Posted:April 4, 2012

The Trouble with Memes

Adaptive Information is a Hammer, but Genes are Not a Nail

Since Richard Dawkins first put forward the idea of the “meme” in his book The Selfish Gene some 35 years ago [1], the premise has struck in my craw. I, like Dawkins, was trained as an evolutionary biologist. I understand the idea of the gene and its essential role as a vehicle for organic evolution. And, all of us clearly understand that “ideas” themselves have a certain competitive and adaptive nature. Some go viral; some run like wildfire and take prominence; and some go nowhere or fall on deaf ears. Culture and human communications and ideas play complementary — perhaps even dominant — roles in comparison to the biological information contained within DNA (genes).

I think there are two bases for why the “meme” idea sticks in my craw. The first harkens back to Dawkins. In formulating the concept of the “meme”, Dawkins falls into the trap of many professionals, what the French call déformation professionnelle. This is the idea of professionals framing problems from the confines of their own points of view. This is also known as the Law of the Instrument, or (Abraham) Maslow‘s hammer, or what all of us know colloquially as “if all you have is a hammer, everything looks like a nail“ [2]. Human or cultural information is not genetics.

The second — and more fundamental — basis for why this idea sticks in my craw is its mis-characterization of what is adaptive information, the title and theme of this blog. Sure, adaptive information can be found in the types of information structures at the basis of organic life and organic evolution. But, adaptive information is much, much more. Adaptive information is any structure that provides arrangements of energy and matter that maximizes entropy production. In inanimate terms, such structures include chemical chirality and proteins. It includes the bases for organic life, inheritance and organic evolution. For some life forms, it might include communications such as pheromones or bird or whale songs or the primitive use of tools or communicated behaviors such as nest building. For humans with their unique abilities to manipulate and communicate symbols, adaptive information embraces such structures as languages, books and technology artifacts. These structures don’t look or act like genes and are not replicators in any fashion of the term. To hammer them as “memes” significantly distorts their fundamental nature as information structures and glosses over what factors might — or might not — make them adaptive.

I have been thinking of these concepts much over the past few decades. Recently, though, there has been a spate of the “meme” term, particularly on the semantic Web mailing lists to which I subscribe. This spewing has caused me to outline some basic ideas about what I find so problematic in the use of the “meme” concept.

A Brief Disquisition on Memes

As defined by Dawkins and expanded upon by others, a “meme” is an idea, behavior or style that spreads from person to person within a culture. It is proposed as being able to be transmitted through writing, speech, gestures or rituals. Dawkins specifically called melodies, catch-phrases, fashion and the technology of building arches as examples of memes. A meme is postulated as a cultural analogue to genes in that they are assumed to be able to self-replicate, mutate or respond to selective pressures. Thus, as proposed, memes may evolve by natural selection in a manner analogous to that of biological evolution.

However, unlike a gene, a structure corresponding to a “meme” has never been discovered or observed. There is no evidence for it as a unit of replication, or indeed as any kind of coherent unit at all. In its sloppy use, it is hard to see how “meme” differs in its scope from concepts, ideas or any form of cultural information or transmission, yet it is imbued with properties analogous to animate evolution for which there is not a shred of empirical evidence.

One might say, so what, the idea of a “meme” is merely a metaphor, what is the harm? Well, the harm comes about when it is taken seriously as a means of explaining human behavior and cultural changes, a field of study called memetics. It becomes a pseudo-scientific term that sets a boundary condition for understanding the nature of information and what makes it adaptive or not [3]. Mechanisms and structures appropriate to animate life are not universal information structures, they are simply the structures that have evolved in the organic realm. In the human realm of signs and symbols and digital information and media, information is the universal, not the genetic structure of organic evolution.

The noted evolutionary geneticist, R.C. Lewontin, one of my key influences as a student, has also been harshly critical of the idea of memetics [4]:

“The selectionist paradigm requires the reduction of society and culture to inheritance systems that consist of randomly varying, individual units, some of which are selected, and some not; and with society and culture thus reduced to inheritance systems, history can be reduced to ‘evolution.’ . . . we conclude that while historical phenomena can always be modeled selectionistically, selectionist explanations do not work, nor do they contribute anything new except a misleading vocabulary that anesthetizes history.”

Consistent with my recent writings about Charles S. Peirce [5], many logicians and semiotic theorists are also critical of the idea of “memes”, but on different grounds. The criticism here is that “memes” distort Peirce’s ideas about signs and the reification of signs and symbols via a triadic nature. Notable in this camp is Terrence Deacon [6].

Information is a First Principle

It is not surprising that the concept of “memes” arose in the first place. It is understandable to seek universal principles consistent with natural laws and observations. The mechanism of natural evolution works on the information embodied in DNA, so why not look to genes as some form of universal model?

The problem here, I think, was to confuse mechanisms with first principles. Genes are a mechanism — a “structure” if you will — that along with other forms of natural selection such as the entire organism and even kin selection [7], have evolved as means of adaptation in the animate world. But the fundamental thing to be looked for here is the idea of information, not the mechanism of genes and how they replicate. The idea of information holds the key for drilling down to universal principles that may find commonality between information for humans in a cultural sense and information conveyed through natural evolution for life forms. It is the search for this commonality that has driven my professional interests for decades, spanning from population genetics and evolution to computers, information theory and semantics [8].

But before we can tackle these connections head on, it is important to address a couple of important misconceptions (as I see them).

Seque #1: Information is (Not!) Entropy

In looking to information as a first principle, Claude Shannon‘s seminal work in 1948 on information theory must be taken as the essential point of departure [9]. The motivation of Shannon’s paper and work by others preceding him was to understand information losses in communication systems or networks. Much of the impetus for this came about because of issues in wartime communications and early ciphers and cryptography. (As a result, the Shannon paper is also intimately related to data patterns and data compression, not further discussed here.)

In a strict sense, Shannon’s paper was really talking about the amount of information that could be theoretically and predictably communicated between a sender and a receiver. No context or semantics were implied in this communication, only the amount of information (for which Shannon introduced the term “bits” [10]) and what might be subject to losses (or uncertainty in the accurate communication of the message). In this regard, what Shannon called “information” is what we would best term “data” in today’s parlance.

The form that the uncertainty (unpredictability) calculation that Shannon derived:

$\displaystyle H(X) = - \sum_{i=1}^np(x_i)\log_b p(x_i)$

very much resembled the mathematical form for Boltzmann‘s original definition of entropy (as elaborated upon by Gibbs, denoted as S, for Gibb’s entropy):

$S = - k_B \sum p_i \ln p_i \,$

and thus Shannon also labelled his measure of unpredictability, H, as entropy [10].

