{"id":4990,"date":"2022-10-31T10:00:00","date_gmt":"2022-10-31T10:00:00","guid":{"rendered":"https:\/\/modernsciences.org\/staging\/4414\/?p=4990"},"modified":"2022-10-21T13:04:04","modified_gmt":"2022-10-21T13:04:04","slug":"noise-in-the-brain-enables-us-to-make-extraordinary-leaps-of-imagination-it-could-transform-the-power-of-computers-too","status":"publish","type":"post","link":"https:\/\/modernsciences.org\/staging\/4414\/noise-in-the-brain-enables-us-to-make-extraordinary-leaps-of-imagination-it-could-transform-the-power-of-computers-too\/","title":{"rendered":"Noise in the brain enables us to make extraordinary leaps of imagination. It could transform the power of computers too"},"content":{"rendered":"\n
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\n \n Shutterstock<\/a><\/span>\n <\/figcaption>\n <\/figure>\n\nTim Palmer<\/a>, University of Oxford<\/a><\/em><\/span>\n\n

We all have to make hard decisions from time to time. The hardest of my life was whether or not to change research fields after my PhD, from fundamental physics to climate physics. I had job offers that could have taken me in either direction \u2013 one to join Stephen Hawking\u2019s Relativity and Gravitation Group<\/a> at Cambridge University, another to join the Met Office<\/a> as a scientific civil servant.<\/p>\n\n

I wrote down the pros and cons of both options as one is supposed to do, but then couldn\u2019t make up my mind at all. Like Buridan\u2019s donkey<\/a>, I was unable to move to either the bale of hay or the pail of water. It was a classic case of paralysis by analysis.<\/p>\n\n

Since it was doing my head in, I decided to try to forget about the problem for a couple of weeks and get on with my life. In that intervening time, my unconscious brain decided for me. I simply walked into my office one day and the answer had somehow become obvious: I would make the change to studying the weather and climate.<\/p>\n\n

More than four decades on, I\u2019d make the same decision again. My fulfilling career has included developing a new, probabilistic way of forecasting weather and climate<\/a> which is helping humanitarian and disaster relief agencies make better decisions ahead of extreme weather events. (This and many other aspects are described in my new book, The Primacy of Doubt<\/a>.)<\/p>\n\n

But I remain fascinated by what was going on in my head back then, which led my subconscious to make a life-changing decision that my conscious could not. Is there something to be understood here not only about how to make difficult decisions, but about how humans make the leaps of imagination that characterise us as such a creative species? I believe the answer to both questions lies in a better understanding of the extraordinary power of noise.<\/p>\n\n

Imprecise supercomputers<\/h2>\n\n

I went from the pencil-and-paper mathematics of Einstein\u2019s theory of general relativity to running complex climate models on some of the world\u2019s biggest supercomputers. Yet big as they were, they were never big enough \u2013 the real climate system is, after all, very complex.<\/p>\n\n

In the early days of my research, one only had to wait a couple of years and top-of-the-range supercomputers would get twice as powerful. This was the era where transistors<\/a> were getting smaller and smaller, allowing more to be crammed on to each microchip. The consequent doubling of computer performance for the same power every couple of years was known as Moore\u2019s Law<\/a>.<\/p>\n\n

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This story is part of Conversation Insights<\/em><\/strong>\n
The Insights team generates long-form journalism<\/a> and is working with academics from different backgrounds who have been engaged in projects to tackle societal and scientific challenges.<\/em><\/p>\n\n

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There is, however, only so much miniaturisation you can do before the transistor starts becoming unreliable in its key role as an on-off switch. Today, with transistors starting to approach atomic size<\/a>, we have pretty much reached the limit of Moore\u2019s Law. To achieve more number-crunching capability, computer manufacturers must bolt together more and more computing cabinets, each one crammed full of chips.<\/p>\n\n

But there\u2019s a problem. Increasing number-crunching capability this way requires a lot more electric power \u2013 modern supercomputers the size of tennis courts consume tens of megawatts. I find it something of an embarrassment that we need so much energy to try to accurately predict the effects of climate change.<\/p>\n\n

That\u2019s why I became interested in how to construct a more accurate climate model without<\/em> consuming more energy. And at the heart of this is an idea that sounds counterintuitive: by adding random numbers, or \u201cnoise\u201d, to a climate model, we can actually make it more accurate in predicting the weather.<\/p>\n\n

A constructive role for noise<\/h2>\n\n

Noise is usually seen as a nuisance \u2013 something to be minimised wherever possible. In telecommunications, we speak about trying to maximise the \u201csignal-to-noise ratio\u201d by boosting the signal or reducing the background noise as much as possible. However, in nonlinear systems<\/a>, noise can be your friend and actually contribute to boosting a signal. (A nonlinear system<\/a> is one whose output does not vary in direct proportion to the input. You will likely be very happy to win \u00a3100 million on the lottery, but probably not twice as happy to win \u00a3200 million.) <\/p>\n\n

Noise can, for example, help us find the maximum value of a complicated curve such as in Figure 1, below. There are many situations in the physical, biological and social sciences as well as in engineering where we might need to find such a maximum. In my field of meteorology, the process of finding the best initial conditions for a global weather forecast involves identifying the maximum point of a very complicated meteorological function<\/a>.<\/p>\n\n

Figure 1<\/strong><\/p>\n\n

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\n A curve with multiple local peaks and troughs.<\/span>\n Author provided<\/span><\/span>\n <\/figcaption>\n <\/figure>\n\n

However, employing a \u201cdeterministic algorithm<\/a>\u201d to locate the global maximum doesn\u2019t usually work. This type of algorithm will typically get stuck at a local peak (for example at point a<\/strong>) because the curve moves downwards in both directions from there.<\/p>\n\n

An answer is to use a technique called \u201csimulated annealing<\/a>\u201d \u2013 so called because of its similarities with (annealing<\/a>), the heat treatment process that changes the properties of metals. Simulated annealing, which employs noise to get round the issue of getting stuck at local peaks, has been used to solve many problems including the classic travelling salesman puzzle<\/a> of finding the shortest path between a large number of cities on a map<\/a>.<\/p>\n\n

Figure 1 shows a possible route to locating the curve\u2019s global maximum (point 9<\/strong>) by using the following criteria:<\/p>\n\n