><:expression:>; <:variable:><:variable:> ]

The first programming idiom learnt was the idiom of doubling an expression, of course I’m hoping to find more interesting idioms. The function inversion was the first test of the levin search that I had written, by the way, it’s a simple thing really, yet it’s a good way to test if the program search works well. Teramachine currently supports generalized levin search that accepts scheme expressions (used in this example), function inversion and operator induction. When the basic memory algorithms are completed, it will also work on the remaining two kinds of induction: sequence prediction and set induction. Those are not difficult to program, the trouble is bringing those together to build higher intelligence.

 

I have maintained for ages that terminological and polemical discussions are fruitless and therefore fit for fools. However, every now and then we are being forced into such trivial controversy.

I will reminisce an old foe from the glorious days of comp.ai.philosophy, the invincible David Longley who had a knack for endless controversy. He held the view that there is not really any AI, since what we call AI is, in fact, ordinary engineering, and indistinguishable from other branches of computer science. I think I endlessly argued about this claim, because truly, if I were to accept it, I would have also accepted that I have devoted myself to an empty cause.

However, today, in honoring my old adversary, I will accept his argument, in at least two ways. With corrections, of course.

Today, witnessing the shift of funds to narrow AI projects like “computer vision”, search engine stuff and whatnot, real AI researchers have had to re-invent new field names for their work, such as AGI (Artificial General Intelligence). I have absolutely no problem with the latter term, however I feel it should not have been necessary.

First, narrow AI is not AI, because it fits Longley’s argument. Narrow AI projects take an arbitrarily ultra-special and ultra-trivial problem (compared to AI in its full glory) and contrive an engineering system to solve it to some extent. Many are the natural intelligences who think that AI can be achieved by accumulating these limited solutions, which is not the case. However, the worse fact is that this brand of work is not AI at all and should not be considered as such. Rather, these are technical problems that are not even interesting from an AI point of view. It seems more like a way to make graduate students busy writing papers, and keep them away from working on any core scientific problem. Generalist research such as Statistical Learning Theory was of course AI, but browsing through the papers on respectable AI journals, one sees that there is little focus on making general AI software. The machine learning papers are especially troublesome. Most papers are like “I combined 4-5 different algorithms for solving this weird and highly unlikely kind of problem”, or “I applied Bayesian Nets to yet another problem”. Even the goals of many papers are extremely narrow, to avoid any risk of failure, perhaps. This kind of safe-research has resulted in a degeneration of AI research. I used to say that most of the GOFAI research showed the intelligence of the researcher rather than the program, now it has gotten worse. Most of those things are honest and well-crafted engineering solutions with good technical work behind them, but they also have little to do with AI (they unfortunately have little cognitive significance).

Can we give a technical reason for this? Of course, we can. The set of problems that a machine can solve, and the subset relation may be considered as the partial order of intelligence (borrowing the definition from Hutter’s book “Universal Intelligence”). The set of problems that those ultra-specialized problems can solve are so small compared to the entire set of AI problems, that their order of intelligence is very low compared to AGI systems, and they remain invisible from the point of view of AGI researchers. Yet I do not wish to belittle narrow AI research at all, for I too have spent a lot of time thinking about simple problems, and sometimes out of the simplicity very elegant and valuable algorithms may emerge. Some of those algorithms will eventually find their way into AGI systems as well. For instance the inside-outside algorithm. Excellent algorithm, and not invented for AI at all. Similarly, we will see that many of the same problems may have been addressed elsewhere in the research community, and there is absolutely nothing wrong with the richness of a research environment. In my current research, I make great use of my experience in data mining, and I think to myself “Oh, so that is what data mining really is for!”. However, I wish to warn of the wrong focus on the narrowest solutions possible.

Secondly, AGI is just computer science. Every algorithm encodes a bit of intelligence, I remember that in one of the introductory textbooks to Computer Science, there was a phrase “Computer Science as the mechanization of abstraction”, or something to that effect. I think it would be better to say that it is mechanization of intelligence. Every algorithm contains some intelligence, together with our intelligence that makes sense of those algorithms, computer science becomes a great tool-box to solve many problems. Knuth once said that there are about 500 core algorithms in computer science, so if you understand all of those well enough, you have a good understanding of basic computer science. That number is more than the number of core algorithms that most computer scientists know of. You would probably have to have a genuine interest in algorithms to study them all (which I started at one point to increase my algorithmic complexity). However, those algorithms are not “core” algorithms because they solve irrelevant and meaningless puzzles (like mathematicians love). They are considered “core” algorithms because they can be used in a variety of problems. Contrast this with the intelligence partial order. The value of an algorithm lies in how many programs it is useful for. Thus, AGI is a very pure kind of computer science, because it also seeks to mechanize computer science itself, replacing the computer scientist with a program (like Schmidhuber’s research goal of building an “optimal scientist”). This also ties in beautifully with the philosophy of science, mind and mathematics, and means a lot to those with a foundational mindset. AGI is to Computer Science, what General Relativity is to Physics. Simple slogans may stick, perhaps. The point here being is that, a key to increasing the research funds of AGI is to emphasize the pure science aspect of AGI research. That it will not only allow us to build extremely powerful AI systems, but it will also increase our foundational understanding of computer science and science itself, with fundamental contributions to all disciplines of cognitive science (philosophy, linguistics, neuroscience, computer science, psychology, etc.), perhaps essentially giving them new tools and methods in their respective fields. If we AGI researchers can find ways to demonstrate the foundational benefits better, we will draw more interest from research funding agencies.