After Shannon, and nearly a century after Boltzmann, work by individuals such as Jaynes in the field of statistical mechanics came to show that thermodynamic entropy can indeed be seen as an application of Shannon’s information theory, so there are close parallels [11]. This parallel of mathematical form and terminology has led many to assert that information is entropy.

I believe this assertion is a misconception on two grounds.

First, as noted, what is actually being measured here is data (or bits), not information embodying any semantic meaning or context. Thus, the formula and terminology is not accurate for discussing “information” in a conventional sense.

Second, the Shannon methods are based on the communication (transmittal) between a sender and a receiver. Thus the Shannon entropy measure is actually a measure of the uncertainty for either one of these states. The actual information that gets transmitted and predictably received was formulated by Shannon as R (which he called rate), and he expressed basically as:

R = H_before – H_after

R, then, becomes a proxy for the amount of information accurately communicated. R can never be zero (because all communication systems have losses). H_before and H_after are both state functions for the message, so this also makes R a function of state. So while there is Shannon entropy (unpredictability) for any given sending or receiving state, the actual amount of information (that is, data) that is transmitted is a change in state as measured by a change in uncertainty between sender (H_before) and receiver (H_after). In the words of Thomas Schneider, who provides a very clear discussion of this distinction [12]:

Information is always a measure of the decrease of uncertainty at a receiver.

These points do not directly bear on the basis of information as discussed below, but help remove misunderstandings that might undercut those points. Further, these clarifications make consistent theoretical foundations of information (data) with natural evolution while being logically consistent with the 2nd law of thermodynamics (see next).

Seque #2: Entropy is (Not!) Disorder

The 2nd law of thermodynamics expresses the tendency that, over time, differences in temperature, pressure, or chemical potential equilibrate in an isolated physical system. Entropy is a measure of this equilibration: for a given physical system, the highest entropy state is one at equilibrium. Fluxes or gradients arise when there are differences in state potentials in these systems. (In physical systems, these are known as sources and sinks; in information theory, they are sender and receiver.) Fluxes go from low to high entropy, and are non-reversible — the “arrow of time” — without the addition of external energy. Heat, for example, is a by product of fluxes in thermal energy. Because these fluxes are directional in isolation, a perpetual motion machine is shown as impossible.

In a closed system (namely, the entire cosmos), one can see this gradient as spanning from order to disorder, with the equilibrium state being the random distribution of all things. This perspective, and much schooling regarding these concepts, tends to present the idea of entropy as a “disordered” state. Life is seen as the “ordered” state in this mindset. Hewing to this perspective, some prominent philosophers, scientists and others have sometimes tried to present the “force” representing life and “order” as an opposite one to entropy. One common term for this opposite “force” is “negentropy” [13].

But, in the real conditions common to our lives, our environment is distinctly open, not closed. We experience massive influxes of energy via sunlight, and have learned as well how to harness stored energy from eons past in further sources of fossil and nuclear energy. Our open world is indeed a high energy one, and one that increases that high-energy state as our knowledge leads us to exploit still further resources of higher and higher quality. As Buckminster Fuller once famously noted, electricity consumption (one of the highest quality energy resources found to date) has become a telling metric about the well-being and wealth of human societies [14].

The high-energy environments fostering life on earth and more recently human evolution establish a local (in a cosmic sense) gradient that promotes fluxes to more ordered states, not lesser unordered ones. These fluxes remain faithful to basic physical laws and are non-deterministic [15]. Indeed, such local gradients can themselves be seen as consistent with the conditions initially leading to life, favoring the random event in the early primordial soup that led to chemical structures such as chirality, auto-catalytic reactions, enzymes, and then proteins, which became the eventual building blocks for animate life [16].

These events did not have preordained outcomes (that is, they were non-deterministic), but were the result of time and variation in the face of external energy inputs to favor the marginal combinatorial improvement. The favoring of the new marginal improvement also arises consistent with entropy principles, by giving a competitive edge to those structures that produce faster movements across the existing energy gradient. According to Annila and Annila [16]:

“According to the thermodynamics of open systems, every entity, simple or sophisticated, is considered as a catalyst to increase entropy, i.e., to diminish free energy. Catalysis calls for structures. Therefore, the spontaneous rise of structural diversity is inevitably biased toward functional complexity to attain and maintain high-entropy states.”

Via this analysis we see that life is not at odds with entropy, but is consistent with it. Further, we see that incremental improvements in structure that are consistent with the maximum entropy production principle will be favored [17]. Of course, absent the external inputs of energy, these gradients would reverse. Under those conditions, the 2nd law would promote a breakdown to a less ordered system, what most of us have been taught in schools.

With these understandings we can now see the dichotomy as life representing order with entropy disorder as being false. Further, we can see a guiding set of principles that is consistent across the broad span of evolution from primordial chemicals and enzymes to basic life and on to human knowledge and artifacts. This insight provides the fundamental “unit” we need to be looking toward, and not the gene nor the “meme”.

Information is Structure

Of course, the fundamental “unit” we are talking about here is information, and not limited as is Shannon’s concept to data. The quality that changes data to information is structure, and structure of a particular sort. Like all structure, there is order or patterns, often of a hierarchical or fractal or graph nature. But the real aspect of the structure that is important is the marginal ability of that structure to lead to improvements in entropy production. That is, processes are most adaptive (and therefore selected) that maximize entropy production. Any structure that emerges that is able to reduce the energy gradient faster will be favored.

However, remember, these are probabilistic, statistical processes. Uncertainties in state may favor one structure at one time versus another at a different time. The types of chemical compounds favored in the primordial soup were likely greatly influenced by thermal and light cycles and drying and wet conditions. In biological ecosystems, there are huge differences in seed or offspring production or in overall species diversity and ecological complexity based on the stability (say, tropics) or instability (say, disturbance) of local environments. As noted, these processes are inherently non-deterministic.

As we climb up the chain from the primordial ooze to life and then to humans and our many information mechanisms and technology artifacts (which are themselves embodiments of information), we see increasing complexity and structure. But we do not see uniformity of mechanisms or vehicles.

The general mechanisms of information transfer in living organisms occur (generally) via DNA in genes, mediated by sex in higher organisms, subject to random mutations, and then kept or lost entirely as their host organisms survive to procreate or not. Those are harsh conditions: the information survives or not (on a population basis) with high concentrations of information in DNA and with a priority placed on remixing for new combinations via sex. Information exchange (generally) only occurs at each generational event.

