From 2d to 3d Information Theory

I've been doing some work on Shannon information theory in collaboration with friends, and wrote a simple program to explore Shannon's idea of mutual information. Mutual information is the measurement of the extent to which two sources of information share something in common. It can be considered as an index of the extent that information source A can predict the messages produced by information source B. If the Shannon information of source A is H_a  and the Shannon information of B is H_b, then the mutual information is calculated by:

H_a  + H_b - H_ab  

There is an enormous literature about this, because mutual information is very useful and practical, whilst also presenting some interesting philosophical questions. For example, it seems to be closely related to Vygotsky's idea of "zone of proximal development" (closing the ZPD = increasing mutual information while also increasing complexity in the messages).

There are problems with Mutual Information. With 3 information sources, its value oscillates between a positive and negative value. What does a negative value indicate? Well, it might indicate that there is mutual redundancy rather than mutual information - so the three systems are generating constraints between them (see https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3030525)

Negative values should not occur in two dimensions. But they do. Loet has put my program on his website, and it's easy to see how a negative value for mutual information can be produced: https://www.leydesdorff.net/transmission

It presents two text boxes. Entering a string of characters in each immediately calculates the entropy (Shannon's H), and the mutual information between the two boxes.

This is fine. But if one of the information sources has zero entropy (which it would if it has no variety), we get a negative value.

So what does this mean? Does it mean that if two systems do not communicate, they generate redundancy? Intuitively I think that might be true. In teaching, for example, with a student who does not want to engage, the teacher and the student will often retreat into generating patterns of behaviour. At some point sufficient redundancy is generated so that a "connection" is made. This is borne out in my program, where more "y"s can be added to the second text, leaving the entropy at 0, but increasing the mutual information: 

But maybe I'm reading too much into it. It seems that it is a mathematical idiosyncrasy - something weird with probability theory (which Shannon depends on) or the use of logs (which he got from Boltzmann). 

Adding redundant symbols is not the same as "adding nothing" - it is another symbol - even if it's zero. 

The bottom line is that Shannon does not have a way of accounting for "nothing". How could it?

This is where I turn to my friend Peter Rowlands and his nilpotent quantum mechanics which exploits quaternions and Clifford Algebra to express nothing in a 3-dimensional context. It's the 3d-ness of quaternions which is really interesting: Hamilton realised that only quaternions could express 3 dimensions.

I don't know what a quaternionic information theory might look like, but it does seem that our understanding of information is 2-dimensional, and that this 2-d information is throwing up inconsistencies when we move into higher dimensions, or try weird things with redundancy.

The turn from 2d representation to 3d representation was one of the turning points of the renaissance. Ghilberti's "Gates of Paradise" represents a moment of artistic realisation about perspective which changed the way representation was thought about forever.

We are at the beginning of our information revolution. But, like medieval art, it may be the case that our representations are currently two-dimensional, where we will need three. Everything will look very different from there.

Out of Chaos - A Mathematical Theory of Coherence

One of my highlights of 2019 was the putting together of a what is beginning to look like a mathematical theory of evolutionary biology, with John Torday of UCLA, Peter Rowlands in Liverpool university, using the work Loet Leydesdorff and Daniel Dubois on anticipatory systems. The downside of 2019 has been that things have seemed to fall apart - "all coherence gone" as John Donne put it at the beginning of the scientific revolution (in "An Anatomy of the world"):

And new philosophy calls all in doubt,
The element of fire is quite put out,
The sun is lost, and th'earth, and no man's wit
Can well direct him where to look for it.
And freely men confess that this world's spent,
When in the planets and the firmament
They seek so many new; they see that this
Is crumbled out again to his atomies.
'Tis all in pieces, all coherence gone,
All just supply, and all relation;
Prince, subject, father, son, are things forgot,
For every man alone thinks he hath got
To be a phoenix, and that then can be
None of that kind, of which he is, but he.

The keyword in all of this (and a word which got me into trouble this year because people didn't understand it) is "Coherence". Coherence, fundamentally, is a mathematical idea belonging to fractals and self-referential systems. It is through coherence that systems can anticipate future changes to their environment and adapt appropriately, and the fundamental driver for this capacity is the creation of fractal structures, which by definition, are self-similar at different scales.

In work I've done on music this year with Leydesdorff, this coherent anticipatory model combines both synchronic (structural) and diachronic (time-based) events into a single pattern. This is in line with the physics of David Bohm, but it also coincides with the physics of Peter Rowlands.

