What does it take to be a global leader in AI and data science?
In this edition of Calculated Conversations, I’m joined by Ms. Parul Pandey, a Kaggle Grandmaster and a leader in AI and data science. Ms. Pandey’s journey spans from making AI more accessible to children, to co-authoring a book on responsible AI, and even earning a spot as one of the top minds in the field. We discussed how AI is transforming education, the ethics of machine learning, and her advice for aspiring data scientists.
Here is what she had to say:
1. You’ve worked in AI, written a book, and even earned the Kaggle Grandmaster title. What first got you interested in data science?
I’ve always had an interest in numbers, but my real experience with Data Science started in my first job. I was part of a team focused on the analysis and planning of the Power Distribution network. Back then, it was just called data analysis. But as I worked more and got curious, I started researching on my own and discovered that there was an entire field built around it, called Data Science. That realization pushed me to dive deeper into learning data science and machine learning.
2. Many people find AI intimidating. How would you explain its real-world impact to someone outside the tech world?
AI can seem intimidating, but it’s already part of our daily lives. When you use Google Maps for directions, ask Siri for the weather, or get personalized recommendations on Netflix or Spotify, AI is helping make those tasks easier and more personalized. These are just a few examples of how AI is improving everyday life.
That said, it’s important to use AI responsibly, especially for high-stakes decisions, like hiring or medical diagnoses. We must ensure AI tools are used ethically and that humans remain involved to ensure fairness and accuracy.
3. You’ve focused on making education more accessible with AI tools. What’s one cool example of how AI can help kids learn better?
AI can provide every child with a personal tutor that adapts to their individual speed and learning style. It can recognize how each child understands material and adjust the lessons accordingly, making learning more effective. But it’s equally important to develop these tools responsibly to ensure they remain fair, protect privacy, and genuinely support each student.
For example, I recently built an AI chatbot for my 4th-grade son to help him during exams. I named it Study Buddy. It’s a simple-to-use tool that gives clear explanations, answers questions, and helps kids organize their study notes. I designed it with safety in mind, by using strict content filters and creating a persona that keeps interactions focused and educational. The app allows kids to ask questions about text or images and store their study notes all in one place. You can find the code for it here: Study Buddy GitHub Repo.
4. You co-founded Women in Coding & Data Science. What’s one change you’d love to see for women in tech in the next five years?
One change I’d love to see is more women gaining real hands-on experience early on. While online courses are great for learning the basics, true understanding comes when you apply what you learn in real situations. I believe more women should be encouraged to write blogs, answer questions on forums, speak at meetups, and share their work on platforms like GitHub. These actions not only build solid skills but also help increase visibility, which is crucial for growth in tech.
5. You’ve worn many hats. From engineer to author to community builder. How do you stay motivated across all these roles?
I enjoy what I do, and staying connected with the community keeps me motivated. Every day, I look forward to the interactions, ideas, and energy that come from being part of something bigger. It’s that daily connection that keeps me inspired, no matter which role I’m in.
6. What advice would you give to a young student who’s curious about AI but doesn’t know where to start?
My advice is to start with a clear idea of what you want to create. Instead of diving into courses or books right away, think about a specific project you want to build. For example, if you want to create an iOS app but don’t know where to begin, focus on what kind of app you want, its functionality and design, first. Then, reverse-engineer the learning process by figuring out what skills and knowledge you need to make it happen. Tools like ChatGPT or Gemini can help guide you through that process.
As you build, you’ll naturally come across areas where you need more knowledge, like math or algorithms. That’s when you can fill in those gaps, learning the theory as you go. This way, the theory feels more relevant because it’s directly tied to what you’re trying to achieve.
While understanding the basics is important, applying them through building makes the learning process more meaningful and engaging.
What an insightful conversation with Ms. Parul Pandey! Her work in AI, from creating tools that help kids learn to ensuring responsible AI in high-stakes applications, is truly impressive.
Key takeaways:
- Start with a clear project in mind, and learn as you build.
- AI is already part of our daily lives – understanding its potential and limitations is key.
- Ethical AI is crucial, especially in high-risk fields like healthcare and hiring.
If you’re interested in exploring her work further, be sure to check out her book, Machine Learning for High-Risk Applications, and follow her on her social platforms:
- Website: parulpandey.com
- LinkedIn: Parul Pandey
Drop any thoughts or questions in the comments below – I’d love to hear what you think about the future of AI!