#355 From Mars to Data Centers: AI that Prevents Cloud Outages.

**Helen Gu** (0:00)
And then, fast track to today, basically, that is about like 10 years, we have been building this product to help a customer to, how to detect those IT outages, and then even like predict and fix them automatically using AI technology.

**Darren Pulsipher** (0:21)
Welcome to Embracing Digital Transformation, where we explore how people process policy and technology drive effective change. This is Dr. Darren, Chief Enterprise Architect, Educator, Author, and most importantly, your host. On this episode, from Mars to the data center, AI that prevents cloud outages with Dr. Helen Gu, Professor at North Carolina State University, and Founder and CEO at InsightFinder.
Helen, welcome to the show.

**Helen Gu** (0:55)
Thank you for inviting me.

**Darren Pulsipher** (0:58)
I'm really interested about this topic because so far in the AI realm and AI agent realm, it's been kind of the Wild West and I haven't seen really good software best practices applied to it like we do in other systems. But you got something unique that we're going to talk about. But before we do, everyone that listens to my show knows that I only have superheroes on the show, and every superhero has a background story, an origin story.
So Helen, what's your background story? What's your origin story? Where does Helen come from?

**Helen Gu** (1:33)
Yeah. So the story go back to almost 30 years ago. So I started actually doing research on how to enable robust like distributed systems using AI technology almost 30 years ago. Can you believe it?

**Darren Pulsipher** (1:49)
Wait, wait, wait.
30 years ago? You can't do AI 30 years ago. The AI was created by OpenAI, right?

**Helen Gu** (1:57)
No.
So OpenAI actually leverage technology like neural networks, right? But neural networks was invented almost like a decade ago, and so like several decades ago. And so the idea of using neural networks to actually extracting patterns and perform predictions and neural classifications is actually, you know, long like established the research topic. So about 30 years ago, talking about my starting point is that I was trying to actually enable like a robust video streaming from Mars to Earth, because my research was founded by NASA, PICE funding project. And so the idea is that you send the rover, right? So believe it or not, there is a manned vehicle.
We send to Mars, and then basically the Mars will collect the scenes on the, the rover will collect the scenes on the Mars, and then we'll send the video back to the Earth. And so my research is to enable this video streaming is reliable, right? And so you can imagine there's all kinds of like potential issues, like, you know, from Mars to Earth, right? Like, you know, glitches or, you know, hardware failures or delays, et cetera. So my research is to use neural networks to actually predict the resource, the usage based on the video content. And so at that time, I built basically a three-tier, 13-neuron network, and it was maybe about like 10 or so parameters. And so that's basically the starting point, right? So fast forward today, and everybody knows ChaiGBT, and they know this is AI. But so my research is about, like, how to use AI technology to make our system more stable, and you can actually enjoy, you know, all the IT services, like, you can enjoy your movies, right, on the Internet smoothly. And so that's basically my research. And fast forward today, like, you know, along the way, I got my PhD from University, you know, Urbana-Champaign, and then after that, I joined IBM Research as a researcher.
So I joined a very interesting project, and so that was actually around 2004 So I started my, like, career being a researcher, study, like, how to enable, like, robust real-time data streaming processing.
So that was actually way before, you know, today you probably heard about, like, Spark or, you know, Hadoop. And so even, like, before, you know, that there was a thing on virtualization or cloud, right? So at that time, IBM trying to build this robust distributed streaming infrastructure, they can actually process video data, audio data, text data in real-time and provide the insights, right? So we detected, for example, fraud transactions. So my research is to work with those AI experts and to make sure those AI models can run smoothly with the right infrastructure support. So starting there, basically, I realized is that there's a lot of AI technologies are invented to analyze text data, like video data or image data. But the very few technology were invented to actually analyze very noisy like machine data, for example, system logs, application logs, telemetry data.