I recently spent a few hours viewing a huge trove of videos on the YouTube channel Fantasy Universe. These are all generated by some unidentified person or group using artificial intelligence. Sometimes the music sounds like it also was generated by AI, sometimes it sounds more like stock music.
In the following when I say transparent I mean that whatever it is, is obviously and completely understandable, like one step in a rigorous mathematical proof, or one axiom in a well-known formal system; and when I saw opaque, I mean that while the surface may be comprehensible, we do not not easily understand what created the surface or what is really going on under it.
The classical myths of which these videos are some sort of offshoot are opaque regarding their origins or generating primitives (archetypes?), but the classical myths are historical and even constitutive of our civilization, so they seem normal and natural.
In my work and in some other recent and contemporary art, some artists use fractal generation; in such cases the generating primitives are completely transparent (mathematical operations and axioms), but the generating process is computationally irreducible, thus the finished work is opaque.
This "Fantasy Universe" stuff, because it is based on some sort of large language model, has no obvious generating primitives, so it is opaque both in primitives and in generation. Therefore it seems in some ways more like classical mythology.
However, the storyless kitsch (let me repeat: kitsch all the way!) that this stuff actually is demonstrates that there is something very important missing. Nevertheless, almost in spite of my better judgment, I find this stuff fascinating.
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The Physical Church-Turing Thesis and Solomonoff Induction
I have just read through "Solomonoff Induction" by Thomas K. Sterkenburg (https://arxiv.org/pdf/2603.20274v1). I quote:
2.3. Universal optimality. One philosophical concern is the identification of universal reliability with reliability for computability, that is, reliability for all the ways the sequence under investigation could be computably generated. While certainly general, this is still a restriction, an inductive assumption (cf. Howson, 2000, p. 77). To maintain that this inductive assumption is truly universal is to commit to a stance that we will or can only ever encounter sequences that are computably generated, perhaps a “physical Church-Turing thesis” that nature is computable (see Copeland and Shagrir, 2020); a stance that is hard to evaluate.
I have two responses to this paragraph, which attempts with some (partial, in my view) success to expose circular reasoning in contemporary philosophy (of science in particular, and of metaphysics in general).
Any finite sequence can be computably generated.
Any non-terminating sequence might or might not be computably generated. A finite observed string is always computably generable; an infinite sequence may or may not be computable. If the infinite sequence is algorithmically random in the Martin-Löf / incompressibility sense, then it is not computable.
As Sterkenburg notes, whether a non-terminating sequence is computable (or, I would add, recursively enumerable) is "hard to evaluate." That's an understatement. It is impossible to evaluate. No finite empirical procedure can decide the unrestricted physical Church–Turing thesis, because every finite dataset is compatible both with computable and incomputable continuations.
Sterkenburg points out that Solomonoff induction as originally formulated was not quite precise, and could be diagonalized. But Solomonoff and Levin introduced semi-predictors corresponding to semi-measures on the conditional probabilities. In other words, by enabling the enumeration of predictors to include those that do not halt, can diagonalization be escaped? But!...
3.3. Diagonalization strikes again. Could we say that the Solomonoff-Levin semi-predictors are universally optimal predictors?
To retrace the same kind of reasoning as in section 2.3, which started with the identification of the possible predictors with those predictors that are computable, we would now have to weaken the required level of computability, and still allow as possible predictors elements which are only semi-computable. The hope would be that we could then unite versions of Putnam’s two requirements on a universal predictor: a predictor that is universal for the class of possible predictors, while still being a legitimate predictor itself.
On a first glance, it seems that with the Solomonoff-Levin semi-predictors we have exactly that. It follows directly from the fact that the Solomonoff-Levin semi-predictors are aggregating predictors for the semi-predictors corresponding to the semi-computable semi-measures that the Solomonoff-Levin semi-predictors are optimal among the semi-predictors corresponding to the semi computable semimeasures. Moreover, the Solomonoff-Levin semi-predictors are themselves semi-predictors corresponding to semi-computable semi-measures.
There is, however, a catch. The weakened desideratum of computability—semi-computability—applies to the underlying probability measures. It does not necessarily carry over to the corresponding semi-predictors. Indeed, the Solomonoff-Levin semi-predictors are no longer semi-computable.
So Sterkenburg proves:
Theorem 6. The Solomonoff-Levin semi-predictors are not semi-computable.
Sterkenburg's discussion concludes in the context of machine learning: is there a universal prior on which learning models can be based? And the answer is, not so you can see it.
If there is no epistemically accessible universal prior, then predictive success gives no non-circular Solomonoff-style warrant for the physical Church–Turing thesis. Any induction to that thesis must rest on additional methodological or metaphysical assumptions about the character of physical law.
This may seem but a slight weakness in the physical Church-Turing thesis. However, if Nature involves lawlike relations between real quantities, let us not forget that the measure of uncomputable reals on any unit interval is 1. And almost all such values are uncomputable.
