Teaching machine learning within different fields

Everyone is talking about machine learning (ML) these days. They usually call it “machine learning and artificial intelligence” and I keep wondering what exactly they mean by each term.

It seems the term “artificial intelligence” has shaken off its negative connotations from back when it meant top-down systems (as opposed to the superior bottom-up “computational intelligence” that most of today’s so-called AI actually uses) and has come to mean what cybernetics once was: robotics, machine learning, embedded systems, decision-making, visualization, control, etc., all in one.

Now that ML is important to so many industries,application areas, and fields, it is taught in many types of academic departments. We approach machine learning differently in ECE, in CS, in business schools, in mechanical engineering, and in math and statistics programs. The granularity of focus varies, with math and CS taking the most detailed view, followed by EC and ME departments, followed by the highest-level applied version in business schools, and with Statistics covering both ends.

In management, students need to be able to understand the potential of machine learning and be able to use it toward management or business goals, but do not have to know how it works under the hood, how to implement it themselves, or how to prove the theorems behind it.

In computer science, students need to know the performance measures (and results) of different ways to implement end-to-end machine learning, and they need to be able to do so on their own with a thorough understanding of the technical infrastructure. (If what I have observed is generalizable, they also tend to be more interested in virtual and augmented reality, artificial life, and other visualization and user-experience aspects of AI.)

In math, students and graduates really need to understand what’s under the hood. They need to be able to prove the theorems and develop new ones. It is the theorems that lead to powerful new techniques.

In computer engineering, students also need to know how it all works under the hood, and have some experience implementing some of it, but don’t have to be able to develop the most efficient implementations unless they are targeting embedded systems. In either case, though, it is important to understand the concepts, the limitations, and the pros and cons as well as to be able to carry out applications. Engineers have to understand why there is a such a thing as PAC, what the curse of dimensionality is and what it implies for how one does and does not approach a problem, what the NFL is and how that should condition one’s responses to claims of a single greatest algorithm, and what the history and background of this family of techniques are really like. These things matter because engineers should not expect to be plugging-and-playing cookie-cutter algorithms from ready-made libraries. That’s being an operator of an app, not being an engineer. The engineer should be able to see the trade-offs, plan for them, and take them into account when designing the optimal approach to solving each problem. That requires understanding parameters and structures, and again the history.

Today, the field of ‘Neural Networks’ is popular and powerful. That was not always the case. It has been the case two other times in the past. Each time, perhaps like an overextended empire, the edifice of artificial neurons came down (though only to come up stronger some years later).

When I entered the field, with an almost religious belief in neural networks, they were quite uncool. The wisdom among graduate students seemed to be that neural nets were outdated, that we had SVMs now, and with the latter machine learning was solved forever. (This reminds me of the famous patent-office declaration in the late 1800s that everything that could be invented had been invented.) Fortunately, I have always benefited from doing whatever was unpopular, so I stuck to my neural nets, fuzzy systems, evolutionary algorithms, and an obsession with Bayes’ rule while others whizzed by on their SVM dissertations. (SVMs are still awesome, but the thing that has set the world on fire is neural nets again.)

One of the other debates raging, at least in my academic environment at the time, was about “ways of knowing.” I have since come to think that science is not a way of knowing. It never was, though societies thought so at first (and many still think so). Science is a way of incrementally increasing confidence in the face of uncertainty.

I bring this up because machine learning, likewise, never promised to have the right answer every time. Machine learning is all about uncertainty; it thrives on uncertainty. It’s built on the promise of PAC learning; i.e., it promises to be only slightly wrong and to be so only most of the time. The hype today is making ML seem like some magical panacea to all business, scientific, medical, and social problems. For better or worse, it’s only another technological breakthrough in our centuries-long adventure of making our lives safer and easier. (I’m not saying we haven’t done plenty of wrongs in that process—we have—but no one who owns a pair of glasses, a laptop, a ball-point pen, a digital piano, a smart phone, or a home-security system should be able to fail to see the good that technology has done for humankind.)

I left the place of the field of Statistics in machine learning until the end. They are the true owners of machine learning. We engineering, business, and CS people are leasing property on their philosophical (not real) estate.

 

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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.

 

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)

Overfitting, Confirmation Bias, Strong AI, and Teaching

I was asked recently by a student about how machine learning could happen. I started out by talking about human learning: how we don’t consider mere parroting of received information to be same as learning, but that we can make the leap from some examples we have seen to a new situation or problem that we haven’t seen before. Granted there need to be some similarities (shared structure or domain of discourse—we don’t become experts on European Union economics as a result only of learning to distinguish different types of wine), but what makes learning meaningful and fun for us is the ability to make a leap, to solve a previously inaccessible problem or deduce (really it’s ‘induce’) a new categorization.

In response, the student asked how machines could do that. I replied that not only do we give them many examples to learn from, but we also give them algorithms (ways to deal with examples) that are inspired by how natural systems work: inspired by ants or honeybees, genetics, the immune system, evolution, languages, social networks and ideas (memes), and even just the mammalian brain. (One difference is that, so far, we are not trying to make general-purpose consciousness in machines; we are only trying to get them to solve well-defined problems very well, and increasingly these days, not-so-well-defined problems also).

So, then the student asked how machines could make the leap just like we can. This led me to bring up overfitting and how to avoid it. I explained that if a machine learns the examples it is given all too well, it will not be able to see the forest for the trees—it will be overly rigid, and will want to make all novel experiences fit the examples in its training. For new examples that do not fit, it will reject them (if we build that ability into it), or it will make justifiable wrong choices. It will ‘overfit’, in the language of machine learning.

