An Introduction to Cybernetics

Cybernetics, due to the complexity and scope of the concept, is often misunderstood, especially by non-specialists. Even during the course of researching this article, finding a relatively concise explanation of cybernetics that a general audience that were not already familiar with the term could grasp was difficult. Thus, the inspiration for this post arose.

Cybernetics, as a field, has been around roughly since the end of the second world war in the manner that it is referred to in a contemporary context. The term comes from the Greek term kybernetike, or governance. It is defined by Norbert Weiner, author of the book ‘Cybernetics: or Control and Communication in the Animal and the Machine’ as “The scientific study of communication and control”. Its foundation as a formal field can be traced back to the Cybernetics Conferences held between 1946 and 1953. These conferences were aimed at pulling together the disparate scholars to create a formalised “general science of the workings of the human mind”. While they did not succeed at this stated aim at the time, these conferences had a significant influence on the development of cybernetics as well as fields such as cognitive science and systems theory.

Part of the complexity of understanding cybernetics as a beginner is that it is a concept that can be applied to almost any field one can think of. While it is primarily associated with computer science and related fields, it can be applied in many other ways. Its interdisciplinary nature, while making it a complex subject to approach, is also what makes the subject so interesting and worthy of study, due to its applicability in a broad swathe of areas. As a way to figure out some of the core thinkers, concepts and ideas behind cybernetics, I recently spoke to Maggie Appleton, a digital anthropologist and designer in order to learn more about the topic. During the course of our discussion, we came up with a board of ideas of these concepts, which can be seen below.

Cybernetics is closely related to complexity theory and complexity studies, and the overlap between the two often creates confusion for those new to cybernetics. Some scholars argue that rather than being two separate fields, they are merely different approaches to the same overall problem. One distinction that is generally made between the two is that systems theory focuses on the structure of systems, while cybernetics focuses more on their functionality and how they control themselves.

To help make more sense of what cybernetics is, I’ll explain one of the core concepts of cybernetic theory, which is the cybernetic loop. A cybernetic loop is the process of how a self-regulatory system works. An example of this can be in the context of a workplace. An initial input would be a task assigned by a manager, an order to fill from a customer or something similar. The response would be to then perform the required work, and the output would be the completed order or objective. During the process, there would likely be some form of feedback, such as further instructions from a manager or customer. Upon receiving this feedback of new information, the work performed would change and the output with it would change. This system is an example of a first-order cybernetic loop or control system.

This post of course barely scratches the surface of cybernetics. Further reading, especially of core texts such as Norbert Weiner’s Cybernetics are highly recommended for those who wish to take a deep dive into the subject. It’s a seminal text of the field and holds up to this day, even if it is somewhat inaccessible in parts.

This is also the first of a series of posts that I will be writing as a means of creating artifacts of learning after exploring topics as part of the Camp Curiosity series of events over at the Interintellect. Held monthly, these events are online-based gatherings of curious minds who want to go down rabbit holes of learning together in order to enhance their understanding of various topics of begin to explore something for the first time with the guidance of others. They will be on the first weekend of every month for the remainder of the year. A link to the next event can be found here, if you’re interested:

Digital Gardening

Recently, I came across the concept of a digital garden. As someone who’s often reading blogs, articles and various other forms of content on the Internet, I’m always interested in new ways to express ideas and thoughts.

So, what is a digital garden? According to Maggie Appleton, a digital garden is a means of organising thoughts and personal notes, as well as a way to share current learning in public. It’s a bit more formalised than simply writing it down on paper or in an app, but less formal and structured than a traditional blogpost or article. It’s an ideal way of quickly sharing thoughts, and also has the benefit of not needing to be fully formed.

An additional benefit to a digital garden is that, because it can be updated easily, it is also easier to curate and maintain than other mediums. Unlike many other forms, where information is static and stuck in the context of when it was published, a digital garden can, and ideally should, change over time.

Having blogged and written more formally for a number of websites in the past, my public output of writing has gone way down in the last year or so, partially because of personal circumstances, but also because I was increasingly finding the time commitment as well as stricter format of blogs and articles more constrained. I want to start experimenting with less structured writing and notes, as well as incorporating other mediums such as audio and video. The digital garden format, requiring less commitment in order to get started, may also help me as I begin to explore different topics and trails of curiosity.

Going forward, I’ll be posting the majority of my shorter-form posts and thoughts there, and leaving my main site for longer-form writing as well as for more technical writing, projects and portfolio work related to things I’m doing in my IT career.

