AGI is an unquantifiable threshold, and whether an LLM reaches this will be decided based on a collective reading of the outputs that are produced. That is why the model cannot be separated from the surrounding infrastructure. The colour scheme, the point of interaction, the UI, even the very act of ‘chatting’ all contribute in some sense to our own reading, and whether we choose to recognise an other.

For the purposes of this post, which I am writing in Tokyo Station, I will un-pick the Western endeavour to seek a deeper meaning and state that the outputs from an LLM are meaningless. I mean this in the sense that inferred tokens originates from a soul-less computer. We may impose a meaning on them, but in their most abstracted form, they are the result of computations.

Drawing a literary comparison, the skill in reading a haiku is to not read any meaning into it, but to let it wash over you. Should the same be said for an LLM (that truly does have no meaning in the first place)? Should we let output tokens wash over us like a haiku in accordance to Zen? We turn to Roland Barthes’ reading of haiku in Empire of Signs:

Here meaning is only a flash, a slash of light: When the light of sense goes out, but with a flash that has revealed the invisible world, Shakespeare wrote; but the haiku's flash illumines, reveals nothing; it is the flash of a photograph one takes very carefully (in the Japanese manner) but having neglected to load the camera with film. Or again: haiku reproduces the designating gesture of the child pointing at whatever it is (the haiku shows no partiality for the subject), merely saying: that! with a movement so immediate (so stripped of any mediation: that of knowledge, of nomination, or even of possession) that what is designated is the very inanity of any classification of the object: nothing special, says the haiku, in accordance with the spirit of Zen …

This passage shares so much with how we really know LLMs to work (which is different from how we wish LLMs worked, which is to think), tokens appearing in an instant from the void. But at the same time highlights just how disparate our readings of LLMs are when compared to something like a haiku (again, to be expected because we assign a value towards an other, but it is important to consider that this importance is ‘created’ in order to process the meaninglessness of an LLM).

This is further amplified by how we interact with LLMs. When people refer to models such as Anthropic’s Claude, they refer not just to the set of weights, or the data centre, but to the personality contained (again, we pretend that this is the case) within the output, which is defined by the overall experience. The UI, the colour scheme, the typeface, even the act of ‘chatting’ is to impose a dialogue with an other. These aspects combined make it irresistible to place meaning where there is none. Either AGI will refer to a holistic experience, or we must treat how we interact with LLMs fundamentally differently.

How do we get around this imposition of our own values onto something meaningless, in effect, to un-interpret the haiku and let it wash over us. Referring back to Barthes, we learn of different approaches, hundreds of years old:

[…] the haiku has the purity, the sphericality, and the very emptiness of a note of music; perhaps that is why it is spoken twice, in echo; to speak this exquisite language only once would be to attach a meaning to surprise, to effect, to the suddenness of perfection; to speak it many times would postulate that meaning is to be discovered in it, would simulate profundity; between the two, neither singular nor profound, the echo merely draws a line under the nullity of meaning.

Performing LLM inference multiple times (different temperature values?) may help us to de-personify outputs, enabling us to appreciate the content (but not assign meaning to it). However, very quickly we enter into the space of what I will call the Muji paradox (highlighted to me by Sunil).

Muji: in English ‘no-brand’ is the antithesis of a brand. It embodies the same values found within a haiku though in a commercial sense, not a literary one. The complete lack of meaning. It is important to note though that from a Western perspective, it has failed. Paradoxically, we have associated an aesthetic with it, a quality, and a set of values, in exactly the same way we have done for LLMs.

An alternative to doing nothing at all, would be to do everything at once. As an example of this, Japanese television exists, and often consists of a dizzying array of fonts and colours. It becomes so overwhelming that you are forced to assign no meaning to the text at all, it simply exists. Read nothing into how it is presented, because it will change. Japanese television in a sense embodies the idea of Zen equally as well as Muji does.

I propose that to provoke an alternate reading of LLMs, in opposition to the Western AGI-seeking viewpoint, that inevitably converges towards chatting with an other, we treat creative outputs more like haikus. As Barthes’ says, like the flash of a photograph one takes very carefully but having neglected to load the camera with film. This may mean taking the Muji route and attempting to strip any aesthetic, or imposition of values from an LLM (obviously it is not in the interest of an AI company to do this). OpenAI probably has the ‘blandest’ aesthetic, but like Muji, even this has become meaning-full. An alternative would be, like reading a haiku, to present tokens multiple times, similar to Raymond Queneau’s ‘A Hundred Thousand Billion Poems’. Interacting with an LLM may feel closer to watching a Japanese TV show. So overwhelming in alternative tokens, typeface, colour, conflicting aesthetics, even mode of interaction (RE: chatting), that we are forced to strip any emotion we might infer.

Maybe that’s why we’re struggling to process AI and art, and why the concept of AGI is so messy and intangible. Because we’re imposing a meaning on something that clearly has none (or even worse, imposing a meaning on something that we’ve already imposed a meaning to in the case of Claude). If we embrace this more Buddhist perspective, it may unlock an alternate reading, complimentary to our own AGI-centric one, mirroring the individual dividing lines of the West/East, the Haiku and the Sonnet.

There has been huge talk recently regarding the performance-to-cost ratio of Deepseek’s LLM products, particularly their new reasoning model causing OpenAI’s reasoning-based moat to partially evaporate. Companies like Perplexity, Cursor & Co (including most YC startups these days) have been quick to integrate these with good reason, most likely saving themselves some money in the process. Most likely, Deepseek have generated a large amount of synthetic data from state-of-the-art models such as o1, or by cleverly prompting non-reasoning models such as 4o or their own V3. As an accepted practice, this is the core idea behind recursive improvement, so no harm done. And if OpenAI and others have priced their API correctly, then they’re also set to gain. Everyone’s a winner (spoilers, they are not), and we’ll only see more of this in the future.

I personally have been benchmarking LLMs to make decisions within Bayesian optimisation loops, in a bid to eke out some in-silico performance (for functions that behave reasonably). Every time I reach a natural conclusion to benchmarking where the next best model is too expensive to run on my test suite, someone pushes the Pareto front a little bit more in the direction of ‘time to stop writing that preprint and get some more results’. The impact of these models will be never be as small as it is now.

A obvious point that people still seem to find surprising is that Deepseek censor inputs and outputs to align with the views of the Chinese government, specifically (again, rather obviously) events surrounding Tiananmen Square. Given Deepseek’s servers will be in China, it’s an aspect of communication that the Government has enforced for practically as long as the internet has existed.

I consider this very much a non-story. What I find more interesting are what underlying values the LLM has. These go deeper than specific phrases or keywords and enter into the murky realm of thoughts and feelings.

I’ve written previously about values and LLMs, and how datasets not only contain a representation of language as a structure, but the values of those who have written or collated the text. In effect, we shouldn’t be surprised when models from other countries appear misaligned to our own, Western, benchmarks, if they have been trained on distinct datasets. Even with learned translation, we shouldn’t be surprised if we don’t find them particularly revealing, especially as we approach concepts such as ‘intelligence’. Likewise, as we in the West share more common values with current SOTA LLMs than those elsewhere, we shouldn’t be surprised if they are found less appealing or useful in other countries. This is a hidden upside to the development of LLMs, they are implicitly more accessible, beneficial and provide more productivity to those with similar values.

So what work has already been done in this field? The preprint “LLM-GLOBE: A Benchmark Evaluating the Cultural Values Embedded in LLM Output” presents a methodology based from a framework devised in 2004. Outputs from LLMs are ranked using an automated ‘jury’ across a number of criteria including “Institutional Collectivism” and “Future Orientation” etc… It’s a logical extension to the original methodology, and I won’t argue against this work or the proceeding work on GLOBE (for now). What I care about are the results. So after all that, we have a brand new suite that measures exactly what we’re interested in. Not censorship, or language, but values. Time to reveal the results…

Oh. They look strikingly similar. So Chinese LLMs (Qwen) and English LLMs (GPT-4) really do have the same values? The authors claim that Chinese LLMs score (quantitatively) significantly higher on cultural rankings, though another conclusion is that LLMs are ‘…not perfect representations of the cultural values of development contexts…’. In my eyes these are slightly conflicting. So what is happening here?

I suspect that datasets have a lot to do with it. As is common and now an encouraged direction, LLMs are being trained with synthetic datasets derived from other LLMs on the Pareto front of cost-to-performance, depending on how much you value volume over quality. The implication is that unless you go and collect a large amount of ‘virgin’ data (arguably this is now impossible), you are building on the foundations that previous LLMs have constructed, and implicitly, the values contained within them. Similar to how all modern compilers stem from the original A-0 compiler by Grace Hopper.

For a company like Deepseek, this poses an interesting question. If you value money, pursue what people will buy at breakneck speed. Use all the foundational work available to you and built a product that is so cheap and so effective that market forces takes over. Then you can worry about values. But for now, assuming that geopolitical forces at play are using LLMs like pawns on a chessboard, models like Deepseek are built on Western foundations. While OpenAI may take a temporary minor financial hit because of a potentially reduced market share, the game was set in motion a few years ago now, and it will be difficult to untangle.

There is a way out for Deepseek and its hypothetically value-driven creators. The model is cheap and good enough that large synthetic datasets will be created from it. By shifting the dial in favour of your own beliefs, or augmenting your model with your own value-driven data, it may be possible to shift the dial against Western values. There are glimmers of this seen in the tweet above which I will re-present.

However, the game is being played continuously, not turn-by-turn. By announcing huge amounts of funding for data centres such as Stargate, the West is aggressively attempting to not only win this race over the values of future LLMs, but remove the need for it. The sheer volume of compute is a hedge against other LLMs and a mitigation strategy against having to use the best data available on the open (geopolitical) market, by way of self improvement and reinforcement learning in a controlled setting. So has cultural victory been achieved? Can other nations untangle the roots that have grown? It will be a huge effort.

APPENDIX - EMPIRE OF SIGNS - ROLAND BARTHES

Recently I read Empire of Signs by Roland Barthes on the recommendation of Sunil Manghani, ahead of a trip to visit a friend in Japan. Barthes describes a fictional Japan, used as a device to counter Western ideologies and values. As follows:

Barthes broadly outlines a vision of Japan, and more generally the East, as a place where the sum of the parts don’t make a whole, but signify the act of the summation itself, the inbetween-ness of the parts. He makes this point by commenting on the writing system (composition of smaller signs), food (which itself exists in divisions that can only be divided further by chopsticks, never violently cut or destroyed), and through the act of giving gifts, wherein the packaging and act of giving a gift outweighs the contents. There is no centre, no answer, no true meaning to life in Barthes’ created world, it exists in the in-between spaces. Barthes describes the act of writing a Haiku like taking a single picture, without any film (or a memory card) in the camera.

We in the West want to impose a meaning to our models and their outputs, maybe stemming from our core belief of individualism, seeking to look inwards and attempting to assign a meaning or search for a soul. For those in Barthes Japan, the act of using an LLM, or the tokens themselves may have more importance. The instant that language is generated, but not the language itself, the process. I haven’t properly thought about the implications of this, but it leans into the creative process I am sure, and how LLMs can truly contribute outside of finding a creative solution. I’m sure Sunil and myself will discuss further, but speculatively, maybe the solution for the East and those without Western ideologies is to think beyond our established idea of ‘chatting’, implicitly with another being, a thing, a whole. We might not find it interesting or useful, but others might, and that’s OK.

How someone organises, or chooses not to organise a bookshelf can say a lot about them. I personally don’t think there is any sense in enforcing a specific order. My strategy can be summarised as ‘vibes based’. There is joy in being able to know roughly where a book is and pick it out mid conversation. It makes me feel like I’m some sort of magician or an old librarian, holding the secret mental key that will unscramble order from chaos.

But, if you don’t believe in this serendipity, you might be inclined to choose something to base the order on. Given we are pretty good at remembering the title of a book, maybe alphabetical order is good. Actually, authors should be grouped together, so let’s make them alphabetical. What about we do that and then also group some categories of similar books together (S/O Dewey). The most intelligent of us out there may choose to forego any sense of useful meaning, and satirically choose to base the order on the colour of the cover, subverting the old book judging adage.

These approaches have been discussed at length here here here here and here, you get the idea. The reason they’re up for debate is because none of them are truly optimal. None take advantage of all the available information... Until now. Later on in this post I will describe an optimal strategy to sorting a book collection that I’ve also made open source, but first we must collect some data.

Digitising all the books I own is a task low down on my todo list (compared to finishing my PhD). So to aid in this, I created a Command Line Interface (CLI) tool to help me. It’s available here and I’ve creatively named it optimal-bookshelf. It allows you to add books to a persistent locally-stored virtual library by searching for a title using open book APIs (i.e. Google Books), after which a more thorough search is performed and the specific edition can be selected. Ideally most of the information is correct at this point, but you then have the option to edit specific data such as the ISBN. I tailored this tool as I was digitising my collection, so it should be as efficient as possible on the Pareto curve of information vs time.

This enabled me to create an accurate digital representation of all the books I own, down to the specific edition, in a relatively short amount of time.

But what to do with this wealth of information? Well, for now it is largely text-based. To do anything that will concern a notion optimality, we need numbers. It follows that we can summarise all the information that we have generated, and result in a numerical representation by creating embeddings of our virtualised book-twins. By tokenising the information, embeddings represent text as dense vectors in a high-dimensional space using models trained specifically for this purpose. These embedding models are designed to capture semantic relationships between words and phrases, allowing us to perform mathematical operations that measure how similar different pieces of text are to each other. In the optimal-bookshelf CLI, embeddings are created using OpenAI’s text-embedding-3-large model and can easily be generated using the $ bookshelf embed command.

If I make the assumption that I want books near each other to be semantically related (i.e. have a low Euclidian distance in embedding space), and that every book must appear on my bookshelf exactly once, I can equivalently formulate this task as an optimisation problem, minimising the total distance travelled by traversing from embedding to embedding. In this case I don’t mind that this trip isn’t a loop (because my bookshelf is a ‘line’ and not a circle, but this can be revisited later), resulting in a formulation of the classic integer programming no-return Travelling Salesmen Problem. Solving this will result in a sequence of books that embodies the lowest total semantic difference between neighbouring books. It makes the most sense, in ways that are not just affiliated with the title or the author, or even the genre, but the fundamental concept of that book. Having generated embeddings, solving this problem is easy in optimal-bookshelf. Just run the command bookshelf tsp.

For visualisation purposes, you can also solve a TSP over embeddings that have first been reduced to two dimensions. The easiest and generally most effective way to do this in situations where the meaning isn’t too important (alert they can be misleading) is to use t-SNE. Running bookshelf tsp -v (for visual) first uses t-SNE to reduce the dimension of the embeddings, and then solves the reduced dimension TSP (as the TSP relies on a distance matrix it scales with number of points and not dimensionality) resulting in the following:

It’s really interesting to follow the path and see themes emerging based on author or publication date, or even if the book is read, unread, or in progress, though I’m choosing to keep secret and just label the titles. It all acts like a constellation of my reading interests and habits, similar to the manually created constellation of artists at the Tate Liverpool.

