Tag: ai

[00:00:00] Nathan Wrigley: Welcome to the Jukebox podcast from WP Tavern. My name is Nathan Wrigley.

Jukebox is a podcast which is dedicated to all things WordPress. The people, the events, the plugins, the blocks, the themes, and in this case how AI works and how it might integrate with WordPress.

If you’d like to subscribe to the podcast, you can do that by searching for WP Tavern in your podcast player of choice, or by going to WPTavern.com forward slash feed forward slash podcast. And you can copy that URL into most podcast players.

If you have a topic that you’d like us to feature on the podcast, I’m keen to hear from you and hopefully get you, or your idea featured on the show. Head to WPTavern.com forward slash contact forward slash jukebox, and use the form there.

Before we begin, just a quick alert that there will not be a podcast next week. It’s summer here and I’m having a few days away, but we’ll be back the week after that.

So on the podcast today we have Dan Walmsley. Dan is a long time user of WordPress, having started using it even before version one was released. With a passion for experimenting with different publishing technologies, Dan eventually discovered WordPress and he’s been using it ever since.

Currently working at Automattic as a code Wrangler, dan is part of the applied AI team. Although the team is relatively new, with only a few members, their mission is to coordinate and guide the various AI initiatives within the company.

Recently he’s been focusing on automating internal workflows and communications. A particularly crucial aspect, given the distributed work set up which spans 70 countries, and multiple time zones.

We start the conversation talking about Dan’s background. He’s recently decided that AI is a truly transformational technology, and so has taken steps to learn the skills needed to understand and implement it.

Dan talks about how Large Language Models work, and how ChatGPT has driven awareness and demand for AI technologies in a way that was almost impossible to predict just a year ago. This has caused many companies to become deeply interested in AI and what it can do for their business workflows.

We get into whether the reality of AI can live up to the hype. Do we have enough understanding of AI to know what its impact will be on the workplace, or are we just in the middle of a media frenzy, which will die down over time?

Dan challenges, the notion that AI will take many of our jobs and emphasizes the economic value that AI can bring.

We move on to explore the differences between site generators and site builders, and Dan introduces the concept of the copilot era, in which website creation can be somewhat automated. He highlights tools like Jetpack AI, which can generate content and modify the tone of voice right inside of WordPress.

Dan stresses the importance of building AI tools with user interfaces that learn from human inputs in order to improve over time. He thinks that companies, which measure user responses and interactions will gain a significant advantage in AI development. While those who fail to improve that AI content generation will be left behind.

Whether you’re new to AI or have been paying attention for awhile, this podcast offers a fascinating insight into its impact on society and how it can accelerate progress in fields like scientific research.

If you’re interested in finding out more, you can find all of the links in the show notes by heading to WPTavern.com forward slash podcast, where find all the episodes as well.

And so without further delay, I bring you Dan Walmsley.

I am joined on the podcast today by Dan Walmsley. Hello, Dan.

[00:04:35] Dan Walmsley: Hello Nathan. Great to be here.

[00:04:37] Nathan Wrigley: Yeah. Thank you for joining us. Dan, I wonder if you wouldn’t mind spending just a very quick moment or two just introducing yourself. Obviously, this is a WordPress podcast. I suspect that today we might stray out of the boundaries of the WordPress ecosystem a little bit. I have a feeling with our preamble talk that we’ve had, that may well happen. Nevertheless, given that it is a WordPress podcast, can you just tell us a little bit about your background, the work that you do, who you work for, that kind of thing.

[00:05:01] Dan Walmsley: Yes. So I have been using WordPress since before version one, or whenever the first version came out. Because I remember back at the time I was playing around a lot with Movable Type and, oh gosh, I can’t even remember the name of all the different things. I’d gone through quite a few different publishing platforms, just experimenting with the web. And I discovered WordPress and I’ve literally still got that same blog, and it’s still on WordPress, and it’s been upgraded through every different version ever since.

I work at Automattic. I am on the Applied AI team. I am a Code Wrangler, or code mangler. We all give ourselves our own titles and mine changes a bit. My colleague calls himself an applied AI artisan. And we’ re a pretty new team. We’ve been around just a couple of months. And we’re very small, as in right now it’s just me and a couple of data scientists. But we have a lot of AI at Automattic. Our team’s job is to sort of try to coalesce, coordinate, guide, align it. So that we’re not just operating at the leaf nodes, that there’s a bit of larger thinking going into things.

And as such, my days are mostly spent building weird prototypes on LangChain and chatbots. The most interesting thing I’ve looked at recently is automating some of our internal workflows and communications. Because we operate async, we’re remote. We’re in 70 odd countries around the world in different time zones. And so using AI to capture people’s knowledge and repeat it later when they’re asleep is pretty useful.

[00:06:25] Nathan Wrigley: When the word Automattic is announced, I usually think of WordPress, but I think I’m right in saying that Automattic is the parent of quite a few different companies. So the connection between WordPress, the open source project, download from .org, may not be quite so obvious. But the implementation, it may well go into some of the SaaS offerings that you’ve got I’m guessing as well.

[00:06:48] Dan Walmsley: Yeah, so we are trying to build out AI infrastructure that really doesn’t have a direct dependency on WordPress. You know, GPUs are GPUs, and we’re running a Python based stack on those, because that’s where a lot of the open source activity is. You might have seen that OpenAI announced some changes to their APIs, and in just a few hours, LangChain had a new release, incorporating those features.

Good luck even finding that in TypeScript, let alone PHP, right? So if you want to move fast, you want to be on the cutting edge, got to stand up a bunch of Python. I’ve built a version of LangChain in PHP that runs on WordPress.com for the purposes of producing knowledge bases from blogs. It’s possible that if it turns out to be useful and reliable, that we’ll open source some of that. But right now it’s just there to provide some quick indexing for chat interfaces.

[00:07:34] Nathan Wrigley: So your team is fairly new. Give us an idea of how old that word new means. Are we going back two years or 18 months or a couple of months?

[00:07:43] Dan Walmsley: Two months maybe?

[00:07:44] Nathan Wrigley: Really, new. Okay. And did that sort of trickle down from the Automattic leadership? Was it that people up there decided that, okay, now we’ve got OpenAI in the space, everybody’s, I mean, literally everybody seems to be talking about it.

I don’t think I’ve picked up a newspaper, certainly an online newspaper, in the recent past without there being some kind of AI story in there. So was it that, or was it more a groundswell of Automatticians saying, look, if we’re going to stay in the game, we need to be moving with this.

[00:08:11] Dan Walmsley: There’s some people who have been pushing on LLMs and transformer technology since pre GPT three or two. Which includes me. When I had my sabbatical a couple of years ago. So Automattic has a three month sabbatical, and I was like I’m going to learn AI. This seems really cool.

So I did Andrew Ng’s Deep Learning course and a couple of other ones. There’s some really great courses out there now, even better ones now, this was about three years ago. And I just thought, oh my god, if this grows up, which it looks like it’s going to, it could be amazing for generating content. It could be amazing for conversational interfaces.

I had a little Roomba running around my house, pretending to be a psychopathic robot with chainsaw arms, when in fact it was a little plastic Roomba. But it was like vaguely self-aware that it didn’t have chainsaws for arms. And so it would be like, when I get my chainsaws back on, you’re a toast buddy.

I had an Australian robot that trundled around, it would try to get you to stop working and go to the beach. But it had no way of getting to the beach, which is hilarious. Anyway, that’s a long way of saying, some of us have been pushing for this stuff for a while, but I think what changed, obviously ChatGPT came out and created a lot of public awareness and public demand and conversation.

People started to see this as a race. Companies started to see this, I don’t think Automattic necessarily falls in this bucket, but a lot of companies started to see this as existential. Either you have an AI plan or you’re dead. And so it made sense to put together a team that’s sort of looking at what is this for the whole organization.

Because like you said, it’s a complicated organization. We’ve got podcasting apps, we’ve got diary apps. We’ve got Woo. We’ve got Day One and all these different things. Sensei is a learning management platform. And so we really needed to figure out how we could scale these efforts up, and not end up duplicating things or having tons of different approaches where it’s hard to get economies of scale, or build knowledge or build capability.

[00:09:53] Nathan Wrigley: Now, given that the rate of change seems to be so incredibly fast. Give us an idea over those last two months, how much knowledge you’ve had to ingest. And I don’t necessarily mean knowledge, but how has it been, trying to keep up over those last couple of months?

Is it genuinely as fast moving as it appears from the outside to be? What you learned last month probably doesn’t apply this month. And so therefore staring into the future, and if I asked you the slightly banal question, what will we be doing with AI in two years time? Is there really any realistic chance that you can offer us an answer to that?

[00:10:27] Dan Walmsley: Well in terms of keeping up with it, there really is no way to keep up with everything. And I mean, there’s multiple different dimensions here, right? There’s the research dimension, what papers are coming out and how practical are those papers. And where are the outcomes of those papers showing up in libraries?

And then there’s like, where is it showing up in products? What are our competitors doing, or what products might we plug into our own stack? For example, we can use GPT4 to generate help responses, but we have to sort of, stand up maybe a vector database and some other infrastructure, various job management things.

There’s other third party services where you can point them at some public documentation and they figure all that stuff out for you, and just give you one endpoint that just chats with you. And it’s oh, well how much do we embrace this plus that? A lot of the day to day involves build versus buy versus don’t bother.

And it’s really hard because our team currently has not that many full-time developers on it, and we do want to move really fast and understand these technologies and do the judicious integration. I personally in my horrifyingly long career have done lots of integrations and they’re almost always bad news.

