Sruthi Pisipati - Why Engineering Foundations Still Matter in the Age of Generative AI

That's amazing.

That paints a great picture of sort of your trajectory through.

And I think a lot of us have taken a similar approach.

I think we're all learning AI as fast as we possibly can.

And the curve is going more and more of a hockey stick.

It seems like every month things are moving faster.

So to have someone say, I've been studying AI for the past 20 years.

It's like that can be useful for a foundation, but things are changing so fast, especially in the world of generative AI.

And so, you know, I want you to maybe talk us through a little bit because AI comes out of machine learning, deep learning.

And I would say generative AI comes out of that.

I consider all artificial intelligence in a lot of ways, but generative AI sort of walks this line of it's not deterministic.

It's a whole different paradigm.

People don't realize that versus some of the statistical analysis and stuff that you're doing around forecasting.

How do you distinguish both of them? How do you try and explain it to people? Maybe we can start there because I have about 100 other questions I want to talk to you about how AI has changed as you've seen in the past 10 years.

Maybe explain and how Gen AI maybe is different.

But I tend to talk to my colleagues or friends when they ask me about how do you see everything as AI? Everything is labeled under AI right now.

So What is generative AI? Is generative AI something which generates, which has the capability to generate? So what people see these days is chat GPT.

It generates text, so it is generative AI.

But when you just take a step back and look at the underlying technology which is running chat GPT or the cutting edge time series foundational models, The architecture is transformer.

Attention is all you need.

It's the basis of all these cutting-edge generative AI stuff, right? I know that the architecture is the same for when we talk about the generative AI capability.

It could be time series or NLP or text-based.

When I explain to someone, I just tell them that the architecture is the same, but the way it manifests or the way it portrays to you, it's different.

That's amazing.

That paints a great picture of sort of your trajectory through.

And I think a lot of us have taken a similar approach.

I think we're all learning AI as fast as we possibly can.

And the curve is going more and more of a hockey stick.

It seems like every month things are moving faster.

So to have someone say, I've been studying AI for the past 20 years.

It's like that can be useful for a foundation, but things are changing so fast, especially in the world of generative AI.

And so, you know, I want you to maybe talk us through a little bit because AI comes out of machine learning, deep learning.

And I would say generative AI comes out of that.

I consider all artificial intelligence in a lot of ways, but generative AI sort of walks this line of it's not deterministic.

It's a whole different paradigm.

People don't realize that versus some of the statistical analysis and stuff that you're doing around forecasting.

How do you distinguish both of them? How do you try and explain it to people? Maybe we can start there because I have about 100 other questions I want to talk to you about how AI has changed as you've seen in the past 10 years.

Maybe explain and how Gen AI maybe is different.

But I tend to talk to my colleagues or friends when they ask me about how do you see everything as AI? Everything is labeled under AI right now.

So What is generative AI? Is generative AI something which generates, which has the capability to generate? So what people see these days is chat GPT.

It generates text, so it is generative AI.

But when you just take a step back and look at the underlying technology which is running chat GPT or the cutting edge time series foundational models, The architecture is transformer.

Attention is all you need.

It's the basis of all these cutting-edge generative AI stuff, right? I know that the architecture is the same for when we talk about the generative AI capability.

It could be time series or NLP or text-based.

When I explain to someone, I just tell them that the architecture is the same, but the way it manifests or the way it portrays to you, it's different.