Herbie Hancock's Chameleon's BPM graph from the Android app 'liveBPM' (v. 1.2.0) by Daniel Bach

Listening to music seems easy.

Listening to music seems easy; it even appears like a passive task.

Listening, however, is not the same as hearing. In listening, i.e., attending, we add cognition to perception. The cognition of musical structures, cultural meanings, conventions, and even of the most fundamental elements themselves such as pitch or rhythm turns out to be a complex cognitive task. We know this is so because getting our cutting-edge technology to understand music with all its subtleties and its cultural contexts has proven, so far, to be impossible.

Within small fractions of a second, humans can reach conclusions about musical audio that are beyond the abilities of the most advanced algorithms.

For example, a trained or experienced musician (or even non-musician listener) can differentiate computer-generated and human-performed instruments in almost any musical input, even in the presence of dozens of other instruments sounding simultaneously.

In a rather different case, humans can maintain time-organizational internal representations of music while the tempo of a recording or performance continuously changes. A classic example is the jazz standard Chameleon by Herbie Hancock off the album ‘HEADHUNTERS’. The recording never retains any one tempo, following an up-and-down contour and mostly getting faster. Because tempo recognition is a prerequisite to other music-perception tasks like meter induction and onset detection, this type of behavior presents a significant challenge to signal-processing and machine-learning algorithms but generally poses no difficulty to human perception.

Another example is the recognition of vastly different cover versions of songs: A person familiar with a song can recognize within a few notes a cover version of that song done in another genre, at a different tempo, by another singer, and with different instrumentation.

Each of these is a task that is well beyond machine-learning techniques that are exhibiting remarkable successes with visual recognition where the main challenge, invariance, is less of an obstacle than the abstractness of music and its seemingly arbitrary meanings and structures.

Consider the following aspects of music cognition.

  • inferring a key (or a change of key) from very few notes
  • identifying a latent underlying pulse when it is completely obscured by syncopation [Tal et al., Missing Pulse]
  • effortlessly tracking key changes, tempo changes, and meter changes
  • instantly separating and identifying instruments even in performances with many-voice polyphony (as in Dixieland Jazz, Big-Band Jazz, Baroque and Classical European court music, Progressive Rock, folkloric Rumba, and Hindustani and Carnatic classical music)

These and many other forms of highly polyphonic, polyrhythmic, or cross-rhythmic music continue to present challenges to automated algorithms. Successful examples of automated tempo or meter induction, onset detection, source separation, key detection, and the like all work under the requirement of tight limitations on the types of inputs. Even for a single such task such as source separation, a universally applicable algorithm does not seem to exist. (There is some commercial software that appear to do these tasks universally, but because proprietary programs do not provide sufficiently detailed outputs, whether they really can perform all these function or whether they perform one function in enough detail to suffice for studio uses is uncertain. One such suite can identify and separate every individual note from any recording, but does not perform source separation into streams-per-instrument and presents its output in a form not conducive to analysis in rhythmic, harmonic, melodic, or formal terms, and not in a form analogous to human cognitive processing of music.)

Not only does universal music analysis remain an unsolved problem, but also most of the world’s technological effort goes toward European folk music, European classical music, and (international) popular music. The goal of my research and my lab (Lab BBBB: Beats, Beats, Bayes, and the Brain) is to develop systems for culturally sensitive and culturally informed music analysis, music coaching, automated accompaniment, music recommendation, and algorithmic composition, and to do so for popular music styles from the Global South that are not in the industry’s radar.

Since the human nervous system is able to complete musical-analysis tasks under almost any set of circumstances, in multiple cultural and cross-cultural settings, with varying levels of noise and interference, the human brain is still superior to the highest-level technology we have developed. Hence, Lab BBBB takes inspiration and direct insight from human neural processing of audio and music to solve culturally specific cognitive problems in music analysis, and to use this context to further our understanding of neuroscience and machine learning.