Human cultural information, however, is of an entirely different nature. Information can be made persistent, can be recorded and shared across individuals or generations, extended with new innovations like written language or digital computers, or combined in ways that defy the limits of sex. Occasionally, of course, loss of living languages due to certain cultures or populations dying out or horrendous catastrophes like the Spanish burning (nearly all of) the Mayan’s existing books can also occur [18]. The environment will also be uncertain.

So, while we can define DNA in genes or the ideas of a “meme” all as information, in fact we now see how very unlike the dynamics and structures of these two forms really are. We can be awestruck with the elegance and sublimity of organic evolution. We can also be inspired by song or poem or moved to action through ideals such as truth and justice. But organic evolution does not transpire like reading a book or hearing a sermon, just like human ideas and innovations don’t act like genes. The “meme” is a totally false analogy. The only constant is information.

Some Tentative Implications

The closer we come to finding true universals, the better we will be able to create maximum entropy producing structures. This, in turn, has some pretty profound implications. The insight that keys these implications begins with an understanding of the fundamental nature — and importance — of information. According to Karnani et al [19]:

“. . . the common contemporary consent, the second law of thermodynamics, is perceived to drive disorder. Therefore, it may appear, at first sight, inconceivable that this universal law could possibly account for the existence and orderly characteristics of information, as well as for its meaningful content. However, the second law, or equivalently the principle of increasing entropy, merely states that difference among energy densities tends to vanish. When the surrounding energy density is high, the system will evolve toward a stationary state by increasing its energy content, e.g, by devising orderly machinery for energy transduction to acquire energy. . . . Syntax of information, when described by thermodynamics, is associated with the entropy of the physical representation, and significance of information is associated with the entropy increase in the receiver system when it executes the encoded information.”

All would agree that the evolution of life over the past few billion years is truly wondrous. But, what is equally wondrous is that the human species has come to learn and master symbols. That mastery, in turn, has broken the bounds of organic evolution and has put into our hands the very means and structure of information itself. Via this entirely new — and incredibly accelerated — path to information structures, we are only now beginning to see some of its implications:

Unlike all other organisms, we dominate our environment and have experienced increasing wealth and freedom. Wealth increases and their universal applicability continue to increase at an exponential rate [20]
We no longer depend on the random variant to maximize our entropy producing structures. We can now do so purposefully and with symbologies and bit streams of our own devising
Potentially all information variants can be recorded and shared across all human individuals and generations, a complete decoupling from organic boundaries
Key ideas and abstractions, such as truth, justice and equality, can operate on a species-wide basis and become adopted without massive die-offs of individuals
We are actively moving ourselves into higher-level energy states, further increasing the potential for wealth and new structures
We are actively impacting our local environment, potentially creating the conditions for our species’ demise
We are increasingly engaging all individuals of the human species in these endeavors through literacy, education and access to global information sources. This provides a still further multiplier effect on humanity’s ability to devise and manipulate information structures into more adaptive and highly-ordered states.

The idea of a “meme” actually cheapens our understanding of these potentials.

Ideas matter and terminology matters. These are the symbols by which we define and communicate potentials. If we choose the wrong analogies or symbols — as “meme” is in this case — we are picking the option with the lower entropy potential. Whether I assert it to be so or not, the “meme” concept is an information structure doomed for extinction.

[1] Richard Dawkins, 1976. The Selfish Gene, Oxford University Press, New York City, ISBN 0-19-286092-5.

[2] This phrase was perhaps first made famous by Mark Twain or Bernard Baruch, but in any case is clearly understood now by all.

[3] According to Wikipedia, Benitez-Bribiesca calls memetics “a dangerous idea that poses a threat to the serious study of consciousness and cultural evolution”. He points to the lack of a coding structure analogous to the DNA of genes, and to instability of any mutation mechanisms for “memes” sufficient for standard evolution processes. See Luis Benitez Bribiesca, 2001. “Memetics: A Dangerous Idea”, Interciencia: Revista de Ciencia y Technologia de América (Venezuela: Asociación Interciencia) 26 (1): 29–31, January 2001. See http://redalyc.uaemex.mx/redalyc/pdf/339/33905206.pdf.

[4] Joseph Fracchia and R.C. Lewontin, 2005. “The Price of Metaphor”, History and Theory (Wesleyan University) 44 (44): 14–29, February 2005.

[5] See further M. K. Bergman, 2012. “Give Me a Sign: What Do Things Mean on the Semantic Web?,” posting on AI3:::Adaptive Information blog, January 24, 2012. See https://www.mkbergman.com/994/give-me-a-sign-what-do-things-mean-on-the-semantic-web/.

[6] Terrence Deacon, 1999. “The Trouble with Memes (and what to do about it)”. The Semiotic Review of Books 10(3). See http://projects.chass.utoronto.ca/semiotics/srb/10-3edit.html.

[7] Kin selection refers to changes in gene frequency across generations that are driven at least in part by interactions between related individuals. Some mathematical models show how evolution may favor the reproductive success of an organism’s relatives, even at a cost to an individual organism. Under this mode, selection can occur at the level of populations and not the individual or the gene. Kin selection is often posed as the mechanism for the evolution of altruism or social insects. Among others, kin selection and inclusive fitness was popularized by W. D. Hamilton and Robert Trivers.

[8] You may want to see my statement of purpose under the Blogasbörd topic, first written seven years ago when I started this blog.

[9] Claude E. Shannon, 1948. “A Mathematical Theory of Communication”, Bell System Technical Journal, 27: 379–423, 623-656, July, October, 1948. See http://cs.yale.edu/homes/yry/readings/general/shannon1948.pdf.

[10] As Shannon acknowledges in his paper, the “bit” term was actually suggested by J. W. Tukey. Shannon can be more accurately said to have popularized the term via his paper.

[11] See further http://en.wikipedia.org/wiki/Information_entropy#Relationship_to_thermodynamic_entropy.

[12] See Thomas D. Schneider, 2012. “Information Is Not Entropy, Information Is Not Uncertainty!,” Web page retrieved April 4, 2012; see http://www.lecb.ncifcrf.gov/~toms/information.is.not.uncertainty.html.

[13] The “negative entropy” (also called negentropy or syntropy) of a living system is the entropy that it exports to keep its own entropy low, and according to proponents lies at the intersection of entropy and life. The concept and phrase “negative entropy” were introduced by Erwin Schrödinger in his 1944 popular-science book What is Life?. See Erwin Schrödinger, 1944. What is Life – the Physical Aspect of the Living Cell, Cambridge University Press, 1944. A copy may be downloaded at http://old.biovip.com/UpLoadFiles/Aaron/Files/2005051204.pdf.

