Jay Shah is pictured in his home podcast studio.
The “Machine Learning Podcast with Jay Shah” now has nearly 4,000 subscribers on YouTube, high numbers on Spotify and Apple Podcasts, and over 150,000 total downloads.
“I’ve learned that the quality of the content you deliver and the delivery of the information is something that sets you apart in the podcast industry,” Shah says.
“I am not a professional podcaster, but in the field of machine learning and deep learning I belong to a top category. I think I’ve matured so much that I can promise that if you’re interested in AI or machine learning, you’ll find something useful in my podcast,” he says.
Shah can back up that claim with some of the attention the podcast has garnered. Earlier this year it was featured in the “5 Best Machine Learning and 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 of the Fulton Schools Artificial Intelligence ClubShah was also chosen as a IEEE Impact Creator By the Institute of Electrical and Electronic Engineers and was interviewed on the “IEEE Spectrum” Podcast and the CurryUp Leadership Podcast†
Samarth Parikh is one of the ardent 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 obtained a bachelor’s degree in information and communication technology.
Parikh, who now works for Oracle Cloud Infrastructure, Shah says he keeps his audience well informed about trends in AI and machine learning. But the podcast is most valuable for how it effectively educates the public and young students about the important contributions these technologies can make to society, he says.
“A lot of the talk in these areas can get very technical and esoteric. So I think Jay being able to describe things in a way that most people can understand is one of his greatest achievements,” says Parikh.
“I always tell engineers that they can do great things, but they won’t get the credit they deserve if they can’t explain it to people who aren’t engineers,” he says. “Jay shows you how to do that.”
Shah has so far received more than 100 messages via LinkedIn and Twitter from students and engineers who say his podcasts have provided information and insights that have helped their efforts.
Neelanshi Varia, an AI consultant for Deloittea major business consulting firm, says Shah’s podcast is “unique in that its content spans a broad spectrum of what’s happening in AI in academia and industry, all communicated in a way that’s understandable and relevant. ”
His podcast “covers everything from tips for students to where the AI industry is going and where the next innovation is,” said Varia, who develops AI solutions for companies in the life sciences, healthcare, retail and financial sectors.
“Given the infancy of our field, it is difficult to get answers online or contact experts. But Jay’s podcast is like an encyclopedia for learning about the latest machine learning research, developments and applications,” she says. “He also processes the feedback, questions and suggestions from his viewers, which is very valuable to listeners.”
Promising prospects for AI, machine learning
Shah’s achievements go beyond enriching the dialogue in his field. It also helps improve performance and multiply the applications of AI and machine learning technologies.
An important focus of his research is the use of AI and deep learning in medical applications. In particular, it will explore the use of medical imaging for the discovery of the biomarkers for: post-traumatic headache and Alzheimer’s disease in collaboration with experts from the ASU-Mayo Center for Innovative Imagingthe Banner Alzheimer Institute and the Barrow Neurological Institute in Arizona.
To be AI-based work on these projects is supervised by professor Teresa Wueco-interim director of the School of Computing and Augmented Intelligence, and professor Baoxin Lic, 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 co-advisors on Shah’s doctoral studies.
Wu describes Shah as creative, motivated, eager to learn and a hardworking and dedicated researcher. All of those positive qualities, along with his desire to serve fellow students and peers, are reflected in the growing success of his podcast, Wu says.
She considers his use of social media to connect with AI and machine learning communities as “a great idea” that can help improve education and fuel innovation. Li considers Shah’s podcast extremely valuable in motivating other students’ interest in AI.
While completing studies and research for his PhD and keeping the podcast going, Shah also updates Amazon as a trainee scientific researcher. He helps develop new computer-based systems to improve health and fitness.
Shah envisions growing opportunities in a wide range of industries, scientific pursuits and technical applications for those with expertise in AI, machine learning, deep learning and computer vision. Big companies, governments and academic institutions are already investing heavily in those technologies, he says.
“That means everyone is going to be generating more and more data and they need experts to use that data as the basis for many big decisions,” Shah said. “So the interest in these fields is not going to stop.”
Likewise, interest in podcasts exploring these areas certainly seems to be rising in the coming years.