#74 Creating Value using Artificial Intelligence with Ru Mitra– Founder and Author

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Ru is a graduate from the University of Cambridge, UK who has built six startups in four countries. His primary interest is to build products with social Value. He is also a mentor of Google Launchpad and a senior AI advisor of EFMA Banking group. Ru has been invited to speak at over 60 events from 21 countries. His talks are about sharing his experiences on growing various startups and building products in Artificial Intelligence and Machine Learning.

In this episode, Ru tells us that he started his career in AI because he loves mathematics and solving problems. He has been making bleeding-edge applications since the early 2000s then got into startup land. Ru was always surprised by how difficult it was selling technology. He thought what he was building was more advanced and better than anything else out there. Just because you make great technology, does not mean you can sell it to a business. Some of his startups failed, so Ru has learned a lot from these many challenges. 

Ru realized that young people paid higher insurance premiums just because they do not have a more extended driving history. So why don’t they track their driving and have the insurance company calculate their premium? When they asked the insurance companies if they would like to be their customers, naturally they said no. This was a challenge that the startup faced. Their users were young drivers, so they tried to create a community of drivers who were interested in what they were building and wanted to be part of it. They had millions of drivers and collected their data. Now, the insurance companies were interested because they had the data. 

Then, Ru talks about his writing career. People from around the world started inviting him to speak at events after they read his work. He’s not a professional public speaker, nor does he want to be. The idea of doing many things is a way to find the true potential that we truly have. Our potential is way more significant than we think, the only way we will know is by trying new things. If you are comfortable, then you are not growing. Now, Ru found what he wants to do for the rest of his life. He has always been interested in the education sector. Ru believes we should contribute to this world and to those who may not be as lucky. Stay tuned to hear Ru discuss his advisory roles, meditation, and overcoming challenges with data.  

Enjoy the show!

We speak about:

  • [01:45] How Ru started in the AI space

  • [06:50] What surprised Ru about startups 

  • [10:15] Getting data first, then finding customers 

  • [15:00] Ru’s writing career  

  • [18:35] How to live a complete life

  • [20:40] What success means to Ru 

  • [31:10] Bringing to life the machine learning models   

  • [35:00] Ru’s advisory roles 

  • [39:00] OpenAI

  • [41:20] Overcoming challenges with data 

  • [44:00] Challenges with user adoption 

  • [50:00] What Ru is most proud of

  • [51:40] Advice for the audience 


Ru’s LinkedIn: https://www.linkedin.com/in/mitrar

Ru’s Website: https://www.mitrarudradeb.com

Creating Value With Artificial Intelligence: Lessons Learned from 10 yrs of Building AI Products and Overcoming Data, Adoption, and Engineering Challenges


  • “I overcome challenges by learning from my failures.”

  • “Two years ago I would have never thought I would be a good public speaker.”

  • “Success cannot be defined by external factors.”

  • “I haven’t been stressed for three years, maybe more.”

Thank you to our sponsors:

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Rudradeb Mitra is based in Stockholm, Sweden.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

#70 Making Black Box Models Explainable with Christoph Molnar – Interpretable Machine Learning Researcher

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Christoph Molnar is a data scientist and Ph.D. candidate in interpretable machine learning. He is interested in making the decisions from algorithms more understandable for humans. Christoph is passionate about using statistics and machine learning on data to make humans and machines smarter.

In this episode, Christoph explains how he decided to study statistics at university, which eventually led him to his passion for machine learning and data. Starting out studying with a senior researcher gave Christoph exposure to many different projects. It is an excellent program for students and companies whom both benefit greatly. Christoph learned so much about statistics that he would not have been able to acquire otherwise. The clients got nine hours of consulting for free, which is very valuable for their businesses. When Christoph started his statistical consulting career, he did patient analysis to assess if a medication was affecting the spine. He found this very interesting as it differed significantly from his previous consulting.

When labeling data, Christoph says to label and always compare continuously. For instance, when a student labeled one photo, later on, Christoph would show a student the same photo and see if it got labeled identically. Sometimes people will see the same image but label it differently; so, this is one thing you can do to ensure labeling data is going smoothly. If you have multiple labelers, you will need to compare how each labeler will mark the same photo. Do not be blind to the quality of your data; it is easy to adjust the numbers. 

Then, Christoph speaks about pursuing his Ph.D. in Interpretable Machine Learning. He publishes his book, Interpretable Machine Learning, on his website chapter by chapter. Christoph gets feedback and uses it while continuing his writing on future chapters. Learning about interpretable machine learning is not exactly present at university now. Some schools and professors are starting to integrate it into the curriculum. Stay tuned to hear Christoph discuss accumulated local effects, deep learning, and his book, Interpretable Machine Learning.

