Digifesto

barriers to participant observation of data science and ethnography

By chance, last night I was at a social gathering with two STS scholars that are unaffiliated with BIDS. One of them is currently training in ethnographic methods. I explained to him some of my quandaries as a data scientist working with ethnographers studying data science. How can I be better in my role?

He talked about participant observation, and how hard it is in a scientific setting. An experienced STS ethnographer who he respected has said: participant observation means being ready for an almost constant state of humiliation. Your competence is always being questioned; you are always acting inappropriately; your questions are considered annoying or off-base. You try to learn the tacit knowledge required in the science but will always be less good at it than the scientists themselves. This is all necessary for the ethnographic work.

To be a good informant (and perhaps this is my role, being a principal informant) means patiently explaining lots of things that normally go unexplained. One must make explicit that which is obvious and tacit to the experienced practitioner.

This sort of explanation accords well with my own training in qualitative methods and reading in this area, which I have pursued alongside my data science practice. This has been a deliberate blending in my graduate studies. In one semester I took both Statistical Learning Theory with Martin Wainwright and Qualitative Research Methods with Jenna Burrell. I’ve taken Behavioral Data Mining with John Canny and a seminar taught by Jean Lave on “What Theory Matters”.

I have been trying to cover my bases, methodologically. Part of this is informed by training in Bayesian methods as an undergraduate. If you are taught to see yourself as an information processing machine and take the principles of statistical learning seriously, then if you’re like me you may be concerned about bias in the way you take in information. If you get a sense that there’s some important body of knowledge or information to which you haven’t been adequately exposed, you seek it out in order to correct the bias.

This is not unlike what is called theoretical sampling in the qualitative methods literature. My sense, after being trained in both kinds of study, is that the principles that motivate them are the same or similar enough to make reconciliation between the approaches achievable.

I choose to identify as a data scientist, not as an ethnographer. One reason for this is that I believe I understand what ethnography is, that it is a long and arduous process of cultural immersion in which one attempts to articulate the emic experience of those under study, and that I am not primarily doing this kind of activity with my research. I have tried to ethnographic work on an online community. I would argue that this was particularly bad ethnographic work. I concluded some time ago that I don’t have the right temperament to be an ethnographer per se.

Nevertheless, here I am participating in an Ethnography Group. It turns out that it is rather difficult to participate in an ethnographic context with ethnographers of science while still maintaining ones identity as the kind of scientist that is being studied. Part of this has to do with conflicts over epistemic norms. Attempting to argue on the basis of scientific authority about the validity of the method of that science to a room of STS ethnographers is not taken as useful information from an informant nor as a creatively galvanizing rocking of the boat. It is seen as unproductive and potentially disrespectful.

Rather than treating this as an impasse, I have been pondering how to use these kinds of divisions productively. As a first pass, I’m finding it helpful in coming to an understanding of what data science is by seeing, perhaps with a clarity that others might not have the privilege of, what it is not. In a sense the Ethnography and Evaluation Working Group of the Berkeley Institute of Data Science is really at the boundary of data science.

This is exciting, because as far as I can tell nobody knows what data science is. Alternative definitions of data science is a joke in industry. The other day our ethnography team was discussing a seminar about “what is data science” with a very open minded scientist and engineer and he said he got a lot out of the seminar but that it reached no conclusions as to what this nascent field is. “What is data science?” and even “is there such a thing as data science?” are still unanswered questions and may continue to be unanswered even after industry has stopped hyping the term and started calling it something else.

So, you might ask, what happens at the boundary of data science and ethnography

The answer is: an epistemic conflict that’s deeply grounded in historical, cultural, institutional, and cognitive differences. It’s also a conflict that threatens the very project of an ethnography of data science itself.

The problem, I feel qualified to say as somebody with training on both sides of the fence and quite a bit of experience teaching both technical and non-technical subject matter, is this: learning the skills and principles behind good data science does not come easily to everybody and in any case takes a lot of hard work and time. These skills and principles pertain to many deep practices and literatures that are developed self-consciously in a cumulative way. Any one sub-field within the many technical disciplines that comprise “data science” could take years to master, and to do so is probably impossible without adequate prior mathematical training that many people don’t receive, perhaps because they lack the opportunity or don’t care.

In fewer words: there is a steep learning curve, and the earlier people start to climb it, the easier it is for them to practice data science.

My point is that this is bad news for the participant observer. Something I sometimes hear ethnographers in the data science space say of people is “I just can’t talk to that person; they think so differently from me.” Often the person in question is, to my mind, exactly the sort of person I would peg as an exemplary data scientist.

Often these are people with a depth of technical understanding that I don’t have and aspire to have. I recognize that they have made the difficult choice to study more of the foundations of what I believe to be an important field, despite the fact that this is (as evinced by the reaction of ‘softer’ social sciences) alienating to a lot of people. These are the people whom I can consult on methodological questions that are integral to my work as a data scientist. It is part of data science practice to discuss epistemic norms seriously with others in order to make sure that the integrity of the science is upheld. Knowledge about statistical norms and principles is taught in classes and reading groups and practiced in, for example, computational manipulation of data. But this knowledge is also expanded and maintained through informal, often passionate and even aggressive, conversations with colleagues.

