Engineering

And that's where what you alluded to earlier, you start to get at the end of this article, they talk about a twilight factory.

And this is like a compromise between, you know, the way of engineering yesterday is gone.

But the way that we're looking at, we're looking down this conveyor belt of like a factory.

And we're not totally bought in on all of the parts.

The Twilight factory is a compromise where agents are handling that work, but they're explicitly designed to know when to bring humans back in.

with the idea of preventing derailment like that, preventing deep misalignment, like what we just witnessed with OpenAI, but also just to be more productive, right? And so you get this compromise, but I just don't know if it's going to be perfect.

What comes to mind for you when you see engineering leaders use the factory analogy, especially with how much we've been covering it on the show?

I feel like the biggest takeaway that we've continually had, and in particular, the biggest takeaway from the great software factory debate and that whole round table was pretty much the unanimous opinion that humans had to be involved at some point in the process, which- Right.

Yeah.

Riker drops by Engineering just as Geordi and Data complete their analysis of the bombing.

It was a device no more than three cubic millimeters in size.

Tiny.

So Back Into The Code is a program that we've run at the Tech Council of Australia.

It's a free course, a one-day agentic engineering course.

It's for software engineers, but it's really for anyone who is interested in tech.

And it's specifically for people who have taken a career break.

And they were saying at the same time, this cohort of parents who are taking time out, they're our biggest concern.

We're so worried that they're going to leave the organization.

And Paul Keane, who's the VP of engineering at CultureAmp, one of Australia's amazing unicorns, he came to me and he said, I'm also seeing this problem and I want to do something about it.

So together we partnered on building this course and we launched the first one in August this year in Sydney.

Or it could also be possible that, you know, open models sort of fork into this more like long tail, hyper specialized niche applications that really are sort of distilled off of the frontier models.

All our listeners out there with the engineering leaders out there listening to us should be thinking about this is that there is a lot of opportunity starting to emerge in these open models that you can run locally or on your own hosted infrastructure.

And, you know, I think it really does like compliment, you know, NVIDIA on one hand, as I mentioned, has all these massive circular financing deals.

with these major companies.

Versus something that where you might be in more of an explore mode where you need to look for things that have big upsides and you're willing to invest enough to go all in on the topic, you know, sort of chase some sort of much larger payout.

And then there's also this sort of like expand mode where you might be looking to scale growth.

And add continuous value to some sort of engineering or operations investment.

And sort of the practical takeaway that he has from this article is that there's no one size fits all for the right amount of risk level that you should take at a management level.

But, yeah, I just wanted to put that out there.

But also, I don't know that I've ever had anybody explain this on here, and it feels like Engineering, mixing, and mastering are something that everybody thinks they understand what it is, but most people don't even think about them.

I'm sure you know.

Most people, when they listen to music, they don't tend to think about the process outside of the producer made the beat, somebody rapped or sang on it, people played instruments.

I'm Professor Arun Thangirala, a faculty with the Department of Chemical Engineering at IIT Tirupati.

and an adjunct faculty at Department of Data Science and AI at IIT Madras.

And I specialize in the areas of process systems engineering, control, identification, applied AI, and data science.

So this workshop essentially is all about going into some final aspects of nonlinear system identification that has been overlooked, but also it talks about results stemming from our research and also research from other groups which focus on the parameter estimates that stem from identifying data-driven models where the main message that we wanted to give is, look, while the mainstay of model development from data has been about developing models with good fits, but that alone is not sufficient.

That one has to zoom into other aspects, especially into directions of parameter spaces.

There has not been much discussion about this.

And this workshop, I believe, would eventually throw light into these aspects of sloppiness.

And we believe that this is useful for the community not only to control and also other engineering, including artificial intelligence.