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The experts reflect on their career

In this article we hear from experts Terence Broad, Rebecca Fiebrink and Luba Elliott who have made AI a key part of their work.
Artists Rebecca Fiebrink, Terence Broad and Luba Elliott

Now that you have had the chance to look for jobs in AI, and thought about the skills required for these roles, we can hear from those who have made AI a key part of their work.

We spoke to artists Luba Elliott and Terence Broad, as well as artist and developer Rebecca Fiebrink. They reflected on their careers and shared their insights on the skills and knowledge required to make a successful career in AI. We’ve summed up some of their answers here.

How did you get involved in creative AI? What led you to explore these technologies in this way?

Terence Broad: I started this work on my Masters in Creative Computing and I am now continuing them on my PhD. Previous to my technical education I was an art school dropout. I left a degree in Sculpture because I was fascinated by the potential for computers to be used in image making and image processing and I decided I needed to learn programming to pursue that properly.

Rebecca Fiebrink: I think there’s a growing recognition that machine learning and artificial intelligence are really powerful tools. But I think it would be a shame if the only people who really got to make use of those tools were computer scientists, or people working at really big tech companies.

What are the skills you need to do your job, and work in Creative AI? How can someone get these skills?

Terence Broad: Patience! It can be incredibly frustrating getting these systems set up, and it can be difficult to debug when it goes wrong. A great deal of patience is required to keep going when things aren’t going well.

Rebecca Fiebrink: Technical work in computing often begins with programming. Programming certainly isn’t the only or most important thing to become good at computing, but it is really important and it’s a gateway to learning and practising other skills like machine learning, or signal processing.

What other advice do you have for our learners who’d like to do what you do?

Rebecca Fiebrink: I think with programming, the advice that I give to my students is find something that you care about, find something that excites you, and make a programme around that, whether that’s in the context of an academic, you know, study programme, or whether that’s a side project on your own, but you’ve got to care about what you’re working on as a programmer if you’re going to build up your skills.

Once you start programming, the other thing I tell my students is that programming is like learning a musical instrument. It’s like learning any other skill. You’ve got to just keep practising it. And it’s not gonna seem easy at first, but the more you do it, the better you get.

Luba Elliott: If you want to work in creative AI, then first of all I think it’s important to have a basic understanding of how the technology works. So I would certainly advise people to experiment a little with generating some text or images or playing around with facial image recognition. Just to understand how the technology works.

Terence Broad: Try and do things other people aren’t doing, and don’t get disheartened if you find out people have already done your ideas or you don’t think your work is as cool as other peoples. It takes time to find a niche but there is still a huge amount of potential for people coming in with fresh ideas and perspectives to contribute to the field.

Have you say

  • What combination of skills do you think have contributed to Rebecca, Luba and Terence’s success?
  • What do you think are the key differences in the approach taken by each person, and what can you learn from this?
Share your reflections with other learners in the Comments section.
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Introduction to Creative AI

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