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.

 

 

The Origins (1840s-1940s)

1843: Ada Lovelace’s Algorithm

The history of programming begins with Ada Lovelace, a British aristocrat that was educated in mathematics and science. In 1843, Lovelace created the very first machine algorithm for Charles Babbage’s Difference Machine. This conceptual leap is what gave birth to the idea of programming languages.

1936: Alan Turing’s Theoretical Foundation

Alan Turing published a paper in 1936 that is now regarded as the founding work of computer science. Turing introduced the concept of a universal machine that could follow instructions. This revolutionary idea eventually evolved into plans for an electricity-powered computer that could run programs, mathematically defining what computation meant.

1940s: First Programming Languages and Electronic Computers

• Konrad Zuse’s Plankalkül: Considered the first programming language for computers, created in the early 1940s to store codes for routine, repetitive tasks
• Assembly Language (1949): A programming language that simplified computer instructions
• Shortcode (1949): Developed by William Schmitt for the BINAC and UNIVAC computers
• ENIAC: The first programmable, electronic, general-purpose digital computer, which required physical rewiring to program

The First Programming Languages (1950s-1960s)

Early 1950s: First Compiler Languages

• Autocode (1952): Developed for the Mark 1 computer at the University of Manchester, it was the first language that could be translated into machine code
• Assembly Language: Further evolved as the first step away from machine code, using symbolic representations of machine instructions

Mid to Late 1950s: Foundation of Modern Programming

• FORTRAN (1957): Developed by John Backus at IBM, FORTRAN (FORmula TRANslation) was the first widely used high-level programming language, designed for scientific and engineering calculations. Remarkably, this language from the 1950s is still used today in supercomputers and scientific computations.
• ALGOL (1958): Created by American and European scientists, ALGOL became the foundation for many renowned languages including Pascal, Java, C, and C++
• LISP (1958): Created by John McCarthy, introducing many ideas still central to programming today, including recursion and tree data structures. Originally intended for AI, companies like Boeing and Genworks still use it.
• COBOL (1959): Developed by a team led by Dr. Grace Murray Hopper, COBOL (Common Business Oriented Language) was designed for business applications like credit card transaction processors, traffic signals, and phone calls. It’s still used in banking and gaming today.

1960s: Programming Becomes More Accessible

• BASIC (1964): Developed by students at Dartmouth College (Beginners All-Purpose Symbolic Instruction Code), it was designed to make programming accessible. Later further developed and marketed by Bill Gates.
• PL/I (1964): IBM’s attempt to create a language combining the best features of FORTRAN and COBOL
• APL (1966): A highly symbolic language focused on array processing
• SIMULA (1967): The first object-oriented programming language, introducing concepts like classes and inheritance

The Structured Programming Era (1970s)

• Pascal (1970): Designed by Niklaus Wirth and named after French mathematician Blaise Pascal, it was created explicitly to encourage structured programming. So easy to learn, it was used by Apple early in its history.
• C (1972): Developed at Bell Labs by Dennis Ritchie for the Unix operating system, combining low-level access with high-level abstractions. Many modern languages like C#, Java, JavaScript, Perl, PHP, and Python are derived from C.
• Smalltalk (1972): Developed by Alan Kay, Adele Goldberg, and Dan Ingalls at Xerox PARC. It pioneered the graphical user interface and pure object-oriented programming. Its principles are present in Java, Python, and Ruby, used by companies like CrowdStrike and Logitech.
• SQL (1972): Originally called SEQUEL, it was developed by Raymond Boyce and Donald Chamberlain for viewing and altering information in databases.
• Ada (late 1970s-early 1980s): Named after Ada Lovelace, it was designed by Jean Ichbiah’s team at CUU Honeywell Bull. A high-level programming language extended from Pascal, Ada is used for air-traffic control in various European countries and space programs.

