Tag Archives: John Kay

Is the autonomous individual ceasing to exist?

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Society consists of a series of bubbles.  A century or so ago, your bubble was largely defined by where you lived, your village or neighbourhood, because few people travelled any significant distance and you probably knew everyone living around you.  A decade or so ago, your bubble was probably defined by the newspaper you read or the radio/TV channels you preferred [see ‘You’re all weird!’ on February 8th, 2017]. Today social media defines bubbles that are geographically widely-dispersed.  This both fractures local communities and gives a global reach to influencers on social media.  Some social media ‘dictates what you shall think, it creates an ideology for you, it tries to govern your emotional life’.  The quote is from George Orwell’s 1941 essay, Literature and Totalitarianism.  He goes on ‘And as far as possible it isolates you from the outside world, it shuts you up in an artificial universe in which you have no standards of comparison.’  Of course, he is writing about totalitarianism not social media but his words seem sinisterly appropriate to the apparent intention of some social media influencers and platforms that promote alternative narratives which are not consistent with reality.  Orwell suggested that if totalitarianism becomes world-wide and permanent then literature, the truthful expression of what one person thinks and feels, could not survive.  Despite Orwell’s fear that he was living ‘in an age in which the autonomous individual is ceasing to exist’, totalitarianism did not abolish freedom of thought in the 1940s.  Now in the 2020s, we have to ensure that social media does not become a modern instrument of totalitarianism, suffocating freedom of thought, isolating large sections of society from reality, dictating ideology and governing emotional life. We need to think for ourselves and encourage others to do the same.  In their book, ‘Radical Uncertainty – Decision-making for an Unknowable Future‘, John Kay and Mervyn King repeatedly ask ‘What is going on here?’ as a device for thinking about and reviewing the evidence before reaching a conclusion.  It is a simple device that we could all usefully deploy in 2025. Happy New Year!

Sources:

George Orwell, Literature and Totalitarianism, 1941 available at https://hackneybooks.co.uk/books/64/1006/LiteratureAndTotalitarianism.html

Nazanin Zaghari-Ratcliffe, The feeling of freedom, FT Weekend, 7th & 8th December 2024.

John Kay and Mervyn King, Radical Uncertainty – Decision-making for an Unknowable Future, Little Brown Book Group, 2020.

Certainty is unattainable and near-certainty unaffordable

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The economists John Kay and Mervyn King assert in their book ‘Radical Uncertainty – decision-making beyond numbers‘ that ‘economic forecasting is necessarily harder than weather forecasting’ because the world of economics is non-stationary whereas the weather is governed by unchanging laws of nature. Kay and King observe that both central banks and meteorological offices have ‘to convey inescapable uncertainty to people who crave unavailable certainty’. In other words, the necessary assumptions and idealisations combined with the inaccuracies of the input data of both economic and meteorological models produce inevitable uncertainty in the predictions. However, people seeking to make decisions based on the predictions want certainty because it is very difficult to make choices when faced with uncertainty – it raises our psychological entropy [see ‘Psychological entropy increased by ineffective leaders‘ on February 10th, 2021].  Engineers face similar difficulties providing systems with inescapable uncertainties to people desiring unavailable certainty in terms of the reliability.  The second law of thermodynamics ensures that perfection is unattainable [see ‘Impossible perfection‘ on June 5th, 2013] and there will always be flaws of some description present in a system [see ‘Scattering electrons reveal dislocations in material structure‘ on November 11th, 2020].  Of course, we can expend more resources to eliminate flaws and increase the reliability of a system but the second law will always limit our success. Consequently, to finish where I started with a quote from Kay and King, ‘certainty is unattainable and the price of near-certainty unaffordable’ in both economics and engineering.

Puzzles and mysteries

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Detail from abstract by Zahrah ReshPuzzles and mysteries are a pair of words that have taken on a whole new meaning for me since reading John Kay’s and Mervyn King’s book called ‘Radical uncertainty: decision-making for an unknowable future‘ during the summer vacation [see ‘Where is AI on the hype curve?‘ on August 12th, 2020]. They describe puzzles as well-defined problems with knowable solutions; whereas mysteries are ill-defined problems, that have no objectively correct solution and are imbued with vagueness and indeterminacy.  I have written before about engineers being creative problems-solvers [see ‘Learning problem-solving skills‘ on October 24th, 2018] which leads to the question of whether we specialise in solving puzzles or mysteries, or perhaps both types of problems.  The problems that I set for students to solve for homework to refine and evaluate their knowledge of thermodynamics [see ‘Problem-solving in thermodynamics‘ on May 6th, 2015] clearly fall into the puzzle category because they are well-defined and there is a worked solution available.  Although for many students these problems might appear to be mysteries, the intention is that with greater knowledge and understanding the mysteries will be transformed into mere puzzles.  It is also true that many real-world mysteries can be transformed into puzzles by research that advances the collective knowledge and understanding of society.  Part of the purpose of an engineering education is to equip students with the skills to make this transformation from mysteries to puzzles.  At an undergraduate level we use problems that are mysteries only to the students so that success is achievable; however, at the post-graduate level we use problems that are perceived as mysteries to both the student and the professor with the intention that the professor can guide the student towards a solution.  Of course, some mysteries are intractable often because we do not know enough to define the problem sufficiently that we can even start to think about possible solutions.  These are tricky to tackle because it is unreasonable to expect a research student to solve them in limited timeframe and it is risky to offer to solve them in exchange for a research grant because you are likely to damage your reputation and prospects of future funding when you fail.  On the other hand, they are what makes research interesting and exciting.

Image: Extract from abstract by Zahrah Resh.

Where is AI on the hype curve?

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I suspect that artificial intelligence is somewhere near the top of the ‘Hype Curve’ [see ‘Hype cycle’ on September 23rd, 2015].  At the beginning of the year, I read Max Tegmark’s book, ‘Life 3.0 – being a human in the age of artificial intelligence’ in which he discusses the prospects for artificial general intelligence and its likely impact on life for humans.  Artificial intelligence means non-biological intelligence and artificial general intelligence is the ability to accomplish any cognitive task at least as well as humans.  Predictions vary about when we might develop artificial general intelligence but developments in machine learning and robotics have energised people in both science and the arts.  Machine learning consists of algorithms that use training data to build a mathematical model and make predictions or decisions without being explicitly programmed for the task.  Three of the books that I read while on vacation last month featured or discussed artificial intelligence which stimulated my opening remark about its position on the hype curve.  Jeanette Winterson in her novel, ‘Frankissstein‘ foresees a world in which humanoid robots can be bought by mail order; while Ian McEwan in his novel, ‘Machines Like Me‘, goes back to the early 1980s and describes a world in which robots with a level of consciousness close to or equal to humans are just being introduced to the market the place.  However, John Kay and Mervyn King in their recently published book, ‘Radical Uncertainty – decision-making beyond numbers‘, suggest that artificial intelligence will only ever enhance rather replace human intelligence because it will not be able to handle non-stationary ill-defined problems, i.e. problems for which there no objectively correct solution and that change with time.  I think I am with Kay & King and that we will shortly slide down into the trough of the hype curve before we start to see the true potential of artificial general intelligence implemented in robots.

The picture shows our holiday bookshelf.