When people talk of a "mathematical theory" we tend to think of something deterministic, or calculative. But this is not at all why maths is important (indeed it is a misunderstanding). Maths is important because it is a richly generative product of human consciousness which provides consciousness with tangible natural phenomena upon which its presuppositions can be explored and developed. It is a search for abstract principles which are generative not only of biological or social phenomena, but of our narrative capacities for accounting for them and our empirical faculties for testing them. Consciousness is entangled with evolutionary biology, and logical abstraction is the purest product of consciousness we can conceive. In its most abstract form, an evolutionary biology or a theory of learning must be mathematical, generative and predictive. In other words, we can use maths to explore the fundamental circularity existing between mind and nature, and this circularity extends beyond biology, to phenomena of education, institutional organisation and human relations.

When human organisations, human relations, learning conversations, artworks, stories or architectural spaces "work", they exhibit coherence between their structural and temporal relations with an observer. "Not working" is the label we give to something which manifests itself as incoherent. This coherence is at a deep level: it is fractal in the sense that the pattern expressed by these things are recapitulations of deeper patterns that exist in cells and in atoms.

These fractal patterns exist between the "dancing" variables involved in multiple perceptions - what Alfred Schutz called a "spectrum of vividness" of perception. The dancing of observed variables may have a similar structure to deeper patterns within biology or physics, and data processing can allow some glimpse into what these patterns might look like.

Fractal structures can immediately be seen to exhibit coherence or disorder. Different variables may be tried within the structure to see which displays the deepest coherence. When we look for the "sense" or "meaning" of things, it is a search for those variables, and those models which produce a sense of coherence. It is as true for spiritual practice as it is for practical things like learning (and indeed those things are related).

2019 has been a deeply incoherent year - both for me personally, and for the world. Incoherence is a spur to finding a deeper coherence. I doubt that we will find it by doing more of the same stuff. What is required is a new level of pattern-making, which recapitulates the deeper patterns of existence that will gradually bring things back into order. 

Human Factors and Educational Technology in Institutions

Educational institutions are now enormously complex technological organisations - particularly universities. They are generally so complex that few people in the university really understand how everything fits together. Computer services will have teams who understand individual systems, although it is unusual to find someone in a computer services department who understands how it all fits together technically. Even less likely is it to find someone who understands the divergences of digital practice either in the classroom by teachers, or among professional service staff who process marks (and often organise assignments in the VLE).

Of course, despite the lack of any synoptic view, things keep on going. This works because whatever complexities are created by different systems, an administrative workforce can be summoned up to handle the complexity. Providing marks are entered, exam boards are provided with data, and students progressed through their courses to completion, it might be tempting to ask whether a lack of a synoptic view matters.

This is where technological infrastructure, human factors and organisational effectiveness meet. An effective organisation is one which organises itself to deal with actual demands placed on it. An effective organisation manages its complexity, understands its environment, and has sufficient flexibility to adapt to change.  In a university, it can be very difficult to define "demand" or be clear about "environment". At a superficial level, there is demand from "students" for teaching and assessment. This demand is increasingly framed as a "market". However at a deeper level, there is a demand from society, and the politicians who steer it (and the policy for higher education).  What does society demand of education? In recent years, the answer to that question has also been framed around "the market" - but many commentators have pointed our that this is a false ontology. Society has a habit of turning on institutions which extend their power beyond reasonable limits. There is no reason to suppose this might not happen to universities, which have extended their power through a variety of what Colin Crouch calls "privatised Keynesianism" - individualised debt to pay for institutional aggrandisement such as real-estate (https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-856X.2009.00377.x)

Then we should ask "What is the actual demand for learning?" A commonsense answer is that a student should expect to be able to do stuff which they weren't able to do before. But this is a very vague notion. As a crude example, how many of the many thousands of computer science graduates can actually program when they graduate? How many creative writers will make it as authors? How many architects will build a building? Now, of course, this is unfair. There are many "transferrable skills" from a degree - people will go into other walks of life, buoyed by their new-found self-confidence. Apart from those who become so worn-down by assessment and institutional rigidity that their mental health took a knock in education. So there is a first demand: "Education should not leave me feeling worse about myself than when I started".

