Becoming the Orchestrator
- From Surveys to Synapses: A Journey to AI
- The Key That Unlocked It All: Language
- The Shift
- Becoming an Orchestrator
- Elevating Performance: Bridging the Skills Gap
- A Mindset Shift
- Exercise: Enhancing Your Questioning Skills
- Conclusion
This chapter is from the book
This chapter is from the book
This chapter is from the book
In 2014, I found myself working in the basement of an academic building, conducting research in a neuroscience lab dedicated to understanding emotional learning and memory. It was a windowless, almost dungeon-like space, but I felt a deep sense of purpose. After earning my first degree in piano performance, I had discovered a passion for science that led me here, to studying the mysteries of the human brain. I was proud of myself for making this leap into research, knowing my work could contribute to new insights about humanity.
From Surveys to Synapses: A Journey to AI
My earliest projects in the lab involved designing and analyzing surveys—a seemingly straightforward tool, yet one that quickly taught me a valuable lesson about human nature. We, as humans, are often poor at understanding and accurately reporting on ourselves. Even when asked simple questions, we tend to embellish, distort, or hide the truth to protect our self-image. Thus, surveys, while valuable, have inherent limitations.
This realization led me to a new approach: working with rodents to study fear responses in a more controlled, measurable way. Over several months, I cared for my furry subjects, carefully gathering data on their reactions and behavior. But when the experiment concluded, there was no happy ending. These little creatures wouldn’t be set free; instead, it was time to euthanize them and study their brains.
Growing up on a farm, I was no stranger to the realities of life and death; I had butchered chickens and understood the cycle of life. Yet, as I prepared to go through with the procedure, I hesitated. My eyes met those of one of my rodent companions, and in that moment, I knew: This wasn’t the path for me.
I left the lab that weekend with a heavy heart, questioning my career path and feeling a pull to make a change. I turned to my usual process for reflection: a pen, a piece of paper, and my computer to help me think through my next steps.
On one side of the paper, I listed everything I loved about my work: science, data, analysis, discovery, coding, and making an impact. On the other side, I noted what I disliked: euthanizing animals, the slow pace of academia, and painstaking data collection. These reflections made one thing clear: I needed a different way to work with data, one that aligned with my values and curiosity.
I typed my list of interests into Google, searching for inspiration. That’s when I stumbled upon an article about people leaving academia for a new field called data science.
Data science? The term was unfamiliar, yet intriguing. Data and science, both on my list of passions. But what truly captivated me was the mention of “data scientists” working with algorithms called neural networks. While I had been working tirelessly in the lab, computer scientists were developing ways to replicate the human brain’s neural networks with code. My mind raced with the implications: You mean I could study human behavior not by analyzing brains directly, but through the digital footprints people naturally leave behind?
That discovery was a revelation. The idea that I could work with artificial neural networks to gain insights into human behavior, without having to study the brain in a traditional sense, ignited something within me. The possibilities felt endless.
That Monday, I quit my lab job, determined to find any opportunity to work with data in the business world. I was ready to dive into this new field and start building my own artificial neural networks.
For the next few years, I immersed myself in learning everything I could about data science. I honed my craft, exploring data, mastering different algorithms, and building models that turned heads. I developed neural network models, created computer vision systems that analyzed retinal scans to predict health outcomes, and crafted marketing models that optimized the flow of products and services. Technologists appreciated the models I built, and business leaders celebrated the results. Still, I could sense that the world wasn’t quite ready to fully embrace AI’s potential.
