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Listen "Dan Porder: From Poetry Teaching to Python Programming for AI – Episode 16"
Episode Synopsis
Dan Porder
A few years ago, Dan Porder was teaching poetry to university students. Now he's at IKEA training large language models to generate useful, usable content for user experiences.
He's picked up new skills along the way, like Python programming, but much of his work still relies on well-established content and design crafts like content strategy and inclusive design.
We talked about:
his role as a senior content designer at IKEA, where he focuses on AI
some of his early experiments in composing and evaluating poetry
his longstanding interest in AI and the development of his tech skills
how content designers can leverage their skills to work in AI
his perception that there is currently more opportunity than threat to content professionals in the AI world
the make-up of the cross-functional teams he works with: data scientists, engineers, developers, content people, designers, subject matter experts
how to brief and guide generative AI to get the outputs your users need
how writing abilities prepare content designers to do prompt engineering
the stack of data and technology that underlies AI and the orchestration mechanisms that connect them
some of the tools he uses in his AI design practice
the role of data in content design for generative AI
the importance of staying aware of bias in training data and always wearing your inclusive design hat
the role of explainability in AI ethics
the importance of knowing how to ask data scientists and engineers questions that reveal as much as possible the inner workings of the "black box" in which AI content is generated
his take on democratization opportunities that arise with the arrival of AI tech
Dan's bio
Dan Porder is a Senior Content Designer and Content Engineer at IKEA. His recent work focuses on the intersection of AI, structured knowledge, and experience design. Outside of work, he runs an international writing community.
Connect with Dan online
LinkedIn
Video
Here’s the video version of our conversation:
https://youtu.be/VFXLG4h6ylE
Podcast intro transcript
This is the Content and AI podcast, episode number 16. AI is quickly changing the way content designers work. New content duties are emerging that require fresh skills, but at the same time traditional skills like content strategy are becoming more important. In his work as a content designer at IKEA, Dan Porder has developed new skills, like Python programming, and has applied the writing skills he perfected as a poetry teacher as well as the inclusive design practices he developed earlier in his content design career.
Interview transcript
Larry:
Hey everyone. Welcome to episode number 16 of the Content and AI podcast. I'm really happy today to welcome to the show Dan Porder. Dan is a senior content designer at IKEA, where he's currently focusing on AI stuff, and his title is content designer, but he is really more of a content architect. So welcome to the show, Dan. Tell me a little bit more about your AI and content adventures.
Dan:
Hey Larry. Thanks for having me on. Yeah, maybe I could just start by giving a little bit of background. I think at heart, despite what I'm doing now, I think of myself as a writer, and that's been my life's focus since I was young. Writing poetry, writing fiction. I did my bachelor's in English literature and later did a masters, masters of fine arts, actually, in poetry. Some of it was more on a conceptual side, thinking of language as data. So there was some unusual experiments in the tech world even then for me. Using Google data to create poems. So imagining Google queries as a representation of the collective zeitgeist, and how can we leverage that data to create meaning in poetry? Or using NLP to find meaningful relationships in texts where you didn't know they were there. But all of that then led me into copywriting, so like brand copywriting, product copywriting, ads, copywriting as creative direction.
Dan:
And then eventually back to the Google data, so SEO copywriting and SEO strategy. And I focused for a while on optimization, research, data analysis for SEO, some technical SEO. And then, yeah, my recent journey has been more in the design world. Content design, content strategy, user experience design. And I'd always been interested in AI and the question was always, how do you do that as a job? Particularly from the position I was coming from as a former student and teacher of poetry and writing. Of course, when ChatGPT came out, like for many people, the connection became clear to me and I started incorporating it immediately into all my work.
Dan:
I realized that I also needed to brush up on my coding skills, and particularly get more invested in Python. And I took some courses specifically on generative AI and machine learning for that purpose, just to make sure I was prepared. But now I think I'm leaning more into the world of knowledge, thinking about the data that we need for AI. The data structures that create meaning for these systems to ingest or to retrieve or to do with what they need. And in the case of generative AI, this is content. This is a task that requires a content designer, content strategist. It's going to be primarily images, text, audio. So that's what I've been up to lately. And yeah, I'm excited to talk to you about it.
Larry:
Well, that's great. I got to say, it's hard to imagine anyone better prepared for this stuff, because to go from playing with Google and poetry stuff, the notion of vectorized word embeddings was just like, "Oh, cool, that's another way to do that." I can almost picture this evolution going pretty smoothly for you. But a lot of content people are not as technically curious as you are, or haven't had the same technical opportunities. And you have a lot of colleagues who are more like conventional content design kind of folks. Have you thought about how people who are less natively technically inclined can jump more into AI stuff?
Dan:
Yeah. I think it's about leaning on their expertise, especially abstracting that expertise. So for a content designer who maybe imagines themselves more as a UX writer or comes from a copywriting background, it's an understanding of information, of messaging, of what content works best for people in what scenarios. And that kind of knowledge, that's less of the craft side and more of the wisdom of content, is incredibly valuable to data scientists and to engineers working on AI.
