My AI Journey: The Tools That Opened Each Door
- S B
- May 16
- 6 min read
Updated: Jul 11
Published in Towards AI
Steve Jobs once said, “Technology is nothing. What’s important is that you have a faith in people, that they’re basically good and smart, and if you give them tools, they’ll do wonderful things with them.”
These were the tools that were given to me so that I could fly, by the giants on whose shoulders I stand.
PyMOL: Seeing Beauty in Science
I remember it like it was yesterday. I was working on an assignment in class. Following the steps carefully, I watched as it happened: proteins appeared as beautiful ribbons on the screen, their intricate structures swirling in vibrant colors. In that moment, I was captivated by PyMOL, a computer program for viewing biological molecules in 3D. Warren Delano’s PyMOL wasn’t just a visualization tool — it was a window into the elegance of science.
PyMOL taught me that data is more than information — it’s art, and that technology and science are deeply intertwined. It was also my first interaction with open-source software — free tools that bring opportunities to anyone, anywhere. This insight, the power of accessible technology, has endured among my fundamental beliefs.
With PyMOL, I found the gateway to the next chapter of my AI journey. An image I created with PyMOL was central to my first scientific publication.
That image remains on the opening page of my portfolio today, a testament to the power of visualization. That publication led to my first professional role in science, where I discovered the tool that would open the door to endless possibilities.
R: Freedom to Create with Code
In that role I discovered R, a programming language for graphics and statistics — it was love at first byte. Unlike PyMOL, R was my first self-taught adventure, mastered at home with just an Amazon-bought book and determination.
While other programming languages felt like strict rule books, R was an artist’s palette. Its quirky symbols and flexible approach felt like an invitation to be creative with code. R became my key to exploring data, ultimately unlocking the most impactful opportunity on my path.
The data manipulation skills I developed in R led me to the frontiers of innovation — a new role in biomedical research. R wasn’t just a tool; it became a trusted companion for weaving together complex data — from genomics to clinical information. With R, data analysis was just the beginning. The next tool allowed me to mesmerize audiences with the beauty of data.
ggplot2: Turning Data into Colorful Stories
Like my discovery of PyMOL, my first encounter with Hadley Wickham’s ggplot2 resonated deeply. This visualization toolkit for R, built on the principles of the grammar of graphics (hence the gg), transcended data into stories told through colors, patterns, and shapes.
I wasn’t just analyzing data anymore; I was uncovering hidden stories. These plots had elements of style that would impress Van Gogh — themes, borders, and vibrant palettes. The result? Multiple scientific publications and a new identity: “the woman who makes pretty plots”.
But like PyMOL and R, ggplot2 taught me that success isn’t just about achievements — it’s about empowering others. Inspired by the open-source community, I created an online ggplot2 course. The most rewarding moment? When a colleague from another continent recognized me from my course and warmly shook my hand. Yet ggplot2 wasn’t the final chapter — it was another stepping-stone on my road of discovery.
Plotly: Making Data Come Alive
ggplot2 revealed the beauty of data, but Plotly in R taught me how to make visualizations interactive with clickable charts and dynamic features. Visualizations were no longer just static images on screens — they could come alive.
Plotly also allowed me to fine-tune my skills in another programming language called Python. Plotly in Python opened doors to freelance opportunities in data visualization. These projects boosted both my skills and confidence.
These experiences prepared me for my leap into industry, where I would turn tools into solutions. But before that transition, there was one more tool in R to master — it would become my most trusted companion.
R Shiny: The Catalyst for Transformation
R had become the backbone of my career when I stumbled upon something unexpected — R Shiny, a tool for creating web apps in R. I stared at the screen in awe, remembering the first time I saw protein ribbons in PyMOL. I used online resources to teach myself R Shiny.
R Shiny brought everything together: R’s analytics, ggplot2’s beauty, and Plotly’s interactivity. Now I could share data through intuitive web apps, no more creating endless PowerPoint presentations. Shiny became my treasured companion and the cornerstone of my budding career.
R Shiny wasn’t just a tool — it was a career catalyst. Making apps wasn’t part of my original plan — honestly, there was no plan. But learning R Shiny gave me the confidence to tackle new challenges beyond the academic environment I called home.
Shiny in Action: Empowering Users and Solving Problems
I joined a startup where I used Shiny to detect fraud — my first venture beyond academia into the world of technology professionals. Then came an opportunity that would tie all my tools together.
Still new to industry, I was unfamiliar with recruiters, hiring practices and corporate culture. But I did what I had always done, I used my best tools. The hiring process required a hands-on use case, so I built a Shiny app in two intense days. That app got me the job.
Within this new role, R Shiny gave me my first industry publication and first published app. Like PyMOL opened the door to science, R Shiny introduced me to the complexities of working in industry. Each new app connected me with different business functions — from Marketing to Medical Affairs — teaching me about collaboration, resilience, and servant leadership.
These experiences prepared me for an unexpected shift — the rise of AI that transformed how we interact with technology.
Generative AI: Redefining Interaction
The release of ChatGPT marked a turning point in how people interacted with technology. I turned to my trusted friend — R Shiny — to quickly build examples of what this new technology could do. Within two months of the release of ChatGPT, we had our first generative AI application running. Once again, R Shiny proved to be an invaluable tool for embracing the future.
By the next year, generative AI had infiltrated industries, creating new opportunities for innovation. At work, I had the chance to contribute to an exciting generative AI project. The increasing demands for flexibility led me to transition to Shiny for Python, combining Shiny’s elegance with Python’s vast AI resources. The application proved successful enough to move from a prototype to an operational solution within the company.
Shiny had evolved, and so had I. No longer just “the woman who makes pretty plots and apps”, I stepped into the future of AI with my trusted companion at my side. Because regardless of the engines that power AI, the need to make data accessible and interactive will always remain.
My AI Journey: The Tools That Became Teammates
Looking back, these weren’t just tools — they were teammates. PyMOL revealed the beauty of science. R offered boundless creativity. ggplot2 and Plotly turned data into stories. Shiny transformed me from a scientist to an innovator, ready for the AI revolution.
Each tool shaped who I am, and together they taught me the most important lesson: technology’s true power lies not in the code, but in how it empowers people to do wonderful things.
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"AI is the tool, but the vision is human." — Sophia B.
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I’m exploring how generative AI is reshaping storytelling, science, and art — especially for those of us outside traditional creative industries.
About the Author
Sophia Banton works at the intersection of AI strategy, communication, and human impact. With a background in bioinformatics, public health, and data science, she brings a grounded, cross-disciplinary perspective to the adoption of emerging technologies.
Beyond technical applications, she explores GenAI’s creative potential through storytelling and short-form video, using experimentation to understand how generative models are reshaping narrative, communication, and visual expression.