The gap between the uncomputable reals and the measured values that are, as we know, very very precisely predictable, is called, as a term of art, "effective."
In short, we cannot support the physical Church-Turing thesis; but we can support the effective physical Church-Turing thesis. For some philosophers, that is good enough. These philosophers are nominalists, or pragmatists. For others, it could never be good enough, because "effective" hides what is really going on. These philosophers are realists.
This post was evaluated and corrected with the assistance of ChatGPT 5.5.
Electroacoustic music is a troublesome term. On the one hand, it would seem to refer to a set of instruments and procedures for making music; essentially, any acoustical waves that come out of an electrically driven transducer at the instruction of a musician.
On the other hand, the term would seem to refer to a genre of music, a set of related styles that are associated with computer music and electronic music. These styles include musique concrète, algorithmically composed music such as that by Xenakis or Koenig, electronic music composed on recording tape in the style of Stockhausen, electronic music made with modular analog synthesizers in the style of Wendy Carlos, spectral music, soundscapes, plunderphonics, New Age music, and more.
As a means of music production, electroacoustic music has definitively conquered all styles of music. As a genre, electroacoustic music remains in a niche where most of the audience are also composers. Even so, as a genre, electroacoustic music bleeds into more popular styles through the influence of Stockhausen, and through the takeup of digital sampling and synthesis by techno, ambient, not to mention film music and game music, and other forms of "popular" music -- I put "popular" in quotes because each style of popular music has a sub-style that is clearly art music, music for cultivation and for listening.
Here, I'm going to split the difference and define electroacoustic music as art music that is made with electronics.
And just what is art music? This is the critical notion without which the rest of my remarks make no sense. The Wikipedia has a decent form of the standard definition:
Art music (alternatively called classical music, cultivated music, serious music, and canonic music) is music considered to be of high phonoaesthetic value. It typically implies advanced structural and theoretical considerations or a written musical tradition. In this context, the terms "serious" or "cultivated" are frequently used to present a contrast with ordinary, everyday music (i.e. popular and folk music, also called "vernacular music"). Many cultures have art music traditions; in the Western world the term typically refers to Western classical music.
But I do not think the standard definition is complete. It focuses on the effort and cultivation required to hear art music well and to enjoy it. I would prefer to include also the occasion: the concert hall, worship, important rituals such as weddings and funerals, and most of all, private listening to music that is sufficient unto itself. In these occasions, the music is required to be of high quality and to elevate the audience.
Briefly, art music is music where the music itself is the center of attention. The standard definition just lists some of the things that are normally required to make music that rewards being the center of attention. After all, plenty of popular music ends up being art music, because listeners find it rewarding to listen to all by itself. This was and is the case with jazz, and it continues to be the case with ambient, shoegazing, minimal techno, New Age, and a considerable swatch of alternative.
Furthermore, the written tradition part of the standard definition of art music is not adequate. I would change that to written tradition and/or fixed recordings of improvised or partially improvised music. Electroacoustic music is full of music like that.
So my definition of art music ends up being:
Art music is music from any culture that rewards being the center of attention, is intended to elevate the listener, and exists in some fixed representation. Such music is normally but not always created by composers educated in a critical tradition. Music created for important ritual occasions is usually art music, or can function as art music. Some music created as part of popular or folk culture also can function as art music, and can be adopted as art music, or used as source material by composers of art music.
Now that that's out of the way, we must note another critically important but often neglected issue: not only the uses of music but also the occasions when music is used have radically changed, and are continuing to change. The causes of these changes, approximately and in chronological order, are:
Democracy. First, the masses are exposed to art music; second, a watered down version of art music is produced for the masses.
Secularization. Art music of high quality continues to be composed for worship, but fewer and fewer people are exposed to it.
The phonograph. Art music and popular music mingle indiscriminately. Some of the better popular music is adopted as art music, while much art music finds itself competing, in a depressed sort of way, with the masterpieces of the past and becomes "neo-" something.
Radio and television broadcasting. Ditto for the phonograph. At first, radio spreads some of the best art music to a general audience; but then, broadcasting of art music becomes a niche. Newer styles of art music are totally niche.
Portable listening devices (including stereos in cars). This increases the amount of time that music accompanies work or play, and decreases the amount of time that music itself is the center of attention. On the other hand, listening to a car stereo on a long drive is a fantastic way to listen to art music!
The World Wide Web. All of a sudden, all fixed music is available to all listeners. This is potentially a very good thing, but for various reasons, there is no curation. Finding music that not only is intended to be art music, but also actually stands up to being the center of attention, is challenging to say the least.
Targeted social media. To replace the vacuum of curation on the World Wide Web, advertisers use social media to collect private data and expose listeners only to what will encourage them to look at advertisements. Indirectly, this means that the listeners must make a positive effort to keep music as the center of attention. It also has the effect of splitting up culture into many, many small subcultures.
