Headline, March 18 2022/ COMPUTING : ''' '' RANDOM NUMBERS RAPPER '' '''



RANDOMNESS IS A VALUABLE COMMODITY. Computer models of complex systems ranging from the weather to the stock-market are voracious consumers of random numbers.

Cryptography, too, relies heavily on random numbers for the generation of unbreakable keys. Better, cheaper ways of generating and handling such numbers are therefore always welcome.

And doing just that is the goal of a project with a slightly tongue-in-cheek name of COINFLIPS, which allegedly stands for Codesigned Improved Neural Foundations Leveraging Inherent Physics Stochasticity.

COINFLIPS operates under the aegis of Brad Aimone, a theoretical neuroscientist at Sandia National Laboratories [originally one of America's nuclear-weapons laboratories, but which has now branded out to other areas, too].

Dr. Aimone's starting point is the observation that, unlike the other circuits of digital computers, which will, if fed a given input, respond with a precise and predictable output, the link between input to and output from a nerve cell is more haphazard - or, in the jargon, ''stochastic''.

He wants to imitate this stochastic behaviour in something less squishy than a nerve cell. By doing so, he thinks he might be able to tune the distribution of digits that a random-number generator spits out, without affecting their underlying randomness.


That would be useful, Existing random number generators produce uniform distributions. {A ''3'', say, is exactly as likely to appear as a ''7''}. But, as Dr. Aimone's colleague Darby Smith notes, the real world that computer modellers are trying to model does not work like this.

For example, the temperature in London in December may vary between -7C and 17C, but is most likely to be in the range 3 degrees C to 8 degrees C. Similarly, vessels are more likely to be in trouble close to busy shipping route than in a remote backwater.

Distorting uniform distribution of random numbers to take account of these realities is tedious and unsatisfactory. As Dr. Smith observes, it would be more efficient if the random numbers used corresponded to the natural distribution in the first place.

There is also an abundance problem. Finding random phenomena in nature that can be transformed into computer bits is not easy. Often the source is computing itself - for example, by gathering the last digits in the numbers of milliseconds between keystrokes made by zillions of users.

Otherwise, specialist, expensive hardware needs to be used to do things such as measuring heat flux through a silicon chip.

To eke out these scarce supplies, such truly random numbers are often then employed to seed programs called pseudo-random-number generators. The algorithms behind those generate sequences of numbers that have the statistical properties of randomness.

But this is not the same thing as the real thing. As John von Neumann, one of computing's pioneers, observed : ''Anyone who considers arithmetical methods of producing random digits is, of course, in a state of sin.''

Moreover, if the purpose is cryptography, this method is particularly risky. The opposition might be able to work out the algorithm involved.

Another member of the project, Shahank Misra, says COINFLIPS' researchers have identified two hardware-based approaches for the production of tuneable, abundant random numbers.

One relies on the pattern magnetic films make when disturbed, the other on how electrons travel through the barrier of quantum-tunneling diode. Both of these things are truly random. And both can be tuned to provide the sort of random-number distributions COINFLIPS requires.


Despite its peregrinations elsewhere, though, Sandia is ultimately in the nuclear business, and one early application of whatever COINFLIPS comes up with is likely to involve interpreting the results of collisions in particle accelerators - something the team have been exploring in collaboration with Temple University, in Philadelphia.

The idea is build a device which incorporates COINFLIPS hardware into the sensor itself. This will allow results from collisions, which will be randomly distributed, but in particular ways, to be compared with artificial randoms distributions, to see if they match.

To be able to this in real-time is useful, because it allows an immediate decision to be made whether or not to store a particular result. Modern colliders generate so many collisions that such immediacy in decision-making is important.

And in the longer run, says Dr. Aimone, COINFLIPS should enable many types of calculation that are currently impossible because of the volume of random members needed - for example, artificial intelligence systems that capture the uncertainty of the world.

This might be done in the form of neural networks which, like the human brain, have randomness available at each synapse. And that, in turn, may lead to COINFLIPS, a project inspired by biology, returning the compliment by providing a better way of understanding of how brains themselves work.

The Honour and Serving of great technical writings on computers and computing bits, continues. The World Students Society thanks The Economist.

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