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. 

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!”.

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.

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. 

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.