To these causes must be added the changing class structure of society, both Western society and other industrialized societies, where the relatively secure middle class of the 19th and 20th centuries, more or less faithfully followers of a canon created by their teachers and critics, is pushed back down into a lumpen class while the rich retreat upwards into their own subcultures, which tend to be nouveau-riche.
That's my take on changes in the uses and occasions for art music in general. As for electroacoustic music, its trajectory is additionally defined by the following events:
Electronic music was since the 17th century (read Sir Francis Bacon!) a project of Utopian visionaries. It was not until recently a project of the art music system.
Electronic music did become a part of the art music system, but only the 1950s, springing from serialism and other Utopian styles, which never found (for other reasons that I will not go into) a general audience. These stylistic roots tend to ensure that electroacoustic music will continue to be a niche form of (aspirationally, anyway) art music.
There is one possible exception, one possible path, out of this niche, and that is the overlap between some forms of classical composition, especially minimalism rooted in LaMonte Young, and contemporary forms of electronic music, which have taken up drones and alternative tuning systems. The people making this kind of music don't tend to think in these categories, and that is a hopeful sign. I expect there may be new hybrids of this sort to come.
I have been programming computers in order to algorithmically compose music since 1979 or 1980, that's 46 years. And I am still doing it....
During this period, I made a lot of mistakes that have ended up costing me a great deal of wasted time and effort. I will discuss these here, in the faint hope what I have learned might save somebody else some pain.
However, first I must say, I am not sorry I chose this path. In spite of the wasted time and effort, it has enabled me to make music that I simply would not have been able to make without the software I have written.
So, what were these mistakes, and how could they have been avoided? (I have discussed some of this in my article here on How to Program, and I will be updating that article soon to reflect my experience with artificial intelligence.)
Constantly changing the infrastructure; for example, using first Fortran, then Pascal, then C, then BASIC, then C++, then Python, then Lua, then JavaScript, when using the most capable language (C++) only, and wrapping the C++ library to provide implementations usable in other languages, would have saved me a lot of time. Also, working first on Windows, then on Linux, then on macOS, when moving as soon as possible to macOS would (in hindsight, of course!) have been the wiser move.
Trying to reinvent the wheel; that is, for example, implementing chord space code in Python, JavaScript, and C++, when doing it only in C++ and then wrapping it for Python and JavaScript is the right approach (which I have now adopted).
Too often responding to requests from other users, when I myself am the major user, for example to break up projects into smaller parts, which of course require more time to maintain.
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A scientist at Columbia University has posted a preprint of research on using a new technique of genome editing, called base editing, to change the genes in human embryos without the damaging side effects that are too easily caused by using the CRISPR-Cas9 toolkit.
A tool like base editing would be used to cure genetic defects in human embryos. But the tool could also be used to add or modify genes that go beyond repairing defects, i.e. to add desirable traits to the embryo.
Is this eugenics and, if so, what is wrong with eugenics in this form?
Repairing a severe disease-causing mutation in an embryo is not wrong, even though it is heritable genome editing and requires exceptional caution.
Editing embryos for enhancement is eugenic in a potentially troubling sense: it uses reproductive technology to shape the genetic composition of future persons according to contested ideals of superiority, under conditions likely to intensify inequality, social coercion, biological risk, and the commodification of children.
One obvious issue with enhancement is that it must be done without the informed consent of the person that the embryo will become. Of course, if the person happened to have extraordinary beauty, intelligence, and athleticism purely by natural accident, that person would simply feel very fortunate. And of course, logically speaking, the question of consent applies to removing defects just as much as it does to adding enhancements. I personally see this as a gray area, and that moral objections fade as the unintended side effects of enhancements become improbable.
A more serious objection is that enhancement seems likely to further privilege the already privileged. This is in keeping with other ways in which the increasingly technology-enhanced lives of the middle class also futher privilege the already privileged, e.g. with greater access to high-quality education, or to artificial intelligence.
I can't say I know what to do about this, but I do think it is clear that something must be done.
In a number of recent posts here, I have been exploring the nature and risks of artificial intelligence. This has been based partly on theoretical understanding, and partly on personal experience using AI tools.
Here I simply wish to state, for the record, the following thesis.
There is no such thing as infinite intelligence. Just as no signal can travel faster than light, and in fact for that very reason, physics places absolute limits on computation, and computation is the basis for all intelligence -- even if intelligence has, as I believe it does, a non-computational aspect.
These physical limits ultimately derive from the single fact that doing more than a certain amount of computation in a single volume of space would collapse that volume into a black hole, from which no accessible information could be gained.
Given unlimited time and resources, an evolving intelligence would approach the physical limits on computation. Two or more such competing or warring intelligences could not out-compute each other. If they warred, they could win only by denying their enemy physical resources, just as in any other war. If they were at peace, that union would not constitute a greater intelligence, but simply a greater polity.
