Sunday, 6 October 2019

Creatively defacing my copy of Simon Critchley's "Tragedy, the Greeks and Us"

I've been defacing my copy of Simon Critchley's "Tragedy, the Greeks and Us". For me, this vandalism is a sign that something has got me thinking. It's not just Critchley. I went back to Jane Harrisson's "Ancient Art and Ritual" the other day, partly in response to my recent experiences in Vladivostok and a central question concerning the structure of drama and the structure of education. Basically: is education drama? Should it be? and, Is our experience online drama? Critchley's not dismissive of Harrison and the Cambridge ritualists - which I find encouraging - and I like his suggestion that art may not be so much "ritual" as "meta-ritual". 

It's funny how things revolve. I was introduced to Harrison by Ian Kemp at Manchester university as a student, who was also a passionate expert on Berlioz. Yesterday evening I took my daughter to hear a performance of Berlioz's Romeo et Juliette, which is Berlioz's brilliant and beautiful refashioning of Shakespeare into the form of a symphony via Greek drama: it has explicit sections of chorus, prologue, sacrifice, feast, etc. Beethoven meets the Greeks!

I'm very impressed with Critchley - and I very much get his vibe at the moment - that tragedy and the ambiguity of dramatic structure was overlooked in favour of philosophy (Plato particularly), and that we are now in a mess because of it. I agree. If we replace "tradegy" with "the drama of learning" or "the dialectic of self-discovery" then I think there are some important lessons for education. Critchley makes the point that our modern lives are determined by endless categorisation, and the resulting incoherence of this drives us back to Facebook and social media:
"We look, but we see nothing. Someone speaks to us, but we hear nothing. And we carry on in our endlessly narcissistic self-justification, adding Facebook updates and posting on Instagram. Tragedy is about many things, but it is centrally concerned with the conditions for actually seeing and actually hearing"
That's what I was missing in "The Twittering Machine". 

But he has an axe to grind about philosophy and Plato - and particularly with his contemporary philosophers, most notably Alain Badiou. Since Badiou also has a deep interest in the arts (and opera particularly) this is interesting, and I think Critchley is seeing a dichotomy where there isn't one. And that is where my doodling starts...

The essence of this goes back to the relationship between the synchronic, categorical frame of rationality and experience which demarcates times, and the diachronic, ambiguous frame which sees time as a continuous process. Critchley doesn't seem to see that the two are compatible. But I think they are in a fundamental way. 

The issue concerns what a distinction is, and the relationship of a distinction to time. We imaging that distinctions are made in time, and that time pre-exists any distinction. But it is possible that a distinction - the drawing of a boundary - entails the creation of time. So this was my first doodle:


The distinction on the right is simply a self-referential process. It embraces something within it, but it occurs within a context which cannot be known (in this case, a "universe" and an "earth" and an "asteroid"...) All of those things are distinctions too, and they are all subject to the same process as I will describe now. The essential point is that all distinctions are unstable.

When we think of distinctions as unstable, think of a distinction about education. There are in fact no stable distinctions about education. Everything throws us into conversation. In fact the conversation started long before anyone thought of education. Indeed, it may have started with the most basic distinctions of matter.

If I was to draw this instability, it is a dark shadow emerging within the distinction. These are the unstable forces which will break apart the distinction unless they are absorbed somehow. So we need something to absorb the uncertainty. It cannot be inside the distinction - it must be outside. So we are immediately faced with a duality - two sides of the Mobius strip.

But more than that, absorbing uncertainty (or ambiguity if you want) is a battle on two fronts. The internal uncertainty within the distinction is one thing, but it must be balanced with what might be known about the environment within which the distinction emerges and maintains itself.

Let's call this "uncertainty mop" a "metasystem".  And here it is. Note the shadow in the distinction and the shadow in the environment. Part of me wants to draw this like a Francis Bacon "screaming pope".


A war on two fronts is hard - the metasystem better get its act together! Part of it must deal with the outside and part of it must deal with the inside - and they must talk to each other.

The interesting and critical thing here is that in order to make sense of this balancing act, the metasystem has to create new distinctions: Past, Present and Future. We might call these "imaginary" but they are entirely necessary, because without them, there is no hope of any kind of coherent stability in our distinction. But what have we done? Our distinction has made time!


By inventing time, we have invented the realm of the "diachronic". This is the realm of drama and music. Whatever time is - and how can we know? - it expands our domain of distinction-making, and helps us to see the connection between past, present and future. In the language of "anticipatory systems" of Daniel Dubois and Robert Rosen, this is the difference between recursion (the future modelled on the past), incursion (the future modelled on the future), and hyperincursion (selection among many possible models of the future).




I ought to say that all of this happens all at once. A distinction is like a bomb going off - or even a "big bang". But these bombs are going off all the time - or rather, they are going off all the time that they themselves make. A distinction entails time, which entails dialectic (and conversation). But it also entails hope for a coherence and stability of a distinction within what it now sees as a changing world.

I think the best picture of coherence is the relationship between a fractal as a kind of map of the environment and the unfolding patterns of action within that environment. It's a fractal because only a fractal can contain seed of the future based on its past. Life goes on in the effort to find a coherent structure. When it does, we die.
Mostly, the search for coherence leads to new distinctions, and so the process goes on in a circle. This, I think, is what education is.

The structure of tragedy unfolds this circle in front of us for us to see. It is a circle of nature - of logic. It is the logic of every atom, cell, fermion, quark, whatever... in the universe. It is the logical structure of a distinction. 

When Critchley says of tragedy that it is about "actually seeing and actually hearing" he is spot-on. But I think his anti-Platonist stance is a reaction to where we are now. "Actually seeing and actually hearing" has been replaced with the processing of data. The part of the metasystem which does that is the lower-part, mopping up the internal uncertainty, but not really thinking about the environment. The diachronic bit - the time-making bit - has been crowded-out by our computer powered categorisation functions. If we continue like this, hope will be extinguished, because hope also sits in the upper bit.

Biological systems do not suffer this imbalance. We have had it forced on us by our rationality. That is our tragedy. Yet we are not as rational as we think - and our rationality is a biological epiphenomenon. It feels as if our technology is out of balance: a dialectical imbalance that presents us with a challenge to be overcome for the future.  But this may be as much of a question as to how we organise our institutions as it is about what kind of technology we produce in the future. 

We have the option to organise our education differently, and learn something from the past. We also have the option to use our technologies differently and use them to reinforce the ambiguities of distinctions, rather than the information-discarding processes of categorisation. 

Wednesday, 2 October 2019

Design for an Institution

To paraphrase Ashby's "Design for a brain", it might be asked "How does an institution produce adaptive behaviour?" But of course, institutions often don't produce adaptive behaviour, or their adaptations lead them to adopt patterns of behavior which are more rigid - which is the harbinger of death for an organism, but institutions seem to be large enough to withstand environmental challenges that they continue to survive even without apparently adapting.

One way of answering this problem is to argue that institutions, in whatever form they come, and in whatever state of adaptability, conserve information. An institution might lose a quantity of information in response to an environmental threat, but maintain some stable behaviour despite this. It's internal processes maintain that new set of information. This is, I think, like what happens when institutions are challenged by a complex environment and become more conservative. They discard a lot of information and replace it with rigid categories, often upheld by computer technology and metrics. But the computer-coordinated information-preservation function with a limited information set is surprisingly resilient. However, for human beings existing within this kind of institution, life can be miserable. This is because human beings are capable of far richer information processing than the institution allows - effectively they are suppressed. There may be distinct phases of information loss and preservation.

But if institutions are information-preserving entities, then rigid low-information preserving entities will not be able to compete with richer information-preserving entities. If information preservation is the criteria for "institution-ness", then new ways of preserving information with technology may well be possible which might challenge traditional institutional models. So what is a basic "design for an institution"?

