Introduction
Xiao-Li Meng, author of Data Science: An Artificial Ecosystem (link) noted “I used the word “artificial” to signal that data science is both human-created and very intensively computer-based” (link). He added “as we advance deeper into the digital age, our societal demands for data scientists naturally rise in both quantity and quality”.
However, Xiao-Li points out “compared to the research progress on data science methods, the research on data science education is still at the dinosaurs’ age” (link). In his discussion of the pedagogical organisation of data science, Xiao-Li suggests “you need to think of data science like a science like a social science, like the humanities”. At the University of California at Berkley “they are creating a division of data science – it’s university wide, a new school – recognizing that data science permeates other fields and requires this scale”. The aim at the University of California is “to comprehensively develop data science and its connections with essentially all fields of inquiry at all levels of teaching and research” (link). The division seeks to “bring together programs, schools, and departments from across campus to create rich educational opportunities and ignite groundbreaking research to meet society’s greatest challenges” (link) my emphasis.
As data science grows its presence in sport, I think there is a real need to explore pedagogy and practice as we seek to normalise the use of data science in sport (link). Xiao-Li suggests that this process involves appointing someone who knows data. “By knowing data, I don’t just mean someone who can program or analyze data. I mean someone who understands the enterprise of data, how they interact with people, how we make decisions with evidence. Data is one form of convincing each other” (link).
This process also involves a conversation about intelligence augmentation. In sport we will need to connect human intelligence (the person who knows data) with machine learning as we become more accomplished at algorithm understanding and development.
This post shares some of the literature about intelligence augmentation.
Intelligence Augmentation
As we develop our practice in data in sport, we need to be clear about the relationship between human intelligence and artificial intelligence. John Aldrin and his colleagues (2019) observe “intelligence augmentation refers to the effective use of information technology to enhance human intelligence” (link). This link between information technology and augmentation was first discussed in the 1950s and 1960s.
Although he makes no explicit mention of augmentation, Vannevar Bush (1945) (link) discusses the explosion of knowledge and the need to ‘navigate’ this ocean of knowledge in a logical way. He wrote “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”. This record of ideas raises fundamental questions about augmentation.
In his Introduction to Cybernetics (1956) (link), Ross Ashby discusses amplifying intelligence (1956:271). Ross points out that “An amplifier, in general, is a device that, if given a little of something, will emit a lot of it”. He points out “Intelligence Augmentation increases your personal intelligence by augmenting your mind/brain, while Artificial Intelligence is a standalone machine-only intelligence”.
Joseph Liklider (1960) (link) discussed man-computer symbiosis as an expected development in cooperative interaction between men and electronic computers. He proposed:
In the anticipated symbiotic partnership,men will set the goals, formulate the hypotheses, determine the criteria, and perform the evaluations. Computing machines will do the routinisable work that must be done to prepare the way for insights and decisions in technical and scientific thinking.
He added:
The hope is that, in not too many years, human brains and computing machines will be coupled together very tightly and the resulting partnership will think as no human brain has ever thought and process data in a way not approached by the information-handling machines we know today.
Douglas Engelbart (1962) in Augmenting Human Intellect: A Conceptual Framework wrote “by “augmenting human intellect” we mean increasing the capability of a man to approach a complex problem situation, to gain comprehension to suit his particular needs, and to derive solutions to problems” (link). Douglas’s report covered the first phase of a program aimed at developing means to augment the human intellect. He notes “individuals who operate effectively in our culture have already been considerably ‘augmented'”.
Oracle has produced a paper titled What Is Augmented Analytics? It was written by Alice LaPlante (2019) (link). Alice suggests that augmented analytics is the latest way to think about data and analytics. She notes that it includes “embedding artificial intelligence , often in the form of machine learning and natural language processing, into traditional analytics”.
Alice suggested the definition of augmented analytics comprises two components: analytics as the process of identifying patterns in data; and artificial intelligence as “the computer science practice of building automated systems that are able to perform tasks that normally require human intelligence”.
Alice added “When you embed machine learning and AI into analytics, you get augmented analytics”. Augmented analytics “automates the selection and preparation of data, the generation of insights, and the communication of those insights”.
Alice discusses the role of governance in the uptake of augmentation. She proposes “organisations must understand how data is collected, how it is used, how it moves through the organisation, how it’s changed, and how it’s stored”.
Gartner’s definition of augmentation is:
Augmented analytics is the use of enabling technologies such as machine learning and AI to assist with data preparation, insight generation and insight explanation to augment how people explore and analyze data in analytics and business intelligence platforms. It also augments the expert and citizen data scientists by automating many aspects of data science, machine learning, and artificial intelligence model development, management and deployment (my emphasis) (link).
Conclusion
The conceptual clarification of intelligence augmentation is, I think, very important for our data practices. We do face an issue Joseph Reger (link) raised, namely:
Anyone would have a hard time to explain the difference between Artificial Intelligence as it stands today and Intelligence Amplification. Intelligence Amplification is like a mighty exo-skeleton for the brain. It assists human intelligence to make better decisions, to work faster and so on. And although the algorithms being applied have become much more sophisticated, most of what the technology industry does currently under the label of Artificial Intelligence is actually intelligence amplification.
The University of California has used two verbs that might help us with this clarification as we develop our pedagogy and practice. The University talks about creating rich educational opportunities and igniting groundbreaking research. I see the former as a way to explore pedagogy in a dynamic knowledge domain and the latter as a way to develop innovative practice that can be shared (link).
Photo Credit
W R Ashby (Copyleft. 2002. No Rights Reserved)
Alice LaPlante (Alice LaPlante)