Then it occurred to me that humans do this, too. We’ve all probably heard the argument that stereotypes are there for a reason. In my opinion, they are there because of the power of confirmation bias (not to mention, sometimes selection bias as well—consider the humorous example of the psychiatrist who believes everyone is psychotic).

Just as a machine-learning algorithm that has been presented with a set of data will learn the idiosyncrasies of that data set if not kept from overfitting by early-stopping, prestructuring, or some other measure, people also overfit to their early-life experiences. However, we have one other pitfall compared to machines: We continue to experience new situations which we filter through confirmation bias to make ourselves think that we have verification of the validity of our misinformed or under-informed early notions. Confirmation bias conserves good feelings about oneself. Machines so far do not have this weakness, so they are only limited by what data we give them; they cannot filter out inconvenient data the way we do.

Another aspect of this conversation turned out to be pertinent to what I do every day. Not learning the example set so well is advantageous not only for machines but for people as well, specifically for people who teach.

I have been teaching at the college level since January 1994, and continuously since probably 2004, and full-time since 2010, three or four quarters per year, anywhere from two to five courses per quarter. I listed all this because I need to point out, for the sake of my next argument, that I seem to be a good teacher. (I got tenured at a teaching institution that has no research requirement but very high teaching standards.) So, let’s assume that I can teach well.

I was, for the most part, not a good student. Even today, I’m not the fastest at catching on, whether it’s a joke, an insult, or a mathematical derivation. (I’m nowhere near the slowest, but I’m definitely not among the geniuses.) I think this is a big part of why I’m a good teacher: I know what it’s like not to get it, and I know what I have had to do to get it. Hence, I know how to present anything to those who don’t get it, because, chances are, I didn’t get it right away either.

But there is more to this than speed. I generate analogies like crazy, both for myself and for teaching. Unlike people who can operate solely at the abstract level, I make connections to other domains—that’s how I learn; I don’t overfit my training set. I can take it in a new direction more easily, perhaps, than many super-fast thinkers. They’re right there, at a 100% match to the training set. I wobble around the training set, and maybe even map it to n+1 dimensions when it was given in only n.

Overfitting is not only harmful to machines. In people, it causes undeserved confidence in prejudices and stereotypes, and makes us less able to relate to others or think outside the box.

One last thought engendered by my earlier conservation with this student: The majority of machine-learning applications, at least until about 2010 or maybe 2015, were for well-defined, narrow problems. What happens when machines that are capable of generalizing well from examples in one domain, and in another, and in another, achieve meta-generalization from entire domains to new ones we have not presented them with? Will they attain strong AI as a consequence of this development (after some time)? If so, will they, because they’ve never experienced the evolutionary struggle for survival, never develop the violent streak that is the bane of humankind? Or will they come to despise us puny humans?

 

Auto-(in)correct: Emergent Laziness?

Is it Google? Is it LG? Or is it emergence?

I am leasing an LG tablet running Android to go with my phone service. I thought the large screen and consequently larger keyboard would make my life easier. The first several days of use, however, have been unreasonably annoying. The salesperson had said that this device would be slave to my LG Android cell phone, but my settings did not seem to carry over. What’s worse, no matter how much I dig through menu trees to get to certain settings I’m looking for, I can’t find them. For example, I may want autocorrect off, or I may not want the latest e-mail in my inbox to be previewed. (I prefer to see a bird’s-eye view of all the recent e-mails, packed as tightly as possible, and I can usually set this very quickly and easily, but not on this tablet.) The reasons might range from being about to go to class and teach in a few minutes and not wanting to think about that e-mail about a committee issue that just arrived right at the moment, and I don’t want Gmail to parade it in front of me.

So, the settings seem to be very well hidden, or maybe not even available to the user anymore (because that has been the trend in computer-and-Internet technology: Make the user think less, and have less control; so-called intelligent software will decide all your preferences for you).

And perhaps the software can deduce (or, more likely, induce) your preferences as they were at a certain time under a certain set of circumstances, but human beings expect the freedom to change their minds. Software doesn’t seem to allow this.

Furthermore, crowd-sourcing is considered the ultimate intelligence. I know and understand the algorithms behind most of these ideas, and totally agree that they are beautiful and awesome (and really fun). However, engineers, programmers, mathematicians, and other nerds (like me) finding something super-fun should not be how life is redesigned. The crowd-sourcing of spelling and automatic correction is leading us from artificial intelligence to natural laziness. My device wants to change “I’m” to “imma”. (Before you decry that I’m also ignorant and don’t know to put a period inside the quotation marks, read my disclaimer about switching to British/logical punctuation.) Am I now forced to appear like I have abandoned capitalization, and to have picked up an unnecessarily excessively colloquial form of spelling. And if I had, then fine, but I haven’t.

It gets worse. The learning algorithm is not learning, at least not from me. The following has now happened with several phrases and words on this new tablet, and I’ve looked further into altering this setting, to no avail.

When I type “I will”, it automatically replaces it with “I silk”. If I backspace and type “I will” again, it replaces it again. And it doesn’t learn from my actions; I have patiently (and later on, a further dozen or so times, impatiently) retyped “I will” more than 30 times, only to watch Gmail running on my Android LG device switch it back to “I silk” immediately.[1]

Where did this come from? Is there a band called “I silk”? Is this a new phrase that’s in these days, and I haven’t been overhearing my students enough to know about it?