It turns out to be surprisingly hard to organise that (https://www.hrmagazine.co.uk/article-details/students-regret-going-to-university) Teachers do their best, but within constraints which they and everyone else in the system find unfathomable. Today, many of those constraints are technical in origin. The computer has swamped the natural human pattern of communication which kept institutions viable for centuries. The space for rich intersubjective engagement - whether it is between teachers and students, or between staff, or even between students and their peers - has been attenuated to lights on a screen and clicks and flicks. And with the problems that this creates, the answer is always the same - more technology.

So do we reach the stage where the art of teaching becomes the art of designing a webpage in the VLE? Instructional design would appear to have a lot to answer for. Deep human virtues of knowledge, openness, generosity and revealing of uncertainty do not fit the digital infrastructure and get unrewarded. Flashy new tech sells staff with ambitions and a latent drive for everyone to "do it their way". Some of these people become senior managers and appoint more like them. It's positive feedback.

The equations are simple. All technology creates more complexity in the guise of offering a "solution" to a problem created by some prior technology. Human beings struggle to deal with the complexity, and demand new technical solutions, which often make things worse. How do we get out of this?

The importance of a synoptic view is that it must entail clear distinctions about the system, its operations, its demands and its boundaries. As long as we have no clear idea of the problem to which we want to put technology to address in education, we will be condemned to repeat the cycle. So what is the purpose? What's the point?

It's probably very simple: we all die. The older ones will die first. While they are alive they can communicate something of how to survive in the future. Some will do things which speak to future generations after they are dead. But this can only happen if people are able to live in a world that is effectively organised. In a world of ineffective organisation, complexity will proliferate and the intergenerational conversation will die in a torrent of tweets and emails. There is a reason why the ice caps melt, the stock market booms, and the world is full of mad dictators.

Institutions, Art and Meaning

Much of what I am reading at the moment - Simondon, Erich Hörl, Stiegler and Luhmann - is leading me to rethinking what an institution is in relation to an "individual". It's like doing a "reverse Thatcher" (which is a good slogan) - there is no such thing as an "individual". There is a continual process of distinction making (and remaking) and transduction by which "institutions" - as biological organisms like you and me - or families, friendship groups, universities, or societies preserve meaning. This is a Copernican shift in perspective, and it is something that I think Luhmann and Simondon saw most clearly, although there are aspects of their accounts which miss important things.

This is a helpful definition, because it seems we live in a time when our institutions don't work very well. Life in them becomes meaningless and alienating. So what's going on?

I think the answer has something to do with technology. Technology did something to institutions, and a hint at an answer is contained in Ross Ashby's aphorism that "any system which categorises throws away information". Those words echo like thunder for me as I'm in the middle of trying to upgrade the learning technology in a massive institution.

So institutions do something with information which preserves meaning. Institutions which lose information risk losing meaning. Thanks to so-called IT systems, most of our institutions from schools to government are losing information.

I've been thinking (again alongside Luhmann) about art and music. A string quartet or an orchestra is an institution, and through their operations, there is little doubt that meaning is preserved. But what is interesting me is that this preservation process is not simply in the current operations of the group - the practice schedule, or performance for example. It is also something to do with history.

Playing Beethoven is to preserve the meaning in Beethoven. And we have a good idea that Beethoven meant his meaning to be preserved: "alle menschen werden brüder" and all that. What is the mechanism for preserving this meaning? A big part of it is notation: a codification of bodily skilled performances to reproduce a historical consciousness.

The art system preserves meaning over a large-scale diachronic period. It seems commonsense to suppose that if the skills to perform were lost, then the process of preserving the meaning would be damaged. Would we lose this stuff? But is this right? What if the skills to perform are lost, but recordings survive? Some information is lost - but is it the technology of recording which loses the information about performance skill, or does the loss of performance skill necessitate recording as a replacement?

In a age of rich media, "performance" takes new forms. There is performance in front of the camera which might end up on social media. There is a kind of performance in the reactions of the audience on Twitter. But is the nuance of "playing Beethoven" (or anything else) lost?

We need a way of accounting for why this "loss" (if it is a loss) is significant for an inability to preserve meaning. Of course, we also need a way of accounting for meaning itself.

So I will have an attempt: meaning is coherence. It is the form something takes which articulates its wholeness. More abstractly, I suspect coherence is an anticipatory system (borrowing this from the biological mathematics of Robert Rosen and Daniel Dubois). It is a kind of hologram which expresses the totality of the form from its beginning to its end in terms of self-similar (fractal) structures.