Here is the link to my digital garden, which is very much a work in progress at present:

If you’re interested, here is the link to the tutorial that I utilised to help me build the site:

I’d like to thank Helena Ng (@herrowna) from the Interintellect for all her help with the process of not only getting the site up and running, but also for introducing me to the digital gardening idea in the first place.

World Development Indicators – Tableau Dashboard

This is a dashboard that I’ve put together as a basic visualization exercise. The aim of the visualization was to provide simple visualisations of key health and economic trends. The indicators that I chose to focus on were GDP in 2017, Average Life Expectancy for men and women between the years 1960 and 2017, and the top 10 countries on average for Healthcare expenditure since the year 2000. The data was obtained from the ‘World Indicators’ dataset, which was used in Week Ten of the #MakeoverMonday Tableau challenge from 2019:

As a first attempt at creating a dashboard, the attempt was mixed. The charts in general could probably display information more clearly. Though the dashboard on Tableau Public is somewhat clearer than the image below, nonetheless, there are issues with the ease of use to work on in the future. The full dashboard, including individual sheets, can be found here:!/vizhome/SummaryofKeyHealthandEconomicMeasures/Dashboard1?publish=yes

There are some interesting insights that can be obtained from this dashboard. Firstly, the trend of life expectancy increasing for both men and women over the last several decades is clear. For both men and women, life expectancy has increased by several years, from a starting point of below 60 years on average worldwide to around 70 years old as of 2017. A further data visualization and analysis could break these statistics down further, by region or compared to national GDP.
The top 10 countries by average health expenditure showed a surprising result. These countries were primarily made up of small island nations such as Nauru, Kiribati and the Marshall Islands. However, nations with a very high overall GDP such as Germany were also present in the top 10. The factors behind this are worth further consideration and would make an ideal topic for a further analytical project. While the dataset being utilized has a sufficient number of measures and dimensions for analysis, the data was not without issues. There were notable gaps in the data, with some measures and some years having considerable gaps in the data. This limited the range of analysis that could be conducted. In addition, while the measures were mostly self-explanatory, there was no data dictionary to go with the dataset, which made analysis somewhat more difficult. Overall, working with this dataset was a worthwhile exercise as a new practitioner of Tableau. My visualization skills were sharpened, as were my analytical skills of looking at a dataset and exploring it for insights to present.

Interintellect Fireside Chat recap: “Reclaiming Control” Book Discussion

This is my first post in a while. Hopefully I’ll be able to write a bit more often going forward.

Earlier this week, I had the privilege of being a co-host of my first Interintellect (I.I) event, alongside the talented Alex Yao, a fellow I.I member. We spoke to Amy McMillen about her debut book, Reclaiming Control: Looking Inward to Recalibrate Your Life. The book is broadly about Amy’s journey from a fast-paced corporate lifestyle, her experience of professional and personal burnout and the subsequent process of reshaping her lifestyle (reclaiming control, if you will).
Over the course of around an hour and a half, the discussion touched on a wide variety of topics, from handling burnout, mental health in modern society, work-life balance, the process of writing and publishing a book independently and more. A small but highly engaged group of attendees were also in attendance, resulting in an intelligent and interesting discussion on the aforementioned topics.

I greatly enjoyed the chance to host an event, even if it was in a co-hosting role. Considering the effort and preparation required of such an event, having the support of Alex was a great help in being able to be a part of this event. It’s been a long process over the last several months since joining the Interintellect at the beginning of the pandemic to being at a place where I’m ready to start hosting. I’m greatly appreciative not only to Alex and Amy for allowing me to be a part of this event, but also to I.I founder Anna Gat and the entire I.I community for their support through some tough personal times. I can’t wait to host my own solo Salon sometime in the near future.

Amy’s book Reclaiming Control is a book I would highly recommend to people who have dealt with or are in the process of dealing with burnout and similar issues. Her style of writing provides an eye-opening account of some of the pressures of modern corporate life, interspersed with research on topics of mental health and wellness. It’s a short but engaging read and, as all good books do, will give you food for thought long after you’ve finished reading it.
Reclaiming Control can be found on Amazon in paperback and kindle formats.

On Accidental Philosophy

Recently, I attended an Interintellect salon on the topic of philosophy. The opening question to the discussion was, ‘Do you consider yourself to be a philosopher?’. In the course of my answer, I stated that I considered myself an ‘accidental philosopher’. By this, I meant that I was someone who, despite not explicitly thinking about philosophy and philosophical questions, as well as someone who does not have a formal background in philosophy, thinks about the fundamental questions of life.

