Tag Archives: predictions

Update on position of AI on hype curve: it cannot dream

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Decorative image of a flowerIt would appear that I was wrong in 2020 when I suggested that artificial intelligence was near the top of its hype curve [see ‘Where is AI on the hype curve?‘ on August 12th, 2020].  In the past few months the hype has reached new levels.  Initially, there were warnings about the imminent takeover of global society by artificial intelligence; however, recently the pendulum has swung back towards a more measured concern that the nature of many jobs will be changed by artificial intelligence with some jobs disappearing and others being created.  I believe that the bottom-line is that while terrific advances have been made with large language models, such as ChatGPT, artificial intelligence is artificial but it is not intelligent [see ‘Inducing chatbots to write nonsense‘ on February 15th, 2023].  It cannot dream.  It is not creative or inventive, largely because it is very powerful applied statistics which needs data based on what has happened or been produced already.  So, if you are involved in solving mysteries (ill-defined, vague and indeterminate problems) rather than puzzles [see ‘Puzzles and mysteries‘ on November 25th, 2020] then you are unlikely to be replaced by artificial intelligence in the foreseeable future [see ‘When will you be replaced by a computer?‘ on November 20th, 2019].  Not that you should trust my predictions of the future! [see ‘Predicting the future through holistic awareness‘ on January 6th, 2021]

Diving into three-dimensional fluids

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My research group has been working for some years on methods that allow straightforward comparison of large datasets [see ‘Recognizing strain’ on October 28th 2015].  Our original motivation was to compare maps of predicted strain over the surface of engineering structures with maps of measurements.  We have used these comparison methods to validate predictions produced by computational models [see ‘Million to one’ on November 21st 2018] and to identify and track changes in the condition of engineering structures [see ‘Out of the valley of death into a hype cycle’ on February 24th 2021].  Recently, we have extended this second application to tracking changes in the environment including the occurance of El Niño events [see ‘From strain measurements to assessing El Niño events’ on March 17th, 2021].  Now, we are hoping to extend this research into fluid mechanics by using our techniques to compare flow patterns.  We have had some success in exploring the use of methods to optimise the design of the mesh of elements used in computational fluid dynamics to model some simple flow regimes.  We are looking for a PhD student to work on extending our model validation techniques into fluid mechanics using volumes of data from measurement and predictions rather than fields, i.e., moving from two-dimensional to three-dimensional datasets.  If you are interested or know someone who might be interested then please get in touch.

There is more information on the PhD project here.

Time travel and rewriting history

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decorative paintingI have written in the past about consciousness being an accumulation of sensory experiences [see ‘Is there are real ‘you’ or ‘I’? on March 6th, 2019].  Our memory consists of fragments of images, sounds, smells and feelings from the past that we can re-assemble into a complete experience often triggered by something in the present that resembles a fragment of a past experience.  We can time travel in our minds by thinking about the past.  It is so ubiquitous that we barely stop to think about it. Yet, we are fascinated by the possibility of time travel into the future.  However, our subconscious minds are constantly time traveling into the future [see ‘Predicting the future through holistic awareness’ on January 6th, 2021].  They are constantly making predictions about what will happen next, whether anticipating the path taken by a ball so that your hand can be positioned to catch it or picking up an umbrella as you leave the house so that you do not get soaked when it rains later in the day.  The further we attempt travel into the future the less dependable our predictions become and I suspect the same is true for travel backwards in time.  The reliability of our recollection of past experiences become less as time and entropy erode the connections between the fragments in our mind so that we struggle to reassemble all of the fragments in the correct order and our personal history is unintentional rewritten.

Source:

Stefan Klein, We are all stardust, Melbourne: Scribe, 2015  (a conversation with Hannah Monyer on memory entitled ‘Do You Remember?’).

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.