Category: AI

Singularity and its Discontents

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Kimmel botIf a machine-based process can outperform a human being is it significant? That weighty question hung in the background as I reviewed Jürgen Schmidhuber’s work on traffic sign classification. Similar results have emerged from IBM’s Watson competition and even on the TOEFL test. In each case, machines beat people.

But is that fact significant? There are a couple of ways we can look at these kinds of comparisons. First, we can draw analogies to other capabilities that were not accessible by mechanical aid and show that the fact that they outperformed humans was not overly profound. The wheel quickly outperformed human legs for moving heavy objects. The cup outperformed the hands for drinking water. This then invites the realization that the extension of these physical comparisons leads to extraordinary juxtapositions: the airline really outperformed human legs for transport, etc. And this, in turn, justifies the claim that since we are now just outperforming human mental processes, we can only expect exponential improvements moving forward.

But this may be a category mistake in more than the obvious differentiator of the mental and the physical. Instead, the category mismatch is between levels of complexity. The number of parts in a Boeing 747 is 6 million versus one moving human as the baseline (we could enumerate the cells and organelles, etc., but then we would need to enumerate the crystal lattices of the aircraft steel, so that level of granularity is a wash). The number of memory addresses in a big server computer is 64 x 10^9 or higher, with disk storage in the TBs (10^12). Meanwhile, the human brain has 100 x 10^9 neurons and 10^14 connections. So, with just 2 orders of magnitude between computers and brains versus 6 between humans and planes, we find ourselves approaching Kurzweil’s argument that we have to wait until 2040.… Read the rest

Methodical Play

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imageMy fourteen-year-old interviewed a physicist yesterday. I had the privilege of being home over the weekend and listened in; my travel schedule has lately been brutal, with the only saving grace being moments like right now en route to Chicago when I can collapse into reading and writing for a few whitenoise-washed moments. And the physicist who was once his grandfather said some remarkable things:

  • Physics consists of empirical layers of untruth
  • The scientific method is never used as formulated
  • Schools, while valuable, won’t teach how to be a scientist
  • The institutions of physics don’t support the creativity required to be a scientist

Yet there was no sense of anger or disillusionment in these statements, just a framing of the distinctions between the modern social model surrounding what scientists do and the complex reality of how they really do their work.

The positives were that play is both the essential ingredient and the missing determinant of the real “scientific method.” Mess around, try to explain, mess around some more. And what is all that play getting this remarkable octogenarian? Possible insights into the unification of electromagnetism and the strong nuclear force. The interview journey passed from alignment of quarks to the beams of neutron stars, igniting the imaginations of all the minds on the call.

But if there is no real large-scale method to this madness, what might we conclude about the rationality of the process of science? I would advocate that the algorithmic model of inference is perhaps the best (and least biased) way of approaching the issue of scientific method. By constantly reshuffling the available parameters and testing the compressibility of models, play is indistinguishable from science when the play pivots on best explanation.… Read the rest

Curiouser and Curiouser

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georgeJürgen Schmidhuber’s work on algorithmic information theory and curiosity is worth a few takes, if not more, for the researcher has done something that is both flawed and rather brilliant at the same time. The flaws emerge when we start to look deeply into the motivations for ideas like beauty (is symmetry and noncomplex encoding enough to explain sexual attraction? Well-understood evolutionary psychology is probably a better bet), but the core of his argument is worth considering.

If induction is an essential component of learning (and we might suppose it is for argument’s sake), then why continue to examine different parameterizations of possible models for induction? Why be creative about how to explain things, like we expect and even idolize of scientists?

So let us assume that induction is explained by the compression of patterns into better and better models using an information theoretic-style approach. Given this, Schmidhuber makes the startling leap that better compression and better models are best achieved by information harvesting behavior that involves finding novelty in the environment. Thus curiosity. Thus the implementation of action in support of ideas.