I think there are a great number of people who fall into this category without realizing it. Philosophy is often associated with sitting around, pondering questions idly and contemplating the ideas of particular philosophers, which are often difficult to read and to comprehend. It’s generally not associated with the layperson or with routine, everyday life. Yet, when you think about it, there is a certain level of philosophical thinking that goes into peoples’ lives, even if it is not explicitly thought of as philosophy per se. For instance, a person does not have to know about Stoicism or think of themselves as a stoic in order to live like a Stoic.  

As far as I can tell, ‘accidental philosophy’ does not exist as a category of philosophy. The closest thing I can find to it is the idea of ‘Accidentialism’, the notion that events can occur haphazardly without a particular cause to ascribe the event. This is not what I’m trying to describe, however.

Is it even possible for one to be an ‘accidental’ philosopher? Or is philosophy inherently something that is done purposefully and intentionally? This is ultimately the question I’m finding myself pondering following this morning’s salon. It’s a question that I’ll likely be wrestling with for some time to come. This piece doesn’t fully articulate my thoughts on the idea and is likely not fully coherent. It’s an idea that’s very much in progress and more a patchwork of related ideas than a coherent view at this stage.

I’d be interested in hearing your thoughts on this topic. Do you think a person can be an accidental philosopher or practice philosophy unintentionally? Or is it something that must be done intentionally?

Connection and Community in the time of Isolation

One of the defining aspects of 2020 has been the temporary halt to everyday communities and means of connection. Almost as soon as the pandemic began and isolation began, online communities and meetups began to fill part of the void left by the lockdown.

Among these sources of online community is the Interintellect(I.I), an online community of curious, optimistic people seeking in-depth and intellectual discourse. Though the group began as a way for these people to meet in small face-to-face meetups, or ‘salons’, the I.I transitioned to socially-distanced gatherings with aplomb. In fact, it is due to this transition that I was even able to join in the first place and be able to interact with the community. As most of the group is based in the United States and Europe, very few members were from Australia, and certainly not enough here in Adelaide to run a salon of my own. I was fortunate to be able to join this community right as the Coronavirus pandemic started to take effect. Through the ups and downs of the last few months, groups such as the I.I have been a means of keeping socially connected in some capacity and providing a source of optimism and hope during moments of despair.

On the I.I Medium page, Alex Yao, a member of the community wrote an article, ‘You’re Part of History Now’ on a similar topic. He details how niche online groups such as the I.I as well as groups on mainstream platforms such as Twitter and Facebook collaborated to support one another during the early stages of the pandemic, providing medical advice as well as support through various distressing circumstances related to the pandemic.

As useful as these online communities have been, they are not a full substitution for face-to-face interaction, even for the most introverted and socially anxious among us. As great as being able to talk to people across the world on Zoom is, it’s also quite taxing in a number of ways. It’s a way of communication that we are not used to, and requires sustained attention in a way that is unusual compared to other ways of communication. Zoom Fatigue is an increasingly-reported issue among people frequently using video calling software.

Aside from issues with Zoom fatigue, the mental and physical health issues associated with being socially distanced and isolated are numerous. Countless studies can be cited to prove this fact. Yet, it doesn’t take a peer-reviewed study to be able to recognize the extent to which this has affected people, myself included. Despite this, the flexibility that online spaces have provided in the wake of the Coronavirus pandemic and will continue to provide have been of considerable benefit. Consider the advances made towards working from home. The pandemic has accelerated the movement and opened up opportunities, particularly in sectors such as IT which weren’t there previously.

If we take no other lessons from the last few months of lockdown, let the one we do take be the importance of community and connection. While we all, of course, lead busy lives with many competing priorities, the extent to which social connections and a sense of community have been reduced and minimized in modern society should be even clearer. A rebalancing is required, and we should aim to place a higher value on community and connection going forward, whether it be online or in face-to-face settings.

How online communities and #tidytuesday took my R skills to the next level

Recently, I’ve been making a more concerted effort in learning R. One of the major hurdles I’ve had to overcome in order to do this was to move from tutorials and to doing more independent work. This can be quite a difficult thing to do, particularly if it isn’t immediately obvious how using R or another programming language can be relevant or beneficial to your work. As a current student who has had very limited coding experience in my current courses and has had to learn coding almost exclusively through self-study, this has been particularly the case.

Once I came across the #tidytuesday hashtag on Twitter and the associated community that participates in this event, doing visualisations without directly following a tutorial become much easier. Having a helpful community as well as a pre-selected source of data to work with each week helped to establish a routine, another key element of establishing a coding practice. As well as having a routine, being able to see experienced R users’ graphs and code and being able to reach out to them for tips has been invaluable in breaking through plateaus I had previously been stuck in.

