EP285 - Quintin McGrath, Sora, and the Ethics Behind Text-to-Video

When you go beyond that or when you cross that border of an ethical line, just make sure that either you do it intentionally or you've got someone saying, but hey, you've crossed that and why are you crossing it?

Welcome to the Church Digital Podcast.

Through this podcast, we'll talk about the technological innovations within the church.

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And now here's your host, Jeff Reed.

Episode 285 of the Church Digital Podcast.

Jeff here, founder of the Church Digital.

Love having this conversation with you.

Glad you all are here.

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Okay, so let's get into this podcast.

Now, you've already done that.

You paused the video.

You came back in.

We're going to talk now about this on the podcast and the video, talking about the ethics surrounding text-to-video and Sora.

So, you know, OpenAI, the inventors of ChatBeatGPT, and the people that made all this noise going back October 22, November 22, and beyond.

Hey, guess what?

They're rolling out this new technology.

It's only getting to certain people, but this new technology isn't just text.

It's not even just text-to-photo.

It's now text-to-video, where now we can create minute-long video clips simply based off of text.

And, you know, things like deepfakes are suddenly becoming much more of a reality.

What are the ethics surrounding this?

As a church, do we like this idea, or are we petrified?

I'll be honest.

I'm terrified at this up front.

And so maybe I need to relax a little bit.

But, hey, let me at least be the guy to ask some questions.

And so who am I going to talk to about that?

I'm bringing in my friend Quentin McGrath.

Quentin, he's got an interesting career in corporate, but has also done a lot of studies recently involving artificial intelligence and ethics and risk assessment, risk management.

And so if I want to talk, and he also works with microchurches in the Tampa Bay, Florida area.

And so if I want to talk with a guy about ethics of artificial intelligence and get a church perspective, well, Quentin's the guy I want to talk to.

And so I re-emailed Quentin recently.

I was like, hey, let's jump on.

Let's talk about Sora, this new technology that OpenAI released or is releasing, and how we, the church, should be thinking about this.

And so here's the conversation.

And I got to tell you, I'm a little surprised walking away from this, Quentin's take.

But, hey, let me introduce to you, Quentin, and really the conversation that I'm calling here, Sora, the ethics of Sora and text-to-video AI.

Okay, everybody, here you go.

Well, thanks, Jeff, and great to be with you again.

This is just fantastic.

A bit of background on myself.

So I was with Deloitte for many years, 24 years.

Actually, I hate to admit that.

24 years I was with Deloitte as an organization.

In fact, I was involved in a number of other organizations in South Africa before that.

So that was a large part of my career.

I was lucky to be able to retire from them in January 22, while I was actually busy with my doctoral studies.

So I spent a couple of years in my doctoral studies focused on AI ethics and risk management.

And in that context, really understanding how we can apply AI in the world.

And you can imagine, now, over the last two years, I was blessed with the opportunity of really understanding that and finding that and being guided to that at the time, because it really has changed.

And it's become such a fundamental thing in businesses.

In fact, in all walks of life, in churches, in academia, just in how do we think about AI, how do we think about ethics, and how do we think about risks.

And so what I'm doing the last two years or so, I've really moved into kind of the give back part of my career.

And I'm involved a lot in mentoring.

I'm involved as an adjunct professor at the University of South Florida, where I've got my doctorate.

And I'm also involved in a number of advisory councils and boards, really trying to figure out how do we put together AI, generative AI, specifically in business,

and how do we take advantage of it.

And then very recently, I joined the AI and Faith as a research fellow.

And so I'm working with that group as well, which is a group that is not only Christian faith, but literally all faiths and AI and the impact of all faiths, AI and all faiths.

And so that's become a fun little exercise as well to understand just the thinking of the spirituality of what we're doing in this space.

That's beautiful.

And so you can imagine, audience, like if I wanted to get into a conversation surrounding ethics of artificial intelligence and Sora and text-to-video and some of that, my gosh, like Quentin is an incredible voice in that.

And, you know, we're even doing studies and background with Deloitte.

And so I'm really looking forward to this.

So, hey, let's do this.

Let's just dive in.

And so we're talking about Sora.