[14] R. Buckminster Fuller, 1981. Critical Path, St. Martin’s Press, New York City, 471 pp. See especially p. 103 ff.

[15] The seminal paper first presenting this argument is Vivek Sharma and Arto Annila, 2007. “Natural Process – Natural Selection”, Biophysical Chemistry 127: 123-128. See http://www.helsinki.fi/~aannila/arto/natprocess.pdf. This basic theme has been much expanded upon by Annila and his various co-authors. See, for example, [16] and [19], among many others.

[16] Arto Annila and Erkki Annila, 2008. “Why Did Life Emerge?,” International Journal of Astrobiology 7(3 and 4): 293-300. See http://www.helsinki.fi/~aannila/arto/whylife.pdf.

[17] According to Wikipedia, the principle (or “law”) of maximum entropy production is an aspect of non-equilibrium thermodynamics, a branch of thermodynamics that deals with systems that are not in thermodynamic equilibrium. Most systems found in nature are not in thermodynamic equilibrium and are subject to fluxes of matter and energy to and from other systems and to chemical reactions. One fundamental difference between equilibrium thermodynamics and non-equilibrium thermodynamics lies in the behavior of inhomogeneous systems, which require for their study knowledge of rates of reaction which are not considered in equilibrium thermodynamics of homogeneous systems. Another fundamental difference is the difficulty in defining entropy in macroscopic terms for systems not in thermodynamic equilibrium.

The principle of maximum entropy production states that the in comparing two or more alternate paths for crossing an energy gradient that the one that creates the maximum entropy change will be favored. The maximum entropy (sometimes abbreviated MaxEnt or MaxEp) concept is related to this notion. It is also known as the maximum entropy production principle, or MEPP.

[18] The actual number of Mayan books burned by the Spanish conquistadors is unknown, but is somewhere between tens and thousands; see here. Only three or four codexes are known to survive today. Also, Wikipedia contains a listing of notable book burnings throughout history.

[19] Mahesh Karnani, Kimmo Pääkkönen and Arto Annila, 2009. “The Physical Character of Information,” Proceedings of the Royal Society A, April 27, 2009. See http://www.helsinki.fi/~aannila/arto/natinfo.pdf.

[20] I discuss and chart the exponential growth of human wealth based on Angus Maddison data in M. K. Bergman, 2006. “The Biggest Disruption in History: Massively Accelerated Growth Since the Industrial Revolution,” post in AI3:::Adaptive Information blog, July 27, 2006. See https://www.mkbergman.com/250/the-biggest-disruption-in-history-massively-accelerated-growth-since-the-industrial-revolution/.

Posted:March 27, 2012

Tortured Terminology and Problematic Prescriptions

Casting My Vote on Revising httpRange-14

The httpRange-14 issue and its predecessor “identity crisis” debate have been active for more than a decade on the Web [1]. It has been around so long that most acknowledge “fatigue” and it has acquired that rarified status as a permathread. Many want to throw up their hands when they hear of it again and some feel — because of its duration and lack of resolution — that there never will be closure on the question. Yet everyone continues to argue and then everyone wonders why actual consumption of linked data remains so problematic.

Jonathan Rees is to be thanked for refusing to let this sleeping dog lie. This issue is not going to go away so long as its basis and existing prescriptions are, in essence, incoherent. As a member of the W3C’s TAG (Technical Architecture Group), Rees has worked diligently to re-surface and re-frame the discussion. While I don’t agree with some of the specifics and especially with the constrained approach proposed for resolving this question [2], the sleeping dog has indeed been poked and is awake. For that we can thank Jonathan. Maybe now we can get it right and move on.

I don’t agree with how this issue has been re-framed and I don’t agree that responses to it must be constrained to the prescriptive approach specified in the TAG’s call for comments. Yet, that being said, as someone who has been vocal for years about the poor semantics of the semantic Web community, I feel I have an obligation to comment on this official call.

Thus, I am casting my vote behind David Booth’s alternative proposal [3], with one major caveat. I first explain the caveat and then my reasons for supporting Booth’s proposal. I have chosen not to submit a separate alternative in order to not add further to the noise, as Bernard Vatant (and, I’m sure, many, many others) has chosen [4].

Bury the Notion of ‘Information Resource’ Once and for All

I first commented on the absurdity of the ‘information resource’ terminology about five years ago [5]. Going back to Claude Shannon [6] we have come to understand information as entropy (or, more precisely, as differences in energy state). One need not get that theoretical to see that this terminology is confusing. “Information resource” is a term that defies understanding (meaning) or precision. It is also a distinction that leads to a natural counter-distinction, the “non-information resource”, which is also an imprecise absurdity.

What the confusing term is meant to encompass is web-accessible content (“documents”), as opposed to descriptions of (or statements about) things. This distinction then triggers a different understanding of a URI (locator v identifier alone) and different treatments of how to process and interpret that URI. But the term is so vague and easily misinterpreted that all of the guidance behind the machinery to be followed gets muddied, too. Even in the current chapter of the debate, key interlocutors confuse and disagree as to whether a book is an “information resource” or not. If we can’t basically separate the black balls from the white balls, how are we to know what to do with them?

If there must be a distinction, it should be based on the idea of the actual content of a thing — or perhaps more precisely web-accessible content or web-retrievable content — as opposed to the description of a thing. If there is a need to name this class of content things (a position that David Booth prefers, pers. comm.), then let’s use one of these more relevant terms and drop “information resource” (and its associated IR and NIR acronyms) entirely.

The motivation behind the “information resource” terminology also appears to be a desire that somehow a URI alone can convey the name of what a thing is or what it means. I recently tried to blow this notion to smithereens by using Peirce’s discussion of signs [1]. We should understand that naming and meaning may only be provided by the owner of a URI through additional explication, and then through what is understood by the recipient; the string of the URI itself conveys very little (or no) meaning in any semantic sense.

We should ban the notion of “information resource” forever. If the first exposure a potential new publisher or consumer of linked data encounters is “information resource”, we have immediately lost the game. Unresolvable abstractions lead to incomprehension and confusion.

