Pressing Snobs into Hell

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

Fish eating fish eating fish

Decompressing in NorCal following a vibrant Hadoop World. More press mentions:

· Big Data, Big News: 10 Things To See At Hadoop World, CRN, October 23, 2012 – (Circulation 53,397)

· Quest Software Announces Hadoop-Centric Software Analytics, CloudNewsDaily, October 23, 2012-coverage of Hadoop product announcements.

· Quest Launches New Analytics Software for Hadoop, SiliconANGLE, October 23, 2012- coverage of Hadoop Product.

· Continuing its M&A software push, Dell moves into ‘big data’ analytics via Kitenga buy, 451 Research

· Cisco Updates Schedule to Automate Hadoop Big Data Analysis Systems, Eweek, October 24, 2012- mention of Kitenga product announcement at Hadoop. (Circulation 196,157)

· Quest Launches New Analytics Software for Hadoop, DABBC, October 24, 2012

And what about fish? Dell == Big Fish, Quest == Medium Fish, Kitenga == Happy Minnow.… Read the rest

Dell Acquires Kitenga

Dell Inc. : Quest Software Expands Its Big Data Solution with New Hadoop-Centric Software Capabilities for Business Analytics

10/23/2012| 08:05am US/Eastern

  • Complete solution includes application development, data replication, and data analysis

Hadoop World 2012-Quest Software, Inc. (now part of Dell) announced three significant product releases today aimed at helping customers more quickly adopt Hadoop and exploit their Big Data. When used together, the three products offer a complete solution that addresses the greatest challenge with Hadoop: the shortage of technical and analytical skills needed to gain meaningful business insight from massive volumes of captured data. Quest builds on its long history in data and database management to open the world of Big Data to more than just the data scientist.

News Facts:

  • Kitenga Analytics: Based on the recent acquisition of Kitenga, Quest Software now enables customers to analyze structured, semi-structured and unstructured data stored in Hadoop. Available immediately, Kitenga Analytics delivers sophisticated capabilities, including text search, machine learning, and advanced visualizations, all from an easy-to-use interface that does not require understanding of complex programming or the Hadoop stack itself. With Kitenga Analytics and the Quest Toad®Business Intelligence Suite, an organization has a complete self-service analysis environment that empowers business and systems analysts across a variety of backgrounds and job roles.
More:

http://www.4-traders.com/DELL-INC-4867/news/Dell-Inc-Quest-Software-Expands-Its-Big-Data-Solution-with-New-Hadoop-Centric-Software-Capabiliti-15415359/Read the rest

Intelligence versus Motivation

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

An Exit to a New Beginning

I am thrilled to note that my business partner and I sold our Big Data analytics startup to a large corporation yesterday. I am currently unemployed but start anew doing the same work on Monday.

Thrilled is almost too tame a word. Ecstatic does better describing the mood around here and the excitement we have over having triumphed in Sili Valley. There are many war stories that we’ve been swapping over the last 24 hours, including how we nearly shut down/rebooted at the start of 2012. But now it is over and we have just a bit of cleanup work left to dissolve the existing business structures and a short vacation to attend to.… Read the rest

Evolutionary Art and Architecture

With every great scientific advance there has been a coordinated series of changes in the Zeitgeist. Evolutionary theory has impacted everything from sociology through to literature, but there are some very sophisticated efforts in the arts that deserve more attention.

John Frazer’s Evolutionary Architecture is a great example. Now available as downloadable PDFs since it is out-of-print, Evolutionary Architecture asks the question, without fully answering it (how could it?), about how evolution-like processes can contribute to the design of structures:

And then there is William Latham’s evolutionary art that explores form derived from generative functions dating to 1989:

And the art extends to functional virtual creatures:

Read the rest

Universal Artificial Social Intelligence

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

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

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