Wednesday, 4 December 2019

Institutions, Art and Meaning

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

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

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

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

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

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

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

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

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

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

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

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

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









Saturday, 16 November 2019

Maximum Entropy

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

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

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



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

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

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

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

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

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

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

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

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

In the end, everything is zero.

Sunday, 10 November 2019

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Tuesday, 5 November 2019

Non-Linear Dynamics, Machine Learning and Physics meets education

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

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

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

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

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

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

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

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

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

Tuesday, 29 October 2019

About Aboutness and Relations: Thoughts on #TheDigitalCondition

As part of the Cambridge Culture, Politics and Global Justice group on the Digital Condition, I made a video response which sought to bring a cybernetic perspective to Margaret Archer's views on the "Practical domain" as pivotal in the relations between nature and the social. I remember challenging Archer on this many years ago when she gave a talk in London about her work on reflexivity and I suggested that Maturana and Varela's concept of "structural coupling" provided a clearer explanation of what she was trying to articulate in terms of the relations between people, practices and things. She brushed the point aside at the time, although more recently I heard her talk more approvingly of autopoietic theory, so I'd be interested to know what she thinks now. This is my video:



One of the things about making a video like this is that it is a very different kind of thing from  Archer's paper that we were all reading. Because it is more conversational, it expresses a certain degree of uncertainty about what it attempts to say. Not just in the messy diagrams, but in the pauses as I try to find the words for what I want to say. Also worth mentioning that having drawn the diagram, making the video was very quick. Why don't we do this more often? My suspicion is that as academics we are rather reluctant to reveal our uncertainty about things. Academic papers full of sophisticated verbiage are safer spaces to hide uncertainty. Personally, I think we should be doing the opposite to hiding uncertainty - and we have the technology to do it.

Anyway, this has elicited some defence of Archer - particularly in arguing that my critique is a misrepresentation of her argument. Well... I'm not sure.

In her paper she begins by focusing on "aboutness" and the relationship between consciousness and reality:
"Deprived of this reference to, or regulation by, reality, then self-referentiality immediately sets in – consciousness becomes to be conscious of our own ideas (generic idealism), experience equates knowledge with the experienced (pragmatism and empiricism), and language becomes the internal relationship between linguistic signs (textualism). Instead, consciousness is always to be conscious of something"
So "consciousness" is a thing which refers to another thing, "reality". So here are two distinctions. They are, of course, unstable and uncertain. What is reality? Well, what isn't reality?? What is consciousness? Well, what isn't consciousness?? (are rocks conscious, for example?) And if whatever is consciousness must refer to whatever is reality in order to be conscious, then what not-consciousness? Does that refer to anything? Lying behind all this is an implicit "facticity" behind the concepts of "consciousness", "reality" and "reference". Imposing the facticity effectively removes the uncertainty.

Archer says "consciousness has to be conscious of something", retreating from self-referentiality. But what if consciousness is self-referential? What does that do? It does two things:

  1. it creates a boundary, since self-reference is a circle.
  2. it creates uncertainty since whatever is contained in the boundary lies in distinction to what is outside it, where the nature of that distinction is unclear. Additionally, the totality of what is contained within the boundary cannot be accounted for within logic of the boundary (Gödel)

As I explain in my video, this then unfolds a topology.

So then what is reference? It must be about the way in which distinctions maintain themselves within the self-referential processes of consciousness. As I explain, this process entails transduction processes which operate both vertically (within the distinction) and horizontally (between a distinction and its environment).

There's a very practical example of this from biology. One of the central questions about DNA is "How does a molecule come to be about another molecule?" (thanks to Terry Deacon for that!). This is a profound question which throws into doubt what is known as the "central dogma" of biology, which places DNA at the centre of life. It really can't be right.

What is more likely is that there are processes of bio-chemical self-reference initially involving lipid membranes maintaining their internal organisation and boundaries in the context of an ambiguous environment. DNA can then be seen as an epiphenomenon of the evolution of this communicative process. In other words, the aboutness of DNA is our distinction concerning the emergent epiphenomena of self-reference.

It's the same with reference more generally. Once we see consciousness as self-reference, then the categories that we invent about "nature" or "the social" can be seen as epiphenomena of the self-referential process. This makes explaining this stuff a lot simpler, in my view. (And of course, explanation is another epiphenomenon of self-reference). It also helps to explain the ways in which we bring technology to bear on processes which help us to maintain our distinctions.

Wednesday, 23 October 2019

Ancestrality and Consciousness

Over the past year, I've been increasingly convinced of the correctness of the evolutionary biological theory of John Torday concerning the connection between consciousness, cellular evolution and "big" evolutionary history (from the deep origins of space and time). Of course, it's hugely ambitious - but we should be hugely ambitious, shouldn't we?

John's work in physiology and medicine (primarily focused on lung physiology and asthma) has presented a number of empirical phenomena that point towards a biological theory that includes evolutionary history, where consciousness is part of a process that explains how cells evolve from lipid bi-layers to sophisticated inter-cellular communication systems. It also addresses what he, and many other biologists see, as a scientific problem within their discipline - that it is not explanatory in the way that physics or chemistry describe causal mechanisms, but descriptive. In our extensive conversations, I have noted that education suffers the same problem: there is no mechanistic explanation for educational phenomena, only description. Since education is also a manifestation of consciousness, I am concerned to make the connection between these different lines of inquiry (biology, physics and evolution).

The central question in evolution is the relationship between diachronic (time-based) process and synchronic structures. What time-based process makes cells absorb parts of their environment (bacteria for example) as mitochondria? What time-based process introduces cholesterol as the critical ingredient to animate life? What time-based process governs the expression of proteins and their transformations in the cell signal-transduction pathways, which despite their complexity, maintain the coherence of the organism?