The long-term goal of our research effort is a feedback cycle:

  1. Neuroscience (in simulation and with human subjects at our collaborators’ sites) informs both music information retrieval and research into neural-network structures (machine learning). We are initially doing this by investigating the role of rhythm priming in Parkinson’s (rhythm–motor interaction) and in grammar-learning performance (rhythm–language interaction) in the basal ganglia. We hope to then replicate in simulation the effects that have been observed with people, verify our models, and use our modeling experience on other tasks that have not yet been demonstrated in human cases or that are too invasive or otherwise unacceptable.
  2. Work on machine learning informs neuroscience by narrowing down the range of investigation.
  3. Deep learning is also used to analyze musical audio using structures closer to those in the human brain than the filter-bank and matrix-decomposition methods typically used to analyze music.
  4. Music analysis informs cognitive neuroscience, we conjecture, as have been done in certain cases in the literature with nonlinear dynamics.
  5. Phenomena like entrainment and neural resonance in neurodynamics further inform the development of neural-network structures and data-subspace methods.
  6. These developments in machine learning move music information retrieval closer to human-like performance for culturally informed music analysis, music coaching, automated accompaniment, music recommendation, and algorithmic composition for multicultural intelligent music systems.

 

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The subjunctive is scientific thinking built into the language.

The subjunctive draws a distinction between fact and possibility, between truths and wishes. The expression “if he were” (not “if he was”) is subjunctive; it intentionally sounds wrong (unless you’re used to it) to indicate that we’re talking about something hypothetical as opposed to something actual.
This is scientific thinking built into the language (coming from its romance-language roots).

This is beautiful. Let’s hold onto it.

You are not disinterested.

Everyone: Stop saying ‘disinterested’. You apparently don’t know what it means. It doesn’t mean ‘uninterested’.

In fact, it means you’re truly interested. ‘Disinterested’ is when you care so deeply as to want to treat the situation objectively. It is a scientific term describing the effort to rid a study of the effects of subconscious biases.

Also, please don’t say ‘substantive’ when all you mean is ‘substantial’. They’re not the same thing. Thanks. (‘Substantial’ is a good word. You’re making it feel abandoned. )

Microsoft: Fix your use of the word ‘both’.
When comparing only two files, Windows says something like “Would you like to compare both files?” As opposed to what, just compare one, all by itself? (like the sound of one hand clapping?)
The word ‘both’ is used when the default is not that of two things. It emphasizes the two-ness to show that the twoness is special, unusual. But when the default is two, you say “the two” (as in “Would you like to compare the two files?”), not ‘both’, and DEFINITELY NOT ‘the both’. (It was cute when that one famous said it once. It’s not cute anymore. Stop saying it.)
Back to ‘both’: A comparison has to involve two things, so ‘both’ (the special-case version of the word ‘two’) only makes sense if the two things are being compared to a third.
English is full of cool, meaningful nuances. I hope we stop getting rid of them.

Seriously, everyone: English is wonderful. Why are you destroying it?

 

PS: same with “on the one hand”… We used to say “on one hand” (which makes sense… either one, any one, not a definite hand with a definite article)

Science-doing

There are (at least) two types of scientists: scientist-scientists and science-doers.

Both groups do essential, difficult, demanding, and crucial work that everyone, including the scientist-scientists, needs. The latter group (like the former) includes people who work in research hospitals, water-quality labs, soil-quality labs, linear accelerators, R-&-D labs of all kinds, and thousands of other places. They carry out the daily work of science with precision, care, and a lot of hard work. Yet, at the same time, in the process of doing the doing of science, they typically do not get the luxury of stepping back, moving away from the details, starting over, and discovering the less mechanical, less operational connections among the physical sciences, the social sciences, the humanities, technology, business, mathematics, and statistics… especially the humanities and statistics.

I am not a good scientist, and that has given me the opportunity to step back, start over, do some things right this time, and more importantly, through a series of delightful coincidences, learn more about the meaning of science than about the day-to-day doing of it.[1] This began to happen during my Ph.D., but only some of the components of this experience were due to my Ph.D. studies. The others just happened to be there for me to stumble upon.