The approach taken by the TAG in requesting new comments on httpRange-14 only compounds this problem. First, the guidance is to not allow any questioning of the “information resource” terminology within the prescribed comment framework [7]. Then, in the suggested framework for response, still further terminology such as “probe URIs”, “URI documentation carrier” or “nominal URI documentation carrier for a URI” is introduced. Aaaaarggghh! This only furthers the labored and artificial terminology common to this particular standards effort.

While Booth’s proposal does not call for an outright rejection of the “information resource” terminology (my one major qualification in supporting it), I like it because it purposefully sidesteps the question of the need to define “information resource” (see his Section 2.7). Booth’s proposal is also explicit in its rejection of implied meaning in URIs and through embrace of the idea of a protocol. Remember, all that is being put forward in any of these proposals is a mechanism for distinguishing between retrievable content obtainable at a given URL and a description of something found at a URI. By racheting down the implied intent, Booth’s proposal is more consistent with the purpose of the guidance and is not guilty of overreach.

Keep It Simple

One of the real strengths of Booth’s proposal is its rejection of the prescriptive method proposed by the TAG for suggesting an alternative to httpRange-14 [7]. The parsimonious objective should be to be simple, be clear, and be somewhat relaxed in terms of mechanisms and prescriptions. I believe use patterns — negotiated via adoption between publishers and consumers — will tell us over time what the “right” solutions may be.

Amongst the proposals put forward so far, David Booth’s is the most “neutral” with respect to imposed meanings or mechanisms, and is the simplest. Though I quibble in some respects, I offer qualified support for his alternative because it:

Sidesteps the “information resource” definition (though weaker than I would want; see above)
Addresses only the specific HTTP and HTTPS cases
Avoids the constrained response format suggested by the TAG
Explicitly rejects assigning innate meanings to URIs
Poses the solution as a protocol (an understanding between publisher and consumer) rather than defining or establishing a meaning via naming
Provides multiple “cow paths” by which resource definitions can be conveyed, which gives publishers and consumers choice and offers the best chance for more well-trodden paths to emerge
Does not call for an outright repeal of the httpRange-14 rule, but retains it as one of multiple options for URI owners to describe resources
Permits the use of an HTTP 200 response with RDF content as a means of conveying a URI definition
Retains the use of the hash URI as an option
Provides alternatives to those who can not easily (or at all) use the 303 see also redirect mechanism, and
Simplifies the language and the presentation.

I would wholeheartedly support this approach were two things to be added: 1) the complete abandonment of all “information resource” terminology; and 2) an official demotion of the httpRange-14 rule (replacing it with a slash 303 option on equal footing to other options), including a disavowal of the “information resource” terminology. I suspect if the TAG adopts this option, that subsequent scrutiny and input might address these issues and improve its clarity even further.

There are other alternatives submitted, prominently the one by Jeni Tennison with many co-signatories [8]. This one, too, embraces multiple options and cow paths. However, it has the disadvantage of embedding itself into the same flawed terminology and structure as offered by httpRange-14.

[1] For my recent discussion about the history of these issues, see M.K. Bergman, 2012. “Give Me a Sign: What Do Things Mean on the Semantic Web?,” in AI3:::Adaptive Information blog, January 24, 2012; see https://www.mkbergman.com/994/give-me-a-sign-what-do-things-mean-on-the-semantic-web/.

[2] In all fairness, this call was the result of ISSUE-57, which had its own constraints. Not knowing all of the background that led to the httpRange-14 Pandora’s Box being opened again, the benefit of the doubt would be that the form and approach prescribed by the TAG dictated the current approach. In any event, now that the Box is open, all pertinent issues should be addressed and the form of the final resolution should also not be constrained from what makes best sense and is most pragmatic.

[3] David Booth‘s alternative proposal is for the “URI Definition and Discovery Protocol” (uddp). The actual submission according to form is found here.

[4] See Bernard Vatant, 2012. “Beyond httpRange-14 Addiction,” the wheel and the hub blog, March 27, 2012. See http://blog.hubjects.com/2012/03/beyond-httprange-14-addiction.html.

[5] M.K. Bergman, 2007. “More Structure, More Terminology and (hopefully) More Clarity,” in AI3:::Adaptive Information blog, July 27, 2007; see https://www.mkbergman.com/391/more-structure-more-terminology-and-hopefully-more-clarity/. Subsequent to that piece, I have written further on semantic Web semantics in “The Semantic Web and Industry Standards” (January 26, 2008), ” “The Shaky Semantics of the Semantic Web” (March 12, 2008), “Semantic Web Semantics: Arcane, but Important,” (April 8, 2008), “When Linked Data Rules Fail” (November 16, 2009), “The Semantic ‘Gap’” (October 24, 2010) and [1].

[6] Claude E. Shannon, 1948. “A Mathematical Theory of Communication,” Bell System Technical Journal, Vol. 27, pp. 379–423, 623–656, 1948.

[7] In the “Call for proposals to amend the “httpRange-14 resolution” (February 29, 2012), Jonathan Rees (presumably on behalf of the TAG), stated this as one of the rules of engagement: “9. Kindly avoid arguing in the change proposals over the terminology that is used in the baseline document. Please use the terminology that it uses. If necessary discuss terminology questions on the list as document issues independent of the 303 question.” The specific template formfor alternative proposals was also prescribed. In response to interactions on this question on the mailing list, Jonathan stated:

If it were up to me I’d purge “information resource” from the document, since I don’t want to argue about what it means, and strengthen the (a) clause to be about content or instantiation or something. But the document had to reflect the status quo, not things as I would have liked them to be.

I have not submitted this as a change proposal because it doesn’t address ISSUE-57, but it is impossible to address ISSUE-57 with a 200-related change unless this issue is addressed, as you say, head on. This is what I’ve written in my TAG F2F preparation materials.

[8] Jeni Tennison, 2012. “httpRange-14 Change Proposal,” submitted March 25, 2012. See the mailing list notice and actual proposal.

Posted:March 14, 2012

TechWiki Gets 400th Document

Phenomenal Growth in Less than Two Years

Today, for the first time, we passed 400 articles published on the open semantic framework (OSF) TechWiki. The TechWiki content is a baseline “starter kit” of documentation related to these OSF projects and their contexts:

conStruct – connecting modules to enable structWSF and sComponents to be hosted/embedded in Drupal
structWSF – platform-independent suite of more than 20 RESTful Web services, organized for managing structured data datasets
Semantic Components – JavaScript or Flex semantic components (widgets) for visualizing and manipulating structured data
irON – instance record Object Notation for conveying XML, JSON or spreadsheets (CSV) in RDF-ready form, and
Various parsers and standard data exchange formats and schema to facilitate information flow amongst these options.