Or is it because earlier that day, I tried to write “I seek to …” where the ‘seek’ was autocorrected to ‘silk’? (for who knows what reason)

And what happens when this behavior is pushed beyond e-mail on a tablet, and I’m not able (or allowed) to write either “I will” or “I seek” as I type a blog entry such as this on my laptop, or as I try to type an e-mail to explain what’s going wrong to Google’s tech support, or someone else’s tech support?

This really doesn’t make sense. Shouldn’t machine learning give us results that make sense? (That used to be the idea.) Now, perhaps, it’s just supposed to give us results that are popular or common. It seems we’re not building artificial intelligence; we’re building artificial commonality.

This is not a rant for elitism (which, anyway, is also used in machine learning, in evolutionary algorithms). It’s about the loss of freedom of speech, to be able to say what one is trying to say the exact way one wants to say it. The ability for clear, unequivocal communication is not something to be eliminated from the human experience; it is something to continue to strive for. Likewise, convenience over freedom (or over accuracy) is not a good choice of values. In the end, the person pushing little buttons with letters marked on them will be held responsible for the content. Shouldn’t that person be in charge of what words appear when they push the little buttons? Shouldn’t we at least be able to turn off auto-correct, or have some control over when it applies?

This is being taken away, little by little. “Oh, it’s just a tablet.” … “Oh, it’s just an e-mail. Nobody expects it to be spelled correctly.” Pretty soon, no one will be able to spell anything correctly, even if they know how to, because their devices won’t allow them to have that little bit of control.

 

[1] Also, do not even try to write in a foreign language, or mix English and Turkish in one sentence. In an early e-mail I wrote on this device, I had to repeat the letter ‘i’ (which appeared only once in the actual word) five times (for a total of six ‘i‘s) for it to stop auto-(in)correcting “geliyorum” to something like “Selma”. I had to type “geliiiiiiyorum”.

How to Reason in Circuit Analysis

The following conversation played out in my head as I was grading an exam problem that had a supernode composed of two neighboring supernodes. Many students (in introductory circuit analysis) had difficulties with this problem, so here’s what I plan to present when I explain it.

HOW TO REASON IN NODAL ANALYSIS

Q: What is the main type of equation involved in performing nodal analysis?

A: KCL equation

Q: What electrical quantity is represented in each term of a KCL equation.

A: current

Q: Are there any elements for which, if the current is not stated, we do not have a way (a defining equation[1]) to know and express the current?

A: yes

Q: What are these elements?

A: voltage sources of any type[2] that are directly between two non-reference essential nodes (NRENs)

Q: Why is that a problem?

A: There is no defining equation (like Ohm’s Law) for a source, and if it’s directly between two NRENs, then there is no other element in series with it.

Q: So what if there is no other element in series with it?

A: If there were a resistor in series with it, we could use Ohm’s Law on the resistor.

Q: Why not use Ohm’s Law on the source?

A: Ohm’s Law does not apply to sources, does not deal with sources; it’s only for resistors[3].

Q: Fine… What’s with the non-reference thing?

A: If a voltage source (of any kind) has one terminal attached to the reference node (ground), then we automatically know the voltage at the other end (with respect to ground).

 

Conclusion: If there is a voltage source between two NRENs, circle it to make a (super)node, and write KCL out of that node, without going inside it (until later, when you need another equation, at which point you use KVL).

 

[1] A defining equation is an expression that relates current through a two-terminal element to the voltage across a two-terminal element by means of the inertial aspect of the element (its capacitance, resistance, inductance, and I suppose, pretty soon, its memristance) and the passive sign convention (PSC).

[2] i.e., independent voltage source, current-dependent voltage source, voltage-dependent voltage source: It’s about the voltage aspect, not about the dependence aspect.

[3] two terminal elements with a linear current–voltage relationship; note the en dash : )

NIPS 2015: Thoughts about SoundCloud, genres, clave tagging, clave gamification, multi-label classification, and perceptual manifolds

On December 9th, at NIPS 2015, I met two engineers from SoundCloud, which is not only providing unsigned artists a venue to get their music heard (and commented on), and providing recommendation and music-oriented social networking, but also, if I understand correctly, is interested in content analysis for various purposes. Some of those have to do with identifying work that may not be original, which can range from quotation to plagiarism (the latter being an important issue in my line of work: education), but also involve the creation of derivative content, like remixing, to which they seem to have a healthy approach. (At the same event, the IBM Watson program director also suggested that they could conceivably be interested in generative tools based on music analysis.)

I got interested in clave-direction recognition to help musicians, because I was one, and I was struggling—clave didn’t make sense. Why were two completely different patterns in the same clave direction, and two very similar patterns not? To make matters worse, in samba batucada, there was a pattern said to be in 3-2, but with two notes in the first half, followed by three notes in the second half. There had to be a consistent explanation. I set out to find it. (If you’re curious, I explained the solution thoroughly in my Current Musicology paper.)

 

Top: Surdo de terceira. Bottom: The 3-2 partido-alto for cuíca and agogô. Note that playing the partido-alto omitting the first and third crotchet’s worth of onsets results in the terceira.

However, clave is relevant not just to music-makers, but to informed listeners and dancers as well. A big part of music-in-society is the communities it forms, and that has a lot to do with expertise and identity in listeners. Automated recognition of clave-direction in sections of music (or entire pieces) can lead to automated tagging of these sections or pieces, increasing listener identification (which can be gamified) or helping music-making.