Time itself introduces further questions. When we look at evolutionary history - maybe at the red-shift of the expanding universe - what are we looking at exactly? "Once upon a time, there was absolutely nothing, and then there was this enormous explosion..." really?

I've been re-reading Quentin Meillassoux's "After Finitude". I have some misgivings about it, but this must surely be one of the greatest philosophical works of the last 20 years. The question of time is one of his central questions, and he calls it "ancestrality". The question is about the nature of reality, and particularly the reality of things like fossils, or electromagnetic radiation from outer-space. We either assume that the world is made by our consciousness, or we assume that the world exists in a pre-existing domain that exists independently of human consciousness and agency (what Bhaskar calls the "intransitive domain"). Meillassoux pursues the Platonist position which denies both (in line with Alain Badiou) arguing that objects are mathematically real to us - logic, in other words, is prior to materiality. At the heart of Meillassoux's (and Badiou's) argument is the contingency of nature. He asks, "Given this contingency, how do things appear stable?"

Pursuing this, the ancestrality of the universe - the big bang, evolutionary history - is (Meillassoux would claim) "logically" real. But this puts the emphasis on the synchronic reality of things - their logical structure out of time - and it assumes that the consciousness that conceives of this logic is similarly structured. Indeed, I'm not convinced that one of Meillassoux's central points - that the Western philosophical position is one of "correlation" between ideas and reality is escaped in his own position. But the care with which he lays out his arguments is nevertheless highly valuable, and his emphasis on contingency seems right to me (but maybe not! - how can anyone say an ontology of contingency is "right"?)

Torday's situating of time in the material and biological evolution of consciousness means that this "logic" has to become a "topo-logic": space and time - the diachronic dimension - are not separable from the "logic" of synchronic structure. What do we get when we have a "topo-logic"? We get contingency, process, uncertainty and the driving necessity for coherence. In essence, we get life.

Somehow, we have to grapple with topology. For a long time, I struggled with the concept of time within cybernetics. After all, you have to have time to have a mechanism, but where did "time" come from? There must be something prior to "mechanism". It turns out that when we think through one of the other key distinctions of cybernetics - difference - we find the answer. A difference results from a distinction. A distinction is a boundary which marks what is inside from what is outside. But distinctions are essentially unstable: whatever mark is made generates a question. It's the same question that Gödel addressed: the distinction demarcates a "formal system", but there are propositions expressible within the formal system which cannot be proved within that system. Uncertainty is inevitable - how is it managed?

Something must be invented in order to mop-up the uncertainty. Time is a powerful invention. By creating past, present and future, ambiguities can be situated in a way where contradictions can be expressed "now it is x" and "now it is not-x". The implications of this are that the topology becomes richer as the distinctions about time must also be negotiated. Part of the richness of this topology may also be the creation of deep symmetries in time and space, including concepts such as "nothingness" or nilpotency, and "dimensionality" - in essence, as my colleague Peter Rowlands would argue, the foundational principles of the material world. The invention of time entails a double management process, where part of it must coordinate with an environment which is also the cause of uncertainty. There are many distinctions in the universe, each creating time, space and matter, and each constraining other distinctions.

So is the "big bang" story a manifestation of a topology? Does the topology pre-exist the consciousness which conceives of it? (what does "pre-" mean in that sentence?) If this is so, what is "evolution"? I feel myself skirting foundationalism while denying the possibility of any "foundation"... and then seeing that process as a foundation... then denying it... then seeing it as a foundation... and so on.

"In the beginning was the word" says St. John's Gospel. That's a distinction - it unfolds a topology. Theologians like Arthur Peacock imagined that "logos" might also mean "information". If there is information, then there is topology, and then the "beginning" is "the word" - the distinction.  And beginnings are everywhere, not least the beginnings created by the distinctions of consciousness. But consciousness's beginnings have their roots in the beginnings of matter - in the "word".

We're very close to Torday's essential point: cells, from which consciousness emerges, are stardust which trace their evolutionary history to the beginning. In the topology of maintaining distinctions, new distinctions must be made as the ambiguity of the environment is dealt with. Indeed, the difference between atoms and organisms may be that atoms in maintaining their distinction, must find a way of organising themselves such that new distinctions may be made. The environment within which atoms organise is the essential driver for new forms of organisation. That way of organising is what life is: a search for new ways of making distinctions which manage the uncertainty generated by those distinctions. It is this, I suspect, which is the mechanism of evolution. It's only about history in the sense that our unstable distinctions require us to invent history to maintain our distinctions about ourselves and our environment.

What we call "homestasis" is the cell's drive for coherence in its distinction-making. What we call "information" or "negentropy" is the cell's interface with its environment. What we call "chemiosmosis" is the disturbance to the cell's equilbrium by forces in its environment and its gaining of energy.

Thought itself is the universe's way of making new distinctions. Since the universe is imprinted in the biology of consciousness, the symmetries of physics, biology and consciousness will contrive to form coherences which enfold both synchronic and diachronic dimensions. This may be why the crazily complex protein dance hangs together - because of the deep coherence between diachronic and synchronic dimensions.

What then of science itself? Of empiricism? In a world produced by thought, something happens within the time we invent to establish coherence between thought and the world. Thought looks closely at what it has made, it discards certain aspects of what it sees, it executes control on what is left, it observes what happens - not just one mind, but many - and then collectively it thinks more deeply. A new level of coherence is arrived at and the topology unfolds once more. 

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.