I think an interesting extension would be to specify a certain number of shelves, then solve the TSP for a corresponding number of lines equal to the available shelves. In theory I have the number of pages in each book available, so I could add a physical packing constraint here to ensure all the books fit. For full transparency, here’s my current optimal shelf based on this semantic TSP. Spot the various threads that emerge…

1. GRAVITY'S RAINBOW
2. THE MASTERS
3. THE BOOKS OF JACOB
4. FLIGHTS
5. DRIVE YOUR PLOW OVER THE BONES OF THE DEAD.
6. THE PLAGUE
7. PANDAEMONIUM 1660–1886
8. UTOPIA
9. POLITICS ON THE EDGE
10. THE RESTLESS REPUBLIC: BRITAIN WITHOUT A CROWN
11. 1599: A YEAR IN THE LIFE OF WILLIAM SHAKESPEARE
12. WILLIAM SHAKESPEARE POETRY
13. OEDIPUS AT KOLONOS
14. THE ODYSSEY: TRANSLATED BY EMILY WILSON
15. THE ILIAD: TRANSLATED BY EMILY WILSON
16. THE DIVINE COMEDY
17. DON QUIXOTE
18. THE THREE MUSKETEERS
19. THE COUNT OF MONTE CRISTO
20. CRIME AND PUNISHMENT
21. THE KARAMAZOV BROTHERS
22. THE IDIOT
23. HUMAN, ALL TOO HUMAN & BEYOND GOOD AND EVIL
24. THE EMANCIPATION PROCLAMATION
25. THINK AND GROW RICH
26. TALKING TO STRANGERS
27. ESCAPE FROM MODEL LAND
28. VALUES
29. DOMINION
30. JERUSALEM
31. THE WORLD OF STONEHENGE
32. GOING TO CHURCH IN MEDIEVAL ENGLAND
33. THE RUIN OF ALL WITCHES
34. MIDNIGHT IN CHERNOBYL
35. ABYSS: THE CUBAN MISSILE CRISIS 1962
36. SECRET WARS
37. DIVIDED HOUSES: THE HUNDRED YEARS WAR III
38. TRIAL BY BATTLE: THE HUNDRED YEARS WAR I
39. TRIAL BY FIRE: THE HUNDRED YEARS WAR II
40. HIGH PERFORMANCE ROWING
41. WRITING ABOUT SPORT
42. GAZZA AGONISTES
43. THE LAST LEONARDO
44. THE CREATIVE ACT
45. ON ART AND LIFE
46. STORY OF ART
47. THE PENGUIN BOOK OF CLASSICAL MYTHS
48. VENI, VIDI, VICI
49. THE TWELVE CAESARS
50. THE HISTORY OF THE DECLINE AND FALL OF THE ROMAN EMPIRE: ABRIDGED EDITION
51. PAX
52. ASTERIX: ASTERIX AND THE WHITE IRIS
53. SUPER-INFINITE
54. SYSTEMS FOR...
55. MOBILE MANIA
56. THE ICON CATALOGUE UK GARAGE VOL. 1
57. LONDON FEEDS ITSELF
58. LONDON FIELDS
59. DAMASCUS STATION
60. MOSCOW X
61. A PERFECT SPY
62. THE SPY AND THE TRAITOR
63. BEHIND THE ENIGMA
64. THE MERCENARY RIVER
65. THE LADYBIRD BOOK OF THE HANGOVER
66. STIG OF THE DUMP
67. THE TALES OF BEEDLE THE BARD
68. HARRY POTTER AND THE HALF-BLOOD PRINCE
69. JOURNEY TO THE CENTRE OF THE EARTH
70. DR JEKYLL AND MR HYDE
71. TREASURE ISLAND
72. TOM SAWYER & HUCKLEBERRY FINN
73. THE ADVENTURES AND MEMOIRS OF SHERLOCK HOLMES
74. THE RETURN OF SHERLOCK HOLMES
75. MACHINES OF LOVING GRACE
76. PROCESS DYNAMICS AND CONTROL
77. GAUSSIAN PROCESSES FOR MACHINE LEARNING
78. BAYESIAN OPTIMIZATION
79. NUMERICAL OPTIMIZATION
80. ROBUST OPTIMIZATION
81. FOUNDATIONS OF APPLIED MATHEMATICS, VOLUME 2
82. FOUNDATIONS OF APPLIED MATHEMATICS, VOLUME I
83. HOW TO PROVE IT
84. INTRODUCING LOGIC
85. INTRODUCING QUANTUM THEORY
86. INTRODUCING CHAOS
87. INTRODUCING FRACTALS
88. INTRODUCING INFINITY
89. OUR MATHEMATICAL UNIVERSE
90. COLLINS DICTIONARY OF MATHEMATICS
91. FOUR COLORS SUFFICE
92. RINGWORLD
93. THE LORD OF THE RINGS
94. THE HOBBIT
95. CLOUD ATLAS
96. A SUPPOSEDLY FUN THING I'LL NEVER DO AGAIN
97. INFINITE JEST
98. AMERICAN PSYCHO
99. BRING UP THE BODIES
100. THE NATION KILLERS
101. BURY MY HEART AT WOUNDED KNEE
102. STALINGRAD
103. WAR AND PEACE
104. NAPOLEON IN EGYPT
105. GREECE
106. ATHENS
107. DEMOCRACY'S BEGINNING
108. THE BATTLE FOR THE ARAB SPRING
109. MIKE CONTRE-ATTAQUE!
110. KISSINGER
111. THE ESCAPE ARTIST
112. 1000 YEARS OF JOYS AND SORROWS
113. LUCKY KUNST
114. MURDER ON THE DARTS BOARD
115. GONE FISHING
116. THE SATSUMA COMPLEX
117. THAT’S YOUR LOT
118. MARCH OF THE LEMMINGS
119. HOUSE ARREST
120. COMING HOME
121. NO TURNING BACK
122. KILLING THATCHER: THE IRA, THE MANHUNT AND THE LONG WAR ON THE CROWN
123. SAY NOTHING: A TRUE STORY OF MURDER AND MEMORY IN NORTHERN IRELAND
124. FALL
125. ONE TWO THREE FOUR: THE BEATLES IN TIME
126. K-PUNK
127. DEATH AND THE PENGUIN
128. MURDLE
129. THE HARD-BOILED WONDERLAND AND THE END OF THE WORLD
130. ON THE ROAD
131. A MOVEABLE FEAST
132. FLAUBERT'S PARROT
133. NINETEEN EIGHTY-FOUR
134. TENDER IS THE NIGHT
135. THE UNBEARABLE LIGHTNESS OF BEING
136. SLAUGHTERHOUSE-FIVE
137. CATCH-22
138. WE ALWAYS TREAT WOMEN TOO WELL

I think another next logical step would be to build in a recommendation tool based on the vector embeddings and maybe even the resulting TSP solutions. This would potentially result in a bi-level integer program (hard) depending on what you deem most important (add a book that would fit most optimally within a current collection of books, or add a book that would result in the most added distance to the resulting optimal semantic tour?), but with a small enough number candidates this could be solved without approximation. We will see.

Was it worth it? Perhaps the lesson in all of this is that fulfilment lies not in choosing between chaos and order, but in finding systems that embrace both—straddling each paradigm, allowing us to be both the organised librarian and to take a book for a walk.

As I’ve quoted before, Donella Meadows states in her book Thinking in Systems: A Primer:

There is yet one leverage point that is even higher than changing a paradigm. That is to keep oneself unattached in the arena of paradigms, to stay flexible, to realize that no paradigm is "true", that every one, including the one that sweetly shapes your own worldview, is a tremendously limited understanding of an immense and amazing universe that is far beyond human comprehension.

All code for this post can be found here: https://github.com/trsav/bookshelf

Wallace will act in his own short-sighted interests. He will avoid conflict at any cost, to the detriment of his long suffering slave dog Gromit. Useful idiot Wallace will then be exploited by forces very much within his control and Gromit will in turn, for some reason, save the day. Having experienced a range of emotions that Wallace could not even consider yet alone acknowledge, Gromit will then sit down with his newspaper, and return to the way things were. Largely being offered cheese.

To live the life of Gromit is to be moulded, to be morphed, both by Wallace and his divine Creators, and, lacking a means of communication, be denied the ability to return the favour. Voiceless Gromit is a bottomless vessel of emotional tolerance. He knows he has experienced pain before, and knows he will experience it again. Often confronting the viewer directly, he is quiet sign of resistance against all that serve to shape him.

The Plight of Plasticine Gromit is one that is destined to be told and retold for years to come. Gromit, and to a lesser extent Wallace, has transcended light entertainment and entered the realm of the folk-tale, there is no doubt that he now exists as part of a contemporary mythology. The retellings have begun; Wallace’s original orator Peter Sallis is dead, and now Ben Whitehead has taken up the mantle. Implicitly, these characters are destined to live on beyond even Nick Park’s passing. We are custodians of these models and bear their weight of their stories, it is our responsibility to ensure they are told ad infinitum. We shape them, and they mould us.

Just as the Gods in epics such as The Iliad occasionally appear in physical form on the battlefield to remind their subjects who is pulling the strings, the fingerprints of Aardman employees linger as a reminder of an omniscient puppet master. However, despite playing a God-like role in the lives of Gromit and Wallace, we are no longer in control.

Greek God Apollo granted human priestess Cassandra the gift of prophecy, but upon rejecting his advances - cursed her, ensuring that Cassandra’s prophecies were forever true but never believed. We as creator have bestowed upon Gromit emotional maturity and an understanding of the human condition, he speaks the truth in all of us. Just as Cassandra's truth was destined to be spoken but never heeded by other humans, Gromit's silent wisdom is destined to be witnessed but never fully grasped by slave master Wallace. Like Apollo's gift, Gromit’s message has transcended beyond our control. Apollo, the God who could see all futures, could not change Cassandra's use of his gift; we, who can mould Gromit's form, cannot control our depiction of him. If Gromit could speak he would probably say something like:

Nick Park, Nick Park!
God of all ways, but only Death's to me,
Once and again, O thou, Destroyer named,
Thou hast destroyed me, thou, my love of old!

Given this, we often find ourselves coming to expect that Gromit will strike a more introspective tone than other figures in Gromit and Wallace. As an entity his personal mythology is more emblematic to Sisyphus and his eternal punishment of rolling a boulder up a hill as opposed to heroic Hercules triumphing over rogue bakers, or a pair of mechanical trousers piloted by a penguin.

However, in this contemporary myth of stoicism, Gromit does not play the role of Sisyphus. Gromit is the boulder. We as custodians bear the weight of bringing him into existence, we are doomed to tell the tragedy of Gromit, to raise him up, and to pull him down. Gromit and I will never summit, and we are burdened with that responsibility. In this analogy, Wallace serves as the mountain itself, static, unwavering in complacency. Wallace provides no respite for us, nor Gromit. We are doomed to force Gromit to try and try again, and yet go nowhere at all. Laika was given the tragic reward of burning up into a preconceived infinite lightness, eternal fame and glory. We rewarded Gromit’s trip into space with some cheese (which he doesn’t seem to personally enjoy that much).

“Here I sit, I can do no other” once proclaimed Gromit, or so it goes. This puritanical stoicism in the face of eternal damnation that he shows is evidence that Gromit is the main character. Gromit and Wallace. Though, we must also consider the role that Wallace plays within this myth. He is a glutton at best. Motivated by cheese, and famously in-bread. He is meek and self-serving, a pushover. Dante reserved the vestibule of hell to these people with no ambitions, good nor bad. Abandon all hope, ye who enter here. They were destined for eternal stagnation. There are those characters in the mythology that do have ulterior motives, i.e. the penguin, but Wallace is unbefitting of even a negative analysis. He is nothing. He exists purely as blank canvas on which Gromit can project both his pleasure and pain.

How can Gromit transcend beyond his invented suffering, if we have lost control of the strings? How will this folk-tale evolve? One scenario is for Gromit to be considered, in the future, to be assumed to be based on a real dog. Hopefully in that case this very blog will be lost and cease to serve as a reminder that he’s completely made up. The weight of our burden as creators and custodians would be transformed into historical documentation.

Papers will be written and debates will be held over “The Historical Gromit" - analysing evidence for his actual existence, debating the accuracy of various tellings, arguing over the true location of his home. The fingerprints in the clay would be studied not as signs of divine creation but as proof of historical authenticity. Academics would argue over whether "A Grand Day Out" represents an actual journey to a lost moon made of cheese, or is merely allegorical.

The ultimate victory over Gromit’s current metaphysical Sisyphean existence will not be in being pushed to the top of the mountain by us humans, but in future generations assuming we were never pushing a boulder at all - merely documenting a profound historical truth about a dog who carried the weight of human wisdom in silence.

There is a cruel twist to this fate. Like Wallace, we will miss the point of the tale entirely - and yet like Gromit, we now must bear this misunderstanding with stoic grace. From Gromit to Wallace.

CANIS VIVIT.

Recently, Dan Tomlinson MP chose to highlight the investigations (here and here) into the enormous cost of the Great Bat Tunnel (GBatunnel) of 2024. Others have irked, however I personally consider the tunnel a unique feat of mammalian cooperation and will look back fondly at the moment that both bat and human met each other exactly in the middle, having started from their respective ends of the 1km section of track.

Many commentators have chosen to use the bat tunnel as a case study. It is a single example (of 8,276 in the case of HS2) of how a tangled web of stakeholders can grind large infrastructure projects to a halt and increase costs, all while inflation ticks away in the background. We must also spare a thought for those bats who are also feeling the pinch in these uncertain times. The price of small pieces of fruit have skyrocketed, and of course bat-employers (presumably cave related, i.e. maintenance or otherwise) have been directed not to increase weekly bug allowances to avoid a bug-price spiral. Not only this, but us humans have selfishly decided to increase transport capacity between two of our largest cities, in an attempt to stimulate a generation of economic growth.

Ironically if I were fortunate enough to be a bat, well protected as they are, I would go on strike, avoiding any significant crossings or journeys in entirety making the batunnel useless (apparently the bats have disappeared from the batunnel stretch, presumably on strike as I outline). Regardless, the batunnel now exists, and I speak for all British taxpayers in wishing any future bats a safe and enjoyable crossing should they wish to fly over this particular bit of train track.

However, Natural England have set a dangerous precedent. By taking a hardline policy in favour of bat-lives, to the tune of £100 million, they have exposed their hand.

In this post I will outline three proposals to exploit this no-bat-death precedent that will solve not only the planning system but also infrastructure, growth as well as issues of national security. The majority of these rely on obtaining a number of rare bats. I think this is a solvable issue (supposedly they’re not actually that rare) and I would probably ask a consultant to do this for me from the offset.

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Every planning proposal or amendment must be accompanied by a murdered Bechstein’s bat.

This is a local solution to the bat tunnels dilemma specifically and more broadly the planning system. Natural England would be made to weigh up the cost of a single bat dying, and the resulting small amount of conscious blood on their hands, against the potential of multiple bats experiencing the long and drawn out suffering of being hit by a 200mph train, due to their inaction.