To date, I’ve only done a couple of visualisations, and relatively simple ones at that. While functional and readable, they’re not overly attractive and appealing. However, having some legible pieces of independent work to show off as the beginning of a portfolio is satisfying as well as a confidence-booster. It’s an indication that I’m going in the right direction and the effort I’ve put into practicing R is yielding results.

There are, of course, many different online communities for all manner of programming languages that one may be interested in learning. With social media and online communities becoming ever more important and ubiquitous for professional development and networking, finding a like-minded community such as that associated with #tidytuesday or #rstats on Twitter may be the step for you to go beyond tutorials and to start working on independent coding tasks and projects.

For those interested, the code to my graph for the Kansas City Chiefs’ total attendance can be found at my GitHub profile.

The Importance of Data Cleaning and Preparation

For the first of my ‘portfolio’ posts, I am going to discuss one of the major stumbling blocks that I, and many others starting out in fields such as business intelligence and data science, have come across. Data cleaning and preparation is among the most important parts of the project lifecycle for any business intelligence and data science project. It is estimated that 80% of the work of people working in these fields relates to data cleaning and preparation in some way. Unfortunately, it’s often overlooked in university programs, online courses and in learning materials in general, despite its obvious importance.

Often, introductory courses will look at more exciting parts of business intelligence and data science, such as data visualization and machine learning. To an extent, this is understandable. These topics are useful ‘hooks’ to get beginners started on interesting and engaging tasks. However, without learning how to clean and prepare data, thoroughly understanding and being able to work through all the stages of a project is not feasible. Insufficient data cleaning and preparation will also compromise the final results obtained. As the saying goes, ‘Garbage In – Garbage Out’.

In this section of the article, I will go through some general principles and best practices for data cleaning and preparation. While there are of course many more techniques and advanced concepts within this area, they are beyond the scope of this article. I intend for this to be a starting point people, who like myself, are new to fields related to data and who want to get an idea of how to clean and prepare data.

When a dataset is obtained, the first thing to do is an exploratory analysis of it. In this stage, you should get a feel for the data within it. One of the first things to look for when doing the exploratory analysis is to make sure that the entries are valid. For example, do the fields that require a number have a numerical entry? On a similar note, entries should also make sense within the dataset provided. This will require a bit of domain knowledge of the subject of the data. For example, if looking at a dataset of wages, do the amounts make sense? If the average value within the dataset is, say, $100,000, and there is an entry that is $1,000,000, there’s a good chance this is an incorrect entry. However, this is all dependent on the context of the dataset.

Duplicate and null entries are also a priority to check for during this stage. Particularly with larger datasets, these entries are likely to arise at some point. They can often be overlooked as they are not always as obvious to find, particularly at an initial glance of a dataset.

Wikipedia provides a useful summary of the dimensions of data quality. They are as follows:

  • Validity (Do measures conform to defined rules or constraints?)
  • Accuracy (Do measures conform to a standardized value?)
  • Completeness (Are all the required measures known?)
  • Consistency (Are the recorded measures the same across the dataset?)
  • Uniformity (Does the dataset use the same units of measurement?)


An awareness of these factors of data quality and some preliminary work to ensure these are adhered to in the preparation and cleaning stages can vastly improve the final results of a project, as well as save a lot of time avoiding confusion and errors in later stages of a project. It can take some time to become accustomed to doing this and can be tedious at times, but establishing good practices of data cleaning and preparation is one of the most valuable things any beginner to business intelligence and data science can do.

Sprinting and Distance Running

One of the essential skills I’ve had to (re)learn this year is time management. In particular, I’ve had to learn this in the context of preparing for exams. Despite having an undergraduate degree and having been in tertiary education for several years, I haven’t been in study programs that had exams at the end of each semester, oddly enough. Thus, having to structure my study not only before the exams but during each week of the semester so as to be adequately prepared and to not cram has been somewhat of a challenge.

Imposing a structure and routine to how I work has helped immensely in this regard. Previously, I would spend a lot of time working, for not a whole lot of output in return. I’d routinely get distracted by anything from social media to YouTube rabbit holes, sometimes losing whole days of ‘study’ sitting in front of my laptop doing basically nothing of worth. At the beginning of the year, this would result in often having to do things at the last minute, having wasted a lot of time and presuming I could wait until the last minute to do work, which had usually worked previously. After one too many close calls and grades that didn’t reflect my actual ability, I decided to make a change.