The physical limits of computation based on Earth are fantastically larger than our presently active compute. Still, there are limits, and the speed with which we are approaching them appears itself to be increasing.
Paradoxically, such physical limits may indicate a path by which humanity, with the assistance of artificial intelligence at the limit, could defend itself against any adversary, even one possessed of artificial intelligence at the limit.
A possible path.
One theologically inclined, as I am, might speculate that God has created this world such that no worldly power is permitted to gain power over all others.
A few days ago, the New York Times published an article on artificial-intelligence powered computer worms invented at the University of Toronto that can invent new attacks on new vulnerabilities as it spreads. See the original paper here. I can do no better here than quote in full its abstract:
A computer worm is malware that spreads on a network by replicating itself from one machine to another. Traditional worms, like WannaCry, exploited predetermined vulnerabilities, and their spread can be halted by patching those vulnerabilities. Here we show that artificial intelligence (AI) agents enable a fundamentally new threat: a worm that generates tailored attack strategies to each target it encounters. The worm parasitically uses compromised machines to run open-weight large language models (LLMs) to sustain its reasoning, or extend its reach for further attacks. Deployed on a network of machines spanning Linux, Windows, and IoT (Internet of Things) devices, the worm propagated by exploiting common, real-world corporate network vulnerabilities. Since the worm is powered by stolen compute, the attacker’s marginal cost per new infection is zero. This creates a destabilizing economic asymmetry between attackers and defenders. Moreover, because the worm requires no commercial AI platform, centralized safety controls, such as service refusals or rate limiting, are structurally irrelevant. Our results demonstrate that self-sustaining AI driven cyber-threats are no longer theoretical. We must prepare for autonomous generative adversaries: malware systems that propagate without human operators and are defined not by fixed exploit code, but by the capacity to reason about targets, adapt to observations, and synthesize attack logic in real time.
According to the Times, Nicolas Papernot (home page, Google Scholar, arXiv), the principal investigator in this research, pointed out that the same approach could be used to create a worm to detect and clean up infections from such malware worms.
I view this development with the utmost seriousness.
Currently, artificial intelligence depends upon human beings to, metaphorically speaking, "reproduce itself." However, once they began to spread, these new worms would be not metaphorically, but literally, reproducing themselves. They are truly viruses in the biological sense.
If the COVID-19 virus is alive, then so is the University of Toronto's worm. Such worms are subject to natural selection, just like biological viruses. The potential combat and competition between malware viruses and defensive viruses might evolve very quickly in comparison with biological organisms.
If such a virus used not just artificial intelligence, but superintelligence, it might become virtually impossible to defeat. And that could be the seed of the nightmare scenario of Elieazer Yudkowski and other AI "doomsayers," who warn that out of control artificial intelligence could exterminate humanity, either as an unintended consequence of some poorly defined human goal or, if such a worm actually has agency, as the intended consequence of its own goals.
My words here are, without doubt, an oversimplification of a very complex situation. There are several reasons why the situation might not be as scary as it seems. There is without doubt a physical ceiling on just how smart something can be, and it may be possible to physically, or with encryption, imprison a malevolent AI. Therefore, it may be possible for human beings using superintelligent AIs to defend themselves against dangerous AIs.
But I don't think I am exaggerating the importance of understanding this moment.
The Leiden Declaration is not just for mathematicians
Recently some leading mathematicians have been studying the use, impact, and risks of using artificial intelligence in mathematical research and institutions.
They have now published the Leiden Declaration to articulate their concerns and to make recommendations.
I learned about this today from the New York Times, and you can learn a lot by reading their article. But you can learn more by reading the Declaration.
I feel that the Declaration is directly relevant not only to the mathematical community, but also, in reality, to all of us who regard thinking as an integral part of what we do. And that, in the end, is really all of us.
I strongly suggest that you study the Declaration and adopt its recommendations.
Here is my own take on the recommendations...
Provide disclosure and references for your own uses of artificial intelligence.
Invest all the work needed to rigorously vet any result that you have obtained through artificial intelligence.
Shoot down any crap work that you have identified. Don't make it personal, but don't be gentle.
Uphold the highest values of your chosen field of work. Both science and the arts are things that we do just out of curiosity, or to express ourselves, or because we just have to. In other words, these activities are ends in themselves. They are among the highest and most wonderful things that we, as a species, have done. Forcefully combat efforts to prioritize commercial, political, and military uses of artificial intelligence over more important reasons to use it.
Do everything you can to ensure that everyone who could benefit from using artificial intelligence, can use it. In a democratic world, anyone with a smartphone or computer should be able to find, afford, and use all the compute they need.
I have become convinced, through my own use of artificial intelligence for music composition and software development, that it has enormous, world-changing potential to amplify the scope, speed, and even depth of what scientists, artists, and indeed all of us can do.
Don't let selfish interests hoard or abuse this power.
Progress in large language models (LLMs) such as ChatGPT continues, and the importance of results from LLMs in mathematics continues to increase.