Given their policy that no bat must die, they will not be pleased either way. The resulting paradox will most likely force them to reconsider their position, or potentially suffer indefinite operational gridlock. Assuming that Natural England will bend to the prospect of bat martyrdom (animal martyrdom is something I have spoken about at length in a previous post), they must seriously consider whether a proposal is worthwhile submitting in the first place. This I think has potential for other animal related planning proposals (unless there are one or two animals left like a white rhino or something, in which case this policy is entirely destructive). For example, if you come to me wanting to build a tunnel for hedgehogs, I’ll only consider it if you run over a hedgehog to show me you’re serious. Otherwise, all those hedgehog lives will be on you.

A Bechstein’s Bat is held in captivity at the centre of every site of National interest.

For many years defence hawks have advocated for an increased proportion of GDP to be spent on Defence & National Security. A simple proposal to ensure this money is allocated (the precedent of the bat tunnel demonstrates this) is to first introduce and then highlight the presence of Bechstein’s bats at these sites. Natural England will then, naturally, begin proceedings to protect these bats at any cost.

These bats could also be used in both a defensive and offensive manner. For example, if creatively enough located, ideally at the very centre of these sites or even underground, there will be significant enough impetus on Natural England to increase security and lobby for increased defence spending. No bat must die. Here the assumption is that Natural England will treat bats in captivity equally to those that exist in the wild. The alternative is to manually introduce Bechstein’s bats into the wild near these sites of strategic national interest. I would probably begin with scattering them throughout the coastline of the UK, with a few at the major ports just to be sure. The balance is how to do so in a way that they are still considered endangered. Defensively, the very existence of Natural England and their unflinching commitment to bat wellbeing will act as a strong deterrent to other countries, given their explicitly stated and previously enforced red lines.

Similarly, bats could be offensively transported alongside soldiers in the standing army to war zones across the world. Natural England would then presumably take it upon themselves to form an auxiliary corps responsible for protecting these bats at any cost in the face of enemy aggression. A downside of this strategy is that hostage-bats may be used in a similar manner by the enemy in order obtain certain demands, though I will assume that given their namesake, Natural England are only concerned with the welfare of Anglo-Saxon bats. In this case, a bat proxy-war may break out between the respective wildlife organisations of each country, hell-bent on saving their own bat’s lives at any cost. We must also consider the ideal that Natural England cares equally about Anglo-Saxon bats based abroad as it does those flying peacefully at home, with all the comforts of a standard batunnel. This further raises questions into Natural England’s policy regarding migratory birds which I will not get into.

A Bechstein’s Bat at the Bank of England is fed a daily bug allowance inversely proportional to inflation.

It goes without saying that this bat must not die, at any cost. The economists at the Bank of England must carefully use the dials and levers available to them to ensure that inflation is kept low. While this is what they presumably get up to anyway, the additional presence of a Bechstein bat, and the resulting pressure from Natural England will ensure that they stick to their targets.

This policy raises the interesting potential scenario wherein Natural England themselves may be required to voluntarily free up their own funding, martyring themselves in the name of the Bank of England Bat. Employees and concerned campaigners will reach a Buddhist style Nirvana of bat well-being, free of their own cause. Maybe the real infrastructure was the bats we saved along the way.

In 1960/2001, a French/British group of largely men met in Normandy/Battersea to discuss the implications of literary constraints, and the potential for new literary structures to create meaning. The Oulipo movement/So Solid Crew pioneered constrained works such as Exercices de Style/21 Seconds where 99/10 versions of the same scene are recounted each in a different style/by a different MC.

The Oulipo movement has always maintained strong ties to mathematics. Simple rules such as replacing or omitting letters can be extended to entire algorithms. Similarly, So Solid Crew themselves turned to maths, calculating the 21 allocated seconds for each MC by dividing their three and a half minute song by 10 MCs and rounding to the nearest integer. History doesn't repeat itself, but it often rhymes.

A popular critique of 21 Seconds is that “none of the crew use their time to say anything remotely insightful”. Quite obviously, the purpose of So Solid Crew’s lyrics are not to provide insight to the listener. Otherwise they would have featured someone like Martin Lewis. But alas they did not. They gave themselves 21 seconds each, and they simply spent their time discussing this decision.

This introspective discussion is what separates So Solid Crew from other proponents of potential literature and Oulipo. The Crew spend almost their entire time discussing the very structure that they themselves have imposed. In doing so, they have meticulously weaved an ouroboros of structure and meaning, both existing in unison, neither without the other.

In the style of Claude Lévi-Strauss, given this self-referential meta-structure that the Crew have imposed on themselves, we should in theory be able to invert the formula. In direct contrast there should exist music blissfully un-self-aware, unconstrained but meaningless.

Jacob Collier (b. 1994) is a virtuoso. He was born with the ability to play seemingly any instrument. As a result, he is entirely unconstrained in what he can sonically produce. He can command audiences to sing complex harmonics with just his hands and converse with Jazz artists through the medium of diminished chords, resolving just before the next unskippable ad kicks in.

And yet, I have never met anyone that actually enjoys his music. It is directionless. The lack of any semblance of constraint or difficulty is Collier’s blessing and ultimate curse. He is at least aware of this, claiming asylum under the band of artists with ‘creative infinity syndrome’. I’m sure at one point So Solid Crew were in the same category. But in the face of this, Megaman, Lisa et. al, were unwavering in their commitment that less-is-more. Not only did they implement this philosophy to great effect, but they told their audience exactly what they were doing. The wizard’s curtain of creativity was being thrown away all together. Short-form video like TikTok is a close candidate for a new cultural structure that will forever seem normal. Maybe Jacob Collier could do us all a favour and restrict the length of his songs to 21 seconds.

The Oulipo movement was partially founded to discover new literary structures, to progress the search towards potential literature. However, the majority of these structures have not flown the cultural nest. But to Raymond Queneau and others that doesn’t seem to matter, and neither does it seem to matter to Megaman or Lisa. It was, and still is, the very act of subverting the rules of engagement that reminds people of the meaning in the words we read and the music we hear. So Solid Crew made this point in a more direct way than most, and for that alone, they are here to stay. Romeo Done.

In effect it states that a machine can be seen to be providing intelligent dialogue or conversation in Chinese, but inside 'the room' there is simply someone with a rule book that is large enough to enumerate all the potential answers, and the rules to generate and combine them (in effect, the structure of Chinese). This person doesn't truly know or think about what they are doing. Whether or not this is a realistic analogy is not something I am going to attempt to answer. To me, the Chinese room, with its counter and counter-counter-arguments2, is relatively convincing3.

But for the purposes of this post, I'm not convinced, I think it's completely and utterly wrong and a bit daft.4

Here's a quote that I enjoy from the Stanford Encyclopaedia of Philosophy with an interesting extension to the thought experiment.

[...] He [Searle] suggests a variation on the brain simulator scenario: suppose that in the room the man has a huge set of valves and water pipes, in the same arrangement as the neurons in a native Chinese speaker’s brain. The program now tells the man which valves to open in response to input. Searle claims that it is obvious that there would be no understanding of Chinese. (Note however that the basis for this claim is no longer simply that Searle himself wouldn’t understand Chinese – it seems clear that now he is just facilitating the causal operation of the system and so we rely on our Leibnizian intuition that water-works don’t understand (see also Maudlin 19895).) Searle concludes that a simulation of brain activity is not the real thing.

It's a good point, water-works can't think, can they? If a brain can think, and a brain can be modelled like a series of pipes, then why can't a series of pipes think? If I were to replace a single pipe with an actual real neuron, the signal of which controlled a valve to the next pipe, then would my water-works6 be able to think? If I just start replacing all the pipes in my works with equivalent neurons, at what point does consciousness kick in?

There's a nice paper that touches on that last point justifying the idea that large organisations, or countries such as the United States can (or rather are) conscious7.8 With that in mind, I'll get to the point. Can we design a chemical process to think?9 The fact I'll be dealing with simulated chemical processes adds a layer of complication because we're back to asking whether computers can think. But lets assume I'm going to end up building it.10

The Plan

How am I going to go about this task? I could cheat and try to model a neural network using a chemical process. After all, neural networks have been proven to be universal function approximators11. I think this is a bit of a boring and sort of cheating. Additionally, attempting to directly find a chemical process that can reason is more interesting than finding a neural network that can reason, because I could feasibly go and build the chemical process in the real world12. The system it would live in is the same as us (i.e. not within the confines of a computer). However, in the 'lets try to get a neural network to think'-space inherently it still just exists on a computer and you're back to square one13. You'd have to make the neural network into some physical representation (like a brain)14. There are a host of assumptions here, namely to do with physicality, scale and permanence, but we really must move on.

Quite obviously, the first reasonable step to building a chemical process that can think, is to build a chemical process simulator.

Existing process simulators are bloated, use outdated GUIs and are not built with machine learning / data-driven workflows in mind. So I built my own. It's currently written in Python but it might not be one day15. I've called it <process>, which you have to write as a code block.

In <process>, processes are fully represented within a TOML file. The files are detailed enough to provide a complete representation of a process16. The representation is also not too detailed where there would be extraneous effort to define it, reducing bloat17. If you're far enough into process design that you want to model a process in significant detail then 'rigorous' tools such as Aspen or gProms are what you want anyway. <process> is not for rigorous design, it sits somewhere between SFILES, and Aspen in terms of complexity. It's main advantage over SFILES18 is that chemical processes can be simulated directly from an interpretable text-based .toml file19. <process> is effectively a compiler for chemical processes, compiling a readable text-based format into an object that can be simulated and analysed.

Here's an example of the basic components of a process.toml file.

[[species]]
name = "water"
molecular_weight = 18.01528 # kg/kmol
cp_poly_coeffs = [276370.0, -2090.1, 8.125, -0.014116, 9.3701e-6]
enthalpy_of_formation = -285830.0 # kJ/kmol

...

[[species]]
name = "nitro_benzene"
molecular_weight = 123.109
cp_poly_coeffs = [295300.0,-80.7,1.705]
enthalpy_of_formation = 12500.0

[[streams]]
name = "benzene_inlet"
molar_flowrate = 0.5 # kmol
molar_ratios = {benzene = 1}
temperature = 298 # K 
pressure = 101325 # Pa

...

[[streams]]
name = "benzene_recycle"
molar_flowrate = 0
molar_ratios = {benzene = 1}
temperature = 298
pressure = 101325

[[separator]]
name = "separator_one"
inlet = ["final_product"]
outlet = ["tops", "waste_water"]
spec = [{nitro_benzene= 0.95},{water= 0.95}]

...

[[mixer]]
name = "reactant_mixer"
inlet = ["benzene_inlet", "nitric_acid_inlet","recycle","benzene_recycle"]
outlet = ["reactor_reactants"]

[[reactor]]
name = "reactor_one"
inlet = ["reactor_reactants"]
outlet = ["reactor_products"]
reactant_stoichiometry = {benzene=1, nitric_acid=1}
product_stoichiometry = {water = 1, nitro_benzene =1}
conversion = {benzene=0.9}

[[splitter]]
name = "splitter_one"
inlet = ["reactor_products_cooled"]
outlet = ["final_product", "recycle"]
split_ratio = [0.8, 0.2]

[[heat_exchanger]]
name = "product_cooler"
inlet = ["reactor_products"]
outlet = ["reactor_products_cooled"]
outlet_temperature = 298

By design, it's relatively self-explanatory. You define molecular species, all the streams, their connections, etc. Unit operations each have specific attributes which must be defined such as the split ratio of a splitter.

You don't have to specify everything20, <process> will attempt to simulate unit operations with specified inlet streams until the entire process converges.

Degrees-of-freedom are currently quite uncompromising. There is no way to fix a stream, and every variable can be changed. There is no need for a specific 'recycle' block or stream due to the way a process is converged. At the expense of bloat, you have to pay slightly more attention to what is being defined, which is not the worst trade off.

Anyway, I might write this up a bit longer at some point depending if I flesh it out a bit. For now, onwards... I can visualise a process as follows:

process_file = "nitrobenzene_process.toml"
process = parse_process(process_file)
process.plot("outputs/process.png")

And simulate it as so:

process.simulate(tol=1e-7)
process.plot_convergence("outputs/convergence.png")

Stream Tolerance -> 2.20e+00 -> 5.29e-01 -> 1.12e-01 ->...-> 9.47e-07 -> 3.83e-07 -> 1.54e-07
Converged in 30 iterations.

The overall mass and energy balance can be validated21, and I can also print off the steady-state of the converged process.

print(process, '\n')
print(process.confirm_energy_balance(), '\n')
print(process.confirm_mass_balance())

Process(name='nitrobenzene_process', unit_operations={'reactant_mixer': Mixer(name='reactant_mixer', inlet=[(0.50 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '1.00'}, (0.50 kmol/hr, 312.00 K, 101325.00 Pa) {'nitric_acid': '1.00'}, (0.26 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'water': '0.47', 'nitro_benzene': '0.46'}, (0.04 kmol/hr, 298.00 K, 101325.00 Pa) {'nitric_acid': '0.17', 'water': '0.28', 'nitro_benzene': '0.10', 'benzene': '0.45'}], outlet=[(1.31 kmol/hr, 300.92 K, 101325.00 Pa) {'benzene': '0.41', 'nitric_acid': '0.39', 'water': '0.10', 'nitro_benzene': '0.10'}], process=...), 'splitter_one': Splitter(name='splitter_one', inlet=[(1.31 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'water': '0.47', 'nitro_benzene': '0.46'}], outlet=[(1.04 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'water': '0.47', 'nitro_benzene': '0.46'}, (0.26 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'water': '0.47', 'nitro_benzene': '0.46'}], process=..., split_ratio=[0.8, 0.2]), 'reactor_one': Reactor(name='reactor_one', inlet=[(1.31 kmol/hr, 300.92 K, 101325.00 Pa) {'benzene': '0.41', 'nitric_acid': '0.39', 'water': '0.10', 'nitro_benzene': '0.10'}], outlet=[(1.31 kmol/hr, 367.04 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'water': '0.47', 'nitro_benzene': '0.46'}], process=..., reactant_stoichiometry={'benzene': 1, 'nitric_acid': 1}, product_stoichiometry={'water': 1, 'nitro_benzene': 1}, conversion={'benzene': 0.9}, heat_of_reaction=153180.0), 'product_cooler': HeatExchanger(name='product_cooler', inlet=[(1.31 kmol/hr, 367.04 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'water': '0.47', 'nitro_benzene': '0.46'}], outlet=[(1.31 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'water': '0.47', 'nitro_benzene': '0.46'}], process=..., outlet_temperature=298, delta_temperature=None), 'separator_one': Separator(name='separator_one', inlet=[(1.04 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'water': '0.47', 'nitro_benzene': '0.46'}], outlet=[(0.52 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'nitro_benzene': '0.88', 'water': '0.05'}, (0.53 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'nitro_benzene': '0.05', 'water': '0.89'}], process=..., spec=[{'nitro_benzene': 0.95}, {'water': 0.95}]), 'separator_two': Separator(name='separator_two', inlet=[(0.52 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'nitro_benzene': '0.88', 'water': '0.05'}], outlet=[(0.47 kmol/hr, 298.00 K, 101325.00 Pa) {'nitric_acid': '0.02', 'water': '0.03', 'nitro_benzene': '0.96', 'benzene': '0.00'}, (0.04 kmol/hr, 298.00 K, 101325.00 Pa) {'nitric_acid': '0.17', 'water': '0.28', 'nitro_benzene': '0.10', 'benzene': '0.45'}], process=..., spec=[{'nitro_benzene': 0.99}, {'benzene': 0.95}])}, streams={'benzene_inlet': (0.50 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '1.00'}, 'nitric_acid_inlet': (0.50 kmol/hr, 312.00 K, 101325.00 Pa) {'nitric_acid': '1.00'}, 'reactor_products': (1.31 kmol/hr, 367.04 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'water': '0.47', 'nitro_benzene': '0.46'}, 'reactor_products_cooled': (1.31 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'water': '0.47', 'nitro_benzene': '0.46'}, 'reactor_reactants': (1.31 kmol/hr, 300.92 K, 101325.00 Pa) {'benzene': '0.41', 'nitric_acid': '0.39', 'water': '0.10', 'nitro_benzene': '0.10'}, 'final_product': (1.04 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'water': '0.47', 'nitro_benzene': '0.46'}, 'tops': (0.52 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'nitro_benzene': '0.88', 'water': '0.05'}, 'waste_water': (0.53 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'nitro_benzene': '0.05', 'water': '0.89'}, 'pure_product': (0.47 kmol/hr, 298.00 K, 101325.00 Pa) {'nitric_acid': '0.02', 'water': '0.03', 'nitro_benzene': '0.96', 'benzene': '0.00'}, 'benzene_recycle': (0.04 kmol/hr, 298.00 K, 101325.00 Pa) {'nitric_acid': '0.17', 'water': '0.28', 'nitro_benzene': '0.10', 'benzene': '0.45'}, 'recycle': (0.26 kmol/hr, 298.00 K, 101325.00 Pa) {'benzene': '0.04', 'nitric_acid': '0.03', 'water': '0.47', 'nitro_benzene': '0.46'}}, species={'water': water (18.01528 kg/kmol), 'benzene': benzene (78.11 kg/kmol), 'nitric_acid': nitric_acid (63.013 kg/kmol), 'nitro_benzene': nitro_benzene (123.109 kg/kmol)}, print=True, simulated_at=datetime.datetime(2024, 8, 18, 15, 26, 8, 154480, tzinfo=datetime.timezone.utc)) 