I tell you, why don't we do this?

Like just maybe set up because I don't know that I fully understand this.

So, you know, text-to-video, Sora, I believe OpenAI is releasing this or has released this in limited capacity now.

Like what are we talking about here as we're talking about Sora or text-to-video?

Yeah.

You know, and a lot of times when I heard text-to-video, it was like, I haven't got this type of thing already.

Haven't we got a number of situations?

And, in fact, I for a long time had a whole string of text and I said, right, put a video on it and create a video.

And so, you know, most people said, oh, we've had text-to-video for a long time, but this is different.

This is really different.

Because essentially what OpenAI have done is they've taken the same approach to prompting and said, right, now use that prompt.

We used to create pictures.

We used to create text.

We used to get it to answer things.

Now, literally, it's taken the same approach and converted it into an up-to-one-minute video clip.

Now, the video clip is rich.

It's high quality.

You can guide it to really be a very rich environment.

You can kind of set the environment as you would use in a prompt in any other place.

So, you know, one of the ones that we often see kind of on the front page of OpenAI's announcement is a Japanese scene.

And there's a Japanese woman walking down in a wet street, you know, evening setting, lights, lights, reflection of the street.

And literally walking down the street in a very natural and no jittering, no stopping, literally a whole smooth, excuse me, AI.

Nice.

Thank you very much, Siri.

That was beautiful.

Apple's not, their technology's not quite up to par yet.

No, no, it's fine.

So, literally, what you've seen, you've seen this woman walk down the street in Japan in the evening in Japan, seeing the reflection and her natural moves.

And eventually it comes and she looks directly into where the camera would be.

But there's no camera.

There's no reflection on the glasses.

She's wearing glasses.

And it's just the whole cycle.

It's so smooth.

And then they've got a whole lot of other examples of just a literal prompt, a couple of sentences, a prompt to creating these magnificent videos, literally, of, you know, short videos.

They can be used.

There's no sound associated with them at this stage, but you can see the richness of the environment.

And literally, if you go to OpenAI slash Sora and you can see all the examples that they've put up there.

And I'm really excited about it because I think it gives a whole lot of creativity openings, just like we've had in terms of, hey, I want to create something.

I don't want to start with a blank sheet of paper.

But this whole process of actually getting something in place and setting it up and moving forward with a lot of really high quality results right from the start.

Have you?

Because technically, this is released at this point, technology, right?

But they're only releasing it to certain people as they're kind of testing it out.

Like, what's the current status of it?

The current status, as you can understand, is they released it.

So there was a blog post, which is what they normally do.

OpenAI normally does everything new they release out in a blog post.

And in that blog post, they said they are releasing it to the red teaming.

And the intention of releasing it to the red teaming is because you can imagine this is so, so complex to begin testing.

They are then releasing it to a small group of people who are going to go through the process of putting guardrails in place.

So just like with large language models, you have the big training of the data, and then you have to put some type of guardrails.

I call it making the child polite, essentially.

You know, putting those guardrails in place to make sure that they then say the right things or don't say the wrong things.

And so that is the process that they're looking at this stage.

It's going to be a complex process because it's very hard to figure out what's going to be prompted and how it's going to be prompted.

And clearly, there are going to be levels of misuse of this.

But they're trying to put guardrails in place, as they have been doing.

And OpenAI has been trying to do that progressively with all their releases.

So that's where they are at this stage.

And how long is it going to be in red teaming they haven't announced?

And I presume their normal style is to open it to a closed group of people and then progressively open it beyond that.

But what they have shown at this stage is very, very interesting.

So, I mean, let's get in here a little bit.

The idea of text a video and create a one-minute video clip of a very detailed woman, Japanese woman, walking on the street in rain, like, artistically, creatively.

That's beautiful.

It's an interesting story.

My son the other day, my son's 13, and he's actually starting to study videography in school.

And so he was creating his first clip of video the other day.

And I don't understand why this is what he chose.

But he was responsible for creating a three-minute video clip of raining hot dogs, where literally, like, hot dogs were falling from the sky.

And so it was just a bunch of video clips that he actually created.