The act of performing is a process of contributing to the articulation of an anticipatory system. If information is lost in an institution, or an art system, then the articulation of coherence becomes more difficult. This may be partly because what is lost in not-performing is not information, but redundancy and pattern. Coherence is borne through redundancy and pattern. How much redundancy has been lost in the rituals of convivial meetings within our institutions, where now email of "Teams" takes over?

If our lives and our institutions have become less coherent it is because technology has turned everything into information in which contingency and ambiguity is lost. As Simon Critchley argued in his recent "Tragedy, the Greeks and Us", this loss of ambiguity is a serious problem in the modern world, and it can only be resolved, in his view, through the diachronic structures of ritual and drama. We have to re-enchant our institutions.

I think he's right, but I think we can move towards a richer description of this process. Technology is amazing. It is not technology per se which has done this. It is the way we think.

Maximum Entropy

On discussing Rossini's music with Loet Leydesdorff a couple of weeks ago (after we had been to a great performance of the Barber of Seville), I mentioned the amount of redundancy in the music - the amount of repetition. "That increases the maximum entropy," he said. This has set me thinking, because there is a lot of confusion about entropy, variety, uncertainty and maximum entropy.

First of all, the relationship between redundancy and entropy is one of figure and ground. Entropy, in Shannon's sense, is a measure of the average surprisingness in a message. That surprisingness is partly produced because all messages are created within constraints - whether it is the constraints of grammar on words in a sentence, or the constraints of syntax and spelling in the words themselves. And there are multiple constraints - letters, words, grammar, structure, meaning, etc.

Entropy is easy to calculate. There is a famous formula without which much on the internet wouldn't work.

Of course, there are lots of questions to ask about this formula. Why is the log there, for example? Just to make the numbers smaller? Or to give weight to something (Robert Ulanowicz takes this route when arguing that the log was there in Boltzmann in order to weight the stuff that wasn't there)

Redundancy can be calculated from entropy.. at least theoretically.

Shannon's formula suggests that for any "alphabet", there is a maximum value of entropy. It is called Maximum entropy. If the measured entropy is seen as a number between 0 and the maximum amount of entropy possible, then to calculate the "ground", or the redundancy, we simply calculate the proportion of the measured entropy to the maximum entropy and subtract it from 1.

Now mathematically, if the redundancy increases, then either the amount of information decreases (H) or the maximum entropy (Hmax) increases. If we simply repeat things, then you could argue that the entropy (H) goes down because it becomes less surprising, and therefore R goes up. If by repeating things we generate new possibilities (which is also true in music), then we could say that Hmax goes up.

No composer, and no artist, ever literally repeats something. Everything is varied (the variation form in music being the classic example). Each new variation is an alternative description. Each new variation introduces a new possibilities. So I think it is legitimate to say the maximum entropy increases. This is particularly true of "variation form" in music.

Now, away from music, what do new technologies do? Each of them introduces a new way of doing something. That too must be an increase in the maximum entropy. It's not an increase in entropy itself. So new technologies introduce redundant options which increase maximum entropy.

If maximum entropy is increased, then the complexity of messages also increases - or rather the potential for disorder and surprise. The important point is that in communicating and organising, one has to make a selection. Selection, in this sense, means to reduce the amount of entropy so that against however many options we have, we insist on saying "it's option x". Against the background of increasing maximum entropy, this selection gets harder. This is where "uncertainty" lies: it is the index of the selection problem within an environment of increasing maximum entropy.

However, there is another problem which is more difficult. Shannon's formula for entropy counts an "alphabet" of signals or events like a, b, c, etc. Each has a probability and each is added to the eventual number. Is an increase in the maximum entropy an increase in the alphabet of countable events? Intuitively it feels like it must be. But at what point can a calculation be made when at any point the full alphabet is incomplete?

This is the problem of the non-ergodic nature of life processes. I've attempted a solution to this which examines the relative entropies over time, considering new events as unfolding patterns in these relations. It's a bit simplisitic, but it's a start. The mechanism that seems to drive coherence is able, through the production of redundancies which increase maximum entropy, to construct over time a pattern which serves to make the selection and reduce the entropy to zero. This is wave-like in nature. So the process of increasing maximum entropy which leads to the selection of entropy to zero is followed by another wave, building on the first, but basically doing the same thing.

