Issue link: https://iconnect007.uberflip.com/i/1357726
APRIL 2021 I SMT007 MAGAZINE 51 the digital twin data. As a soware developer of more than 30 years, I find there to be two choices of types of algorithms. My favorites are the heuristic-based algo- rithms, which model the thought processes of humans. Rules, oen complex, are followed that determine calculations that lead to a spe- cific answer. e difference between the so- ware and my limited biological approach is that the computer will follow all possible tracks, rather than being limited to those within my own attention span. e danger of this algo- rithm, however, is that unless written in a very clever way, it is harder to change the thought process based on new ideas or concepts. e benefit, however, is that the results appear very quickly and are effective. e second type of algorithm is a random mathematical model originally termed "genetic algorithms." e connection of facts, such as the order in which a process could be done, is laid out at random, the effectiveness mea- sured, then the order changed, and effective- ness re-measured. How the changes are made vary, the original genetics-based idea being to divide them in the same way as genes are shared from parents to a child—slice and dice, then try again, potentially billions of times. No matter how sophisticated genetic algorithms and the like become, the result will take time, geometrically increasing in proportion to the number of variables. e benefit is that unlike the heuristic model, there are no assumptions; a solution that no one may ever have consid- ered could be found to be the best. e down- side is that it takes time—a long time—to come up with the best solution. One of my own heu- ristic machine program optimization algo- rithms was once beaten by a genetic algorithm, shaving off a second or two of the machine's run time. I did like to point out that the heuris- tic algorithm had taken five minutes whereas the genetic algorithm was still going aer five days, four days aer the production was supposed to have started. An "I'm bored" but- ton then appeared to stop the genetic algo- rithm and take whatever has been the best result thus far. e interesting thing, however, about the genetic algorithm is that the "health" of each potential solution discovered is qualified by a function that measures the value of the solu- tion. e need to have a defined method pro- vokes similar limitations as seen with the heu- ristic method; to truly find new and original solutions is questionable. It is easier to change the ruleset of the genetic algorithm as com- pared to the logic of a heuristic model. If a solution were able to automatically change the ruleset, based on feedback of the real effective- ness of solutions over time, this would lead to the potential of actual "AI." e human abil- ity to change the constraints associated with a problem can be termed either intelligence or recklessness, depending on the nature of influ- encing factors. As a digital twin can be used for good or evil, will we trust the AI to mod- ify itself in a way that we would assume to be in our interest? We see the trend of increasing amounts of data, more decisions to be made, more com- plexity, and more security, so the next stage of intelligence may be a hybrid of the two algo- rithmic types, whereby the simple cause and effect decisions are implemented as heuris- tic elements of an advanced genetic algorithm type of approach. is can include the building in of "laws" to protect human interest. Saturation point reached; I don't want to have to understand all of this "nerd talk" as As a digital twin can be used for good or evil, will we trust the AI to modify itself in a way that we would assume to be in our interest?