The sources of these discoveries took the form of two electrical-engineering professors, three philosophy professors, one music professor, one computer-science professor, some linguistics graduate students, and numerous philosophy, math, pre-med, and other undergrads. All of these people exposed me to ideas, ways of thinking, ways of questioning, and ways of teaching that were new to me.

As a result of their collective influence, my studies, and all my academic jobs from that period, I have come to think of science not merely as the wearing of lab coats and carrying out of mathematically, mechanically, or otherwise challenging complex tasks. I have come to think of science as the following of, for lack of a better expression, the scientific method, although by that I do not necessarily mean the grade-school inductive method with its half-dozen simple steps. I mean all the factors one has to take into account in order to investigate anything rigorously. These include double-blinding (whether clinical or otherwise, to deal with confounding variables, experimenter effects, and other biases), setting up idiot checks in experimental protocols, varying one unknown at a time (or varying all unknowns with a factorial design), not assuming unjustified convenient probability distributions, using the right statistics and statistical tests for the problem and data types at hand, correctly interpreting results, tests, and statistics, not chasing significance, setting up power targets or determining sample sizes in advance, using randomization and blocking in setting up an experiment or the appropriate level of random or stratified sampling in collecting data [See Box, Hunter, and Hunter’s Statistics for Experimenters for easy-to-understand examples.], and the principles of accuracy, objectivity, skepticism, open-mindedness, and critical thinking. The latter set of principles are given on p. 17 and p. 20 of Essentials of Psychology [third edition, Robert A. Baron and Michael J. Kalsher, Needham, MA: Allyn & Bacon, 2002].

These two books, along with Hastie, Tibshirani, and Friedman’s The Elements of Statistical Learning and a few other sources that are heavily cited papers on the misuses of Statistics have formed the basis of my view of science. This is why I think science-doing is not necessarily the same thing as being a scientist. In a section called ‘On being a scientist’ in a chapter titled ‘Methodology Wars’, the neuroscientist Fost explains how it’s possible, although not necessarily common, to be on “scientific autopilot” (p. 209) because of the way undergraduate education focuses on science facts and methods[2] over scientific thinking and the way graduate training and faculty life emphasize administration, supervision, managerial oversight, grant-writing, and so on (pp. 208–9). All this leaves a brief graduate or a post-doc period in most careers for deep thinking and direct hands-on design of experiments before the mechanical execution and the overwhelming burdens of administration kick in. I am not writing this to criticize those who do what they have to do to further scientific inquiry but to celebrate those who, in the midst of that, find the mental space to continue to be critical skeptical questioners of methods, research questions, hypothesis, and experimental designs. (And there are many of those. It is just not as automatic as the public seems to think it is, i.e., by getting a degree and putting on a white coat.)

 

Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building, George E. P. Box, William G. Hunter, and J. Stuart Hunter, New York , NY: John Wiley & Sons, Inc., 1978 (0-471-09315-7)

Essentials of Psychology, third edition, Robert A. Baron and Michael J. Kalsher, Needham, MA: Allyn & Bacon, A Pearson Education Company, 2002

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, second edition, Trevor Hastie, Robert Tibshirani, and Jerome Friedman, New York, NY: Springer-Verlag, 2009 (978-0-387-84858-7 and 978-0-387-84857-0)

If Not God, Then What?: Neuroscience, Aesthetics, and the Origins of the Transcendent, Joshua Fost, Clearhead Studios, Inc., 2007 (978-0-6151-6106-8)

[1] Granted, a better path would be the more typical one of working as a science-doer scientist for thirty years, accumulating a visceral set of insights, and moving into the fancier stuff due to an accumulation of experience and wisdom. However, as an educator, I did not have another thirty years to spend working on getting a gut feeling for why it is not such a good idea to (always) rely on a gut feeling. I paid a price, too. I realize I often fail to follow the unwritten rules of social and technical success in research when working on my own research, and I spend more time than I perhaps should on understanding what others have done. Still, I am also glad that I found so much meaning so early on.

