Thesis Musings 2: Potential Pivot and Clarification

Reflection

This week I had some thoughts that might lead me down a path to a clearer thesis topic. I was interviewed by a group of Penn Integrated Product Design grad students about my Biodesign Competition project. At one point, they asked me if I considered myself a biodesigner, and I hesitated, and ultimately said no. Why? I’ll come back to that later.

I studied decision processes and public policy in undergrad. When I graduated in 2010, there were very few jobs called “UX Designer.” In fact, my concentration, Decision Processes, could have easily been renamed “Behavioral Economics,” but that field was also still in its infancy in popular understanding. Yet even as students, we could see how the emerging theories we studied were already shaping software design and policy. “Nudges” were being implemented to set default behaviors when signing up for, say, savings accounts -- something informed by behavioral research. Particular colors were being used to incentivize or disincentive actions. It was increasingly clear that what we were studying was being implemented in practice, both in software and within public policy, and that the field of design was expanding beyond fashion, furniture, hardware, and graphics to include more interdisciplinary philosophies and applications.

Now in 2020, we’ve seen both the bright and dark sides of the influence of behavioral research on product design and policy, and we have an entire industry built around incorporating our academic understanding of behavior into the process of design. “UX Design” is a common job title that has subfields (i.e. “UX research”) and is even a field of study itself now. If I were a Decision Processes student today, I would have a set of obvious jobs to consider upon graduating.

We’re currently at a similar inflection point, I believe, in the field of “biodesign.” There is an emerging set of beliefs, tools, and practices that are loosely tied together with the term “biodesign,” but with a very fluid definition of what it might mean, and few job opportunities explicitly penned. 

A Fast Company article from 2017 says that biodesign is “a growing movement (literally) of scientists, artists, and designers that integrates organic processes and materials into the creation of our buildings, our products, and even our clothing.”  In perhaps the definitive text on the current state of Biodesign, William Myers says that “unlike biomimicry or the popular but vague "green design," biodesign refers to the incorporation of living organisms as essential components in design, enhancing the function of the finished work.” These definitions leave a lot of room for further clarification, and might beg more questions than they answer.

One question that follows immediately: are there “biodesigner” jobs? What does LinkedIn look like when searching “biodesigner?” How does that compare with “UX designer?”

In the “Biodesign” search, there are only a few results with “biodesigner” in the job title, and few companies that are oriented around “biodesign.” UX, on the other hand, has an entirely different page layout given how popular both the title and the field is.

In addition to how biodesign might look on LinkedIn, another few questions might be:

“What philosophies or frameworks might guide a biodesign practice?” 

“What is the equivalent of “Human Centered Design” for biodesign… or are they actually compatible, and biodesign is a discipline, in a different part of the design ecosystem from a framework like HCD?” 

“What would distinguish biodesign from, say, industrial design?”

All of these circle around the bigger question of defining what biodesign is today, where it might go in the future, both pragmatically in the form of jobs, companies, and labels, but also in the form of philosophies that shape how we approach thinking about designing the world around us and around the other life forms we live with.

When I was asked whether I was a biodesigner, I said “no” because I felt the word was too vague; that it could be too easily misconstrued, or misunderstood. I said I might be more comfortable with bio-artist; perhaps because “artist” itself is such a broad, open-ended label at this point, that it could subsume the prefix “bio” without batting an eyelid…

As I reflected about my thought processes and my design processes, however, I noticed that I did have a pattern that might not fit under an “established” design school of thought. I begin my design process with a process of decomposition; of breaking the whole of an entity (a problem, a feeling, a material…) into its constituent parts. I often think about how those parts can be transformed, recombined, recycled into new wholes. I realized that my thought process is informed both by the computer science-y idea of “decomposition” and the biological idea of “decomposition” -- and that with some further thought, I may be able to articulate a philosophy and set of tools for using this sort of decompositional framework for designing -- ideating, prototyping, problem solving.

So, in a nutshell, I may focus my thesis on elaborating on the definition(s) of biodesign, of placing it within the world of existing design school of thought, and then establishing a sub-framework of biodesign of my own, which right now would loosely be described at “Decomposition Design.”