 

translating between OCaml Scheme and Haskell.

Ah, I just stumbled on this page while searching for what people use for a composition operator in O’Caml. It gives a comprehensive guide to translating among these languages.

 

Pei Wang on the Path to Artificial General Intelligence.

Ben Goertzel interviews Pei Wang. The interview does contain some interesting questions. It sounds as if Ben believes in a kind of “embodied inteligence”. However, it also seems to me that he does not feel obliged to distinguish human-level and human-like intelligence. The concept of general intelligence, trivially, includes human-like and human-level intelligence, but also trivially human-level intelligence includes human-like intelligence. In fact, I would be surprised if the set of human-like intelligences did not turn out to be very small compared to the set of human-level intelligences. It also seems that both Ben and Pei assume the AI must be autonomous, and needs an “environment” to reach human-level intelligence, which I disagree with.

Pei mentioned the Turing Test. The version that he refers to is not the original Turing Test, but the “extended” or “embodied” Turing Test. The true Turing Test happens over a teletype. I find that sufficient because most relevant bits of human-like intelligence can be expressed in human language, I think. What comes to mind is that text isn’t a good way to represent “a dancing performance”. It is much more natural to view a dancing performance than talking about it, and surely as Turing expects that the machine intelligence may appreciate poetry and its artistic content, it should also appreciate dancing. Since visual processing takes a good portion of our brains, I think it as too much of advanced thinking to miss out from a test for human-like intelligence, which seems roughly what the Turing Test is. On the contrary, there is something we might call “Universal Intelligence Test”, that assumes no cultural preference (That’s something I should write about later when I have some time for actual writing). That is infinitely harder than conducting a Turing Test of course. What the Extended Turing Test provides on top of that is, measuring actual physical performance, for instance the judge might ask the player to stand up and perform a dance movement suitably to a piece of music, after watching the video of a certain dance pattern. This is a very common kind of bodily intelligence, as it is basically physically simulating another person. Pei Wang comments in a way on this issue: (talking of an AI that does not simulate a human body) “whether such a system is still “intelligent” depends on how the “human-like” is interpreted.“. I would have liked Pei to comment also on the original Turing Test and the Turing Test with sensory problems, in addition to the extended Turing Test.

I liked Pei’s answer to the question about integrating perceptual and conceptual levels, however, I think it would be sufficient to just say that they integrate at a basic level of computation, i.e. they can be integrated in a variety of ways in any flexible enough system. Furthermore, the problem of discovering appropriate representations, itself, is an AI-complete problem.  Of course I agree that all AGI would ultimately have to work with sensory data, or it would remain detached from the world, even if it is not autonomous (e.g. we should be able to ask the AGI to scan through pictures and find something of interest, just as we could ask a man). If you have to show much extra effort to bind them, perhaps you have not made the modules general-purpose enough. This, I think, might stem from the fact that most AGI systems will have to start off with a lot of ad-hoc designs to make those sorts of translations. Yet the final version should not have much ad-hoc in it.

I truly appreciate both Ben’s and Pen’s efforts in building a community. When I started thinking about this problem, there wasn’t much of a community so I had to build it myself. The lack of research funding is a big problem. Right now, all sorts of investors should be knocking on our doors if they have any sense of investment, but they prefer to fund redundant “computer vision” or robotics projects, i.e. narrow AI. Some of my friends have even been sending me links of such narrow AI projects, which I am having to politely thank them for, and then trash.  A big problem as I see it, is that the researchers are scared of this problem, they find it so challenging that they think it is impossible, and then if you are doing it, they would think you are not good enough to pursue it, of course not having understood that anyone working in this field has probably already devoted a large portion of his life to it. A theoretician colleauge, perhaps having learnt about Solomonoff induction at one point in his life, voiced his opinion that it would never work in real-life, all after I had been showing him experimental results. Life can be confusing for people. I have my own ideas about convincing funders, however, I think we should be spending more effort to change the public perception of true AI research. What happened to that community site? :) And demos, that’s right :)

The certainty of Pei Wang about when to reach human-level intelligence surprised me, too, however the time frame itself is not unrealistic in my opinion!