I don’t know where this leaves the project of ethnography of data science. One possibility is that it can abandon participant observation as a method because participant observation is too difficult. That would be a shame but might simply be necessary.

Another strategy, which I think is potentially more interesting, is to ask seriously: why is this so difficult? What is difficult about data science? For whom is it most difficult? Do experts experience the same difficulties, or different ones? And so on.

statistics, values, and norms

Further considering the difficulties of doing an ethnography of data science, I am reminded of Hannah Arendt’s comments about the apolitically of science.

The problem is this:

  • Ethnography as a practice is, as far as I can tell, descriptive. It is characterized primarily by non-judgmental observation.
  • Data scientific practice is tied to an epistemology of statistics. Statistics is a discipline about values and norms for belief formation. While superficially it may appear to have no normative content, practicing statistical thinking in research is a matter of adherence to norms.
  • It is very difficult to reconcile ethnographic epistemology and statistical epistemology. They have completely different intellectual histories and are based in very different cognitive modalities.
  • Ethnographers are often trained to reject statistical epistemology in their own work and as a result don’t learn statistics.
  • Consequently, most ethnographies of data science practice will leave out precisely that which data scientists see as most essential to their practice.

“Statistics” here is not entirely accurate. In computational statistics or ‘data science’, we can replace “statistics” with a large body of knowledge spanning statistics, probability theory, theory of computation, etc. The hybridization of these bodies of knowledge in, for example, educational curricula, is an interesting shift in the trajectory of science as a whole.

A deeper concern: in the self-understanding of the sciences, there is a transmitted sense of this intellectual history. In many good textbooks on technical subject-matter, there is a section at the end of each chapter on the history of the field. I come to these sections of the textbook with a sense of reverence. They stand as testaments to the century of cumulative labor done by experts on which our current work stands.

When this material is of disinterest to the historian or ethnographer of science, it feels like a kind of sacrilege. Contrast this disinterest with the treatment of somebody like Imre Lakatos, whose history of mathematics is so much more a history of the mathematics, not a history of the mathematicians, that the form of the book is a dialog compressing hundreds of years of mathematical history into a single classroom discussion. Historical detail is provided in footnotes, apart from the main drama of the narrative–which is about the emergence of norms of reasoning over time.

Observations of ethnographers

This semester the Berkeley Institute of Data Science Ethnography and Evaluation Working Group (EEWG) is meeting in its official capacity for the first time. In this context I am a data scientist among ethnographers and the transition to participation in this strange, alien culture is not an easy one. I have prepared myself for this task through coursework and associations throughout my time at Berkeley, but I am afraid that integrating into an “Science and Technology Studies” ethnographic team will be difficult nonetheless.

Off the bat, certain cultural differences seem especially salient:

In the sciences, typically one cares about whether or not the results of your investigation are true or useful in an intersubjective sense. This sense of purpose leads to a sense of concern for the logical coherence and rigor of ones method, which in turn constrains the kinds of questions that can be asked and the ways in which one presents ones results. Within STS, methodological concerns are perhaps secondary. The STS ethnographer interviews people, reads and listens carefully, but also holds the data at a distance. Ultimately, the process of writing–which is necessarily tied up with what the writer is interested in–is as much a part of the method as the observations and the analysis. Whereas the scientist strives for intersubjective agreement, the ethnographer is methodologically bound to their own subjectivity.

A consequence of this is that agonism, or the role of argumentation and disagreement, is different within scientific and ethnographic communities. (I owe this point to my colleague, Stuart Geiger, an ethnographer who is also in the working group.) In a scientific community argument is seen as a necessary step towards resolving disagreement and arriving at intersubjective results. The purpose of argument is, ideally, to arrive at agreement. Reasons are shared and disagreements resolved through logic. In the ethnographic community, since intersubjectivity is not valued, rational argument is seen more as form of political or social maneuvering. To challenge somebody intellectually is not simply to challenge what they are intellectualizing; it is to challenge their intellectual authority or competence.

This raises an interesting question: what is competence, to ethnographers? To the scientist, competence is a matter of concrete skills (handling lab equipment, computation, reasoning, presentation of results, etc.) that facilitate the purpose of science, the achievement of intellectual agreement on matters within the domain. Somebody who succeeds by virtue of skills other than these (such as political skilfulness) is seen, by the scientist, as a charlatan and a danger to the scientific project. Many of the more antisocial tendencies of scientists can be understood as an effort to keep the charlatans out, in order to preserve the integrity (and, ultimately, authority) of the scientific project.

Ethnographic competence is mysterious to me because, at least in STS, scientific authority is always a descriptively assessed social phenomenon and not something which one trusts. If the STS ethnographer sees the scientific project primarily as one of cultural and institutional regularity and leaves out the teleological aspects of science as a search for truth of some kind, as has been common in STS since the 80’s, then how can STS see its own intellectual authority besides as a rather arbitrary political accomplishment? What is competence besides the judgement of competence by ones bureaucratic superiors?

