Interview With The CEO of AIClub, Nisha Talagala

In the latest instalment of our interviews speaking to leaders throughout the world of tech, we’ve welcomed CEO of AIClub, Nisha Talagala to share her thoughts. Nisha has significant experience in introducing technologies like Artificial Intelligence to new learners.

Previously, Nisha co-founded ParallelM which pioneered the MLOps practice of managing Machine Learning in production for enterprises prior to their acquisition by DataRobot. Nisha is a recognized leader in the operational machine learning space, having also driven the USENIX Operational ML Conference, the first industry/academic conference on production AI/ML. Nisha was also previously a Fellow at SanDisk and Fellow/Lead Architect at Fusion-io, NVM Software Lead and Intel and CTO of Gear6.

Tell us about the business you represent, what is their vision & goals?

I am the CEO of AIClub, an EdTech company bringing AI Literacy to K-12 students and individuals worldwide. We believe that Artificial Intelligence and related technologies are going to fundamentally change every aspect of our work and daily life and that AI Literacy will be a requirement for many jobs moving forward.

We provide learning programs, curriculums, content and tools for students to learn AI, for schools to teach AI and for practitioners to develop AI competencies.

Can you tell us a bit more about ParallelM?

ParallelM was a startup company focused on enterprise production machine learning. ParallelM was a pioneer in production ML and originally defined MLOps for the enterprise, including creating MLOps Center, the first enterprise platform for Production ML. ParallelM was acquired by DataRobot and became the foundation of the DataRobot MLOps Platform.

Can you share a little bit about yourself and how you got into the field of artificial intelligence?

I have a PhD in Computer Science from UC Berkeley where I did research on distributed systems. I have always been fond of AI. During college, I used to experiment with Neural Networks in my spare time. About seven years ago I decided to focus on AI full time, starting with ParallelM and now with AIClub.

How would you describe your experience with driving the USENIX Operational ML Conference?

It was a great experience. USENIX is a great organization to work with and the conference attracted a combination of researchers both academic and industrial, as well as practitioners from companies like Facebook, Google and LinkedIn who have a great deal of experience with the challenges and requirements of production ML at scale.

What do your day to day responsibilities look like at your organisation?

Since the pandemic, our entire team has been working remotely. My day to day responsibilities are a mix of different areas. I spend as much time as I can with our users and customers, which can include students, teachers, parents and school administrators.

I review product roadmaps and user experiences and work with both engineering and marketing. I am very hands-on and use our products myself every day, although I do not have time to write code!

What are the key differences between computer science, machine learning and AI?

Computer Science is a broad field encompassing every area of computation, whether it is computer hardware, software, applications, algorithms, or others. Artificial intelligence has classically been a subset of computer science, although as AI moves more into the mainstream, there are aspects of AI, such as ethics, that cross over into other fields.

AI itself is an umbrella term that tends to cover any technology that enables computers to mimic human brain functions- such as vision, language understanding, reasoning, and so on. Machine Learning is a subset of AI that focuses on techniques that machines can use to learn patterns and extract insights from data.

What are some misconceptions that you believe the average person has about AI?

Because of all the noise and buzz around AI, people can get two misconceptions, one that AI is magical and all-knowing, and the other that AI is dangerous. Neither is true. AI is a technology that in itself is neither magical nor dangerous. Like all technologies, their benefits and dangers come from how it is used or misused.

This is also why we at AIClub believe that AI Literacy is crucial for our future. AI now intersects with the lives of everyday people, who need some understanding of it to navigate the technology safely and effectively.

What advice would you give to someone wishing to start their career in artificial intelligence?

AI as a topic is quite easy to learn, but the hype and noise can make it difficult to determine what to learn and what resources to use. At AIClub, we routinely have Middle School students who build their own AIs and their own AI-powered projects, who have demonstrated that AI is indeed accessible to learners.

Can you tell us about some of your favourite technology patents that our audience might be unaware of?

I don't really have a favourite patent. There is one recent patent that I have found fascinating, and indicative of where we are headed with AI. Amazon filed a patent for a CAPTCHA test where, unlike other tests, humans are detected by failing the test and not bypassing it! I have always liked this example because it demonstrates how far machine intelligence has progressed.

Another recent patent-related news item - several countries have recognized AI as a patent author, although other countries have rejected this proposal. Another interesting intersection of technology and the intellectual property process.

Would you like to share any artificial intelligence forecasts or predictions of your own with our readers?

My projections for AI-related trends 2022 includes data marketplaces, AI in the metaverse, the AI-enabled practitioner, everything personalized, new education standards, home robots, and augmented creativity.

__What is your experience of using AI-backed data analysis or log management tools? __

I think this would be very helpful. We have developed mostly homegrown tools but managing logs and getting useful information from them is valuable for a company like ours. AI can be used well for this purpose.

Particularly for companies like us that provide a SaaS offering that integrates many cloud tools, parsing logs manually is painful. AI-backed log analysis can help us find issues that did not manifest as customer escalations, track trends, and ideally forecast issues.

If you enjoyed this article then why not check out our previous interview with Charles Denyer or our guide on how to export to CSV from Kibana?