Ben Shneiderman's work is deeply influential in HCI; his work has assisted in creating strong connections between tech and creativity, especially when applied to fostering innovation.

his 2007 national science foundation funded report on creativity support tools, led by UMD, provides a seminal overview of the definitions of creativity at that time.

Margaret Boden, research professor at University of Sussex, has provided pivotal work in the exploration of creativity using interdisciplinary research across music, game, story, physics, and artificial intelligence to explore human creativity in arts, science, and life.

The Creative Mind is available to all on archive.org.

This is another way of achieving concurrency (progress made while waiting for other things) besides wrapping the timer's sleep in a separate thread like https://github.com/rubyworks/facets/blob/main/lib/standard/facets/timer.rb does.

The importance of data structures (related to memory)

Thanks Sascha for an excellent primer on the internal machinations of our favorite machines beyond the usual focus on the storage/memory and indexing portions of the process.

Said another way, a zettelkasten is part of a formal logic machine/process. Or alternately, as Markus Krajewski aptly demonstrates in Paper Machines (MIT Press, 2011), they are early analog storage devices in which the thinking and logic operations are done cerebrally (by way of direct analogy to brain and hand:manually) and subsequently noted down which thereby makes them computers.

Just as mathematicians try to break down and define discrete primitives or building blocks upon which they can then perform operations to come up with new results, one tries to find and develop the most interesting "atomic notes" from various sources which they can place into their zettelkasten in hopes of operating on them (usually by juxtaposition, negation, union, etc.) to derive, find, and prove new insights. If done well, these newly discovered ideas can be put back into the machine as inputs to create additional newer and more complex outputs continuously. While the complexity of Lie Algebras is glorious and seems magical, it obviously helps to first understand the base level logic before one builds up to it. The same holds true of zettelkasten.

Now if I could only get the printf portion to work the way I want...

https://lu.ma/2u5f7ky0

I attended this live this morning from 9:20 - 10:45 AM

Maths, Logic, Computer Science, Chess, Music, and Dance

A similar observation could be made about mathematics, logic, and computer science. Sadly, public education in the states seems to lose sight that the formalisms in these domains are merely the tools of the trade and not the trade itself (ie, developing an understanding of the fundamental/foundational notions, their relationships, their instantiations, and cultivating how one can develop capacity to "move" in that space).

Similarly, it's as if we encourage children that they need to merely memorize all the movements of chess pieces to appreciate the depth of the game.

Or saying "Here, just memorize these disconnected contortions of the hand upon these strings along this piece of wood. Once you have that down, you've experienced all that guitar, (nay, music itself!) has to offer."

Or "Yes, once, you internalize the words for these moves and recite them verbatim, you will have experienced all the depth and wonder that dance and movement have to offer."

However, none of these examples are given so as to dismiss or ignore the necessity of (at least some level of) formalistic fluency within each of these domains of experience. Rather, their purpose is to highlight the parallels in other domains that may seem (at first) so disconnected from one's own experience, so far from one's fundamental way of feeling the world, that the only plausible reasons one can make to explain why people would waste their time engaging in such acts are 1. folly: they merely do not yet know their activities are absurd, but surely enough time will disabuse them of their foolish ways. 2. madness: they cannot ever know the absurdity of their acts, for "the absurd" and "the astute" are but two names for one and the same thing in their world of chaos. 3. apathy: they in fact do see the absurdity in their continuing of activities which give them no sense of meaning, yet their indifference insurmountably impedes them from changing their course of action. For how could one resist the path of least resistance, a road born of habit, when one must expend energy to do so but that energy can only come from one who cares?

Or at least, these 3 reasons can surely seem like that's all there possibly could be to warrant someone continuing music, chess, dance, maths, logic, computer science, or any apparently alien craft. However, if one takes time to speak to someone who earnestly pursues such "alien crafts", then one may start to perceive intimations of something beyond their current impressions

The contorted clutching of the strings now seems... coordinated. The pensive placement of the pawns now appears... purposeful. The frantic flailing of one's feet now feels... freeing. The movements of one's mind now feels... marvelous.

So the very activity that once seemed so clearly absurd, becomes cognition and shapes perspectives beyond words

argument and parameter

https://en.wikipedia.org/wiki/Read%E2%80%93eval%E2%80%93print_loop

aliases: interactive toplevel, language shell

read-eval-print loop (REPL)

Webb's use case here sounds like she's got the mass data, but that what she really desired was a database which she could more easily query to do her work and research. As a result, she took the flat file data and made it into a manually sortable and searchable database.

https://introtcs.org/public/index.html

Repository: https://github.com/boazbk/tcs

While he's couching it in the computer science milieu of his day, this is not dissimilar to the Llullan combinatorial arts.

definition of computer science

Computer science definition

Exercises

2.1.b

Counterexample: \(\to := {(a, c), (b, c)}\)

2.3

\(a \to b\) iff \(a\) encodes Turing machine \(M_a\) and \(b\) encodes a valid terminating computation (sequence of states) of \(M_a\).

