What does Artificial Intelligence and Agile Change Management Mean for Us?

Dear Reader, please ruminate upon these thoughts before reading the article. Then, together we will explore the nature of AI and acquire some coaching tools to help our Inner Dynamics proactively respond and adapt to coping with change and chaos. Inner Dynamics play out within our minds and sensibilities.

The point where we could turn off all of our computers
without causing the collapse of modern civilisation is long past.
— Pedro Domingos in The Master Algorithm

Where is all the knowledge we lost with information?
— T.S. Elliot

And don’t speak too soon for the wheel’s still in spin
And there is no tellin’ who it’s namin’
— Bob Dylan, The Times They are A-changin’

Victoria Palacios


Bye-bye humankind?

The idea that we as humans are replaceable at all is one of the most cringeworthy and all-encompassing apprehensions that we could imagine. Just the thought can shatter our souls and causes our foundations to quake.

Topic number one among many clients is the effect artificial intelligence (AI) will have on all of us. I cannot recall a subject which has received so much attention for so long and in such a wide array of publications and news outlets, as AI.

Many believe that AI could challenge and ultimately destroy our society: jobs will disappear in droves and we, unable to compete in any form with robots, will all descend into a life of poverty and boredom.

However, there are also many supporters of AI who believe that it will transform the world by leaps and bounds into a more advanced and humane civilisation.

This article will not be a treatise on the politics or ethics of AI. Instead, it is an insight in to the world of AI and how it not only affects us but how we might proactively prepare and respond to AI.

Definitions and the nature of AI

According to Stefan Finlay in his book Artificial Intelligence and Machine Learning for Business, AI, or machine learning (ML), is “the replication of human analytical and/or decision making capabilities.” ML is “the use of mathematical procedures (algorithms) to analyse data.” (Finlay) An algorithm is a detailed succession of instructions for implementing an operation or solving a problem. In non-technical terminology, we use algorithms in everyday tasks, such as a recipe for cooking or an instruction manual – even coaching tools.

Algorithms are only as good as the data used to create them. UK mathematician Clive Humby describes data as: “ the new oil. It is valuable but if unrefined it cannot really be used … It has to be changed into gas, plastic, chemicals etc. to create a valuable entity that drives profitable activity.”

In his book, The Master Algorithm, Pedro Domingos writes that: “Control of data and ownership of the models learned from it is what many of the 21st century’s battles will be about – between governments, corporations, unions, and individuals. But you also have an ethical duty to share data for the common good.”

The transformation may not be easy …

According to an article in The Economist (24th April 2018): “Job-grabbing robots are not longer science fiction. In 2013, Carl Benedikt Frey and Michael Osborne of Oxford University used –what else? – a machine-learning algorithm to assess how easily 702 different jobs in the US could be automated. They concluded that fully 47% could be done by machines ‘- “over the next decade or two.”

Stephen Hawking was also cautious: “I fear that AI may replace humans altogether,” he said in an interview with Wired magazine, seen by Cambridge News. “If people design computer viruses, someone will design AI that improves and replicates itself. This will be a new form of life that outperforms humans.”

The Economist writes (28th March 2018): “using AI managers can gain extraordinary control over their employees.” One firm peddles smart ID badges that can “track employees around the office and reveal how well they interact with colleagues.” The author speculates whether “the choice in some jobs will be between being replaced by a robot or being treated like one.”

....but will also supposedly bring astounding benefits:


Also from The Economist (28th March 2018): “the McKinsey Global Institute, “reckons that just applying AI to marketing, sales and supply chains could create economic value, including profits and efficiencies, of $2.7 trillion over the next 20 years. The same article quotes Google’s boss as declaring that AI “will do more for humanity than fire or electricity.”

Pedro Domingos predicts that machines, which can learn more in a shorter amount of time will create an environment in which we have greater lifetime unemployment benefits, no human-fought wars, and future generations who are comfortable interacting with robots in ways we are not. “Humans are not a dying twig on the tree of life. On the contrary, we’re about to start branching.”

Coping with the change

Since the world of artificial intelligence will continue to bring about enormous change, we must develop methods to affect balance and smooth out chaos. Increasing the amount of new choices you have by expanding your “map of the world” is essential. Our personal map of the world is the prism through which we experience and give meaning to situations and experiences. Alfred Korzybski’s principle that the map is not the territory is a healthy attitude to develop. By adjusting our Inner Dynamics we are able to challenge our beliefs, view things from different perspectives and discover new information.