An institution, like a brain, must be a collection of components which communicate - or converse - with one another. In the process of conversation, essential distinctions about the institution are made: what is it, what is it for, what functions must it perform, and so on. Each of these distinctions is essentially uncertain: conversation is necessary to uphold each distinction. Within the conversations there are details about different interpretations of these distinctions. Not all of these differences can be maintained: some must be attenuated-out. So information is lost in the goal of seeking generalisable patterns of practice and understanding to coordinate the whole.

Having said this, the generalisations produced may well be an inadequate representation. So what must be done is that whatever generalisations are produced are used to generate multiple versions of the world as it is understood by these generalisations. The multiplicity of this generated reality and the multiplicity of "actual" reality mus be compared continuously, and the question asked "in what ways are we wrong?" This generation of multiple descriptions is a niche-making function that can generate new information about the world. It is like a spider spinning a web to create a home, but also to detect what is in the environment.

An institution will connect the generalisations it makes with the new information produced through its multiple expressions of its understanding. In traditional institutions, both functions were performed by humans: an "operations" team that identified what needed to be done and did it; and a "research and development" team which looked at the future and speculated on new developments. Technology has shifted the balance between operations and strategy, where research and development is now seen as "operational" in the sense that it has become "data driven". This collapse of distinction-making is dangerous.

But lets say, in a technology-enhanced institution (such as all of ours are now), a clear division could be established between the operational, synergistic parts of the organisation, and the strategic, future-looking parts. We need two kinds of machines. One, the purpose-driven analytical engine that the modern computer is, in order to maintain operations and synergy. The other, a "maverick machine" as Gordon Pask put it, which uses the information it has at its disposal to produce a rich variety of artefacts from paintings and music to usual correlations of data. The maverick machine's purpose (if it can be said to have one) is to stimulate thought and shake it out of the rigid confines of the analytical engine. It presents people with orders of things which are unfamiliar to them, and challenges them to correct it, or embrace a new perspective. By stimulating thought, the maverick machine generates new information to counteract the information that is lost through the analytical engine. The maverick machine is an information-preserving machine  because it maintains a living record of human order and distinction-making within the institution.

It is through the stimulation of thought that changes might be made to the analytical engine and new strategic priorities defined, or new orders identified. In this way, an institution must be a collaboration between humans and machines. To think of institutions as essentially human, with technological "support" is a mistake and will not work.

What is particularly interesting about the maverick machine is that it is a creating entity. Not only does that mean that the viable institution maintains its information. It also means that the viable institution is putting stuff out into the environment in the form of new things. This "generous" behaviour will result in patterns of engagement with the environment which will help it to survive. People will support the institution because not only does its information-preserving processes help itself, but helps other things and people in the environment too.

What does this actually look like? Well, imagine two friends who have similar intellectual interests. They meet every now and then and discuss what they are reading and are interested in. But they don't just discuss it, they video their discussions. They process the video to extract text and images. They use machine learning to mine the text and explore new resources, which software is then able to produce new representations of (a maverick machine). A weekly meeting is generative of a rich range of different kinds of things. Others see these things and think "that's cool - how do I get involved?" Using the same techniques, others are able to do similar things, where the software is able to create synergies between them. Slowly an information-preserving "institution" of two friends becomes something bigger.

This is not Facebook: that is an institution which loses vast amounts of information. It is more akin to a university - an institution for preserving information and creating the conditions for conversation.  

Monday, 30 September 2019

Technology and the Institution of Education

There's a lot of stuff about technology in education on the internet at the moment. A lot of it is increasingly paranoid: worries about the "platform" university, surveillance, brain scanning, boredom scanning, omniscient AIs, idiot AIs, and big corporations making huge sums of money out of the hopes and dreams of our kids.  Whitehead noted once that if one wants to see where new ideas are going to arise, one has to see what people are not talking about. So, most likely the future is going to be "none of the above". But what are we not talking about?

The Golem-like AI is, and always has been, a chimera. What is real in all this stuff? Well, follow the money. Educational institutions are enormous financial concerns, boosted by outrageous student fees, burgeoning student numbers, increasingly ruthless managerial policies of employment, and an increasing disregard for the pursuit of truth in favour of the pursuit of marketing-oriented "advances" and "high ranking" publication - bring on the Graphene! (condoms, lightbulbs, and water purification here we come. perhaps.) Of course, tech companies are massive financial concerns too, but while we are all on Facebook and Twitter, we are not taking out thousands of pound loans to feed our Facebook habit. Naturally, Facebook would like to change that. But it seems a long-shot.

So we come back to the institution of education. Why has it become such a dreadful thing? When I think about my own time in the music department at Manchester University in the late 80s, I think of how my best professors would probably not be able to get a job in the metrics-obsessed University now. This is a disaster.

Ross Ashby (another genius who would have struggled) noted that any system that distinguished categories effectively "throws away information". The profundity of that observation is worth reflecting on. All our educational IT systems, all our bibliometric systems, NSS, REF, TEF, etc are category-making systems. They all throw away information. The result of the impact of technology - Information technology no less - in education has been the loss of information by its institutions.

What happens to institutions when they lose information? They lose the capacity to adapt their operations in an increasingly complex environment. As a result, they become more rigid and conservative. Information systems create problems that information systems can solve, each new wave of problems loses more information than the previous wave. We are left with a  potentially irrelevant (although still popular) operation which has little bearing on the real world. This is where we are, I think.

Let's be a bit clearer about institutions: institutions which lose information become unviable. So, a viable institution is an entity which conserves information. Traditionally - before technology - this was done by balancing the institution's operational (information losing) function with a reflexive (information gaining) function that would probe the environment, where academics had space for thinking about how the world was changing and making informed interventions both in the world and in the institution. When technology entered the institution, the operational function - which was always a "categorising" function - was amplified, and to many excited by the apparent possibilities of new techno-coordinating powers, the loss of information was welcomed, while at the same time, the reflexive function was dismissed as a waste or irrelevant. Basically, everything became "operations", and thought went out of the window.

Many AI enthusiasts see AI as a further step towards the information-losing side of things, and welcome it. AI can lose information better than anything - basically, a technology for soaking up a large amount of information redundancy for the sake of producing a single "answer" which saves human beings the labour of having to talk to each other and work out the nuances of stuff.

But in the end, this will not work.

But AI, or rather deep learning, is a different kind of technology. In working with redundancy rather than information, it is something of a counterbalance to information systems. Redundancy is the opposite of information. Where information systems amplified the "categorising" operational processes, might deep learning technology amplify the reflexive processes? I am particularly interested in this question, because it presents what might be a "feasible utopia" for technologically-enhanced institutions of education in the future. Or rather, it presents the possibility of using technology to conserve, not destroy, information.

The key to being able to do this is to understand how deep learning might work alongside human judgement, and particularly the ordering of human judgement. If deep learning can operate to preserve human order, coordinate effective human-based correction of machine error, whilst supporting human judgement-making, then a virtuous circle might be possible. This, it seems to me, is something worth aiming for in technologically embedded education.

Conserving information is the heart of any viable institution. States and churches have survived precisely because their operations serve to preserve their information content, although like everything, they have been challenged by technology in recent years.

In Stafford Beer's archive, there is a diagram he drew of the health system in Toronto. At the centre of the diagram is a circle representing a "population of healthy people". This is a system for preserving health, not treating illness. And more importantly it is a system for preserving information about health.


We need the same for education. In the centre of a similar diagram for education, perhaps there should be a box representing a population of wise people: everyone from children to spiritual leaders. What is the system to preserve this wisdom in society? It is not our current "education" system that seeks to identify the deficit of ignorance and fill it with lectures and certificates. Those things make wise people go mad! It is instead a set of social coordination functions which together preserve information about wisdom, and create the conditions for its propagation from one generation to the next. We don't have this because of the information loss in education. Can we use technology to correct the loss of information and turn it into conservation? I think we might be able to do this. We should try.