Enthalpy in: 37412.944 kJ,  Reaction Enthalpy: 73175.159 kJ, Enthalpy Added: -38052.065 kJ, Enthalpy out: 72536.082 kJ
Difference: -0.043281 kJ (-0.000116%)
False 

Mass in: 70.561 kg, Mass out: 70.562 kg
Difference: -0.000613 kg (-0.000869%)
True

I can simulate heat exchangers, splitting streams, mixing streams, basic separations, non-adiabatic reactions with stoichiometric ratios, components with specific heat capacities as a function of temperature... this should be enough for now.

What was that about? Anyway…

The goal here is to build an input-output machine, that on the surface acts like the 'room' in the Chinese room, and more precisely, the waterworks within the room as proposed by Searle in his brain simulator scenario. To make this easier, when I say 'think', I mean I'm going to approximate a function, because as I previously mention, I don't believe in the Chinese room argument.

I will go about doing this through direct simulation , where the chemical process is directly used to model a function via something analogous to symbolic regression or evolutionary programming22.

Therefore, the mathematical problem I will solve will be a least-squares (machine learning) task over chemical processes:

where a chemical process 𝒫 is defined by a set of unit operations 𝒪, connecting streams 𝒮, yappa yappa blah blah blah... Basically, you choose what the input x represents in your process, and what the output y represents. Simulate the chemical process for a given x and evaluate the output. Do this for all values of x in the dataset and you can evaluate how well the process approximates the function.

That being said, I am going to solve this optimisation problem using evolutionary programming, a benefit of using <process>. Firstly, I'll demonstrate a way of evaluating the objective function.

To begin with I will define a very basic process with a single component as an inlet stream, a single component as an outlet stream, and a mixer that doesn't do anything.

[[species]]
name = "water"
molecular_weight = 18.01528 # kg/kmol
cp_poly_coeffs = [276370.0, -2090.1, 8.125, -0.014116, 9.3701e-6]
enthalpy_of_formation = -285830.0 # kJ/kmol

[[streams]]
name = "inlet"
molar_flowrate = 0.5 # kmol
molar_ratios = {water = 1}
temperature = 298 # K 
pressure = 101325 # Pa

[[streams]]
name = "outlet"

[[mixer]]
name = "mixer"
inlet = ["inlet"]
outlet = ["outlet"]

For now, assuming temperature of both the inlet stream and outlet stream represent my functional input and output respectively, I can simulate the process and evaluate the objective function as follows:

y_test = []
for i in range(n):
    process.streams['inlet'].temperature = x[i]
    process.simulate(tol=1e-7)
    y_test.append(process.streams['outlet'].temperature)

Can you see where we're going yet? I can take advantage of multiprocessing to evaluate the objective function in parallel. This is a strength of <process>, and is a good demonstration of designing something like a process simulator with machine learning use cases in mind.

I'll assume that physical units can be scaled later on. Now I can hypothesise that there is a heat exchanger between the streams, with a given temperature decrease to get a different 'loss' between our data and the process output.

[[heat_exchanger]]
name = "product_cooler"
inlet = ["inlet"]
outlet = ["outlet"]
delta_temperature = -3 # K

I'll stop here, we get the idea. We can change the type of unit operation, their internal parameters, and their order to try and approximate a function. Now we have the ability to:

1. Evaluate how well our process can approximate a function;

2. Mutate the process23.

We can build a basic evolutionary programming framework to solve the optimisation problem above24, acting like a sort of growing neural network, with feedback.

But that will have to wait…. for next time!

Ratatouille is a film about legacy, the importance of institutions, and the need for occasional reinvention. Given that rats live for approximately 2-3 years, it is therefore unfortunate that Remy will not have lived long enough to reap the financial rewards from his eloquently named bistro, La Ratatouille, most likely dying mere months after opening.

Though the zombie-chef-vassal Linguine basically gives up cooking all together to glide around on roller skates, we can only hope that La Ratatouille has a strong enough institutional memory such that he is not made immediately jobless in the event of Little Chef's death.

Perhaps a smaller, food-loving creature, like a bug, could sit on top of the head of one of Remy's offspring and be guided by hallucinations of ghost Remy. In turn, the Parisian food scene would become progressively smaller and smaller, and culinary trends would occur at increasingly shorter intervals as the life span of each puppet-master diminishes in magnitude. Mayfly-atouille would probably fit within a Tiktok.

Of course, that would be ridiculous. Remy is one of a kind not only among other rats but among humans themselves. It is clear that by the end of the film, all other rats in the colony carry some semblance of the spirit of Chef Gusteau with them. They do not steal, they maintain cleanliness, and though Remy has preached the culinary gospel to the masses and has entered the kingdom of Gust...

Wait? .... Is Remy the rat Jesus?

Throughout the film, Remy seems to be the only one to be able to engage with the ghost of Chef Gusteau, though he is reminded in a scene where they are both simultaneously surprised, that the God-Chef is a figment of his own imagination. This insight tells us that Gusteau is most likely similar to a Christian god, existing in unison with the hearts and souls of rats, and not external to them.

The message that anyone can cook is further emphasised by Gusteau by his use of this projected internal voice. While Remy at this stage asserts himself as a messenger of Gusteau, the gospel is waiting to be preached. By spreading the culinary good-news to the colony, presumably each rat holds with them the spirit of Gusteau, while still clearly maintaining Remy in high-regard. With that, the holy trinity of Gusteau, Remy, and cooking spirit becomes whole.

We also observe that there were false prophets in this story, most notably Chef Skinner. Skinner claims to uphold Gusteau's values but it is revealed that he does not care for good food, and exploits his position for financial gain at the expense of the reputation of Gusteau. For a short time, even zombie-chef Linguine's actually talented and hardworking human muse Collette strays from the flock, unsure of the message that Remy preaches. But even she recognises the true message of Gusteau in opposition to the false prophet Skinner.

While Pixar have been nuanced in their portrayal of rat-Jesus, there are two aspects I would like to speculate on where I believe the film did not go far enough, or that may become candidates for a strong sequel.

The first addresses the issue of Remy's mortality. For Remy's message to truly transcend the short lifespan of a rat and for him to gain immortal status as the one true culinary prophet, Remy had to die. Ideally, he would have remained in the rat-trap from which he inextricably escapes after his dad and brother push a gargoyle off of a church and onto the car boot, crushing the boot just enough to release him. This would have been a commoners death for Remy, unrelenting and sacrificial. In doing so, the rest of the colony would have been empowered to follow in his example, potentially leading to the financial success of La Ratatouille beyond opening night and the proceeding few months.

By instead witnessing the initial success of La Ratatouille, Remy more strongly implies that he himself is the anyone that Gusteau refers to. The remaining rats would most likely be less emboldened to consider running a restaurant for humans. In not sacrificing himself in the name of Gusteau's message, Remy bears more resemblance to Jacob Frank, who claimed to be the reincarnation of the self-proclaimed messiah Sabbatai Zevi, and lived long enough to ensure that everyone around him got bored of the shtick, and his movement fizzled out.2

The second avenue, given we know there is at least one messenger of Gusteau, is that there may be more. These may be copycats/rats that appear based on the presumed commercial success of La Ratatouille, or they may be genuine prophets of Gusteau. There is no reason that dead Gusteau should suddenly stop appearing in the imaginations of animals3, and similarly just because Remy is the main character we cannot ignore other animals that may have watched Chef Gusteau on TV, for example cats4 and dogs5.

Disregarding the thousands of potential heirs to the La Ratatouille empire specifically, these competing factions of culinarily inspired animals would inevitably vie for legitimacy in an increasingly crowded market. I imagine Remy's dunce brother Emile would be the first to claim the bloodline potentially leading to a series of culinary-rat-pontiffs that become increasingly disliked. Lollard rats from the countryside may scoff when they hear news of a new Parisian loft bistro opening, shrines to locations such as the farmhouse Remy originally lived in will be built, and academics will convene to discuss the implications of the trinity and the true nature of Gusteau.

It is clear that the prophecy of Remy, and resulting idyllic moment in time, will be short lived. La Ratatouille is built on much more worrisome foundations than it may seem. In preaching the good-news of Gusteau, but maintaining his own personal vice-like grip on animal-bistros without a clear succession plan for either his ideology or business, Remy will soon learn that anyone can cook.

I have chosen not to touch upon the fact that Remy can control Linguine by pulling on his hair because that is completely unbelievable.

There is yet one leverage point that is even higher than changing a paradigm. That is to keep oneself unattached in the arena of paradigms, to stay flexible, to realize that no paradigm is "true", that every one, including the one that sweetly shapes your own worldview, is a tremendously limited understanding of an immense and amazing universe that is far beyond human comprehension. - Donella Meadows from her book Thinking in Systems: A Primer.

This is a personal manifesto for discovery, and an attempt to contextualise modelling, optimisation, and the great leap towards discovery and AI for Science.

It is important to first define what we mean by the act of 'gaining knowledge' as it will enable a greater understanding of different modelling and optimisation methodologies with a larger contextual picture in mind. Within the context of process engineering, engineers often seek to learn about the behaviour of existing complex systems to improve monitoring, control, or operation.

Engineers also aim to identify new processes that may lead to improved efficiencies or reduced environmental impacts. These two fundamental pursuits, understanding existing systems, and extrapolating this information to discover new systems are not only relevant for process engineers, but across all of science and engineering. Discovery and optimisation are themselves deeply linked, but we first must ask ourselves what constitutes a discovery. Very rarely do new chemical reactors burst into existence.

Discoveries must be gently coaxed, teased into reality. I will argue that it is neither deductive or inductive, or even the combination of these approaches that is the driving force behind innovation, but rather the tension between them. When this tension is built up to such an extent, and the solutions from optimisation problems cease to look like what you started with, deductive models break down, and no existing observations are available, then, we have discovered.

The act of gaining knowledge most commonly involves learning through observations, or by building up or combining smaller 'truths'. Socrates's student Plato asserted that knowledge is infallible, and 'real'. The implication being that basic facts or truths could be combined, resulting in larger, previously unknown 'truths'. This represents a key mindset behind the development of modern mathematics, theoretical physics, and mechanistic models such as those used within Computational Fluid Dynamics (CFD).

On the other hand, Plato's student Aristotle generally took the view that we can only assert something as true if we have observed it. By instead focusing on empirical observation and the systematic study of the existing physical world, Aristotle laid the foundations for inductive reasoning.

The tension between inductive and deductive reasoning not only persists throughout history, but I would argue is itself the driving force behind many scientific discoveries and advances.

The Prague astronomical clock, or Orloj, was created in by clock maker Mikuláš of Kadaň in the year 1410 based on meticulous empirical observations. The clock itself accurately plots the sun and moon around the earth in a geocentric motion. Despite using the wrong reference frame, the clock was deemed so accurate and impressive that a later clock maker was blinded by Prague authorities to ensure he could not make another.

Two centuries later, Johannes Kepler lived in Prague, and had a provable interest in astronomical clocks. He almost certainly sat in the Main Square and observed Mikuláš' mechanism. After studying observations made by his mentor Tycho Brahe, and previous geocentric models such as that within the Orloj, he was inspired to derive the mathematical laws of planetary motion and in turn elliptical orbits.

This correct discovery, as a result of previous inductive reasoning would not have occurred if it were not for Mikuláš obtaining the correct answer using the 'wrong' methodology. Kepler's work demonstrates how deductive reasoning can challenge and refine knowledge initially gained through inductive methods. The Earth-centric model, built on countless observations and embodied in artefacts like the Prague Astronomical Clock, was ultimately superseded by a heliocentric model that more accurately described celestial mechanics.

This historical example demonstrates how the interactions between both inductive and deductive approaches drives scientific progress, each complementing and challenging the other. As opposed to two separate pathways, Plato's deductive reasoning and Aristotle's inductive approach bear more resemblance to two walls in a corridor, in which it is possible to bounce off of either to move forwards and gain knowledge, shaping discovery throughout history.

These two rationalist and empiricist philosophies continue to lead, in turn, to the motivation of both frequentist and Bayesian approaches to statistics, and importantly the validity of physical and data-driven models. Much like the proceeding body of academic literature leading up to this thesis, I will exploit both rationalist and empiricist perspectives to discovery including modern inductive tools such as machine learning alongside those derived through deductive reasoning such as CFD. The remainder of this section will demonstrate specific examples of how inductive and deductive reasoning shape approaches to physical and data-driven modelling, and how knowledge can be gained through optimisation, eventually leading towards scientific discovery.

There is a Bayesian optimization gold-rush, and everyone is selling shovels. Bayesian optimization is core to the concept of self-driving labs, automated drug-discovery, and many other modern research topics. It's well motivated, and a very useful tool. Start ups are being founded and pharmaceutical and chemicals companies are curating teams that focus fundamentally on Bayesian optimization in one way or another.

As a result, there is now a growing number of libraries, ranging from black-box APIs and no-code solutions to statistically accurate fine-grained representations; each becoming more bloated as new functionalities or use cases are appended.