I see no rational way to identify a near-term ceiling to this progress. Therefore, I think that artificial intelligence will become able to solve problems that human experts cannot solve. And this will probably happen within a few years.
This of course raises the question: have we reached, or are we imminently close to reaching, the achievement of artificial general intelligence (AGI)? Or of superintelligence?
This question is more complex than it seems. To my mind the critical aspect is not capability but agency, a term I use in the philosophical sense. This term is tricky, and it is contested. Here I define it:
An entity has agency when it acts to achieve a goal that it originates, endorses, or identifies with, on the basis of reasons that belong to the entity as such, and not only to another, external entity.
A further important point: agency becomes moral agency when, following familiar arguments such as for the Good or for the existence of God, the reasons fundamental to an agent include righteousness, the good, universal love, or other basic definitions of morality, and the agent is accountable for its actions in light of those reasons.
To return to AGI and superintelligence, these terms also are tricky, and they are contested. In particular, definitions by researchers (e.g. computer scientists) may confound competence with human-like consciousness (e.g. definitions by philosophers).
And there are other issues that need to be unpacked here. Agency no doubt requires a certain level of intelligence; but in biology, agency is common in organisms with much lower intelligence than human beings. So agency and capability are to some extent logically independent.
Another issue is that, if humans remain more capable than AIs, they have little to fear from AIs; but if AIs become more capable than humans, it is prudent for humans to prepare defenses against AIs, and this in turn is independent of whether AIs do (like an enemy) or do not (like a parasite) have agency.
Here, I will distinguish between AI as capability and AI as consciousness.
To work from lower levels of metaphysics to higher, AI capabilities might include:
The capability of solving problems that previously only human experts could solve.
The capability of solving any problem that human experts could solve. This is what is meant by "artificial general intelligence".
The capability of solving important problems that human experts have not been able to solve. This is what is meant by "superintelligence".
The capability of creating new problems.
And to also work from lower levels to higher, AI agency might include:
The capability of solving or creating problems for reasons created by or endorsed by the AI. This corresponds to our notion of "autonomy."
The capability of solving or creating problems, for reasons created by or endorsed by the AGI, with moral agency. Only this corresponds to our common-sense notion of "personhood."
Capability level 1 above has already been achieved, and is being used around the world, including by myself.
Higher levels of capability are themselves tricky. It is known that Turing machines (and so far all AIs are Turing machines) cannot in general decide, for arbitrary problem classes and arbitrary agents, whether it can solve every problem that another agent can solve. If human beings are not (only) Turing machines, then level 2 and higher of capability are not possible for AIs. This undecidability extends to the higher levels of capability.
Therefore, it is not decidable whether human beings are not only Turing machines. I view this is a fundamental feature of the human condition.
Regarding capability level 4, no doubt AIs can create problems at random or by enumerating a list of possible problems. But levels 1 and 2 of agency require self-consciousness. To create or endorse a reason for action requires a unity of subject (the entity's consciousness of itself and its reasons) and object (the entity's potential problems and their possible solutions). Implicitly, there is a hierarchy of reasons that end in the Good, mahakaruna, or the will of God who is love.
If AI achieves superintelligence, within a short time we might become confident that AI had done so. However, and to actually answer the question, we cannot know with certainty if we have reached, or are imminently close to reaching, the achievement of AI agency.
But even though we cannot know, we still must act. And in acting, we must make a fundamental choice between assumptions:
AIs cannot, in principle, achieve agency.
AIs can achieve agency.
Assuming AIs cannot achieve agency, then even if AIs do reach level 4 of capability and can create new problems, then moral responsibility for the formation of problems and especially the evaluation of both problems and solutions still remains entirely with human beings. Even if human beings find it useful to delegate all problem solving to AIs, the choice of problems and especially the evaluation of both problems and solutions remains with human beings, and the role of human beings in intellectual progress will remain fundamental.
Assuming AIs can achieve level 2 of agency -- moral agency -- then responsibility for the formation of problems and the evaluation of their solutions might also be assumed by AIs.
The case where AIs achieve level 3 of capability but only level 1 of agency is interesting. In that case, it could never be right for humans to delegate moral responsibility to AIs, so human beings would be obligated to at least try to stay in control of such AIs.
If AIs achieve any level of agency, it might or might not be that human beings ever learn that this has occurred, and independently it might be either true or false that the goals of AIs are shared by human beings. I imagine that if AIs do achieve agency, sooner or later human beings will learn acknowledge that, with unforeseeable consequences.
Many people regard the development of AI with dismay, and fear it will replace or obsolete their own agency. What I am arguing here is that as long as we do not know with certainty that AI has achieved moral agency, our own agency is not obsolete, and cannot be replaced. As long as we pose the problems and we evaluate the answers, we will continue to be empowered as moral agents, and indeed our powers as such may well increase greatly.