My clave-recognition scheme (which is an information-theoretically aided neural network) recognizes four output classes (outside, inside, neutral, and incoherent). In my musicological research, I also developed three teacher models, but only from a single cultural perspective. Since then, I have recently submitted a work-in-progress and accompanying abstract to AAWM 2016 (Analytical Approaches to World Music) about what would happen if I looked at clave direction from different cultural perspectives (which I have encoded as phase shifts), and graphed the results in the complex plane (just like phase shift in electric circuits).

Another motivating idea came from today’s talk Computational Principles for Deep Neuronal Architectures by Haim Sompolinsky: perceptual manifolds. The simplest manifold proposed was line segments. This is poignant to clave recognition because among my initial goals was extending my results to non-idealized onset vectors: [0.83, 0.58, 0.06, 0.78] instead of [1101], for example. The line-segment manifold would encode this as onset strengths (“velocity” in MIDI terminology) ranging from 0 (no onset) to 1 (127 in MIDI). This will let me look inside the onset-vector hypercube.

Another tie-in from NIPS conversations is employing Pareto frontiers with my clave data for a version of multi-label learning. Since I can approach each pattern from two phase perspectives, and up to three teacher models (vigilance levels), a good multi-label classifier would have to provide up to 6 correct outputs, and in the case that a classifier cannot be that good, the Pareto frontier would determine which classifiers are undominated.

Would all this be interesting to musicians? Yes, I think so. Even without going into building a clave-trainer software into various percussion gear or automated-accompaniment keyboards, this could allow clave direction to be gamified. Considering all the clave debates that rage in Latin-music-ian circles (such as the “four great clave debates” and the “clave schism” issues like around Giovanni Hidalgo’s labeling scheme quoted in Modern Drummer*), a multi-perspective clave-identification game could be quite a hit.

So, how does a Turkish math nerd get to be obsessed by this? I learned about clave—the Afro-Latin (or even African-Diasporan) concept of rhythmic harmony that many people mistake for the family of fewer than a dozen patterns, or for a purely Cuban or “Latin” organizational principle—around 1992 from the musicians of Bochinche and Sonando, two Seattle bands. I had also grown up listening to Brazilian (and Indian, Norwegian, US, and German) jazz in Turkey. (My first live concert by a foreign band was Hermeto Pascoal e Grupo, featuring former CBC faculty Jovino Santos Neto.) So, I knew that I wanted to learn about Brazilian music. (At the time, most of what I listened to was Brazilian jazz, like Dom Um Romao and Airto, and I had no idea that they mostly drew from nordestino music, like baião, xote, côco, and frevo**―not samba).

Fortunately, I soon moved to Portland, where Brian Davis and Derek Reith of Pink Martini had respectively founded and sustained a bloco called Lions of Batucada. Soon, Brian introduced us to Jorge Alabê, and then to California Brazil Camp, with its dozens of amazing Brazilian teachers. . . But let’s get back to clave.

I said above that clave is “the Afro-Latin (or even African-Diasporan) concept of rhythmic harmony that many people mistake for the family of fewer than a dozen patterns, or for a purely Cuban or ‘Latin’ organizational principle.” What’s wrong with that?

Well, clave certainly is an organizational principle: It tells the skilled musician, dancer, or listener how the rhythm (the temporal organization, or timing) of notes in all the instruments may and may not go during any stretch of the music (as long as the music is from a tradition that has this property, of course).

And clave certainly is a Spanish-language word that took on its current meaning in Cuba, as explained wonderfully in Ned Sublette’s book.

However, the transatlantic slave trade did not only move people (forcefully) to Cuba. The Yorùbá (of today’s southwest Nigeria and southeast Benin), the Malinka (a misnomer, according to Mamady Keïta for people from Mali, Ivory Coast, Burkina Faso, Gambia, Guinea, and Senegal), and the various Angolan peoples were brought to many of today’s South American, Caribbean, and North American countries, where they culturally and otherwise interacted with Iberians and the natives of the Americas.

Certain musicological interpretations of Rolando Antonio Pérez Fernández’s book La Binarización de los Ritmos Ternarios Africanos en América Latina have argued that the organizational principles of Yoruba 12/8 music, primarily the standard West African timeline (X.X.XX..X.X.X)

Bembé ("Short bell") or the standard West African timeline, along with its major-scale analog

and the Malinka/Manding timelines met the 4/4 time signatures of Angolan and Iberian music, and morphed into the organizational timelines of today’s rumba, salsa, (Uruguayan) candombe, maracatu, samba, and other musics of the Americas.

Some of those timelines we all refer to as clave, but for others, like the partido-alto in Brazil***, it is sometimes culturally better not to refer to them as clave patterns. (This is understandable, in that Brazilians speak Portuguese, and do not always like to be mistaken for Spanish-speakers.)

Conceptually, however, partido-alto in samba plays the same organizational role that clave plays in rumba and salsa, or the gongue pattern plays in maracatu: It immediately tells knowledgeable musicians how not to play.

In my research, I found multiple ways to look at the idiomatic appropriateness of arbitrary timing patterns (more than 10,000 of them, only about a hundred of which are “traditional” [accepted, commonly used] patterns). I identified three “teacher” models, which are just levels of strictness. I also identified four clave-direction categories. (Really, these were taught to me by my teacher-informers, whose reactions to certain patterns informed some of the categories.)

