Category: Psychology

Universal Artificial Social Intelligence

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Continuing to develop the idea that social reasoning adds to Hutter’s Universal Artificial Intelligence model, below is his basic layout for agents and environments:

A few definitions: The Agent (p) is a Turing machine that consists of a working tape and an algorithm that can move the tape left or right, read a symbol from the tape, write a symbol to the tape, and transition through a finite number of internal states as held in a table. That is all that is needed to be a Turing machine and Turing machines can compute like our every day notion of a computer. Formally, there are bounds to what they can compute (for instance, whether any given program consisting of the symbols on the tape will stop at some point or will run forever without stopping (this is the so-called “halting problem“). But it suffices to think of the Turing machine as a general-purpose logical machine in that all of its outputs are determined by a sequence of state changes that follow from the sequence of inputs and transformations expressed in the state table. There is no magic here.

Hutter then couples the agent to a representation of the environment, also expressed by a Turing machine (after all, the environment is likely deterministic), and has the output symbols of the agent consumed by the environment (y) which, in turn, outputs the results of the agent’s interaction with it as a series of rewards (r) and environment signals (x), that are consumed by agent once again.

Where this gets interesting is that the agent is trying to maximize the reward signal which implies that the combined predictive model must convert all the history accumulated at one point in time into an optimal predictor.… Read the rest

Multitudes and the Mathematics of the Individual

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The notion that there is a path from reciprocal altruism to big brains and advanced cognitive capabilities leads us to ask whether we can create “effective” procedures that shed additional light on the suppositions that are involved, and their consequences. Any skepticism about some virulent kind of scientism then gets whisked away by the imposition of a procedure combined with an earnest interest in careful evaluation of the outcomes. That may not be enough, but it is at least a start.

I turn back to Marcus Hutter, Solomonoff, and Chaitin-Kolmogorov at this point.  I’ll be primarily referencing Hutter’s Universal Algorithmic Intelligence (A Top-Down Approach) in what follows. And what follows is an attempt to break down how three separate factors related to intelligence can be explained through mathematical modeling. The first and the second are covered in Hutter’s paper, but the third may represent a new contribution, though perhaps an obvious one without the detail work that is needed to provide good support.

First, then, we start with a core requirement of any goal-seeking mechanism: the ability to predict patterns in the environment external to the mechanism. This is well-covered since Solomonoff in the 60s who formalized the implicit arguments in Kolmogorov algorithmic information theory (AIT), and that were subsequently expanded on by Greg Chaitin. In essence, given a range of possible models represented by bit sequences of computational states, the shortest sequence that predicts the observed data is also the optimal predictor for any future data also produced by the underlying generator function. The shortest sequence is not computable, but we can keep searching for shorter programs and come up with unique optimizations for specific data landscapes. And that should sound familiar because it recapitulates Occam’s Razor and, in a subset of cases, Epicurus’ Principle of Multiple Explanations.… Read the rest

Reciprocity and Abstraction

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Fukuyama’s suggestion is intriguing but needs further development and empirical support before it can be considered more than a hypothesis. To be mildly repetitive, ideology derived from scientific theories should be subject to even more scrutiny than religious-political ideologies if for no other reason than it can be. But in order to drill down into the questions surrounding how reciprocal altruism might enable the evolution of linguistic and mental abstractions, we need to simplify the problems down to basics, then work outward.

So let’s start with reciprocal altruism as a mere mathematical game. The iterated prisoner’s dilemma is a case study: you and a compatriot are accused of a heinous crime and put in separate rooms. If you deny involvement and so does your friend you will each get 3 years prison. If you admit to the crime and so does your friend you will both get 1 year (cooperation behavior). But if you or your co-conspirator deny involvement while fingering the other, one gets to walk free while the other gets 6 years (defection strategy). Joint fingering is equivalent to two denials at 3 years since the evidence is equivocal. What does one do as a “rational actor” in order to minimize penalization? The only solution is to betray your friend while denying involvement (deny, deny, deny): you get either 3 years (assuming he also denies involvement), or you walk (he denies), or he fingers you also which is the same as dual denials at 3 years each. The average years served are 1/3*3 + 1/3*0 + 1/3*3 = 3 years versus 1/2*1 + 1/2*6 = 3.5 years for admitting to the crime.

In other words it doesn’t pay to cooperate.… Read the rest

Science, Pre-science, and Religion

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Francis Fukuyama in The Origins of Political Order: From Prehuman Times to the French Revolution draws a bright line from reciprocal altruism to abstract reasoning, and then through to religious belief:

Game theory…suggests that individuals who interact with one another repeatedly tend to gravitate toward cooperation with those who have shown themselves to be honest and reliable, and shun those who have behaved opportunistically. But to do this effectively, they have to be able to remember each other’s past behavior and to anticipate likely future behavior based on an interpretation of other people’s motives.