In the end, everything is zero.

Design for an Institution: the role of Machine Learning #TheoryEDTechChat

There's an interesting reading group in Cambridge on the theory of educational technology at the moment. Naturally enough, the discussion focuses on the technology, and then it focuses on the agency of those operating the technology. Since the ontology of technology and the ontology of agency are mired in metaphysics, I'm not confident that the effort is going to go anywhere practical - although it is good to see focus on Simondon, and the particularly brilliant Yuk Hui.

But that raises the question: What is the thing to focus on if we want to get practical (i.e. make education better!)? I don't think it's technology or agency. I think it's institutions - we never really talk about institutions! And yet all our talk is framed by institutions, institutions pay us (most of us), and institutions determine that it is (notionally) part of our job to think about a theory of educational technology. But what's an institution? And what has technology done to them?

It is at this point that my theoretical focus shifts from the likes of Simondon, Heidegger, and co (great though I think this work is), to Luhmann, Stafford Beer, Leydesdorff, von Foerster, Ashby and Pask.

Luhmann is a good place to start. What's an institution? It is a autopoietic system which maintains codes of communication. "Autopoietic" in this sense means that codes of communication are reproduced by people ("psychic systems"), but that the "agency" of people in communicating is driven by the autopoietic mechanism (in Luhmann's jargon, it is "structurally coupled"). "Agency" is the story we tell ourselves about this, but it is really an illusion (as Erich Hörl has powerfully discussed in his recent "The archaic illusion of communication")

By this mechanism, institutions conserve meaning. I wonder if they also conserve information, and Leydesdorff has done some very important work in applying Shannon's information theory to the academic discourse.

Ashby's insight into information systems becomes important: "Any system that categorises effectively throws-away information" he wrote in his diary. That seems perverse, because it means that our so-called information systems actually discard information! But they do.

For Luhmann, discarding information means that the probability that communications will be successful (i.e. serve the mechanism of autopoiesis in the institution) will be reduced. As he pithily put it in his (best) book "Love as Passion": "All marriages are made in heaven, and fall apart in the motorcar". What he means is that when one person in a couple is driving, their lifeworld is completely different to their partner's. The context for meaningful communication is impaired by the mismatch in communicative activity which each is engaged in.

In our social media dominated world, where alternative lifeworlds metastasise at an alarming rate, the effect of technology in damaging the context for the effective conservation of meaning is quite obvious.

In the technocractic world of the modern university, where computer systems categorise students with so-called learning analytics, it is important to remember Ashby: with each categorisation, information is thrown away. With each categorisation, the probability that communications will be successful is diminished as the sphere of permissible speech acts becomes narrower. Instead of talking about the important things that matter most deeply, conversations become strategic, seeking to push the right buttons which are reinforced by the institutional systems: not only the bureaucratic systems of the university, but the discourse system of the publishers, and the self-promotion system of social media. This is the real problem with data.

The problem seems quite clear: Our institutions are haemorrhaging information. It is as if the introduction of information systems was like putting a hole in the hull of the institutional "ship".

Stafford Beer knew this problem. It is basically what happens when the coordination and control part of his "viable system model" (what he called "System 3") takes over, at the expense of the more reflective and exploratory curious function that probes the environment and examines potential threats and opportunities (what he called "System 4"). In companies, this is the R&D department. It is notable that universities don't have R&D departments! Increasingly, R&D is replaced by "analytics" - the system 4 function is absorbed into system 3 - where it doesn't belong.

But let's think more about the technology. System 3 tools categorise stuff - they have to - it's part of what system 3 has to do. This involves selecting the "right" information and discarding the rest. It is an information-oriented activity. However, the opposite of information is "redundancy" - pattern, repetition, saying the same thing in many ways... in education, this is teaching!

Machine learning is also predominantly a redundancy-based operation. Machine learning depends on multiple descriptions of the same thing from which it learns to predict data that it hasn't seen before. I'm asking myself whether this redundancy-oriented operation is actually a technological corrective. After all, one of the things that the curious and exploratory function of system 4 has to do is to explore patterns in the environment, and invent new interventions based on what it "knows". Machine learning can help with this, I think.

But only "help". Higher level coordination functions such as system 4 require human intelligence. But human intelligence needs support in being stimulated to have new kinds of conversations within increasingly complex environments. Machine learning can be incredibly and surprisingly creative and stimulating. It can create new contexts for conversations between human beings, and find new ways of coordinating activities which our bureaucratic systems cannot.