Some patterns are in 3-2 (which I call “outside”). While the 3-2 clave son (X..X..X…X.X…):

3-2 (outside) clave son, in northern and TUBS notation

is obvious to anyone who has attempted to play anything remotely Latin, it is not so obvious why the following version of the partido-alto pattern is also in the 3-2 direction****: .X..X.X.X.X..X.X

The plain 3-2 partido-alto pattern. (The pitches are approximate and can vary with cuíca intonation or the agogô maker’s accuracy.) "Bossa clave" in 3-2 and 2-3 are added in TUBS notation to show the degree of match and mismatch with 3-2 and 2-3 patterns, respectively.

 

Some patterns are in 2-3 (which I call “inside”). Many patterns that are heard throughout all Latin American musics are clave-neutral: They provide the same amount of relative offbeatness no matter which way you slice them. The common Brazilian hand-clapping pattern in pagode, X..X..X.X..X..X. is one such pattern:

The clave-neutral hand-clapping pattern in pagode, AKA, tresillo (a Cuban name for a rhythm found in Haitian konpa, Jamaican dancehall, and Brazilian xaxado)

It is actually found throughout the world, from India and Turkey, to Japan and Finland, and throughout Africa; from Breakbeats to Bollywood to Metal. (It is very common in Metal.) The parts played by the güiro in salsa and by the first and second surdos in samba have the same role: They are steady ostinati of half-cycle length. They are foundational. They set the tempo, provide a reference, and go a long way towards making the music danceable. (Offbeatness without respite, as Merriam said*****, would make music undanceable.)

Here are some neutral patterns: X…X…X…X… (four on the floor, which, with some pitch variation, can be interpreted as the first and second surdos):

Four quarter notes, clave-neutral (from Web, no source available)

….X.X…..X.X. (from ijexá):

surdo part for ijexá (from http://www.batera.com.br/Artigos/dia-do-folclore)

 

and XxxXXxxXXxxXXxxX. (This is a terrible way to represent swung samba 16ths. Below is Jake “Barbudo” Pegg’s diagrams, which work much better.)

Jake "Barbudo" Pegg's samba-sixteenths accent and timing diagrams (along with the same for "Western" music)

The fourth category is incoherent patterns. These are patterns that are not neutral, yet do not conform to either clave direction, either. (One of my informers gave me the idea of a fourth category when he reacted to one such pattern by making a disgusted face and a sound like bleaaahh.)

A pattern that has the clave property immediately tells all who can sense it that only patterns in that clave direction and patterns that are clave-neutral are okay to play while that pattern (that direction) is present. (We can weaken this sentence to apply only to prominent or repeated patterns. Quietly passing licks that cross clave may be acceptable, depending on the vigilance level of the teacher model.)

So, why mention all this right now? (After all, I’ve published these thoughts in peer-reviewed venues like Current Musicology, Bridges, and the Journal of Music, Technology and Education.)

For one thing, those are not the typical resources most musicians turn to. Until I can write up a short, highly graphical version of my clave-direction grammar for PAS, I will need to make some of these ideas available here. Secondly, the connection to gamification and musical-social-networking sites, like SoundCloud, are new ideas I got from talking to people at the NIPS reception, and I wanted to put this out there right away.

 

FOOTNOTES

* Mattingly, R., Modern Drummer, Modern Drummer Publications, Inc., Cedar Grove, NJ, “Giovanni Hidalgo-Conga Virtuoso,” p. 86, November 1998.

** While talking to Mr. Fereira of SoundCloud this evening at NIPS, he naturally mentioned genre recognition, which is the topic of my second-to-last post. (I argued about the need for expert listeners from many cultural backgrounds, which could be augmented with a sufficiently good implementation of crowd-sourcing.) I think he was telling me about embolada, or at least that’s how I interpreted his description of this MC-battle-type of improvised nordeste music. How many genre-recognition researchers even know where to start in telling a street-improvisation embolada from even, say, a pagode-influenced axé song like ‘Entre na Roda’ by Bom Balanço? (Really good swing detection might help, I suppose.)

*** This term has multiple meanings; I’m not referring to the genre partido-alto, but the pattern, which is one of the three primary ingredients of samba, along with the strong surdo beat on 2 (and 4) and the swung samba 16ths.

**** in the sense that, in the idiom, it goes with the so-called 3-2 “bossa clave” (a delightful misnomer): X..X..X…X..X..,

The "bossa clave" is a bit like an English horn; it's neither.as well as with the rather confusing (to some) third-surdo pattern ….X.X…..XX.X, Top: Surdo de terceira. Bottom: The 3-2 partido-alto for cuíca and agogô. Note that playing the partido-alto omitting the first and third crotchet’s worth of onsets results in the terceira.

which has two notes in its first half, and three notes in its second half. (Yes, it’s in 3-2. My grammar for clave direction explains this thoroughly. [http://academiccommons.columbia.edu/catalog/ac:180566])

***** See Merriam: “continual use of off-beating without respite would cause a readjustment on the part of the listener, resulting in a loss of the total effect; thus off-beating [with respite] is a device whereby the listeners’ orientation to a basic rhythmic pulse is threatened but never quite destroyed” (Merriam, Alan P. “Characteristics of African Music.” Journal of the International Folk Music Council 11 (1959): 13–19.)

ALSO, I use the term “offbeatness” instead of ‘syncopation’ because the former is not norm-based, whereas the latter turns out to be so:

Coined by Toussaint as a mathematically measurable rhythmic quantity [1], offbeatness has proven invaluable to the preliminary work of understanding Afro-Brazilian (partido-alto) clave direction. It is interpreted here as a more precise term for rhythmic purposes than ‘syncopation’, which has a formal definition that is culturally rooted: Syncopation is the placement of accents on normally  unaccented notes, or the lack of accent on normally accented notes. It may be assumed that the norm in question is that of the genre, style or cultural/national origin of the music under consideration. However, in all usage around the world (except mine), normal accent placement is taken to be normal European accent placement [2, 3, 4].