An interesting place to begin is by considering the some of the components of Domingos’s Master Algorithm. According to Domingos, the Master Algorithm is a sort of ultimate solution in AI/machine learning. It involves processes with which you are already familiar. Here is a very brief overview.

  • Use logic and induction to find out what knowledge is missing in order to make something complete. It starts with a conclusion and works backwards to close the gaps.

  • The technique of natural selection: choosing the best possible alternative given the available data. Select what has survival potential.

  • Calculate the probability, what are the chances that something will happen or not happen. This is the Bayesian way.

  • Recognise similarities between experiences and events in order to infer other similarities and conclusions. According to Douglas Hofstadter “all intelligence is nothing but analogy.”

By guiding your thought processes to adapting these may assist your Inner Dynamics in to broaden your “map of the world” and your array of choices.

Accept the idea that life-long learning will be the norm. According to the OECD AI working paper, not only are the better-educated better poised to cope with AI-imposed changes, but firms invest much more in retraining the better educated:

The human experience – how to deal with people – will become even more important. Algorithms don’t comprehend context so the more context-based a job is, the less likely a robot could replace it.

Thought viruses: swish them away!

Suppose you are haunted by the “thought virus” of being replaced by ML. A thought virus is “a limiting belief that interferes with one’s own efforts to improve.” (Robert Dilts) It can infect our mind and neurology just as a bacterial virus can attack our body.

The Swisch Pattern developed by Richard Bandler is a helpful combatant coaching tool against “thought viruses”. It can assist you in adjusting your Inner Dynamics to your advantage. Let’s go!

  1. Create a picture of in your head of your perceived present negative state. Experience it in the “first person”. You are left unemployed due to ML and there is nothing you can do about it.

  2. Now create a picture of the situation you would rather have. This is your desired state. Envision perhaps learning new skills and working in a job you really enjoy. This time, look at yourself in the picture, from the outside, i.e. dissociated or from a third person perspective. Change this positive picture to make it more forceful. Make it bigger, bring it closer, paint it in your favourite colour, have your favourite music playing in your head.

  3. Close your eyes and bring back the first, negative present state picture.

  4. In the lower left hand corner of the present-state image paste in a small picture of the desired state.

  5. Confirm you have the large, negative picture and the smaller, pleasant picture in your mind. Make a loud “swishing” sound and at the same time, absolutely as fast as possible, explode the small picture to become so large that it completely covers the negative picture. Watch the negative picture whimper away into the void. A blitz-like speed is of the essence. This exercise must be done in a blitz-like manner, as quickly as possible.

  6. Repeat number 5 at least five times: large, negative picture.....tiny, positive picture in the corner...SWISH!!! Open your eyes, close your eyes...big picture, little picture SWISH!

If necessary, repeat a few more times until your Inner Dynamics guide your mind straight to the desired image.

Summary

AI and ML have already cast the dice and put us on a trajectory of great change. As with any change there will be advantages and disadvantages. In the time we live, a large segment of the world’s population has a standard of living that even the richest of our ancestors could not have fathomed. No one knows where the new paths will lead, but as Alice points out in Alice in Wonderland: “it’s no use going back to yesterday, because I was a different person then.”

And, lest we forget: “the wheel is still in spin.”

References

  • Video about Deep Mind: https://www.youtube.com/watch?v=TnUYcTuZJpM&list=RDTnUYcTuZJpM&start_radio=1

  • Hawking Interview: https://www.independent.co.uk/life-style/gadgets-and-tech/news/stephen-hawking-artificial-intelligence-fears-ai-will-replace-humans-virus-life-a8034341.html

  • Artificial Intelligence and Maching Learning for Business, by Dr Steven Finlay, Copyright © 2017 Steven Finlay, Relativistic Books, UK

  • The Master Algorithm, Dr Pedro Domingos, Text copyright © Pedro Domingos, 2015 Penguin Random House UK

  • Encyclopedia of Systemic NLP and NLP New Coding, Robert Dilts and Judith DeLozier, NLP University Press, USA ©Copyright 2000 by NLP University Press

  • OECD Working Paper: Nedelkoska, L. and G. Quintini (2018), “Automation, skills use and training”, OECD Social, Employment and Migration Working Papers, No. 202, OECD Publishing, Paris. http://dx.doi.org/10.1787/2e2f4eea-en