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

A Head of Football Analytics

Earlier this year, the canoe slalom program in Great Britain advertised for a performance data analyst.

I though this marked a fascinating change in the sport and underscored for me the opportunities that are now appearing in sport that signal a fundamental shift in how learning journeys are being experienced in industry and in education systems.

This week, Leicester City are adding to this momentum with the advertisement of a Head of Football Analytics opportunity. I hoped the club would extend its expertise in an area they energised with their Tactical Insights Day in 2016.

The Role

  • Produce unique and insightful performance metrics and analysis, using data modelling.
  • Ensure that existing and new databases are maintained and updated promptly.
  • Collaborate with appropriate members of staff at the club, and develop strategies to raise the overall levels of data literacy, analysis and visualisation.
  • Develop the integrated, club-wide approach to providing data driven insights for performance evaluation, player recruitment, sports science and medical aspects of the club.
  • Pro-active in the organisation and implementation of data analysis-based CPD learning activities within the club.

The person Leicester is seeking:

  • Significant experience of working as part of a professional sports organisation, or other sports-related industries.
  • Experience of managing large datasets, and producing high-quality data insights and visualisations for end-users.
  • Experience from other areas may still be considered, based on the relevance to this role.
    Masters or PhD in a numerate subject, this may include Statistics, Economics, Applied Mathematics, Engineering, Computer Science or related subjects.
  • Advanced coding ability (R, Python, XML/XSLT manipulations).
  • Demonstrable working knowledge of databases, SQL and database design.
  • Knowledge of using API’s to manage data sets, and experience using JSON scripts.
  • Familiarity with raw data files such as Opta F24 & TracAb.
  • Good time management & organisational skills, and ability to adhere to deadlines.
  • Excellent written and communication skills in English, with the ability to present results clearly (verbally & visually), and to develop close working relationships with existing staff members with varied levels of data analysis experience.
  • Demonstrable knowledge of football, and of how data analytics are currently being used to impact decision making processes in professional sport.

I noted Ted Knutson’s tweet about this opportunity.

Photo Credit

Leicester City ready for kick off (Ronnie Macdonald, CC BY 2.0)