What might this look like:

• Research “paper”

  • Does not need to be entirely written -- could include multimedia, especially video and animation

  • Does not need to be a linear experience for the “reader”

    • There are many questions to answer, and many paths that might follow from the broader question of defining biodesign

  • Should include interviews with people across the biodesign world, broader design world, and lay people who might not currently know what biodesign is/might become

• A series of experiments

  • objects as examples of the schools of thought within biodesign

    • Especially important to do this if I decide to develop/articulate my own design paradigm

    • Schools of thought

      • Resilience design

      • Multi-species-centered design

      • Biomimicry

      • Decomposition design (my framework)

Digital Currencies, Blockchain, Future of Finance Reaction 2

From October 2020// reaction essay for NYU Stern/Law School Digital Currency, Blockchain, Future of Finance course with Drew Hinkes and David Yermack

Bitcoin has come under fire for a whole host of issues: its energy consumption, the political ideology of many enthusiasts, its volatility, the shady transactions that fueled early usage, the potentially unwarranted hype, and so on. We’ve picked apart a number of these critiques in class through a much more nuanced prism. One critique that I think has more validity than many of the others is that the Bitcoin proof-of-work task, finding a random number, is pointless-yet-effective. The question raised in class and by other blockchain thinkers is whether we can devise a proof-of-work task that is cryptographically and algorithmically sound while also useful in the broader world. I have one idea I would like to explore -- protein folding as proof-of-work. I’ll start with a brief summary of proof-of-work as a verification tool, review the basics of protein folding and why it is important, then analyze whether protein folding could be a useful proof-of-work task for a blockchain. Finally, I’ll highlight some folks who seem to be thinking along the same lines already.

Let’s start with a quick summary of the mechanics of proof-of-work verification. Every blockchain is, at its simplest, a series of transactions that account for the exchanges of coins between parties. Those transactions need to be verified so that the current state of ownership of coins is accurate; this process of verification-in-chunks is what ultimately creates “blocks” that are “chained” together to form a blockchain. The process of verification itself can take different forms, but the one I’m interested in in this paper, proof-of-work (POW), works as follows:

1. All transactions since the previous verification event are grouped together into a block.

2. The blockchain in question poses a challenge to all prospective verifiers-- miners-- that is challenging to complete, but easy to verify.

3. Miners compete to see who can complete the challenge as quickly as possible.

4. Miners who believe they are correct will broadcast their answer for others to check.

5. After enough miners (also called “nodes”) agree that the answer is correct, the winning miner will receive a reward.

The criteria that is most intriguing to me when thinking of alternative POW designs is that the verification challenge must be difficult to solve but easy to verify. In the Bitcoin blockchain protocol, this involves finding a random number, which when combined with all previous block encryptions, will produce another very-difficult-to-guess specific encryption value, or target. The challenge issuer -- in this case, an algorithm -- can simply announce a target for this round, allow miners to attempt to find a hash value that is lower than the target, and then, after someone has announced that they won, wait for other miners to combine the announced value with the other components of the encryption formula, and verify that it produces the right target value. 

Abstracted a level up, in designing a new proof-of-work, we need to issue a challenge that has a solution, likely a single number or small combination of solutions, which can be plugged in to an algorithm to produce a very-unlikely-to-fake value. This lends itself to challenges that have discrete answers with measurable qualities of “correctness.” that could be used to plug in to this pseudo-algorithm:

Encrypt [POW solution + (previous blockchain info)] = Target value

If we asked people to find one way to arrive at the number 112 using multiplication of more than 2 numbers, we would be able to verify answers -- of which there are many -- and could use timestamps of answer submissions to determine who was correct first. If we issued a challenge that asked people to submit a paragraph about the nature of truth, we would not be able to evaluate the correctness of the output, and would not be able to complete the hashing function. In all of these configurations, we can also ask whether the challenge itself is worthwhile: what is the value of asking people to use their time and computational effort to find random numbers? Is there a better use for this effort than can still maintain a sound cryptographic and incentive design?

Let’s hold the algorithm design thoughts for a moment and switch gears to protein folding. What is it/why is it important? 

Proteins, one of the most important building blocks of life, are composed of a chain of many amino acids. There are 20 unique amino acids found in our bodies, and each one has special characteristics that affect how they interact with each other, and with other molecules. Proteins can be visualized on paper as a chain of linked amino acids, but in 3D space, the electrical charges associated with different amino acids cause the chain to attract and repel itself, leading to a “folding” that gives proteins their unique shapes. The shapes of proteins expose some of those positive or negative charges to passing molecules, which may attract or repel those molecules. This property allows proteins to function in the myriad ways that they do; as enzymes, as membranes, as tissues, and many other important components of life and living systems.

