Fran Bartolić Of Salient: How AI Is Disrupting Our Industry, and What We Can Do About It
An Interview With Cynthia Corsetti
Recognize the limitations of AI tools. AI is not going to solve all your problems. Use common sense and make sure to understand the limitations of any given tool.
Artificial Intelligence is no longer the future; it is the present. It’s reshaping landscapes, altering industries, and transforming the way we live and work. With its rapid advancement, AI is causing disruption — for better or worse — in every field imaginable. While it promises efficiency and growth, it also brings challenges and uncertainties that professionals and businesses must navigate. What can one do to pivot if AI is disrupting their industry? As part of this series, we had the pleasure of interviewing Fran Bartolić.
Fran Bartolić is Data Scientist at Salient, a leading pioneer in weather forecasting analytics. He has extensive experience in applied Bayesian statistics and machine learning. Prior to Salient, Fran worked at Cervest, a London-based climate tech startup, focusing on predicting the effects of climate change in dense urban environments. He has a doctorate in Astrophysics from the School of Physics and Astronomy at the University of St. Andrews.
Thank you so much for joining us in this interview series. Before we dive into our discussion our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path?
Even before starting my PhD, I was aware of the strong possibility that I might eventually work outside academia in the tech industry. With this in mind, I chose a project that was not only scientifically interesting but also required robust skills in data science, programming, and machine learning. Additionally, the PhD program I selected included an unusual (for non-applied science) yet valuable component: a six-month industry placement at a tech company of our choice. I knew this experience would be invaluable if I decided to pursue a career outside academia.
During my placement, I worked at a climate tech startup in London, focusing on predicting the effects of climate change in urban areas. The work I did there was in many ways similar to my academic research, and I thoroughly enjoyed it. After completing my PhD, I decided to leave academia and search for jobs at interesting startups in the climate/weather space. This led me to Salient, where I’m currently working as a data scientist.
What do you think makes your company stand out? Can you share a story?
What makes Salient stand out are two key factors. First is our outstanding science and engineering team, which is dedicated to developing the best weather prediction model using the latest research from the fields of weather forecasting and machine learning. Second, our product and business teams play a crucial role. They are committed to understanding our customers’ specific needs and figuring out how to effectively assist them in making “decisions” based on our forecasts. It’s not just about having a model with superior metrics; understanding how to optimally utilize those forecasts is equally, if not more, important.
You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?
I would have to say these were the most instrumental character traits:
- First principles thinking
- Continuous learning
- Risk management
Let’s now move to the main point of our discussion about AI. Can you explain how AI is disrupting your industry? Is this disruption hurting or helping your bottom line?
In the last couple of years, the application of machine learning to weather forecasting has revolutionized our industry. Major tech companies have unveiled models that match or even surpass the accuracy of state-of-the-art physical models. These physical models, developed over decades by government-funded research institutes, are now being challenged by AI-driven approaches.
What’s remarkable about these AI models is not just their predictive accuracy, but also their cost-efficiency. They are significantly less expensive to train and operate compared to their physical counterparts. This shift has been immensely beneficial for us as a startup. We’re in an advantageous position, being nimble and technologically agile, to leverage these advancements.
Which specific AI technology has had the most significant impact on your industry?
The availability of exceptional open-source machine learning libraries, including PyTorch, JAX, and Keras, has significantly impacted startups in our sector. Without these resources, it would be considerably more challenging to move quickly and experiment with innovative machine learning architectures.
Can you share a pivotal moment when you recognized the profound impact AI would have on your sector?
The pivotal moment for me was when I read a preprint of a paper by Ryan Keisler, “Forecasting Global Weather with Graph Neural Networks,” submitted early last year. He presents a data-driven approach for forecasting global weather using graph neural networks. The system learns to step forward the current 3D atmospheric state by six hours, and multiple steps are chained together to produce skillful forecasts going out several days into the future. I realized then that it is only a matter of time before machine learning models become dominant in weather forecasting.
How are you preparing your workforce for the integration of AI, and what skills do you believe will be most valuable in an AI-enhanced future?
The key to preparing our workforce for the integration of AI is to fully embrace these new technologies rather than resist them. We are on the cusp of a new technological revolution potentially even more significant than the advent of personal computing. Companies that fail to adapt to this change risk going out of business. In other words, don’t be a Luddite.
