Grazing on the periphery

It has been a great week for grazing … much of it enabled by Mara Averick’s open sharing.

It started with news of Alison Hill’s speakerdeck presentation.

Alison discusses courage, enchantment, permission, persistence and trust as elements of creative learning. She concludes with this slide:

What fascinated me about Alison’s presentation was her synthesis of profound ideas about sharing and learning with each other in an aesthetic that grabbed and held my attention for 94 slides.

She is part of a remarkable R community that shares openly.

Three other members of this community enabled even more grazing this week. Each offered me possibilities to extend my knowledge of visualisation using R.

Matt Dancho has shared the Anomalize package that enables a “tidy” workflow for detecting anomalies in time series data. There is a vignette for the package to share the process of identifying these events. I think this will be very helpful in my performance research as I investigate seasonal and trend behaviours.

Ulrike Groemping shared the prepplot package in which “a figure region is prepared, creating a plot region with suitable background color, grid lines or shadings, and providing axes and labeling if not suppressed. Subsequently, information carrying graphics elements can be added”.  There is a detailed vignette to support the package.

Guangchuang Yu shared the ggplotify package that converts “plot function call (using expression or formula) to ‘grob’ or ‘ggplot’ object that compatible to the ‘grid’ and ‘ggplot2’ ecosystem”.  Guangchang shares a detailed vignette that illustrates the potential of the package.

Mara, Alison, Matt, Ulrike and Guangchuand epitomise for me the delights in open sharing. A post in The Scholarly Kitchen, written by Alice Meadows, added to my grazing on the margins of openly sharing.

In the post Alice shares a wide range of resources. She makes a particular mention of the Metadata 2020 project that is “a collaboration that advocates richer, connected, and reusable, open metadata for all research outputs, which will advance scholarly pursuits for the benefit of society.”

The opportunities for such collaboration are increasing as we find new ways to share synchronously and asynchronously. These become easier as we make a bold decision to think out loud and share our thoughts with others.

Alison’s presentation includes this slide as a stimulus for that sharing:

This sharing permits grazing for me in the sense of the word used in Leonard Cohen’s Preface to the Chinese translation of his collection of Beautiful Losers poems includes this passage:

When I was young, my friends and I read and admired the old Chinese poets. Our ideas of love and friendship, of wine and distance, of poetry itself, were much affected by those ancient songs. … So you can understand, Dear Reader, how privileged I feel to be able to graze, even for a moment, and with such meager credentials, on the outskirts of your tradition.

Photo Credits

Slide grabs from Alison Hill’s speakerdeck.

Pictures from Twitter and Beuth Hochschule.

Collaboration image from Alice Meadow’s post.

Intelligence Augmentation: meeting Vannevar and Douglas

Introduction

It has taken me some time but I have managed to unearth some primary sources in the discussion of intelligence augmentation.

I do think this is a profoundly important concept to consider when we contemplate our relationship to artificial intelligence in our cultural contexts.

A slide shared by Melanie Cook (2017) sent me off on my reading journey.

Peter Skagestad (1993:157) set me on the way too. He observed:

the pioneers of the personal-computer revolution did not theorize about the essence of the computer, but focused rather on the essence of human thinking, and then sought ways to adapt computers to the goal of improving human thinking.

His discussion took me to Douglas Engelbart’s report Augmenting Human Intellect: A Conceptual Framework (October 1962) prepared for the Director of Information Sciences in the Air Force Office of Scientific Research.

Douglas introduced me to Vannevar Bush’s article As We May Think (1945).

In this post I introduce you to Vannevar and Douglas. I apologise if you have met them both already.

Vannevar Bush

There is a very detailed account of Vannevar’s life and work in Wikipedia. His 1945 article provides an introduction to his thinking and a vision for the scientific endeavour he nurtured in the next four decades. The Atlantic Editor notes “Dr. Bush calls for a new relationship between thinking man and the sum of our knowledge”.

Douglas Engelbart (1962:48) quotes Vannevar extensively: “it was deemed appropriate to our purpose here to summarize it in detail and to quote from it at considerable length”.

There are some key passages for me in Vannevar’s article. The first is to do with communication:

Science has provided the swiftest communication between individuals; it has provided a record of ideas and has enabled man to manipulate and to make extracts from that record so that knowledge evolves and endures throughout the life of a race rather than that of an individual.

And how we manage these communications:

There is a growing mountain of research. But there is increased evidence that we are being bogged down today as specialization extends. Professionally our methods of transmitting and reviewing the results of research are generations old and by now are totally inadequate for their purpose. If the aggregate time spent in writing scholarly works and in reading them could be evaluated, the ratio between these amounts of time might well be startling. The difficulty seems to be, not so much that we publish unduly in view of the extent and variety of present day interests, but rather that publication has been extended far beyond our present ability to make real use of the record.

Then there is:

The summation of human experience is being expanded at a prodigious rate, and the means we use for threading through the consequent maze to the momentarily important item is the same as was used in the days of square-rigged ships.

Vannevar notes that there is help on the horizon, namely, “there are signs of a change as new and powerful instrumentalities come into use”.Vannevar’s example of an instrumentality was an imagined device, a memex:

in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility. It is an enlarged intimate supplement to his memory.

A memex is primarily a piece of furniture. It is designed to support the human mind’s associative inquiry (an “intricate web of trails carried by the cells of the brain”) and “any item may be caused at will to select immediately and automatically another”.