I proposed a similar model to explain aesthetic preferences for mid-ordered complex systems of notes, brush-strokes, etc. around 1994, but Schmidhuber’s approach has the benefit of not just characterizing the limitations and properties of aesthetic systems, but also justifying them. We find interest because we are programmed to find novelty, and we are programmed to find novelty because we want to optimize our predictive apparatus. The best optimization is actively seeking along the contours of the perceivable (and quantifiable) universe, and isolating the unknown patterns to improve our current model.… Read the rest

Industrial Revolution #4

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Paul Krugman at New York Times consumes Robert Gordon’s analysis of economic growth and the role of technology and comes up more hopeful than Gordon. The kernel in Krugman’s hope is that Big Data analytics can provide a shortcut to intelligent machines by bypassing the requirement for specification and programming that was once assumed to be a requirement for artificial intelligence. Instead, we don’t specify but use “data-intensive ways” to achieve a better result. And we might get to IR#4, following Gordon’s taxonomy where IR stands for “industrial revolution.” IR#1 was steam and locomotives  IR#2 was everything up to computers. IR#3 is computers and cell phones and whatnot.

Krugman implies that IR#4 might spur the typical economic consequences of grand technological change, including the massive displacement of workers, but like in previous revolutions it is also assumed that economic growth built from new industries will ultimately eclipse the negatives. This is not new, of course. Robert Anton Wilson argued decades ago for the R.I.C.H. economy (Rising Income through Cybernetic Homeostasis). Wilson may have been on acid, but Krugman wasn’t yet tuned in, man. (A brief aside: the Krugman/Wilson notions probably break down over extraction and agribusiness/land rights issues. If labor is completely replaced by intelligent machines, the land and the ingredients it contains nevertheless remain a bottleneck for economic growth. Look at the global demand for copper and rare earth materials, for instance.)

But why the particular focus on Big Data technologies? Krugman’s hope teeters on the assumption that data-intensive algorithms possess a fundamentally different scale and capacity than human-engineered approaches. Having risen through the computational linguistics and AI community working on data-driven methods for approaching intelligence, I can certainly sympathize with the motivation, but there are really only modest results to report at this time.… Read the rest

Pressing Snobs into Hell

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Paul Vitanyi has been a deep advocate for Kolmogorov complexity for many years. His book with Ming Li, An Introduction to Kolmogorov Complexity and Its Applications, remains on my book shelf (and was a bit of an investment in grad school).

I came across a rather interesting paper by Vitanyi with Rudi Cilibrasi called “Clustering by Compression” that illustrates perhaps more easily and clearly than almost any other recent work the tight connections between meaning, repetition, and informational structure. Rather than describing the paper, however, I wanted to conduct an experiment that demonstrates their results. To do this, I asked the question: are the writings of Dante more similar to other writings of Dante than to Thackeray? And is the same true of Thackeray relative to Dante?

Now, we could pursue these questions at many different levels. We might ask scholars, well-versed in the works of each, to compare and contrast the two authors. They might invoke cultural factors, the memes of their respective eras, and their writing styles. Ultimately, though, the scholars would have to get down to some textual analysis, looking at the words on the page. And in so doing, they would draw distinctions by lifting features of the text, comparing and contrasting grammatical choices, word choices, and other basic elements of the prose and poetry on the page. We might very well be able to take parts of the knowledge of those experts and distill it into some kind of a logical procedure or algorithm that would parse the texts and draw distinctions based on the distributions of words and other structural cues. If asked, we might say that a similar method might work for the so-called language of life, DNA, but that it would require a different kind of background knowledge to build the analysis, much less create an algorithm to perform the same task.… Read the rest

Intelligence versus Motivation

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Nick Bostrom adds to the dialog on desire, intelligence, and intentionality with his recent paper, The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents. The argument is largely a deconstruction of the general assumption that there is somehow an inexorable linkage between intelligence and moral goodness. Indeed, he even proposes that intelligence and motivation are essentially orthogonal (“The Orthogonality Thesis”) but that there may be a particular subset of possible trajectories towards any goal that are common (self-preservation, etc.) The latter is scoped by his “instrumental convergence thesis” where there might be convergences towards central tenets that look an awful lot like the vagaries of human moral sentiments. But they remain vagaries and should not be taken to mean that advanced artificial agents will act in a predictable manner.… Read the rest

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

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

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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