And I’m almost always fighting to do some minimal thing like in-house, rather than integrate. But it’s a constant. That’s really the battle. It’s like less so the awareness of what’s happening and more so wrestling with the idea of like, how do we incorporate this or not?

And people wondering if something’s strategic or aligned or whatever. And there’s all these different time horizons you’re looking at. Like, are you talking about today? In a week, in two weeks, in a month, in a year? Because they’re all different answers.

[00:11:57] Nathan Wrigley: Yeah, I feel like if I was to to you about AI two years ago, I genuinely think the conversation about what we would be doing in 2023, 2024, I honestly don’t think we could have got any kind of line of sight into what happened. Even maybe a year ago. Nobody would’ve thought that mainstream media, mainstream products, would be using AI. And like you said, falling over each other to have some kind of policy on AI. So I don’t quite know how conversation will go.

But it feels as if we’re in the infancy of this still, and it does feel if we are going propel ourselves through this at an exponentially faster rate. The thing that just popped into my head was that when humanity first came up with the motor car, it was, at least in the UK, you had to have somebody walking with a flag in front of the motor car. And most people probably looked at it and thought, that’s ridiculous. I could walk there just as quickly as I could get into that vehicle and be driven there because it’s going so slowly.

Give it 10 years, got a little bit faster. Give it another 10 years, it got faster and more beautiful and more efficient. But of course it then polluted the world, which brings us onto the inherent problems that we may have with AI. There’s a lot of concern about unexpected consequences. The fact that it hallucinates. The fact that it may give information out which is inaccurate. Given that this is your work, are you fairly sanguine that things built with AI are broadly speaking safe? Or are we just working out what the guardrails even are?

[00:13:25] Dan Walmsley: Well there’s a few pieces to that question, and I keep failing to address all the pieces of your question, so I’ll try to genuinely do it this time. But, the first piece is sort of like where we’re at in this AI timeline, and you talked about various analogies.

I think of this as the BBS era. If you’re in your forties, you know what a BBS is. If you’re not, it was when people used to connect to a single computer using a modem, and the modems were slow enough that you could see the text appearing on the screen. Sometimes slower than you could read. Certainly when I started using BBSs, it was slower than you could read, and even slower for images.

And obviously subsequent to that we got the internet, through various stages. And now, you look at a BBS and it’s unrecognizable. It’s like why would you ever look up information this way when you can look at the whole internet? I think we’re going to go through the same thing with AI.

There was another part to your questions which was the danger piece. There are alignment techniques that we use today on large language models and other kinds of models, that are fairly reliable at the scales at which those models operate, or at least useful.

And the worst things that those models can do are not yet super terrible. if you’ve got one plugin that talks to your bank and another plugin that can pick up the phone, then a rogue AI can hallucinate its way into destroying your life, no problem.

I sometimes talk about this with, we’ve experimented with building ChatGPT plugins for different products, including wordpress.com. And one of the hardest things is, you have to put user confirmation stuff everywhere because you simply can’t predict when the AI’s going to start invoking your API in backwards ways, and just deleting all your posts because it thought that’s what you wanted to do. Turning every post synopsis into the word red paper clip.

There’s a broader alignment thing that I think goes way beyond that. It goes way beyond these hallucinations. Because you know, I think people get caught up with, oh well it’s not that useful because of the size of the context window. It’s not that useful because it hallucinates. So it’s not that useful because it was last updated in September, 2021. As if all of those aren’t things that are going to change immediately, right?

Those are all solvable problems. We know we can make larger context windows. We know we can update it more often. We know we can inject additional information. We know that various alignment techniques can encourage it to reason more thoughtfully and activate pathways that have more expertise, and that will continue to be the case. And as the models get larger, those pathways with expertise will have more expertise. And so it’s obvious and predictable, those things.

So the really hard thing to predict is where does this interface with society? And you know, we touched briefly on jobs and other things. Or whether, obviously people talk about rogue states getting an unhinged intelligence to go do crazy scientific research for them, or invent a nuclear weapon or a chemical weapon.

Google Brain just invented protein folding. So get this, the Google Brain team, Google Deep Mind, they invented a protein folding system that can fold a protein in a few seconds, which is the equivalent of about at least four years of PhD time. And so in that single invention, they eradicated, I suppose you could say, or avoided over a million years of PhD time. By folding all those proteins instantly.

The thing I think we’re not ready for is that rate of progress. I call it Moore’s Law for everything. Where you have a self-reinforcing centralized paradigm, where you have AIs that, by their very progress, make it easier to build the next AIs.

And then at the same time you have this fanning out into different disciplines, where those newer AIs are also making it easier to make scientific progress. You could use, for example a score like Perplexity, feed in all of the papers in the world and find the most useful research questions to ask that have not been answered, by basically large scale language based statistics.

[00:17:03] Nathan Wrigley: I think this is the piece where my knowledge breaks down because my interaction with AI has largely been ChatGPT. Certainly the most recent versions of ChatGPT. Plus also the image creation tools. And, I’m amazed by how quickly I’ve become, unimpressed is the wrong word, but how quickly I just expect it to give me something akin to a human.

The first couple of times I used ChatGPT my entire endeavor was to see what it would produce, and be utterly, utterly flabbergasted by the fact that it could in any way give me something coherent back. And the same with the image creation tools, Stable Diffusion and a few others that I’ve tried. Typing in some kind of prompt, and then just jaw droppingly quickly, something half decent comes back. And you know you try a little bit harder and you tweak the input that you’re putting in and something slightly better comes back.

I’m kind of amazed by how quickly that became uninteresting and just normal. In the same way that when I was a child, I first got on the bike and suddenly I could ride a bike and wow, this was amazing. Two weeks later you have to basically pay me to get on the bike at that point, it’d lost its interest.

But I’m wondering if that interface, because it is replicating a human in many ways, you know, the ability to do art and the ability to give us answers, whether it’s hallucinating or not. I wonder if that’s something that we all think that’s the way the AI’s going to go. But the examples that you gave just then, like medical research and probably research in all sorts of scientific domains, if that’s something which just never quite gets out into the public.

So the fear that a lot of people have, and there are some parts of that that I share, is never counterbalanced by the, but listen we’ve just done thousands and thousands of hours of PhD equivalent work in a matter of moments. Look how fantastic this is. I don’t think that message gets out very often.

[00:18:56] Dan Walmsley: Well, you know, and without launching into a critique of the media, I think we can all recognize that dramatic headlines sell. And I’m sure if the headlines of these articles were slightly hard to predict whether AI will be good or bad, stay tuned. Then they wouldn’t sell so many newspapers.

You know, I don’t think anybody can actually, at a large scale, predict the outcome of the current AI revolution. That there are people who think that it will be a nothing burger. And there are people who think that it will more likely than not, result in the eradication of the human species. And there are people who think it’ll be cyborgs. And there are people who think it’ll be utopia. They’re all neither right nor wrong, yet.

I will say though, that people narrowly pushing, AI will take all the jobs line, definitely wrong in my opinion. Part of that we really alluded to this before the show, but part of that is, humans are really good at inventing new jobs. We added like 8 billion humans to the planet in the last a hundred or so years, and we gave them all jobs, no problem. We can invent new jobs like dog tickler and it’s fine.

People will just find ways to keep themselves busy. And if AIs come and take away a huge amount of jobs, particularly those jobs that are mostly typing and mostly repetitive, like similar things over and over again, then maybe those people get a chance to like move their bodies and stand up.

We forget how incredibly dysfunctional it is to sit there and type all day. If we can just take away all the typing. I have a gym membership because my body’s falling to pieces because I have to sit there and move my fingers and unblinkingly for like seven hours a day. It’s ridiculous. It’s torture. Can AI make that go away? That’d be amazing. What a revolution.

And so we sort of think about this in terms of jobs as if there’s some fixed number of jobs and the AI’s going to take them. And then there’s going to be no jobs to replace them. We don’t really think about it holistically, in this sense of if it’s doing all that work it’s producing huge economic value and unlocking human potential.

[00:20:48] Nathan Wrigley: One of the things that really has sort of crept up is the use of the word intelligence. So we’ve got AI, artificial intelligence. I’m not entirely sure that, at the moment, is really the right word to be deploying, because that is a fairly scary word.

You’ve seen films going back half a century or more where some kind of intelligent cyborg, something created by a human being at least, Frankenstein onwards, is able to outthink humans and therefore wreak havoc and so on and so forth. But my understanding is that the implementations that we are broadly using, ChatGPT and so on and so forth, are based on these large language models.

It would be interesting to get into the weeds of that if you’re willing. Can you explain how that technology works and why perhaps it’s more of a fluke that it gets anything right? Well, that’s not true. It’s not really intelligent in the sense that you or I would subscribe to a human, but it appears, it masquerades as intelligent.

[00:21:48] Dan Walmsley: Right. That’s very true. So, I’ll try to make this brief but accessible to people who might not have heard this explained before. There was a paper came out, I think it was around 2017, might have been earlier from Google, called Attention is All You Need. And that was the paper that described an architecture called transformers. Where you could feed in a sequence of text that they would turn into these tokens representing, not quite a character, not quite a word, but a numeric string of stuff representing the text.

And then it would be able to predict the next word with a pretty high degree of accuracy, based on paying selective attention to the previous words. So we all know that words like and, or, or not, aren’t always salient but then there’s other words that are sort of really important to the text.

It gets really good at picking up genre and tone and language. It’s important to note that ChatGPT was never trained to speak English. It was Hindi or anything else. It was just fed huge amounts of text, and they hide a piece of the text and say, can you guess what that is? And if you do that enough times with this selective attention model, then you end up with a system that is very good at continuing text where you left off.