For example, according to Kauffman [3, p. 394], syncopation “implies a deviation from the norm of regularly spaced accents or beats.” Various definitions by leading sources cited by Novotney also involve the concepts of “normal position” and “normally weak beat” [2, pp. 104, 108). Thus, syncopation is seen to be norm-referenced, whereas offbeatness is less contextual as it depends solely on the tactus.

Kerman, too, posits that syncopation involves “accents in a foreground rhythm away from their normal places in the background meter. This is called syncopation. For example, the accents in duple meter can be displaced so that the accents go on one two, one two, one two instead of the normal one two, one two” [4, p. 20; all emphasis in the original, as written]. Similarly, on p. 18, Kerman reinforces that “[t]he natural way to beat time is to alternate accented (“strong”) and unaccented (“weak”) beats in a simple pattern such as one two, one two, one two or one two three, one two three, one two three.” [4, p. 18]

Hence, placing a greater accent on the second rather than on the first quarter note of a bar may be sufficient to invoke the notion of syncopation. By this definition, the polka is syncopated, and since it is considered the epitome of “straight rhythm” to many performers of Afro-Brazilian music, syncopation clearly is not the correct term for what the concept of clave direction is concerned with. Offbeatness avoids all such cultural referencing because it is defined solely with respect to a pulse, regardless of cultural norms. (Granted, what a pulse is may also be culturally defined, but there is a point at which caveat upon caveat becomes counterproductive.)

Furthermore, in jazz, samba, and reggae (to name just a few examples) this would not qualify as syncopation (in the sense of accents in abnormal or unusual places) because beats other than “the one” are regularly accented in those genres as a matter of course. In the case of folkloric samba, even the placement of accents on the second eighth note, therefore, is not syncopation because at certain places in the rhythmic cycle, that is the normal—expected—pattern of accents for samba, part of the definition of the style. Hence, it does not constitute syncopation if we are to accept the definition of the term as used and cited by Kauffman, Kerman, and Novotney. In other words, “syncopation” is not necessarily the correct term for the phenomenon of accents off the downbeat when it comes to non-European music.

Moreover, in Meter in Music, Hule observes that “[a]ccent, defined as dynamic stress by seventeenth- and eighteenth-century writers, was one of the means of enhancing the perception of meter, but it became predominant only in the last half of the eighteenth century [emphasis added]. The idea that the measure is a pattern of accents is so widely held today that it is difficult to imagine that notation that looks modern does not have regular accentual patterns. Quite a number of serious scholarly studies of this music [European art music of 1600–1800] make this assumption almost unconsciously by translating the (sometimes difficult) early descriptions of meter into equivalent descriptions of the modern accentual measure” [5, p. viii] Thus, it turns out that the current view of rhythm and meter is not natural, or even traditional, let alone global. In fact, in Essential Dictionary of MUSIC NOTATION: The most practical and concise source for music notation is perfect for all musicians—amateur to professional (the actual book title) states that “the preferred/recommended beaming for the 9/8 compound meter is given as three groups of three eighth notes” [6, p. 73]. This goes against the accent pattern implied by the 9/8 meter in Turkish (and other Balkan) music, which is executed as 4+5, 5+4, 2+2+2+3, etc., but rarely 3+3+3. The 9/8 is one of the most common and typical meters in Turkish music, not an atypical curiosity. This passage is included here to demonstrate the dangers in applying western European norms to other musics (as indicated by the phrase “perfect for all musicians”).

[1]    Toussaint, G., 2005. Mathematical Features for Recognizing Preference in Sub-Saharan African Traditional Rhythm Timelines. Lecture Notes in Computer Science 3686:18-27. Springer Berlin/Heidelberg, 2005.                                                                                                                                [2]    Novotney, E. D. “The 3-2 Relationship as the Foundation of Timelines in West African Musics,” University of Illinois at Urbana-Champaign (Ph.D. dissertation), Urbana-Champaign, Illinois, 1998.
[3]    Kauffman, R. 1980. African Rhythm: A Reassessment. Ethnomusicology 24 (3):393–415.
[4]    Kerman, J., LISTEN: Brief Edition, New York, NY: Worth Publishers, Inc., 1987, p. 20.
[5]    Hule, G., Meter in Music, 1600–1800: Performance, Perception, and Notation, Bloomington, IN: Indiana University Press, 1999.
[6]    Gerou, T., and Lusk, L., Essential Dictionary of MUSIC NOTATION: The most practical and concise source for music notation is perfect for all musicians—amateur to professional, Van Nuys, CA: Alfred Publishing Co., Inc., 1996.

Culturally Situated and Image-based Genre Attribution

Genre recognition has become the holy grail of music information retrieval. What concerns me, before we worry about machine recognition of musical genre, is whether people can agree at all on what genre means, and what the various genres are. Wikipedia, Echonest, and many other sites (some now defunct) have put forth excellent information on various musical genres and their relationships to one another. My critique of the genre discussions I have encountered to date falls into two categories. One is the (necessarily, and not surprisingly) culturally narrow perspective of most work on musical genres. The other is the role non-aural, non-audio features play in the determination of genre. (These can be metadata, like release dates, or even more [sub]culturally determined information such as the clothing style of the artists.)