The TechWiki covers all aspects of this open source OSF software stack. Besides the specific components developed and maintained by Structured Dynamics as listed above, the OSF stack combines many leading third-party software packages — such as Drupal for content management, Virtuoso for (RDF) triple storage, Solr for full-text indexing, GATE for natural language processing, the OWL API for ontology management, and others.

The TechWiki is the one-stop resource for how to install, configure, use and maintain these components. The best entry point to the OSF content on the TechWiki is represented by this entry page covering overall workflows in use of the system:

Since our first release of the TechWiki in July 2010, we have been publishing and releasing content steadily. We post a new article about every 1.5 calendar days, or about one per working day. This content is well-organized into (at present) 72 categories and is supported by nearly 500 figures and diagrams. Users are free to download and use this content at will, solely by providing attribution. The content has proven to be a goldmine for local use and modification by our clients, and for training and curriculum development.

The TechWiki represents a part of our commitment that we are successful when our customers no longer need us. As one of our most popular Web sites with fantastic and growing user stats, we invite you to visit and see what it means to provide open source semantic technologies as a total open solution.

Posted:February 27, 2012

OSF Gains Powerful, New Mapping Component

Ontology-driven Application Meshes Structured Data with Public APIs

Locational information — points of interest/POIs, paths/routes/polylines, or polygons/regions — is common to many physical things in our real world. Because of its pervasiveness, it is important to have flexible and powerful display widgets that can respond to geo-locational data. We have been working for some time to extend our family of semantic components [1] within the open semantic framework (OSF) [2] to encompass just such capabilities. Structured Dynamics is thus pleased to announce that we have now added the sWebMap component, which marries the entire suite of Google Map API capabilities to the structured data management arising from the structWSF Web services framework [3] at the core of OSF.

The sWebMap component is fully in keeping with our design premise of ontology-driven applications, or ODapps [4]. The sWebMap component can itself be embedded in flexible layouts — using Drupal in our examples below — and can be very flexibly themed and configured. sWebMap we believe will rapidly move to the head of the class as the newest member of Structured Dynamics’ open source semantic components.

The absolutely cool thing about sWebMap is it just works. All one needs to do is relate it to a geo-enabled Search structWSF endpoint, and then all of the structured data with geo-locational attributes and its facets and structure becomes automagically available to the mapping widget. From there you can flexible map, display, configure, filter, select and keep those selections persistent and share with others. As new structured data is added to your system, that data too becomes automatically available.

Key Further Links

Though screen shots in the operation of this component are provided below, here are some further links to learn more:

Live demo of the sWebMap component on the Citizen Dan sandbox
An explanation of some of the technical details behind the sWebMap component
sWebMap documentation on the TechWiki, including configuration and styling options
General background information on ODApps, ontology-driven applications.

sWebMap Overview

There is considerable functionality in the sWebMap widget, not all immediately obvious when you first view it.

NOTE: a wide variety of configuration options — icons and colors — matched with the specific data and base tiling maps appropriate to a given installation may produce maps of significantly different aspect from the screenshots presented below. Click on any screenshot to get a full-size view.

Here is an example for sWebMap when it first comes up, using an example for the “Beaumont neighborhood”:

views

It is possible to set pre-selected items for any map display. That was done in this case, which shows the pre-selected items and region highlighted on the map and in the records listing (lower left below map).

The basic layout of the map has its main search options at the top, followed by the map itself and then two panels underneath:

The left-hand panel underneath the map presents the results listing. The right-hand panel presents the various filter options by which these results are generated. The filter options consist of:

Sources – the datasets available to the instance
Kinds – the kinds or types of data (owl:Classes or rdf:types) contained within those datasets, and
Attributes – the specific attributes and their values for those kinds or sources.

As selections are made in sources or kinds, the subsequent choices narrow.

The layout below shows the key controls available on the sWebMap:

You can go directly to an affiliated page by clicking the upper right icon. This area often shows a help button or other guide. The search box below that enables you to search for any available data in the system. If there is information that can be mapped AND which occurs within the viewport of the current map size, those results will appear as one of three geographic feature types on the map:

Markers, which can be configured with differing icons for specific types or kinds of data
Polylines, such as highways or bus routes, or
Polygons, which enclose specific regions on the map through a series of drawn points in a closed area.

At the map’s right is the standard map control that allows you to scroll the map area or zoom. Like regular Google maps, you can zoom (+ or – keys, or middle wheel on mouse) or navigate (arrow direction keys, or left mouse down and move) the map.

Current records are shown below the map. Specific records may be selected with its checkbox; this keeps them persistent on the map and in the record listing no matter what the active filter conditions may be. (You may also see a little drawing icon [ Update record ], which presents an attribute report — similar to a Wikipedia ‘infobox‘ — for the current record). You can see in this case that the selected record also corresponds to a region (polygon) shape on the map.

sWebMap Views, Layers and Layouts

In the map area itself, it is possible to also get different map views by selecting one of the upper right choices. In this case, we can see a satellite view (or “layer”):

Or, we can choose to see a terrain layer:

Or there may optionally be other layers or views available in this same section.

Another option that appears on the map is the ability to get a street view of the map. That is done by grabbing the person icon at the map left and dragging it to where you are interested within the map viewport. That also causes the street portion to be highlighted, with street view photos displayed (if they exist for that location):

By clicking the person icon again, you then shift into walking view:

Via the mouse, you can now navigate up and down these streets and change perspective to get a visual feel for the area.

Multi-map View

Another option you may invoke is the multi-map view of the sWebMap. In this case, the map viewing area expands to include three sub-maps under the main map area. Each sub-map is color-coded and shown as a rectangle on the main map. (This particular example is displaying assessment parcels for the sample instance.) These rectangles can be moved on the main map, in which case their sub-map displays also move:

You must re-size using the sub-map (which then causes the rectangle size to change on the main map). You may also pan the sub-maps (which then causes the rectangle to move on the main map). The results list at the lower left is determined by which of the three sub-maps is selected (as indicated by the heavier bottom border).

Searching and Filter Selections

There are two ways to get filter selection details for your current map: Show All Records or Search.

NOTE: for all data and attributes as described below, only what is visible on the current map view is shown under counts or records. Counts and records change as you move the map around.