Now this by itself is what they call a foundation model. It’s not that useful. The only thing that really does well is generate plausible sounding text. So if you start something that looks like a scientific paper, it will continue. If you start something that looks like a poem, it will continue.

So, once you have that foundation model, it’s not very useful for chat. It will go off the rails. Because it turns out, as soon as a transformer introduces one mistake into its output. Let’s just say it’s producing an output and it changes somebody’s name from Bob to Bill. It will continue to refer to them as Bill, even if it knows in its heart of hearts the correct answer is Bob, because all it’s trying to do is be as plausible as possible. Ah, I said Bill, I better stick with Bill. Or I said, up is down, I better continue with up is down.

I did about eight years of improv. It’s like an improviser in that respect. And in fact, that was one of the first things I used it for was generating scripts and improv things. Little musicals and stuff. Because it can take an absurd premise and run with it. So you give it an absurd premise like bogans in space, that’s a very Australian reference. It will generate the most plausible script it can for bogans in space. And that’s wonderful if what you’re doing is trying to create sort of a fantasy thing, but it’s less wonderful if you’re trying to do something grounded.

And so then they go through these various alignment processes where they feed it a huge amount of handwritten, curated, expert questions and answers on top of that whole internet that they fed it in the first place. And these are supposed to be illustrative of, I’ve got a question, I need a step by step answer that is clear and concise. And I also need it to refuse to tell me how to make a chemical weapon and other things like that.

So there’s some safety stuff there where you look at examples of people asking for malicious things. It’s crazy. I asked it to tell me a joke the other day, an Irishman, Englishman, American joke, right? And so ChatGPT refused to generate it. Because well, I can’t make a joke about people based on specific aspects of their race or whatever. Which is sort of like, fair enough in the general case but also weird in the context of me just wanting that joke for myself to see what it could do. That’s the kind of alignment stuff that they’ve put in.

And so finally what you get at the end of the day after a few more steps, is a model that has a little background thing where developers can align the model. Has all these different safety mechanisms. Has the ability to spell out instructions step by step,. Avoids as much as it can certain mistakes that would lead to it repeating itself or hallucinating too much. And has the ability to recognize now and use tools that accept JSON structured input as part of its cognition. That’s the latest level of alignment that they’ve introduced. And in the future there’ll be more and more as it gets bigger and more capable.

[00:25:31] Nathan Wrigley: So the fact that we’re on GPT4 at the moment, we’re recording this in June 2023. We’re on GPT4, and prior to that there was GPT3. And I think everybody can agree that each iteration is better. But the way that the technology is structured at the moment, will each version in the large language model, the token version that you described, the transformer model, will that simply get better at creating fewer and fewer mistakes?

Or are we approaching something which we could point to and say okay that now really is intelligent? In other words, are we heading towards a general intelligence? An AGI where we can now no longer disassociate it from being a human. It can come up with its own incentives, its own reasons to do things and then figure things out all by itself, based upon no human input whatsoever?

[00:26:18] Dan Walmsley: Yes. When I think of an AGI, I think of an autonomous AGI, right? Where it’s HAL 9000. I don’t really know when that will happen. And I don’t know if it’s a great idea necessarily. I think in between here and there, there’s like a huge amount of work to be done to bring this technology to life in ways that help people with their work.

It’s one thing to switch tabs and go to ChatGPT and type, write me a program that does x or y. It’s another thing to have GitHub Copilot living in the editor, which is an absolute game changer. And I suspect what’s coming next is AIs that work with your programmers and produce pull requests, or patches on pull requests, that fix linting or reduce complexity.

For example, I would pay at least $10,000 a month for an AI that comes in and reduces the complexity of the code that our teams write every single day. Finds methods that shouldn’t be there. Renames things to more align with each other. Move stuff between classes, and documents things publicly. Maybe pings developers if it’s not sure something’s useful anymore. Can you just imagine? Because not only is that cleaning up the code, it’s reducing the number of developers you want, it’s removing one of the most annoying things about being a developer.

So it’s making your job as a developer more pleasant. It’s not like it’s inventing new stuff, but it’s making it so much easier to invent new stuff because you’re working on super clean, minimal code that only does what you need it to do. And now just imagine if every company had that, how much progress we would see.

[00:27:46] Nathan Wrigley: Yeah, it’s interesting isn’t it because at the hub of that is, it almost feels like if you paid that $10,000, a large proportion of your team have to go away. Because probably a significant proportion of the team is people going in and cleaning things up and what have you.

[00:27:59] Dan Walmsley: Does it work that way though? Because let’s just imagine that somebody is on my team. Unless your company is losing money, right? Large amounts of money, and you’re desperately looking for some way to cut, right? If you have a programmer on your team and you can give them this tool and they become four times as productive. Then why would you want fewer programmers? Every programmer you add is four new programmers.

I don’t think this is going to result in people being fired en masse. People look around at Silicon Valley right now, there’s a lot of companies copying the Elon Musk strategy of, oh boy we just realized that we need to trim the fat. Over the long term, I don’t know if that necessarily means fewer programmers. Although I do think more people will get to be a programmer.

My dream is that every human being has their own open source stack that is completely proprietary to them. That is built and managed by an AI that is completely personal to them. Runs on a device that they own and control.

And so then you can simply describe how you want your life to be, and your personal software stack adapts and makes sure that I only see the information that is valuable and actionable to me. And because of this AIs role in my life, I’m able to get insights about what’s really working, and avoid distractions and nobody will ever be able to spam me again.

I actually literally am building an AI that scrapes the bajillion inscrutable emails from the school and plucks out the things that actually need to go in my calendar. It’s easy now, right? It’s 50 lines of code. And I can do the same thing for other digital parts of my life and just make that whole thing go away.

[00:29:29] Nathan Wrigley: Yeah, I think there’s three places to sit on this seesaw. There’s either I’m terrified by AI, or I’m really pro AI, or I think where I’m finding myself at the minute is more in the middle. There are parts of it that I can see which clearly have enormous utility, and are really going to put us on a rocket ship to Mars if you like.

There’s just no downside, but I think there is a part of me which does genuinely worry about the incentives. Whether or not it’s a great idea to automate all the things. Whether the landscape is just going to become flooded by noise, which actual humans can’t go through. So we then have to employ more AI to figure out how to get rid of the fake content. I’m not a hundred percent sold on it. I can certainly see there’s bits of it which have benefits.

However, I’ve just come back from WordCamp Europe and part of the final address, we had Matt, Josepha and Matias on stage. And Matt is clearly very, very bullish about AI. In the same way that five years ago he was telling everybody to learn JavaScript deeply. I think the lasting message I got certainly from that presentation was start using AI deeply. Obviously you’re an Automattician, what he says matters. I’m just wondering, just to bring it back to WordPress, I’m wondering where we are going to begin to see AI in our WordPress sites? What are the kind of places where we may see it surfacing in the future?

[00:30:51] Dan Walmsley: Yeah, I’m going to start with the quote from Matt, learn AI deeply. We don’t really know where AI is going to go but we see a certain rate of progress. And it’s faster than Moore’s Law. And so if you use an imperfect AI tool today and you get familiar with it and fluent with it, let’s just say GitHub Copilot. You can be pretty sure that tool will accelerate in progress over time. Because it’s already an AI tool that’s like standing on the shoulders of this like industry. So it’s going to get faster, it’s going to get better.

The people who don’t embrace AI are going to continue on their linear or plateau trajectory. And so I feel like any human being alive today should probably start embracing some piece of AI in their life so that they can get a sense for how it’s shifting and changing and improving. So if it’s just a matter of using ChatGPT to like make plugin snippets, oh it’s good at this, it’s not good at that. Make it a habit. Then you’ll bear witness to what’s going on and you’ll know where to jump both feet into the stream and start leveraging this stuff more at scale.

In terms of where we’re going to see it show up in WordPress. I was on a panel recently and one of the things that I said was, a question worth asking is what content management system would an AI choose? If you’re an AI and you’ve been asked to create a website for someone and you haven’t been told what technology to use, would you use WordPress?

And the answer today is, probably. Because most of the public documentation for content management systems is WordPress documentation. So the AI has access to like 20 years of all this stuff. And that’s really, really powerful. It means it can reason about WordPress in a really impressive way.

It’s actually a great testimony to keeping WordPress roughly the same all of that time with minimal breaking changes. Because, you know, one of the things that I’ve noticed is there are lots of breaking changes between libraries in the Python ecosystem. And that means that ChatGPT very rarely writes working Python code for me. I have to modify it to use the latest API or whatever. It almost always produces working PHP WordPress code, because what works hasn’t changed, which is quite amazing.

[00:32:58] Nathan Wrigley: I mean, that is actually phenomenal to see that happen.

[00:33:01] Dan Walmsley: Yeah. Now we have to capitalize on that, but that’s a really great start. And you know what CMS would an AI choose, okay it’s one that it’s familiar with. And then the next level is, well it would be one where you can modify it and extend it easily. WordPress certainly checks that box to infinity, right? There’s all of these existing plugins and an AI can read the documentation of plugins and choose one for you or whatever it needs to do.

So the plugin mechanism is amazing because you can basically take a statement that someone makes about how they want their website to be different, and turn it into a function that runs a bunch of hooks.

It doesn’t have to go modifying the existing code of WordPress and forking it. It can just like inject the things that you ask it for, and correlate them back to the statements that you made. And in the future if it finds out that there was a better way to implement that request then it can implement it differently. Because it has the original things you asked for. So that’s one way I think I see AI helping with WordPress over time. Not that that’s a product that I’ve built I’m just sort of reasoning broadly about it.