My concern with the paranoia about technology in education at the moment is that it is entirely framed around the perspective of the traditional institution in its current information-losing form. It is effectively realising that the path of information-loss leads to the abyss. It does indeed. But it is not technology that takes us there. It is a particular approach to technology which turns its hand to categorisation and information loss which takes us there. Other technologies are possible. Another technological world is possible. Better education, which preserves information and maintains a context for the preservation of wisdom between the generations is possible. But only (now) with technology. 

Tuesday, 24 September 2019

Time, Ritual and Education

The Global Scientific Dialogue course at the Far Eastern Federal University, which I co-designed with Russian colleagues last year, is running for a second year. I was happy with how it went last year, but this year it seems better. I must admit that on returning to this transdisciplinary mix of stuff (art, science, technology, intersubjectivity, machine learning, etc), I had a few worries about whether I was deceiving myself that any of this was any good – particularly as this year, we are running it with nearly 500 students. But after getting back into it, and particularly after talking to the teachers, I was reassured that there was something really good in it, which was of special benefit to teachers and students across the management and economics school in the university.

Although the course is about trying to provide a broader perspective on the rapid changes in the world of work for our students (particularly, this year, with a focus on machine learning), I think this is really as much a course for teachers: it demands and gets great team teaching. Last year we recruited and trained 30 teachers from the school, and 20 helped us run the course. This year we recruited and trained another 17. But it was so much easier because last years’ teachers have become experts: from being a very small team trying to encourage innovative teaching practice (basically me and a couple of Russian colleagues), it has been transformed into a movement of more than 20 teachers all pulling in the same direction. Their internal communication has been conducted through WhatsApp, and this year, the level of cooperation and coordination has been superb. It’s really wonderful. Eventually, they may not need me any more – but that’s as it should be!

Technologically, it’s very simple. There is video to keep things coordinated so many teachers can conduct similar activities in small groups together, there is comparative judgement to keep the students thinking and submitting their thoughts, and patchwork text to provide flexibility in assessment. We tried as hard as we could to get away from rigid learning outcomes. We ended up with a compromise.

Ok. So it really works (although much could be refined). Why?

I gave a presentation to the senior academic board of the faculty last week. I explained that I see the course as a cybernetic intervention, inspired by Beer’s work on syntegration. But only inspired. Really, I think the interventions of the course all contribute to an uncertain environment for teachers and students (syntegration does this too). The uncertainty means that they cannot rely on their pre-existing categories for dealing with the world (existing within what Beer calls the “meta-system”), and must find ways of reconfiguring their meta-system, expressing their uncertainty, which they do through dialogue. Importantly, this helps to level the positioning between teachers and students.

I’m fairly happy with that as an explanation: the evidence fits the model. But I think there’s something more. I’m wondering if the course’s structure over its two weeks is also important.

The structure is highly varied. It begins with a “big lecture” from me. I was never that comfortable with this but its an administrative requirement, and the room we do it in is enormous and echoey. So I start by getting them all to sing, and I introduce the idea of multiple description through examining the sound frequencies in a single sound (“A single sound is made of many frequencies. A single concept is produced by many strands of dialogue, etc…”). It’s great having a spectrum analyser providing real-time feedback and intellectual challenge!

Thinking about it, the singing is a “chorus” - does all this have the structure of ancient Greek drama?

I try not to talk for too long before getting them to turn their chairs round and play a game. We play Mary Flannagan’s "Grow-a-game", asking the students to invent a new game that addresses a global challenge (inequality, homelessness, global warming, etc) by changing the rules of a game they already know.

There is much argument and debate in the groups. A kind of “Agon” (bear with me….)
We get the students to make a short video of their game, which we play to everyone. This is a presentation of ideas and themes, maybe a “Parados?

More talk follows (chorus), followed by games (agon 2).

Then students go to separate groups and talk about different topics. These topics too have a similar structure, except the “chorus” is usually a video that sets the scene  (could be a “prologue”). Then there is more activity (agon 3), and a presentation of their ideas ("stasimon"?)

In the middle of the course, we have a “feast”: a gathering of experts where all 500 students are free to wander around and talk to interesting people. I told the students to think of it as a “party”. It wasn’t quite a party, but had a great atmosphere for everyone.





At the end of the course, the students parade their work. We end with a final procession ("exodus"?).

Maybe I’m being overly grand, but the thing has a structure, and I can’t help feeling that the structure has a deeper significance which may relate to ancient drama and ritual - although things may be in an unconventional order in comparison to Euripides.

What was the point of ancient drama and ritual? It must have had a function in producing coherence of experience among the group of spectators. That is exactly what we are trying to do with Global Scientific Dialogue. Why is so much formal learning incoherent? Because it doesn’t have any clear structure: it’s just one thing after another. There’s no dramatic thrust. This may explain why some talk about the "accelerated academy" (like this: http://accelerated.academy/). I don't buy it - "acceleration" is the feeling one gets when things start to run out of control. The real problem is that things just don't make sense. Our traditional ways of operating in education are out of control.

The connection must be made between the message that is given, how it is given, the structure of proceedings, and the role of time. So what is the connection?

We can think of a message as a distinction. When we draw a distinction we immediately create uncertainty: what is inside and what is outside the distinction? The internal uncertainty must be managed and balanced with external uncertainty. In order to manage the external uncertainty, the invention of time is necessary. It's only by creating past, present and future that the essential contingency of a distinction can be maintained.

With the creation of past, present and future, the relationship between diachronic structure and synchronic structure becomes an important element in the coherence of the whole: exactly in the way that music operates. Could it be shown that the moments of the dramatic ritual are necessary to maintain coherence? Well, these elements like “agon”, “parados”, “feast”, “chorus” rotate around in different configurations. They are discrete moments produced by dialogue. Each is characterised by a different form of pattern or "redundancy". I suspect these are different ways of saying the same thing - or maybe different ways of saying "nothing". What is the switch from one “nothing” to the next “nothing”?

I'm thinking that this brings Peter Rowlands’s idea from physics of mass, space, time and charge all dancing around each other in a cyclic group is useful. I strongly suspect there is some deep contact between the nature of the universe and the structure of the moments of experience. We could go much deeper.

But whatever theoretical construct we might indulge in, Global Scientific Dialogue has presented a phenomenon which demands a better explanation than “everyone seems to like it!”.

Monday, 16 September 2019

Topology and Technology: A new way of thinking

I'm back in Russia for the Global Scientific Dialogue course. We had the first day today with 250 first year students. Another 250 second year students follow on Wednesday. They seemed to enjoy what we did with them. It began with getting them to sing. I used a sound spectrum analyzer, and discussed the multiplicity of frequencies which are produced with any single note that we might sing. With the spectrum analyzer, it is possible to almost "paint" with sound: which in itself is another instance of multiple description. A very unusual way to begin course on management and economics!

The message is really about conversation, learning and systems thinking. Conversation too is characterised by multiple descriptions - or rather the "counterpoint" between multiple descriptions of things. This year, largely due to my work on diabetic retinopathy, the course is spending a lot of time looking at machine learning. Conversations with machines are going to become an important part of life and thanks to the massive advances in the standardisation of ML tools (particularly the Javascript version of tensorflow meaning you can do anything in the web), you don't have to look far to find really cool examples of conversational machine learning. I showed the Magic Sketchpad from Google fantastic Magenta project (a subset of their Tensorflow developments): https://magic-sketchpad.glitch.me/. This is clearly a conversation.