The Rise of Personal Computing (1980s)

• C++ (1983): Bjarne Stroustrup modified the C language at Bell Labs to create C++, adding object-oriented features, classes, templates, and virtual functions. It’s now used in MS Office, Adobe Photoshop, and various types of high-performance software.
• Objective-C (1983): Developed by Brad Cox and Tom Love as another object-oriented extension of C, later becoming the foundation for Apple’s operating systems.
• Perl (1987): Created by Larry Wall, initially for text editing. Today its primary uses include database applications, data processing, graphic programming, and system administration.
• Haskell (1990): Named after mathematician Haskell Brooks Curry, this primarily mathematical programming language is used in industries that need complex calculations and record-keeping.
• Visual Basic (1991, but conceptualized in the late 1980s): Microsoft’s attempt to make Windows programming accessible. It allows users to drag and drop sections of code via a graphical user interface (GUI). Parts of Visual Basic are used in applications like Word and Excel.

The Internet Age (1990s)

• Python (1991): Created by Guido Van Rossum and named after the British comedy Monty Python, it emphasizes code readability. Today Python is one of the most popular programming languages worldwide, used by giants like Google and Spotify.
• Ruby (1993): Created by Yukihiro Matsumoto as a high-level programming language primarily for web applications development. Today it’s used by companies like Groupon.
• HTML (1993): Not strictly a programming language, but fundamental to web development
• Java (1995): Developed by James Gosling at Sun Microsystems with the promise of “write once, run anywhere.” It’s one of the world’s most popular programming languages, used in everything from cell phones to parking machines.
• JavaScript (1995): Created by Brendan Eich, now ubiquitous in web development. Used for web development, PDF documents, and desktop widgets, it’s on almost every major website, including Adobe, Gmail, and Mozilla Firefox.
• PHP (1995): Originally known as “Personal Home Page” but now stands for “Hypertext Preprocessor,” it was developed by Rasmus Lerdorf. Used for building and maintaining web pages and server-side development, PHP powers huge platforms like Facebook, Wikipedia, and WordPress.

The Modern Era (2000s-2010s)

• C# (2000): Microsoft developed C# in an attempt to combine elements of Visual Basic and C. It’s now used in all the company’s products.
• Scala (2003): Created by Martin Odersky, it can work with Java, giving it an advantage in Android development. Scala is used by social media and entertainment giants like Twitter, LinkedIn, and Netflix.
• Groovy (2003): An easy-to-learn language developed by James Strachan and Bob McWhirter, used by companies like Starbucks, Craftbase, and Transferwise.
• Go (2009): Developed by Google to resolve challenges faced by large software systems. Go is popular with big tech companies like Uber, Dropbox, and of course, Google.
• Rust (2010): Mozilla’s language designed for memory safety without garbage collection
• TypeScript (2012): Microsoft’s statically-typed superset of JavaScript
• Swift (2014): Developed by Apple to replace C, C++, and Objective-C, intended to be easier and more versatile. Swift can be used for cloud applications, mobile, and desktop. Language platform Duolingo used it to launch a new app.

The AI and Cloud Native Era (2010s-Present)

• Julia (2012): Designed for high-performance numerical analysis and computational science
• Kotlin (2011, widely adopted late 2010s): Modern alternative to Java, particularly for Android development
• WebAssembly (2017): Not a traditional language, but a binary instruction format for a stack-based virtual machine
• Dart (2011, popularized with Flutter 2017): Google’s language for multi-platform development
• Python’s resurgence: Becoming the dominant language for data science, AI, and machine learning

The Future of Programming

As we look ahead, programming continues to evolve in exciting new directions. Low-code and no-code platforms are democratizing software development, making it accessible to those without traditional programming skills. Meanwhile, AI-assisted coding tools like GitHub Copilot are revolutionizing how developers work by generating code from natural language prompts. The emergence of quantum computing is driving the development of specialized quantum programming languages to harness this powerful new paradigm. Simultaneously, domain-specific languages are becoming increasingly sophisticated, offering tailored solutions for specific industries and problems. These trends collectively point to a future where programming becomes both more specialized and more accessible, continuing the remarkable journey that began with Ada Lovelace’s algorithm nearly two centuries ago.