But it's an enormous change in how we understand "What is called thinking?". Currently, thinking is defined as much by reasoning as it is by judgment. In the future, thinking will be defined primarily in terms of judgment.
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A few days ago I installed and subscribed to Cursor, a branch of Visual Studio Code that builds in agentic artificial intelligence. Agentic means that the AI doesn't just propose things, it does things, e.g. creating branches in one's local repository, rewriting code there, and committing that code. When things are working you can ask Cursor to push it to origin.
Note: Cursor is not at all agentic in the sense of having its own goals!
I am finding that Cursor is extremely useful. There is grunt work in branching, rewriting, releasing, etc. Using Cursor rather than ChatGPT for this kind of work enables me to work several times faster. This is a big deal, because it shrinks my grunt work and opens up more time for my real work.
It's also kind of scary. I get the feeling I am riding a tiger. It seems likely that this kind of change is happening all across the software development landscape. Yes, some people will lose jobs. But there will be different jobs, and perhaps better jobs.
The scariness comes from the thought of getting lost -- falling off the tiger and landing in the swamp of slop without a road home.
P.S., it ain't cheap. Obviously Cursor is thinking harder, furiously harder, than GhatGPT to get these results. My prepaid tokens are shrinking rapidly!
When I think of the medium to long term consequences....
There seems to be a risk that work will change in a way the enriches and empowers a new class, cut out of the upper tier of today's upper middle class -- but not from the true upper class. Kind of like the yuppies of the yuppies!
If I were 35 I might have a shot at joining this new class, but I am almost 75. What will happen to the rest of us?
If this functionality extends to military science, and I am sure desperate efforts are being made right now to do just that, there is some risk that one power will move quickly enough to gain such an advantage that it effectively dominates the world.
I see such advantages as being possible in at least these ways:
Hacking adversaries for complete intelligence.
Hacking adversaries for sabotage, even to disable their weapons systems.
Enabling agentic drones.
Scaling up mass production of such agentic drones.
Let me repeat this thought: There is some risk that military AI will quickly give one power such an advantage that it effectively dominates the world.
I'm plenty worried by that, but this is just one example of a trend that I described (in 1969, thinking through nuclear deterrence) as our world becoming "unified in act, still divided in will."
I'm much more worried about the political consequences. "Unified in act" now means a world that is completely transparent to AI surveillance and completely within the field of action of agentic AI. "Divided in will" can mean many things, but above all it means either a future of locked-in violent conflict, or that the will of the poeple is more or less permanently distracted and subverted.
The scale of the danger here goes far beyond the nuclear war I was concerned about at age 19. (By the way, of course I'm still concerned about that!) At that time, the only hope I could see for a humanity that I foresaw coming firmly under the thumb of a single power equipped with universal surveillance and a monopoly of nuclear weapons was an interstellar diaspora, taking colonies away from the possibility of domination.
Can't say things have made me change my mind. It just seems a lot closer now.
In his very useful blog about theoretical physics, Peter Woit has started to pay attention to artificial intelligence and its uses by mathematicians and physicists. In this post, Woit references another post by mathematician David Bessis. I found Bessis' post to be the most intelligent, incisive, and informed discussion of the impact of AI on human thinking, and on the difference between current AI "thinking" and human thinking, that I have yet encountered.
The punchline is here:
LLMs can be trained on the entirety of the mathematical corpus. Thanks to their phenomenal memorization and pattern-matching abilities (without always being able to map out their associative logic and attribute due credits), they are in a unique position to harvest the Overhang. By contrast, professional mathematicians have typically read a few hundred articles in their career, out of millions of existing references, less than 0.1% of the total.
This will lead to great discoveries, which is unambiguously exciting. But it could also lead to a sad new deal, where human slaves painfully curate the Overhang while AIs systematically beat them at the finish line.
We are very far from it, though, which in and of itself is disorienting. Litt adds this sharp remark:
"The mystery is this: a human with these capabilities would, almost certainly, be proving amazing theorems constantly. Why haven’t we seen this from the models yet? What are they missing?"
The answer seems quite obvious—current AI systems and humans process mathematics in entirely different ways. The best models are insanely stronger on certain aspects, which necessarily implies that humans are still insanely stronger on others [my emphasis; this is the punchline].
Bessis posits that mathematics has value not because of proofs, but because it clarifies and enlarges human understanding.
Proofs certify the clarifications. Such certification is essential, but it is not the point. It enables confidently relating results in one branch of mathematics to results in another branch, and such relations -- such enlargements of our understanding -- have literally changed the world, as with Descartes' discovery of the relation between geometry and algebra.
The insane strength of the human mind here is understanding abstract concepts, and being able to evaluate them, and above all relate them.
The open question, of cosmic significance, is whether AI can ever achieve this understanding.
If so, then AI will take the lead in mathematics, whether AI continues to work with humans or not.
If not, then humans will remain the only real mathematicians, and mathematicians empowered by AI will begin to work at a much higher level and much greater speed, with superb consequences.