[2] In one of my previous academic positions, I was on a very active subcommittee that designed critical-thinking assessments for science, math, and engineering classes with faculty from chemistry, biology, math, and engineering backgrounds. We talked often about the difference between teaching scientific facts and teaching scientific thinking. Among other things, we ended up having the university remove a medical-terminology class from the list of courses that counted as satisfying a science requirement in general studies.

Time Travel? (It comes down to causality’s unawareness.)

Forget the usually discussed paradox about time travel; how about how one would know whether one had come back to the proper (intended) present?

Would one rely on clocks and calendars? Clocks move at different speeds (as explained by relativity). And how much should the calendar have changed while you were off in the past? Does the travel itself take any time? How about if everyone else also time-traveled while you were off doing that? Who, or what, decides what the correct time is “back home”? (To say that it would take so much energy that only one person should be able to time-travel or get close to the speed of light at a time would be an economic argument, not a physical one, so in a discussion of physics, we must allow for the possibility that everyone is time-traveling concurrently—let’s say, by starting together but going off in different directions in different amounts.) Then who or what keeps so-called real time going in its regular pace? (It doesn’t even have one, thanks to relativity!)

What we mean by time travel—if it’s not kept track of via clocks—is that causality is unaware of the traveler’s appearance and disappearance. So, what happens to the air molecules where the traveler will suddenly appear? Are they pushed out, or do they somehow combine or coexist with the traveler’s? If they are pushed out, then how massive an object can time-travel? (Because, at some point, we’d be moving so much air, it would lead to catastrophic damage to someone or something. So, the paradoxes extends to the legal and safety realms as well, although those are merely practical.)

Science, Clave, and Understanding

When Dr. Eben Alexander defended, in one of the major news magazines, his book (“proof[i]”) [1] about a spiritual non-physical afterlife realm, part of his argument was that he is a surgeon, and therefore a scientist. Surgeons are highly trained, highly specialized people who perform a very difficult and critically important service. It would be absurd not to recognize their value. Their work is without a doubt science-based, but does that make it “science”? (There are, of course, surgeons who publish scholarly work (although, I’ve noticed that in some cases, it’s not about surgery, but on fields as distant as music), and thus function as scholars, and therefore scientists.)

A scientist is not anyone who functions as a professional practitioner of a difficult and science-based field; a scientist is someone who sets up, tests, and evaluates (mostly via statistical data analysis) testable hypotheses (about anything, including the afterlife and spiritual realms, if necessary), and more importantly, does so within the guidelines of rigor, accuracy, objectivity, skepticism, and open-mindedness [2][ii]. It is worrisome to imagine that surgeons are setting up double-blinded clinical trials of surgical practices as part of their work, choosing to apply a known good technique on one patient and an as-yet-unsupported one on another patient. (In other words, I really hope surgeons do not act as scientists.) Maybe they do; I’d like to know, so please give me feedback on this question.

Assuming, though, that they don’t endanger patients’ lives for the sake of science, as we tend not to do anymore, it seems safe to assume that surgeons are highly trained specialists who practice state-of-the-art medicine. In this sense, they are not scientists. They use the findings and results of science in their practical, applied work (medicine). They must, then, fall somewhere between applied scientists and technologists (inclusive).

To say that someone who practices a specialty that is based on scientific findings is therefore a scientist is like saying a sandwich-shop employee is a farmer because they use bacon, lettuce, and tomato in their work. (The fact that surgery is far more specialized does not invalidate the argument.)