In the first case, we pick the Show All Records option at the bottom of the map view, which then brings up the detailed filter selections in the lower-right panel:

Here are some tips for using the left-hand records listing:

If there are more than 10 records, pagination appears at the bottom of the listing
Each record is denoted by an icon for the kind of thing it is (bus stops v schools v golf courses, for example)
If we mouse over a given record in the listing, its marker icon on the map bounces to show where it resides
To the right of each record listing, the checkbox indicates whether you want the record to be maintained persistently. If you check it, the icon on the map changes color, the record is promoted to the top of the list where it becomes sticky and is given an alphabetic sequence. Unchecking this box undoes all of these changes
To the right of each record listing is also the view record [] icon; clicking it shows the raw attribute data for that record.

The records that actually appear on this listing are based on the records scope or Search (see below) conditions, as altered by the filter settings on the right-hand listing under the sWebMap. For example, if we now remove the neighborhood record as being persistent and Show included records we now get items across the entire map viewport:

Search works in a similar fashion, in that it invokes the filter display with the same left- and right-hand listings appear under the sWebMap, only now only for those records that met the search conditions. (The allowable search syntax is that for Lucene.) Here is the result of a search, in this case for “school”:

As shown above, the right-hand panel is split into three sections: Sources (or datasets), Kinds (that is, similar types of things, such as bus stops v schools v golf courses), and Attributes (that is, characteristics for these various types of things). All selection possibilities are supported by auto-select.

Sources and Kinds are selected via checkbox. (The default state when none are checked is to show all.) As more of these items are selected, the records listing in the left-hand panel gets smaller. Also, the counts of available items [as shown by the (XX) number at the end of each item] are also changed as filters are added or subtracted by adding or removing checkboxes.

Applying filters to Attributes works a little differently. Attributes filters are selected by selecting the magnifier plus [ Filter by attribute ] icon, which then brings up a filter selection at the top of the listing underneath the Attributes header.

The specific values and their counts (for the current selection population) is then shown; you may pick one or more items. Once done, you may pick another attribute to add to the filter list, and continue the filtering process.

Saving and Sharing Your Filters

sWebMaps have a useful way to save and share their active filter selections. At any point as you work with a sWebMap, you can save all of its current settings and configurations — viewport area, filter selections, and persistent records — via some simple steps.

You initiate this functionality by choosing the save button at the upper right of the map panel:

When that option is invoked, it brings up a dialog where you are able to name the current session, and provide whatever explanatory notes you think might be helpful.

NOTE: the naming and access to these saved sessions is local to your own use only, unless you choose to share the session with others; see below.

Once you have a saved session, you will then see a new control at the upper right of your map panel. This control is how you load any of your previously saved sessions:

Further, once you load a session, still further options are presented to you that enables you to either delete or share that session:

If you choose to share a session, a shortened URI is generated automatically for you:

If you then provide that URI link to another user, that user can then click on that link and see the map in the exact same state — viewport area, filter selections, and persistent records — as you initially saved. If the recipient then saves this session, it will now also be available persistently for his or her local use and changes.

NOTE: two users may interactively work together by sharing, saving and then modifying maps that they share again with their collaborator.

[1] A semantic components is a JavaScript or Flex component or widget that takes record descriptions and irXML schema as input, and then outputs interactive visualizations of those records. Depending on the logic described in the input schema and the input record descriptions, the semantic component may behave differently or provide presentation options to users. Each semantic component delivers a very focused set of functionality or visualization. Multiple components may be combined on the same canvas for more complicated displays and controls. At present, there are 12 individual semantic widgets in the available open source suite; see further the sComponent category on the TechWiki. By convention, all of the individual widgets in the semantic component suite are named with an ‘s’ prefix; hence, sWebMap.

[2] The open semantic framework, or OSF, is a combination of a layered architecture and an open-source, modular software stack. The stack combines many leading third-party software packages — such as Drupal for content management, Virtuoso for (RDF) triple storage, Solr for full-text indexing, GATE for tagging and natural language processing, the OWL2 API for ontology management and support, and others. These third-party tools are extended with open source developments from Structured Dynamics including structWSF (a RESTful Web services layer of about a dozen modules for interacting with the underlying data and data engines), conStruct (a series of Drupal modules that tie Drupal to the structWSF Web services layer), semantic components (data display and manipulation widgets, mostly based either in Flash or JavaScript, for working with the semantic data), various parsers and standard data exchange formats and schema to facilitate information flow amongst these options, and a ontologies layer, that consists of both domain ontologies that capture the coherent concepts and relationships of the current problem space and of administrative ontologies that govern how the other software layers interact with this structure.

[3] structWSF is a platform-independent Web services framework for accessing and exposing structured RDF (Resource Description Framework) data. Its central organizing perspective is that of the dataset. These datasets contain instance records, with the structural relationships amongst the data and their attributes and concepts defined via ontologies (schema with accompanying vocabularies). The structWSF middleware framework is generally RESTful in design and is based on HTTP and Web protocols and open standards. The current structWSF framework has a baseline set of more than 20 Web services in CRUD, browse, search, tagging, ontology management, and export and import.

[4] For the most comprehensive discussion of ODapps, see M. K. Bergman, 2011. ” Ontology-Driven Apps Using Generic Applications,” posted on the AI3:::Adaptive Information blog, March 7, 2011. You may also search on that blog for ‘ODapps‘ to see related content.

Posted:February 13, 2012

The Conditional Costs of Free

Shun the Frumious Bandersnatch?

The Web and open source have opened up a whole new world of opportunities and services. We can search the global information storehouse, connect with our friends and make new ones, form new communities, map where stuff is, and organize and display aspects of our lives and interests as never before. These advantages compound into still newer benefits via emergent properties such as social discovery or bookmarking, adding richness to our lives that heretofore had not existed.

And all of these benefits have come for free.

Of course, as our use and sophistication of the Web and open source have grown we have come to understand that the free provision of these services is rarely (ever?) unconditional. For search, our compact is to accept ads in return for results. For social networks, our compact is give up some privacy and control of our own identities. For open source, our compact is the acceptance of (generally) little or no support and often poor documentation.

We have come to understand this quid pro quo nature of free. Where the providers of these services tend to run into problems is when they change the terms of the compact. Google, for example, might change how its search results are determined or presented or how it displays its ads. Facebook might change its privacy or data capture policies. Or, OpenOffice or MySQL might be acquired by a new provider, Oracle, that changes existing distribution, support or community involvement procedures.

Sometimes changes may fit within the acceptable parameters of the compact. But, if such changes fundamentally alter the understood compact with the user community, users may howl or vote with their feet. Depending, the service provider may relent, the users may come to accept the new changes, or the user may indeed drop the service.

