13 Aspirations

Mid the silence that pants for breath,
when I thought myself at my last gasp,
haine ou de l’ambition et qui se,
the pale motor vessel withdrew its blue breath toward the island’s horizon.

As pure and simple as a powder puff,
such also was the ambition of others upon the like occasion,
there was hardly a breath of air stirring,
mon ancien cœur en une aspiration vers la vertu.

After drawing a long breath,
the silver ring she pull’d,
the suitor cried, or force shall drag thee hence.

For wild ambition wings their bold desire,
and with thine agony sobbed out my breath,
I will pull down my barns.

Developing a software product rarely finishes. It is maintained, refactored, repurposed, updated, extended, etc. Especially with creative products, where the functional requirements are more fluid perhaps, it is always tempting to change things.

For the purpose of this doctoral project, the artefact pata.physics.wtf is a snapshot of a product in constant motion. The state of the code at the time of submission of this thesis is described in chapter 10 and further elaborated on in the Patanalysis chapter. But it may very well continue to evolve.

Here, in this chapter I will lay out some of the potential further work for this project. This may continue on a private basis or in a more academic environment.

13.1 Performance

The website can be slow to load. Currently speed performance was not a priority during development. In fact it is not built for speed from the ground up. Each time the server restarts, the indexing process takes place from scratch (see chapter 10.1). This takes time. Google and other big web search engines do this continuously in the background to keep data up to date. The index is currently cached after startup but perhaps preprocessing it and storing it more permanently in a database would help speed up the start. However this may not be necessary, as it only affects the server startup.

Query Response
The time it takes from the user entering a query term and the system displaying the results page varies between unnoticable short and impatiently long. This is due to the pataphysicalisation process. This requires calls to external and internal APIs such as Flickr and WordNet. See analysis on speed issues in table 12.4.

Preprocessing Corpora
At this point the texts in the corpora consist of almost unedited plaintext (‘.txt’) files1 (see chapter 10.1.1). Newlines and whitespace formatting varies, as does language and quality of spelling. Generally, chapter headings, chapter numberings, etc. were left untouched. The Shakespeare corpus contains poetry and plays for example. With the plays, scene information, stage directions, and voice details were kept. This means sentences that appear in the results of the search tool can contain peripheral words such as in this example: “…Athens and a wood near it ACT I …” from A Midsummer Night’s Dream or this example: “…Exit SHERIFF Our abbeys and our priories shall pay This expedition’s charge …” from King John. This could be addressed by preprocessing the individual texts in advance and removing any text that might interfere with the readablility of results.

Image Sizes
At the moment images are retrieved at one specified size through the various calls even though they are displayed at various different sizes depending on their location in the image spiral (unless they are displayed as a list). This process could certainly be optimised. Smaller image sizes could be accessed via the s.

13.2 Design

Responsive Spirals
Currently the image and video spirals (see chapter 10.3) are fixed size. This means that when the webpage is resized the spiral stays the same size and is left-aligned on the page. Ideally it would be better to scale the spiral with the width of the browser page. This could be achieved using percentage widths, although it would require a lot of work to adapt the current code for the spirals (see chapter B.7).

Scalable Image Sizes
As mentioned above, images are retrieved at one size through the various API calls. Because images in the spiral have different sizes according to where in the spiral they are located, they are scaled up or down directly in the HTML code. This means that some of the images look distorted and pixelated if they have to be scaled up or down too much.

Square Aspect Ratio
Another issue is the aspect ratio of images and videos. For the spiral they need to be square. They are currently distorted as opposed to cropped. It might be possible to specify an option in the API calls to only retrieve square images which would help this problem.

Responsive Poems
A similar problem to the responsive spirals exists with the display of the Queneau poems. The random poems are centered on the page but the Queneau poems require a lot more formatting and styling to render and currently this is achieved by left-aligning them and having a fixed ‘absolute’ position on the page. Ideally this would also be centered as in the random poems.

Paginate Results
For the text-by-source and text-by-algorithm search as well as the image- or video-as-list search results, it may improve the loading speed of the results page to split the results into smaller chunks and display them on several pages instead of one long scrolling page. This is called pagination.

Random Sentences
Adding to the source of random sentences used in the top and bottom banner on the website should be an ongoing endeavour. The current list of sentences used is shown in appendix A.1.

13.3 Text

Result Sentences
Currently the way result sentences are retrieved for the text search is based on punctuation (see chapter 10.2.2). This means once a pataphysicalised keyword has been found, the system retrieves up to words prior until it reaches a punctuation mark and the same for after. The idea here was to get suitable sentence fragments. This could be changed to rely on POS tags for example or simply retrieving complete sentences.

When the index is created only words that are not considered stopwords are added. We could modify the list of stopwords (see appendix B.6) to include a few more uninteresting words. Or we could simply remove everything but nouns for example. This would drastically influence the results produced by the system.

