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The Science Behind AI

April 22, 2025

When I wrote my first line of code in 8th grade, I found a passion that would grow with me through high school and beyond. But as I delved deeper into both scientific concepts and artificial intelligence, I realized something profound: science builds AI, and AI transforms science right back. This two-way relationship creates a cycle of innovation that’s reshaping our world faster than ever before.

The foundation of modern AI isn’t just computer science – it’s a blend of mathematics, neuroscience, psychology, and computational theory all working together in harmony. This mashup of disciplines creates something greater than its parts. Every AI breakthrough begins with scientific principles and the same process of hypothesis, testing, and refinement that powers all scientific discovery.

With classes like Data Structures and Machine Learning Principles, “universities are preparing students to continue this scientific tradition” (Frueh). The principles that scientists like Alan Turing (look at the evolution of programming languages article to learn more!) developed decades ago still form the backbone of today’s most advanced AI systems. The mathematics of linear algebra and calculus provide the quantitative tools for modeling complex relationships, while neuroscience contributes insights about how biological neural networks function, inspiring artificial ones.

But the relationship goes both ways. AI has become science’s most powerful tool, scanning astronomical data to find celestial objects human researchers might miss or predicting protein structures to accelerate drug discovery. It excels at seeing patterns in massive datasets that would take humans lifetimes to analyze. Climate scientists employ neural networks to improve weather forecasting, while medical researchers use deep learning to detect cancer in medical images with unprecedented accuracy.

As a student studying Computer Science, these applications fascinate me because they show how AI isn’t just about creating smart assistants or recommendation systems – it’s about expanding human capabilities in every scientific domain. The research opportunities in this field seem endless, from artificial intelligence to warehouse-scale computing systems.

Most intriguing is how AI transforms scientific discovery itself. While traditional science follows hypothesis-driven research where scientists form theories and then test them, AI introduces a complementary data-driven approach where algorithms identify patterns first. Systems like AlphaFold solve 50-year scientific challenges independently by accurately predicting protein structures from amino acid sequences. This represents a fundamental shift in how discovery happens.

This partnership raises big philosophical questions. If an AI predicts phenomena accurately but can’t explain its reasoning in human terms, have we truly gained scientific understanding? Is prediction equivalent to explanation? These questions reflect deeper tensions about what knowledge really means in an age of increasingly capable but opaque AI systems.

Looking ahead, science and AI will continue evolving together, creating new forms of inquiry that blend human creativity with machine intelligence. This partnership will enable us to tackle humanity’s most pressing challenges – from climate change to disease – with previously unimaginable capabilities. The possibilities and opportunities in this field are truly endless.

 

Citations

 

Frueh, Sara. “How AI Is Shaping Scientific Discovery.” Nationalacademies.org, 6 Nov. 

2023, www.nationalacademies.org/news/2023/11/how-ai-is-shaping-scientific-discovery.

 

“Science in the Age of AI | Royal Society.” Royalsociety.org, 2024, royalsociety.org/news-resources/projects/science-in-the-age-of-ai/.

 

Wang, Hanchen, et al. “Scientific Discovery in the Age of Artificial Intelligence.” Nature, vol. 620, no. 7972, 1 Aug. 2023, pp. 47–60, www.nature.com/articles/s41586-023-06221-2, 

https://doi.org/10.1038/s41586-023-06221-2.

 

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  Timothy Durham was convicted of rape when a test showed his DNA genotype matched the DNA recovered from the crime scene. Eye witnesses testified that Durham was in a different state at the time of the incident, but he wasn’t exonerated from his 3000 year sentence until follow up DNA analysis showed that the preliminary forensic analysis used in the trial was misinterpreted (Thomas et al.). DNA testing serves an invaluable part in our criminal justice system, but it comes with complications and bias that can lead to wrongful convictions and misinformed juries.  

Background:

