Digital Things: FRIENDS, Algorithms, and Making a Trifle

Digital Things: FRIENDS, Algorithms, and Making a Trifle

Do you remember the episode of FRIENDS, where Rachel makes the Thanksgiving trifle? She adds a layer of ladyfingers, then a layer of jam, custard, raspberries, the infamous beef (sautéed with peas and onions), bananas, and whipped cream, and makes a quirky “delicacy”.

While the result of the experiment was a disaster, the recipe is exactly what an algorithm is — steps to perform a task; a set of rules that precisely defines a sequence of operations.

Rachel followed the step-by-step recipe to make the dessert, just like a computer program may follow an algorithm. Had she not missed a few steps, the trifle would have turned out perfect.

There are many ways to make a trifle, maybe with different ingredients, different layering, or with a different process altogether. Similarly, depending on the result you want to achieve, algorithms too are simple, elaborate, or somewhere in between. A simple task like adding two numbers will need a simpler algorithm, whereas other elaborate tasks may require thousands of lines of coding.?But one must ensure to apply the algorithm in the right manner to avoid getting less than desirable results.

Water utility leaders also leverage tools and technologies, built with many algorithms, to help with several tasks. Take, for example, a tool for service line inventory development and Lead and Copper Rule Revisions (LCRR) compliance. Digitizing the entire lifecycle of LCRR compliance into an all-in-one solution requires careful use of algorithms to prevent costly mistakes. Add predictive modeling to the mix, and you get even more intricate layers. For example, using machine learning to tackle data challenges for sewer anomaly detection.

Do watch the video to understand the basics of algorithms. Also, watch?more episodes of our Digital Things?series to explore all things digital. We will see you the next time!?


#Algorithms #DigitalThings #TrustInWhatsNext #PredictiveModeling #MachineLearning #Data #ArtificialIntelligence

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