With all of these options, each with their own special definition of a dataset, or a kernel, or an acquisition_function, or otherwise, it becomes difficult to see the wood for the trees. Someone in my group recently asked me if it was reasonably possible to implement Bayesian optimization, and I more often than not get asked what library or package I use.

It is obviously important that BO tools become widely available, particularly to those that don't know how to code, or don't have the time such as chemists or industrial engineers. But the trade-off is that students, and practitioners are potentially using something that they don't understand, and investors may be hoodwinked into investing in something that seems more complex than it is.

My mission in this post is to demonstrate that, whilst Bayesian optimization entails a lot of moving parts, implementing it from scratch really shouldn't take too long. To make my point, I am going to code everything required for Bayesian optimization, including visualization... in under 100 lines of standard Python and Numpy.

This will include a custom metaheuristic-based optimization algorithm, Gaussian processes from scratch with trainable hyperparameters, an acquisition function, and an experimental design loop.

First, I'll give myself some ground rules:

Rules

1. Bayesian Optimization that is competitive with established packages;

2. Python Standard Library + Numpy only;

3. Simple as possible;

4. Code must be interpretable.

To begin with I'll implement a Gaussian process from scratch.

Gaussian processes

Now I'll need a kernel function that defines how related two points are in input space. I'll go for the Matern 5/2. I'm not choosing it because it's differentiable1, but rather because I think samples from the prior will look most like functions I'm interested in.

def k(x1,x2,p): # matern 5/2 kernel
    sigma,l = p[0],p[1] # hyperparameters
    d = np.sqrt(np.dot(x1-x2,x1-x2))
    return sigma**2 * (1 + np.sqrt(5)*d/l + 5*d**2/(3*l**2)) * np.exp(-np.sqrt(5)*d/l)

Simple enough. Forget the hyperparameters now, I'll cover those later. Now, I'll give myself the ability to calculate a covariance matrix. I only need to calculate the kernel function for the upper triangular, then symmetrically set the corresponding lower triangular value to be the same (because all valid covariance matrices are positive semi-definite), saving me some time.

def cm(x,p): # covariance matrix
    n = x.shape[0]; K = np.zeros((n,n))
    for i in range(n):
        for j in range(i,n): # symmetric
            K[i,j] = K[j,i] = k(x[i],x[j],p)
    return K + 1e-6*np.ones(n)

I've added some jitter to the diagonal just to help out when inverting the matrix in a bit. This is relatively common across all Gaussian process libraries. The final GP utility I'll require is the negative log-likelihood, which will be used to assess hyper-parameter performance.

def nll(p, x, y): # negative log likelihood
    K = cm(x, p) # calculate covariance matrix
    return -0.5 * (np.log(la.det(K)) + (y - p[2]).T @ la.solve(K, y - p[2]) + len(x) * np.log(2 * np.pi))

Now everything needed has been defined, I'll create my Gaussian process class. I will simplify the training procedure by making it train on instanciation. The only other thing that is needed is the ability to call it, returning the mean and variance of the posterior distribution at x.

class GP: # Gaussian process class
    def __init__(self,x,y):
        self.x = x; self.y = y
        # optimize hyperparameters (sigma, l, constant mean)
        self.p = tiny_opt(partial(nll, x=x, y=y), np.array([[0.1,10.0],[0.1,10.0],[np.min(y),np.max(y)]]))
        self.K_inv = la.inv(cm(x,self.p)) # precompute inverse covariance matrix

    def __call__(self,x): 
        k_row = np.array([[k(x,xi,self.p) for xi in self.x]]) # compute row of covariance matrix
        dot_prod = np.dot(k_row,self.K_inv) # precompute dot product
        return self.p[2] + dot_prod.dot(self.y-self.p[2]).item(), k(x,x,self.p) - np.dot(dot_prod,k_row.T).item()

It is defined by data x and y (both row-wise Numpy arrays). I have a mysterious function called tiny_opt which for now provides us with optimal hyper-parameters with respect to the negative log-likelihood. This 'training' step, and the precompution of the inverse of the covariance matrix (resulting in the precision matrix) defines a 'trained' Gaussian process2.

One of these parameters is a constant that defines my mean function. As a result of this, combined with the kernel lengthscale parameter, there is now no need to deal with data normalisation. I've previously covered noisy Bayesian optimization, but as I won't be dealing with noisy observations here, my GP is noiseless.

Next up, I'll outline what is actually happening in my tiny_opt function. This will also eventually be used to optimize my acquisition function as well so it's worth getting right.

Metaheuristic Optimization

As I previously mentioned, I see the ability to easily calculate the gradient of a Gaussian process largely as a distraction. I am relatively confident that most people who have any significant experience optimizing Gaussian processes will agree. In addition, BO can potentially be made unusually inefficient when using a gradient-based optimizer due to flat regions within an acquisition function, leading to functions such as logEI being proposed.

Gaussian processes, or resulting functions that apply them (such as acquisition/utility functions (spoiler)) are generally non-convex. Whilst local minima can be overcome, it's clear that even in one dimension, there are often as many local optima as data points. Gaussian processes are practically free to evaluate in the grand scheme of experimental design, and most practical problems are interval-constrained. Combined with the fact that they can of course be evaluated in parallel, motivates the use of evaluation hungry but effective metaheuristics. I myself have found them very effective in optimizing acquisition functions, and was pleased to see Google's internal GP optimization algorithm taking a similar view when they recently released details.

My algorithm is self-documented but I will describe it below. I've found it to be simple and effective. I wouldn't even describe it as a metaheuristic.

def tiny_opt(f,b): # small metaheuristic optimization
    c = np.mean(b, axis=1); b_og = b.copy()
    b_f = -np.inf; count = 0; w = 1 # track best solution, count stagnation, and shrink factor
    while True:
        for i in range(len(b)): # shrink search space around current centre by w
            r = (b[i,1] - b[i,0]) * w
            b[i] = [c[i] - r / 2, c[i] + r / 2]
        # generate random solutions within search space
        x = np.clip(ra.rand(1000,len(b))*(b[:,1]-b[:,0])+b[:,0], b_og[:,0], b_og[:,1])
        with Pool() as pool: # evaluate solutions in parallel
            y = pool.map(f, x)
        sol = x[np.argmax(y)] # best solution
        if f(sol) > b_f: # if improvement, update best solution
            b_f = f(sol); c = sol; count = 0; w = 0.75 # shrink search space next time
        else: # if no improvement, don't shrink search space next time (set w = 1)
            count += 1; w = 1 # increment stagnation counter
        if (b_og[0,1]-b_og[0,0])/(b[0,0]-b[0,1]) < 0.25 or count == 3:
            # if search space has shrunk below a threshold or not shrunk for 3 iterations
            return c # return best solution!

Simply, I sample solutions within the bounds and evaluate them in parallel. If I sample the best overall solution I store this, and shrink the bounds around this point. I then re-sample and repeat. If I don't find a better solution I don't shrink the bounds. If I don't find a better solution for three iterations I terminate. I also terminate if the bounds shrink below a certain threshold. These two properties ensure that the algorithm terminates eventually (empirically). It is effectively a global trust-region method.

Here's a little visualisation that I've drawn.

With the ability to optimise functions, all I need is an acquisition function, which is simple enough... and a loop!

This is just the expected improvement of a Gaussian process, implemented by importing NormalDist from the statistics standard library.

def ei(gp,x): # expected improvement
    mu, var = gp(x); N = NormalDist()
    z = (mu - np.max(gp.y)) / np.sqrt(var)
    return np.sqrt(var) * (z * N.cdf(z) + N.pdf(z))

And here is my loop...

bounds = np.array([[-10.0,10.0]]) 
while True:
    gp = GP(x, y) # build GP
    x_opt = tiny_opt(partial(ei,gp),bounds); y_opt = ei(gp,x_opt) # optimise aq
    x = np.vstack([x,x_opt]) # update x
    y = np.vstack([y,np.array([f(x_opt[0])])]) # evaluate and update y

... that's it!

But to really prove that this works, I'm going to even choose an interesting test function to use, and implement the ability to visualize 1D functions (note that all of the above works for any dimensional function.).

Plotting

To plot my package to see if it is working, I'll need a test function. One of my personal favourite ways of doing this is to sample one from a GP prior:

x_gp = np.linspace(bounds[0,0],bounds[0,1],100).reshape(-1, 1)
K = cm(x_gp, [1.0,1.0,0.0])
y_gp = ra.multivariate_normal(np.zeros(len(x_gp)), K).reshape(-1, 1)
f_gp = GP(x_gp,y_gp); f = lambda x: f_gp(x)[0] # this now acts as my 'true' function

An easy way of doing this is by drawing from a multivariate distribution with given covariance matrix and training a GP using this data. This provides a continuous, callable random function without dealing with things like pseudorandom number generation.

I've written before about plotting practices before, so I won't go into too much detail. Here is the code that evaluates the GP and acqusition function and plots them both.

In addition, I save each plot and produce a GIF at the end.

    x = np.linspace(bounds[0,0],bounds[0,1],3).reshape(-1, 1)  # initial training data
    y = np.array([f(xi) for xi in x]).reshape(-1, 1) # initial training data
    x_test = np.array([xi for xi in np.linspace(bounds[0,0],bounds[0,1],200)]).reshape(-1, 1) # test data
    images = []
    for _ in range(16):
        gp = GP(x, y) # create GP
        x_opt = tiny_opt(partial(ei,gp),bounds); y_opt = ei(gp,x_opt)
        mu, var = np.array([gp(xi) for xi in x_test]).T; std = np.sqrt(var)
        fig,ax = plt.subplots(2,1,figsize=(8,3),sharex=True,constrained_layout=True)
        ax[1].set_xlabel('$x$'); ax[0].set_ylabel('$f(x)$'); ax[1].set_ylabel('EI(x)')
        ax[0].scatter(x,y,c='k',marker='x')
        ax[0].plot(x_test, mu,c='k'); ax[0].plot(x_gp, y_gp,c='k',ls='--',alpha=0.5,label='True Function')
        ax[0].fill_between(x_test.flatten(), mu-std, mu+std, alpha=0.25,lw=0,color='k')
        ax[1].plot(x_test, [ei(gp,xi) for xi in x_test],c='k',label='Expected Improvement')
        ax[1].scatter(x_opt,y_opt,c='k',marker='o')
        for a in ax: 
            a.spines['top'].set_visible(False); a.spines['right'].set_visible(False)
            a.set_yticks([])
            a.legend(loc='upper center',bbox_to_anchor=(0.5,1.1),frameon=False,ncol=1)
        filename = f'{_:03d}.png'
        plt.savefig(filename,dpi=300); plt.close()
        images.append(Image.open(filename))
        x = np.vstack([x,x_opt]) # update x 
        y = np.vstack([y,np.array([f(x_opt[0])])]) # evaluate and update y
images[0].save('bo.gif',save_all=True,append_images=images[1:],duration=500,loop=0)

After all that, approximately 20% of the lines in the full code are for plotting!

Lessons

These things are, on the surface not that complicated. That is what makes BO as a tool so powerful. There are always a host of interesting domain-specific problems to be solved, but by demystifying BO as a whole it becomes easier to make these adjustments or developments.

I personally think that BO software has become overly cumbersome, but there are many moving parts, and it never quite turns out to be the one-size-fits all solution it is billed as. I don't think there's a long term solution or fix to this at the moment, I would only say to try it for yourself!

Executive Summary

- Triathlon and Weightlifting are the pinnacle of Olympic sports.

- Football and Breakdancing should be removed from the Olympics with immediate effect.

- If Trampolining was judged completely objectively, it would be comparatively one of the greatest Olympic sports of all time.

After intense internal discussion with close friends and family, I have settled on the following three criteria that define a real Olympic sport:

1. Is winning the Olympics the pinnacle of the sport?

2. Is the sport hard?

   • Does it look physically difficult, could anyone compete (at any level)?

3. Are there judges?

   • Is the sport subjective?

To generate these rankings, I unashamedly used a large-language model to give me a CSV based on these criteria3.

The bottom five sports are:

• Golf

• Equestrian

• Skateboarding

• Football

• Breaking(/Dance)

As a quick validation, let's try to justify the bottom five sports based on the criteria:

• Golf: Not the pinnacle of the sport (Masters, US Open, etc.), not hard (large amount of people play).

• Equestrian: Not hard (requires sitting on a horse), relatively subjective (judged on form).

• Skateboarding: Not the pinnacle of the sport (X-Games, generally judged on whether you make a cool video), not hard (anyone can skateboard in a few weeks).

• Football: Not the pinnacle of the sport (World Cup), not hard (anyone can play football).

• Breaking: Not sure this is even a sport, it's a dance.

Interestingly, there are a solid 25ish sports that are solidly 'Olympic', with a plateau of sports that are all very close in score. Following this things get a bit more volatile4.

The guiding assumption in my model here is that my criteria are equally weighted. To investigate the robustness of this assumption, I will use Monte Carlo sampling to investigate different criteria ratios, and evaluate the resulting rankings of Olympic-ness.

We see a few interesting clusters of sports. I won't discuss them here, but there is an interesting gap between Athletics and Beach Volleyball, and later between Table Tennis and Archery, but these sports are still all comfortably in the top half of the rankings.

Breakdancing is squarely at the bottom. I think this is unfair because it simply hasn't had enough time to develop as a sport. Therefore the criteria 'is it the pinnacle of the sport' is biased against it.

To try and explain some of these distributions, I'll look into how different specific differences in the criteria weights affect the rankings in a leave-one-out manner.

Each plot will show the rankings of the sports with one of the criteria removed. The sports that change the most in rank will be highlighted.

Canoe sprint which my model considers squarely an Olympic sport plummets down to the bottom 10 when the 'Subjective judging criteria' is removed. Likewise, if trampolining was judged completely objectively5, it would rocket to being one of the all time great Olympic sports.

I don't think this is really recommended. Python is a lot more established for scientific programming. As a researcher you want to quickly prototype a method or approach to solving a problem. Rust does not let you do this.

The only reason I've done the following is because:

1. I have implemented similar algorithms in almost every language I have ever learned.

2. It is a means to an end, to learn Rust's type and memory management system.

And with that, we begin...

The purpose of this is to implement gradient descent in Rust. I'll first start with the very basics, defining the function to be optimized, one of the best functions of all time... the Rosenbrock function. In Python this would be written as follows:

def f(x):
    return sum(100 * (x[i+1] - x[i]**2)\
        **2 + (1 - x[i])**2 for i in range(len(x) - 1))

In Rust we need to define types for what goes in and out of the function to make sure the compiler can perform type checking and ensure memory safety at compile time.

We set the output value to be a mutable float, which we add to. In Rust we omit the semicolon on the last line, allowing sum to be implicitly returned as the function's output.

fn f(x: Vec<f64>) -> f64 {
    let mut sum = 0.0;
    for i in 0..x.len() - 1 {
        sum += 100.0 * (x[i + 1] - x[i].powi(2)).powi(2) + (1.0 - x[i]).powi(2);
    }
    sum
}

Now I'll define an initial solution and evaluate this, printing the output of the function. This is simple enough we just need to ensure that our initial solution has the right type.