2.9

Let \(|w|_a := \varphi_a(w)\).

\(\varphi(w) := 3^{|w|_a} 2^{|w|_b}\)

Proof

1. Let \(u \to_1 v\). Then \(\varphi(v) = 3^{|v|_a} 2^{|v|_b} = 3^{|u|_a+1} 2^{|u|_b-2} = 3^{|u|_a} 2^{|u|_b} \frac{3}{4} = \varphi(u) \frac{3}{4} < \varphi(u)\).
2. Let \(u \to_2 v\). Then \(\varphi(v) = 3^{|v|_a} 2^{|v|_b} = 3^{|u|_a-1} 2^{|u|_b+1} = 3^{|u|_a} 2^{|u|_b} \frac{2}{3} = \varphi(u) \frac{2}{3} < \varphi(u)\).

2.17

No.

Let \(a > b\). Then \([b^n a | n \in [0, 1, \ldots]]\) is an infinite chain according to \(>_{Lex}\).

Note: This exercise completes the discussion of Lemma 2.4.3.

4.2

Let \(s, t\) be terms. Run BFS from \(s\) using \(\leftrightarrow^E\). If \(t\) is encountered, conclude that \(s \approx_E t\). If the BFS finishes enumerating the equivalence class without encountering \(t\), conclude that \(\lnot s \approx_E t\).

4.4

Let \(x \in Var(r) \setminus Var(l)\). Let \(p\) be a position of \(x\) in \(r\).

Infinite chain:

• \(t_0 = x\)
• \(t_{i+1} = r[t_i]_p\)

4.18

1. a
   • Unifier: \({x \to h(a), y \to h(a)}\)
   • Matcher: \({x \to h(a), y \to x}\)
2. b
   • Unifier: Unsolvable
   • Matcher: \({x \to h(x), y \to x}\)
3. c
   • Unifier: \({x \to h(y), z \to b}\)
   • Matcher: Unsolvable
4. d
   • Unifier: Unsolvable
   • Matcher: Unsolvable

5.2

Counterexample TRS \(R\):

1. \(a \to b\)
2. \(b \to b\)

https://en.wikipedia.org/wiki/Computer_Lib/Dream_Machines

Google translation:

9/8g The Zettelkasten technique is based on experience: You can't think without writing—at least not in contexts that require selective access to memory.

That also means: you can't think without notching differences.

There's something interesting about the translation here of "notching" occurring on an index card about ideas which can be linked to the early computer science version of edge-notched cards. Could this have been a subtle and tangential reference to just this sort of computing?

The idea isn't new to me, but in the last phrase Luhmann tangentially highlights the value of the zettelkasten for more easily and directly comparing and contrasting the ideas on two different cards which might be either linked or juxtaposed.

Link to:

• Graeber and Wengrow ideas of storytelling

• Shield of Achilles and ekphrasis thesis

• https://hypothes.is/a/I-VY-HyfEeyjIC_pm7NF7Q With the further context of the full quote including "with selective access to memory" Luhmann seemed to at least to make space (if not give a tacit nod?) to oral traditions which had methods for access to memories in ways that modern literates don't typically give any credit at all. Johannes F.K .Schmidt certainly didn't and actively erased it in Niklas Luhmann’s Card Index: The Fabrication of Serendipity.

Note here how the author creates the acronym CBU out of central bibliographic unit as a means of creating a connection to computer jargon like CPU (central processing unit). I suspect that CBU was not an acronym used at the time.

bacrkonym?

Budak, C., Soroka, S., Singh, L., Bailey, M., Bode, L., Chawla, N., Davis-Kean, P., Choudhury, M. D., Veaux, R. D., Hahn, U., Jensen, B., Ladd, J., Mneimneh, Z., Pasek, J., Raghunathan, T., Ryan, R., Smith, N. A., Stohr, K., & Traugott, M. (2021). Modeling Considerations for Quantitative Social Science Research Using Social Media Data. PsyArXiv. https://doi.org/10.31234/osf.io/3e2ux

From a computer science perspective, isn't the index card functioning like an external memory, albeit one with somewhat pre-arranged linked paths? It's the movement through the machine's various paths that is doing the "thinking". Or the user's (active) choices that create the paths creates the impression of thinking.