Rhyming Scheme
One of the biggest points for future work is to introduce a rhyming scheme for the poetry results. This might involve some more NLP during the creation of the index. It would make the poems much more readable. This could include pronounciation POS tags or other like data (for example using an API like Wordnik (“Developer.wordnik.com” 2016) or a library like NLTK). So a word in the index dictionary might contain the following items.

  (``tree'': [``l_00'': [24,566,4990], ``s_14'': [234,5943]], ``[tri]'')

By doing POS tagging with pronounciation data, we could retrieve sentences that match the sound of the last word of the previous line for example.

13.4 Pataphysicalisation

The vocabulary in WordNet is limited. According to it’s website (“What Is Wordnet? WordNet: A Lexical Database for English” n.d.) it contains 117000 ‘synsets’2 This affects two of my algorithms (namely the Syzygy and Antinomy algorithms). See also discussion in chapter 12.2.5. An option might be to somehow widen the amount of word matches by including different word-types/forms and relationships, such as troponyms, homonyms and heteronyms. Using these could introduce a whole new kind of pataphysical result.

Homonyms are pronounced the same but mean something else (e.g. ‘write’ and ‘right’). Heteronyms are words that are spelled the smae but have a different meaning (e.g. ‘close to the edge’ and ‘to close the door’). Homophones are often used to create puns (and remember—puns are syzygys of words), for example “past your eyes” and “pasteurize”.

You can tune a guitar, but you can’t tuna fish. Unless of course, you play bass.

(attributed to Douglas Adams)

The antinomy algorithms relies on WordNet’s antonyms. A lot of words simply do not have an opposite and no fallback is currently defined. This means a lot of the time the antinomy function will not produce any results. Andrew Dennis implemented the algorithm in the same way, as discussed in chapter 11.1. It would be great to come up with a better way of dealing with this concept to ensure results are produced everytime.

Stemming could increase the number of results found by all algorithms (see chapter 6.2). A danger of increasing the output of the pataphysicalisation is always that results become more boring. Currently queries such as ‘clear’ and ‘clearing’ are treated as separate entities and would produce different results. Stemming would turn both of these words into the stem ‘clear’ and they would return the same results. Now it becomes immediatly clear (no pun intended) though that this might not always be desirable as just illustrated in this sentence: the root meaning of ‘clear’ can be very different to the meaning of ‘clearing’.

Queneau’s poems
It would be nice to actually add Queneau’s poems (Queneau 1961) into the Faustroll corpus as little easter egg (see chapter 2.8).

Image Algorithms
The image and video search currently rely on external APIs (see chapter 10.3). One option to approach this in a totally differnet way would be to write algorithms that analyse and pataphysicalise the actual image or video data themselves. This might involve manipulating histograms or pixel maps.

Maximum Obscurity
N-grams are a NLP technique introduced in chapter 6.2.2. The idea is that it allows for prediction of likely word pairs, meaning if the word ‘sunny’ often occurs just before the word ‘day’ in a given training text or corpus then the probability for this particular n-gram is higher than say for ‘sunny dog’. This can be increased to predict the probability of longer chains of words. One can immediately see the attraction of abusing this to generate pseudo sentences or even of creating a formula similar in nature but for example ranking obscure combinations of words higher than common ones. So for example instead of having a Maximum Likelihood Estimation (MLE) (see equation 6.8) we could have a ‘Maximum Obscurity Estimation’ which returns the highest probabilty for word sequences that happen the rarest.

Pataphysical Entropy
Similarly, we could could play with maximum entropy models as shown in chapter 6.2.2 together with POS tagging by rigging given probability for tags. There are endless possipilities of abusing these kinds of techniques. This is also very reminiscent of OULIPO techniques.

We could create a whole new language grammar based on pataphysical principles. Examples of using a standard grammar (see chapter 6.2.2) for generating ‘random’ text are as follows3.


Generates artist statements.


A system for generating random text from grammars.


Generates random Computer Science research papers.

In chapter 7.2.5 I discussed the concepts of uninspiration and aberration by Wiggins and Ritchie (2006; 2012) in relation to their CSF. We could define a ‘Pataphysical Search Framework’ in the same way. Table 13.1 shows some of their original definitions for various forms of aberration and uninspiration. Table 13.2 then shows some rough ideas about how pataphysical concepts might be defined.


smallest possible aberration to make the biggest difference


reachable, abnormal concepts with value


reachable concepts outside the norm


criteria for value and norm must be perfectly matched

Syzygy 1

concepts reachable within 3 steps from the query

Syzygy 2

transformed set of concepts Sobj → Smeta → Sobj

This is definitely work in progress and it would be out of the scope of this thesis to elaborate much further.

