A view of Moscow

A couple of days ago, Maha Bali wrote about connecting virtually (link). I was very interested in the ways she explored personal and continuing learning in her post.

Part of her experience was ‘attending’ conferences through the presence of others. I see this as vital in a world where there are so many conferences with a variety of registration, travel and subsistence costs. Partnering someone remotely overcomes the difficulties of cost and distance.

It does nor replace attendance. I am mindful that many people gain immense satisfaction from being present and part of “the hallway and social conversation at the conference”.

However, connecting virtually does offer the possibility of connection in a different kind of way. It is an issue I have been thinking about a great deal.

This Summer, in July, I am facilitating an unmeeting and (un)hack in Moscow with Malte Siegle, Martin Lames and Alexander Danilov prior to the Symposium of the International Association of Computer Science in Sport (link). We have a tentative program to explore data from the 2018 FIFA World Cup Finals and prospecting to Qatar in 2022:

Saturday: 6 July

18:00 PM: Arrival, Registration and Brainstorming

Start of hacking, working and analysing.

Sunday: 7 July

09:00 AM: Morning coffee and recap of Saturday

09:30 AM: Networking and brainstorming

10:00 AM: Hacking, working and analysing

Noon: Lunch

13:00 PM: Hacking, working and analysing

16:00 PM: Break out session

17:00 PM: Elevator pitch presentations of ideas (3 slides, 3 minutes per group).

18:00 PM: Announcement of winners and on to the opening reception of IACCS conference.

This is a framework that we can adapt. Throughout the process, I have been aware that not everyone can attend. There are lots of other opportunities around the world including Seattle and Paris.

I am travelling from Australia to Moscow for the (un)meet and (un)hack. I do take Maha’s point strongly that those who are attending in person can partner with those not there. I wondered if we might connect in real or lapsed time through social media and online platforms. I am going to use Twitter (link), Mastodon (link) and GitHub (link) as part of my aim to connect. I will be using the hashtag #iacss19connect to support this remote sharing.

Photo Credit

Tom Grimbert (@tomgrimbert) on Unsplash

Augmenting, interacting, reflecting

Helping with a shoe lace

I have revisited Douglas Engelbart’s 1962 paper Augmenting Human Intellect: A Conceptual Framework (link). I did so after Mark Upton shared links with me to Dan McQuillan’s Towards an anti-fascist AI (link) and Joi Ito’s (2018) Resisting Reduction manifesto (link).

Joi’s manifesto includes reference to Norbert Wiener’s 1950 The Human Use of Human Beings (link). (By coincidence, I have been researching Norbert’s work in cybernetics for a paper I have been writing about computer science in sport developments in Russia.)

Another nudge in this direction came from an alert to Ben Shneiderman’s (2019) Encounters with HCI Pioneers (link). It is Ben’s personal history of the intellectual arguments and people he encountered.

The final impetus for this post came from a Stephen Downes post today (link) that concludes “We can discuss ethics, we can refer to them – but you can’t make people ethical – at least, not in the sense that everybody is ethical in exactly the same way everyone else is ethical. And if you depend on this in order to succeed, you won’t succeed.” (Original emphasis)

I see all of these links as important prompts to explore our taken-for-grantedness views of the world. Joi points out “the paradigms that set our goals and drive the evolution of society today have set us on a dangerous course”. This would include, I think, a consideration of how the discipline Douglas envisaged aimed at understanding and harnessing “neural power” might be sufficiently reflective to pose questions about it own paradigmatic certainty.

I take this to be the essence of Dan McQuillan’s argument about artificial intelligence (AI):

AI is political. Not only because of the question of what is to be done with it, but because of the political tendecies of the technology itself. The possibilities of AI arise from the resonances between its concrete operations and the surrounding political conditions. By influencing our understanding of what is both possible and desirable it acts in the space between what is and what ought to be.

He concludes:

Real AI matters not because it heralds machine intelligence but because it confronts us with the unresolved injustices of our current system. An antifascist AI is a project based on solidarity, mutual aid and collective care. We don’t need autonomous machines but a technics that is part of a movement for social autonomy.

These are profound issues for us. Sport has to be part of this debate about how we might all flourish in changing times. I take Stephen’s point about different ethical views of the world that inform our practices. I am hopeful that the ‘collective care’ Dan mentions can give us a shared journey embedded in the harmony discussed by Joi.

Photo Credit

Photo by Adrià Crehuet Cano on Unsplash

Discussing data

A tilt-shift photography of HTML codes

Three posts popped up recently that explored our understanding of data.

In a recent post, Cassie Kozyrkov proposes “we need to learn to be irreverently pragmatic about data” (link).

She observes:

Take a moment to realize how glorious it is to have a universal system of writing that stores numbers better than our brains do. When we record data, we produce an unfaithful corruption of our richly perceived realities, but after that we can transfer uncorrupted copies of the result to other members of our species with perfect fidelity. Writing is amazing! Little bits of mind and memory that get to live outside our bodies.

Cassie notes that when we analyse data, we are accessing someone else’s memories. If we regard ourselves as data analysts then we are engaged in the discipline of making data useful (an in doing so make decisions about analytics, statistics and machine learning). We can demystify data and talk simply about what we do, how we do it, and what we share.

After reading Cassie’s post, I followed up with Nick Barrowman’s (2018) Why Data Is Never Raw (link). He points out:

A curious fact about our data-obsessed era is that we’re often not entirely sure what we even mean by “data”: Elementary particles of knowledge? Digital records? Pure information? Sometimes when we refer to “the data,” we mean the results of an analysis or the evidence concerning a certain question. On other occasions we intend “data” to signify something like “reliable evidence” …

Like Cassie, Nick cautions against “the near-magical thinking about data”. He notes:

How data are construed, recorded, and collected is the result of human decisions — decisions about what exactly to measure, when and where to do so, and by what methods. Inevitably, what gets measured and recorded has an impact on the conclusions that are drawn.

He adds:

We tend to think of data as the raw material of evidence. Just as many substances, like sugar or oil, are transformed from a raw state to a processed state, data is subjected to a series of transformations before it can be put to use. Thus a distinction is sometimes made between “raw” data and processed data, with “raw data” often seen as a kind of ground truth

Nick argues that when people use the term raw data “they usually mean that for their purposes the data provides a starting point for drawing conclusions”. (Original emphasis) He adds:

the context of data — why it was collected, how it was collected, and how it was transformed — is always relevant. There is, then, no such thing as context-free data, and thus data cannot manifest the kind of perfect objectivity that is sometimes imagined

By coincidence, I was reading Will Koehrsen’s suggestions (link) for a non-technical reading list for data science that starts with this introduction:

we can never reduce the world to mere numbers and algorithms. When it comes down to it, decisions are made by humans, and being an effective data scientist means understanding both people and data

I thought all three posts were excellent nudges to enhance our reflexive practice. They reminded me also of EH Carr’s (1961) discussion of historical ‘facts’. He noted that far from being self-evident, historians give facts their significance and do so selectively. They are in effect “a selective system of cognitive orientations”.

Photo Credit

Photo by Markus Spiske on Unsplash