As Global Practice Lead for AI in Google Cloud Professional Services, Valentine Fontama spends his days working with powerful leaders across industries to help transform their businesses using AI. Learn about his journey from being a young boy in Cameroon where he encountered computers only a few times to his PhD and eventual career in AI.
We interviewed Valentine as part of AI4ALL’s Role Models in AI series, where we feature the perspectives of people working in AI in a variety of ways.
As told to Nicole Halmi of AI4ALL by Valentine Fontama; edited by Panchami Bhat
NH: Can you describe what you do as the Global Practice Lead for AI in Google Professional Services?
VF: My job involves leading AI strategy for Google Cloud Professional Services. I work with leadership to define the right strategy to bring AI to more organizations. Our product goal is to democratize AI, and my job is finding the best way to do that, particularly from a services standpoint.
As a consultant, I get to work with some of Google Cloud’s largest customers around the world. I really consider that a privilege, because we have so many important customers who are very interested in using the power of AI to transform their products or their businesses.
How did you get interested in this work?
I first encountered AI and machine learning in the early 90s when I started my PhD in the UK. I was looking for a scholarship, and I ran into a research assistant job which involved applying AI to mechanical engineering. I used AI and computer vision to solve some pretty hard problems in mechanical engineering, specifically in fluid dynamics and heat transfer. This research exposed me to the power of AI and machine learning algorithms, which really opened my mind.
You have degrees in math, computer science, AI, and management. Did you always know that you wanted to pursue those subjects? If not, was there a turning point for you?
In high school, I loved math and physics, and I excelled at both. At the time I actually thought I would do two PhDs in math and physics, because I wanted to become a professor of math and physics.
I did all my schooling through high school in Cameroon, West Africa, where we had very limited exposure to computers. I only saw computers a few times before graduating high school. I was first introduced to computer science in college in the UK, where I learned programming languages like BASIC, Pascal, and assembly language. I learned about AI in grad school, where my research topic was to investigate whether AI methods could be used to solve two well-known problems in mechanical engineering. Through my research, I found that neural networks were very effective, so that became the topic of my dissertation.
After my PhD, I had the opportunity to apply AI in many domains. As a post-doctoral research fellow at Staffordshire University in the UK, I applied AI to solve specific problems in environmental science. In the late 90s-early 2000s, I got the chance to work with Equifax at their European headquarters in London, and there we pioneered the application of AI and machine learning to the banking industry. We were in a small team of advanced analysts, and we consulted with most of the top High Street banks in the UK. We applied machine learning very early on to many of the problems they have in risk management and marketing.
How has the conversation around AI changed since you finished your PhD in the mid-90s?
AI and machine learning are now much more mainstream than they were back in the 90s. Back then, it was really still in R&D. Today, people are much more aware of what AI can do because they use it and see it in everyday products like Google Home and recommended videos on YouTube.
With increased awareness comes increased scrutiny. Members of the public are starting to raise concerns about the potential effects of AI.
I’m glad that awareness and everyday uses of AI have started to stimulate healthy conversation about important aspects of this technology.
These discussions raise very important questions about the business opportunities, of course, but they also raise important social questions that should be addressed. For example, how do we prevent AI models from inadvertently discriminating against minority groups. Google takes these issues very seriously and is taking specific steps in addressing them. For example, researchers at Google Brain are actively researching fairness in machine learning. Google is also a member of the industry group, Partnership on AI and internally trains its developers on fairness in machine learning.
What are some of the important things people should be doing to create a positive future for AI?
AI is a very powerful technology, and it has a lot of potential. AI is not a silver bullet, though. Like other transformational technologies before it, we need to think deliberately about how we build it.
If we’re not intentional in the way we collect data and the way we train these algorithms, we’ll end up building algorithms that reflect societal biases.
One thing we can do to set a better foundation for the future is to be more thoughtful about how we build AI models. AI algorithms rely on data; these algorithms will learn whatever is in the data.
What has been the proudest or most exciting moment in your work so far?
I worked on a pioneering AI and IoT application for one of the largest global elevator manufacturers. There, we used AI to predict if an elevator will fail before it did. It’s basically the area of predictive maintenance. This implementation made headlines around the world and has become a poster child for predictive maintenance. I feel very proud of that work.
Who were your role models growing up?
I’ve been really blessed with many good role models. The first one is Dr. Timah, who was my physics teacher in high school. He instilled a good love of physics in me.
The second one is my uncle, Dr. Augustine Kinni who is a real academic at heart. He studied prolifically and earned so many degrees — he’s a hard act to follow! He has a PhD in law and economics from Sorbonne University in Paris, he has several master’s degrees in international relations, sociology, and anthropology. He instilled in me that deep thirst for learning.
Third would be my friend and mentor, Professor Sama Nwana who’s a real AI pioneer. He did his PhD in robotics 7–10 years before me. It was very inspiring to see. Last, and by no means least, would be my friend and brother Dr. Teddy Ngu, a seasoned finance executive who is also making his mark in business. He is one of the people who inspired me to go to business school.
What advice do you have for young people who are interested in AI?
I’d say start small. There are so many different AI tools and public websites you can go learn something about AI, even at a very basic level. One good website I’d recommend is Experiments with Google. It has many fun applications of AI to learn what you can do with AI. You can play with an app that recognizes doodles. There’s an app you can use to see how you can train an AI model with your own images in a short period of time.
The other advice I have is to be curious. There are a lot of opportunities for improvement in AI.
AI is powerful and it’s solving many challenges today but it’s a constantly evolving area. I truly believe we are at the start of the new phase in AI. Many young people today have the opportunity to really make their mark in this field.
About Valentine Fontama
Valentine Fontama is the Global Practice Lead for AI at Google Cloud Professional Services, where he advises strategic customers around the world. Prior to this he was a principal data scientist manager on Microsoft’s AI Data Science team in Azure. Valentine was also a new technology consultant at Equifax in London, where he pioneered the use of data mining to improve risk assessment and marketing in the consumer credit industry; principal data scientist in the Data & Decision Sciences Group (DDSG) at Microsoft, where he led consulting to key customers, including ThyssenKrupp and Dell; and a senior product manager for big data and predictive analytics in cloud and enterprise marketing at Microsoft, where he was the first product manager for Azure Machine Learning, HDInsight, Parallel Data Warehouse (Microsoft’s first ever data warehouse appliance).
He has published 11 academic papers and co-authored three books on big data: Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes (2 editions) and Introducing Microsoft Azure HDInsight.
Valentine holds a PhD in Artificial Intelligence, an MSc. in computing, an MBA in strategic management and marketing from the Wharton School, and a BSc. (First Class Honors) in mathematics and electronics.
Follow along with AI4ALL’s Role Models in AI series on Twitter and Facebook at #rolemodelsinAI. We’ll be publishing a new interview with an AI expert on Wednesdays this winter. The experts we feature are working in AI in a variety of roles and have taken a variety of paths to get there. They bring to life the importance of including a diversity of voices in the development and use of AI.