Amino acid chains that make up individual protein molecules can be very large in humans; small ones are chains of 100 amino acids, normal ones can be 1000+. These chains can be a combination of essentially any of the 20 amino acids, leading to large combinatorial sets even for the smaller proteins. Any one of these combinations can take on different shapes when folded. Knowing all of the possible shapes of proteins is important in advancing our understanding of biological systems, but getting to a comprehensive understanding is currently extremely computationally intensive.

The stability of a shape, and the performance qualities of a protein can be measured along a number of dimensions. For the sake of space in this paper, I am going to link to an example from a protein folding competition website, where you can see how the challenge is designed. Suffice to say that there is a leaderboard with scores, and a number of factors that roll up in to that final score. Any of these components, from the final score to a lower level performance factor, could be used as the input that leads to a target value. 

 The structure for a competition that is difficult to complete but easy to verify already exists, as exhibited by fold-it. Foldit encourages manual folding and is used as an educational tool, but there are also computational versions of Foldit. In fact, there seems to be a project called FoldingCoin out of Stanford that attempts to tokenify protein folding, although judging by the current coin price (way less than 1c per coin), it does not seem to have taken off. The promise, however, is real: if we use our manual or computational firepower towards identifying the shape of more and more proteins, we will be able to produce a more robust understanding of the structure of life, and with that knowledge, better medicine and health, better biomaterials for manufacturing (a potential solution to some of our climate change issues), better food production, and more. 

I’ve run out of space for this exercise (I know you all have a lot of these to read!) -- but this seems to be a potential topic/project for the final paper, specifically: 

1. What are the mechanics for a protein folding-based blockchain, in much more detail

2. What efforts have already launched and why have they not succeeded

3. What are the potential security vulnerabilities and performance issues in this POW setup

4. If successful, what value could this sort of blockchain add to the world

Thesis Musings

I’m at the beginning of my dive into thesis research. We officially began our thesis class at ITP 2 weeks ago, and were asked to write a little bit about what was on our mind.

Prompt: Write a short blog post on the big concept or passion or interest or questions you want to tackle (not the technology).

Yogurt is a food. Yogurt is alive. Yogurt was an accident. Yogurt is intentional. Yogurt is recursive. Yogurt is an archive.  

A spoonful of yogurt might sound like nothing more than an occasional breakfast snack; but what if I told you that within that yogurt, we can find questions and answers touching on everything from experience design to metaphysics; systems architecture to the future of computing?

Yogurt has been in the human diet in various parts of the world for thousands of years. We know it as a tangy, smooth-textured dairy product that is in a distinct class from cheese, milk, kefir, butter, and the rest. We know how it’s made now, too — heat milk, let it cool, add a bit of previous yogurt, give it time, and voila– you’ve got yogurt. But it wasn’t always this simple. 

Yogurt likely began as an accident. It probably went something like this: milk was left outdoors in a hot environment near some plants, where lactobacillus — a lactic acid producing bacteria — was crawling around. That bacteria found its way into the milk and metabolized the lactose in the milk to produce lactic acid. That lactic acid lowered the pH of the milk. The hot environment “cooked” the milk, changing the shape (denaturing) many of the proteins in the milk. The more acidic environment and the denatured proteins encouraged a re-formation of protein networks in the milk. Those new networks of proteins created a firmer texture, and the lower pH gave the milk its tangy taste. Some brave soul took a bite of that substance, liked it, maybe even felt good rather than sick, and perhaps tried to recreate it. Over many centuries and experiments, we now have a relatively reliable way of making yogurt. 

The mechanics of the inner workings of yogurt, which I can go to in much more detail, are ripe for analogy to the ways that human systems form, de-form, transform, and re-form. Bookmark “resilience in networks,” and “transformation” as topics of interest, and yogurt as a lens through which to look at these topics. 

Also bookmark “experience design” as a practice of interest, and the cultivation of spoiled milk into a repeatably delicious product as something that we can analyze as an act of intentional design, and extend into other food and non-food design processes.