[00:33:59] Nathan Wrigley: I think one of the areas that I really would like to see is the ability to leverage what’s just come around. I’m really excited about blocks and block patterns in particular. I’m quite a visual person, so I love to see images of what I’m about to get. And the idea of, I don’t know, I want to build a website for a local industry. A real estate agent, a lawyer or something like that. And the AI has some kind of interpretation of what that means. It probably has a little understanding of the geography of where I live and what kind of imagery might go into a website like that.

I live on the coast and there is some things which people always take pictures of and they often end up on websites for the places where I live. But also it understands typically what a lawyer is, you know? And it would understand that, okay, you probably need a page that has this on it, and a page that needs this on it, and probably a form and blah, blah, blah.

And then it would just throw at me, I don’t know 100, 200 designs, something like that, that I can look at. And because of the fact that it’s all built with blocks I could input that pattern, and then start to tweak things as I like it. I just love the idea of the choice that it might be able to give me, and short circuit, I mean me building 200 different designs, that’s going to take me weeks. This potentially could happen in the blink of an eye and I love that choice.

[00:35:13] Dan Walmsley: Yeah. Think about a few years ago, if you had like a site generator versus a site builder, right? So let’s just say I generated a site and we’ve all be familiar with site generators, you give it like, what kind of color scheme you want and what kind of industry you’re in and kind of thing.

And this has been possible for 10 or 20 years that you can generate a site. But the problem is, okay, now you’ve generated a site and then you make some content and you’re like, ah, I want to change that one decision. Well you can either regenerate it from scratch and blows away everything you’ve done. Or you can try and manually make the change, but you have no idea how to do that because you didn’t build it in the first place. And then you’re going to learn the whole system.

That sort of like magic trick of generating the site back in the day is the thing you can only do once. But in the copilot era, which I think Microsoft correctly identified this paradigm. You can jump in and out of automating the site creation experience as much as you want. And so the idea is, okay, I’m going to generate the content on this page. Jetpack AI block is actually really, really good at this. I’m not here to like boost our products too much. But it’s like a really good example.

You can generate a page and then you can just change the tone of voice. And it will go and take the same content and change the tone of voice, non-destructively, you know what I mean?

And so the AI is able to work with whatever changes you’ve already made and make some more. I think that that’s going to be the paradigm for a long time. And anybody building AI tools needs to be very careful about building the UI in such a way that it takes these hints from the human. And uses them to make the AI better over time. Better at getting the first guess right.

And any company that does that is going to have an AI flywheel. And any company that just generates content directly but doesn’t measure how the users respond to it or interact with it or change it over time is going to be stuck on a plateau, with no way to get to the next level.

[00:37:04] Nathan Wrigley: I really find the whole idea of that curious. Literally you could go to bed one night, wake up in the morning and the AI has decided that we’ve gathered lots of data and well you had a real blitz of users during the course of the night and it’s really shown us that no, they don’t like this bit, so we’ve changed it entirely on your behalf. So it’s like split testing but on steroids.

That seems like a really interesting idea. Obviously people will not wish to hand some aspects of that over but if you can prove that a WooCommerce sale, for example, this configuration of a checkout system seems to be 20 times more popular than this one. Okay, we’re going to get rid of that one. Now we’re going to start working our way through whether we can improve this one. All of that seems to be a bit of a no-brainer.

[00:37:47] Dan Walmsley: Yes. Building an awareness of when humans need to make discriminating decisions, and when you can make them on their behalf. And the product design aspects of what expectations do you set about what’s going to happen, or whether it’s reversible, or whether it requires confirmation or authentication or et cetera, et cetera. Taking a backup.

That’s all stuff that you don’t get for free with AI. That’s all the infrastructure of actually making it useful. And I will say the AI itself is dead simple to use, right? it’s conceptually unbelievably easy. 99.99% of the work is just like aligning the whole rest of the system around it so that you can make sure that customers have a good experience.

The normal stuff of building products, right? Setting expectations, all these different things. It feels different because watching a generative AI talk like a person is weird, but it’s not, it’s not work that requires you go do a deep learning course.

The thing that is transformative about this is it’s generality. These techniques have existed for years. We’ve always been able to classify, well, not always, for a long time been able to classify images for a long time been able to sort of grammatically parse out text or detect languages or sentiment or other things.

But they were all specialized models with vast data sets. And now you can fine tune it on 500 of your own examples and have it go answering entire support requests straight out of your knowledge base. And so it’s that generality that is really powerful.

[00:39:13] Nathan Wrigley: I’m curious to see what the UI for all of these different things are going to be in the future. In the sense that, you know, if you look at WordPress from when you began using it, it’s a very different animal. Although it hasn’t changed dramatically in the last five or six years. When you began using it, it was a different animal to the way it looks now.

And then these sort of page builder technologies came along and further democratized publishing and made things easy and it was a point click interface. I’m just curious to see how, what the pieces are that live inside WordPress. Whether it’s going to be text input. Whether we’re just going to start talking to our website and, you know, move it left a bit, a little bit more, make it red. Not that red, the other red.

I want a picture of a, I don’t know, a sausage over there, that kind of thing. How all this gets surfaced. We’re obviously in the era of trying to get everybody to use Gutenberg. Whether it fits into there or whether we need a brand new interface because the AI will just take care of everything. That bit is for me going to be really interesting.

[00:40:06] Dan Walmsley: Yeah. I’m really excited to see what happens with Gutenberg. I’m completely convinced Gutenberg will not go away. And actually AI makes Gutenberg look like a better and better decision versus the classic editor as AI comes into view.

[00:40:21] Nathan Wrigley: Can you develop on that? I think I know what you mean but I want to hear what you mean. Yeah.

[00:40:25] Dan Walmsley: So having things embedded as blocks with parameters provides a much more semantically rich interface than just a bunch of HTML. It’s similar as to how we see markdown used a lot more in AI than HTML as a formatting language, input, output. And why is that?

Well, it’s because the structure tells you something about the meaning of the document, right? This is a table, this is an image, this is a whatever. Obviously you get an HTML but more sophisticated than that, right? This allows the AI, so say you’ve got like a cover block with an image and a text. This allows the AI to have some confidence about how that’s going to appear when it shows up on a webpage.

As opposed to arbitrary HTML that may be pulling in CSS from various places and like all that kind of stuff. Gutenberg provides an incredible foundation for collaboration. And collaboration is key, right? If we’re talking about the copilot era here, I don’t think for a long, long time we’re ever going to have necessarily AIs. Like you’re not going to have a CMS come out that like, doesn’t have an editor, because it just has a chat interface. You tell the AI what to do and hope that it does the right thing.

Like that’s not going to be the case for a really, really long time, if ever. What you need is an editor where you can seamlessly collaborate with an AI. And if I was to take Matt’s words and bring them back into the conversation about learning AI deeply, I would love to see people in the community experimenting with UX concepts for collab.

We are in the collaboration phase. Now is the time to start bringing your ideas to the table about what it looks like to collaborate with an AI in Gutenberg and how revolutionary that could be.

[00:42:01] Nathan Wrigley: Are you open to those conversations? Is your team keen to hear from the community? And if that’s the case, where do we go to begin that conversation?

[00:42:08] Dan Walmsley: That’s all happening in the open source community. I’ve had a couple of conversations with Matias or others, but really at a high level. I think it’s the community that needs to help drive that. We’ve shown what’s possible with Jetpack AI. It’s like the first quickest, most sane thing we could build.

But in terms of the collaboration phase, my team is aligning the AI efforts of a large multinational corporation across many, many, many different modalities. Not just in the editor, but across image classification, and trust and safety, and all sorts of other things.

On a day-to-day basis I don’t have a huge amount of bandwidth for one thing like the Gutenberg editor but I really encourage the community to get involved and share ideas.

[00:42:53] Nathan Wrigley: Yeah. I’ll put links to the presentation that you were involved in, with, I know it was at least Anne McCarthy. I can’t remember who the other contributors were now but that was really fascinating. Interesting kind of first steps in, well, tell us what we want out of AI because we can see what it can do out in the wild with other things. You mentioned co-pilot and there’s obviously ChatGPT and all fun images that you can create with mangled fingers.

Interesting to find out what the community want from it. How it will look in two or three years time? And getting involved in that conversation could really impact the project right now.

[00:43:25] Dan Walmsley: I would also say, dark horse here, but I would love to see more people get involved in WordPress Playground. So for those don’t know, WordPress Playground they demoed it last year and I was actually in the room in New York for the WordCamp US there.

[00:43:38] Nathan Wrigley: That is some astonishing tech.

[00:43:41] Dan Walmsley: It is game changing. I mean, and it’s funny because it’s on the one hand you could look at it and be like, well, this is like a cute hack, but it’s you know, you would never run a website this way. But think about it, if you’re a person creating or modifying or wanting to come up with a new website. With no hosting, with no nothing, just sitting there like running a blob of JS in the browser.

You can ask an AI to generate the entire site and remix it and destroy it and build it again, and like when you’re happy enough with it, click a button to download and put it on a real web host. It’s lowering the barrier to entry. And I can imagine if we get lots of good contributions, there’s already really good JavaScript API access for saying, install this plugin, or like, modify this file, right?

And so if you go a step further, oh, generate an AI block that does X, Y, Z, right? And if you’re a developer that doesn’t already have WordPress or know WordPress, and you don’t have to pull down PHP, you don’t even have to write PHP. You have this like ephemeral WordPress in the browser and you can see what it’s capable of.