Then, language allows transmission of historical patterns (largely gossip in tight-knit social groups) and abstractions about ethical behaviors until, ultimately:

The ability to create mental models and to attribute causality to invisible abstractions is in turn the basis for the emergence of religion.

But this can’t be the end of the line. Insofar as abstract beliefs can attribute repetitive weather patterns to Olympian gods, or consolidate moral reasoning to a monotheistic being, the same mechanisms of abstraction must be the basis for scientific reasoning as well. Either that or the cognitive capacities for linguistic abstraction and game theory are not cross-applicable to scientific thinking, which seems unlikely.

So the irony of assertions that science is just another religion is that they certainly share a similar initial cognitive evolution, while nevertheless diverging in their dependence on faith and supernatural expectations, on the one hand, and channeling the predictive models along empirical contours on the other.… Read the rest

Bloodless Technonomy

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The last link provided in the previous post leads down a rabbit hole. The author translates a Chinese report and then translates the data into geospatial visualizations and pie charts, sure, but he also begins very rapidly to layer on his ideological biases. He is part of the “AltRight” movement with a focus on human biodiversity. The memes of AltRight are largely racially charged, much less racist, defined around an interpretation of Darwinism that anoints difference and worships a kind of biological determinism. The thought cycles are large, elliptical constructs that play with sociobiology and evolutionary psychology to describe why inequities exist in the human world. Fair enough, though we can quibble over whether any scientisms rise far enough out of the dark waters of data to move policy more than a hair either way. And we can also argue against the interpretations of biology that nurtured the claims, especially the ever-present meme that inter-human competition is somehow discernible as Darwinian at all. That is the realm of the Social Darwinists and Fascists, and the realm of evil given the most basic assumptions about others. It also begs explanation of cooperation at a higher level than the superficial realization that kin selection might have a role in primitive tribal behavior. To be fair, of course, it has parallels in attempts to tie Freudian roles to capitalism and desire, or in the deeper contours of Marxist ideology.

But this war of ideologies, of intellectual histories, of grasping at ever-deeper ways of reinterpreting the goals and desires of political actors, might be coming to an end in a kind of bloodless, technocratic way. Specifically, surveillance, monitoring, and data analysis can potentially erode the theologies of policy into refined understandings of how groups react to changes in laws, regulations, incentives, taxes, and entitlements.… Read the rest

The Teeming Masses and Bigotry

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A new 14-year-old is an odd place to begin with a discussion of nature and nurture, but my new 14-year-old set me off on the topic of the is-ought barrier when we were discussing the hows and whys of his incredibly athletic cat, who is a natural born killer. 500 million birds each year! It was all theoretical because our cats are indoor only; a dozen moths and flies, maybe.

But related is “Bright Minds and Dark Attitudes: Lower Cognitive Ability Predicts Greater Prejudice Through Right-Wing Ideology and Low Intergroup Contact,” a fascinating study by Hodson and Busseri at Brock University in Canada, which apparently is also involved in the NASA Curiosity project. The study suggests that stupidity (in the form of low g or “general intelligence”) leads to right-wing ideals, which is perhaps comforting to those opposed to right-wing ideals but has limited utility otherwise.  Conservatives, of course, shot at the messenger while liberals endorsed it.

Drilling down into the results reveals some intricacies, however. Low g or IQ correlated with low abstract thinking and also with limited contact with social groups that were not like-minded. This leads, in turn, to questions about g and its stability as a measure: for instance, the Flynn Effect might be explained by a broadly more stimulating environment for individuals. Now, let’s say that the stimulating environment is a result of greater social contact and social requirements for intelligence as manifested through school and complex interactions in urban and suburban environments (as distinct from isolated agrarian communities in the past). After all, one explanation for enhanced verbal and mathematical psychometric performance among Ashkenazi Jews is the so-called “shtetl” effect wherein urban channeling and genetic isolation might have produced a “founder effect” with selective pressure towards certain capabilities.… Read the rest

On the Structure of Brian Eno

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I recently came across an ancient document, older than my son, dating to 1994 when I had a brief FAX-based exchange of communiques with Brian Eno, the English eclectic electronic musician and producer of everything from Bowie’s Low through to U2’s Joshua Tree and Jane Siberry. Eno had been pointed at one of my colleague’s efforts (Eric in the FAXes, below) at using models of RNA replication to create music by the editor of Whole Earth Catalog who saw Eric present at an Artificial Life conference. I was doing other, somewhat related work, and Eric allowed me to correspond with Mr. Eno. I did, resulting in a brief round of FAXes (email was fairly new to the non-specialist in 1994).

I later dropped off a copy of a research paper I had written at his London office and he was summoned down from an office/loft and shook his head in the negative about me. I was shown the door by the receptionist.

Below is my last part of the FAX interchange. Due to copyright and privacy concerns, I’ll only show my part of the exchange (and, yes, I misspelled “Britain”). Notably, Brian still talks about the structure of music and art in recent interviews.