As I mentioned above, yogurts are created from previous yogurts. They don’t have to be done this way, but in common practice, they are. In that sense, yogurts are recursive. A piece of the whole begets the next whole; the “next” is dependent on the “previous.” In Indian households it is very common to make yogurt at home, and yogurt starters are often an important item to bring along when moving from one place to another, or to share with family and friends when they come to a new place. We can trace lineage through yogurt — where did it come from, where did it branch, how did it transform? We can tell stories of migration, of immigration, through yogurt; both through people and through bacteria. 

There are a few ways we could go about tracing the lineage of yogurt. One of those ways would take advantage of recent dramatic improvements in our collective ability to understand the biological makeup of the world around us. Genome sequencing has become orders of magnitude cheaper, with handheld DNA sequencing tools now available to hobbyists, with room for further improvements in hardware and cost well within reach. The study of genomics coupled with the techniques of bioinformatics, among other related fields, are giving us new information about both the “hardware” and “software” of life, and allowing us to identify specific species of invisible microorganisms in our environment. We even have the ability to “program” some genes. We’ve figured out ways to store information in DNA, and we’re beginning to understand the possibilities of using DNA instead of bits as the basis for computing. Taken together, we are starting to learn techniques that may give us new infrastructure-level tools to reimagine the ways in which we build the materials around us — both physical and digital. The uses of these technologies will not be neutral; we have to imagine and execute the uses of the technologies that we want to see exist. 

It’s possible that yogurt already is a kind of archive in itself; I would like to explore whether we can use the bacterial makeup of yogurt as a way of identifying its ancestors in ways that are roughly similar to how we are able to identify our relatives using DNA. I’d also like to explore using DNA storage to embed oral histories of the Indian community in New York — my mom’s family was part of the early batch of Indian immigrants to arrive in Queens in the 70s — in the DNA of lactobacillus, and use that lactobacillus to make yogurt. I’d then want to demonstrate the ability to read out those files from the yogurt DNA.

Zooming out — I am trying to weave together a variety of interests and questions through an exploration of yogurt. It is possible that I’ll narrow in on one specific area: resilience in networks, transformation, lineage, stories of the Indian-American community’s roots, the future of biology and computing, infrastructure technology versus end uses, Vedic philosophy. It’s also possible that all of these can be refracted through one prism. Let the journey begin…

Genetic Evolution Simulations and Athletic Performance

This week, we covered Genetic Evolution algorithms and surveyed a few approaches to designing simulations that implement this technique. Below, I’ll lay out a plan for a simulation in p5.js that takes inspiration from the improvement in athletic performance over the last 100+ years.

Scenario

Describe the scenario. What is the population? What is the environment? What is the "problem" or question? What is evolving? What are the goals?

When we watch athletes compete today, it is remarkable just how far they’re able to push the human body to achieve the feats they achieve during the games. Crowds at earlier athletic events were similarly mesmerized by the best athletes in the world during their eras. However, when we watch film of athletic competitions even 30-40 years ago, it often seems like we’re watching an amateur version of today’s sport. When we look at world record times for competitions like the 400m dash, we see a steady improvement over the years; most likely these are due to advances in technology, diet, strategy, training methods and other tools more readily available to modern athletes.

This steady march towards reaching an upper limit on athletic performance reminds me of a genetic evolution simulation. Thousands of athletes have tried to run around a track as fast as they could. Each generation of new athletes learns from the previous ones and finds little ways to improve. Over time, we see world records broken, little by little.

I would like to build a simulation that has objects try to “run” around a “track” as quickly as they can, under a number of constraints that loosely model those of real athletes. The viewer will be able to manually run each generation rather than having the simulation evolve as fast as possible, and we’ll record “world record” for the best individual “athlete” in that generation’s competition. We can display a record book on screen to see how the world records vary and eventually improve over time. We can also display the “stats” for the top n athletes to see how things like “strategy,” “technology,” and “muscle mass” change over time as the athletes improve. 

Phenotype and Genotype

What is the "thing" that is evolving? Describe it's phenotype (the "expression" of its virtual DNA) and describe how you encode its genotype (the data itself) into an array or some other data structure.

Athletes, represented by shapes on the screen, are the “things” that are evolving. The athletes will be physics objects that have a position, velocity and acceleration. They’ll also have other traits like “strategy” and “technology” that loosely model real world factors that can limit or increase their max speed and vary from generation to generation. Instead of thinking of technology/strategy as properties of the object, they could be thought of as forces that are applied to the object’s acceleration or velocity vector and vary from object to object in the system.