The professions that discover, invent, develop, and apply are all different. The roles can overlap—scientists do develop and build new equipment to perform their experiments, but these are not mass-produced. Anything we can purchase repeatedly on amazon or at Best Buy, say, was not made by scientists. It was designed, developed, tested, and manufactured by engineers, technologists, technicians, and other professionals, not by scientists, even if scientists were involved in the early stages. As for applied scientists, including those who work at laboratories, characterizing soil samples, say, or performing tests, they are also highly trained specialists of scientific background who are not doing science at that point. As one XKCD comic suggested [3], you can simply order a lab coat from a catalog; no one will check your publication record. Science is not solely about what you’re wearing or what degrees you have; it’s about what, exactly, you’re doing.

The public’s idea of what science is seems to be “mathy and difficult, preferable done in a lab coat while uttering multisyllabic words you don’t want to see in your cereal’s list of ingredients.” This may be a decent shortcut for pop-culture purposes, but it is not what science really is. I will not go into the inductive-method-vs-hypothetico-deductive-method-vs-what-have-you debate here because there are people who do that professionally, and do it very well. (I have been enjoying Salmon’s The Foundations of Scientific Inference [4] immensely.) What I do want to do is draw two parallels in succession, first from the preceding discussion to explanation and understanding, and from those concepts, to explanations and understanding of clave (in music).

The former has been done quite successfully in Paul Dirac-medal-and-prize-winning physicist Deutsch’s earlier book The Fabric of Reality [5]. I am not concerned here with the bulk and main point of his book, but only with his opening argument about the role of science (explanation) and what it means to understand. Deutsch criticizes instrumentalism because of its emphasis on prediction at the cost of explanation (pp. 3–7). He gives rather good examples of situations in which no scientist (or layperson, for that matter) would be satisfied with good predictions without explanations (p. 6, for example). He does not deny the role and importance of predictions, but argues that “[t]o say that prediction is the purpose of scientific theory is [. . .] like saying that the purpose of a spaceship is to burn fuel” (similar to another author’s argument that the purpose of a car is not to make vrooom–vrooom noises; they just happen to do that as part of their operation[iii]). Deutsch states that just like spaceships have to burn fuel to do what they’re really meant to do, theories have to pass experimental tests in order “to achieve the real purpose of science, which is to explain the world.” (Think about it: Why did we all, as children, get excited about science? To understand the world!)

He then moves on to explain that theories with greater explanatory power than the ones they’ve replaced are not necessarily more difficult to understand, and certainly do not necessarily add to the list of theories one has to understand the be a scientist (or an enthusiast). Theories with better explanatory power can be simpler. Furthermore, not everything that could be learned and understood needs to be: See his example of multiplication with Roman numerals (pp. 9–10). It might be fun, and occasionally necessary to have some source in which to look it up (for purposes of the history of mathematics, say), but it’s not something anyone today needs a working knowledge of; it has been superseded. His example for this is how the Copernican system superseded the Ptolemaic system, and made astronomy simpler in the process (p. 9). All of this is discussed in order to make the point that there is a distinction between “understanding and ‘mere’ knowing” (p. 10), which is where my interest in clave comes into play.

Several “explanations” of clave (sometimes even with that word in the title) that were published in recent years have been of the “mere knowing” type in which clave patterns are listed, without any explanation as to how and why they indicate what other patterns are allowed or disallowed in the idiom. Telling someone that x..x..x…x.x… is 3-2, and ..x.x…x..x..x. is its opposite, so 2-3, and (essentially) “there you go, you now know clave” does nothing towards explaining why a certain piano pattern played over one is “sick” (good) and over the other, sickening (bad) within the idiom.

Imagine if the natural sciences went about education the way we musicians do with clave. A chapter in a high-school biology book would contain a diagram of the Krebs cycle, with all the inputs, outputs (sorry for the electrical-engineer language), and enzymes given by name and formula, followed by “and now you know biochemical pathways,” without any explanation as to how it has anything to do with an organism being alive. I’m flabbergasted that musicians and music scholars find mere listings of clave son, clave rumba, [and . . . you know, the other one that won’t be named[iv]] sufficient as so-called explanations[v].