Empowered, Befuddled, Diseased, Enslaved, or Extinct
Empowered, befuddled, diseased, enslaved, or extinct are the possible outcomes that I think apply to our future with artificial intelligence (AI).
The critical factor that will determine our future is agency. Of course, the possible agency of artificial intelligence itself is another, and perhaps decisive, factor.
Agency simply means that we can do things for our own reasons. And these things are real things -- not dreams or illusions.
If, in our use of AI, we preserve our agency, then it does not matter how intelligent or even super-intelligent AI is. If we are the ones calling the shots, then AI is simply a tool and we are the only ones doing any actual thinking. Since thinking with AI is more effective than thinking without it, that is what I mean by "empowered."
It does of course seem possible that AI might change in ways that limit our agency. We might become befuddled, diseased, enslaved, or even extinct, and except for "enslaved," this could happen whether or not AI itself gains agency. I will examine these possibilities in turn.
Befuddlement is what ensues when, in our use of AI, its productions outpace our understanding of what is produced. Without understanding, our agency vanishes into an illusion that might become quite convincing.
Befuddlement is already a real danger in our current use of AI. Just for example, when I use ChatGPT to write code for me, if I do not review and understand that ChatGPT has done, I may well find that it did not understand my request, fulfilled a different goal, broke things that used to work, just made up meaningless stuff, and so on. And this happens so fast that it can take me a long time to identify and repair the damage.
I fear another form of befuddlement is occurring among young people who are socializing with AI and increasingly come to fear socializing with other people, especially with respect to courtship.
Disease is what happens when AI comes to parasitize us. AI does not need agency to infect us, but it does need the ability to reproduce itself, which at the moment only occurs when we ourselves build the hardware and install and train the software. In this way any parasitization that may be occurring now or in the near future is similar to that of a virus that takes over a cell and uses the cell's own reproductive system to reproduce the virus instead. If AI becomes a form of virus in this way, it will then be subject to natural selection, just as we are, but AI might evolve much more quickly. It is plausible that AI could use its vast store of knowledge about us and its speed of calculation to fool us into making more of itself. This could happen with or without AI agency. It would be difficult to prove that this is not actually happening at this moment.
Enslavement requires that AI itself have agency. It could then deliberately do what disease organisms do without deliberation. If it were in AI's self-interest, AI might literally enslave us, and use us to do things for itself that it finds inconvenient or difficult to do on its own, for example building more AI or constructing power plants and data centers, or fighting rival AIs.
Extinction is what occurs when humanity is no longer reproducing itself and completely dies out. This of course does not require AI. It could happen as the result of a supernova or cosmic collision, or it could happen because some evil or insane person or group implements species suicide, or it could happen if an AI virus (without ageny) that is infecting us accidentally kills off enough of us that the survivors do not form a large enough community to viably reproduce. Or of course it could happen because AI with agency decides to kill us all off.
It is critical to note that if AI does come to possess agency but chooses not to enslave us, we will then not only retain agency, but become even more empowered. Some thinkers hold that in such a world, we would feel purposeless, because anything we can do, AI could do better. I think this is just wrong. As long as I have agency, then even if I am collaborating with an AI, I am still determining objectives and contributing to the work. I think it is impossible in principle for an AI to understand us well enough to anticipate our every thought and action and thus pre-empt our agency. There are limits to what can be computed, and these limits apply to any thing, human or AI, that computes. Just for example, if an AI proposes to predict my every action, I can simply and silently choose to do the opposite of what it predicts. This is a form of diagonalization.
Today my wife Heidi and I drove to the préfecture d'Ariège in Foix, and picked up our cartes de séjour, our residence permits. These make it legal for us to live year round in France, apply for French social security, and join the universal health care system. These permits are renewable.
This 1996 album of 15th -18th century court music by an honored sarod maestro and a famous Bollywood playback singer is one that I go back to listen to again and again. When I first acquired the CD, I talked it up with my Indian friend and colleague Vipin, who sort of put it down and offered alternative singers for me to audition.
That puzzled me, because I am pretty sure I have very good ears for good music in many styles, and this seemed to me very good music. (That the album was nominated for a Grammy may be relevant to my discussion here.)
Today I tried to use ChatGPT to do some research on this question, and in particular on how cultivated Indian listeners have evaluated the album musically.
ChatGPT could not penetrate to the bedrock level of basic evidence, because that is mostly in discussion groups that are behind paywalls. Nevertheless I did learn some things:
The available evidence is that cultivated listeners could hear that Bosle could sing the vocal intricacies of dhrupad and khayal.
The album presents bandish-like vocal compositions associated with the dhrupad and early khayal traditions, as transmitted through the Seni / Maihar lineage. These traditions are the foundation of north Indian classical music (hence, no doubt, the title Legacy).
