The “Machine Learning Podcast with Jay Shah” now has nearly 4,000 subscribers on YouTube, high numbers on Spotify and Apple Podcasts, and overall has more than 150,000 downloads.
“I’ve learned that the quality of the content you provide and the delivery of the information is something that can set you apart in the business of podcasts,” Shah says.
“I am not a professional podcaster, but at least in the machine learning and deep learning space, I am in a top category. I think I have matured to the point where I can promise that if you are interested in AI or machine learning, you will find something useful in my podcast,” he says.
Shah can back up that claim with some of the attention the podcast has drawn. Earlier this year, it was featured in the “5 Best Machine Learning & AI Podcasts” by the Unite.AI website. Welp Magazine selected it as one of the “20 Best Machine Learning Podcasts of 2021.”
A former technical director for the Fulton Schools Artificial Intelligence Club, Shah was also chosen as an IEEE Impact Creator by the Institute of Electrical and Electronics Engineers and has been interviewed on the “IEEE Spectrum” podcast and the “CurryUp Leadership Podcast.”
Samarth Parikh is among avid longtime followers of the podcast. He is Shah’s former classmate at the Dhirubhai Ambani Institute of Information and Communication Technology in India, where they each earned a bachelor’s degree in information and communication technology.
Parikh, who now works for Oracle Cloud Infrastructure, says Shah does a good job keeping his audience up to date on AI and machine learning trends. But the podcast is most valuable for how it is effectively educating the public and young students about the important contributions these technologies can make to society, he says.
A lot of the talk in these fields can get highly technical and esoteric. So I think how Jay is able to describe things in ways that most people can grasp is one of his biggest achievements,” Parikh says.
“I tell engineers all the time that they can do amazing things, but they won’t get the credit they deserve for it if they can’t explain it to people who are not engineers,” he says. “Jay is showing how to do that.”
Shah has so far received more than 100 messages through LinkedIn and Twitter from students and engineers who say his podcasts have provided information and insights that have aided their endeavours.
Neelanshi Varia, an AI consultant for Deloitte, a major business consulting company, says Shah’s podcast “is unique in that the content spreads across a wide spectrum of what is happening in AI in academia and industry, all communicated in a way that is understandable and relevant.”
His podcast “covers everything from tips for students to where the AI industry is going and where the next innovation is happening,” says Varia, who develops AI solutions for companies in the life sciences, health care, retail and finance industries.
“Given the infancy of our field, it is difficult to get answers online or to contact experts. But Jay’s podcast is like an encyclopedia for finding out about the latest machine learning research, development and applications,” she says. “He also incorporates his viewers’ feedback, questions and suggestions, which is very valuable for listeners.”
Promising outlook for AI, machine learning
Shah’s achievements extend beyond enriching the dialogue in his fields of expertise. He is also helping to enhance the performance and multiply the applications of AI and machine learning technologies.
A major focus of his research is the use of AI and deep learning in medical applications. Specifically, it involves exploring uses of medical imaging for the discovery of biomarkers for post-traumatic headache and Alzheimer’s disease in collaboration with experts at the ASU-Mayo Center for Innovative Imaging, the Banner Alzheimer’s Institute and the Barrow Neurological Institute in Arizona.
His AI-based work on these projects is overseen by Professor Teresa Wu, co-interim director of the School of Computing and Augmented Intelligence, and Professor Baoxin Li, a faculty member in the school’s computer science and engineering program and co-director of ASU. -Mayo Center for Innovative Imaging. Both are also Shah’s doctoral studies co-advisers.
Wu describes Shah as creative, motivated, an eager learner and a hard-working and committed researcher. All of those positive traits, along with his desire to be of service to fellow students and professional colleagues, are reflected by the growing success of his podcast, Wu says.
She sees his use of social media to reach out and connect to AI and machine learning communities as “a great idea” that can help improve education and spark innovation. Li views Shah’s podcast as exceptionally valuable for motivating other students’ interest in AI.
While completing studies and research for his doctoral degree and keeping the podcast going, Shah is also working at Amazon as a research scientist intern. He’s helping develop new computer-based systems to improve health and fitness.
Shah foresees growing opportunities in a wide range of industries, scientific pursuits and engineering applications for those with expertise in AI, machine learning, deep learning and computer vision. Major corporations, governments and academic institutions are already heavily investing in those technologies, he says.
“That means everyone is going to be generating more and more data, and they’ll need experts in using that data as the basis for guiding a lot of big decisions,” Shah says. “So interest in these fields is not going to stop.”
Likewise, interest in podcasts exploring these fields seems certain to be on the upswing for years to come.