It feels like everything is converging. Over the summer we had two important conferences in Liverpool. One was on Topology - George Spencer-Brown's Laws of Form. The other was on physics (Alternative Natural Philosophy Association) - which ended up revolving around Peter Rowlands's work. The astonishing thing was that they were fundamentally about the same two key concepts: symmetry and nothing. At these conferences there were some experts on machine learning, and other experts on consciousness. They too were saying the same thing. Symmetry and nothing. And it is important to note that the enormous advances in deep learning are happening as a result of trial and error, and there is no clear theoretical account as to why they work. That they work this well ought to be an indication that there is indeed some fundamental similarity between the functioning of the machine and the functioning consciousness.

My work on diabetic retinopathy has basically been about putting these two together. Potentially, that is powerful for medical diagnostics. But it is much more important for our understanding of ourselves in the light of our understanding of machines. It means that for us to think about "whole systems" means that we must see our consciousness and the mechanical products of our consciousness (e.g. AI) as entwined. But the key is not in the technology. It is in the topology.

Any whole is unstable. The reasons why it is unstable can be thought of in many ways. We might say that a whole is never a whole because something exists outside it. Or we might say that a whole is the result of self-reference, which causes a kind of oscillation. Lou Kauffman, who came to both Liverpool conferences, draws it like this (from a recent paper):


Kauffman's point is that any distinction is self-reference, and any distinction creates time (a point also made by Niklas Luhmann). So you might look at the beginning of time as the interaction of self-referential processes:
But there's more. Because once you create time, you create conversation. Once the instability of a whole distinction is made, so that instability has to be stabilised with interactions with other instabilities. Today I used the idea of Trivial Machine, proposed by Heinz von Foerster. Von Foerster contrasted a trivial machine with a non-trivial machine. Education, he argues, turns non-trivial machines into trivial machines. But really we need to organise non-trivial machines into networks where each of them can coordinate their uncertainty.
I think this is an interesting alternative representation of Lou's swirling self-referential interactions. It is basically a model of conversation.

But this topology opens out further. Stafford Beer's viable system model begins with a distinction about the "system" and the "environment". But it unfolds a necessary topology which also suggests that conversation is fundamental. Every distinction (the "process language" box) has uncertainty. This necessitates something outside the system to deal with the uncertainty. If we assume that this thing outside is dealing with the uncertainty, then we have to assume that it must both address uncertainty within the system, and uncertainty outside it. Since it cannot know the outside world, it must perform a function of probing the outside world as a necessary function of absorbing uncertainty. Quickly we see the part of the system which "mops up" the uncertainty of the system develops its own structure, and must be in conversation with other similar systems...


What does this mean?

Beer's work is about organisation, and organisation is the principle challenge we will face as our technology throws out phenomena which will be completely new to us. It will confuse us. It is likely that the uncertainty it produces will, in the short run, cause our institutions to behave badly - becoming more conservative. We have obvious signs right now that this is the case.

But look at Beer's model. Look at the middle part of the upper box: "Anticipation". Whole distinctions make time, and create past and future. But to remain whole, they must anticipate. No living system cannot anticipate.

With the rapid development of computers over the last 80 years, we have had deterministic systems. They are not very good at anticipation, but they are good at synergy and coordination (the lower part of the upper box). But we've lacked anticipation - having to rely on our human senses which have been diminished by the dominance of deterministic technology.

I could be wrong on this. But our Deep Learning looks like it can anticipate. It's more than just a "new thing". It's a fundamental missing piece of a topological jigsaw puzzle.






Monday, 9 September 2019

Organisation and Play in Education

"Play" in learning has become a dominant theme among pedagogical innovators in recent years. Far from the stuffy lecture halls, the enthusiasts of play will bring out the Lego and Plasticine as a way of motivating engagement from staff and students. Sometimes, the "play" objects are online. Often it is staff who are exhorted to play with their students, and I've done a fair bit of this myself in the past - most recently on the Global Scientific Dialogue (GSD) course at the Far Eastern Federal University in Russia.

I first encountered playful learning approaches in three brilliant conferences of the American Society for Cybernetics which were organised by the late Ranulph Glanville. I was initially skeptical at first, but on reflection I found that these conferences deeply influenced my thinking about what happens in not only in scientific conferences, but also in educational experience. The last of these ASC conferences, which took place in Bolton, UK, was the subject of a film, and the concept of the conference led to a book. There was lots of music (participants had to bring a home-made musical instrument). The previous conference featured the great American composer Pauline Oliveros, who had a bunch of engineers and cyberneticians singing every morning around the swimming pool of the hotel we stayed in the Midwest!


In 2018 I organised the Metaphorum conference in Liverpool, and attempted to bring a more playful approach encouraging delegates not to "talk at" each other when presenting their ideas, but to organise an activity. This conference was attended by two academics from Russia, and the experience of it led directly to the design of the Global Scientific Dialogue module: a course like a conference, a conference with a set of activities and lots of discussion, focused on science and technology.

The important point about this approach to pedagogy (and to conferences) is that "play" is not an end in itself. As an end in itself, play is empty - and there is nothing worse that being forced to play when you either don't want to, or can't see the point. Games only work when people want to play - and overwhelmed academics are sometimes understandably sceptical about the pedagogical exhortation to "get out the Lego".

So what is play about?

Fundamentally, it is about organising conversations. More specifically, it concerns creating the conditions for conversations which would not otherwise occur within the normal contexts of education. This is what matters, because the "normal contexts" create barriers between people and ideas which shouldn't be there, or at least should be challenged. Play does this by introducing uncertainty into the educational process. In an environment where everyone - teachers and learners together - are uncertain, they have to find new ways of organising themselves to express their uncertainty, and coordinate their tenuous understanding with others.

The organisational reasons for introducing play are to break down barriers and to create the conditions for new conversations. On the Global Scientific Dialogue module, this is precisely how it works, and the elements of uncertainty which are amplified are not just contained in the activities, but in the content which draws on current science and technology about which nobody is certain. Inevitably, everyone - learners and teachers - are in the same boat, and what happens is a kind of social reconfiguration.

However, if play is imposed on the unwilling, then it reinforces barriers between the pedagogical idealists and exhausted teachers struggling to manage their workload. This raises the question as to how an organisational intervention might serve the purpose of reorganising relationships between exhausted academics in such a way that the underlying causes of exhaustion might be reconceived and addressed together.

In the final analysis, effective play is the introduction of a particular set of constraints within which the reorganisation that we call "learning" occurs. But every teacher knows they can get their constraints wrong, and it can have an oppressive effect. Play in itself cannot be the thing to aim for. Like all teaching, the effective manipulation of constraints, or the effective organisation of contexts for learning conversations is what matters. The magic of this is that in coordinating this, teachers reveal their understanding of the world, their students and themselves. 

Saturday, 7 September 2019

Information Loss and Conservation

One of the ironies of any "information system" is that they discard information. Quite simply, anything which processes large amounts of data to produce an "answer", which is then acted on by humans, is attenuating those large amounts of data in various ways. Often this is done according to some latent biases within either the humans requesting the information, bias within the datasets that are processed, or bias within the algorithms themselves. Bias is also a form of attenuation, and the biases which have recently been exposed around racial prejudice in machine learning highlight the fundamentally dangerous problem of loss of information in organisations and society.

In his book "The human use of human beings" Norbert Wiener worried that our use of technology sat on a knife-edge between it either being used to destroy us, or to save us from ourselves. I want to be more specific about this "knife edge". It is whether we learn how to conserve information within our society and institutions, and avoid using technology to accelerate the process of information destruction. With the information technologies which we have had for the last 50 years, with their latency (which means all news is old news) and emphasis on databases and information processing, loss of information has appeared inevitable.