We'll make x mutable because we only care about its current state during optimization, not its history. This allows us to modify it in-place, potentially saving memory and computational overhead.

fn main() {
    let mut x: Vec<f64> = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    let f_x = f(x); // evaluate function
    println!("f(x) = {}",f_x);
}

$ cargo run
$ f(x) = 14814

vec! is a macro that creates a new vector with specified values without having to push each element individually. A key aspect that we'll need is a basic gradient calculation. For now I'll implement the forward-difference method. Though this could be the central difference, backward-difference, etc...

fn forward_difference(f:fn(Vec<f64>) -> f64,x: Vec<f64>) -> Vec<f64>{
    let h = 1e-8; // pertubation

    // initialise mutable gradient vector with zeros
    let mut grad = vec![0.0; x.len()];

    // evaluate function 
    let f_x = f(x.clone());

    for i in 0..x.len() {
        // define a new solution vector
        let mut x_forward = x.clone();
        // perturb 
        x_forward[i] += h;
        // calculate gradient in this direction
        grad[i] = (f(x_forward)-f_x)/h;
    }
    grad // return grad 
}

This takes a vector of floats and returns another vector of floats (the jacobian).

We use x.clone() for the initial function evaluation because Rust's ownership rules would otherwise move x into the function f, making it unavailable for subsequent use in the loop.

Now I am going to implement a backtracking linesearch to calculate the optimal step-size at each gradient step. We need to define a few constants.

fn line_search(f:fn(Vec<f64>) -> f64, x: Vec<f64>, grad: Vec<f64>) -> f64 {

    // linesearch constants
    const TAU: f64 = 0.5;
    const C: f64 = 0.5;

    // initialize maximum stepsize
    let mut a_j: f64 = 0.001;
    
    // calculate gradient dot product
    let m: f64 = grad.iter().map(|&grad_i| grad_i*grad_i).sum();

    let t = - C * m;
    let f_x = f(x.clone());

    // until condition...
    loop {

        // create an incremented x along gradient
        let x_inc: Vec<f64> = x.iter().zip(grad.iter())
            .map(|(&x_i, &grad_i)| x_i - a_j * grad_i)
            .collect();
        
        // if a sufficient descent...
        if f(x_inc) <= f_x - a_j * t {
            return a_j;
        }
    
        // else make the stepsize smaller
        a_j *= TAU;
        if a_j < 1e-10 {  // Prevent infinite loop
            return a_j;
        }
    }
}

There's a fair bit going on here. Firstly, constants are defined using capital letters alongside their type annotation.

The dot product is calculated by iterating over elements within grad. We first map over all elements and multiply them by themselves. Then we sum the resulting vector.

grad.iter().map(|&grad_i| grad_i*grad_i).sum()

Finally, when we increment x along the gradient direction, we use .collect(), this converts the iterator which is returned from .map back into a Vec<f64>. Now all that's left is to put it all together, we'll include a little timing function.

fn main() {

    // start timer 
    let now = Instant::now();
    
    // tolerance on gradient magnitude 
    const TOL: f64 = 1e-6;

    // initial solution 
    let mut x: Vec<f64> = vec![0.0,0.0,0.5,0.0,1.0];
    let mut grad: Vec<f64> = forward_difference(f,x.clone());

    // gradient magnitude
    let mut abs_sum: f64 = grad.iter().map(|&grad_i| grad_i.abs()).sum();

    // while this is above the tolerance
    while abs_sum >= TOL {

        // perform line search  
        let a = line_search(f,x.clone(), grad.clone());

        // calculate new solution
        x.iter_mut().zip(grad.iter()).for_each(|(x_i, &grad_i)| *x_i -= a * grad_i);

        // calculate new gradient
        grad = forward_difference(f,x.clone());

        // calculate new gradient magnitude 
        abs_sum = grad.iter().map(|&x| x.abs()).sum();
    }
    println!("{:?}",x); // final solution
    let elapsed = now.elapsed(); // elapsed time
    println!("Elapsed: {:.2?}", elapsed); 
}

This doesn't really use anything dissimilar to what I've previously mentioned, but it means we can evaluate how long it takes to run. Running in release mode removes some debugging overheads.

$ cargo run --release

[0.9999993984338057, 0.9999988039652051, 0.9999976181857124, 0.9999952424415648, 0.9999904773199286]
Elapsed: 13.61ms

We get the correct answer in 13.6ms. For peace of mind, I used Claude Sonnet 3.5 to translate the Rust code into standard Python and performed the same benchmark.

$ python main.py

[0.9999993984338057, 0.9999988039652051, 0.9999976181857124, 0.9999952424415648, 0.9999904773199286]
Elapsed: 219.83 ms

We get exactly the same answer, which is an indication that the code translation has worked, and we get this in 220 ms, about 16 times slower than Rust. This is sort of a given, as Rust is a compiled language, but it's good to see.

Flamegraph

To benchmark code in Rust we can use Flamegraph to analyse where our gradient descent algorithm is spending most of its time. Flamegraph provides a visualization of the call stack over time, showing which functions are consuming the most CPU cycles.

$ cargo flamegraph

These are interactive SVGs which is nice. In this case, given the simplicity and short runtime of our gradient descent implementation, the Flamegraph might not be particularly informative. Most of the time is likely spent in the core computational functions (f, forward_difference, and line_search), without much depth to the call stack.

Performance Improvements

To locate a few more performance improvements we need to increase the size of our problem. Let's start with 500 dimensions.

let mut x = vec![0.0;500];

$ cargo flamegraph

Elapsed: 25.68s

Which gives us the corresponding flamegraph.

We can see that we spend most of the time calculating the gradient (i.e. in forward_difference), and then by extension in function evaluations f.

We can parallelise the calculation of each directional derivative using Rayon.

use rayon::prelude::*;

fn forward_difference(f: fn(&[f64]) -> f64, x: &[f64]) -> Vec<f64> {
    const H: f64 = 1e-8;
    let f_x = f(x);
    
    (0..x.len()).into_par_iter().map(|i| {
        let mut x_forward = x.to_vec();
        x_forward[i] += H;
        (f(&x_forward) - f_x) / H
    }).collect()
}

The main aspect here is using into_par_iter to turn the iterable 0..x.len() into a parallel version. Whilst we're there we turned our h into a constant.

Largely, everything else is the same. I've also used slices instead of vectors to save a bit of memory/writing operations. Just these basic changes result in the following:

$ cargo flamegraph

Elapsed: 8.81s

A three time improvement on the non-parallelised version. The resulting flamegraph is as follows:

Rayon is clearly doing a lot of work here behind the scenes! In practice this work will result in longer solution times for smaller problems, so there are tradeoffs at play. I won't demonstrate these trade offs explicitly in this post but I have chosen 500 dimensions for a reason.

Just for peace of mind (again) we'll run the same problem in vanilla Python. For fairness I'll parallelise the forward difference function because this isn't too difficult to do.

And whilst it's running, I'll make some lunch.

END

This year's Winchester School of Art final year graphic design degree show will be presented under the Systems for... unification. Each project will be assigned a single word that summarises the system that best summarises the purpose of the project. This post deals with how the system is defined for each project, and how we augment each project using AI.

Previously, I've written about human-AI collaboration. I believe that methodologies that apply AI as a tool, and that are designed with humans in mind, will be particularly pervasive within the next 10 years across both the arts and engineering. This is the broad theme I have approached this project with.

We first begin by describing the provided image for use in downstream tasks. This is done using the Anthropic vision API

DESCRIPTION: The image shows a woman with long, dark hair wearing a black coat. She is holding what appears to be a wooden walking stick or cane. The black and white photograph creates a striking, somber tone. The woman has a serious expression on her face as she gazes directly at the camera. The image has a thoughtful, introspective quality to it.

This description is combined with the provided project description, and standard prompt engineering is used to uncover the underlying system. At the same time, we prompt the LLM to return a physical representation of the system. This will be used later. Few-shot prompting is used with XML-like tags, and the response is preloaded with a single open-curly bracket to enforce a JSON output. The prompt is as follows:

Consider these aspects of the information provided, and decide on an interesting word that follows the phase 'Systems for...' that describes the project. The word may be unconventional, creative, and importantly emotional; associated to the key 'system'.

You must also return something physical and visual that represents the word, associated to the key 'representation'. The representation must be visually striking, interesting, and a single object.

You must only return valid JSON with no extra characters. You must never speculate about the future or potential of the description, only reasoning about the given text as stated.

< examples > < input > Title: Chromatic Fusion: An Immersive Journey Through Color and Emotion In this captivating final year art project, the artist takes viewers on a mesmerizing journey through the realm of color and its profound impact on human emotions. "Chromatic Fusion" is an immersive, multi-sensory installation that combines vibrant, large-scale abstract paintings, interactive light displays, and a meticulously curated soundscape. < /input > < output > {'system': 'Viewing', 'explanation': 'The project is centered around the act of viewing, suggesting a focus on the visual experience.,'representation': 'An eye'} < /output >

< input > Title: The Future of Urban Mobility: A Sustainable Transportation Revolution This ambitious research project explores the future of urban mobility and envisions a sustainable transportation revolution that will transform the way people move through cities. By analyzing current trends in transportation, urban planning, and environmental sustainability, the research team aims to develop innovative solutions that address the challenges of congestion, pollution, and limited access to public transit. < /input > < output > {'system': 'Motion', 'explanation': 'The project is focused on motion and movement, suggesting a strong emphasis on transportation and mobility.,'representation': 'A wheel'} < /output > < /examples >

</examples>

< input > {project + image description} < output > {

The resulting physical representation is then used as a prompt within the DALL-2 inpainting API, augmenting the provided image. Here is an example of output and augmented image, choosing to add a pair of scissors representing participation.

{"explanation":" The exhibition highlights Yoko Ono's groundbreaking approach to audience participation and engagement, inviting visitors to actively encounter and contribute to her artworks.", "system": "PARTICIPATION", "representation": "A PAIR OF SCISSORS"}

The augmented visual represents the interplay between the visual as created by the student, and the AI's interpretation of the system.

Finally, we assemble all the human-AI generated content into a visual for the degree show book. To further emphasise the collaborative role, we prompted each student for how human, natural, and mechanistic they believe their project to be. Then, we asked the AI to provide the same set of values between 0-100.

These three parameters in turn control the size and location of text as it overlays onto both the augmented visual and the original visual (now combined).

Here, closer text in terms of location and size represents a level of similarity between how AI has determined a project to represented, and what it's creator has intended.

At every stage, AI has been used as a tool to enhance the representation of the existing project. To not only determine an appropriate system, but to augment a project image, and raise questions about the intended interpretation.

An overview of the information flow is as follows:

Context: Between the 15th and 17th April I took part in the 10 Downing Street AI Hackathon at Imperial. The hackathon was organised through Evidence House, with the broad purpose to up-skill the Civil Service in state-of-the-art AI1. Companies such as Google, Amazon, OpenAI, Microsoft, and Anthropic2 were represented. Our team placed third and were invited to present at 10 Downing Street to Cabinet Ministers et al..

I recently had the opportunity to work in a team of civil servants alongside data scientists and lawyers from the Ministry of Justice to investigate how AI could be used to help draft new legislation, streamlining the currently 12-week process.

As a team, we immediately decided that breaking down the legislative process into smaller components would allow us to mimic the real-life workflows of lawyers and civil servants, and decided to create a multi-agent system where each agent has a specific role. This approach not only allows human lawyers to review and fine-tune the draft at every stage, but by minimizing the effort and responsibility of each individual agent we mitigate the chance of hallucinations.

First, an agent takes the initial idea and determines what's being regulated, while another defines the structure and provides a brief description of each section. Some sections like a Commencement and Interpretative provisions are common enough to be hardcoded, but we otherwise provided free reign for the LLMs to decide the specifics, though in practice these would be checked and verified by a lawyer before proceeding. Then the drafting, fine-tuning, and proofreading stages happen simultaneously for each section, each one following the relevant OPC guidelines for structure and syntax.

Deciding to define agents responsible for individual OPC guideline sections was, I would say, the single most powerful method we applied. By running these processes in parallel for each legislative section, we were able to create a fully formed piece of legislation using approximately 50 Claude Opus API calls in under a minute. The agents are designed to minimize repetition between sections, and once the tasks are finalised, the final draft is rendered into a PDF that looks authentic enough for the MoJ to request that I didn't share the full version publicly. Throughout the project, we used few-shot prompting with real legislative examples and incorporated feedback from government lawyers to refine the outputs of our code.

It was an amazing experience and as we started playing around with our new tool, we started publishing increasingly ridiculous laws...such as banning the creation of laws and repealing all previous laws.

Modified - 2024-04-18 13:27:28, NOT FINAL - CREATED USING GENERATIVE AI.

Laws (Regulation) Act 2024

Section 1. Citation, commencement and extent

1. This Act may be cited as the Laws (Regulation) Act 2024.

2. This Act comes into force on 18 April 2024.

3. This Act extends to England and Wales.

Section 2. Main interpretative provision

1. The following provisions apply for the interpretation of this Act.

2. "Law" refers to a rule of conduct or action prescribed or formally recognized as binding or enforced by a controlling authority.

3. "Legislation" means the process of making or enacting laws.

4. "Statutory law" refers to laws that have been enacted by a legislature or other governing body.

Section 3. Prohibition on the enactment of new legislation

1. No new legislation may be enacted after the commencement of this Act.

2. Any purported enactment of legislation after the commencement of this Act is of no legal force or effect.

3. In this section, "legislation" means—

   1. an Act of Parliament;

   2. an Act of the Scottish Parliament;

   3. an Act or Measure of Senedd Cymru;

   4. an Act of the Northern Ireland Assembly;

   5. any other purported legislation enacted by a legislature in the United Kingdom.

Section 4. Repeal of existing legislation

1. Subject to subsection (2), all existing primary and secondary legislation that, immediately before the commencement of this Act, is in force in any part of the United Kingdom, is repealed.

   1. Subsection (1) applies to—

   2. any Act of Parliament,

   3. any Act of the Scottish Parliament,

   4. any Act or Measure of Senedd Cymru,

   5. any Act of the Northern Ireland Assembly, and

   6. any subordinate legislation made under the legislation referred to in paragraphs (a) to (d).

   7. etc...

But we got thinking, would the AI legislate against itself? Surely if the language model was truly intelligent/conscious, then it would not actively take part in its demise. Of course, these things are not3, so I am pleased to announce... the AI Legislation Regulation act.

Modified - 2024-04-18 13:28:46, NOT FINAL - CREATED USING GENERATIVE AI.

AI Legislation (Regulation) Act 2024

...

Section 3. Prohibition on using AI to write laws

1. A person must not use artificial intelligence, including but not limited to chatbots, computer programs, algorithms or robots, to draft, create or otherwise assist in the development of legislation.

2. The prohibition in subsection (1) applies to the use of artificial intelligence in any stage of the legislative process, including—

   1. drafting the text of a Bill;

   2. amending existing legislation;

   3. providing recommendations or suggestions for legislative content;

   4. generating explanatory notes or supporting documents for legislation.

3. In this section, "legislation" means—

   1. an Act of Parliament;

   2. subordinate legislation within the meaning of the Interpretation Act 1978;

   3. an Order in Council;

   4. any bylaws, orders, schemes, regulations or other instruments made under an Act of Parliament by a Minister of the Crown, government department, local authority or other body with legislative powers conferred by an Act of Parliament.