Perhaps it's the pre-arranged links where the thinking has already happened (based on "work" put into the system) and then traversing the paths gives the appearance of "new" thinking?

How does this relate to other systems which can be thought of as thinking from a complexity perspective? Bacteria perhaps? Groups of cells acting in concert? Groups of people acting in concert? Cells seeing out food using random walks? etc?

From this perspective, how can we break out the constituent parts of thought and thinking? Consciousness? With enough nodes and edges and choices of paths between them (or a "correct" subset of paths) could anything look like thinking or computing?

Microarchitecture (µarch) What Does Microarchitecture (µarch) Mean? Microarchitecture, abbreviated as µarch or uarch, is the fundamental design of a microprocessor. It includes the technologies used, resources and the methods by which the processor is physically designed in order to execute a specific instruction set (ISA or instruction set architecture). Simply put, it is the logical design of all electronic components and data paths present in the microprocessor, laid out in a specific way that it allows for optimal execution of instructions. In academe this is called computer organization.

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Techopedia Explains Microarchitecture (µarch) Microarchitecture is the logical representation of how a microprocessor is designed so that the interconnections between components – the control unit, the arithmetic logic unit, registers and others – interact in an optimized manner. This includes how buses, the data pathways between components, are laid out to dictate the shortest paths and proper connections. In modern microprocessors there are often several layers to deal with complexity. The basic idea is to lay out a circuit that could execute commands and operations that are defined in an instruction set.

A technique that is currently used in microarchitecture is the pipelined datapath. It is a technique that allows a form of parallelism that is applied in data processing by allowing several instructions to overlap in execution. This is done by having multiple execution pipelines that run in parallel or close to parallel.

Execution units are also a crucial aspect of microarchitecture. Execution units perform the operations or calculations of the processor. The choice of the number of execution units, their latency and throughput is a central microarchitectural design consideration. The size, latency, throughput and connectivity of memories within the system are also microarchitectural decisions.

Another part of a microarchitecture is system-level design. This includes decisions on performance such as level and connectivity of input, as well as output and I/O devices.

Microarchitectural design pays closer attention to restrictions than capability. A microarchitecture design decision directly affects what goes into a system; it heeds to issues such as:

Performance Chip area/cost Logic complexity Ease of debugging Testability Ease of connectivity Power consumption Manufacturability A good microarchitecture is one that caters to all of these criteria.

Instruction Set Architecture defines all logical steps (performed by their corresponding digital logical design hardware) which realizing all computing tasks facilitating our life.

Hoffman, R., Mueller, S., Klein, G., & Litman, J. (2021). Measuring Trust in the XAI Context. PsyArXiv. https://doi.org/10.31234/osf.io/e3kv9

Running time is asymptotically tight bound. "Asymptotically" because it matters for only large values of nnn. "Tight bound" because we've nailed the running time to within a constant factor above and below.

Running time has lower bound and upper bound once the number of computations get large enough.

Computation takes time to run. The notation used for running time of computations is Big-Theta.

Tollefson, J. (2021, April 16). The race to curb the spread of COVID vaccine disinformation. Nature. https://www.nature.com/articles/d41586-021-00997-x?error=cookies_not_supported&code=0d3302c0-59b3-4065-8835-2a6d99ca35cc

Can talk about how this applies to other areas/problem-solving/impact on world.

Kozlowski, Diego, Jennifer Dusdal, Jun Pang, and Andreas Zilian. ‘Semantic and Relational Spaces in Science of Science: Deep Learning Models for Article Vectorisation’. ArXiv:2011.02887 [Physics], 5 November 2020. http://arxiv.org/abs/2011.02887.

the crux of the problem is: trade-offs

how can he expect people to read that with scrutiny?

Yang, Scott Cheng-Hsin, Chirag Rank, Jake Alden Whritner, Olfa Nasraoui, and Patrick Shafto. ‘Unifying Recommendation and Active Learning for Information Filtering and Recommender Systems’. Preprint. PsyArXiv, 25 August 2020. https://doi.org/10.31234/osf.io/jqa83.