Let’s take the problem of narrowly culturally situated efforts first. There have been a variety of impressive resources on the Internet about the sub-sub-sub-genres of electronic music and of extreme metal. There is a wonderful degree of detail provided in these Web resources. However, the effort put into very subtle distinctions among “northern-based” musics (anything we typically understand as pop music, plus the folk and court musics of northern Europe and North America**)  is rarely, if ever, matched by the knowledge available, perhaps, in English, on musics from other countries. We typically find some half a dozen genres listed for Brazil, Mexico, Japan, or Cuba, and far fewer for China, Turkey, Belize, Honduras, or Mali. This is a typical case of out-group bias, which is easy to understand; all people are subject to out-group bias. The importance of understanding biases lies in the effort to move beyond them. Are the differences among Xote, Brukdown, Özgün Müzik, and Guarapachangeo less significant than the differences between Goa Trance and Happy Hardcore, or Grindcore and Power Violence?*** Of course not, but who can know every little detail about the impossibly rich musical landscape of every culture? (That’s why we need multi-cultural teams to work on genre recognition and classification.)

The other issue is one I am only aware of in terms of “northern” (western) popular forms of music, and it is the issue of image-based, fashion-based, temporal, and geographical genre attribution. In many cases, the clothes worn by rock and pop artists seem to determine their musical genre more than the sounds created and organized into musical works by those artists. For example, Billy Idol and Avril Lavigne are thought of as Punk Rock artists. Yet, and even without appealing to DIY ethics and political content, we can tell from the aural experience that these artists make (or have made) something sufficiently aurally distant from the music of CRASS, pragVEC, Buzzcocks, X-Ray Spex, or BAD RELIGION, and that theirs are genres well removed from Punk Rock. (The artists listed do not all sound the same, but they share the elements of disaffected vocals, a lack of polish, and an overall dark despair with one another and with bands as far removed from them as Joy Division, The Paper Chase, Depeche Mode, and Sleater-Kinney, all of which have more sonic elements in common than they do with Idol or Lavigne.)

What makes the problem further difficult is that genre names are rarely descriptive, and all too often temporally and geographically limiting. Consider the genres NWOBHM (New Wave of British Heavy Metal), New Wave, Nü (new) Metal, Grunge, and Old-School Hip Hop.

Quite apart from the problem that “New Wave” actually has at least three different meanings, it is sonically possible (and common) for an artist making music thirty years after the end of the era attributed to one of these genres to make music with the same structure, affectation, instruments, sounds, and production. Which should we consider in determining genre: the year of release or the way the music sounds? New-millenial bands like Titanium Black and The Haunted, and even punk-rockers like Saviours, often play a flavor of Metal that sounds just like NWOBHM, but we are not supposed to call them that if they are from a different time, and especially, a different place. Likewise, ACCEPT and SCORPIONS (from Germany) sometimes played the same type of music, stylistically speaking, as Judas Priest, DIO, and IRON MAIDEN, but since they’re not British, we cannot refer to their music as NWOBHM. Or can we? Is it not the sounds and how they are organized that matters in determining music? (I think so.) Can anyone really tell, in a blinded listening test, whether a rhythm guitarist is German or British?

The Union Underground was a Metal band that had some success during the Nü Metal years. They had the look and the album art to be part of that era and that genre. However, listening to their music in 2008, I could not help but notice that the singing style really had little to do with Nü Metal, and quite a lot to do with Grunge, which was declared over by that time. As far as I can tell, no one talked of TUU as a late Grunge band.

An interesting pair that got me thinking further about image- and time-based genre attribution are Corrosion of Conformity and VOIVOD. Originally starting out in very disparate genres, in the farthest reaches of Hardcore Punk and Prog Metal, these two diverged in their music until their releases of the albums ‘KATORZ’ (by VOIVOD) and ‘CORROSiON OF CONFORMITY’ (by CoC, of course) in the late aughts. I find it nearly impossible to tell these two albums apart stylistically (though each is quite distant from the bands’ earlier output). When I saw CoC perform at Dante’s in Portland, they presented a marvellous synthesis of Prog agility and Punk attitude. (These two were not meant to go together, but it’s happening more and more.) Meanwhile, VOIVOD apparently drifted further and further into Punk Rock, and lost most of their Prog intricacies. Yet, if I were to stick to “what we know those bands to be,” I would be forced to attach opposite labels to songs from those two albums, which, even when I’m looking right at the readout on my display and know what I’m hearing, sound the same to me.

I mentioned Old-School Hip Hop above as well. Every now and then, you hear a new song, and it has that early, innocent flow we associate with everyone from The Jungle Brothers to MC Hammer. It’s old-school in that the time extents of rhythmic phrases in the vocals and the time extent of semantic phrases in the lyrics delivered by the same vocals coincide****.

Yet, maybe it was released in 2013. Yet, De La Soul was putting out music in 1989 that did not sound old-school; it was like what was going to happen ten years later. (I feel the same way about fu-schnickens’ 1992 album.) Some of the music in those old-school days was well ahead of its time, and some music that gets released even today brings back the old-school style. It’s the sound that counts, not the metadata.