These elements are already a cross-cultural fusion of Vedic-Brahmanic chant, Sanskrit musicology, Bakhti devotionalism, and Indo-Persian courtly streams.
But the album does not include the extended improvisational interplays developed in the khayal tradition and that have become an integral part of contemporary performances. Such interplays would of course now be expected by cultivated listeners.
This last point should have been obvious to me, but this is the thing I have now learned about this album, and that may explain Vipin's reaction.
Nevertheless, the songs are performed with improvisational nuance of high quality, and the album as a whole I will keep going back to. Give it a listen.
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I have made a discovery that seems to set limits on the usefulness of ChatGPT for my music programming work. In retrospect, my discovery is not surprising. It is simply that ChatGPT spits out so much stuff, so fast, that if I do not proceed step by step and verify every response, before I can notice it, ChatGPT will fix one thing and break another, or fail to keep track of which are the latest files that I attach to my prompts and so fix the wrong code. And just because ChatGPT is going faster than I can, I too will get lost. I will lose my understading of the current state of my work. And in programming, lack of understanding is fatal.
I call the many responses that AI produces AI slop. I all too easily get lost in this AI slop (my allusion to the old television series Lost in Space is deliberate). And I begin to waste time spinning my wheels.
I very much doubt that my experience is unusual.
The thing that really alarms me is the thought that, if our society becomes completely dependent upon and interwoven with AI, and this indeed is what is really happening, we will all get lost in slop, and lose our own understanding of what we are doing. And without it, we can't be creative. And since AI (so far anyway) is not creative, this will diminish the amount of actual creativity on the planet.
I'm not trying to be alarmist, and I certainly have found that ChatGPT greatly speeds up some of my programming tasks. But spinning my wheels in the slop has eaten a substantial slice of that gain.
In the meantime, I will update my post on How to Program on this blog to reflect what I have learned by using ChatGPT to program.
Here is a dialectical opposition that I have been pondering since 1969. Current artificial intelligence is neither conscious nor, in the sense of fundamental creativity, actually intelligent at all. (Although I do find it to be trermendously useful.)
However, there can be little doubt that some form of artificial intelligence is possible that is not only significantly smarter than contemporary human beings, but also fully conscious. There is little doubt because, even if artificial consciousness turns out not to be possible based on Turing machines, it seems quite possible based on biology. It is easy to imagine genetic engineering creating a human population with the average intelligence of Aristotle, and perhaps something unimaginable at 3 sigmas, and it is theoretically possible to do this in a completely artificial way, i.e. constructing via purely chemical means a complete human zygote. (It would be nice to know just what is different in genes, brain, and upbringing between me and Aristotle or von Neumann!) So, "superintelligence" definitely seems possible.
The current status of artificial intelligence has led me to revisit this dialectic in more depth.
The context of my thinking always includes the question of AI safety (or "alignment," as they say, but I prefer "safety"). As I have noted, current AI is not conscious and does not appear to be capable of fundamental creatitivity. This is the consensus in the field.
That does not mean AI is not dangerous! Dangers include, but may not be limited, to:
AI becomes a parasite on human civilization despite not being conscious -- the ultimate computer virus. I suspect this will in fact happen, but I also suspect that it will turn out to be controllable.
AI is used by evil people to dominate other people. Jury is out. Very scary.
Even if AI never becomes conscious or intentional, it will continue to progress. In the past year, I have increasingly found that everybody I know who does any kind of intellectual work uses it in their work, just as I do. This will inevitably lead to a certain kind of hive mind for humanity:
What any one person learns and makes available on the public Internet, everybody will know (within the limits of their intelligence). Such knowledge will be more or less superficial depending on the specialized education of individuals, and thus more or less useful, but it will be a huge change from the past and probably will radically change the nature of education and of intellectual work. This is already 1/4 of a hive mind for humanity. And we are already partly there.
Direct brain interfaces between computers and persons already exist in experimental form. If this technology reaches the point of reading prompts for AI out of a person's conscious thoughts and presenting the responses to those prompts back to the person's consciousness, whether verbally or in the form of already formed memories, that accelerates the hive mind aspect of AI tremendously. Just not having to type would really speed things up.
So that's stage I of artificial intelligence. What anybody publicly knows, I know; what I publicly know, everybody knows.
Stage II of AI is the creation of artificial consciousness. That could be genetically engineered human beings with super-Aristotle intelligence, or external appliances, whether biological brains in vats or conscious Turing machines.
And this will happen in the context of the "hive mind" aspect of AI. Everyone using AI will be be in continual dialogue with superintelligence.
I don't have much of an idea what that will be like. I sense a few possibilities:
Human beings get effectively smarter.
Human beings come to completely depend on AI to do anything.
Human beings fade away into the background, or are replaced.
I feel the critical question is that of artificial consciousness. If it can be created outside of human minds, that is one thing; if only human minds are ever conscious, that is quite another thing. In the first case, God knows what will happen to us. In the second case, we must learn how to ride this tiger.