I am not sure that these questions, which seem pertinent to me as a scientist, are even askable within the language and culture of STS. As they concern the normative elements of intellectual inquiry, not descriptions of the social institutions of inquiry, they are in a sense “unscientific” questions vis-a-vis STS.

Perhaps more importantly, these questions are unimportant to the STS ethnographer because they are not relevant to the STS ethnographer’s job. In this way the STS ethnographer is not unlike many practicing scientists who, once they learn an approved method and have a community in which to share their work, do not question the foundations of their field. And perhaps because of STS’s concern with what others might consider the mundane aspects of scientific inquiry–the scheduling of meetings, the arrangement of events, the circulation of invitations and rejection letters, the arrangement of institutions–their concept of intellectual work hinges on these activities more than it does argument or analysis, relative to the sciences.

The recalcitrance of prediction

We have identified how Bostrom’s core argument for superintelligence explosion depends on a crucial assumption. An intelligence explosion will happen only if the kinds of cognitive capacities involved in instrumental reason are not recalcitrant to recursive self-improvement. If recalcitrance rises comparably with the system’s ability to improve itself, then the takeoff will not be fast. This significantly decreases the probability of decisively strategic singleton outcomes.

In this section I will consider the recalcitrance of intelligent prediction, which is one of the capacities that is involved in instrumental reason (another being planning). Prediction is a very well-studied problem in artificial intelligence and statistics and so is easy to characterize and evaluate formally.

Recalcitrance is difficult to formalize. Recall that in Bostrom’s formulation:

\frac{dI}{dt} = \frac{O(I)}{R(I)}

One difficulty in analyzing this formula is that the units are not specified precisely. What is a “unit” of intelligence? What kind of “effort” is the unit of optimization power? And how could one measure recalcitrance?

A benefit of looking at a particular intelligent task is that it allows us to think more concretely about what these terms mean. If we can specify which tasks are important to consider, then we can take the level of performance on those well-specified class of problems as measures of intelligence.

Prediction is one such problem. In a nutshell, prediction comes down to estimating a probability distribution over hypotheses. Using the Bayesian formulation of statistical influence, we can represent the problem as:

P(H|D) = \frac{P(D|H) P(H)}{P(D)}

Here, P(H|D) is the posterior probability of a hypothesis H given observed data D. If one is following statistically optimal procedure, one can compute this value by taking the prior probability of the hypothesis P(H), multiplying it by the likelihood of the data given the hypothesis P(D|H), and then normalizing this result by dividing by the probability of the data over all models, P(D) = \sum_{i}P(D|H_i)P(H_i).

Statisticians will justifiably argue whether this is the best formulation of prediction. And depending on the specifics of the task, the target value may well be some function of posterior (such as the hypothesis with maximum likelihood) and the overall distribution may be secondary. These are valid objections that I would like to put to one side in order to get across the intuition of an argument.

What I want to point out is that if we look at the factors that affect performance on prediction problems, there a very few that could be subject to algorithmic self-improvement. If we think that part of what it means for an intelligent system to get more intelligent is to improve its ability of prediction (which Bostrom appears to believe), but improving predictive ability is not something that a system can do via self-modification, then that implies that the recalcitrance of prediction, far from being constant or lower, actually approaches infinity with respect the an autonomous system’s capacity for algorithmic self-improvement.

So, given the formula above, in what ways can an intelligent system improve its capacity to predict? We can enumerate them:

  • Computational accuracy. An intelligent system could be better or worse at computing the posterior probabilities. Since most of the algorithms that do this kind of computation do so with numerical approximation, there is the possibility of an intelligent system finding ways to improve the accuracy of this calculation.
  • Computational speed. There are faster and slower ways to compute the inference formula. An intelligent system could come up with a way to make itself compute the answer faster.
  • Better data. The success of inference is clearly dependent on what kind of data the system has access to. Note that “better data” is not necessarily the same as “more data”. If the data that the system learns from is from a biased sample of the phenomenon in question, then a successful Bayesian update could make its predictions worse, not better. Better data is data that is informative with respect to the true process that generated the data.
  • Better prior. The success of inference depends crucially on the prior probability assigned to hypotheses or models. A prior is better when it assigns higher probability to the true process that generates observable data, or models that are ‘close’ to that true process. An important point is that priors can be bad in more than one way. The bias/variance tradeoff is well-studied way of discussing this. Choosing a prior in machine learning involves a tradeoff between:
    1. Bias. The assignment of probability to models that skew away from the true distribution. An example of a biased prior would be one that gives positive probability to only linear models, when the true phenomenon is quadratic. Biased priors lead to underfitting in inference.
    2. Variance.The assignment of probability to models that are more complex than are needed to reflect the true distribution. An example of a high-variance prior would be one that assigns high probability to cubic functions when the data was generated by a quadratic function. The problem with high variance priors is that they will overfit data by inferring from noise, which could be the result of measurement error or something else less significant than the true generative process.

    In short, there best prior is the correct prior, and any deviation from that increases error.