Strategy will control the efficiency of the path taken on the track. Each frame, the object will have to decide the angle of its trajectory. If the object zig-zags around the track, it will not be taking the most efficient route. Over time, the objects should learn to take the path with the least amount of wasted movements to get to the finish line. The code will need to be written in a way that the object only goes clockwise, rather than immediately going to the finish line to the objects’ left.

The objects will also have other constraints besides knowing the best route to run. They’ll a “technology” constraint — this will correspond to a max speed or max force property’s ceiling that applies to all athlete objects. Each athlete object’s specific technologies will allow it to reach a percentage of the ceiling — with some having “better” technology, or a higher chance of reaching the ceiling, and some will only reach, say, 50% of the ceiling, which means they will likely complete the race slower than athlete objects with better technology.

Athlete objects will also have different diets that behave similarly to technology. Diet could potentially control a “max force” constraint that affects acceleration, or could be a small velocity multiplication factor that works in conjunction with technology. This factor would apply to all objects, and each individual athlete object’s diet could vary to allow it to have a percentage of the max factor.

These properties encode the genotype and allow the athlete the potential to perform as well as their ceilings will allow them to perform. Over time, those ceilings will go up, which will allow individual athletes to go faster than the fastest in the previous generations.

The athletes’ phenotype, or expressed traits, will be their shapes and speeds of movement around the track. Perhaps diet can modulate size of the circle a bit and tech can change color or control some sort of blur effect.

Fitness Function

What is the fitness function? How do you score each element of the population?

Each generation, 20 athletes will compete. The fitness function will look for the elapsed time for the athlete to reach the finish line. Assume an oval track where the athletes begin at the top of the canvas and run clockwise as shown above. The pseudo-code to calculate the fitness function will be something like:

If athlete.pos.x equals finish.pos.x and  athlete.pos.y is between a range of finish.pos.y, trigger a function that will log the time elapsed since the start of the race and the point at which the if conditions are met..

Mutation and Crossover

Are there any special considerations to mutation and crossover to consider or would the standard approaches work?

Technology and diet ceilings should probably increase on some set interval -- maybe determined by user but set at a default of every 10 generations (like a decade). Crossover can continue in a standard way and so can mutation.

Listening to Bio-Signal (Or: JAWZZZ)

Assignment: Expose a signal from some under-represented part of your body.

Idea

Our bodies produce signals that we can’t see, but often can feel in one dimension or another. Whether pain or restlessness, euphoria or hunger, our body has mechanisms for expressing the invisible.

Some of its signals, however, take time to manifest. Small amounts of muscle tension only convert into pain after crossing some threshold — which, in some cases, could take years to reach. I clench my teeth at night, which only became visible a few years ago when the enamel on my teeth showed significant wear and tear. At various times, I had unexplainable headaches or jaw lock; but for the most part, my overnight habits were invisible and not sense-able.

With technological prostheses, however, we can try to shift the speed at which we receive signal. This week, I built a muscle tension sensor to wear on my jaw while sleeping with the hope that I could sense whether I still clench my jaw. Long story short: I most likely do still clench my jaw, but without spending more time on statistical analysis of my results, it’s not wise to read too deeply into the results.

I’ll go over the process and results, but perhaps the most important reflection in this whole process is that even in my 3-day experiment, it was possible to see the possible pitfalls that accompany trying to quantify and infer meaning from data in situations that include even minimal amounts of complexity.

Process

This experiment required the following pieces:

• Wire together a muscle tension sensor and microcontroller

  • I used a MyoWare muscle sensor and Arduino MKR WiFi 1010

• Send data from the sensor to a computer

  • I used the MQTT protocol to wirelessly send data from my Arduino to a Mosquitto server

• Write the data from the server to a database

  • I used a node.js script to listen to the MQTT data and write it to a local SQLite database on my computer

• Analyze data from the database

  • I used Pandas, Matplotlib — common Python libraries— and the SQLIte package to shape and analyze my data

  • Here’s a link to the analysis in a Jupyter notebook

[As a side note: prior to this assignment, I had not used a number of these different technologies, especially not in such an interconnected way. The technical challenge, and the opportunity to learn a number of useful skills while tackling these challenges, was a highlight of the week!]