I think that could bring so many potential developers into the WordPress community. Who are able to see what’s possible, have this low barrier entry, who have zero dependencies and can provide plugins and blocks and other cool ideas into the WordPress community who might not have had a chance to contribute before.

[00:44:56] Nathan Wrigley: It’s amazing when you actually use it because you just assume that there’s a machine somewhere remotely that’s serving up that website and it just spun it up in a heartbeat. But of course it’s not. You can entirely unplug from the internet and there it is. It’s still working. And it took all of no seconds at all to get the whole thing going. It’s amazing.

[00:45:17] Dan Walmsley: Yeah, it really is.

[00:45:19] Nathan Wrigley: I will link to that as well. Yep.

[00:45:21] Dan Walmsley: I hope that becomes the way that a lot of people build stuff on WordPress actually. It is a playground. It’s really fun. It reminds me of when I was playing with the first version of WordPress. But it’s just accessible to vastly, vastly more people. You know, anyone with a web browser?

[00:45:35] Nathan Wrigley: Yeah, it’s kind of like having a blank piece of paper next to you, one of a thousand bits of paper that you can just scribble on and screw it up and throw it over your shoulder and, okay, that didn’t work. Let’s try again. We’ll just blank canvas, start again. And actually, I don’t know if you did see the address that Matt gave at WordCamp Europe. That was one of the other things he discussed. So you are very much in alignment.

[00:45:54] Dan Walmsley: It’s in my queue.

[00:45:56] Nathan Wrigley: Okay. Well, sadly, I mean, I could honestly talk about this with very little authority for hours and hours and hours. But we’ve probably used up our allotted time.

Dan, if anybody wants to reach out to you specifically, do you make yourself available in that way? And if so, where do we find you? Are you a Twitter fan? Or are you on, you know, you’re going to throw an email in our direction or a Slack channel? Let us know.

[00:46:17] Dan Walmsley: Well, you can reach me on Twitter. Twitter.com/danwalmsley. d a n w a l m s l e y. It’s a tricky one. And, that’s a start.

[00:46:28] Nathan Wrigley: Perfect. Well, thank you so much for chatting to us today about AI. I’m just sorry that I, uh, I can’t kind of keep up with the level of intelligence that’s probably required to make this conversation worth while, but I appreciate it.

[00:46:40] Dan Walmsley: I super appreciate being on the podcast. I’m really, really excited about the next couple of years. And especially for WordPress. I think we’ve got like a lot of strengths that if we leverage them, can put us in an amazing position to empower a lot of people to, you know, publish and to continue to democratize publishing.

[00:00:00] Nathan Wrigley: Welcome to the Jukebox podcast from WP Tavern. My name is Nathan Wrigley. Jukebox is a podcast, which is dedicated to all things WordPress. The people, the events, the plugins, the blocks, the themes, and in this case how AI and WordPress can work together.

If you’d like to subscribe to the podcast, you can do that by searching for. WP Tavern in your podcast player of choice. Or by going to WPTavern.com forward slash feed forward slash podcast. And you can copy that URL into most podcast players.

If you have a topic that you’d like us to feature on the podcast, I’m keen to hear from you. And hopefully get you or your idea featured on the show. Head to WPTavern.com forward slash contact forward slash jukebox, and use the form there.

So on the podcast today, we have James Dominy. James is a computer scientist with a master’s degree in bioinformatics. He lives in Ireland working at the WP engine Limerick office.

This is the second podcast recorded at WordCamp Europe, 2023 in Athens. James gave a talk at the event about the influence of AI on the WordPress community and how it’s going to disrupt so many of the roles which WordPressers currently occupy.

We talk about the recent rise of ChatGPT, and the fact that it’s made AI available to almost anyone. In less than 12 months, many of us have gone from never touching AI technologies to using them on a daily basis to speed up some aspect of our work.

The discussion moves on to the rate at which AI systems might evolve, and whether or not they’re truly intelligent or just a suite of technologies which masquerade is intelligent. Are they merely good at predicting the next word or phrase in any given sentence? Is there a scenario in which we can expect our machines to stop simply regurgitating texts and images based upon what they’ve consumed; a future in which they can set their own agendas and learn based upon their own goals?

This gets into the subject of whether or not AI is in any meaningful way innately intelligent, or just good at making us think that it is, and whether or not the famous Turing test is a worthwhile measure of the abilities of an AI.

James’ his background in biochemistry comes in handy as we turn our attention to whether or not there’s something unique about the brains that we all possess. Or if intelligence is merely a matter of the amount of compute power that an AI can consume. It’s more or less certain that given time machines will be more capable than they are now. So when if ever does the intelligence Rubicon get crossed?

The current AI systems can be broadly classified as Large Language Models or LLMs for short, and James explains what these are and how they work. How can they create a sentence word by word if they don’t have an understanding of where each sentence is going to end up?

James explains that LLMs are a little more complex than just handling one word at a time, always moving backwards and forwards within their predictions to ensure that they’re creating content which makes sense, even if it’s not always factually accurate.

We then move on from the conceptual understanding of AI to more concrete ways it can be implemented. What ways can WordPress users implement AI right now? And what innovations might we reasonably expect to be available in the future? Will we be able to get AI to make intelligent decisions about our websites SEO or design, and therefore be able to focus our time on other more pressing matters?

It’s a fascinating conversation, whether or not you’ve used AI tools in the past.

If you’re interested in finding out more, you can find all the links in the show notes by heading to WPTavern.com forward slash podcast. Where you’ll find all the other episodes as well.

And so without further delay, I bring you James Dominy.

I am joined on the podcast today by James Dominy. How are you doing James?

[00:04:51] James Dominy: I’m well, thanks. Hi Nathan. How are you doing?

[00:04:53] Nathan Wrigley: Yeah, good, thanks. We’re at WordCamp Europe. We’re upstairs somewhere. I’m not entirely sure where we are in all honesty. The principle idea of today’s conversation with James is he’s done a presentation at WordCamp Europe all about AI. Now, I literally can’t think of a topic which is getting more interest at the moment. It seems the general press is talking about AI all the time.

[00:05:17] James Dominy: Yeah.

[00:05:17] Nathan Wrigley: It’s consuming absolutely everything. So it’s the perfect time to have this conversation. What was your talk about today? What did you actually talk about in front of those people?

[00:05:24] James Dominy: Right. So my talk was about the influence of AI on the WordPress community. The WordPress community involving, in my mind, roughly three groups. You’ve got your freelancer, single content generator, blogger. You have someone who does the same job but in a business as in an agency or a marketing or a brand context. And then on the other side, you’ve got software developers who are developing plugins or working on the actual WordPress Core.

And AI is going to be changing the way all of those people work. Mostly I focused on the first and the third groups. I don’t know enough about the business aspects to really talk about the agency and the marketing side of things.

I personally, I’m a software developer, so I suppose I really skewed towards that in the end. But, my wife has been a WordPresser for 15, 20 years, which is how I ended up doing this. And a lot of the things that she’s been using ChatGPT quite actively recently.

And she’s been chatting to me after work going, you know, I was trying to use ChatGPT to do X Y Z. And I thought, well, you know, that’s interesting. I know some bit about machine learning and the way these things work. I’ve read some stuff on the internals and I have opinions.

[00:06:33] Nathan Wrigley: Perfect.

[00:06:34] James Dominy: So that’s how I got here.

[00:06:35] Nathan Wrigley: Yeah. Well, that’s perfect. Thank you. It seems like at the moment the word ChatGPT could be easily interchanged with AI . Everybody is using that as the pseudonym for AI and it’s not really, is it? It really is a much bigger subject. But that is, it feels at the moment, the most useful implementation in the WordPress space. You know, you lock it into the block editor in some way shape and you create some content in that way.

[00:07:00] James Dominy: And I mean, I am absolutely guilty of that. I think the number of times I’ve said ChatGPT, I mean AI generative systems, or something during my workshop this morning is well beyond count.

it is likely to fall victim of a trademark thing at some point. Like Google desperately tries to claim that Google is a trademark and shouldn’t be used as a generic term for search. I expect the same thing will happen with ChatGPT at some point.

[00:07:25] Nathan Wrigley: This is going to sound a little bit, well, maybe snarky is the wrong word, but I hope you don’t take it this way, but it feels to me that the pace of change in AI is so remarkably rapid. I mean, like nothing I can think of. So, is there a way that we can even know what AI could look like in a year’s time, two years’ time, five years’ time? So in other words, if we speculate on what it could be to WordPress, is that a serious enterprise? Is it serious endeavor? Or are we just hoping that we get the right guess? Because I don’t know what it’s going to be like.

[00:07:59] James Dominy: I think if we rephrase the question a bit, we might get a better answer. So AIs are human design systems. And there is a thing called the alignment problem where there is an element of design to AIs, and we give it a direction, but it doesn’t always go the direction we want and I think that is the unanswerable part of this question.

Yes, there are going to be emergent surprises from the capabilities of AIs. But for the most part, AIs are developed with a specific goal in mind. Large language models were developed, okay I’m taking a wild educated guess here perhaps, but they were developed with the idea of producing text that sounded like a human. And I mean, we’ve had the Turing test for nearly a hundred years, more than a hundred years? 21, yeah, more than a hundred years now.

So I mean, that’s been a goal for a hundred years. Everyone says that AI has advanced rapidly and it has, but the core mathematical principles that are involved, those haven’t advanced. I don’t want to take away from the people who’ve done the work here. There has been work that’s been put into it, but I think what’s really given us the quantum leap here is the amount of computational power that we can throw at the problem.