Enjoy the show!

We speak about:

  • [02:10] How Christoph started in the data space

  • [09:25] Understanding what a researcher needs

  • [15:15] Skills learned from software engineers 

  • [16:00] Statistical consulting 

  • [19:50] Labeling data  

  • [23:00] Christoph is pursuing his Ph.D.

  • [29:00] Why is interpretable machine learning needed now? 

  • [31:00] Learning interpretability  

  • [33:50] Accumulated local effects (ALE)

  • [37:00] Example-based explanations  

  • [39:15] Deep learning  

  • [43:35] The illustrations in Interpretable Machine Learning.

  • [49:50] How Christoph maximizes the impact of his time


Christoph’s LinkedIn: https://www.linkedin.com/in/christoph-molnar-63777189/
Christoph’s Website: https://christophm.github.io

Interpretable Machine Learning: https://christophm.github.io/interpretable-ml-book/


  • “Always look at the process when labeling data.”

  • “After each chapter of my book, I publish it and get feedback.”

  • “I randomly read a lot of papers and structure the knowledge to fit them together.”

  • “I express what I want easier with illustrations in my book.”

Christoph Molnar is based in Munich, Bavaria, Germany.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!

#64 Intersections of Analytics, AI, Linguistics and Culture with Prashant Natarajan – Principal, AI & Analytics

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Prashant Natarajan has 18+ years’ experience in building EMRs, ERP, big data platforms, actionable analytics, and machine/deep learning applications. Before joining Deloitte, he served in hands-on global consulting and product leadership roles at H2O.ai, Oracle, McKesson Payer Solutions, Healthways, and Siemens. Prashant is Co-Faculty Instructor of Data Science and AI at Stanford University School of Medicine, Palo Alto, CA, USA. He volunteers as an industry expert and guest lecturer at leading Australian universities. Prashant serves as an industry advisor at the CIAPM computer vision project in University of California San Francisco, Council for Affordable Health Coverage, and Pistoia Alliance Center for Excellence in Artificial Intelligence.  

In this episode, Prashant describes how essential human interaction is for success. In a technology-heavy space, human interaction and linguistics were not very common. Instead of complaining about it, Prashant went and got his masters to focus on English in the technology space. To have success, we need a clear understanding of culture. Culture is language, and language at its core is mathematics. How do we interact with people to figure out what their strengths are? Prashant considers himself the luckiest person on earth to have the experiences he has had in his career. 

Then, Prashant discusses how to identify business problems and integrate it with data science. Data analytics is as old as when humans started interacting with each other. By nature, all human beings are data analytics, consuming creatures. Historically, the application of computing data and analysis have been humans trying to define a problem, find data to solve the problem, and then writing algorithms that will create insights. Prashant is a massive admirer of the human mind and the human brain, which is far superior to any artificial machine. When it comes to AI, computing can do things that the human mind cannot. Business leaders must look at data science as a way to help them define business problems, rather than purely using deduction. Later, Prashant advises companies moving into data-driven products, explains horizontal capabilities, and the use of machine learning in healthcare. 

Enjoy the show!

We speak about:

  • [01:25] How Prashant started in the data space 

  • [03:45] Studying communications and linguistics  

  • [08:45] Mentoring young professionals  

  • [11:45] Work with people who are smarter than you  

  • [15:00] Merging business problems with data science  

  • [19:45] The value business leaders see in data  

  • [25:00] Advice for companies who are moving into data-driven products

  • [29:45] What excites Prashant about the future of data 

  • [34:05] Horizontal capabilities 

  • [37:20] The use of machine learning in healthcare  

  • [44:20] Improving product development 

  • [48:40] Prashant’s proudest moment

  • [50:15] The manufacturing industry 

  • [52:20] We learn more from our failures than our successes 


Prashant’s LinkedIn: https://www.linkedin.com/in/natarpr/

Demystifying Big Data and Machine Learning for Healthcare (Himss Book)


  • “Human interaction is the most key determiner of success or not.”

  • “Today, we have the technology that has caught up with the human need.”

  • “Data science is increasingly a horizontal capability that will impact all of us.”

  • “I celebrate relationships because they allow me to learn.”

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Thank you to our sponsors:

UNSW Master of Data Science Online: studyonline.unsw.edu.au

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Prashant Natarajan is based in Melbourne, Victoria, Australia.

And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Thank you so much for listening. Enjoy the show!