Now that we have enumerate the ways in which an intelligent system may improve its power of prediction, which is one of the things that’s necessary for instrumental reason, we can ask: how recalcitrant are these factors to recursive self-improvement? How much can an intelligent system, by virtue of its own intelligence, improve on any of these factors?

Let’s start with computational accuracy and speed. An intelligent system could, for example, use some previously collected data and try variations of its statistical inference algorithm, benchmark their performance, and then choose to use the most accurate and fastest ones at a future time. Perhaps the faster and more accurate the system is at prediction generally, the faster and more accurately it would be able to engage in this process of self-improvement.

Critically, however, there is a maximum amount of performance that one can get from improvements to computational accuracy if you hold the other factors constant. You can’t be more accurate than perfectly accurate. Therefore, at some point recalcitrance of computational accuracy rises to infinity. Moreover, we would expect that effort made at improving computational accuracy would exhibit diminishing returns. In other words, recalcitrance of computational accuracy climbs (probably close to exponentially) with performance.

What is the recalcitrance of computational speed at inference? Here, performance is limited primarily by the hardware on which the intelligent system is implemented. In Bostrom’s account of superintelligence explosion, he is ambiguous about whether and when hardware development counts as part of a system’s intelligence. What we can say with confidence, however, is that for any particular piece of hardware there will be a maximum computational speed attainable with with, and that recursive self-improvement to computational speed can at best approach and attain this maximum. At that maximum, further improvement is impossible and recalcitrance is again infinite.

What about getting better data?

Assuming an adequate prior and the computational speed and accuracy needed to process it, better data will always improve prediction. But it’s arguable whether acquiring better data is something that can be done by an intelligent system working to improve itself. Data collection isn’t something that the intelligent system can do autonomously, since it has to interact with the phenomenon of interest to get more data.

If we acknowledge that data collection is a critical part of what it takes for an intelligent system to become more intelligent, then that means we should shift some of our focus away from “artificial intelligence” per se and onto ways in which data flows through society and the world. Regulations about data locality may well have more impact on the arrival of “superintelligence” than research into machine learning algorithms now that we have very faster, very accurate algorithms already. I would argue that the recent rise in interest in artificial intelligence is due mainly to availability of vast amounts of new data through sensors and the Internet. Advances in computational accuracy and speed (such as Deep Learning) have to catch up to this new availability of data and use new hardware, but data is the rate limiting factor.

Lastly, we have to ask: can a system improve its own prior, if data, computational speed, and computational accuracy are constant?

I have to argue that it can’t do this in any systematic way, if we are looking at the performance of the system at the right level of abstraction. Potentially a machine learning algorithm could modify its prior if it sees itself as underperforming in some ways. But there is a sense in which any modification to the prior made by the system that is not a result of a Bayesian update is just part of the computational basis of the original prior. So recalcitrance of the prior is also infinite.

We have examined the problem of statistical inference and ways that an intelligent system could improve its performance on this task. We identified four potential factors on which it could improve: computational accuracy, computational speed, better data, and a better prior. We determined that contrary to the assumption of Bostrom’s hard takeoff argument, the recalcitrance of prediction is quite high, approaching infinity in the cases of computational accuracy, computational speed, and the prior. Only data collections to be flexibly recalcitrant. But data collection is not a feature of the intelligent system alone but also depends on its context.

As a result, we conclude that the recalcitrance of prediction is too high for an intelligence explosion that depends on it to be fast. We also note that those concerned about superintelligent outcomes should shift their attention to questions about data sourcing and storage policy.

Nissenbaum the functionalist

Today in Classics we discussed Helen Nissenbaum’s Privacy in Context.

Most striking to me is that Nissenbaum’s privacy framework, contextual integrity theory, depends critically on a functionalist sociological view. A context is defined by its information norms and violations of those norms are judged according to their (non)accordance with the purposes and values of the context. So, for example, the purposes of an educational institution determine what are appropriate information norms within it, and what departures from those norms constitute privacy violations.

I used to think teleology was dead in the sciences. But recently I learned that it is commonplace in biology and popular in ecology. Today I learned that what amounts to a State Philosopher in the U.S. (Nissenbaum’s framework has been more or less adopted by the FTC) maintains a teleological view of social institutions. Fascinating! Even more fascinating that this philosophy corresponds well enough to American law as to be informative of it.

From a “pure” philosophy perspective (which is I will admit simply a vice of mine), it’s interesting to contrast Nissenbaum with…oh, Horkheimer again. Nissenbaum sees ethical behavior (around privacy at least) as being behavior that is in accord with the purpose of ones context. Morality is given by the system. For Horkheimer, the problem is that the system’s purposes subsume the interests of the individual, who is alone the agent who is able to determine what is right and wrong. Horkheimer is a founder of a Frankfurt school, arguably the intellectual ancestor of progressivism. Nissenbaum grounds her work in Burke and her theory is admittedly conservative. Privacy is violated when people’s expectations of privacy are violated–this is coming from U.S. law–and that means people’s contextual expectations carry more weight than an individual’s free-minded beliefs.