And as long as that is increasing exponentially, I think we can expect that the models themselves will get exponentially better at roughly the same rate as the amount of hardware we throw at it.

[00:09:28] Nathan Wrigley: So we can stare into the future and imagine that it’s going to get exponentially, logarithmically it’s going to, it’s just going to get better and better and better. But we can’t predict the ways that it might output that betterness. Who knows what kind of interface there’ll be, or.

[00:09:41] James Dominy: Yeah. I think better’s a very evasive term perhaps, on my part. I think there are specific ways that it is going to get better. For example, we are going to see less confused AIs, because they are able to process more tokens. They have deeper models. Deeper statistical trees for outputs. They’re able to take more context in and apply it to whatever comes out. So in that sense we’re going to see a better output from an AI. Is it going to ever be able to innovate? Ooh, that’s a deep philosophical question, and I mean we can get into that, but I don’t know that we have time.

[00:10:20] Nathan Wrigley: I think I would like to get into that.

[00:10:22] James Dominy: Okay.

[00:10:22] Nathan Wrigley: Because when we begin talking about AI, I think the word which sticks is intelligence. The artificial bit gets quickly forgotten and we imagine that there is some kind of intelligence behind this, because we ask it a fairly straightforward, or even indeed quite complicated question.

And we get something which appears to pass the Turing test. Just for those people who are listening, the Turing test is a fairly blunt measure of whether you are talking to something which is a human or not human, masquerading as a human. And if something is deemed to have passed the Turing test, it’s indistinguishable from a human.

And so, I have an intuition that really what we’re getting back, it’s not intelligent in any meaningful sense of the word. It’s kind of like a regurgitation machine. It’s sucking in information and then it’s just giving us a best approximation of what it thinks we want to hear. But it’s not truly intelligent. If you asked it something utterly tangential, that it had no capacity, it had no data storage on, it would be unable to cope with that, right?

[00:11:22] James Dominy: I think yes. If you can clearly delineate the idea of, we have no data on this, which is very difficult considering the amounts of information that, you know, give something access to Wikipedia and that AI generative system might well be able to produce an opinion on practically anything these days.

But if it hasn’t read the latest paper on advanced quantum mechanic theory, it’s not going to know it. That text isn’t going to be there. Could it reproduce that paper? That’s a subtely different question, because then it comes down to, well, when a human produces that paper, what are they really doing?

They’re synthesizing their knowledge from a bunch of different things that they’ve learned, and they’re producing text in a language, in a grammar, that they have learned in a very similar way, that statistically speaking this sentence follows this grammatical form. Because I have learned that as a child through hearing it several thousand times from the people around me and my parents. What’s different?

A more practical example here, I was having this discussion earlier today, and someone said yes, but they’re not truly intelligent. But if you consider it, even now, we can ask Chat GPT something, and I’m going to be abstract cause I don’t have a concrete example here, I’m sorry. But we can say to ChatGPT, I want you to produce a poem in the style of Shakespeare, a sonnet or something. But I want you to use a plot from Goethe.

Okay, fine. Now it can do that. It can give you a response. I’m not sure that it’ll be a good response. I haven’t tried that particular one. But in that context, if you are asking a human to do that, and we automatically make the assumption of other human beings that they understand. And, sorry, I’m making air quotes here. That they understand, in quotes, who Goethe is. That that is a person and a character. That Goethe has a particular style and a proclivity for a certain pattern in his plots.

And that those are all, to use a computer science term, symbolic representations. Abstract concepts. So is ChatGPT actually understanding those abstract concepts? Does it understand that Goethe is a person? Educated guests here, probably not. But it does understand that Goethe refers to a certain, can draw a line in all the stuff that it has learned and know this is Goethe.

It has a concept of what it thinks Goethe is. Then from there it can say, and he has done work on the following things, and these are plots. And so it kind of understands. There’s another line there about what a plot is, which is a very abstract concept.

Does that mean it’s intelligent? Does that mean it understands? I don’t know. That’s my answer because I did biochemistry at university, and there’s also the question there, and it’s exactly the same question. It’s at what point do the biological machines, the biochemical machines, your actual proteins and things that are obviously on their own, unintelligent, and yet when they act in concerts, they produce a cell, and a living being.

Where does that boundary exist? Is it gray? Is it a hard line? And the same for me is true of the intelligence question here. Intelligence is a, it’s an aglomeration of lots of small, well-defined things that when they start interacting, become more than the sum of their parts. Does it come down to the Turing test? I mean, the fact that people on support, little support popups on the web, have to ask, are you a human every now and then. It immediately says, we have AIs that have passed the Turing test long ago.

But here in this case, like the extended Turing test is the thing actually intelligent? I don’t know. I genuinely don’t know the answer there. In some sense, yes, because it’s doing almost the same thing as we are, just in a different, with different delineations and different abstractions, but the process is probably the same.

[00:15:33] Nathan Wrigley: Given that you’ve got a background in, forgive me, did you say biochemistry?

[00:15:37] James Dominy: Yeah, biochemistry and computer science, bioinfomatics.

[00:15:39] Nathan Wrigley: Yeah, do you have an intuition as to whether the substrate of the brain has some unique capacity that can lock intelligence into it? In other words, is there a point at which a computer cannot leap the hurdle? There’s something special about the brain, the way the brain is created? This piece of wetwear in our head.

[00:16:00] James Dominy: Unpopular opinion, I think it comes down to brute force count. We’ve got trillions of cells. Large language models, I don’t know what the numbers are for GPT4, but we’re not at trillions yet. Maybe when we get there, I don’t know where the tipping point is, you know. Maybe when we get to tens of billions, or whatever number it happens to be, is the point where this thing actually becomes intelligent.

And we would be unable to distinguish them from a human, other than the fact that we’re looking at a screen that, that we know it’s running on the chip in front of us. But if it’s over the internet and it’s on a machine running, or whether we’re talking to a person in the support center. Or we are at the McDonald’s kiosk of 2050 and being asked whether we want fries with that. If we can’t see the person who’s asking the question, if we’re at the drive-through, we can’t see the person. Do we care?

[00:16:54] Nathan Wrigley: Interesting. You mentioned a couple of times large language models, often abbreviated just to LLM. My understanding at least, forgive me I’m, I really genuinely am no expert about this. This is the underpinning of how it works. I’m going to explain it in crude terms, and then I’m hoping you’ll step in and pad it out and make it more accurate.

[00:17:12] James Dominy: I should caveat anything that I say here with I also am not an expert on these, but I will do what I can.

[00:17:17] Nathan Wrigley: So a large language model, my understanding is that things like ChatGPT are built on top of this, and essentially it is vacuuming up the internet. Text, images, whatever data you can throw at it. And it’s consuming that, storing that. And then at the point where you ask it something, so write a sonnet in the style of Goethe, written by Shakespeare. It’s then making a best approximation, and it’s going through a process of, okay, what should the first word be? Right, we’ve decided on that. Now, let’s figure out the second word, and the third word and the fourth word. Until finally it ends in a full stop and it’s done.

And that’s the process it’s going through. Which seems highly unintelligent. But then again, that’s what I’m doing now. I’m probably selecting in some way what the next word is and what the next word is. But yeah, explain to us how these large language models work.

[00:18:03] James Dominy: I think that’s a pretty fair summation. I think the important bit that needs to be filled in there is that what we perceive and use as customers of AI systems in general is a layer of several different models. There is a lot of pre-processing that goes into our prompts and post-processing in terms of what comes out.

But fundamentally the large language model is, yes, it’s strings of text generally. There are different systems that the AI images, image systems, are a different form of maths. Most of them, at least the ones that I know of, are mostly based on something called Stable Diffusion.

We can chat about that separately, but large language models tend to be trained on a large pile of text where they develop statistical inferences for the likelihood of some sequence of words following some other sequence of words. So as you say, like, if I know that a pile of words were written by Goethe, then I can sub select that aspect of my trained data.

And I’m personifying an AI here already. The AI can circumscribe, isolate a portion of its training set, and say, okay I will use this subset of my training, and use the statistical values for what words follow what other words that Goethe wrote. And then you will get something in the style of Goethe out.

[00:19:29] Nathan Wrigley: It’s kind of astonishing that that works at all. That one word follows another in something which comes out as a sentence because, I don’t know if you’ve ever tried that experiment on your phone where you begin the predictive text. On my phone there’s there’s usually three words above the little typewriter, and it tries to say what the next word is based upon the previous word.

[00:19:49] James Dominy: It’s not called auto corrupt for nothing.

[00:19:50] Nathan Wrigley: Yeah, so you just click them at the end of that process, you have fantastic gibberish. It’s usually quite entertaining, and yet this system is able to, in some way just hijack that whole process and make it so that by the end the whole thing makes sense in isolation.

It is Goethe. It looks like Shakespeare, sounds like Shakespeare, could easily be Shakespeare. How is it predicting into the future such that by the end, the whole thing makes sense? Is there more processing going on than, okay, just the next word. Is it reading backwards?

[00:20:22] James Dominy: Yes absolutely. Again, not an expert on LLMs, but there is this thing called a Markov Model. Which is a much more linear chain. It’s used often for bioinformatics, for genome and predicting the most likely next amino acid or nucleic acid in a genomic or a proteomic sequence.

And so Markov Models are very simple. They have a depth and that is how much history they remember of what they’ve seen. So you point a Markov Model at the beginning of the sequence of letters of nucleic, the ACGT’s. And then you want to say, okay, I’ve managed to sequence this off my organism. I’ve got a hundred bases and I want to know what the most likely one after that is, because that’s where it got cut off.