The model I use uses the analogy of running, in particular, the idea of sprinting and distance running, which is outlined below. It’s not particularly groundbreaking, but it’s an analogy that’s worked for me.


Sprinting, in this model, is basically work that is done under a strict time or other constraints. For instance, allotting a small but clearly defined period of time (say, an hour) and working flat out during then. Basically, it’s the Pomodoro technique, though I didn’t realize the technique had a name until I began writing this article. When used right, it can net a great deal of output in a relatively short amount of time. However, it is mentally taxing. I can only work under this condition for a fairly short amount of time before needing to take a break for a while.

This concept is similar to an idea raised by Cal Newport in his book ‘Deep Work’. Specifically, cutting out all distractions and imposing strict constraints in order to produce deep, quality work in a fairly short, focused session.

Distance Running

Distance running, in this model, is less structured than sprinting. If I have a more open-ended goal, such as doing some research and exploration of a question or topic I want to know more about, I employ this approach. This allows me to go on some tangents and make connections I may not otherwise make in a highly-structured sprinting session, but still has a clear goal in mind, unlike my previous method of working. By nature, I’m prone to doing this. I take advantage of this personality trait, but with some other conditions to maximise its benefit and minimize the downsides.

Ideally, this will be the last post on productivity, study and similar topics for a while, for a couple of reasons. In a few short weeks, at the end of my exams, I’ll have finished with study for the year and will have a few months off entirely, save for a bit of work and volunteering here and there. Secondly, I recently looked back at my first post on this new blog a few months back. Within it, I outlined that I wanted this blog to be more lighthearted and spontaneous than my previous writing, which so far hasn’t really happened. Old habits die hard, of course, but I can’t help but feel I’ve started to fixate on what aren’t particularly interesting or fun topics to write about, even if they are personally useful to a degree.

Thinking from First Principles

An enduring topic of interest to me is the science of how we as humans learn and think. This concept, known as metacognition, has been a valuable aid to my personal development this year and earlier. Metacognition, broadly speaking, has three component parts – knowledge, regulation and experiences. Knowledge refers to what you know about thinking and learning processes. Regulation involves the strategies and activities used to control learning. Experiences are the thoughts and feelings experienced while learning.

The idea of bigger picture thinking – or thinking from ‘first principles’ has been among the most important principles of thinking I have come across this year. One of the most famous proponents of first principles thinking is entrepreneur Elon Musk. On first principles thinking, Musk says the following:

“I tend to approach things from a physics framework,” Musk said in an interview. “Physics teaches you to reason from first principles rather than by analogy. So I said, okay, let’s look at the first principles. What is a rocket made of? Aerospace-grade aluminum alloys, plus some titanium, copper, and carbon fiber. Then I asked, what is the value of those materials on the commodity market? It turned out that the materials cost of a rocket was around two percent of the typical price”

First principles thinking, then, is reducing a problem or function to its fundamental parts, then working from there. It is a basic assumption that cannot be deduced any further. Thinking back to metacognition from earlier in the article, this is an example of metacognitive thinking – knowing about thinking and learning processes.

First principles thinking has been a powerful tool for my learning this year as I begin life within the field of IT. Initially, I was getting too caught up in the minutiae of the field, such as focusing on specific concepts within coding, instead of looking at a broader picture. Take, for instance, a large Business Intelligence assignment I recently completed. The assignment initially seemed too large to complete. It involved research, data entry, data analysis, visualization, machine learning and modelling. Having done none of these things previously, I was initially overwhelmed by the technical nature of many of these tasks.

However, by using first principles thinking, I was able to break down the assignment to its fundamental parts, which was to attain data related to flu cases in Australia and to create a report justifying its importance for analysis and study to a business manager. Digging deeper into the initial research stage and business context, the fundamental tasks of the assignment, and things I am already good at, made the more technical tasks which I was less proficient in later in the assignment more manageable. The steps in between suddenly became less daunting. Prioritising function (viable, relevant data communicated simply) over form (complex, fancy data science modelling algorithms and software) ensured a better final result as well as a simpler work process over time.

By nature, I’m a details-oriented person, as well as somewhat of a perfectionist. Being naturally inclined this way, it was difficult to move beyond these details. This is in stark contrast to how I approached problems I faced when studying during my teaching courses, or when I do freelance writing such as writing this article. By not being bogged down in smaller details, the writing flows easily, the solutions to problems appear more readily. Without knowing it, I was using first principles thinking – breaking down a problem or task into its most fundamental form, then completing it with a focus on function over form.