As we constantly strive to create solutions for every problem: most of these solutions are merely complete, if not inadequate. Take, for example, CRISPR-Cas9, the gene-editing technology, where scientists can alter the genetic makeup of humans to help prevent diseases or long-term ailments. It’s still a problem when such technology as the Cas9 enzyme—that’s supposed to slice a specific DNA sequence—can make cuts in other parts of the genome that could cause mutations that result in cancer risks(Kaiser, 2016). It is thus evident that no solution comes 100% risk-free, and the strive for an ideal/perfect solution is what drives humans, specifically scientists, the urge to wake up and improve on previous research.
The Center for Disease Control and Prevention(CDC) claims that more than 50% of U.S. people will be diagnosed with a mental health disorder at some point in their lifetime (U.S. Department of Health & Human Services, 2021). Imagine with such a major problem globally, most mental health diseases like bipolar disorder are only realized after strenuous interviews or questionnaires that are further subjected to expected and unexpected biases and errors. Here is another example of, yet again, an incomplete solution that, as previously mentioned, does not fully solve the problem we have today.
Is a solution ever complete? How do we know whether we have a complete solution? Imagine if we had a way of predicting solutions and checking whether they are complete or not, before actually implementing them. Would we as humans be missing out on something? Would the absence of intermediate solutions affect us in any way?
The absence of intermediate solutions means one crucial thing: humans have to wait long for the final solution before they can get solutions to problems. This would mean that the first cancer patient would have to wait for 3000 years, from the first discovery of the disease around 400 BC in Egypt to the evolution of CRISPR-which itself is not yet a complete solution (MatchTrial, 2020). This implies that, without intermediate solutions, every cancer patient between 400 BC and 2022 will die, while society works to develop the most complete solution.
This then brings us to a bigger question, how do we find a complete solution quickly?
A basic understanding of this question is crucial and computers can help us do so. The way humans persist through life is different—different from the way computers solve problems. Of course, we go through a given set of solutions but not always as fully and completely as a computer is capable of doing so. Deciding between a cup of coffee or tea can seem trivial at times, but even such small decisions are never truly trivial. A human may base his choice of drink based on factors like mood and economic ability without considering other factors such as health benefits or proximity from the purchasing shop. The way we think, act, and operate is based on what we observe in our current environment and the variables we see—the variables we can make sense of.
In the 1950s, the first Greedy algorithms started coming into the picture after being coined by the famous Dutch computer scientist and mathematician, Edsger W. Dijkstra (TechVidvan). The Greedy algorithm selects the best optimal solution at the specific moment without worrying about the overall optimal result. Sounds equivalent to how we solve problems, right?
However, this is not the ideal way to solve problems. Solving problems 100% risk-free would require solving problems while taking into account all the factors, whether currently present or not. And so, the other option is the Brute Force algorithm. With this approach, every single set of choices is considered before coming up with a solution.
Imagine if we could potentially check every single source of information before coming up with a solution—if we had the knowledge to do so, the right tools and resources to look into hundred years from now and understand every single case scenario. Wouldn’t that be incredible? Would it make us superhumans? Perhaps not, but the steps in technological advances would be much more transient and efficient.
Code Academy. Cheatsheets/Learn Data structures and Algorithms with Python, 2023. https://www.codecademy.com/learn/learn-data-structures-and-algorithms-with-python/modules/brute-force-algorithms/cheatsheet
TechVidvan. Greedy Algorithm with Applications. https://techvidvan.com/tutorials/greedy-algorithm/
U.S Department of Health & Human Services. About Mental Health. https://www.cdc.gov/mentalhealth/learn/index.htm#:~:text=More%20than%2050%25%20will%20be,some%20 point%20in%20the%20 lifetime.&text=1%20in%205%20Americans%20will,illness%20in%20a%20given%20year.&text=1%20in%205%20children%2C%20 either,a%20seriously%20debilitating%20mental%20illness.
Kaiser, Jocelyn .The gene editor CRISPR won’t fully fix sick people anytime soon. Here’s why. May 3, 2016. https://www.science.org/content/article/gene-editor-crispr-won-t-fully-fix-sick-people-anytime-soon-here-s-why.
MatchTrial. History of Cancer Disease, 05/06/2020. https://matchtrial.health/en/history-of-cancer/