All of this reminds me of an argument I once had with a very intelligent person. I had said, in my talk at Tuesday Talks, that science is concerned with ‘why’ and ‘how’, not just ‘how’. He disagreed, which I think is because he thought of a different type of ‘why’: the theological ‘why’. I, instead, had in mind Deutsch’s type of ‘why’: “about what must be so, rather than what merely happens to be so; about laws of nature rather than rules of thumb” (p. 11). I would add, about consistency (even given Goedel, because I’m Bayesian like that, and not so solipsistic), which Deutsch mentions immediately afterwards, calling it ‘coherence’.[vi]

I understand that Hume, Goedel, and others have shown us that our confidence in science, or even math, ought not to be infinite. It isn’t. Even in a book like The God Delusion, even Richard Dawkins makes it clear that he is not absolutely certain. Scientific honesty requires that we not be absolutely certain. But we can examine degrees of (un)certainty, and specifically because of the solipsists, we have to ignore them[vii], and be imperfect pursuers of an imperfect truth, improving our understanding, all the while knowing that it could all be wrong.

To that end, I continue to test my clave hypothesis under different genres. Even if it’s wrong, it definitely is elegant.

[1] Alexander, M.D., E., Proof of Heaven: A Neurosurgeon’s Near-Death Experience and Journey into the Afterlife, Simon & Schuster, 2012.

[2] Baron, R. A., and Kalsher, M. J., Essentials of Psychology, Needham, MA: Allyn & Bacon, A Pearson Education Company, 2002.

[3] http://xkcd.com/699/ (last accessed 12/25/2015).

[4] Salmon, W. C., The Foundations of Scientific Inference, Pittsburgh, Pennsylvania: University of Pittsburgh Press, 1966.

[5] Deutsch, D., The Fabric of Reality: A leading scientist interweaves evolution, theoretical physics, and computer science to offer a new understanding of reality, New York: Penguin Books, 1997.

[i] Scientists do not speak of proof; they deal with evidence. Proofs are limited to the realm of mathematics. There are no scientific proofs; there are just statistically significant results, which are presented to laypersons as ‘proof’ because even scientists have quite a lot of difficulty interpreting measures of statistical significance, and the average person has no patience for or interest in the details of philosophy of science.

[ii] The authors of [2] give the following excellent definitions for these precise terms. Accuracy: “gathering and evaluating information in as careful, precise, and error-free a manner as possible”; objectivity: “obtaining and evaluating such information in a manner as free from bias as possible” [Ibid.]. ‘Bias’ in this case refers to the cognitive biases that are natural to human thinking and judgment, such as confirmation bias, Hawthorne effect[ii], selection bias, etc.; skepticism: the willingness to accept findings “only after they have been verified over and over”; and open-mindedness: not resisting changing one’s own views—even those that are strongly held—in the face of evidence that they are inaccurate [2]. To these we can add principles like transferability and falsifiability, and the key tools of double-blinding, randomization, blocking, and the like. Together, all these techniques and principles constitute science. Simply being trained in science and carrying out science-based work is not sufficient.

[iii] I think it was Philips in The Undercover Philosopher, but I’m not sure.

[iv] If you’ve read my post about running into cool people from SoundCloud at NIPS ’15, you’ll know what pattern I’m talking about: the English-horn-like-named pattern.

[v] Fortunately, we do have work from the likes of Mauleón and Lehmann that show causal relationships between individual notes or phrases in different instrumental lines, but since their work and mine, the trend has reverted to listing three patterns, and calling that an explanation.

[vi] Perhaps this paragraph needs its own blog post. . .

[vii] Because, according to them, they don’t exist.

“Math is hard”: Math, Science, the Arts, and Humanist Spirituality

It is a false dichotomy that those who seek to understand nature without imposing on it their own wishes of how it ought to be cannot, then, appreciate the majesty of its beauty. This false dichotomy is distressingly commonly held among people who consider themselves spiritual, and worse, also by those who consider themselves the opposite.