The tension could be resolved when free individuals determine the purpose of the systems they participate in. Indeed, Nissenbaum quotes Burke in his approval of established conventions as being the result of accreted wisdom and rationale of past generations. The system is the way it is because it was chosen. (Or, perhaps, because it survived.)

Since Horkheimer’s objection to “the system” is that he believes instrumentality has run amok, thereby causing the system serve a purpose nobody intended for it, his view is not inconsistent with Nissenbaum’s. Nissenbaum, building on Dworkin, sees contextual legitimacy as depending on some kind of political legitimacy.

The crux of the problem is the question of what information norms comprise the context in which political legitimacy is formed, and what purpose does this context or system serve?

The relationship between Bostrom’s argument and AI X-Risk

One reason why I have been writing about Bostrom’s superintelligence argument is because I am acquainted with what could be called the AI X-Risk social movement. I think it is fair to say that this movement is a subset of Effective Altruism (EA), a laudable movement whose members attempt to maximize their marginal positive impact on the world.

The AI X-Risk subset, which is a vocal group within EA, sees the emergence of a superintelligent AI as one of several risks that is notably because it could ruin everything. AI is considered to be a “global catastrophic risk” unlike more mundane risks like tsunamis and bird flu. AI X-Risk researchers argue that because of the magnitude of the consequences of the risk they are trying to anticipate, they must raise more funding and recruit more researchers.

While I think this is noble, I think it is misguided for reasons that I have been outlining in this blog. I am motivated to make these arguments because I believe that there are urgent problems/risks that are conceptually adjacent (if you will) to the problem AI X-Risk researchers study, but that the focus on AI X-Risk in particular diverts interest away from them. In my estimation, as more funding has been put into evaluating potential risks from AI many more “mainstream” researchers have benefited and taken on projects with practical value. To some extent these researchers benefit from the alarmism of the AI X-Risk community. But I fear that their research trajectory is thereby distorted from where it could truly provide maximal marginal value.

My reason for targeting Bostrom’s argument for the existential threat of superintelligent AI is that I believe it’s the best defense of the AI X-Risk thesis out there. In particular, if valid the argument should significantly raise the expected probability of an existentially risky AI outcome. For Bostrom, it is likely a natural consequence of advancement in AI research more generally because of recursive self-improvement and convergent instrumental values.

As I’ve informally work shopped this argument I’ve come upon this objection: Even if it is true that a superintelligent system would not for systematic reasons become a existentially risky singleton, that does not mean that somebody couldn’t develop such a superintelligent system in an unsystematic way. There is still an existential risk, even if it is much lower. And because existential risks are so important, surely we should prepare ourselves for even this low probability event.

There is something inescapable about this logic. However, the argument applies equally well to all kinds of potential apocalypses, such as enormous meteors crashing into the earth and biowarfare produced zombies. Without some kind of accounting of the likelihood of these outcomes, it’s impossible to do a rational budgeting.

Moreover, I have to call into question the rationality of this counterargument. If Bostrom’s arguments are used in defense of the AI X-Risk position but then the argument is dismissed as unnecessary when it is challenged, that suggests that the AI X-Risk community is committed to their cause for reasons besides Bostrom’s argument. Perhaps these reasons are unarticulated. One could come up with all kinds of conspiratorial hypotheses about why a group of people would want to disingenuously spread the idea that superintelligent AI poses an existential threat to humanity.

The position I’m defending on this blog (until somebody convinces me otherwise–I welcome all comments) is that a superintelligent AI singleton is not a significantly likely X-Risk. Other outcomes that might be either very bad or very good, such as ones with many competing and cooperating superintelligences, are much more likely. I’d argue that it’s more or less what we have today, if you consider sociotechnical organizations as a form of collective superintelligence. This makes research into this topic not only impactful in the long run, but also relevant to problems faced by people now and in the near future.

Bostrom and Habermas: technical and political moralities, and the God’s eye view

An intriguing chapter that follows naturally from Nick Bostrom’s core argument is his discussion of machine ethics writ large. He asks: suppose one could install into an omnipotent machine ethical principles, trusting it with the future of humanity. What principles should we install?

What Bostrom accomplishes by positing his Superintelligence (which begins with something simply smarter than humans, and evolves over the course of the book into something that takes over the galaxy) is a return to what has been called “the God’s eye view”. Philosophers once attempted to define truth and morality according to perspective of an omnipotent–often both transcendent and immanent–god. Through the scope of his work, Bostrom has recovered some of these old themes. He does this not only through his discussion of Superintelligence (and positing its existence in other solar systems already) but also through his simulation arguments.

The way I see it, one thing I am doing by challenging the idea of an intelligence explosion and its resulting in a superintelligent singleton is problematizing this recovery of the God’s Eye view. If your future world is governed by many sovereign intelligent systems instead of just one, then ethics are something that have to emerge from political reality. There is something irreducibly difficult about interacting with other intelligences and it’s from this difficulty that we get values, not the other way around. This sort of thinking is much more like Habermas’s mature ethical philosophy.