This apparent "inevitable" loss of information is tacitly accepted by all institutions from government downwards. Given the hierarchical structures of our institutions, we can only deal with "averages" and "approximations" of what is happening on the ground, and we have little capacity for assessing whether we are attenuating out the right information, or whether our models of the world are right. To think this is not inevitable, is to think that our organisations are badly organised - and that remains an unthinkable thought, even today. Beyond this, few organisations run experiments to see if the world they think they are operating in is the actual world they operate in. Consequently, we see catastrophe involving the destruction of environments, whether it is the corporate environment (banking crisis), social environment (Trump, Brexit), the scientific environment (university marketisation), global warming, or the economic system.

Of course, attenuation is necessary: individuals are less complex than institutions, and institutions are less complex than societies. Somehow, a selection of what is important among the available information must be made. But selection must be made alongside a process of checking that whatever model of the world is created through these selections is correct. So if information is attenuated from environment to individual, the individual must amplify their model of the world and themselves in the environment. This "amplification" can be thought of as a process of generating alternative descriptions of the information they have absorbed. Many descriptions of the same thing are effectively "redundant" - they are not strictly necessary, but at the same time, the capacity to generate multiple descriptions of the world creates options and flexibility to manage the complexity of the environment. Redundancy creates opportunities to make connections with the environment - like creating a niche, or a nest - rather in the same way that a spider spins a web (that is a classic example of amplification).

The problem we have in society (and I believe the root cause of most of our problems) is that the capacity to produce more and more information has exploded. This has produced enormous unmanageable uncertainty, and existing institutions have only been able to mop-up this uncertainty by asserting increasingly rigid categories for dealing with the world. This is why we see "strong men" (usually men) in charge in the world. They are rigid, category-enforcing, uncertainty-mops. Unfortunately (as we see in the UK at the moment) they exacerbate the problem: it is a positive-feedback loop which will collapse.

One of the casualties of this increasing conservatism is the capacity to speculate on whether the model of the world we have is correct or not. Austerity is essentially a redundancy-removal process in the name of "social responsibility". Nothing could be further from the truth. More than ever, we need to generate and inspect multiple descriptions of the world that we think we are living in. It is not happening, and so information is being lost, and as the information is lost, the conditions for extremism are enhanced.

I say all this because I wonder if our machine learning technology might provide a corrective. Machine learning can, of course, be used as an attenuative technology: it simplifies judgement by providing an answer. But if we use it like this, then the worst nightmares of Wiener will be realised.

But machine learning need not be like this. It might actually be used to help generate the redundant descriptions of reality which we have become incapable of doing ourselves. This is because machine learning is a technology which works with redundancy - multiple descriptions of the world - which determine an ordering of judgements about the things it has been trained with. While it can be used to produce an "answer", it can also be used to preserve and refine this ordering - particularly if it is closely coupled with human judgement.

The critical issue here is that the structures within a convolutional neural network are a kind of fractal (produced through recursively seeking fixed points in the convolutional process between different levels of analysis), and these fractals can serve the function of what appears to be an "anticipatory system". Machine learning systems "predict" the likely categories of data they don't know about. The important thing about this is, whatever we think "intelligence" might be, we can be confident that we too have some kind of "anticipatory system" built through redundancy of information. Indeed, as Robert Rosen pointed out, the whole of the natural world appears to operate with "anticipatory systems".

We think we operate in "real time", but in the context of anticipatory systems, "real-time" actually means "ahead of time". An anticipatory system is a necessary correlate of any attenuative process: without it, no natural system would be viable. Without it, information would be lost. With it, information is preserved.

So have we got an artificial anticipatory system? Are we approaching a state where we might preserve information in our society? I'm increasingly convinced the answer is "yes". If it is "yes", then the good news is that Trump, Brexit, the bureaucratic hierarchy of the EU, are all the last stages of a way of life that is about to be supplanted with a very different way of thinking about technology and information. Echoing Wiener, IF we don't destroy ourselves, our technology promises a better and fairer world beyond any expectations that we might allow ourselves to entertain right now.


Thursday, 22 August 2019

Luhmann on Time and the Ethical Reaction to New Technology

In the wake of some remarkable technical developments in predictive and adaptive technologies, there has been a powerful - and sometimes well-funded - ethical reaction. The most prominent developments are Oxford's Digital Ethics Lab (led by Luciano Floridi), and the Schwarzman Institute specifically looking at AI ethics benefiting from "The largest single donation to the university since the Renaissance". I wonder how Oxford's Renaissance academics sold the previous largest donation! And there are a lot of other initiatives which google will list. But there is an obvious question here: what exactly are these people going to do? How many ethicists does it take to figure out AI?

What they will do is write lots of papers in peer-reviewed journals which will be submitted to the REF for approval (a big data analytical exercise!), compete with each other to become the uber-AI-ethicist (judged partly by citation counts, and other metrics), compete for grants (which after this initial funding will probably become scarcer as the focus of investment shifts to making the technology work), and get invited to parliamentary review panels when the next Cambridge Analytica strikes. Great. It's as if society's culture of surveillance and automation can be held at bay safely within a university department focusing on the rights and wrongs of it all. And yet, it is to miss the obvious point that Cambridge Analytica itself had very strong ties to the university! Are these "Lady Macbeth" departments wringing their hands at the thought of complicity? And what is it with ethics anyway?

In a remarkable late paper called "The Control of Intransparency", Niklas Luhmann observed the "ethical reaction" phenomenon in 1997. There are very few papers which really are worth spending a long time with. This is one. Most abstractly, Luhmann shows how time and anticipation lie implicit in the making of a distinction - something which had been prefigured in the work of Heinz von Foerster and something that Louis Kauffman, who elaborated much of the maths with von Foerster (see my previous post), had been saying. I suspect Luhmann got some of this from Kauffman.

It all rests in understanding that social systems are self-referential, and as such produce "unresolvable indeterminacy".  Time is a necessary construct to resolve this indeterminacy, where the system imagines possible futures, distinguishing between past and future, and choosing which possible futures meet the goals of the system and which don't. This raises the question: What are the selection criteria for choosing desired futures and how are they constructed?

"One may guess that at the end of the twentieth century this symphony of intransparency reflects a widespread mood. One may think of the difficulties of a development policy in the direction of modernizing, as it was conceived after the Second World War. [...] One may think of the demotivating experiences with reform politics, e.g. in education.[...] The question is, to what degree may we accommodate our cognitive instruments and especially our epistemologies to this?
As we know, public opinion reacts with ethics and scandals. That certainly is a well-balanced duality, which meets the needs of the mass media, but for the rest promises little help. Religious fundamentalists may make their own distinctions. What was once the venerable, limiting mystery of God is ever more replaced by polemic: one knows what one is opposed to, and that suffices. In comparison, the specifically scientific scheme of idealization and deviation has many advantages. It should, however, be noticed that this is also a distinction, just like that of ethics and scandals or of local and global, or of orthodox and opponents. Further, one may ask: why is one distinction preferred over the other?"
The scope of Luhmann's thinking here demands attention. Our ethical reactions to new technologies are inherent in the distinctions we make about those technologies. The AI ethics institutes are institutions of self-reference attempting to balance out the indeterminacy of the distinctions that society (and the university) is making about technology. Luhmann is trying to get deeper - to a proper understanding of the circular dynamics of self-referential systems and their relation to time. This, I would suggest, is a much more important and productive goal - particularly with regard to AI, which is itself self-referential.

Luhmann considers the distinction between cause and constraint (something which my book on "Uncertain Education" is also about). Technologies constrain practices, but we cannot determine the interference between different constraints among the different technologies operating in the world. Luhmann says:

"The system then disposes  of a latent potentiality which is not always but only incidentally utilized. This already destroys the simple, causal-technical system models with their linear concept and which presuppose the possibility of hierarchical steering. With reflective conditioning the role of time changes. The operations are no longer ordered as successions, but depend on situations in which multiple conditionings come together. Decisions then have to be made according to the actual state of the system and take into account that further decisions will be required which are not foreseeable from the present point in time. Especially noteworthy is that preciseley complex technical systems have a tendency in this direction. Although technology intends a tight coupling of causal factors, the system becomes intransparent to itself, because it cannot foresee at what time which factors will be blocks, respectively released. Unpredictabilities are not prevented but precisely fostered by increased precision in detail."
So technology creates uncertainty. It does it because the simple causal-technical system produces new options (latent potentialities) which exist alongside other existing options which carry their own constraints. All of these constraints interfere with one another. Indeterminacy increases. Something must mop-up the indeterminacy.