As for the second part of the question, the truth is that no one really knows. Personally, I believe skills that have been valuable for a long time — math, reading, writing, rhetoric, logic — will continue to be valuable in the future. This is called the “Lindy effect.” It’s hard to imagine that skills that have only recently emerged, such as “prompt engineering,” will still be relevant in five years.
What are the biggest challenges in upskilling your workforce for an AI-centric future?
It’s difficult to prepare for the AI-centric future if you don’t even know what the next year is going to look like. Startups have an advantage in this aspect due to their agility and ability to adapt more quickly compared to larger, more bureaucratic organizations.
What ethical considerations does AI introduce into your industry, and how are you tackling these concerns?
In the context of AI applied to weather forecasting, the ethical considerations are indeed less critical compared to sectors like media or biotech. This is primarily because the core function of AI in weather forecasting is to analyze vast data sets and improve prediction accuracy, rather than making decisions that directly impact human lives or societal norms. The focus in our sector is more on technical accuracy and reliability, which are less fraught with ethical dilemmas compared to areas where AI decisions might directly affect individual health outcomes or social dynamics.
What are your “Five Things You Need To Do, If AI Is Disrupting Your Industry”?
- Embrace AI tools and educate your workforce. Tools like ChatGPT (with the GPT-4 model) represent a form of artificial general intelligence with capabilities that are often underestimated. There are many obvious and less-obvious use cases for it that can significantly boost productivity. Encourage creativity in exploring these applications.
- Leverage AI to enhance the productivity of your existing employees and processes. At least in the short term, tools such as large language models are unlikely to completely replace entire jobs. Instead of focusing on automating existing jobs with AI, the right model is to think about how to make everyone 10 times more productive by integrating AI tools into their existing workflows.
- Prepare for radical business changes. Surviving the incoming technological revolution requires adaptability and preparedness for uncertainty. Staying agile and open to significant shifts in your core business model is crucial for survival.
- Invest in specialized talent. Hiring talented machine learning engineers is costly but if your business model cannot be sustained with off-the-shelf AI solutions, acquiring people with the necessary expertise is essential.
- Recognize the limitations of AI tools. AI is not going to solve all your problems. Use common sense and make sure to understand the limitations of any given tool.
What are the most common misconceptions about AI within your industry, and how do you address them?
A common misconception in our industry is the belief that AI will replace meteorologists entirely. In the short term, this is unlikely to be true. Understanding the limitations of various models and interpreting forecasts requires a level of skill that is challenging to automate.
Can you please give us your favorite “Life Lesson Quote”? Do you have a story about how that was relevant in your life?
I can’t pinpoint one that specifically influenced me, but I’m fond of several quotes by investor, entrepreneur, and developer Nat Friedman, listed on his website: https://nat.org/. One of my favorites is, “The efficient market hypothesis is a lie; The best things in life occur where EMH is wrong.” This quote challenges the Efficient Market Hypothesis (EMH), which asserts that markets are completely efficient in reflecting all available information. It suggests that real-world opportunities, in finance and life, often arise from imperfections and inefficiencies in systems. These inefficiencies, the quote implies, are what create spaces for innovation, unexpected gains, and personal growth, leading to some of the most fulfilling experiences and achievements.”
Off-topic, but I’m curious. As someone steering the ship, what thoughts or concerns often keep you awake at night? How do those thoughts influence your daily decision-making process?
A genuine concern that often keeps me awake at night is the paranoia that there might be serious bugs in our code, or that our model evaluation process is fundamentally flawed in some way and the models we’ve built aren’t making useful predictions. I think that is a healthy attitude to have in this field because the systems we build are quite complex, and we’re working on difficult problems.
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger. :-)
It would be a movement whose core set of principles is centered around technological and social progress and the belief that most problems are soluble if one possesses the right mindset. The movement should also have good aesthetics.
How can our readers further follow you online?
https://www.linkedin.com/in/fbartolic/
Thank you for the time you spent sharing these fantastic insights. We wish you only continued success in your great work!
About the Interviewer: Cynthia Corsetti is an esteemed executive coach with over two decades in corporate leadership and 11 years in executive coaching. Author of the upcoming book, “Dark Drivers,” she guides high-performing professionals and Fortune 500 firms to recognize and manage underlying influences affecting their leadership. Beyond individual coaching, Cynthia offers a 6-month executive transition program and partners with organizations to nurture the next wave of leadership excellence.