I started by assembling the hardware and testing on my forearm to make sure it worked properly:

I then moved to testing that it could sense jaw clenching (it did):

Ultimately, I put it to the test at night. The first night I tried to use the sensor, my beard seemed to interfere with the electrodes too much. In true dedication to science, I shaved off my beard for the first time in years :P It seemed to do the trick:

Results

OK, so— what happened?

First, the basics: This data was collected on Saturday night into Sunday morning for ~8 hours. I wore the sensor on my right jaw muscle and took 2 readings per second the entire time.

And a few caveats: this is only one night’s worth of data, so it is really not conclusive whatsoever. It’s really just a first set of thoughts, which can hopefully be refined with more data and Python know-how. I also did not capture film of my sleeping to crosscheck what seems to be happening in the data with what actually happened in real life.

With that said, here’s one explanation of what happened.

Throughout the night, it’s likely that I shifted positions 5-10 times in a way that affected the sensor. In the graph below, there are clusters of datapoints that appear like blue blocks. Those clusters are periods where the readings were fairly consistent, suggesting that I may have been sleeping in one consistent position. These clusters are usually followed by a surge in reading values, which happen when the sensor detects muscle tension, but also happened when I would touch the sensors with my hand to test calibration. When sleeping, it’s possible that I rolled over onto the sensor, triggering periods where the readings were consistently high.

During those fairly-stable periods, there are still a lot of outlying points. By zooming into one “stable” area, we can look at what’s happening with a bit more resolution:

This is a snapshot of 1 minute. During the beginning of the snapshot, the sensor values are clustered right around a reading of 100. Then there is a gap in readings— the readings were higher than 400 and I didn’t adjust the y-axis scale for this screenshot— then they return to ~100 before spiking to 400. The finally begin returning to an equilibrium towards the end of the minute.

This could be evidence of the jaw-clenching that I was looking for initially. It would be reasonable to expect jaw clenching to last only for a few seconds at a time, but that it could happen many times in a row. Perhaps this data shows this in action — I am sleeping normally, clench my jaw for a few seconds, relax again for 5 seconds, and then clench my jaw for another 5 seconds before letting up.

Ultimately, it looks like this sensor data may unveil 2 behaviors for the price of 1: shifts in sleeping position + jaw clenching!

Reflections

In order to make these insights somewhat reliable, I need to do a few things:

• Collect more data

  • This is only one night’s worth of data. It’s possible that this is all noise, the sensor didn’t actually work at all, and I’m just projecting meaning onto meaningless data. A bigger sample size could help us see what patterns persist day after day.

• Collect data from different people

  • In order to validate the hypothesis that high-level clusters explain shifts in position and more granular clusters/outliers show jaw clenching, I’d need to try this with other people. I know that I clench my jaw, but if someone who doesn’t clench still has similar patterns in data, I’d need to revisit these hypothesis.

• Validate insights against reality

  • If I had video of my night, or if some house elf took notes while I slept, we could tag different actual behaviors and timestamps. Capturing shift in position should be relatively easy to do, as long as I get the lighting figured out. Clenching might be harder to capture on video.

• Statistical analysis

  • I used the scatterplot to see obvious visual patterns. Using some clustering analysis, I could understand the relationships between clusters and outliers at a more detailed level.

Beyond what I could do to improve this analysis, I think there’s a bigger point to make: we should be skeptical of the quantified data we are presented with and ask hard questions about the ways in which the presenters of data arrived at their conclusions. In my experiment above, I could have made some bold claim about my sensor being able to detect sleep positions and TMJ-inducing behavior, but the reality is that the data needs a lot of validation before any insights can be made confidently. While academia has checks and balances (which themselves have a lot of issues), the rise of popular data science and statistics has not been coupled with robust fact-checking. So — before going along with quantified-self data, make sure to ask a lot of questions about what might be causing the results!

Thanks to Don Coleman and his course Device to Database — extremely helpful for this technical implementation of this project.

My Microbial Companion: Nukazuke

Assignment:

Start a microbial culture that you will keep as a companion for the rest of the semester.