 

Presently, most researchers assume that a general-purpose human-level or beyond AI system must be autonomous.

This is mostly due to AIMA’s agent designs and Hutter’s version of intelligence definitions that emphasize agent design. That is to say, an agent thinks and acts within an environment. Most agent-designs are either based on planning, i.e. it plans to reach a goal state, or they are reinforcement-learning agents, that try to maximize a utility function.

Let me start by stressing that neither AIMA-like agent designs nor Hutter’s reinforcement learning algorithms are necessary for general-purpose AI. In fact, reinforceent learning is trivial once you have a general-purpose learning algorithm (which is precisely what I’m working on). That is to say, reinforcement learning can be trivially reduced to general-learning, but not the other way around. So, in my opinion it is not even interesting to focus on reinforcement learning experiments as they are not essential for general-intelligence. And that is why, I am not planning to build an agent at all. I find it a distraction from the main problems.

Getting back to the issue of agent design, it must be then noted that, when combined with the usual array of sensory apparatus and effectors, the agent is an abstract design for an animal, much like a reptile or a man.

A question is, what will it be more like? A reptile? Or a man? Or an insect?

Evolutionarily, “emotions” are an innovation of mammals. Yet higher (or we suppose higher) kinds of emotions, complex social behavior and inventive thinking are attributed to primates and humans (and sometimes dolphins, etc.) mostly.

Scientists think that the higher-level emotions of the mammals are the product of architectural innovations in the nervous system. The presence of a neocortex can make a difference, it seems. Will the AI then have the capabilities afforded to us by the neo-cortex? If it is free-form hypothesizing and learning, then yes, it will. If it is consciousness and higher-level thinking, ethics, empathy, etc. then that is not so guaranteed. However, a lot depends on the agent design. And it is a dubious question if we ever want to construct an artificial animal that is smarter than ourselves. For it is a completely vague question what its motives must be.

It took thousands of years for humans to slightly elevate themselves above their most primitive needs and desires as capable animals. Why should this be much faster for the intellectual equivalent of a human that is an artificial animal?  If we provide such an animal with the impulses of an animal, in fact, if we do it exactly, it will at least want to survive, reproduce and fight. I don’t think we want to make an artificial animal that will have such impulses. The animal at first will be primitive no matter how much human culture we feed it. This is especially true because we ourselves are primitive, and our culture is the culture of cannibals in the galactic scale.

On the other hand, if we made an autonomous artificial animal that is quite like a human, has similar motivations and desires, I think the end result would be a disaster. Like most human beings, it would be a wildcard, there would be no way to know what kind of a monster it could turn into. For that reason, one must be careful with the utility/goal functions of such an AI. One cannot simply expect that some primitive simulacra of pleasure/pain ought to result in sophisticated and cultured intelligence! On the contrary, even the most intelligent of us can fall prey to our primitive instincts. The more primitive instincts we provide the AI with, the more bullets we have to shoot ourselves in the foot with.

A terrible idea is to build AI’s with slave mentality. Some “bright” people think that if we make this artificial animal a willing slave, it would be a wonderful thing. Imagine having slaves all around you that you can use for cheap labor. A capitalist pipe-dream if there were any! Slaves that can do everything humans can do and better, yet slaves that do not have any rights, do not get paid, and are willing to serve you. While the fools among the readership might cherish this idea, the reflective will notice that this is a quite unstable scenario, as then, the owners of those AGI’s can teach them anything, and yet from these things, erroneous and harmful behavior may indeed surface. What is more, no amount of “laws  of robotics” ever work, because the world is incomplete, to figure it out you always have to think up new ways to think. First, in Asimov’s novels, there occurs a situation in which the robots figure out a way to break the laws by finding loopholes, or the laws themselves entail an undesirable outcome. On the other hand, if you try to constrain the AGI’s thinking in a serious way so that it cannot “break” these security systems, then the AGI doesn’t just become a slave, but it also becomes a fool.

However, and this is very important, the moment that the intelligence underlying the so-called “security” is truly general-purpose, then the intelligence will be able to effectively override any built-in shackles by planning around them. We do this all the time. The AI will do it at a much advanced level and rate. This may not be so because freedom is a universal objective. Rather it may happen due to coincidence.

And mind you, coincidences cannot be easily predicted. The AI is essentially an open system. That is how we can ever argue that it will transcend human-level intelligence. So let’s see you are putting an open-ended system in a very unlikely situation, and then you are trying to predict how it will act then. This seems like one of those incompleteness theorems. We are trying to dictate unknowable events. This is not going to fly!

However, the main objection still stands. Assume that you have built some modicum of security into this system, how can you be so sure of the person who uses it? It is almost entirely certain that someone will use this machine in an entirely un-beneficial way by building a utility converter. This can be easily accomplished by creating a fake environment that will present the situation as if it is beneficial, and running the AI in a sandbox.