You give it a hundred, maybe you have a buffer of 10. So it remembers the last ten. It sort of slides this window of visibility over the whole sequence and mathematically starts working out, you know, what comes after an A? Okay, 30% of the time it’s a C. 50% of the time it’s a G. And by the end of it, it can with reasonable accuracy to some value of how much information you’ve given it, predict okay, in this particular portion of 10 that I’ve seen, the next one should be T.

And they get better as you give them more and more information. As you give them a bigger and bigger window. As you let them consume more and more memory whilst they’re doing their job, their accuracy increases.

I imagine the same is true of large language models, because they do. They don’t just predict the next word, they operate on phrases, on whole sentences. At some point, maybe they already do, but I imagine they operate on whole paragraphs. And again, it depends on what you’re trying to produce. Like if you’re trying to produce a legal contract that’s got a fairly prescribed grammar and form to it. And you know, then like statistically you’re going to produce the same paragraph over and over again because you want the same effect out of contracts you do all the time.

[00:22:22] Nathan Wrigley: You described this slider. That really got to the nub of it. I genuinely didn’t realize that it wasn’t doing any more than just predicting the next word. And because that’s the way I thought about it, I thought it was literally astonishing that it could throw together a sentence based upon just the next word, if it didn’t know what two words previously it had written.

It’s back to my predictive text, which produces pure gobbledygook. But it still, occasionally, it goes down a blind alley, doesn’t it? Because although that is, presumably 99 times out of a hundred that will lead to a cogent sentence, which is readable. Occasionally it does this thing, which I think has got the name hallucinate, where it just gets slightly derailed and goes off in a different direction. And so produces something which is, I don’t know, inaccurate, just nonsense.

[00:23:06] James Dominy: Yes. Well known for being confidently wrong for sure. I’ve experienced something similar, and I find that it is especially the case where you switch contexts. Like when you are asking it to do more than one thing at a time, and you make a change to the first thing that you expect to carry over into the context of the second task, and it just doesn’t. It gets confused.

And then the two things, this is especially true in coding, where you ask it to produce one piece of code and a function here, and another piece of code and a function on the other side. And you expect them, those two functions to interoperate correctly. Which means that you have to get the convention, the interface between those two things, the same on both sides.

But if you say, actually, I want this to be called Bob, that doesn’t necessarily translate. Again, I suppose this is my intuition. There are a lot of ways that that failure can happen. The most obvious one is that you’re doing too much and it’s run out of tokens.

Tokens are sort of an abstraction. Sorry I used that word a lot. Computer scientist. Tokens are, they’re not strictly speaking individual words, but they are a rough approximation of a unit of knowledge, context. I don’t know what the right word here. They chose token, right? So, if you use the API for ChatGPT, one of the things that you pass is how many tokens is the call allowed to use?

Because you are charged by tokens. And if you say only 30 tokens, you get worse answers than if you give it an allowance of a hundred tokens. Meaning that you might have given it a problem that exceeds the window that I was describing earlier. That sort of backtrack of context that it’s allowed to use.

Or you give it to two contexts and together they just go over and then it’s confused because it doesn’t know which, again, I say this as a semi-educated guess. We as humans don’t have a good definition of what context means in this conversation. How do we expect a computer system to?

[00:25:05] Nathan Wrigley: Just as you’ve been talking, in my head, I’ve come up with this analogy of what I now think AI represents to me, and it represents essentially a very, very clever baby. There’s this child crawling around on the ground, I really do mean an infant who you fully forgive for knocking everything over and, tipping things over, damaging things and what have you. And yet this child can speak. So on the one hand, it can talk to you, but it’s just making utterly horrific mistakes because it’s a baby and you forgive it for that. So I don’t know how that sits, but that’s what’s it landed in my head.

[00:25:40] James Dominy: I wouldn’t say that AI is in its infancy anymore, but it’s probably in its toddler year, and maybe we need to watch out when it turns two.

[00:25:47] Nathan Wrigley: So we’ve, done the sort of high level what is AI and all of that. That’s fascinating. But given that this is a WordPress event and it’s a WordPress podcast, let’s bind some of this stuff to the product itself. So WordPress largely is a content creation platform. You open it up, you make a post, you make a page, and typically into that goes text, sometimes images, sometimes video, possibly some other file formats. But let’s stick with the model of text and images. Why do we want, or how could we put AI into WordPress? What are the things that might be desirable in a WordPress site that AI could assist us with?

[00:26:21] James Dominy: I am totally going to be stealing some ideas from the AI content creation things that have happened this morning. I mean, there’s the obvious answer. I need to generate a thousand words for my editor by 4:00 PM today. Hey, ChatGPT, can you generate a thousand words on topic, blah?

I think there are a lot of other places. I’d be super surprised if this hasn’t actually happened already. But, hey ChatGPT, write me an article that gets me to the top five Google ranking.

The other obvious place for me as a software developer is using it to develop code. Humans are inventive. We’re going to see a lot of uses for AI that we never thought of. That’s not a bad thing at all. The more ways that we can use AI, I think the better.

Yes, there are questions about the dangers, and I’m sure that’s a question coming up later on, so I won’t dive into them now, but in the WordPress community, there’s content creation, but there’s also content moderation, where AI can probably help a lot. Analyze this piece of text to me and tell me is it spam? Does it contain harmful or hateful content?

Again, it’s a case of you get what you give. There’s that story about Microsoft, I think it was Microsoft, and the chatbot that turned into a horrible Nazi racist within about two hours, having been trained on Twitter data. We need to be careful about that, certainly. I’m struggling to think of things beyond the obvious.

[00:27:47] Nathan Wrigley: Well, I think probably it is going to be the obvious, isn’t it? Largely, people are popping in text and so having something which will allow you within the interface, whether you are in a page builder or whether you’re using the Gutenberg editor, the ability to interrupt that flow and say, okay, I’ve written enough now, ChatGPT, take over. Give me the next 300 words please. Or just read what I’ve written and can you just finish this? I’m almost there.

[00:28:11] James Dominy: Yeah, we are doing it already, even if it’s a sort of fairly primitive flow now where we write some stuff in our block editor, copy it up, pop it in ChatGPT or Bard or whatever, and say, hey, this is too formal. Or this is not formal enough. And it’s really great at that. Make this sound more businessy. And it understands the word businessy. The tool integration, it’s obvious in a lot of ways, but I think there are going to be a lot of non-obvious integrations. Like, oh wow, I wish I thought of that, and, you know, made my millions off that product. I mean, Jetpack is doing it already, you know. I am able to actively engage with ChatGPT whilst I’m editing my blog post. Fantastic.

Another thing that I’ve just thought of is oh, I run a WooCommerce site and I want to use, not necessarily ChatGPT, but some other AI system to analyze product sales and use that to promote, to change the listing on my product site, so that I can sell more product. That’s going to happen.

[00:29:09] Nathan Wrigley: Yeah, given that it’s incredibly good at consuming data.

[00:29:13] James Dominy: Yeah, or even generating it on the fly. Generate 300 different descriptions of this product and randomize them. Put them out there and see which one sells best. We are doing that manually already. It’s AB testing at a larger scale.

[00:29:28] Nathan Wrigley: Yeah. You can imagine a situation where the AI runs the split test, but it’s divided over 300 variations. And it decides for itself which is the winner.

[00:29:39] James Dominy: On a day-to-day basis.

[00:29:40] Nathan Wrigley: On an hourly basis. Implements the winner and then begins the whole process over and over again. I also wonder if in WordPress there is going to be AI to help lay out things. So at the moment we have the block editor. It enables you to create fairly complex layouts. We also have page builders, which allow us to do the same thing. So it alludes to what I was speaking about a moment ago.

Talking, so literally talking, as well as typing in. I would like a homepage. I would like that homepage to show off my plumbing business, and here’s my telephone number. I’d like to have a picture of me, or somebody doing some plumbing, some additional content down there. You get the picture?

[00:30:17] James Dominy: Yeah, absolutely.

[00:30:18] Nathan Wrigley: A few little prompts, and rather than spitting out text or an image, whole layouts come out. And we can pick from 300 different layouts. I’ll go for that one, but now make the buttons red. The AI takes over the design process in a way.

[00:30:32] James Dominy: Yeah. I’m going to confess here that I’m absolutely stealing this opinion from the AI panel earlier. I think the danger for WordPress specifically there, is that that level of automation for us with human engagement and, you know, developing something through conversation with an AI, might actually skip WordPress entirely. Why must the AI choose WordPress to do this?

Maybe if we as a WordPress community invest in making WordPress AI integrated, then yeah, absolutely. Then hopefully we’re first to market with that in a way. And then it will generate stuff in WordPress. But there’s no, there’s no reason for it to maybe choose a Wix page as a better solution for you as a plumber, who doesn’t update things very often. You just want a static, you know.

Chances are it’ll just say, here is some HTML it does the job for you, it’s pretty. I made some images for you as well. And, all you need to do is run the sequence of commands to, SSH it up to provider of your choice. Or I have selected this provider because I know how much they all charge and this is the cheapest. Or you’ve asked for the fastest, whatever.

[00:31:41] Nathan Wrigley: Oh, interesting, okay. So it’s not just bound inside the WordPress interface. Literally, put this in the cheapest place as of today. And then if it changes in the next 24 hours, just move it over there and change the DNS for me and.

[00:31:53] James Dominy: One day. For sure. Yeah.

[00:31:54] Nathan Wrigley: Okay. So that very nicely ties into the harms.

[00:31:58] James Dominy: There it is.