I’ve written about how to apply Habermas to the design of networked publics that mediate political interactions between citizens. What I built and offer as toy example in that paper, @TheTweetserve, is simplistic but intended just as a proof of concept.

As I continue to read Bostrom, I expect a convergence on principles. “Coherent extrapolated volition” sounds a lot like a democratic governance structure with elected experts at first pass. The question of how to design a governance structure or institution that leverages artificial intelligence appropriately while legitimately serving its users motivates my dissertation research. My research so far has only scratched the surface of this problem.

Recalcitrance examined: an analysis of the potential for superintelligence explosion

To recap:

  • We have examined the core argument from Nick Bostrom’s Superintelligence: Paths, Dangers, Strategies regarding the possibility of a decisively strategic superintelligent singleton–or, more glibly, an artificial intelligence that takes over the world.
  • With an eye to evaluating whether this outcome is particularly likely relative to other futurist outcomes, we have distilled the argument and in so doing have reduced it to a simpler problem.
  • That problem is to identify bounds on the recalcitrance of the capacities that are critical for instrumental reasoning. Recalcitrance is defined as the inverse of the rate of increase to intelligence per time per unit of effort put into increasing that intelligence. It is meant to capture how hard it is to make an intelligent system smarter, and in particular how hard it is for an intelligent system to make itself smarter. Bostrom’s argument is that if an intelligent system’s recalcitrance is constant or lower, then it is possible for the system to undergo an “intelligence explosion” and take over the world.
  • By analyzing how Bostrom’s argument depends only on the recalcitrance of instrumentality, and not of the recalcitrance of intelligence in general, we can get a firmer grip on the problem. In particular, we can focus on such tasks as prediction and planning. If we discover that these tasks are in fact significantly recalcitrant that should reduce our expected probability of an AI singleton and consequently cause us to divert research funds to problems that anticipate other outcomes.

In this section I will look in further depth at the parts of Bostrom’s intelligence explosion argument about optimization power and recalcitrance. How recalcitrant must a system be for it to not be susceptible to an intelligence explosion?

This section contains some formalism. For readers uncomfortable with that, trust me: if the system’s recalcitrance is roughly proportional to the amount that the system is able to invest in its own intelligence, then the system’s intelligence will not explode. Rather, it will climb linearly. If the system’s recalcitrance is significantly greater than the amount that the system can invest in its own intelligence, then the system’s intelligence won’t even climb steadily. Rather, it will plateau.

To see why, recall from our core argument and definitions that:

Rate of change in intelligence = Optimization power / Recalcitrance.

Optimization power is the amount of effort that is put into improving the intelligence of system. Recalcitrance is the resistance of that system to improvement. Bostrom presents this as a qualitative formula then expands it more formally in subsequent analysis.

\frac{dI}{dt} = \frac{O(I)}{R}

Bostrom’s claim is that for instrumental reasons an intelligent system is likely to invest some portion of its intelligence back into improving its intelligence. So, by assumption we can model O(I) = \alpha I + \beta for some parameters \alpha and \beta, where 0 < \alpha < 1 and \beta represents the contribution of optimization power by external forces (such as a team of researchers). If recalcitrance is constant, e.g R = k, then we can compute:

\Large \frac{dI}{dt} = \frac{\alpha I + \beta}{k}

Under these conditions, I will be exponentially increasing in time t. This is the “intelligence explosion” that gives Bostrom’s argument so much momentum. The explosion only gets worse if recalcitrance is below a constant.

In order to illustrate how quickly the “superintelligence takeoff” occurs under this model, I’ve plotted the above function plugging in a number of values for the parameters \alpha, \beta and k. Keep in mind that the y-axis is plotted on a log scale, which means that a roughly linear increase indicates exponential growth.

Plot of exponential takeoff rates

Modeled superintelligence takeoff where rate of intelligence gain is linear in current intelligence and recalcitrance is constant. Slope in the log scale is determine by alpha and k values.

It is true that in all the above cases, the intelligence function is exponentially increasing over time. The astute reader will notice that by my earlier claim \alpha cannot be greater than 1, and so one of the modeled functions is invalid. It’s a good point, but one that doesn’t matter. We are fundamentally just modeling intelligence expansion as something that is linear on the log scale here.

However, it’s important to remember that recalcitrance may also be a function of intelligence. Bostrom does not mention the possibility of recalcitrance being increasing in intelligence. How sensitive to intelligence would recalcitrance need to be in order to prevent exponential growth in intelligence?

Consider the following model where recalcitrance is, like optimization power, linearly increasing in intelligence.

\frac{dI}{dt} = \frac{\alpha_o I + \beta_o}{\alpha_r I + \beta_r}

Now there are four parameters instead of three. Note this model is identical to the one above it when \alpha_r = 0. Plugging in several values for these parameters and plotting again with the y-scale on the log axis, we get:

Plot of takeoff when both optimization power and recalcitrance are linearly increasing in intelligence. Only when recalcitrance is unaffected by intelligence level is there an exponential takeoff. In the other cases, intelligence quickly plateaus on the log scale. No matter how much the system can invest in its own optimization power as a proportion of its total intelligence, it still only takes off at a linear rate.