But as Luhmann says, the ethical distinction which attempts to address the uncertainty behave in a similar way: uncertainty proliferates despite and because of attempts to manage it. This may keep the AI ethics institutes busy for a long time!

Yet it may not. AI is itself an anticipatory technology. It relies on the same processes of distinction-making and self-reference that Luhmann is talking about. Indeed, the relationship of re-entry between human distinction-making and machine distinction-making may lead to new forms of systemic stability which we cannot yet conceive of. Having said this, such a situation is unlikely to operate within the existing hierarchical structures of our present institutions: it will demand new forms of human organisation.

This is leading me to think that we need to study the ethics institutes as a specific form of late-stage development within our traditional universities. Benign as they might appear, they might have a similar institutional and historical structure to an earlier attempt to maintain traditional orthodoxy in the wake of technological development and radical ideas: the Spanish Inquisition.

Monday, 19 August 2019

Emerging Coherence of a New View of Physics at the Alternative Natural Philosophy Association

The Alternative Natural Philosophy Association met in Liverpool University last week following a highly successful conference on Spencer-Brown's Laws of Form (see http://lof50.com).  There is a profound connection between Spencer-Brown and the physics/natural science community of ANPA, not least in the fact that Louis Kauffman is a major contributor to the development of Spencer-Brown's calculus, and also a major contributor to the application of these ideas in physics.

Of central importance throughout ANPA was the concept of "nothing", which in Spencer-Brown maps on to what he calls the "unmarked state". At ANPA 4 speakers, all of them eminent physicists, gave presentations referencing each other, with each of them saying that the totality of the universe must be zero, and that "we must take nothing seriously". 

The most important figure in this is Peter Rowlands. Rowlands's theory of nature has been in development for 30 years, and over that time he has made predictions about empirical findings which were dismissed when he made them, but subsequently discovered to be true (for example, the acceleration of the universe, and the ongoing failure to discover super-symmetrical particles). If this was just a lucky guess, that would be one thing, but for Rowlands it was the logical consequence of a thoroughgoing theory which took zero as its starting point.

Rowlands articulates a view of nature which unfolds nothing at progressively complex orders. He argues that the dynamic relationship between the most basic elements of the universe (mass, space, time and charge) arrange themselves at each level of complexity in orders which effectively cancel each other out through a mathematical device where things which multiply each other create zero, called a nilpotent.

This brilliant idea cuts through a range of philosophical problems like a knife. It is hardly surprising that, as John Hyatt pointed out in a brilliant presentation, Shakespeare had an intuition that this might be how nature worked:
Our revels now are ended.
These our actors,
As I foretold you,
were all spirits, and
Are melted into air, into thin air:
And like the baseless fabric of this vision,
The cloud-capp'd tow'rs, the gorgeous palaces,
The solemn temples, the great globe itself,
Yea, all which it inherit, shall dissolve,
And, like this insubstantial pageant faded,
Leave not a rack behind.
We are such stuff
As dreams are made on;
and our little life
Is rounded with a sleep.
But Rowlands needs a mechanism, or an "engine" to drive his "nothing-creating" show. He uses group theory in mathematics, and William Rowan Hamilton's concept of Quaternions: a 3-dimensional complex number, notated as i, j, k, where i*i = j*j = k*k = i*j*k = -1. Mapping these quaternions on to the basic components of physical systems (plus a unitary value which makes up the 4), he sees mass, time, charge and space represented in a dynamic numerical system which is continually producing nilpotent expressions. This provides an ingenious way of re-expressing Einstein's equation of mass-energy-momentum, but most importantly it allows for the Einstein equation to be situated as entirely consistent with Dirac's equation of quantum mechanics. Rowlands is able to re-express Dirac's equation in simpler terms using his quaternions as operators in a similar and commensurable way to how he deals with Einstein's equation.

As Mike Houlden argued at the conference, this way of thinking helps to unpick some fundamental assumptions made about the nature of the universe and the beginning of time. For example, the concept held by most physicists that there is a fixed amount of dark matter in the universe which was created instantly at the big bang is challenged by Rowlands's system. It articulates a continual creation process that sees a recursive process of symmetry-breaking throughout nature, from quantum phenomena through to biology, and by extension consciousness.

Rowlands articulates a picture similar to that of Bohm - particularly in upholding the view of nature as a "hologram" - but his thoroughgoing mathematics produces what Bohm was arguing for: an algebra for the universe.

Empirical justification for these ideas may not be far off. As Mike Houlden argued, the discovery of dark energy (presumed to be the driver for the acceleration of the universe) and the assumption that the proportion of dark matter in the universe was fixed at the big bang (whatever that is) are likely to be questioned in the future. Rowlands's theory helps to explain the creation of dark matter and dark energy as balancing processes which are the result of the creation of mass, and which serve to maintain the nilpotency of the universe.

From an educational perspective this is not only extremely exciting, but also relevant. The fundamental coherence of the universe and the fundamental coherence of our understanding of the universe are likely to be connected as different expressions of the same broken symmetry. Learning, like living, as Shakespeare observed, is also much ado about nothing. It's not only the cloud capp'd towers which disappear. 

Sunday, 4 August 2019

China's experiments with AI and education

At the end of Norbert Wiener's "The Human Use of Human Beings", he identified that there was a "new industrial revolution" afoot, which would be dominated by machines replacing, or at least assisting, human judgement (this is 1950).  Wiener, having invented cybernetics, feared for the future of the world: he understood the potential of what he and his colleagues had unleashed, which included computers (John von Neuman), information theory (Claude Shannon) and neural networks (Warren McCulloch). He wrote:
"The new industrial revolution is a two-edged sword. It may be used for the benefit of humanity, but only if humanity survives long enough to enter a period in which such a benefit is possible. It may also be used to destroy humanity, and if it is not used intelligently it can go very far in that direction." (p.162)
The destructive power of technology would result, Wiener argues, from our "burning incense before the technology God". Well, this is what's going on in China in their education system right now (see https://www.technologyreview.com/s/614057/china-squirrel-has-started-a-grand-experiment-in-ai-education-it-could-reshape-how-the/)

There has, unsurprisingly, been much protest by teachers online to this story. However, sight must not be lost of the fact that there are indeed benefits that the technology brings to these students, autonomy being not the least of them. But we are missing a coherent theoretical strand that connects good face-to-face teaching to Horrible Histories, Khan academy and this AI (and many steps in-between). There is most probably a thread that connects them and we should seek to articulate it as precisely as we can, otherwise we will be beholden to the rough instinct of human beings unaware of their own desire to maintain their existence within their current context, in the face of a new technology which will transform that context beyond recognition.

AI gives us a new powerful God in front of which we (and particularly our politicians) will need to resist the temptation to light the incense. But many will burn incense, and this will fundamentally be about using this technology to maintain the status quo in education in an uncertain environment. So this is AI to get the kids through "the test" more quickly. And (worse) the tests they are concerned with are STEM. Where's the AI that teaches poetry, drama or music?