The adjective has come to indicate a proper subset of the actual set of spiritual people. The set of all spiritual people includes the religious and the non-religious, the scientifically oriented and the unscientifically oriented, and all those who create or enjoy art. Instead, the commonly understood meaning today is one who prefers metaphysical explanations and vague feel-good explanations to the rigorous objective pursuit of truth. This is not necessary, and the scientifically and artistically oriented (humanists, in short) perhaps ought to claim their form of spirituality.

I say this because there can be genuine spirituality in honest, rigorous science. Note that there is a distinction between science and technology — a distinction that has been all but lost in the understandable environmental guilt (and accompanying desire to make up for the mistakes of the past) felt by many progressive people in the northern hemisphere. This distinction is also blurred by the existence of applied science and applied research in general, specifically in the case of chemistry, and, I’m told, by the day-to-day work of physicists and chemists in environmental analysis. Nonetheless, for the sake of clarifying a point in the issue of scientific spirituality, I ask that you bear with me and assume that science and technology are essentially distinct and different.

It appears to be the case that, drunken with the power of knowledge and of technological development, humans worldwide (especially in the developed nations) have brought various forms of environmental harm and extensive threats to human health … yet, all the while making great strides in the elimination of many human-health problems. I understand the desire to lash out at “science and technology” *, but I think it’s a fallacy to blame environmental destruction on science and scientists alone, and to confuse science with technology.

People choose, time after time and around the world, to put comfort or profit before caution, safety, and a sufficient understanding of the consequences of our actions. Is it not true that critics of fossil-fuel use, for the most part, continue to drive cars, ride motorcycles, or fly in airplanes? And if that is inevitable, how many are either working on or supporting the development of clean(er) or more sustainable technologies? **

Some people are, of course. And I even know many who give up certain fundamental comforts and amenities that all citizens of developed nations have come to take for granted—and which most citizens of developing nations look forward to. Nonetheless, such people are in the minority.

My point is that pointing the finger at “science and technology” is hypocritical. We are all responsible. What scientists and technologist have made possible would not have caused harm if people weren’t eager to use technology  in ways that cause harm (directly or indirectly).

Furthermore, scientists are people, too. They need to put food on the table. I expect that many, if not most, scientists would prefer to live in a world where curiosity about the universe was encouraged and funded, and future funding depended solely on merit, not on the applicability of research to corporate profits or defense. Can you imagine a world in which research in mathematics, pedagogy, or music-information retrieval with no defense or business potential would be awarded the same level of importance as research that promises great profits or great military advantage? (I can’t.) Scientists, mathematicians, researchers, doctors, and technologists need to make a living, just like baristas, acupuncturists, and civil servants. What I find most problematic about blaming “science and technology” alone for environmental destruction and other ills (and they certainly were to blame some of the time, to some extent) is that it leads to anti-science attitudes and even pseudo-scientific beliefs.

I lived for over seventeen years in a progressive city that prides itself on its liberal politics, its arts, and its (so-called) alternative medicine, which I call non-evidence-based/non-mechanism-based medicine. (Let’s just call it NEB.) The reasoning for many who pursue NEB either as a career or as their primary health-care choice is that it involves “holistic” care, which sometimes is indeed holistic, but mostly has come to mean wishy-washy or that something has its roots in non-dominant/global-southern cultures or deep in the past (before humans were such environmental bullies).

Aside from the fact that math, science, and critical-thinking are grossly neglected by many education systems — hence many people simply do not have the tools to understand what math or science are like —  I believe, based on my observations, that it is the need to turn away from the harmful effects and shameful colonial history of the global north’s dominant nations that fuels the preference for unproven, vague, and in some cases, impossible treatments just because they originated far in the past or in foreign countries.

Acupuncture, for example, is said to be from ancient China. It posits the existence of chi, and the meridians along which it flows. Chiropractic is based on the (alleged) innate intelligence of bones, and although it, like its cousin osteopathy and  like homeopathy, is of European origin, it is old enough*** to be considered attractive by those who feel the need to dissociate themselves from the evils of technology. (Yet, I don’t see them giving up their cars and smart phones.)