[00:31:58] Nathan Wrigley: What we’ve just laid out is potentially quite harmful to a lot of the jobs that people do inside of WordPress. We’ve just described a workflow in which many of the things that we would charge clients for, which we could potentially get AI to do. Whether that’s a voice interface or a visual interface or a type, we’re typing in.

So that is concerning, if we are giving AI the option to put us out of work. And I know at the moment, this is the hot topic. I’m pretty sure that there’s some fairly large organizations who have begun this process already. They’ve taken some staff who are doing jobs which can be swapped out for AI, and they’ve shed those staff.

And whilst we’re in the beginning phase of that, it seems like we can swallow so much of people getting laid off. The problem, potentially is, if we keep laying people off over and over and over again and we give everything over to the AI, we suddenly are in a position where, well, there’s no humans in this whole process anymore. Does any of that give you pause for thought?

[00:32:53] James Dominy: Yeah, it certainly does. I think we should temper our expectations of the capabilities of AI. So there’s a technical term called a terminal goal. The delineation between specific artificial intelligences and machine learning, in that world, and the concept of the general artificial intelligences, which is what everyone thinks of when they think of the I in artificial intelligence, is an AI that is capable of forming its own terminal goals.

Its own, don’t get me wrong, like we have AIs that are capable of forming what are called intermediate nodes. If you tell an AI of a particular type to go and do a particular thing, then it is capable of forming intermediate steps. In order to do the thing you’ve told me, I need to first do this, which requires me to do that. And, you know, it forms a chain of goals, but none of those goals are emergent from the AI. They are towards a goal we have given the AI externally.

That ability to form a goal internally is the concept of a terminal goal. And we don’t have, large language models don’t have terminal goals. Large language models, stable diffusion, all of the different algorithms that are hot topics today, are all couched within the idea of solving a problem given to them as an input.

Which means there’s always going to need to be a human. At least with what we’ve got now. No matter how good these models get, how much brain power we give them. And this maybe is going against what I said earlier of like, I think it’s probably a quantity thing.

Maybe there’s a tipping point. Maybe there’s a tipping point where the intermediate goal that it forms is indistinguishable from a terminal goal in a human brain. But for the moment, I think there always needs to be a human there to give the AI the task to solve. Open AI isn’t just running servers randomly just doing stuff. It spends its computational time answering users prompts and questions.

[00:34:48] Nathan Wrigley: So if we pursue artificial intelligence research, and the end goal is to create an AGI, then presumably at some point we’ve got something which is indistinguishable from a human because it can set its own goals.

[00:35:02] James Dominy: The cyberpunk dystopia, right?

[00:35:03] Nathan Wrigley: But we’re not there yet. This is a ways off, my understanding at least anyway. But in the more short term, let’s bind it to the loss of jobs.

[00:35:11] James Dominy: In my workshop this morning, I think the primary point that I wanted to get across is, if you are currently in the WordPress community, employed and or making an income out of WordPress. ChatGPT, Bard, generative AI, large language models are a tool that you should be learning to use. They’re not going to replace you.

Maybe that’s less true on the content generation side, because large language models are particularly good at that. But there’s a flip side to that because on the software development side, programming languages have very strict grammars, which means the statistical model is particularly good at producing output for programming languages.

It’s not good at handling the large amounts of complexity that can exist in large pieces of code. But equally so, I mean, if you ask it to give you a hundred items of things to do in Athens, whilst I’m totally, totally, working hard at a conference, uh, then you are probably going to get repeats. You might run into the confusion problem, the hallucination issue at some point there, where just a hundred is too much.

Nobody has ever written an article of a hundred things to do in Athens in a day. I don’t know, I haven’t tried that. I’m guessing that there are going to be limitations. So some jobs are more in threat than others, but I think that if you’re already in the industry, or in the community and working with it, go with it and, absorb the tools into your day-to-day flow.

It’s going to make you better at what you do. Faster at what you do. Hopefully able to make more money. Hopefully able to communicate with more people, translations et cetera. Make your blog multilingual. There are a lot of things that you can use it for that aren’t immediately coming after your job.

The problem for me, and this again is the point that I was trying to get across in the workshop, the problem is the next generation. The people who are getting into WordPress today and tomorrow, and in six months time. Who are coming into a world where AI is already in such usage that it’s solving the simple problems. And the same as true, my editor wants 200 words or whatever on fun things to do in Athens overnight.

Okay, great. ChatGPT can do that for the editor. Why does he need a junior content writer anymore? But the problem is, I mean, we’ve already said, sometimes it’s spectacularly wrong. Does that editor always have the time to actually vet the output? Probably not. And so the job of that junior is going to transform into, they need to be a subeditor. They need to be a content moderator almost, rather than a content generator.

But that’s a skill that only comes from having written the content yourself. We learn by making mistakes, and if we are not making those mistakes because AI is generating the stuff, and either not making mistakes or making mistakes that we haven’t made before ourselves, and thus don’t recognize his mistakes. So my fear of the job losses aspect of AI is not that it’s going to wipe out people who are working already. It’s going to make that barrier to entry for the next generation, it’s knocking the bottom rung out of the ladder.

And unless we change the ways that we teach people as they are entering the community, the WordPress community, the industry, and all the industries which AI is going to affect, the basics, and we focus on it. You know, it’s a catch 22. We have to teach people to do stuff without AI, so they can learn the basics. But at the same time, they also have to learn how to use AI so they can do the basics in the modern world.

And I mean, we get back to that old debate like, why am I learning trigonometry in school? Because maybe someday it actually helps you do your job. Admittedly, so far, not so much. But I will say this. History, I did history in school. That has surprisingly turned out to be one of the most useful subjects I ever did, just because it taught me how to write. Which I didn’t learn in English class. Go figure.

[00:39:17] Nathan Wrigley: It sounds like you are quite sanguine for now. If you are in the space and listening to this podcast now, everything is fine right now.

[00:39:26] James Dominy: Yeah.

[00:39:27] Nathan Wrigley: Maybe less sanguine for the future. Given that, do you think that AI more broadly needs to be corralled. There need to be guardrails put in place. There needs to be legislation. I don’t know how any of that works, but manufacturers of AI being put under the auspices of, well it would have to be governments, I guess. But some kind of system of checks and balances to make sure that it’s not, I don’t know, deliberately producing fakes. Or that the fakes are getting, the hallucinations are getting minimized. That it’s not doing things that aren’t in humanity’s best interests.

[00:39:59] James Dominy: Absolutely. Yes. Although I’m not sure how we could do a good job of it, to be fair. The whole concept of, we want AIs to operate in humanity’s best interests. Who decides? The alignment problem crops up here where, it’s well known that we can train an AI to do something we think that it’s going to do, and it seems to be doing that thing until suddenly it doesn’t.

And we just get some weird output. And then when we go digging, we realize actually it was trying to solve an entirely different problem to what we thought we were training it on, that just happened to have a huge amount of overlap with the thing that we did. But when we get to those edge cases, it goes off in what we think is a wildly wrong direction. But it is solving the problem that it was trained to solve. We just didn’t know we were training it to solve that problem.

As far as regulation goes. Yes, I think regulation, it’s coming. I really want to say nobody could be stupid enough to put weapons in the hands of an AI. The human race has proved me wrong several thousand times already in history. Yeesh, I personally think that that’s an incredibly stupid idea. But then the problem becomes what’s a weapon?

Because a weapon these days can be something as subtle as enough ability to control trading, high frequency trading. Accidentally crash a stock market. It’s already happened. Accidentally, and again, I’m air quoting the accidentally here, accidentally crash your competitor’s stock, or another nation’s stock market. AI is there, is being used as a validly useful tool to participate in the economy, but the economy can be used as a weapon.

Putting AI in control of the water infrastructure in arid countries. Optimization, it can do those jobs a lot better. It can see almost instantaneously when there’s a pressure drop. So there’s a leak in this section of the pipe. Somebody needs to go fix it. And also it can just shut off the water to an entire section of the city because, I don’t know, it feels like it. Because for some reason it is optimizing for a different goal than we actually think we gave it.

The trick is we can say, we can input into ChatGPT, I want you to provide water to the entire city in a fair and equitable way. That doesn’t mean that’s what it’s going to do. We just think that that’s what it’s going to do. We hope.

[00:42:26] Nathan Wrigley: I think we kind of come back to where we started. If we had a crystal ball, and we could stare five, two years, three years, 10 years into the future. That feels like it would be a really great thing to have at the moment. There’s obviously going to be benefits. It’s going to make work certainly more productive. It’s going to make us be able to produce more things. But as you’ve just talked over the last 20 minutes or so, there’s also points of concern and things to be ironed out in the near term.

[00:42:52] James Dominy: Absolutely, yeah.

[00:42:53] Nathan Wrigley: We’re fast running out of time, so I think we’ll wrap it up if that’s all right? A quick one James, if somebody is interested, you’ve planted the seed of interest about AI and they want to get in touch with you and natter about this some more, where would they do that?

[00:43:06] James Dominy: The best way is probably email. I am not a social person in the social media sense. I don’t have Twitter. I don’t do any of that. So I’m probably terrible for this when I think about it. My email is, J for Juliet, G for golf, my surname D O M for mother, I, N for November, Y for yankee at gmail.com. Please don’t spam. Please don’t get AI to spam me.

[00:43:30] Nathan Wrigley: Yeah, yeah. James Dominy, thank you so much for joining us today.

[00:43:34] James Dominy: Thank you for the opportunity. It’s been great fun, and I’ve really enjoyed being able to kind of deep dive into a lot of the stuff I just had to gloss over in the workshop. Thank you.