Douglas Engelbart

 

Seventeen years after Vannevar’s article, Douglas presented his report Augmenting Human Intellect: A Conceptual Framework to the Director of Information Sciences in the Air Force Office of Scientific Research.

Mike Cassidy (2013) wrote of Douglas:

He believed that computers, which were primarily for crunching numbers and spitting out answers when he started his work, had the ability to empower people and enhance their intellect in ways that would improve lives.

Douglas’s 1962 report addressed this empowerment. It was “an initial summary report of a project taking a new and systematic approach to improving the intellectual effectiveness of the individual human being” (1962:ii).

‘Augmenting the human intellect’ for Douglas meant increasing a person’s capability to approach a complex situation and derive solutions to the problem. This will involve providing access to “the services of a digital computer” and “developing the new methods of thinking and working” that allow the human to capitalize on these services” (1962:3). (He notes elsewhere “Individuals who operate effectively in our culture have already been considerably ‘augmented’. (1962:15))

Douglas’s report presents a conceptual framework that orients us “toward the real possibilities and problems associated with using modern technology to give direct aid to an individual in comprehending complex situations, isolating the significant factors, and solving problems” (1962:8).

He suggests that our capabilities are augmented by:

  • artifacts
  • language
  • methodologies for problem-solving
  • training

He proposes:

The system we want to improve can thus be visualized as a trained human being together with his artifacts, language, and methodology. The explicit new system we contemplate will involve as artifacts computers,and computer-controlled information-storage, information-handling, and information-display devices. (1962:9)

Douglas named this system H-LAM/T (Human using Lauguage, Artifacts, Methodology, in which he/she is Trained).

As he explored his conceptual approach to augmentation, Douglas discussed Vannevar’s memex ideas at length (“This material is so relevant and so well put that I quote it in its entirety” (1962:50).)

Douglas concludes his report with ta discussion of how the intellectual effectiveness of a human can be significantly improved by an engineering-like approach toward redesigning changeable components of a system. (1962:128)

The aim of such an engineering-like approach is to provide:

potential users in different domains of intellectual activity with a basic general-purpose augmentation system from which they themselves can construct the special features of a system to match their jobs, and their ways of working—or it could be used on the other hand by researchers who wanted to pursue the development of special augmentation systems for special fields. (1962:130)

Douglas concludes his report with advocacy for a dynamic discipline aimed at understanding and harnessing ‘neural power’.

Conclusion

Vannevar and Douglas’s visions for intelligence augmentation make for fascinating reading seventy-three and fifty-six years on respectively. I do think they should be essential reading for anyone exploring sport informatics and analytics and contemplating special augmentation systems for such a special field.

I hope both of them would take some delight in Ross Goodwin’s suggestion (2016):

When we teach computers to write, the computers don’t replace us any more than pianos replace pianists—in a certain way, they become our pens, and we become more than writers. We become writers of writers.

I trust that in becoming writers of writers, we create new opportunities to incorporate artificial intelligence into our own augmentation and our transformation of practice.

Photo Credits

Telephone Switchboard Operators 1914 (Reyner Media, CC BY 2.0)

Vannevar Bush (This image is a work of the United States Department of the Treasury, taken or made as part of an employee’s official duties. The image is in the public domain in the United States.)

Douglas Engelbart (Smithsonian Magazine)

On the way to the training ground (Keith Lyons, CC BY 4.0)

180309 Finds

During this week I came across some excellent resources. This post is an aide memoir for me but I hope it might be of interest.

Open Badge Template

This design your own open badge template appeared in the Wapisasa sandbox.

Data Culture

Rahul Bhargava, Catherine D’Ignazio and the Stanford Center on Philanthropy and Civil Society, have worked together with 25 organizations to create the Data Culture Project.

Data Science Text

Pablo Casas (2018) has published a data science live book. The introduction to Why this book?

The book will facilitate the understanding of common issues when data analysis and machine learning are done.

Building a predictive model is as difficult as one line of R code:

my_fancy_model=randomForest(target ~ var_1 + var_2, my_complicated_data)

That’s it.

But, data has its dirtiness in practice. We need to sculpt it, just like an artist does, to expose its information in order to find answers (and new questions).

Michael Clark’s Documents

Michael notes of his online resource shared on GitHub:

Here you’ll find documents of varying technical degree covering things of interest to me or which I think will be interesting to those I engage with. Most are demonstration of statistical concepts or programming, and may be geared towards beginners or more advanced. I group them based on whether they are more focused on statistical concepts, programming or tools, or miscellaneous.

Michael is the Statistician Lead for the Advanced Research Computing consulting group, CSCAR, at the University of Michigan. He provides analytical, visualization, code, concept, and other support to the larger research community.

March Madness

A tweet from Mara Averick led me to Sam Firke’s discussion of March Madness college basketball.

Sam shares his guide for March Madness predictions (an estimate how likely it is that Team A beats Team B, for each of the 2,278 possible matchups in the tournament).

Sam shares a link to Gregory Matthews and Michael Lopez’s (2014) paper Building an NCAA mens basketball predictive model and quantifying its success. Gregory and Michael were winners of the 2014 Kaggle competition to predict the outcome of the tournament.

Photo Credits

…so… you’re saying the round one would please her more? (Pim Geerts, CC BY-NC-ND 2.0)

sweet16wide (Andy Thrasher, public domain)