Dan:
This is some of the expertise that's needed, is subject matter expertise, including on content. So generative AI consumes data and puts out data. That data is content. You need a content person to figure out what it will be, what the use case is, and what content you want these models to produce on the other end, either for a system or for an end user. So you're giving up a bit of control on the craft side, but on the strategic side you're actually, if you're willing to have those conversations with the technical people, you are asserting control in a way.
Larry:
Right. That's so interesting because, as you're saying that, I'm picturing... it's sort of like the way, a lot of these models, there's attempts to capture subject matter expertise and incorporating that in there. But you also need that subject matter expertise to train the models. To write the prompts, do all the other stuff as well. Can you talk a little bit about that relationship between... and this gets at people's concerns of AI replacing them, because if we capture all that subject matter expertise, then all of a sudden it's like, "Oh, we don't need content designers." I personally don't think that's coming, but what do you think about that idea?
Dan:
Yeah. People have talked a lot about this. I think some of the concerns are overblown. Of course there's a grain of truth in this. Theoretically, if we were to all give all of our best data, most of which is just in our minds as experts, so it doesn't exist in the right data format, but if we were and we were to train models that somehow are still usable and not unwieldy as a result of that, you would start to replace people.
Dan:
That's not what's going on right now. That's not the technical capabilities. Anyone who's using these tools or working with them can see that. And also just the actual process of properly curating the data and testing and iterating on methods of fine-tuning and reward functions, and getting the right feedback from the right experts. That's a lot of work. That's a lot of resources, even for small use cases. So I don't think that's the worry. I think it's more like an opportunity. This is an exciting opportunity to make your work more scalable faster. I think, especially from the content design perspective, also to be able to assert governance over content creation through the consistency of machines that doesn't necessarily exist in people always.
Larry:
Right. And what you just said, I realized that my question was sort of like I'm projecting the alarm that I feel in a lot of circles. But I think more often the answers are like what you just said. It's much more hopeful and optimistic in that, at every juncture, there's going to be more need for our expertise that will, probably not for the next couple of decades, anyway, be codified in machines. So that kind of leads me back to one of the things that wanted to talk about a little earlier, actually. It's just, done both conventional content design for regular, old digital products. And then now you're working more on the AI side. Can you talk a little bit about the evolution of the practice as you go from one realm to another?
Dan:
Yeah. Well, I think, as we were just talking about, one thing to notice is the importance of cross-functional teams. So having not just the tech people in there, the data scientists and engineers and developers, but also the content people,
A few years ago, Dan Porder was teaching poetry to university students. Now he's at IKEA training large language models to generate useful, usable content for user experiences.
He's picked up new skills along the way, like Python programming, but much of his work still relies on well-established content and design crafts like content strategy and inclusive design.
We talked about:
his role as a senior content designer at IKEA, where he focuses on AI
some of his early experiments in composing and evaluating poetry
his longstanding interest in AI and the development of his tech skills
how content designers can leverage their skills to work in AI
his perception that there is currently more opportunity than threat to content professionals in the AI world
the make-up of the cross-functional teams he works with: data scientists, engineers, developers, content people, designers, subject matter experts
how to brief and guide generative AI to get the outputs your users need
how writing abilities prepare content designers to do prompt engineering
the stack of data and technology that underlies AI and the orchestration mechanisms that connect them
some of the tools he uses in his AI design practice
the role of data in content design for generative AI
the importance of staying aware of bias in training data and always wearing your inclusive design hat
the role of explainability in AI ethics
the importance of knowing how to ask data scientists and engineers questions that reveal as much as possible the inner workings of the "black box" in which AI content is generated
his take on democratization opportunities that arise with the arrival of AI tech
Dan's bio
Dan Porder is a Senior Content Designer and Content Engineer at IKEA. His recent work focuses on the intersection of AI, structured knowledge, and experience design. Outside of work, he runs an international writing community.
Connect with Dan online
Video
Here’s the video version of our conversation:
https://youtu.be/VFXLG4h6ylE
Podcast intro transcript
This is the Content and AI podcast, episode number 16. AI is quickly changing the way content designers work. New content duties are emerging that require fresh skills, but at the same time traditional skills like content strategy are becoming more important. In his work as a content designer at IKEA, Dan Porder has developed new skills, like Python programming, and has applied the writing skills he perfected as a poetry teacher as well as the inclusive design practices he developed earlier in his content design career.
Interview transcript
Larry:
Hey everyone. Welcome to episode number 16 of the Content and AI podcast. I'm really happy today to welcome to the show Dan Porder. Dan is a senior content designer at IKEA, where he's currently focusing on AI stuff, and his title is content designer, but he is really more of a content architect. So welcome to the show, Dan. Tell me a little bit more about your AI and content adventures.
