Tag Archives: unpredictability

Forecasts and chimpanzees throwing darts

During the coronavirus pandemic, politicians have taken to telling us that their decisions are based on the advice of their experts while the news media have bombarded us with predictions from experts.  Perhaps not unexpectedly, with the benefit of hindsight, many of these decisions and predictions appear to be have been ill-advised or inaccurate which is likely to lead to a loss of trust in both politicians and experts.  However, this is unsurprising and the reliability of experts, particularly those willing to make public pronouncements, is well-known to be dubious.  Professor Philip E. Tetlock of the University of Pennsylvania has assessed the accuracy of forecasts made by purported experts over two decades and found that they were little better than a chimpanzee throwing darts.  However, the more well-known experts seemed to be worse at forecasting [Tetlock & Gardner, 2016].  In other words, we should assign less credibility to those experts whose advice is more frequently sought by politicians or quoted in the media.  Tetlock’s research has found that the best forecasters are better at inductive reasoning, pattern detection, cognitive flexibility and open-mindedness [Mellers et al, 2015]. People with these attributes will tend not to express unambiguous opinions but instead will attempt to balance all factors in reaching a view that embraces many uncertainties.  Politicians and the media believe that we want to hear a simple message unadorned by the complications of describing reality; and, hence they avoid the best forecasters and prefer those that provide the clear but usually inaccurate message.  Perhaps that’s why engineers are rarely interviewed by the media or quoted in the press because they tend to be good at inductive reasoning, pattern detection, cognitive flexibility and are open-minded [see ‘Einstein and public engagement‘ on August 8th, 2018].  Of course, this was well-known to the Chinese philosopher, Lao Tzu who is reported to have said: ‘Those who have knowledge, don’t predict. Those who predict, don’t have knowledge.’


Mellers, B., Stone, E., Atanasov, P., Rohrbaugh, N., Metz, S.E., Ungar, L., Bishop, M.M., Horowitz, M., Merkle, E. and Tetlock, P., 2015. The psychology of intelligence analysis: Drivers of prediction accuracy in world politics. Journal of experimental psychology: applied, 21(1):1-14.

Tetlock, P.E. and Gardner, D., 2016. Superforecasting: The art and science of prediction. London: Penguin Random House.

When will you be replaced by a computer?

I have written before about extending our minds by using external computing power in our mobile phones [see ‘Science fiction becomes virtual reality‘ on October 12th, 2016; and ‘Thinking out of the skull‘ on March 18th, 2015]; but, how about replacing our brain with a computer?  That’s the potential of artificial intelligence (AI); not literally replacing our brain, but at least taking over jobs that are traditionally believed to require our brain-power.  For instance, in a recent test, an AI lawyer found 95% of the loopholes in a non-disclosure agreement in 22 seconds while a group of human lawyers found only 88% in 90 minutes, according to Philip Delves Broughton in the FT last weekend.

If this sounds scary, then consider for a moment the computing power involved.  Lots of researchers are interested in simulating the brain and it has been estimated that the computing power required is around hundred peta FLOPS (FLoating point Operations Per Second), which conveniently, is equivalent to the world’s most powerful computers.  At the time of writing the world’s most powerful computer was ‘Summit‘ at the US Oak Ridge National Laboratory, which is capable of 200 petaFLOPS.  However, simulating the brain is not the same as reproducing its intelligence; and petaFLOPS are not a good measure of intelligence because while ‘Summit’ can multiply many strings of numbers together per second, it would take you and me many minutes to multiply two strings of numbers together giving us a rating of one hundredth of a FLOP or less.

So, raw computing power does not appear to equate to intelligence, instead intelligence seems to be related to our ability to network our neurons together in massive assemblies that flicker across our brain interacting with other assemblies [see ‘Digital hive mind‘ on November 30th, 2016]. We have about 100 billion neurons compared with the ‘Summit’ computer’s 9,216 CPUs (Central Processing Unit) and 27,648 GPUs (Graphic Processing Units); so, it seems unlikely that it will be able to come close to our ability to be creative or to handle unpredictable situations even accounting for the multiple cores in the CPUs.  In addition, it requires a power input of 13MW or a couple of very large wind turbines, compared to 80W for the base metabolic rate of a human of which the brain accounts for about 20%; so, its operating costs render it an uneconomic substitute for the human brain in activities that require intelligence.  Hence, while computers and robots are taking over many types of jobs, it seems likely that a core group of jobs involving creativity, unpredictability and emotional intelligence will remain for humans for the foreseeable future.


Max Tegmark, Life 3.0 – being human in the age of artificial intelligence, Penguin Books, 2018.

Philip Delves Broughton, Doom looms over the valley, FT Weekend, 16 November/17 November 2019.

Engelfriet, Arnoud, Creating an Artificial Intelligence for NDA Evaluation (September 22, 2017). Available at SSRN: https://ssrn.com/abstract=3039353 or http://dx.doi.org/10.2139/ssrn.3039353

See also NDA Lynn at https://www.ndalynn.com/