And we used, not Sora, because we don't have access to it, but we used other AI to kind of insert raining hot dogs.

And once again, he's a middle schooler, so I don't understand why.

But Sora, for him, would be incredible, because I'm sure Sora could make hot dogs rain from the sky.

13-year-old boys, whatever.

Moving past that, like, moving past the creatives, like, ethically, what concerns are we seeing with this idea of text-to-video?

Like, to me, it seems like this might be a win, but it also could be a loss.

Like, what's the negative as we're looking at this?

Yeah, so my context and ethics is always the two sides of the coin.

So I do believe that there's an important part of ethics that we often don't look at is the good side, so the human flourishing element.

And I love to think of the context of shalom.

So literally everything that goes with shalom and the wholeness is one of the critical parts of ethics.

And then the other part, as you say, is not doing bad stuff and making sure we don't do bad stuff.

So I think if we take a look at it from a holistic perspective, the opportunities for good are definitely there, whether it's just being creative or just helping people.

In fact, interestingly, on the website, OpenAI site, they start, their first sentence says, and let me quote it to you,

We are teaching AI to understand and simulate the physical world in motion with the goal of training models to help people solve problems that require real-world interaction.

So they're really saying the aim of this thing is to create an environment so you can understand and you can understand the real-world interaction more effectively.

So that's on the positive side.

But I think if you then start saying, right, now what else opens up?

And if I was trying to do bad things, what could I do?

And you can imagine this whole space of when you add visual images, the ability to influence people just steps up.

So we know just out of experience that if we look at something, we can keep a critical thinking, apply critical thinking to something.

But over time, if we look at something and we get used to something, we get comfortable with that image.

And so we start to lose now critical thinking.

And so the ability over time for the system or people to use it to create beautiful images or peaceful images,

but at the same time then lean towards nudging someone to go to some place that they wouldn't normally go.

And essentially through the beauty and through the imagery that's being put in, being able to soften a person's resistance to thinking about in detail.

So my big worries are the ability to use the visual images to essentially reduce the critical thinking and reduce our logical mind,

kind of shifting from left brain logical mind to right brain, which is much more able to accept things without judging them quite or harshly.

So that, to me, is one big element.

The other part, of course, is you've got your fundamental biases that are built into training data.

As much as we love to think about it, we might think we're totally unbiased.

We're all biased.

And so that data that's been used to train has got some implicit biases in it.

And we know that those biases are in the training data.

And a lot of the guardrails that are put around that these large tech companies put in place try and take out some biases and in some ways also add some additional biases based on their own decisions in terms of what is ethical and what is normal.

And so we've got this whole situation where the training data has got some biases in it.

And so we may get results coming out that are biased.

Now, whether those are boldly biased and visibly biased, like saying all doctors are men, you know, and clearly that's incorrect.

Being able to then say, right, are there maybe even deeper biases which are less obvious that are going to start pushing us in one or other way?

So that whole thing about bias and what's underlying in terms in terms of bias certainly does worry me.

And then just kind of the deeper things where you start looking at deep fakes and people starting to think that this is this is real.

We really have a problem because the quality of things, I'm thinking about gaming even, the quality of the visuals and the sound and the environment is becoming so real that as humans, it's become harder and harder for us to say this is real.

And this is not real.

And so the ability, this is now even bringing it even closer because the quality is so good that we start to say, oh, that must be real or that could be real or isn't that real?

You know, and you can see very quickly the steps that we move towards saying, oh, that has to be real versus otherwise.

And so I think those to me are probably the biggest worries.

It's a bias.

It's the ability to defake.

It's the ability to actually shift us and not just in a direction we don't really want to go and bringing down other barriers that we'd normally apply from a logic perspective.

Now, I know at some level, like there's a there's a maybe filters the right word.

Maybe it's actually another stage of bias.

But like there is in the chat GPT currently like there is some level of filter.

And when I filter, you know, I mean, like I've asked chat GPT, you know, what's the best religion?

I've talked about this in the podcast before.

Like, well, there are four religions or, you know, what are the what religion is right?

I think that's actually and it gave me a very filtered kind of everything is right.

And it defined the Christianity, Judaism, Hinduism, maybe I forget what the fourth was, but it defined it.