Dan:
Hey Larry. Thanks for having me on. Yeah, maybe I could just start by giving a little bit of background. I think at heart, despite what I'm doing now, I think of myself as a writer, and that's been my life's focus since I was young. Writing poetry, writing fiction. I did my bachelor's in English literature and later did a masters, masters of fine arts, actually, in poetry. Some of it was more on a conceptual side, thinking of language as data. So there was some unusual experiments in the tech world even then for me. Using Google data to create poems. So imagining Google queries as a representation of the collective zeitgeist, and how can we leverage that data to create meaning in poetry? Or using NLP to find meaningful relationships in texts where you didn't know they were there. But all of that then led me into copywriting, so like brand copywriting, product copywriting, ads, copywriting as creative direction.
Dan:
And then eventually back to the Google data, so SEO copywriting and SEO strategy. And I focused for a while on optimization, research, data analysis for SEO, some technical SEO. And then, yeah, my recent journey has been more in the design world. Content design, content strategy, user experience design. And I'd always been interested in AI and the question was always, how do you do that as a job? Particularly from the position I was coming from as a former student and teacher of poetry and writing. Of course, when ChatGPT came out, like for many people, the connection became clear to me and I started incorporating it immediately into all my work.
Dan:
I realized that I also needed to brush up on my coding skills, and particularly get more invested in Python. And I took some courses specifically on generative AI and machine learning for that purpose, just to make sure I was prepared. But now I think I'm leaning more into the world of knowledge, thinking about the data that we need for AI. The data structures that create meaning for these systems to ingest or to retrieve or to do with what they need. And in the case of generative AI, this is content. This is a task that requires a content designer, content strategist. It's going to be primarily images, text, audio. So that's what I've been up to lately. And yeah, I'm excited to talk to you about it.
Larry:
Well, that's great. I got to say, it's hard to imagine anyone better prepared for this stuff, because to go from playing with Google and poetry stuff, the notion of vectorized word embeddings was just like, "Oh, cool, that's another way to do that." I can almost picture this evolution going pretty smoothly for you. But a lot of content people are not as technically curious as you are, or haven't had the same technical opportunities. And you have a lot of colleagues who are more like conventional content design kind of folks. Have you thought about how people who are less natively technically inclined can jump more into AI stuff?
Dan:
Yeah. I think it's about leaning on their expertise, especially abstracting that expertise. So for a content designer who maybe imagines themselves more as a UX writer or comes from a copywriting background, it's an understanding of information, of messaging, of what content works best for people in what scenarios. And that kind of knowledge, that's less of the craft side and more of the wisdom of content, is incredibly valuable to data scientists and to engineers working on AI.
Dan:
This is some of the expertise that's needed, is subject matter expertise, including on content. So generative AI consumes data and puts out data. That data is content. You need a content person to figure out what it will be, what the use case is, and what content you want these models to produce on the other end, either for a system or for an end user. So you're giving up a bit of control on the craft side, but on the strategic side you're actually, if you're willing to have those conversations with the technical people, you are asserting control in a way.
Larry:
Right. That's so interesting because, as you're saying that, I'm picturing... it's sort of like the way, a lot of these models, there's attempts to capture subject matter expertise and incorporating that in there. But you also need that subject matter expertise to train the models. To write the prompts, do all the other stuff as well. Can you talk a little bit about that relationship between... and this gets at people's concerns of AI replacing them, because if we capture all that subject matter expertise, then all of a sudden it's like, "Oh, we don't need content designers." I personally don't think that's coming, but what do you think about that idea?
Dan:
Yeah. People have talked a lot about this. I think some of the concerns are overblown. Of course there's a grain of truth in this. Theoretically, if we were to all give all of our best data, most of which is just in our minds as experts, so it doesn't exist in the right data format, but if we were and we were to train models that somehow are still usable and not unwieldy as a result of that, you would start to replace people.
Dan:
That's not what's going on right now. That's not the technical capabilities. Anyone who's using these tools or working with them can see that. And also just the actual process of properly curating the data and testing and iterating on methods of fine-tuning and reward functions, and getting the right feedback from the right experts. That's a lot of work. That's a lot of resources, even for small use cases. So I don't think that's the worry. I think it's more like an opportunity. This is an exciting opportunity to make your work more scalable faster. I think, especially from the content design perspective, also to be able to assert governance over content creation through the consistency of machines that doesn't necessarily exist in people always.
Larry:
Right. And what you just said, I realized that my question was sort of like I'm projecting the alarm that I feel in a lot of circles. But I think more often the answers are like what you just said. It's much more hopeful and optimistic in that, at every juncture, there's going to be more need for our expertise that will, probably not for the next couple of decades, anyway, be codified in machines. So that kind of leads me back to one of the things that wanted to talk about a little earlier, actually. It's just, done both conventional content design for regular, old digital products. And then now you're working more on the AI side. Can you talk a little bit about the evolution of the practice as you go from one realm to another?
Dan:
Yeah. Well, I think, as we were just talking about, one thing to notice is the importance of cross-functional teams. So having not just the tech people in there, the data scientists and engineers and developers, but also the content people,
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