Jazayeri, A., & Yang, C. C. (2020). Motif Discovery Algorithms in Static and Temporal Networks: A Survey. ArXiv:2005.09721 [Physics]. http://arxiv.org/abs/2005.09721

Katz, D. M., Coupette, C., Beckedorf, J., & Hartung, D. (2020). Complex Societies and the Growth of the Law. ArXiv:2005.07646 [Physics]. http://arxiv.org/abs/2005.07646

Edelmann, A., Wolff, T., Montagne, D., & Bail, C. A. (2020). Computational Social Science and Sociology. Annual Review of Sociology, 46(1), annurev-soc-121919-054621. https://doi.org/10.1146/annurev-soc-121919-054621

https://docs.google.com/forms/d/e/1FAIpQLSeUXG-DsxF-nMJEidB6Ie83NPwUDwEuwHFPel36NoArBaMTrA/viewform

key point

no hidden cost, eg., memory growth on the heap or pauses during garbage collection. but there is a cost in stack memory usage and "scoping" among multiple stack frames, CPU caching, etc.

eg., shared via the "pointer" to an address in heap memory

But it is very difficult to fully express these utility functions in code. The goal is to literally turn our ethics into code -- to translate them into coherent data structures, algorithms, and decision trees. We want to deduce our moral intuitions and more.

http://www.unit-conversion.info/texttools/convert-text-to-binary/

binary is anything with two and only two states, a code that is binary represents information using only these two states.

Computer Science, B.S.

a.k.a. Dynamic Method Dispatch

Alan Kay is on quora.

Class = General description of form and functions of data. Object = A member or instance of a class.

Neil Fraser says Vietnam is doing well with computer science education.

"If grade 5 students in Vietnam are performing at least on par with their grade 11 peers in the USA, what does grade 11 in Vietnam look like? I walked into a high school CS class, again without any advance notice. The class was working on the assignment below (partially translated by their teacher for my benefit afterwards). Given a data file describing a maze with diagonal walls, count the number of enclosed areas, and measure the size of the largest one."

Q&A session with Alan Kay, June 2016.

This sounds really similar to the concept of "abstraction".

Algorithm visualizations: sorting, binary search, graph search

Tutorial on using Rust to write an interpreter for an invented programming language.

Great Principles of Computing<br> Peter J. Denning, Craig H. Martell

This is a book about the whole of computing—its algorithms, architectures, and designs.

Denning and Martell divide the great principles of computing into six categories: communication, computation, coordination, recollection, evaluation, and design.

"Programmers have the largest impact when they are designers; otherwise, they are just coders for someone else's design."

Introduction to Computer Organization with x86-64 Assembly Language and GNU/Linux, Robert G. Plantz

Tutorial series on distributed systems.

Discussion about Obama's computer science for K-12 initiative. CS programs in high school are about 40 years overdue. It is a valid concern that much of this money may be wasted on overpriced proprietary software, hardware, and training programs. And of course, average schools will handle CS about like they handle other subjects -- not very well.

Another concern raised, and countered, is that more programmers will mean lower wages for programmers. But not everyone who studies CS in high school is going to become a programmer. And an increase in computer literacy may help increase the demand for programmers and technicians.

The concept of trust, in the sociological and economic sense, underlies exchange. In the 15th-17th centuries, the Dutch and English dominance of trade owed much to their early development of instruments of credit that allowed merchants to fund and later to insure commercial shipping without the exchange of hard currency, either silver or by physically transporting the currency of the realm. Credit worked because the English and Dutch economies trusted the issuers of credit.

Francis Fukuyama, a philosopher and political economist at Stanford, wrote a book in 1995, Trust: The Social Virtues and the Creation of Prosperity, on the impact of cultures of trust on entrepreneurial growth. Countries of ‘low trust’ have close family culture who limit trust to relations: France, China, S. Italy. Countries of ‘high trust’ have greater ‘spontaneous sociability’ that encourages the formation of intermediate institutions between the state and the family, that encourage greater entrepreneurial growth: Germany, England, the U.S. – I own the book and (shame on me!) haven’t yet read it.

I thought of this article in those contexts – of the general need for trusted institutions and the power they have in mediating an economy, and the fascinating questions raised when a new facilitator of trust is introduced.

How do we trust? Across human history, how have we extended the social role of trust to institutions? If a new modality of trust comes available, how does that change institutional structures and correspondingly the power of individuals, of institutions. How would it change the friction to growth and to decline?

Prior to reading this article, I had dismissed Bitcoin as a temporary aberration, mostly for criminal enterprises and malcontents. I still feel that way. But the underlying technology and it’s implications – now that’s interesting.

The story of Walter Pitts and Warren McCulloch, whose theory of brain function was followed by John von Neumann's design for the stored program computer.

Category Theory for the Sciences by David I. Spivak<br> Creative Commons Attribution-NonCommercial-ShareAlike 4.0<br> MIT Press.

Pamela Fox on careers that involve both coding and teaching.

Mike Malone examines JavaScript's Math.random() in v8, argues that the algorithm used should be replaced, and suggests alternatives.