Feedback loops - the ability to continually refine and improve something in response to feedback - are innocuous, pervasive phenomena. For this blog post I’m particularly interested in instances where these loops have been deliberately built into a particular technology or solution to solve a specific problem. I’m writing this post because I want to try to put forward the following argument:
The most successful, `disruptive’ changes in society and technology have been (and are being) primarily brought about by innovations that are able to harness the power of this simple loop.
Potted History
Throughout the 20th century, this simple loop has dominated technological progress. We will proceed to look at some examples. It is worth highlighting that these are treated both in a rough chronological order. However (and I don’t believe this is an accident) they are also ordered according to the rate at which feedback is provided; one of the reasons I am writing this blog entry is that (I believe) it appears that this rate is constantly increasing.
In the early 1900’s, Bell Labs were experiencing a lot of variability in the quality of their telephone wiring. Walter Shewhart, a statistician, was charged with devising a means by which to eliminate this variability. In order to refine the cabling process, he proposed the a cycle - later popularised by Edwards Deming - as the Plan-Do-Check-Act cycle. In essence, any procedure is devised as follows. You “Plan” your procedure, you “Do” it, you “Check” the quality of the outcome, identifying any problems, and you “Act” by refining the plan, and repeat the cycle.
The approach was one of the major innovations that formed the basis for the American manufacturing boom in the early 20th century. Shewhart, Deming and their colleagues travelled to Japan in the aftermath of the Second World War, and carried on to spread this ethos of continuous feedback. This set the foundations for the Toyota Production System and similar manufacturing procedures that underpinned the Japanese “economic miracle” and since inspired various modern equivalents, such as ``lean manufacturing’’ or Continuous Process Improvement.
These principles eventually fed into the most enduring approaches within the software engineering. Incremental Iterative Development and their successors in Agile techniques all centrally play upon feedback. Regular cycles to enable continual feedback from the client about the product, often daily team meetings to enable feedback from developers, the increasing use of technologies such as Github, which enable feedback on individual code-changes, etc.
Within web applications continuous feedback from users to providers (or to each other) has become a core means by which to maintain and enforce service standards. One can think of the likes of Uber, AirBnB, and Ebay.
At an algorithmic level, most Machine Learning algorithms play upon some form of internal feedback loop. Two obvious examples are Genetic Algorithms and “Deep Learning” Recurrent Neural Networks. At their heart is a capability to continually modulate an inferred model by adapting to feedback. The last couple of years have seen enormous advances in Machine Learning technology. Computers can now beat humans at Go. They can recognise details in enormously complex patters, they can drive cars. I do not think that this is because of enormous advances in terms of the algorithms themselves. It is because there has been a sudden surge in the availability of data, and specifically in the availability of feedback.
What next?
What links the emergence of powerful Machine Learning algorithms with the sudden rise of apps such as Uber?
I believe that both have become as powerful as they are because it has been easier to collect data that can be channeled into feedback. This is as true for Machine Learning as it is for apps, where there are suddenly millions of users, all of whom have smartphones with virtually uninterrupted internet access.
The potential that could be gained from exploiting this loop first became apparent to me when I read a 2004 Nature paper: “The Robot Scientist”; this nicely embodied the feedback loop - replacing a human scientist with a Machine Learner with the goal of carrying out experiments (automatically via robotic equipment) to infer a model of a particular genetic pathway. In their loop the hypothesis model was refined after each experiment and tested in each subsequent cycle. At the time this appeared to be impossibly futuristic to me. We are now almost 13 years down the line, and the process of fully automated drug discovery has been realised.
To me, this renders the coming years as exciting as they are terrifying. For every noble cause, such as automated drug discovery, there is an equally disconcerting one, especially when humans become a direct part of this loop. An unrelenting, unwavering mechanised channel of feedback is necessarily reductive. If the feedback has direct implications for someone’s livelihood, the consequences can be brutal (c.f. the consequences for Uber drivers who receive consistently low ratings).
As organisations are seduced by the apparent “direct democracy” and self-regulating properties of these mechanisms, it is easy to see how, for its workers, this can turn into tyranny at the hands of a capricious, remote customer.
Even in my field of university teaching, similar changes are becoming increasingly tangible. Students are now customers, and their feedback on teaching (however subjective that may be) plays at least as much of a role as research in ranking departments and universities against each other, a trend that will no doubt become more pronounced with the emergence of the Teaching Excellence Framework. I noticed this year that the Panopto lecture-recording software, mandated across a growing list of UK universities, even has a feature that gives students the option (in a Netflix-esque way) to rate individual lectures out of five stars…
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