Name Equation
Universal set of concepts $U$ and $X \subseteq U$
Aberration $B$ where $B \notin N_\alpha(X) \wedge B \neq \emptyset$
Perfect Aberration $V_\alpha(B) = B$
Productive Aberration $V_\alpha(B) \neq \emptyset \ \wedge \neq B$
Pointless Aberration $V_\alpha(B) = \emptyset$
Hopeless Uninspiration $V_\alpha(X) = \emptyset$
Conceptual Uninspiration $V_\alpha(N_\alpha(X)) = \emptyset$
Generative Uninspiration $elements(A) = \emptyset$
Table 13.1 – CSF concept definitions of uncreativity
Name Equation
Norm $N_\alpha(X) = \{ c \in X \ | \ N(c) > \alpha \}$ where $N \in [0,1]^X$
Value $V_\alpha(X) = \{ c \in X \ | \ V(c) > \alpha \}$ where $V \in [0,1]^X$
Pata $P_\alpha(X) = \{ c \ | \ c \in (CLI(X) \cup ANT_\alpha(X) \cup SYZ(X) \cup ANO_\alpha(X) \cup ABS(X)) \}$
Clinamen $CLI(X) = \{ c \in X \ | \ N_{0.9}(N_{0.1}(c)) \}$
Antinomy $ANT_\alpha(X) = \{ c \in X \ | \ V(N_0(c)) > \alpha \}$
Anomaly $ANO_\alpha(X) = \{ c \in X \ | \ N(c) < \alpha \}$
Absolute $ABS(X) = \{ c \in X \ | \ V_1(N_1(X)) \neq \emptyset \}$
Syzygy 1 $SYZ(query) = \bigcup_{n=0}^3 elements(Q(N,V)^n(query))$
Syzygy 2 $SYZ(X) = S'(X)$ where $S_{obj} → S^{meta} → S′_{obj}$
Table 13.2 – Possible definitions of pataphysical concepts in terms of the CSF

13.5 Extensions

Additional APIs
Currently 5 APIs4 are used in pata.physics.wtf. This could be increased to include more varied sources of data. Sites like Flickr are heavily based on user tags (‘folksonomies’) which can be unreliable and a bit random at times. Possible additional APIs to consider would be Instagram, Imgur, Facebook, Google Image Search, DeviantArt, Pinterest, Vimeo, Twitter, SoundCloud, etc.

Additional Algorithms
It would be nice to implement some more algorithms for the search tool. This could include the two additional algorithms suggested by Andrew Dennis (see chapter 11.1) or developing more of my own. This could involve implementing some of the other pataphysical principles, such as equivalence or anomaly. Or it could consist of implementing some of the more famous OULIPO techniques. The repetoire of them is huge (see tables 4.1 and 4.2).

Custom API
Finally, it would be great to develop a custom API for the algorithms of pata.physics.wtf. This would allow other people to use the search remotely without going through the interface and to use the results as they want. This would have been beneficial for the Digital Opera project and certainly for other researchers/developers like Andrew Dennis.

13.6 User Testing

Focus Group
It might be interesting to look at opinions of various people (general public and experts) about the interpretation/evaluation framework. This could be done by asking them to provide their own definition of computer creativity and then to analyse and evaluate a product (such as pata.physics.wtf) according to their own criteria. Then follow this up by getting the same people to use my proposed framework to compare the results. This would include asking them about whether or not they thought that using the framework was beneficial to them or confusing.

To study the effects of using different styles of presenting the same results, an eye-tracking experiment could be done. This would involve setting up participants with the necessary equipment and then introduce them to pata.physics.wtf and moniter their eye movements as they navigate the site. This could also provide details about how long users spend on each results page, what kind of style of results they prefer, etc. Some may prefer image or video search over the text search while others may not be interested in that at all. Generally of course one has to take into account that this is a creative piece of work and not everybody will like it. It purposefully purposeless and highly subjective, so user feedback may not provide unbiased and useful results.


“Developer.wordnik.com.” 2016. link.

Queneau, Raymond. 1961. One Hundred Thousand Billion Poems. Gallimard.

Ritchie, Graeme. 2012. “A closer look at creativity as search.” In Proceedings of the International Conference on Computational Creativity, 41–48.

Stribling, Jeremy, Max Krohn, and Dan Aguayo. 2016. “SCIgen - an Automatic Cs Paper Generator.” link.

“The Dada Engine.” 2016. dev.null.org. link.

“What Is Wordnet? WordNet: A Lexical Database for English.” n.d. Princeton University. link.

Wiggins, Geraint. 2006. “A preliminary framework for description, analysis and comparison of creative systems.” Knowledge Based Systems 19 (7): 449–58.

Winter, Joke de. 2016. “ArtyBollocks Generator.” link.

  1. For text files downloaded from Project Gutenberg, the Gutenberg specifc copyright notices have been removed to only contain the relevant body of text

  2. Synonyms—“words that denote the same concept and are interchangeable in many contexts”—are grouped into unordered sets called synsets (“What Is Wordnet? WordNet: A Lexical Database for English” n.d.).

  3. (Winter 2016; “The Dada Engine” 2016; Stribling, Krohn, and Aguayo 2016)

  4. Flickr, Getty, Bing, MicrosoftTranslator and YouTube