Plot of takeoff when both optimization power and recalcitrance are linearly increasing in intelligence. Only when recalcitrance is unaffected by intelligence level is there an exponential takeoff. In the other cases, intelligence quickly plateaus on the log scale. No matter how much the system can invest in its own optimization power as a proportion of its total intelligence, it still only takes off at a linear rate.

The point of this plot is to illustrate how easily exponential superintelligence takeoff might be stymied by a dependence of recalcitrance on intelligence. Even in the absurd case where the system is able to invest a thousand times as much intelligence that it already has back into its own advancement, and a large team steadily commits a million “units” of optimization power (whatever that means–Bostrom is never particularly clear on the definition of this), a minute linear dependence of recalcitrance on optimization power limits the takeoff to linear speed.

Are the reasons to think that recalcitrance might increase as intelligence increases? Prima facie, yes. Here’s a simple thought experiment: What if there is some distribution of intelligence algorithm advances that are available in nature and that some of them are harder to achieve than others. A system that dedicates itself to advancing its own intelligence, knowing that it gets more optimization power as it gets more intelligent, might start by finding the “low hanging fruit” of cognitive enhancement. But as it picks the low hanging fruit, it is left with only the harder discoveries. Therefore, recalcitrance increases as the system grows more intelligent.

This is not a decisive argument against fast superintelligence takeoff and the possibility of a decisively strategic superintelligent singleton. Above is just an argument about why it is important to consider recalcitrance carefully when making claims about takeoff speed, and to counter what I believe is a bias in Bostrom’s work towards considering unrealistically low recalcitrance levels.

In future work, I will analyze the kinds of instrumental intelligence tasks, like prediction and planning, that we have identified as being at the core of Bostrom’s superintelligence argument. The question we need to ask is: does the recalcitrance of prediction tasks increase as the agent performing them becomes better at prediction? And likewise for planning. If prediction and planning are the two fundamental components of means-ends reasoning, and both have recalcitrance that increases significantly with the intelligence of the agent performing them, then we have reason to reject Bostrom’s core argument and assign a very low probability to the doomsday scenario that occupies much of Bostrom’s imagination in Superintelligence. If this is the case, that suggests we should be devoting resources to anticipating what he calls multipolar scenarios, where no intelligent system has a decisive strategic advantage, instead.

Instrumentality run amok: Bostrom and Instrumentality

Narrowing our focus onto the crux of Bostrom’s argument, we can see how tightly it is bound to a much older philosophical notion of instrumental reason. This comes to the forefront in his discussion of the orthogonality thesis (p.107):

The orthogonality thesis
Intelligence and final goals are orthogonal: more or less any level of intelligence could in principle be combined with more or less any final goal.

Bostrom goes on to clarify:

Note that the orthogonality thesis speaks not of rationality or reason, but of intelligence. By “intelligence” we here mean something like skill at prediction, planning, and means-ends reasoning in general. This sense of instrumental cognitive efficaciousness is most relevant when we are seeking to understand what the causal impact of a machine superintelligence might be.

Bostrom maintains that the generality of instrumental intelligence, which I would argue is evinced by the generality of computing, gives us a way to predict how intelligent systems will act. Specifically, he says that an intelligent system (and specifically a superintelligent) might be predictable because of its design, because of its inheritance of goals from a less intelligence system, or because of convergent instrumental reasons. (p.108)

Return to the core logic of Bostrom’s argument. The existential threat posed by superintelligence is simply that the instrumental intelligence of an intelligent system will invest in itself and overwhelm any ability by us (its well-intentioned creators) to control its behavior through design or inheritance. Bostrom thinks this is likely because instrumental intelligence (“skill at prediction, planning, and means-ends reasoning in general”) is a kind of resource or capacity that can be accumulated and put to other uses more widely. You can use instrumental intelligence to get more instrumental intelligence; why wouldn’t you? The doomsday prophecy of a fast takeoff superintelligence achieving a decisive strategic advantage and becoming a universe-dominating singleton depends on this internal cycle: instrumental intelligence investing in itself and expanding exponentially, assuming low recalcitrance.

This analysis brings us to a significant focal point. The critical missing formula in Bostrom’s argument is (specifically) the recalcitrance function of instrumental intelligence. This is not the same as recalcitrance with respect to “general” intelligence or even “super” intelligence. Rather, what’s critical is how much a process dedicated to “prediction, planning, and means-ends reasoning in general” can improve its own capacities at those things autonomously. The values of this recalcitrance function will bound the speed of superintelligence takeoff. These bounds can then inform the optimal allocation of research funding towards anticipation of future scenarios.


In what I hope won’t distract from the logical analysis of Bostrom’s argument, I’d like to put it in a broader context.

Take a minute to think about the power of general purpose computing and the impact it has had on the past hundred years of human history. As the earliest digital computers were informed by notions of artificial intelligence (c.f. Alan Turing), we can accurately say that the very machine I use to write this text, and the machine you use to read it, are the result of refined, formalized, and materialized instrumental reason. Every programming language is a level of abstraction over a machine that has no ends in itself, but which serves the ends of its programmer (when it’s working). There is a sense in which Bostrom’s argument is not about a near future scenario but rather is just a description of how things already are.