Idea:

Nukazuke are Japanese rice-bran ferments. They’re made by first preparing a bed of rice bran, salt, water and additional ingredients like mustard powder, kombu, dried red chili flakes and garlic. Other add-ons can be used instead of these. After mixing together the ingredients, bury a vegetable, chopped if necessary but with skin-on, in the rice bran bed. The salt in the rice bran bed functions just like salt in a lacto-ferment; it promotes healthy lactobacilus growth and inhibits other pathogenic bacteria that can’t survive in higher-salt concentrations. The rice bran supplies sugar for the naturally occurring bacteria in the air, and the buried vegetable (especially its skin) often carries a lactobacilus biome that will help begin culturing the rice bran bed.

Each day until mature, swap out the vegetable in the bed. This helps promote a more diverse microbiome in the rice bran bed. I’ve been tasting the pickles each of the last two days; they are noticeably transformed from their normal state, slightly sweeter and saltier, but still very young for a nukazuke. I expect that within a week I will have a mature bed.

Once the bed is mature, I’ll be burying vegetables or ~12 hours or more to get the fermented final product. I’ll try different vegetables, and eventually I may take some of the mature culture to

I followed this recipe, and also learned a lot from The Art of Fermentation by Sandor Katz.

Embodied Intuition as the Rest of You

I jolted awake. 7:44am — one minute before my alarm was set to ring. Time and again throughout my life, this same thing has happened; my internal clock has known when to get up and shot some sort of signal behind my eyes and forced them open, just ahead of my alarm. Not every day. I still need alarms. But it’s happened enough times in enough varied instances that it is unlikely to be coincidental. Something else is going on.

Close your eyes. Move your mind out of your head. Live in your right arm. Now breathe into your gut. Stop thinking. Heal. At the front of the room, our teacher sits cross-legged, leading us through a Somatic Meditation practice that emphasizes “connecting with the inherent, self-existing wakefulness that is already present within the body itself.” He tells stories of people in distant, old cultures being able to sense incoming rains more accurately than technological systems, all from years of living outdoors, tuning their bodies’ sensing machinery subconsciously. He speaks of “embodied intuition,” a way of sensing phenomena outside of the mind.  More advanced students are exorcising physical pains lodged deep in their bodies, caused by emotional traumas that they talk about gingerly during our post-class reflection. Color me intrigued.

Attendees at Steve Jobs’ funeral famously left with a copy of Autobiography of a Yogi, a book that sits on my shelf, half-read. During my various stops-and-starts with this book, one feature of the writing stood out: the number of seemingly miraculous events that occur. The pages are peppered with stories of yogis who never eat yet live for a long time; yogis who can teleport into other bodies; yogis who can control their “involuntary” biological systems. During earlier moments in life, I was highly skeptical of these accounts. Now, I’m willing to be more open. Maybe these are symbolic anecdotes that still connect to an unbelievable reality, or maybe our bodies and minds are capable of far more than I had suspected.

—————

Where do thoughts live? When I visualize an answer, I see them floating around in my head. In each of the stories above, however, thoughts — or maybe a different word, something like “conscious activity”– exist in the body, outside of the head. If our mental model for consciousness is a powerful computer in our brains, the model suggested by these anecdotes is that we have distributed computing power in far more parts of our body. That our limbs and organs are not just sensors feeding data to the head, but that they may have their own local processing units as well.

What if every time we have a negative experience, our muscles encode that negativity in the form of tension. And what if that network of tense muscles were able to self-correct after taking on too much tension? What if positive thoughts were encoded as well, perhaps by storing and/or releasing hormones that increase alertness. What if we could access those positive stores of hormones whenever we needed to? 

We already do a version of this: we have immune responses to stress, physical responses to pain, etc. But perhaps the rest of us lies in building a deeper connection with our embodied intuition, our embodied consciousness. 

The explosion of mindfulness practices in the West seems to be, at least in part, a response to the strain we are placing on our brains to process an ever-increasing amount of signal. While these practices are often marketed as cures to anxiety and stress or superchargers for mental focus and productivity, perhaps they are really a way of training ourselves to load-balance signal processing from the mind to the rest of the body — and in the process unlock wholly new human capabilities.

Beyond mindfulness training, it would be interesting to see whether we could measure what our bodies are doing when they bypass conscious thought to make autonomous decisions. Measuring tension, hormone production, and perhaps other biological markers when people are exposed to a variety of situations would allow us to refine our mental model of the mind/body connection– or at least start to sketch it in low resolution while our instruments of measurement catch up to our actions.