Forensic genetics aims to identify the origin of a biological sample. It can be traced back to Karl Landsteiner who discovered human ABO blood groups. With blood groups, suspects could be eliminated based on their blood type. For example, if a suspect had type A blood and there was type B blood left at the scene by the perpetrator, the investigators could rule out the suspect. Unfortunately, the reverse didn’t work. If the suspect and perpetrator both had the same blood type, it can’t be concluded that the suspect was guilty. Blood identifiers evolved into 8 distinct categories which improved the ability to eliminate suspects, but could only conclude that (if the system matched) it might be the perpetrator or this person might be related to the perpetrator (Jobling and Gill). The field evolved in the 1980s with the discovery of Minisatellites, lines of repetitive DNA that don’t encode proteins. Minisatellite patterns in DNA create what we know today as the DNA fingerprint, and it has a much higher accuracy rate than previous methods (Augustyn et al.). Around the same time, a breakthrough for victims of sexual assault was discovered. In the case of assault, bodily fluids of both the victim and the assaulter can get mixed. A process called differential lysis was created which could selectively increase the concentration of sperm in a fluid sample thereby allowing for accurate DNA testing (Jobling and Gill). Forensics evolved to use single-locus probe testing after several years because of its ease of interpretation. It looked at specific minisatellites that had high variability between subjects. This was the first DNA-based method to be used in a criminal investigation. Two murder homicides from several years apart were connected using DNA evidence. The evidence was then used to exonerate an innocent man, and the guilty party was found when he tried to evade mandatory DNA testing for all men in the small town. His guilt was then reinforced when the DNA profiles matched, and he gave a confession (Jobling and Gill). Finding DNA at a crime scene doesn’t guarantee that you’ll find a murderer. There are issues with non-pure samples and limited samples that make DNA work difficult, and accreditation of facilities and new techniques and take time (Jobling and Gill).  

Subjectivity of DNA Interpretation:

In a 2009 publication, the National Academy of Science discusses the possibility for subjective influence in lab based forensics. The interpretation bias happens in complex cases where, for example, there could be a mixture of DNA. A study published in 2011 looked specifically at this issue. A gang rape happened in Georgia, and the mixture in DNA from the case was used to prosecute one suspect after another suspect took a plea bargain to testify against him. With DNA mixtures, there are three possible conclusions: suspect excluded, suspect cannot be excluded, and inconclusive. If the suspect is excluded, it means that there’s little to no possibility that his DNA is part of the sample. If the suspect cannot be excluded, it means that there’s a possibility that his DNA is part of the sample. In the trial, the DNA examiners who knew the context of the case concluded that the suspect being prosecuted could not be excluded. The 2011 study gave the DNA evidence from this case and the DNA profiles of the suspects to 17 forensic examiners. These examiners had no context of the case, and they all worked independently. Of the 17, only 1 came to the same conclusion (suspect cannot be excluded) as the examiners who knew the context of the case. Twelve of the 17 concluded that the suspect who was prosecuted and convicted had no relation to the DNA found in the sample. Not only does this show that subjectivity occurs due to the context of the case, but the 17 examiners came up with different findings based on their past experiences meaning that not all DNA analysis is objective. Some organizations like the Scientific Working Group on DNA Analysis Methods say that quantitative statistical evidence should be presented with every categorical conclusion, but the US doesn’t require this (Dror and Hampikian).

False Positives:

There are two categories that cause wrongful identification in a DNA report: coincidental matches and false positives. Coincidental matches are DNA matches between two unrelated people. The probability of these matches is about 1 to 500,000 for white individuals and about 1 to 350,000 for black individuals. This small probability of coincidental matches makes people feel confident in DNA evidence. False positives, on the other hand, are matches between DNA and test DNA that result from issues in the forensics lab. There have been multiple cases of forensics labs accidentally switching victim and suspected rapist DNA which lead to convictions that were later overturned when the lab realized the error. There are also cases of misreading or misunderstanding a sample. In a case from 1993, a man was convicted of rape because someone interpreted a mixed sample of victim and perpetrator DNA as an individual sample. The conviction was again overturned. To prevent false convictions, retesting DNA is allowed, but it’s not an efficient solution. Many people are pushing for statistical information on the probability of false positives, so that the jurors can make accurate judgements based on DNA evidence (Thomas et al.).  

Conclusion:

DNA analysis is a useful tool for the criminal justice system, but subjectivity can be an issue in forensic testing. To combat wrongful convictions, there are organizations like The Innocence Project who use DNA testing to free the innocent, but reformed forensic testing standards should be put in place to prevent subjectivity and to ensure juries are correctly informed during trials. https://www.innocenceproject.org/    

Citation:

Augustyn , Adam, et al. “DNA Finerprinting.” Encyclopedia Britannica , 26 Apr. 2018, www.britannica.com/science/DNA-fingerprinting#ref153186. Dror, Itiel E., and Greg Hampikian. “Subjectivity and Bias in Forensic DNA Mixture Interpretation.” Science & Justice, vol. 51, no. 4, 2011, pp. 204–208., doi:10.1016/j.scijus.2011.08.004. Jobling, Mark A., and Peter Gill. “Encoded Evidence: DNA in Forensic Analysis.” Nature Reviews: Genetics, vol. 5, no. 10, Oct. 2004, pp. 739–751., doi:10.1038/nrg1455. Thompson, William C, et al. “How the Probability of a False Positive Affects the Value of DNA Evidence.” Journal of Forensic Sciences, vol. 48, no. 1, Jan. 2003, pp. 1–8., doi:10.1520/jfs2001171.