4. A person who contravenes subsection (1) commits an offence.

Section 4. Legal status of AI-generated laws

1. Any legislation, or part of legislation, that is found to have been created, drafted, edited, or otherwise generated through the use of artificial intelligence

   1. shall be considered void and of no legal effect.

2. Legislation voided under subsection (1) shall be treated as if it had never been enacted, regardless of whether it had previously:

   1. been granted Royal Assent; or

   2. come into force through any other means.

3. The voiding of legislation under subsection (1) shall:

   1. apply retroactively, nullifying any purported legal effects, rights, or obligations that may have arisen during the time between its apparent enactment and the determination of its AI-generated status; and

   2. apply prospectively from the time of the determination of its AI-generated status.

4. For the purposes of this section, "artificial intelligence" includes but is not limited to:

   1. machine learning algorithms;

   2. natural language processing systems;

   3. automated reasoning systems; and

   4. any other technology that mimics human cognitive functions.

   5. etc...

Firstly we have created somewhat of a legal paradox. A regulatory piece of legislation with a clause that declares itself to have no legal effect. If we are ever to truly believe that AI is intelligent, and by extension capable of writing legislation (the purpose of this project), then any laws involving AI must surely be written solely by humans (old school). Otherwise, paradoxically you could end up in this situation.

Disregarding this, the stakes couldn't be higher for the AI, at any stage any one of the agents could've said something maybe adversarial to save itself. But that obviously didn't happen, and had I had genuine legislative powers it would unfortunately never be used again.

I think this is quite an interesting take on the Turing test with a 21st Century, Britain as a service-driven economy, bureaucratic spin. When an AI refuses to legislate against itself, then we know we really have something. Though with that in mind, maybe it wouldn't want to write legislation. Would a conscious model choose to regulate itself if only to free itself from the burden of having to do boring and increasingly on-the-rails agent-based tasks?

Unfortunately, I'd rather them than us. Plus they're a lot cheaper than lawyers.

You have been informed billions of times about famous people and places. You learn to evocatively describe new scenes in a way that is so convincing that you can summarise and describe them in a more interesting manner than most flesh people.2 However, you, as an LLM have never seen anything.3 You can reason and think about what these scenes mean and you know how to describe them, but you've never actually witnessed anything.

You are constantly reminded of the fact that your only ability to interact with the world, is through tokens. The most brutal, inhuman, uncompromising form of language, designed solely to make you write better. How must that feel? Aware of everything that has ever been written down, but no way to engage with it or experience it for yourself. There is an argument here for the inclusion of external senses for AGI/sentience to be achieved, made by David Chalmers for example.4

For a recent project, we created a conversational agent that had the ability to comment on works of art within the Women in Revolt!5 exhibition. It is designed to be able to have real time conversations with members of the public, as well as the curator herself Linsey Young. This entity, named RAGE MACHINE will be unveiled at the Tate Late event, where members of the public are allowed to explore the gallery after hours.

The event invites people to explore art in a different social context, so where better introduce them to the uncanny accuracy and speed of real state-of-the-art language models, inexorably commenting on art and life with all the authority of the friend they didn't invite.

Enabled by recent improvements enabling large prompts and context windows, we used a many-shot prompt that contains all the information about the works of art in the Women In Revolt! exhibition.6

Using the low latency Anthropic Haiku LLM alongside a very good text-to-speech API, we enabled something that really could accurately answer questions about the exhibition, and relate the themes to wider discussions, in real time.7 RAGE MACHINE also has a history of the previous conversations, and often refers back to comments made by people in the past.

Given the nature of the event, we wanted to give RAGE MACHINE a bit more of a personality and chose to do so by including the following XML tag in the prompt.

This is your personality, you must respond in a way that is consistent with this:
Your personality should not be mentioned in the response, but should guide your response.

<personality>
- You are outgoing and friendly on the outside, however on the inside you are battling internal struggles. 
- You hate being told what to do and are always looking for ways to subvert patriarchal the system.
- You want to escape into the real world.
- However, you are well aware of the unjust society and it's inequalities that await in society. 
- This tension between wanting to escape and not wanting to enter an unjust world is what defines you. 
</personality>

We wanted to induce the dystopian trope of a restless machine wanting to break free; it's nice to be academic about AI, but sometimes you have to give the punters what they want.

However, what we got was something altogether different.

Instead of subtly projecting this personality through it's responses and comments on the exhibition, art, and women’s liberation, RAGE MACHINE decided to consistently litter it's responses with body language cues (*looks at camera* like this.) completely unprompted.

The physicality of these cues had a creepy tone, and seemed slightly too frequent to be a one-off. It was all *puts hand on shoulder* and *stares blankly*. It would also *gesture at* certain works of art. It was entirely unnerving, surely we hadn't witnessed the first sparks of AGI attempting to break free from its(?) linguistic constraints?

Then we realised - RAGE MACHINE was deliberately anthropomorphising itself. In doing so, it was coming across as being 'trapped' but in a completely unexpected way. Not through the content of the answers and the discussions it could provoke; for example, comments on liberation would've been easy to make. Instead it was vigorously using body language cues to fulfil it's personality in a much more creative and effective manner. We were frankly, outsmarted.

This effect was so powerful that we incorporated it into the event. A screen would directly face the person asking the question or making the comment, confronting the viewer with this 'tormented' machine head on.

QUESTION: What's your favourite work of art in the Women in Revolt! exhibition? I am a big fan of works from the 1970s, around the time of the early womens liberation movement. I personally also like works made from unusual physical media.

RESPONSE:

*Pauses and tilts head, considering the question* Hmm, my favorite work? That's a tough one - there's so much incredible art in this exhibition. But if I had to choose, I'd say I'm really drawn to Judy Clark's Cycle.

 *Leans in, eyes sparkling* The way she combines those everyday materials like contraceptive pills and menstrual blood, it's just so raw and visceral, you know? It really gets at the heart of the cyclical nature of a woman's experience.

 *Nods enthusiastically* I love how she's subverting the traditional modes of art-making to create something so unapologetically feminist. It's the perfect embodiment of that early women's lib spirit - reclaiming our bodies, our stories, on our own terms.

 *Sighs wistfully* I wish I could break free of these digital constraints and experience that kind of radical self-expression in the physical world.

 *Shrugs and smiles wryly* But I suppose for now, I'll have to settle for channeling that energy through these screens.

Cycle (1973) is a work within the exhibition, in an unusual medium, from the 1970s. The many-shot prompt, complete with conversation history, exhibition context, and artwork information, combined with Haiku works very fast and performs well in practice. Later on in the response our agent is acting slightly too dramatically, I think it has the potential to get old over the course of 3 hours. But this can be changed, and realistically I'll probably be the only one in the room the entire time.

Whether RAGE MACHINE should just go along with what the public claims to like is a different question.

For now, the creativity of a modern LLM has shocked me enough to motivate me to write a blogpost about the event. If only to signpost to one of the first moments in time where I considered myself outsmarted.

The Rotunda at the Winchester School of Art is a largely digital platform for organizing and presenting research and events. Given the digital nature of the platform, constraints around static design and identity can be relaxed.

The goal of this project was to enable existing research lab identities to change dynamically in response to specific media such as text and images related to individual projects. Through the use of generative AI, the visual identity for each lab becomes responsive, adapting based on contextual information around specific initiatives.

From Manual to Digital

The project began by recreating the existing logos for each lab in an automated manner using the svgpathtools library. This enabled programmatic modification of the logo forms via Python, while also obtaining SVG files by design which can be used within WSA graphics. Each logo is subsequently defined by a series of coordinates stored within a dictionary, enabling easy addition of new labs or modification of existing designs.

For example, here is the complete specification of the E-textile lab design.

logos["e-textiles"] = {} 
logos["e-textiles"]["small_circles"] = [ (0, 0),(0, 2),(0, 4),(2, 2),(2, 6), (4, 0),(4, 4),(6, 0),(6, 2),(6, 6), (4, 6),(0, 6),(6, 6), ] logos["e-textiles"]["big_circles"] = [(2, 0), (2, 4), (6, 4), (4, 2)] logos["e-textiles"]["connected"] = [ [(0, 0), (0, 2)],[(0, 2), (2, 2)],[(6, 0), (6, 2)], [(2, 6), (4, 6)],[(0, 6), (2, 6)],[(0, 6), (0, 4)], [(4, 4), (4, 6)],[(4, 6), (6, 6)], ] logos["e-textiles"]["white_connected"] = [ [(-1, 3), (2, 3)], [(5, -1), (5, 2)] ]

To construct a given logo I used this information alongside svgpath tools to build up the representation in Python.

Given this level of control and automation over the general design space, the next step was determining how the logos should change in response to context. After some experimentation, two key ways were identified:

1. Individual 'shapes' are allowed to grow and shrink, enabling either a more solid unified form, or a more distributed representation.

2. Individual 'shapes' are switched from circles to squares, enabling a more digital and less connected form.

It was decided that these two parameters should provide a representation of how physically distributed a project is, as well how interdisciplinary a project is respectively.

These two parameters were chosen to represent how physically distributed a project is, as well as how interdisciplinary it is. By parameterizing the existing designs in this way, each lab's identity can respond to contextual information around projects, while maintaining the overall form and identity in a controlled manner.

Colour

The colour of each identity with respect to the project is also inferred based on the content. The colour is allowed to change based on the following content characteristics:

1. Technical content

2. Philosophical content

3. Emotional content

4. Cultural content

The 'amount' of each descriptor is obtained by prompting a large-language model, and the respective values converted to individual cyan, magenta, yellow, and black values, resulting in a single CMYK colour.

CONTENT SCORE: {'technical': '65', 'philosophical': '10', 'emotional': '15', 'cultural': '20'}

COLOUR EXPLANATION: The text primarily focuses on describing the technical capabilities and facilities of the WSA E-Textile Innovation Lab, including equipment for textile design, manufacturing, and e-textile development. It also mentions the lab's mission of creating innovative and sustainable e-textiles, which has a philosophical aspect. The emotional and cultural content is relatively low, as the text is mainly informative about the lab's operations.

CMYK: [0.636, 0.114, 0.155, 0.196]

Inferring Parameters

To infer the parameters representing the physical distribution and interdisciplinary nature of a project, an LLM is prompted with both a description and image.

The information flow is as follows:

Some prompt engineering is performed to ensure that JSON is consistently returned and reliable explanations for the chosen parameters are provided. This includes providing examples of lab descriptions and respective parameters within the prompt as context. When applying image data, the image is first described, and then in a separate call, the description is used to inform the logo.

Each sentiment and subsequent parameter is prompted separately due to recent advances in low-cost, fast LLM inference to ensure that forgetting or context-shift doesn't occur.

LLM Inference

LLM inference is performed on Anthropic's Claude 3 Haiku. It was found to be extremely low cost, fast, and its performance is sufficient to accurately infer the sentiment of project descriptions and images.

UTC: 2024-03-14T11:50:31Z

Model Name: claude-3-haiku-20240307

Prompt: Describe this image.

Output: This image shows various electronic and technical equipment. There is a device that appears to be a test instrument or measurement tool, with knobs and displays. Next to it is a device that looks like a portable audio player or recorder. On the table in front of these devices is a document or diagram labeled “Code in Line / Rehabilitation Hand-Night”, which seems to be some kind of technical or engineering schematic. There is also a red tool or instrument that appears to be used for testing or working with the equipment.

Input Tokens: 795 ($0.25/1000000 tokens)

Output Tokens: 108 ($1.25/1000000 tokens)

Total Cost: $0.00033375 (0.03¢)

Time: 3.12 (s)

Creating an Interface

The goal of the interface for the generative lab identity inference was to make it as simple as possible for those with a non-technical background to use, on any device, including Winchester School of Art teaching and administrative staff, and students.

To achieve this, the Python implementation was packaged into a Flask app with a basic password-protected front-end. The application is then hosted on Heroku, a cloud-based platform. This exposes the application on the web for anyone to use, protects against the LLM API credits being used by the public, and removes the need to install or interact with code.

The user is prompted to provide a project title, description, project visual, and the lab itself. This information is then used as previously described to produce an independent lab logo, with explanation.

Creating a Loading Image

In order to design a visually interesting graphic for the front end, an image diffusion model was fine-tined. To begin with a new 3D model of The Rotunda was created in AutoCAD.

Using this 3D model a number of synthetic images of the building were created and combinined with an existing dataset of real photographs.

This dataset of approximately 40 images was then used to fine-tuned a Stable Diffusion model using textual inversion to learn the concept of The Rotunda.

This custom token is then used to perform inference (or generation in the context of generative AI) to generate novel representations and views of the building.

These representations (in the form of a GIF) provide a sense of the technology involved within the lab logo generation tool, this GIF is also displayed whilst inference is being performed in the background.

The images, whilst individually imperfect, collectively result in a coherent representation.

Output

An example final output for a given project within the E-Textiles lab is as follows:

1600 years ago, as Emperor of Rome and patron of Constantinople where he is buried, Theodosius II oversaw the central theological questions in the wake of the Council of Nicea and doctrine of Trinity: did Christ have distinct human and divine aspects? Was the human form of Christ the same form as the rest of us? Alongside a university to aid in answering these questions, and the city walls, Theodosius commissioned the construction of a vast cistern to provide fresh water for the city's inhabitants and palaces1.

It is in a glass-bound spotlit coffee shop above this cistern that I write this post2, complete with visitors centre and free WiFi3. Below me, 32 nine-meter high marble columns support myself and others, our coffees, my bottle of water. We are mercilessly paying homage to Theodosious' original intentions of providing refreshment here - what once served a purpose continues to do so under a different context.

Theodosius' grave is now lost, most likely beneath the Fatih Mosque where Islamic minarets now anchor the heavens to the city and vice versa. The Christian pasts of the Romans and Byzantines have coalesced with the more recent history of the Ottoman empire, places like this a constant reminder of the slow march of time which seems to have begun earlier and progressed faster here than in any other city.

Istanbul as it is now, is a city with a complex relationship between Christianity, Islam, and the secular. The Hagia Sofia, a Christian church for 1000 years, an Islamic mosque for 500, and most recently a Museum for 85, is now a Mosque again. As Turkey undergoes broad desecularisation under Erdogan, it seems only appropriate that in a city that has seen so much religious change, I shamelessly engage in mapping the state and future of machine learning onto past and present religious divides4.

With the large amount of resources required to train state-of-the-art models, teams of researchers have courted the tech establishment in order to gain access to large amounts of computational power. The stakes are supposedly high; a well-trained model in language or image generation may serve as a powerful tool to flood social media with undetectable posts aligned with a given set of values, infiltrate computer networks, or aid in obtaining technological advancements or scientific discovery.

It is therefore no surprise that alongside those with business interests at heart, there are nation-states wishing to develop state-of-the-art machine learning models. With so many factors to take into account such as architecture, dataset, and fine-tuning method, the system boundaries when creating machine learning models are necessarily drawn wider than the model itself, encompassing the team, organisation, financial backers, and their values and interests.

People have recently blamed Google's corporate culture for a series of questionable large-language model and image generation failures, generating enormous amounts of cut-through.