 

 

 

Citations

Aron, Jacob. “Alan Turing.” New Scientist, www.newscientist.com/people/alan-turing/.

Hamilton, Tim. “History of Programming Languages.” Praxent, 4 Oct. 2022, praxent.com/blog/history-of-programming-languages.

The Editors of Encyclopedia Britannica. “Ada Lovelace | Biography & Facts.” Encyclopædia Britannica, 23 Nov. 2018, www.britannica.com/biography/Ada-Lovelace.

 

Without the analysis of historical artifacts, much of the history we know today would be non-existent. It is through the scientific exegesis of many artifacts that we are able to understand the history of the human race. When looking back on the most critical and historic artifacts we come across many varieties, such as the Rosetta Stone, the Venus of Willendorf, the Dead Sea Scrolls and many more. But one historical artifact that stood out amongst the others has a unique backstory. This artifact is the most scientifically studied artifact in all of history…The Shroud of Turin. In this article we delve deeper into what the Shroud Of Turin is,  how it came about and understanding both sides of the spectrum.
The Shroud of Turin is a 14.5 x 3.5 foot linen cloth bearing the faint image of a man who appears to have suffered physical trauma consistent with crucifixion. The cloth has been preserved in the Cathedral of Turin, Italy, since 1578 (Wilson, 2010). While its origins are debated, the first historically documented appearance of the shroud was in France in the 1350s.

The image on the shroud shows both the front and back of a male figure, with apparent wounds corresponding to biblical accounts of Jesus’s crucifixion. The image’s unique properties have puzzled scientists, as it appears to be a photographic negative and contains three-dimensional information that becomes visible when analyzed with modern imaging technology (Jackson et al., 2019).

In 1988, radiocarbon dating tests conducted by laboratories in Oxford, Zurich, and Arizona dated the shroud to between 1260 and 1390 CE, suggesting it was a medieval creation (Damon et al., 1989). However, some researchers have challenged these results, arguing that the tested sample may not have been representative of the entire shroud due to repairs and environmental contamination (Rogers, 2005).

The shroud’s weave pattern matches first-century Middle Eastern textiles, and pollen samples found on the cloth include species specific to the Jerusalem area (Frei, 1982). However, skeptics argue that such materials could have been introduced during the shroud’s documented travels through these regions in medieval times.

The Shroud of Turin continues to generate significant interest from both religious and scientific communities. While the Catholic Church has neither officially endorsed nor rejected the shroud’s authenticity, it recognizes its significance as an important icon of Christian faith. The shroud remains a powerful symbol that draws millions of pilgrims to Turin during its rare public exhibitions.

 

 

 

Citations

Damon, P. E., Donahue, D. J., Gore, B. H., Hatheway, A. L., Jull, A. J. T., Linick, T. W., … & Valladas, H. (1989). Radiocarbon dating of the Shroud of Turin. Nature, 337(6208), 611-615.
Di Lazzaro, P., Murra, D., Santoni, A., Fanti, G., Nichelatti, E., & Baldacchini, G. (2010). Deep ultraviolet radiation simulates the Turin Shroud image. Journal of Imaging Science and Technology, 54(4), 40302-1.
Frei, M. (1982). Nine years of palynological studies on the Shroud. Shroud Spectrum International, 1(3), 3-7.
Jackson, J. P., Jumper, E. J., & Ercoline, W. R. (2019). Correlation of image intensity on the Turin Shroud with the 3-D structure of a human body shape. Applied Optics, 58(9), 2683-2691.
Rogers, R. N. (2005). Studies on the radiocarbon sample from the Shroud of Turin. Thermochimica Acta, 425(1-2), 189-194.
Wilson, I. (2010). The Shroud: The 2000-Year-Old Mystery Solved. Bantam Press.

When looking at the past, it’s very hard to decipher how exactly certain situations unfolded. We must look to historical texts in order to garner a revelation on how this domino effect of history unfolded to our present day. The issue with analyzing historical texts is that it is under the umbrella of human error and with human error comes human bias which when analyzing historical texts to reveal historic events to the world, this bias can become very dangerous. Dangerous to the point where it can (and has) altered our perception of history. In this article we will look to AI and how analyzing historical texts through AI can help suppress these human errors.