It's the STEM thing which is the real problem here, and ironically, it is the thing which is most challenged by the AI/Machine learning revolution (actually, I think the best way to describe the really transformative technology is to call it an "artificial anticipatory system", but I won't go into that now). This is because in the world that's going to unfold around us - the world that we're meant to be preparing our kids for - machine learning will provide new "filters" through which we can make sense of things. This is a new kind of technology which clearly works - within limits, but well beyond expectations. Most importantly, while the machine learning technology works, nobody knows exactly how these filters work (although there are some interesting theories: https://medium.com/intuitionmachine/the-holographic-principle-and-deep-learning-52c2d6da8d9)

Machine learning is created through a process of "training" - where multiple redundant descriptions of phenomena are fed into a machine for it to understand the underlying patterns behind them. Technical problems in the future will be dealt with through this "training" process, in the way that our current technical problems demand "coding" - the writing of specific algorithms. It is also likely that many professionals in many domains will be involved in training machines. Indeed, training machines will become as important as training humans.

This dominance of machine training and partnership between humans and machines in the workplace means that the future of education is going to have to become more interdisciplinary. It won't be enough for doctors to know about the physiological systems of the body; professionally they will have to be deeply informed about the ways that the AI diagnostic devices are behaving around them, and take an active role in refining and configuring them. Moreover, such training processes will involve not only the functional logic of medical conditions, but the aesthetics of images, the nuances of judgement, and the social dynamics of machines and human/organisational decision-making. So how do we prepare our kids for this world?

The fundamental problems of education have little to do with learning stuff to pass the test: that is a symptom of the problem we have. They have instead to do with organising the contexts for conversations about important things, usually between the generations. So the Chinese initiative basically exacerbates a problem produced by our existing institutional technologies (I think of Wiener's friend Heinz von Foerster: "we must not allow technology to create problems it can solve"). So AI is dragged out of what Cohen and March famously called the "garbage can of institutional decision-making" (see https://en.wikipedia.org/wiki/Garbage_can_model), when the real problem (which is avoided) is, "how do we reorganise education so as to prepare our kids for the interdisciplinary world as it will become?"

This is where we should be putting our efforts. Our new anticipatory technology provides new means for organising people and conversations. It actually may give us a way in which we might organise ourselves such that "many brains can think as one brain", which was Stafford Beer's aim in his "management cybernetics" (Beer was another friend of Wiener). My prediction is that eventually we will see that this is the way to go: it is crucial to local and planetary viability that we do.

Will China and others see that what they are currently doing is not a good idea? I suspect it really depends not on their attitude to technology (which will take them further down the "test" route), but their attitude to freedom and democracy. Amartya Sen may well have been right in "Development as Freedom" in arguing that democracy was the fundamental element for economic and social development. We shall see. But this is an important moment.

Wednesday, 31 July 2019

Fractals of Learning

I've been doing some data analysis on responses of students to a comparative judgement exercise I did with them last year. Basically, they were presented with pairs of documents on various topics in science, technology and society, and asked "Which do you find more interesting and why?"

The responses collected over two weeks from about 150 students were surprisingly rich, and I've become interested in drawing distinctions between them. Some students clearly are transformed by many of the things which they read about (and this was in the context of a face-to-face course which also gravitated around these topics), and their answers reflect an emerging understanding. Other students, while they might also appear to engage with the process, are a bit more shallow in their response. 

To look at this, I've looked at a number of dimensions of their engagement and plotted the shifts in entropy in each dimension. So, we can look at the variety of documents or topics they talk about: some students stick to the same topic (so there is continually low entropy), while others choose a wide variety (so entropy jumps around). The amount of text they write also has an entropy over time, as does the entropy of the text itself. This last one is interesting because it can reveal key words in the same way that a word cloud might: key concepts get repeated, so the entropy gets reduced. 

What then would we expect to see of a student gradually discovering some new concept which helps them connect many topics? Perhaps an initial phase of high entropy in document choice, high entropy in concepts used and low entropy in the amount of text (responses might be a similar length). As time goes on, a concept might assert itself as dominant in a number of responses. The concept entropy goes down, while the document entropy might continue to oscillate. 

The overall pattern is counterpoint, rather like this graph below:


The graphical figure above is a representation of the positive and negative shifts in entropy of the main variables (going across the top), followed by the positive and negative shifts in the relative entropy of variables to one another. The further over to the right when patterns change is an indication of increasing "counterpoint" between the different variables. The further to the left is a sign of particular change in particular variables. From top to bottom is time, measured in slots where responses were made.


Not all the graphs are so rich in their counterpoint. This one (admittedly with fewer comparisons) is much more synchronous. There's a "wobble" in the middle where things are shifted in different directions, while at the end, the comments on the documents, the type of documents, and the type of topics all vary at once. If there was a common concept that had been found here, one would expect to see that the entropy of the comments would really be lower. But the graph and the diagram provide a frame for asking questions about it.
This one is more rich. It has a definite structure of entropies shifting up and down, and at the end there is a kind of unity which is produced. Looking at the student comments, it was quite apparent that there were a number of concepts which had an impact.

It doesn't always work as a technique, but there does appear to be a correlation between the shape of these graphs and the ways in which the students developed their ideas in their writing which merits further study.

More interestingly, this one (below) produced a richly contrapuntal picture, but when I looked at the data, it was collected over a very short period of time, meaning that this was the result of a one-off concentrated effort, rather that a longitudinal process. But that is interesting too, because there is a fractal structure to this stuff. A small sample can be observed to display a pattern which can then be contextualised within a larger context where that pattern might be repeated (for example, with a different set of concepts), or it might be shown to be an isolated island within a larger pattern which is in fact quite different.
Either way, the potential is there to use these graphs as a way of getting students to reflect on their own activities. I'm not sure I would go so far as to say "your graph should look like this", but awareness of the correlations between intellectual engagement and patterns of entropy is an interesting way of engaging learners in thinking about their own learning processes. Actually, it also might be possible to produce a 3d landscape from these diagrams, and from that a "google map" of personal learning: now that is interesting, isn't it?

Monday, 29 July 2019

Recursive Pedagogy, Systems thinking and Personal Learning Environments

Most of us are learning most of what we know, what we can do, what we use on an everyday basis, what we talk about to friends and colleagues, online. Not sat in lectures, gaining certificates, or sitting exams. Those things (the formal stuff) can provide 'passports' for doing new things, gaining trust in professional colleagues, getting a new job. But it is not where the learning is really happening any more. The extent to which this is a dramatic change in the way society organises its internal conversations is remarkably underestimated. Instead, institutions have sought to establish the realm of 'online learning' as a kind of niche - commodifying it, declaring scarcity around it, creating a market. This isn't true of just educational institutions of course. Social media corporations saw a different kind of marketing opportunity: to harness the desire to learn online into a kind of game which would continually manipulate and disorient individuals in the hope that they might buy stuff they didn't want, or vote for people who weren't good for them. But the basic fact remains: most of us are learning most of what we know online.

That means machines are shaping us. One senses that our sense of self is increasingly constituted by machines. I wonder if the slightly paranoid reactionaries who worry about the power of digital 'platforms' are really anxious about an assault on what they see as 'agency' and 'self' by corporations. But are we so sure about the nature of self or agency in the first place? Are we being naive to suppose autonomous agents acting in an environment of machines? Wasn't the constitution of self always trans-personal? Wasn't it always trans-personal-mechanical? The deeper soul-searching that needs to be done is a search for the individual in world of machines. Some might say this is Latour's project - but seeing 'agency' everywhere is not helpful (what does it mean, exactly?). Rather more, we should look to Gilbert Simondon, Luhmann, Kittler, and a few others. There's also a biological side to the argument which situates 'self' and consciousness with cells and evolutionary history, not brains. That too is important. It's a perspective which also carries a warning: that the assertion of agency, autonomy and self against the machine is an error in thinking which produces in its wake bad decision, ecological catastrophe and the kind of corporate madness which our platform reactionaries complain about in the first place!

