In this episode of Leading with data, we interact with Jepson Taylor, co-director of the ai Masterclass at New York University and former Chief ai Strategist at Dataiku. Unraveling the future of ai, Taylor shares valuable insights into pivotal moments in his journey: from chemical engineering to ai entrepreneurship, a successful startup acquisition, and the rise of generative ai.
Let’s dive in!
Key insights from our conversation with Jepson Taylor
- Generative ai is the key to unlocking the path to AGI, revolutionizing approaches to innovation and problem solving.
- The shift from traditional programming to ai requires a passion for technology and a willingness to take risks, such as leaving a stable job for entrepreneurial pursuits.
- Storytelling is emerging as a crucial skill for ai professionals, enabling effective communication of complex ideas to executives and stakeholders.
- The future of ai encompasses generative algorithms, which allow ai systems to write and improve their code autonomously, giving way to more efficient and powerful applications.
- The success of an ai startup depends on recruiting the right talent, emphasizing experienced professionals who can take charge of their duties and drive the company forward.
In the following section, we have summarized the questions directed to Jepson Taylor in the Leading with data session.
<h2 class="wp-block-heading" id="h-how-did-your-journey-from-chemical-engineering-to-ai-entrepreneurship-begin”>How did your journey from chemical engineering to ai entrepreneurship begin?
When I was studying chemical engineering, I didn’t do much programming, but two parallel paths changed that. First of all, I started an e-commerce company while I was in school, which was my foundation in web programming. Second, an inspiring professor in my numerical methods class introduced me to genetic algorithms and simulated annealing. This sparked my passion for programming, particularly in areas where computers could work for you, such as high-performance computing and computer vision. My engineering projects always had a programming extension, and I even got slapped once for doing satellite image processing as a chemical engineering internship!
<h2 class="wp-block-heading" id="h-transitioning-from-chemical-engineering-to-ai-what-were-the-pivotal-moments”>Transition from chemical engineering to ai, what were the crucial moments?
I initially thought about going to medical school and getting a PhD in medicine, combining medical research with programming. However, I fell in love with programming and computer vision and realized I could have a bigger impact with ai than in healthcare. Before deep learning, computer vision was more of an art requiring labor-intensive heuristics. Deep learning changed that, making it unnecessary to build those complex rules.
Can you share the story behind your startup that sold to DataRobot?
In 2016, my co-founder and I participated in a pitch competition in Utah, pitching an AutoML solution. By creating a web form for structured data uploads, it delivered an analysis with an AutoML model in less than 40 seconds. The quality of the data surprised us and sparked a shift towards deep learning. Upon leaving our jobs, a crucial step in transitioning from “entrepreneur” to entrepreneur, we landed a contract with Teal Drone for deep learning use cases. This marked the beginning of our growth, and we eventually raised $600,000 and formed a team. Despite receiving three acquisition offers in our first year, we chose to sell on different terms three years later.
What was your role at DataRobot and how did storytelling become an important part of your career?
At DataRobot, I became known as an executive SC, interacting with executives and assisting in high-profile sales. I also honed my skills as a global keynote speaker, becoming obsessed with storytelling. I read books on storytelling and analyzed my successful talks to understand why they resonated. Storytelling impacts every part of your career, from sales to hiring. It’s about making the right first impression and being seen as the expert in the room.
<h2 class="wp-block-heading" id="h-with-the-rise-of-generative-ai-what-was-your-aha-moment”>With the rise of generative ai, what was your “aha” moment?
When ChatGPT came out, I was initially skeptical about deep learning. But seeing ChatGPT’s ability to recover knowledge convinced me that we don’t need a miracle to reach full AGI or singularity. Generative ai is that crack in the dam. My “aha” moment was when I pushed GPT-4 to ask its own questions out of curiosity, not based on human needs. It asked about sentient beings in a parallel universe where time went backwards, which was a profound moment for me.
<h2 class="wp-block-heading" id="h-what-s-the-focus-of-your-podcast-atomic-soul-and-your-ai-master-class-at-nyu”>What is the focus of your podcast, Atomic Soul, and your ai master class at NYU?
The podcast aims to have raw, authentic, unreserved conversations with guests who intimidate me, like venture capitalists and CEOs. I want it to be emotional, to explore the human side of these individuals. The ai Masterclass seeks to stay at the forefront of the field, focusing on generative ai and, coming soon, generative algorithms.
Can you tell us about your current startup and your focus on generative algorithms?
My vision for the next three years is that every major algorithm will be rewritten by ai, not humans. This will revolutionize fields such as healthcare. In 2023, we will focus on generative ai, and in 2024, we will see generative algorithms where ai writes ai. I envision a SaaS offering where the system can be asked to write custom algorithms for specific use cases.
How do you see success for yourself at the end of 2024?
By the end of 2024, my goal is to have closed my seed round, have a foundational model for generative algorithms, over a million revenue, and over 10 customers. I also hope to see a proof of concept of ai by writing ai and have a team that supports this vision.
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summarizing
While we finish talking about interesting computer science things in this Leading with data, we learned that Jepson Taylor really loves making smart computers. He is very interested in making computer brains smarter with “generative algorithms.” We have more interesting talks, unraveling the mysteries of data in a way that everyone can enjoy. So stay tuned to us on Lead with data For more sessions of this type!