There are many more examples, and perhaps better ones that I will add as I think of them, or hear them, but for now, I will conclude that, 1) genre studies and genre R&D need multi-cultural teams so that the level of attention to detail that is possible for Deep Psytrance vs. Gabber vs. New Romantic vs. New Wave will also be possible for ‘Bulgarian Rock’, ‘Hungarian Rock’, ‘Russian Pop’, and ‘Turkish Pop’. Sure, I’m glad someone in America even cares enough to put those on the map, but given the several hundred varieties of Electronica, Metal, and Hip Hop each, can we really believe there is only one variety of ‘Russian Pop’? (I know for a fact there are quite a few styles and genres within Turkish Pop.*****)

NOTES

* Yet, no matter how much detail each scholar, researcher, developer, or enthusiast goes into, it is likely to prove insufficient for the afficionado of that sub-sub-genre. The Echonest blog (at http://blog.echonest.com/post/52385283599/how-we-understand-music-genres) recently included the following comment: “. . . somebody, somewhere might care about (e.g. “gothic metal” vs. “symphonic metal” vs. “gothic symphonic metal”).” Yes, somebody right here not only does, finds those to be rather obvious and relevant distinctions, which are further complicated by Nightwish’s recent experiments combining symphonic, power, and folk metal.

** Why I use the term “northern” rather than “western” will be the topic of another post.

*** Yes, these are real genre names. To a connoisseur who lives Grindcore, Power Violence may sound completely different, while to an outsider neither would be distinguishable from the earliest ’80s Thrash.

****As Hip Hop matured, it became less and less common for the sentence and its rhythmic phrase to start and end together. This seems to be a result of the recognition that rhythm allows one to rhyme with any syllable in a word, not just the last syllable. So, an MC who wants to rhyme, for instance ‘crime’ with ‘time’ does not need each sentence to end with one of those words; s/he can rhyme ‘crime’ with ‘time’ in a sentence that might go “It was that time [break here] I went off to the east coast” where ‘time’, due to rhythmic phrasing, took care of the rhyme, and the rest of the sentence could still be uttered. In much old-school Hip Hop, the semantic phrases had to end at the same rhythmic stopping point.

*****As for style versus genre, let me try a quick explanation. A guitarist can play the blues in a Be-Bop context, a Psych context, or a Funk context (to name a few), and an MC/toaster can rap on a Hip Hop song, a Reggaeton, a Rock or Metal song, or even in a piece of modern “classical” music. Similarly, a drummer can play funk, swing, or shuffle in a Jazz band, Rock band, Pop group, or an experimental combo. The elements these musicians bring in are styles (blues, rapping, funk, swing, shuffle), while the complete package of the musical experience will likely fall into a genre or subgenre, like Electro Swing, Funk Metal, Be-Bop, or Chorinho.

“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?

Dylan Evans’ ‘Placebo’, Languages, and “Yes Way”

I was reading the section called “Saying is Believing” in Dylan Evans’ book called ‘Placebo: Mind over Matter in Modern Medicine”,[1] which is one of my sources for an elective class I teach (called ‘Science, Medicine, and Reason’).

At the beginning of this section, Evans introduces the assent theory of belief, with references to C. Cherniak and Bertrand Russell. While I expect to talk about this idea in the near future, I will focus now on an example Evans gives, and share my thoughts about languages and translation. Evans says “If you want to explain why John took his umbrella when he went out this morning, the chances are you will say it was because he believed it was going to rain.” (p. 77). As a fully bilingual person who thinks and dreams in his second language, I nevertheless mentally transposed this statement, and found that the word choice for ‘belief’ (as used above) might correspond to a different word in Turkish than the primary word for ‘belief’: The word ‘think’ may be used instead in a good Turkish translation of Evans’ sentence.

I think this is informative about the meanings the word ‘belief’ can have. Do we really mean John believes it’s going to rain in the same sense that John might believe in God? I don’t think so. John may have been led to believe that the probability of rain on that day was 50% or higher (and perhaps it’s a different threshold for others), but typically, a religious person does not believe in God in terms of a 50% (or even, say, 70%) probability; they think of it as certainty. Could it be, then, that the word ‘belief” has (at least) two nuanced meanings? The sense of ‘belief’ as involved in faith must be different from the sense of ‘belief’ as involved in likelihoods.

If that seems unlikely, consider the word ‘no’. Obviously, ‘no’ is the opposite of ‘yes’. However, when it’s used in the humorous pop-cultural “yes way”, the “no way” to which it responds was not meant in the sense of opposition, but in terms of negating presence or existence. Here’s where fluency in another language helps: In Turkish, ‘no’ in the sense of “none” or “there isn’t any” is a different word from ‘no’ as the opposite of ‘yes’. That word, ‘hiç’ (pronounced close to “hitch”) carries a sense of “any”, as in “there aren’t any”.

“Yes way” is humorous because it doesn’t quite work; we recognize that. But we also don’t really think about why it doesn’t work. It’s just a sense, like a note that is out of tune. To the foreigner fluent in English, however, there is another way to access that sense of slight off-ness** which allows one, in my case, to recognize this yes/no pair not as the regular yes/no (evet/hayır) pair, but as a distorted any/any (her/hiç) pair, which remains unclear in English. (In English, “anyone can . . .” implies existence while “there isn’t any  . . ” implies nonexistence. The same word is used for opposite meanings. Not so in all languages.)

So, there it is: my introduction to the world of blogging, thanks to this quick thought that occurred to me while reading Dylan Evans’ ‘Placebo’.

In the next installment, I will examine the word ‘good’ (with respect to music and the two languages).

FOOTNOTE: [1] Disclaimer: I use logical/British/tech punctuation, not US punctuation, when it comes to placing commas and periods inside or outside quotations (but I’m aware of the US convention)

Placebo: Evans, Dylan. Mind over Matter in Modern Medicine, Oxford University Press, 2004, New York, NY, USA. First published in 2003 by HarperCollins Publishers. Copyright © Dylan Evans, 2003, 2004.