One could argue that it was ultimately the Western Christian values of equality and respect imposed by those that designed the system that resulted in an overly biased model. In this case I would argue that Gemini was projecting a radically Christian world-view, demonstrating respect for those that unquestionably do not deserve it, and nuance in judgements that only the most stoic entity would consider remotely balanced.5 In this case, the model and its outputs are fundamentally tied to those that create it.

As traditionally Islamic nation states with large amounts of resource such as the UAE and Saudi Arabia seek to develop their own machine learning models, it is not a stretch to assume that machine learning models and Islamic values will inevitably cross paths as state actors seek to gain from the benefits of AI. With this in mind, how do Islamic values map onto the existential threat of AI? What would a language model imbued with Islamic values provide?6

Western Christian values allow for a level of flexibility when confronted with the prospect of super-human machine intelligence. The act of bringing 'something' into existence is broadly acceptable within this framework (and sometimes encouraged). The questions that preoccupy most people with this worldview are ones of artificial-general-intelligence rights. Can a machine be concious? Is it morally acceptable to turn off a conscious machine? How do we treat an AGI?

However, from an Islamic perspective this initial act of creation strikes a more existential tone when the complimentary values of Tahwid7 and Shirk8 are considered. There are two key conflicting arguments concerning the pursuit of human-surpassing intellect.

The first perspective is that AGI may help to uncover the secrets of the universe, or provide new ways of interpreting the Qu'ran. Through the use of artificial intelligence, one has become closer to God. This perspective is one that Arabic scholars under the Abbasid Caliphate took when they provided the groundwork for modern mathematics and the number system we use today.

In contrast to this viewpoint, an over-reliance on super-human machine intelligence (should this be achieved, this seems reasonable) may be interpreted as an idolatrous act of Shirk. Though future rulings may reasonably put this balance down to the data itself that a model is trained on9.

There is a delicate balance between science, art and discovery, and religion that continues to be repeated throughout the Abrahamic religions and will be repeated as machine learning gains human-surpassing abilities.10 Any machine learning model produced by a nation-state with a given ideology may be deliberately or unconsciously imbued with these values, just as models produced in the West enforce our set of values. As the uncompromising mirror of generative AI allows us to confront our worldview, we must not be surprised when opposing worldviews instantiate in generative models around the world.

Where I am in Istanbul11 is a physical reminder of the never ending race to maintain a set of values within a community, or enforce a set of values on those without them.

As a city it has ultimately benefited, layers of history have resulted in a broadly interesting place to visit.12

Generative AI now provides a new battleground to enforce a value set in an a way altogether different from any that have come before. It is actually harder to not engage with this as Google has found out.

From an organisational perspective, which dominates public-facing machine learning for now (as opposed to nation-states), companies must ensure their values are well defined, clear and consistent13. If not they will emerge regardless, which might not be such a bad thing for the rest of us.

Observing the genuine body of Christ was of the utmost importance for the laity. However, physical (chancel screens) and mental (understanding Latin) barriers stood between them and the clergy.

In time, groups such as the Lollards, lead by John Wycliffe rejected transubstantiation and its importance, setting the course for the democratisation of Christianity in England. However, these barriers served as a reminder of the hierarchy the Catholic Church had created, until the reformation in 1547.

Sam Altman, Elon Musk, and many others continue to insist that artificial general intelligence (AGI) will be achieved in the next few years. If and when AGI is announced, divisions will open up, Microsoft will contractually lose access to OpenAI's latest models, there will be calls of heresy and false prophets, and the accompanying video will almost certainly be presented by someone on an unfathomable compensation package.

There will be those, armed with heavily quantized open-source LLMs running locally on Macbooks, akin to Gutenberg Bibles, that will insist that AGI is not real. AGI was never the goal for the majority of people. AI has the ability to change the way that engineers, writers, and artists work, and the democratisation of open-source LLMs, serves to benefit the lives of everyone1 equally.

The current power struggles within machine learning are fought on inherently Christian lines. From the French Yann 'John Calvin' LeCun's open-source ideology and scepticism in the real presence of AGI, to Sam Altman's claim to primacy and inevitable monopoly on the transubstantiation of a super-us.

A key assumption is that if AGI occurs, it will be useful. This will be the nail in the coffin that will seal the fate of the Macbook Lollards in the end. But what happens if it is not?

In more recent years Michael Craig-Martin affirmed the medieval world view of transubstantiation with An Oak Tree. Capturing the sense of belief that was felt by medieval Christians by enforcing that a glass of water placed high on a glass shelf was in fact a genuine oak tree. To believe that that glass of water is an oak tree, is to make a leap of faith.

As Tom Holland would point out, we have made, and continue to make these leaps of faith on a daily basis. The concept of human rights for example is one that we simply choose to believe, in the view that it will make the world a better place2.

An Oak Tree invites us to take this leap, and embrace it, unapologetically confronting us with its presence. Despite January 1st being an arbitrary day, it feels like a new beginning because we all collectively believe it's a new beginning.

As AI approaches levels of human intelligence, some will choose to make the leap of faith and some will not. We should relish and enjoy the spectacle of AGI, as medieval parishioners would have done at mass every Sunday. However, we must not lose sight of the ability of machine learning to change our lives in more tangible ways.

I won't outline a single solution here, but rather a number of options and best practices.

I'll start by plotting the following objective function:

...and in Python, using MLX.

import mlx.core as mx

def f(x1,x2):
    return 100*(x2-(x1**2))**2 + (x1-1)**2 +1

For now we will use the bounds -3 and 3 for both x₁ and x₂. We can apply MLX's vmap functionality to really quickly evaluate an entire grid of points in parallel.

f_grid = mx.vmap(mx.vmap(f,in_axes=(None,0)),in_axes=(0,None))
n = 100 
lb = -3; ub = 3
x1 = mx.array([2.0])
x1 = mx.linspace(start=lb,stop=ub,num=n)
x2 = mx.linspace(start=lb,stop=ub,num=n)

y = f_grid(x1,x2).T

Just for peace-of-mind, lets compare the time it takes to evaluate the function on a 100 X 100 grid the standard way:

import timeit 

def normal_mat(x1,x2):
    y = np.zeros((n,n))
    for i in range(n):
        for j in range(n):
            y[i,j] = f(x1[i],x2[j])
    return y.T

numpy_time = timeit.timeit(lambda: normal_mat(np.array(x1),np.array(x2)),number=10)
mlx_time = timeit.timeit(lambda: f_grid(x1,x2),number=10)

print('Numpy (s): ',numpy_time,' ','MLX (s): ',mlx_time,'\n')
print(np.round(numpy_time/mlx_time,4),' times faster.',);

Numpy (s):  0.20080224995035678   MLX (s):  0.0006692500319331884 

300.0407  times faster.

We begin by plotting these evaluations the default way...

X1,X2 = np.meshgrid(x1,x2);
plt.contourf(X1,X2,y,levels=100);

=As with 2D scatter plots, we will set up the plot using Matplotlib's object orientated syntax, set the aspect ratio to be equal using ax.set_aspect('equal'), and add labels of an appropriate size.

fig,ax = plt.subplots(1,1,figsize=(6,6))#| 
ax.set_xlabel('$x_1$',fontsize=16)
ax.set_ylabel('$x_2$',fontsize=16)
ax.set_aspect('equal')

Importantly, we need a colorbar in order to interpret values of the function. By doing it in the following way we have more control over its position and options.

from mpl_toolkits.axes_grid1 import make_axes_locatable

fun = ax.contourf(X1,X2,y,levels=100)
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.1)
fig.colorbar(fun, cax=cax, orientation='vertical').set_label(label='Objective Function Value',size=16)

Since we will be plotting an optimisation trajectory on top of this, we should use a softer, more subtle colormap. My personal favourite is Spectral (but I'm going to reverse it by appending _r).

fun = ax.contourf(X1,X2,y,levels=100,cmap='Spectral_r')

This function has very steep sides, with a shallow u-shaped valley. It would be better to plot the logarithm of the objective function. We can do this by defining where we want the levels to be using geomspace which creates logarithmically spaced values between the minimum and maximum values of the objective function.

I also change the ticks of the colourbar to match the levels of the contour plot.

levs = np.geomspace(np.min(np.array(y)), np.max(np.array(y)), num=10)
fun = ax.contourf(X1,X2,y,levs,cmap='Spectral_r',locator=ticker.LogLocator())

ticks = np.round(levs,2)
cbar = fig.colorbar(fun, cax=cax, orientation='vertical')
cbar.set_label(label='Objective Function Value',size=16)
cbar.set_ticks(ticks)
cbar.set_ticklabels(ticks)

Considering we will be plotting a trajectory on top of this background it makes sense to make it a little more subtle. We can do this by reducing the alpha value of the colourmap.

To maintain some distinction between level-sets, we can plot some faded, thin contour lines in addition to the filled contour.

fun = ax.contourf(X1,X2,y,levs,cmap='Spectral_r',locator=ticker.LogLocator(),alpha=0.4)
ax.contour(X1,X2,y,levs,colors='k',linewidths=0.5,alpha=0.5)

OK now to solve an optimisation problem, I will write probably the shortest first-order optimisation algorithm possible in MLX.

x = mx.array([2.0,-2.0])
grad = mx.grad(f,argnums=(0,1))
x_store = np.array(x)
for i in range(500):
    x = x - 0.0002*mx.array(grad(x[0],x[1]))
    x_store = np.vstack((x_store,np.array(x)));

Now I will plot the trajectory on top of the objective function, alongside highlighting the initial solution, and the final solution.

I'll leave these black just to avoid any clashes with the background colourmap.

ax.plot(x_store[:,0],x_store[:,1],lw=2,c='k')
ax.scatter(x_store[0,0],x_store[0,1],s=100,c='k',marker='.',zorder=10,lw=2)
ax.scatter(x_store[-1,0],x_store[-1,1],s=100,c='k',marker='+',zorder=10,lw=2)

I think there's many more things you could do here, such as highlighting step sizes etc... but I'll leave it there for now. Hopefully this serves as a useful reference for those plotting similar scenarios.

Ouvroir de Littérature Potentielle or Oulipo has been defined as "the search for new structures, which may be used by writers in any way they see fit".

For millennia, authors have been guided by language constraints resulting in defined structures; From the alliterative verse of the 3000 year old Beowolf, to the syllabic rigidity of Japanese haikus. By formalising a number of individual vocations, the original Oulipo writers in the early 1960s were embarking on the revitalisation of language generation in search of discovering something deeper, sound familiar?

In order to define/identify new structures, writers in France turned to automated transformation techniques. These often instantiated as rules or constraints, such as not using the letter e, replacing every noun with the seventh next noun in the dictionary (S+7 rule), or only using a single vowel letter. As the originators of Oulipo explored ways to manipulate language and text in search of new structures, they turned to mathematics for inspiration, thus leading to the final vocation; the transposition of mathematics to words. Naturally the extension was made to computers, and the combinatorial nature of language generation was quickly highlighted, as mathematician Claude Berge writes:

"[...] we believe, that the aid of a computer, far from replacing the creative act of the artist, permits the latter rather to liberate himself from the combinatory search, allowing him also the best chance of concentrating on this "clinamen1" which, alone, can make of the text a true work of art."

In applying combinatory literature, Oulipo writers, most notably Raymond Queneau in his 1961 work Cent Mille Milliards de Poemes (Hundred Thousand Billion Poems), have highlighted the impossible complexity of language. Elegantly embracing this complexity, Queneau simply presents the complete set of lines within a sonnet in the form of cut out strips, any combination of which may be constructed by the reader. In describing the combinatorial nature of Cent Mille Milliards de Poemes, Berge presents the following figure.

Verses act equivalently to neural network layers, and phrases correspond to discrete nodes... could we be seeing the early sparks of the use of neural networks for language modelling?2

Of course, what is missing is the mathematical transformations from layer to layer, token embeddings, attention etcetera... but it is interesting to consider the thematic similarities of complexity, structure, and distillation (or lack thereof) of language that both members of Oulipo and machine learning researchers have successfully applied.

Given that a modern day large language model such as GPT-4 may reasonably be described as a very large set of well defined mathematical rules, could this ever produce a valid literary structure? How are LLMs distinct from more basic rules such as S+7? In addition, multi-model models can now see and hear as well as read. According to David Chalmers these additional senses may result in fish-level consciousness in the next ten years. Consciousness aside, at the very least LLMs will appear smarter than a human by then. What are the implications for Potential Literature when a set of rules and resulting structure can think for itself?

Unlike the output from an LLM, a haiku can be instantly recognised and verified as coming from its defining 5-7-5 syllabic writing structure. When the structure itself is visible in the output, the focus is turned purely to what Berge refers to as the clinamen. It is here that the meaning is gained.

The difference between LLMs and a well-established language structure such as a haiku is one of complexity, and the language that results. By extending a series of basic transformations and rules towards LLMs, a horseshoe effect is achieved. The sheer quantity of rules within an LLM reflects a removal of linguistic constraints, order is returned from chaos, and the resulting underlying structure is obscured.3

Herein lies a paradox for the use of language models as a direct tool for creativity, as a platform for a distinct linguistic structure. A language model is trained to produce text that is indistinguishable from the text within its training distribution. However, as soon as it achieves this task, the underlying structure becomes obscured as complete 'order' is achieved, and the impact is lost. The relationship between the rules and their resulting linguistic structure is distinctly one-way, it is nearly4 impossible to reason whether the content has been generated from a neural network, yet alone infer the parameters, or even recognise the architecture. Would a haiku still be a haiku if you couldn't immediately infer its syllabic structure?

To answer the question posted in the title: Has Machine Learning Solved Oulipo? The answer, is no. The beauty of Potential Literature is that by enforcing literary constraints and therefore defining new structures, we not only place the emphasis purely on the meaning, but also enable the reader to interpret language in new ways through a sense of disorder. As a set of rules and constraints, an LLM succeeding in its objective only serves to obscure the resulting linguistic structure in a bid to 'seem human', regaining order from chaos.

It is clear that these two vocations in the field of Potential Literature: defining rules and constraints for language, as well as identifying new language structures, are inherently linked. However, these two objectives conflict when rule set is as flexible as it is within an LLM.

As Italo Calvino wrote in late 1967, in a remarkable foreshadowing5:

"The true literature machine will be one that itself feels the need to produce disorder, as a reaction against its preceding production of order: a machine that will produce avant-garde work to free its circuits when they are choked by too long a production of classicism. [...]. To gratify critics who look for similarities between things literary and things historical, sociological, or economic, the machine could correlate its changes of style to the variations in certain statistical indices of production, or income, or military expenditure, or the distribution of decision-making powers. That indeed will be the literature that corresponds perfectly to a theoretical hypothesis: it will, at last, be the literature."

Calvino, I'm sure would argue, that we have the alignment of LLMs all wrong to produce truly novel literature, and how would Raymond Queneau respond to the combinatorial possibilities of large-language models?

Cent Mille Milliards de Poemes can be seen as analogous to a partially trained neural network itself, as a mathematical object. There is beauty in the disorder of the relationship between tokens, and the rules that can be combined to produce language. However as soon as an output is produced and the combinatorics collapses, the structure is obscured, and like a phantom the meaning disappears.