#EL30 Graphing

Week 3 of Stephen Downes’ E-Learning 3.0 course is looking at Graphs.

Stephen recommended some resources for this topic. These included:

Vaidehi Joshi’s (2017) gentle introduction to graph theory. In her discussion of graphs, Vaidehi observes “in mathematics, graphs are a way to formally represent a network, which is basically just a collection of objects that are all interconnected”.  She distinguishes between directed graphs and undirected graphs and explains the ways edges connect nodes in these kind of graphs. An example of the former is Twitter (each edge created represents a one-way relationship), and of the latter Facebook (its edges are unordered pairs).

Vaidehi suggests a number of resources to provide details about graphs, one of them is Jonathan Cohen’s slide show Graph Traversal. He defines a graph as a “general structure for representing positions with an arbitrary connectivity structure” that has a collection of vertices (nodes) and edges (arcs). An edge connects two vertices and makes them adjacent.

A second resource shared by Stephen is Fjodor Van Veen’s (2016) Neural Network Zoo. In his post Fjodor shares a “mostly complete chart of Neural Networks’ and includes a detailed list of references to support his visualisation of the networks.

A third resource continues the visualisation theme. Vishakha Jha (2017) uses this diagram to inform the discussion of machine learning:

A fourth resource recommendation is Graph Data Structure and Algorithms (2017). This article aggregates a large number of links to graph topics. It includes this explanation:

One of the E-Learning 3.0 course members, Aras Bozkurt, exemplified this theme in this tweet and in doing so underscored the skills available within self-organising networks :

It was a great way to end and start conversations about graphs.

Photo Credits

Title image is from Gonçalves B, Coutinho D, Santos S, Lago-Penas C, Jiménez S, Sampaio J (2017) Exploring Team Passing Networks and Player Movement Dynamics in Youth Association Football. PLoS ONE 12(1): e0171156. https://doi.org/10.1371/journal.pone.0171156

Other images are frame grabs for the resources cited in this post.

A Coaching Mind

Lewis Lapham starts his post about the mind’s ability to reinterpret the past with a quote from Jeffrey Eugenides: “Biology gives you a brain, life turns it into a mind”.

Lewis’s post and Michio Kaku’s (2014) Future of the Mind prompted my thoughts about a coaching mind and consciousness.

I sense that these philosophical issues will become more important as coaches deal with performance data flows in their daily working environments. For example, Lewis’s observes:

The scientific-industrial complex focuses its efforts on the creation of artificial intelligence—computer software equipped with functions of human cognition giving birth to machines capable of visual perception, speech and pattern recognition, decision making and data management.

He adds:

Mind is consciousness, and although a fundamental fact of human existence, consciousness is subjective experience as opposed to objective reality and therefore outdistances not only the light of the sun and the moon but also the reach of the scientific method.

Michio notes “human consciousness … creates a model of the world and then simulates it in time, by evaluating the past to simulate the future”.

In my concept of a coaching mind, I have a sense of coaches’ experiencing different kinds of consciousness. Alain Morin (2006) identifies four kinds of consciousness that help me reflect on coaching as an emerging experience and learning journey:

  • Unconscious: being non-responsive to self and environment
  • Conscious: focusing attention on the environment; processing incoming external stimuli
  • Self-awareness: focusing attention on self; processing private and public self-information
  • Meta-self-awareness: being aware that one is self-aware

Alain concludes his paper with a recognition that there are other conversations to be had:

A great deal of effort still needs to be deployed in order to examine and compare additional consciousness-related concepts such as “meta-cognition”, “higher-order thought,” “autonoetic,” “visceral,” “first-order consciousness,” and “immediate self-awareness.”

Lewis, Michio, and Alain have helped me reflect on how coaches might flourish in an occupational culture that will extend the reach and application of artificial intelligence.

I am hopeful that the move towards and within meta-self-awareness might help us discuss how we, as mindful people, create our own intelligence augmentation and achieve a symbiosis with the tools that we integrate mindfully in our praxis.

Photo Credit

In the mind’s eye (Robert Couse-Baker, CC BY 2.0)

Feedback Loop (Robert Couse-Baker, CC BY 2.0)


Earlier this year, Teppo Felin wrote a post for Aeon titled The fallacy of obviousness.

It reappeared this week on the Medium platform and gave me an opportunity to read it. I had been off re-reading some of the early 1960s intelligence augmentation and human-computer interaction literature so Teppo’s article was an excellent opportunity to focus on mind, cognition and rationality.

The starting point for Teppo is Daniel Simons and Christopher Chabris’ (1999) paper Gorillas in Our Midst: Sustained Inattentional Blindness for Dynamic Events. Daniel and Christopher’s abstract starts with these sentences:

With each eye fixation, we experience a richly detailed visual world. Yet recent work on visual integration and change direction reveals that we are surprisingly unaware of the details of our environment from one view to the next: we often do not detect large changes to objects and scenes (‘change blindness’). Furthermore, without attention, we may not even perceive objects (‘inattentional blindness’). Taken together, these findings suggest that we perceive and remember only those objects and details that receive focused attention.

Teppo notes:

Now, it’s hard to argue with the findings of the gorilla experiment itself. It’s a fact that most people who watch the clip miss the gorilla. But it does not necessarily follow that this illustrates – as the study’s authors argue – that humans are ‘blind to the obvious’. A completely different interpretation of the gorilla experiment is possible.

In his discussion, he proposes:

… the very notion of visual prominence or obviousness is extremely tricky to define scientifically, as one needs to consider relevance or, to put differently, obviousness to whom and for what purpose?

and asserts:

The alternative interpretation says that what people are looking for – rather than what people are merely looking at – determines what is obvious. Obviousness is not self-evident.

The final part of Teppo’s paper resonated strongly with my reading of JCR Licklider’s (1960) discussion of human-computer interaction. Teppo suggests:

the current focus on human blindness and bias – across psychology, economics and the cognitive sciences – has contributed to the present orthodoxy that sees computers and AI as superior to human judgment.

He adds “computers and algorithms – even the most sophisticated ones – cannot address the fallacy of obviousness”.

Intelligence and rationality are more than just calculation or computation, and have more to do with the human ability to attend to and identify what is most relevant.

Why I was delighted to read Teppo’s article was that it gave me an opportunity to reflect on the generative and creative qualities of the human mind and the dynamic nature of observation … and reflect on long-standing conversations about intelligence amplification and augmentation initiated in the 1960s.

I did see the gorilla in the video when I first saw it and was surprised when colleagues did not. Our differences led to some fascinating conversations … which I take to be the force of Teppo’s post too.