My hunch is that the artists need to get on to this. The new institutional system 4, enhanced by machine learning, is the artist's workshop, engaging managers and workers of an organisation into ongoing creative conversation about what matters. When I think about this more deeply, I find that the future is not at all as bleak as some make out.

Non-Linear Dynamics, Machine Learning and Physics meets education

In my recent talk about machine learning (in which I've been particularly focussing on convolutional neural networks because they present such a compelling case for how the technology has improved), I explored the recursive functions which can be used to classify data such as k-means. The similarity between non-linear dynamics of agent-based modelling and the recursive loss functions of convolutional neural network training are striking. It is hard for people new to machine learning to understand that we know very little of what is going on inside. The best demonstration of why we know so little comes from demonstrating the non-linear dynamic emergent behaviour in an agent-based model. Are they actually the same thing in different guises? If so, then we have a way of thinking about their differences.

The obvious difference is time. A non-linear agent-based model's behaviour emerges over time. Some algorithms will settle on fixed points (if k-means didn't do this it would be useless), while other models will continue to feed their outputs into their inputs endlessly producing streams of emergent behaviour. The convolutional process appears to settle on fixed points, but in fact it rarely fully "settles" - one can run the python "model.fit()" function for ever, and no completely stable version emerges, although stability is established within a small fluctuating range.

I discussed this fluctuation with Belgian mathematician Daniel Dubois yesterday. Daniel's work is on anticipatory systems, and he built a mathematical representation of the dynamics that were originally introduced by biologist Robert Rosen. Anticipation, in the work of Dubois, results from fractal structures. In a sense, this is obvious: to see the future, the world needs to be structured in a way in which patterns established in the past can be seen to relate to the future. If machine learning systems are anticipatory (and they appear to be able to predict categories of data they haven't seen before), then they too will contain a fractal structure.

Now a fractal is produced through a recursive non-linear process which results in fixed points. This all seems to be about the same thing. So the next question (one which I was asking both Daniel Dubois, and Loet Leydesdorff who I saw at the weekend) is how deep does this go? For Loet, the fractal structures are in communication systems (Luhmann's social systems), and (importantly) they can be analysed using Shannon's information theory. Daniel (on whose work Loet has constructed his system), agrees. But when we met, he was more interested to talk about his work in physics on the Dirac equation and what he believes to be a deeper significance of Shannon. I don't fully understand this yet, but we both agreed that if there is a deeper significance to Shannon, then it was a complete accident because Shannon only half-understood what he was doing... Half-understanding things can be way forwards!

Daniel's work on Dirac mirrors that of both Peter Rowlands in Liverpool and Lou Kauffman in Chicago (and now Novosibirsk). They all know each other very well. They all think that the physical world is basically "nothing". They all agree on the language of "nilpotents" (things multiplying to zero) and quaternions (complex numbers which produce a rotational geometry) as the fundamental building blocks of nature. There is an extraordinary intellectual confluence emerging here which unites fundamental physics with technology and consciousness. Who could not find that exciting?? It must have significance for education!

What's it all about? The clue is probably in Shannon: information. And I think it is not so much the information that is involved in learning processes (which has always been the focus of cognitivism). It is the way information is preserved in institutions - from the very small institutions of friendship and family, to larger ones like universities and countries.

Our technologies are technologies of categorisation and they throw away information. Since the computer revolution, holes have appeared in our social institutions which have destabilised them. The anticipatory function, which is essential to all living things, was replaced with a categorising function. The way we use machine learning also tends to categorise: this would make things worse.  But if it is an anticipatory system, it can do other things - it can provide a stimulus for thought and conversation, and in the process put information back into the system.

That is the hope. That is why we need to understand what this stuff does. And that is why, through understanding what our technology does, we might understand not only what we do, but what our institutions need to do to maintain their viability.

Education is not really about schools and universities. Those are examples of institutions which are now becoming unviable. Neither, I think, is it really about "learning" as such (as a psychological process - which ultimately is uninspectable). Education is about "institutions" in the broadest sense: families, friendships, coffee bars, businesses, hospitals... in fact anywhere which maintains information. To understand education is to understand how the processes which maintain information really work, how they can be broken with technologies, and how they can be improved with a different approach to technology.