… Read the rest

Bostrom on the Hardness of Evolving Intelligence

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At 38,000 feet somewhere above Missouri, returning from a one day trip to Washington D.C., it is easy to take Nick Bostrom’s point that bird flight is not the end-all of what is possible for airborne objects and mechanical contrivances like airplanes in his paper, How Hard is Artificial Intelligence? Evolutionary Arguments and Selection Effects. His efforts to try to bound and distinguish the evolution of intelligence as either Hard or Not-Hard runs up against significant barriers, however. As a practitioner of the art, finding similarities between a purely physical phenomena like flying and something as complex as human intelligence falls flat for me.

But Bostrom is not taking flying as more than a starting point for arguing that there is an engineer-able possibility for intelligence. And that possibility might be bounded by a number of current and foreseeable limitations, not least of which is that computer simulations of evolution require a certain amount of computing power and representational detail in order to be a sufficient simulation. His conclusion is that we may need as much as another 100 years of improvements in computing technology just to get to a point where we might succeed at a massive-scale evolutionary simulation (I’ll leave to the reader to investigate his additional arguments concerning convergent evolution and observer selection effects).

Bostrom dismisses as pessimistic the assumption that a sufficient simulation would, in fact, require a highly detailed emulation of some significant portion of the real environment and the history of organism-environment interactions:

A skeptic might insist that an abstract environment would be inadequate for the evolution of general intelligence, believing instead that the virtual environment would need to closely resemble the actual biological environment in which our ancestors evolved … However, such extreme pessimism seems unlikely to be well founded; it seems unlikely that the best environment for evolving intelligence is one that mimics nature as closely as possible.

… Read the rest

Randomness and Meaning

Mark Davis1 Comment

The impossibility of the Chinese Room has implications across the board for understanding what meaning means. Mark Walker’s paper “On the Intertranslatability of all Natural Languages” describes how the translation of words and phrases may be achieved:

  1. Through a simple correspondence scheme (word for word)
  2. Through “syntactic” expansion of the languages to accommodate concepts that have no obvious equivalence (“optometrist” => “doctor for eye problems”, etc.)
  3. Through incorporation of foreign words and phrases as “loan words”
  4. Through “semantic” expansion where the foreign word is defined through its coherence within a larger knowledge network.

An example for (4) is the word “lepton” where many languages do not have a corresponding concept and, in fact, the concept is dependent on a bulwark of advanced concepts from particle physics. There may be no way to create a superposition of the meanings of other words using (2) to adequately handle “lepton.”

These problems present again for trying to understand how children acquire meaning in learning a language. As Walker points out, language learning for a second language must involve the same kinds of steps as learning translations, so any simple correspondence theory has to be supplemented.

So how do we make adequate judgments about meanings and so rapidly learn words, often initially with a course granularity but later with increasingly sharp levels of focus? What procedure is required for expanding correspondence theories to operate in larger networks? Methods like Latent Semantic Analysis and Random Indexing show how this can be achieved in ways that are illuminating about human cognition. In each case, the methods provide insights into how relatively simple transformations of terms and their occurrence contexts can be viewed as providing a form of “triangulation” about the meaning of words.… Read the rest

The Comets of Literary Cohesion

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Every few years, with the hyperbolic regularity of Kahoutek’s orbit, I return to B.R. Myers’ 2001 Atlantic essay, A Reader’s Manifesto, where he plays the enfant terrible against the titans of serious literature. With savagery Myers tears out the elliptical heart of Annie Proulx and then beats regular holes in Cormac McCarthy and Don DeLillo in a conscious mockery of the strained repetitiveness of their sentences.

I return to Myers because I currently have four novels in process. I return because I hope to be saved from the delirium of the postmodern novel that wants to be written merely because there is nothing really left to write about, at least not without a self-conscious wink:

But today’s Serious Writers fail even on their own postmodern terms. They urge us to move beyond our old-fashioned preoccupation with content and plot, to focus on form instead—and then they subject us to the least-expressive form, the least-expressive sentences, in the history of the American novel. Time wasted on these books is time that could be spent reading something fun.

Myers’ essay hints at what he sees as good writing, quoting Nabakov, referencing T.S. Eliot, and analyzing the controlled lyricism of Saul Bellow. Evaporating the boundaries between the various “brows” and accepting that action, plot, and invention are acceptable literary conceits also marks Myers’ approach to literary analysis.

It is largely an atheoretic analysis but there is a hint at something more beneath the surface when Myers describes the disdain of European peasants for the transition away from the inscrutable Latin masses and benedictions and into the language of the common man: “Our parson…is a plain honest man… But…he is no Latiner.” Myers counts the fascination with arabesque prose, with labeling it as great even when it lacks content, as derived from the same fascination that gripped the peasants: majesty is inherent in obscurity.… Read the rest