The Hidden Costs of Dependence

But there is another aspect of the use of free services, the implications of which have been largely unremarked. What happens if a service we have come to depend upon is no longer available?

Abandonment or changes in service may arise from bankruptcy or a firm being acquired by another. My favorite search service of a decade ago, AltaVista, and Delicious are two prominent examples here. Existing services may be dropped by a provider or APIs removed or deprecated. For Google alone, examples include Wave and Gears, Google Labs, and many, many APIs. (The howls around Google Translate actually caused it to be restored.) And existing services may be altered, such as moving from free to fee or having capabilities significantly modified. Ning and Babbel are two examples here. There are literally thousands of examples of Web-based free services that have gone through such changes. Most have not seen widespread use, but have affected their users nonetheless.

There is nothing unique about free services in these regards. Ford was able to cease production of its Edsel and change the form factor of the Thunderbird despite some loyal fans. Sugar Pops morphed into a variety of breakfast cereal brands. Sony Betamax was beat out by VHS, which then lost out to CDs and now DVDs. My beloved Saabs are heading for the dustbin, or Chinese ownership.

In all of these cases, as consumers we have no guarantees about the permanence of the service or the infrastructure surrounding it. The provider is solely able to make these determinations. It is no different when the service or offering is free. It is the reality of the marketplace that causes such changes.

But, somehow, with free Web services, it is easy to overlook these realities. I offer a couple of personal case studies.

Case Study #1: Site Search

I have earlier described the five different versions of site search that I have gone through for this blog. The thing is, my current option, Relevanssi, is also a free plug-in. What is notable about this example, though, is the multiple attempts and (unanticipated) significant effort to discover, evaluate and then implement alternatives. Unfortunately, I rather suspect my current option may itself — because of the nature of free on the Web — need to be replaced at some time down the road.

Case Study #2: FeedBurner

Part of what caused me to abandon Google Custom Search as one of the above search options was the requirement I serve ads on my blog to use it. So, when I decided to eliminate ads entirely in 2010 I not only gave up this search option, but I also lost some of the better tracking and analytics options also provided for free by Google. Fortunately, I had also adopted FeedBurner early in the life of this blog. It was also becoming increasingly clear that feed subscribers — in addition to direct site visitors — were becoming an essential metric for gauging traffic.

I thus had a replacement means for measuring traffic trends. Google (strange how it keeps showing up!) had purchased FeedBurner in 2007, and had made some nice site and feature improvements, including turning some paid services into free. The service was performing quite well, despite FeedBurner’s infamous knack to lose certain feed counts periodically. However, this performance broke last Summer when my site statistics indicated a massive drop in subscribers.

The figure below, courtesy of Feed Compare, shows the daily subscriber statistics for my AI3 blog for the past two years. The spikiness of the curve affirms the infamous statistics gaps of the service. The first part of the curve also shows nice, steady growth of readers, growing to more than 4000 by last Summer. Then, on August 16, there was a massive drop of 85% in my subscriber counts. I monitored this for a couple of days, thinking it was another temporary infamous event, then realized something more serious was afoot:

It was at this point I became active on the Google group for FeedBurner. Many others had noted the same service drop. (The major surmise is that FeedBurner now is having difficulty including Feedfetcher feeds, which is interesting because it is the feed of Google’s own Reader service, and the largest feed aggregation source on the Web.)

Over the ensuing months until last week I posted periodic notices to the official group seeking clarification as to the source of these errors and a fix to the service. In that period, no Google representative ever answered me, nor any of the numerous requests by others. I don’t believe there has been a single entry on any matter by Google staff for nearly the past year.

I made requests and inquiries no fewer than eight times over these months. True, Google had announced it was deprecating the FeedBurner API in May 2011, but, in that announcement, there was no indication that bug fixes or support to their own official group would cease. While it is completely within Google’s purview to do as it pleases, this behavior hardly lends itself to warm feelings by those using the service.

Finally, last week I dropped the FeedBurner stats and installed a replacement WordPress plugin service [1]. It was clear no fixes were forthcoming and I needed to regain an understanding of my actual subscriber base. The counts you now see on this site use this new service; they show the continuation of this site’s historical growth trend.

Is Google Becoming More Frumious?

It is not surprising that in the prior discussions Google figures prominently. It is the largest provider of APIs and free services on the Web. But, even with its continuing services, I am seeing trends that disturb me in terms of what I thought the “compact” was with the company.

I’m not liking recent changes to Google’s bread and butter, search. While they are doing much to incorporate more structure in their results, which I applaud, they are also making ranking, formatting and presentation changes I do not. I am now spending at least us much of my search time on DuckDuckGo, and have been mightily impressed with its cleanliness, quality and lack of ads in results.

I also do not like how all of my current service uses of Google are now being funneled into Google Plus. I am seeing an arrogance that Google knows what is best and wants to direct me to workflows and uses, reminiscent of the arrogance Microsoft came to assume at the height of its market share. How does that variant of Lord Acton’s dictum go? “Market share tends to corrupt, and absolute market share corrupts absolutely.”

We are seeing Google’s shift to monetize extremely popular APIs such as Maps and Translate. My company, Structured Dynamics, has utilized these services heavily for client work in the past. We now must find alternatives or cost the payment for these services into the ongoing economics of our customer installations. Of course, charging for these services is Google’s right, but it does change the equation and causes us to evaluate alternatives.

I fear that Google may be turning into a frumious Bandersnatch. I’m not sure we will shun it, but we certainly are changing our views of the basis by which we engage or not with the company and its services. Once we shift from a basis of free, our expectations as to permanence and support change as well.

Big Boys Don’t Cry

This is not a diatribe against Google nor a woe is us. Us big kids have come to know that there is no such thing as a free lunch. But that message is getting reaffirmed now more strongly in the Web context.

There can be benefits from seeking, installing or adapting to new alternatives with different service profiles when dependent services are abandoned or deprecated. Learning always takes place. Accepting one’s own responsibility for desired services also leads to control and tailoring for specific needs. Early use of free services also educates about what is desired or not, which can lead to better implementation choices if and when direct responsibility is assumed.

But, in some areas, we are seeing services or uses of the Web that we should adopt only with care or even shun. Business opportunities that depend on third-party services or APIs are very risky. Strong reliance on single-provider service ecosystems adds fragility to dependence. Own systems should be designed to not depend too strongly on specific API providers and their unique features or parameters.

Free is not forever, and it is conditional. Substitutability is a good design practice to embrace.

[1] I may detail at a later time how this replacement service was set up.

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Author: Mike Bergman