Our very concepts of “technology” and “instrument” are so related that it can be hard to see any distinction at all. (c.f. Heidegger, “The Question Concerning Technology“) Bostrom’s equating of instrumentality with intelligence is a move that makes more sense as computing becomes ubiquitously part of our experience of technology. However, if any instrumental mechanism can be seen as a form of intelligence, that lends credence to panpsychist views of cognition as life. (c.f. the Santiago theory)

Meanwhile, arguably the genius of the market is that it connects ends (through consumption or “demand”) with means (through manufacture and services, or “supply”) efficiently, bringing about the fruition of human desire. If you replace “instrumental intelligence” with “capital” or “money”, you get a familiar critique of capitalism as a system driven by capital accumulation at the expense of humanity. The analogy with capital accumulation is worthwhile here. Much as in Bostrom’s “takeoff” scenarios, we can see how capital (in the modern era, wealth) is reinvested in itself and grows at an exponential rate. Variable rates of return on investment lead to great disparities in wealth. We today have a “multipolar scenario” as far as the distribution of capital is concerned. At times people have advocated for an economic “singleton” through a planned economy.

It is striking that contemporary analytic philosopher and futurist Nick Bostrom’s contemplates the same malevolent force in his apocalyptic scenario as does Max Horkheimer in his 1947 treatise “Eclipse of Reason“: instrumentality run amok. Whereas Bostrom concerns himself primarily with what is literally a machine dominating the world, Horkheimer sees the mechanism of self-reinforcing instrumentality as pervasive throughout the economic and social system. For example, he sees engineers as loci of active instrumentalism. Bostrom never cites Horkheimer, let alone Heidegger. That there is a convergence of different philosophical sub-disciplines on the same problem suggests that there are convergent ultimate reasons which may triumph over convergent instrumental reasons in the end. The question of what these convergent ultimate reasons are, and what their relationship to instrumental reasons is, is a mystery.

Further distillation of Bostrom’s Superintelligence argument

Following up on this outline of the definitions and core argument of Bostrom’s Superintelligence, I will try to narrow in on the key mechanisms the argument depends on.

At the heart of the argument are a number of claims about instrumentally convergent values and self-improvement. It’s important to distill these claims to their logical core because their validity affects the probability of outcomes for humanity and the way we should invest resources in anticipation of superintelligence.

There are a number of ways to tighten Bostrom’s argument:

Focus the definition of superintelligence. Bostrom leads with the provocative but fuzzy definition of superintelligence as “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains of interest.” But the overall logic of his argument makes it clear that the domain of interest does not necessarily include violin-playing or any number of other activities. Rather, the domains necessary for a Bostrom superintelligence explosion are those that pertain directly to improving ones own intellectual capacity. Bostrom speculates about these capacities in two ways. In one section he discusses the “cognitive superpowers”, domains that would quicken a superintelligence takeoff. In another section he discusses convergent instrumental values, values that agents with a broad variety of goals would converge on instrumentally.

  • Cognitive Superpowers
    • Intelligence amplification
    • Strategizing
    • Social manipulation
    • Hacking
    • Technology research
    • Economic productivity
  • Convergent Instrumental Values
    • Self-preservation
    • Goal-content integrity
    • Cognitive enhancement
    • Technological perfection
    • Resource acquisition

By focusing on these traits, we can start to see that Bostrom is not really worried about what has been termed an “Artificial General Intelligence” (AGI). He is concerned with a very specific kind of intelligence with certain capacities to exert its will on the world and, most importantly, to increase its power over nature and other intelligent systems rapidly enough to attain a decisive strategic advantage. Which leads us to a second way we can refine Bostrom’s argument.

Closely analyze recalcitrance. Recall that Bostrom speculates that the condition for a fast takeoff superintelligence, assuming that the system engages in “intelligence amplification”, is constant or lower recalcitrance. A weakness in his argument is his lack of in-depth analysis of this recalcitrance function. I will argue that for many of the convergent instrumental values and cognitive superpowers at the core of Bostrom’s argument, it is possible to be much more precise about system recalcitrance. This analysis should allow us to determine to a greater extent the likelihood of singleton vs. multipolar superintelligence outcomes.

For example, it’s worth noting that a number of the “superpowers” are explicitly in the domain of the social sciences. “Social manipulation” and “economic productivity” are both vastly complex domains of research in their own right. Each may well have bounds about how effective an intelligent system can be at them, no matter how much “optimization power” is applied to the task. The capacities of those manipulated to understand instructions is one such bound. The fragility or elasticity of markets could be another such bound.

For intelligence amplification, strategizing, technological research/perfection, and cognitive enhancement in particular, there is a wealth of literature in artificial intelligence and cognitive science that addresses the technical limits of these domains. Such technical limitations are a natural source of recalcitrance and an impediment to fast takeoff.

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