Having said this, we then need to think about 'personal' learning in a context where the 'personal' is constituted by its mechanical and social environment. Machine learning gives us an insight into a way of thinking about 'personal' learning. Deep down, it means 'system awareness': to see ourselves as part of a system which constitutes us being aware of a system. It's recursive.

Some people object to the word 'system', thinking that it (again) denies 'agency'. Ask them to define what they mean by agency, and we end up confused. So its useful to be a bit clearer about 'system'. Here's my definition:

To think of 'systems' is a thought that accepts that the world is produced by thought.

This is why I'm a cybernetician. I think this is critically important. To deny that thought produces the world is to set thought against those things which constitute it. When thought is set against that which constitutes it, it becomes destructive of those things it denies: the planet, society, love.

So what of learning? What of learning online? What of personal learning?

It's about seeing our learning as a recursive process too. To study something is to study the machines through which we learn something. It may be that the machine learning revolution will make this more apparent, for the machines increasingly operate in the same kind of way that our consciousness operates in learning the stuff that is taught by the machines. It's about closing the reflexive loop.

So what about all that stuff about certificates, trust, passports, etc? It seems likely to me that closing the reflexive loop will produce new ways of codifying what we know: a kind of meta-codification of knowledge and skill. Against this, the institutional stamp of authority will look as old-fashioned as the wax seal. 

Monday, 15 July 2019

Interdisciplinary Creativity in Marseille

Last week I was lucky enough to go to this year's Social Ontology conference in Marseille. I've been going to southern France for a few years now to sit with economists and management theorists (no cyberneticians apart from me!) and talk about everything. Academic "authority" was provided by Tony Lawson (whose Cambridge social ontology group was the model for the meeting) and Hugh Willmott, whose interdisciplinarity helped established Critical Management Studies. Three years ago, I hosted the event in Liverpool, and more and more it feels like a meeting of friends - a bit like the Alternative Natural Philosophy Association (http://anpa.onl) which I'm hosting in Liverpool in August, but with management studies instead of physics.

This year, Tony didn't come, but instead we had David Knights from Lancaster University. It's always been an intimate event - and usually better for that, where the discussion has been of a very high level. Gradually we have eschewed papers, and focused entirely on dialogue for two days on a topic. This year's topic was Creativity.

If I'd read David Bohm before I'd started coming to these conferences, I would have known exactly what this was and why it was so good. Now I know Bohm, and I know he would have absolutely understood what we were doing. And with a topic like creativity, understanding what we were doing, where we were going, or where we would end up, was often unclear. Dialogue is a bit scary - it's like finding your way through the fog. Sometimes people get frustrated, and it is intense. But it is important to have faith that what we manage to achieve collectively is greater than what could be achieved by any individual.

So what conclusions did we reach? Well, I think I can sum up my own conclusions:
  • Creativity is not confined to human beings. It is a principle of nature. It may be the case that creative artists tune-in to natural processes, since this would explain how it is that their labours can result in something eternal. 
  • Creativity is connected to coherence. It is an expression of fundamental underlying patterns. In an uncertain environment, the necessity for the creative act is a necessity to maintain coherence of perception.
  • Creativity can be destructive. However (my view) I think that "creative destruction" needs unpicking. Creativity may always create something new which is additional to what was there before. This creates an increase in complexity and a selection problem. The "destruction" is done in response to this increase in complexity - often by institutions ("from now on, we are going to do it like this!")
  • The difference between creativity with regard to technical problems and creativity in human problems was discussed. Technical creativity is also driven by the drive for individual coherence - particularly in addressing ways of managing complexity - but it loses sight of the institutional destructive processes that may follow in its wake. 
  • The conversion of everything to money is, I think, such a "technical" innovation. On the one hand, money codifies expectations and facilitates the management of complexity. However, it prepares the way for the destruction of richness in the environment. 
  • The idea of "origin-ality" was explored. "Original" need not be new, but rather, connected to deeper "origins" in some way. This relates directly to the idea of creativity as a search for coherence.
  • Time is an important factor in creativity - it too may feature as a fundamental dimension in the coherence of the universe to which artists respond (particularly musicians, dancers, actors). Time raises issues about the nature of anticipation in aesthetic experience, and the perception of "new-ness"
  • A genealogy of creativity may be necessary - a process of exploring through dialogue how our notions of creativity have come to be. 
  • The genealogical issue is important when considering the role of human creativity in failures of collective decision-making and the manifest destruction of our environment. I'm inclined to see the issue of genealogy as a kind of laying-out of the levels of recursion in the topics and discourses of creativity, and this laying out may be necessary to provide sufficient flexibility for humankind to address its deepest problems.
  • Psychoanalytic approaches to creativity are useful, as are metaphors of psychodynamics. Michael Tippett's discussion of his own creative process had a powerful effect on everyone. However, the value of psychodynamics may lie in the fact that similar mechanisms are at work at different levels of nature (for example, cellular communication).
Michael Tippett Interview.mov from Directors Cut Films on Vimeo.

I took my Roli Seaboard with me, which inspired people to make weird noises. Music is so powerful to illustrate this stuff, and I invited people to contribute to a sound collage of the conference... which you can hear here. Actually, it's the first time I've heard a reflexology technique being used on the Seaboard!



Tuesday, 9 July 2019

Creativity and Novelty in Education and Life

A number of things have happened this week which has led me to think about the intellectual efforts that academics engage in to make utterances which they claim to be insightful, new or distinct in some other way. The pursuit of scholarship seems to result from some underlying drive to uncover things, the communication of which brings recognition by others that what one says is in some way "important" or "original", and basically confers status. Educational research is particularly interesting in this regard since very little that is uttered by anyone is new, yet it is often presented as being new. I don't want to criticise this kind fakery in educational research (but it is fakery), of which we are all guilty, but rather to ask why it is we are driven to do it. Fundamentally, I want to ask "Why are we driven to reclaiming ideas from the past as new and rediscovered in the present?" Additionally, I think we should ask about the role of technology in facilitating this rediscovery and repackaging of the past.

Two related questions accompany this. The first is about "tradition". At a time when we see many of the tropes of statehood, politics and institutional life becoming distorted in weird ways (by the Trumps, Farages and co), what is interesting is to observe what is retained in these distortions and what is changed. Generally it seems that surface appearance is preserved, but underlying structure is transformed from the structures that were once distributed, engaging the whole community in the reproduction of rituals and beliefs, to structures which leave a single centre of power responsible for the reproduction of rituals and beliefs.  This is, in a certain sense, a creative act on the part of the individual who manages to subvert traditions to bend to their own will.

Central to this distortion process is the control of the media. Technology has transformed our communication networks which, before the internet, were characterised by personal conversations occurring within the context of global "objects" such as TV and newspapers. Now the personal conversations are occurring within the frame of the media itself. The media technologies determine the way the communication game is played, and increasingly intrude on personal conversations where personal uncertainties could be resolved. The intrusion of media technologies increasingly serves to sway conversation in the direction of those who control the media, leaving personal uncertainties either unresolved, or deliberately obfuscated. The result is both a breakdown in mental health and increasingly lack of coherence, and increased control by media-controlling powers.

Where does creativity and novelty sit in all of this? Well, it too is a kind of trope. We think we are rehearsing being Goethe or Beethoven, but while the surface may bear some similarity, the deep structure has been rewired. More importantly, the university has become wired into this mechanism too. Is being creative mere appearance in a way that it wasn't in a pre-internet age?

At the same time, there's something about biology which is driven to growth and development to overcome restriction. Our media bubble is restriction on growth, and right now it looks menacing. The biological move is always to a meta-level re-description. Epochs are made when the world is redescribed. But we cannot redescribe in terms of "creativity" or "innovation" because those things are tropes wired into the media machine. Seeing the media machine for what it is may present us with some hope - but that is very different from our conventional notions of creativity.