And, it is those individuals who most commonly claim spirituality as their exclusive domain. Otherwise progressive, fair-minded, usually educated people who buy “holistic” pet food and gluten-free everything are also the ones who prefer acupuncture, homeopathy, reflexology, and cranio-sacral therapy to “invasive” “allopathic” **** conventional therapy, the last adjective also being one calculated to sound boring, old-fashioned (ironically), and non-innovative.

The only other group to claim spirituality are the overly religious. Oregon is home to a branch of Christianity that requires faith healing alone to be used for health care. (Criminal cases related to this have been in the news several times in the past decade.) Various organizations have sponsored or carried out studies on the power of prayer to heal. All of this sometimes drives the mechanism-minded, evidence-minded, and the mathematically or logically minded to abandon the concept of spirituality to the pseudoscientific, the anti-scientific, and the heavily religious.

I was one of those spirituality-abandoners for many years. I allowed myself to be robbed of my true nature. Spirituality is not the sole domain of the wishful thinker or the tradition-follower. Science, mathematics, and the arts are spiritual pursuits: They reveal the beauty of nature and the human mind (which is part of nature anyway). One of the most harmful expressions I have ever heard is “Math is hard.” Most things worth pursuing are.

Being an athlete is hard. Carpentry is hard. Cooking (well) is hard. Prioritizing is hard. Learning to drive a car or ride a bicycle is hard. (Learning to drive a semi truck is harder.) Being an auto mechanic is hard. Being a nurse is hard. Raising kids is hard. Being a professional musician is really hard. The same people who typically claim that math is hard have no concomitant fear about going into and succeeding in these other areas.

“Math is hard” is irrelevant. Everything is hard. At least, to be good enough to make a living in anything is as difficult as in math. People who make a living by playing basketball, League of Legends, or the guitar are the ones who worked tirelessly on their passion through years of frustration and failure. Somehow, society (in America) seems to say that it is okay to strive to be successful in sports, business, law, medicine, or even music, but not in math, science, and technology (except for IT and programming, and except very recently… and those “STEM” efforts really don’t seem to be going anywhere).

This doesn’t make sense. First of all, STEM fields are supposed to be financially rewarding careers (and sometimes, they are). Secondly, the joy of science is more satisfying than of League of Legends. I say this because the joy of math is deeper than any other wordly pleasure, perhaps with the exception of deep intimacy. The joy of engineering is on par with the joy of music-making (though a little different) because both are centered on creating through problem-solving.

Those who have been able to unite in themselves a sense of awe for the arts and the sciences are sometimes called humanists. Part of this is to be able to appreciate in art those ideas and feelings that one may not give creedence to in daily life. A humanist may truly appreciate Christian music (from Bach to OUT KAST, say), the wabi-sabi aesthetic of Japanese Zen art, Islamic architecture, or a painting that reflects Taoist values, just as people who identify primarily as followers of those beliefs or philosophies use the Internet ( a product of electromagnetics and semiconductor technology, hence physics) or may prefer seeing a physician to relying on prayer for health care. There is no reason not to coexist, disagree, and still respect one another as people, as long as no one forces (either through violence or the force of law) their ways on unwilling others. And while doing that (coexisting in peace), there is also no need for any one group to give up its right to spirituality.

Science rocks. Math is beautiful. Engineering is creative. Art is life. And each is its own spirituality.

 

* See song lyrics by Living Coloür, ANTI-FLAG, and countless other bands.

** Some do, of course, and I even know many people who give up some fundamental comforts and amenities that all citizens of developed nations take for granted — and which most citizens of developing nations look forward to. Nonetheless, they are in the minority.

*** (and sufficiently looked down upon by the mainstream)

**** Have you looked up the meaning of the prefix “allo-” ?   It seems to mean “other” or “outside” . . . How are herbs any less “other” to our bodies?