The implementation of AI in historical analysis has revolutionized how we approach primary sources. Machine learning algorithms can now process vast quantities of historical documents, identifying patterns and connections that human researchers might miss or take years to discover. For instance, the Venice Time Machine project, led by researchers at École Polytechnique Fédérale de Lausanne, has successfully digitized and analyzed thousands of historical Venetian documents, revealing previously unknown patterns in trade relationships and social networks (Williams, 2022). This breakthrough in historical research demonstrates the potential for AI to uncover hidden narratives and connections that traditional methods might overlook.

AI systems offer several significant advantages in historical analysis that extend beyond mere pattern recognition. Advanced natural language processing (NLP) models can analyze historical texts in multiple languages and adapt to archaic writing styles, making cross-cultural comparison more accessible than ever before (Chen & Rodriguez, 2023). Furthermore, these systems can provide comprehensive statistical analysis of historical trends, helping to validate or challenge existing historical narratives with data-driven insights that would be impossible to generate through traditional research methods alone.

However, it’s crucial to acknowledge that AI itself is not free from bias. The algorithms are trained on data sets that may contain their own inherent biases, and the programming decisions made by developers can inadvertently influence results. As noted by Dr. Sarah Thompson (2023) in her work on digital humanities, “AI should be viewed as a complementary tool to traditional historical research methods, not a replacement.” This understanding has led to the development of various safeguards in the field, including regular calibration of AI models against peer-reviewed historical research and cross-validation of findings using multiple AI systems. The collaboration between historians and AI experts has become essential to ensure appropriate interpretation of results and maintain the integrity of historical research.

The integration of AI in historical analysis represents a significant step forward in our ability to understand the past more objectively. Projects like the Historical Document Analysis Initiative (HDAI) demonstrate how machine learning can help identify and correct for biases in historical narratives while preserving the essential human element of historical interpretation (Garcia et al., 2024). These developments have opened new avenues for research and understanding, allowing historians to approach their subjects with unprecedented depth and precision.
The more we refine these technologies, the greater the relationship between AI and historical research becomes. Researchers have found that combining traditional historical methods with AI-powered analysis can provide a more complete and nuanced understanding of historical events and their interconnections. This hybrid approach allows historians to maintain their crucial role in interpretation and analysis while leveraging the powerful capabilities of AI to process and analyze vast amounts of historical data.

The future of historical research lies in this careful balance between human expertise and technological capability. While AI offers powerful tools for analyzing historical texts with reduced human bias, it’s essential to remember that complete objectivity remains elusive. The goal is not to remove human judgment entirely but to enhance it with technological capabilities that can help identify and minimize unconscious biases. As we continue to develop more sophisticated AI tools, the partnership between human historians and machine learning systems will become increasingly valuable in our quest to understand the past more accurately.

By developing more accurate and comprehensive understandings of historical events, we can better inform current decision-making processes and policy development. The ability to analyze large amounts of historical data quickly and efficiently while also accounting for potential biases helps create a more nuanced and accurate picture of our past, which in turn helps us better understand our present and prepare for our future.

 

 

 

 

 

Citations

Donovan, M. (2023). How AI is helping historians better understand our past. *MIT Technology Review*.
Williams, R. (2022). The Venice Time Machine: Digital Humanities at Scale. *Digital History Quarterly*, 18(2), 45-67.
Chen, L., & Rodriguez, A. (2023). Natural Language Processing in Historical Research: Advances and Challenges. *Journal of Digital Humanities*, 12(4), 89-112.
Thompson, S. (2023). Digital Tools and Historical Method: A Critical Assessment. *History and Computing*, 15(3), 201-224.
Garcia, M., et al. (2024). The Historical Document Analysis Initiative: First Year Results. *Digital Scholarship in the Humanities*, 39(1), 1-28.