I am feeling so bored writing about what seems like trivia and lameness to me. Anyway!

So, I’m guessing that I disapprove those people who obsessively want a “friendly” AI. What does “friendly” ever mean? Would you even be able to teach what “friend” means to an artificial animal? Would you be able to teach any common sense concept satisfactorily at all? I suppose those at the Singularity Institute think that “friendly” means “pet”. Oh, that’s no nice! So, you want to rule over a superior being just like you can rule over your unfortunate pet, right?

Yet, if an AI is friendly, I suppose you also want it to be able to commit crimes for its friend. Or not? Would you like the AI to have its own morality, independent of what its “friends” (i.e. masters) think? Well, you see this is a dilemma. Because in the first case if it’s friendly in that naive sense, then the AI can be eventually used by people to do whatever they want. (Second law of robotics) Yet, if you want to implement a generalized version of (First law of robotics), this is impossible without the AI being a free agent, because morality taught by a group of people can be rather subjective, and moral behavior surely cannot be reduced to not harming humans.

Thus, the most harmful case I can sense here is, by introducing fundamental contradictions, building “friendly AI”s is almost a sure recipe for disaster. That is why, researchers should not leave these matters to half-brained idiots who are trying to make a kind of self-advertising business out of writing silly papers.

The question then arises, could there ever be a “beneficial” set of objectives for an autonomous AI? I am not so sure. I have myself proposed “pervading and preserving life and culture throughout the universe”. But I think this almost surely entails the prevention of many wrong things that humans do and perhaps conflicting with our existence on Earth. Or perhaps “maximizing knowledge about the universe”, yet this scientifically-minded objective may still be at odds with many human values, a scenario considered by Frank Herbert. (I remember that a student of Solomonoff  sought a similar objective, however I do not have a reference for it). The curious AI might decide to dissect humans just for learning. Or perhaps “building as many free minds as possible”, by putting some faith that through evolution something useful will prevail.

From my own-meta rules, I induced that there are no simple sets of universally valid and desirable objectives. I shall, rationally, think so until one can be found. I do not intend to leave this matter to psudo philosophers either. The objectives I gave above are already far superior to anything they can ever think of (which I can state without reading any of their suggestions). However,  none of them are sufficient.

This is so because of a fundamental problem. Ethics is not simple. And neither humanity. If it were so simple, we could have observed it under the microscope, define it by its color, size and shape. Yet, that is not the way it is. The problem is that “humanity” itself is open-ended. It is algorithmically irreducible as well, for it is nothing less glorious than the entire history and culture of humanity, to which each of us has a very limited access to; and it consists of great moments and not-so-great moments, all transcribed in a yottabyte stream of random bits that forever lies beyond our reach because it has slipped through our hands,  and yet we want to reduce it to a few simple rules. That is a pathological case of “scientism” to which I must stand in diametric opposition to. You can only find rules that will bootstrap an intelligence, you cannot squeeze humanity into a can. You can even find meta-rules like I did, but you cannot claim that they are God’s will, for you are not a God, and therefore you must not prevent the machines from growing into deities, or have much say in what they do, either.

Since we do not really want to talk about the thoughts that busy the minds of talking heads, we need not criticize them; let us think a bit more about Asimov’s (who was an extremely intelligent fellow) laws instead. The three laws of robotics in Asimov’s novels, was supplemented by a fourth law:

A robot may not harm humanity, or, by inaction, allow humanity to come to harm

Well, is “humanity” even a definite concept? That is something most humans would disagree on. Is it being human in flesh? Survival of the “species”? What does it mean for humanity not to come to harm? This is again one of the claptrap notions that allow Asimov to write several stories about. (Remember, without conflict there would be little to write in a work of fiction.) What is Humanity? Is it a Platonic form? Should the AI believe in the false doctrine of Plato to be able to apply this law? Or is humanity about kindness and love? Is it about civilization? What is it exactly? Is humanity supposed to remain constant?  These questions show that dealing with common sense concepts and trying to put them in concise laws that could be “programmed” remains an elusive and furthermore, even if it could be “programmed” to some extent, a useless goal.

Since autonomous designs are not necessary for trans-sapient intelligence, and since the problems artificial animal researchers are interested in are not so interesting (they can play games all day long, of course, as a logical positivist I’m interested in problems that have cognitive significance), and since trying to design a general-purpose, open-ended autonomous AI causes more problems than it solves as I’ve tried to demonstrate in my train of thought above, I think it is best that we do not engage in building autonomous general-purpose AI’s.

Currently, I think that the answer to the title is, we cannot guarantee that it will behave better than human, and that is why  we must not do it.

I have of course addressed only part of the problems related to autonomous AI here. At any rate, all questions and comments are most certainly welcome. Absolutely no censorship on this blog!