And it said the strengths of all four.

And it said that they were all right.

And so like and just I got bored one day and decided to ask a whole bunch of stuff of Jasper and chat GPT and couldn't get anything.

And so somewhere in the nebulous fear, somebody coded that kind of filter or bias.

They, you know, do that is is it possible to filter or will they be filtering this to like I mean, on the deepfake side?

I mean, I'm thinking, hey, let's put the president of the United States in a in a brothel.

I'm thinking, hey, let's create the next Scarlet Widow Marvel movie that without permission, you know, they just opened up Mickey Mouse where he's now public domain.

And so Steamboat Willie, you can do like there's all of a sudden we've got this immense potential to do something that arguably like if we're talking to betterment of mankind, this isn't the betterment of mankind.

This is actually violating, you know, ethical principles, moral principles, business principles like this.

This power is way beyond what I feel like, well, almost, you know, my 13 year old son should have access to.

Yeah, and I think you're right in that it goes to that whole bias and consent side of things.

Maybe rights and consent is probably a better way of putting it.

So what what do I do I have rights to do?

And I in this space, I always encourage people to say not what can I do, but what should I do?

And part of what we need to be saying is what is our ethical framework that we're going to use to say, even though I can do this, I will not do it or I should not do it because I've got certain certain framings.

Now, some of those are built in in terms of guardrails.

And yes, I've got no doubt that that OpenAI is building in to make sure that there are certain things that are just not displayed, certain things that would be R-rated, for instance, and making sure I presume that they're going to be putting guardrails in to make sure that that's not displayed.

But I think there are certain things that even so we need to be saying in our own personal lives, in our own business lives, in our own spiritual lives, what things do we say is within a frame that we're comfortable with?

And one of the things I was actually chatting to someone the other day, they're doing some AI work and, you know, helping people just from a dying perspective and really helping people process death.

And they were just saying, right, they will not go past this point.

And I was encouraging them to say, right, make sure you're writing that down and have a group of people around you, if you wish, your personal border directors, who are going to continually hold you responsible for that.

And when you go beyond that or when you cross that border of an ethical line, just make sure that either you do it intentionally or you've got someone saying, but hey, you've crossed that and why are you crossing that?

And so I really think it's important for us to say, I'm not going to rely on someone else to put the boundaries in place.

I'm going to hold to certain ethical frameworks and ethical stances or positions based on what I believe is important.

Now, my personal belief system is Judeo-Christian basis.

And so I've got certain ethics based on the Bible that I'll use as a foundation to say, right, this crosses the line or no, I will not go there.

Or this is good and yes, we should go there.

Or if we go there, we can actually get some advantage.

So let's be careful, but we'll not go further than that.

And so the whole ability for us to be very, very careful and very clear on this is the line we are drawing.

And so that might be in terms of visual images.

Just know how close are you going to go to, you know, beauty and what is beautiful and at what point do you stop and say, oh, that's no longer, you know, honoring of God or honoring of the individual.

And so being very careful on how we look at things.

And then the other side, of course, is also when I'm looking at creating something, am I actually trying to convince someone to go a particular way?

And I always remember C.S. Lewis, that he used his humor, he used his children's stories as a way of, in some ways, sliding in the gospel underneath in a children's story.

And I think in some ways, when we do that, we've got to be intentional about it and say, yes, I am doing that.

And then that's for a reason.

And then we need to be transparent about why we do it and how we're doing it.

So I'm a Christian.

I'm using this as a way of displaying my Christian faith.

And here's an example.

You know, and just being absolutely transparent about that, rather than just trying to get it out there and hope people will be moved in that direction,

almost in an underhand, nefarious type of way.

So part of it is, how do we think about it?

How do we make sure that we ourselves are not taking advantage of the nudging, even if we believe it is for what we believe a good purpose, without being transparent about that?

I mean, so, I mean, really, you're suggesting there's two different checks.

One, we've got to check ourselves in place to make sure that we, the users of the tool, are using it with ethical considerations.

And so, you know, 13-year-old son, check.

You know, at least I keep him in check, hopefully, as a parent.

But the second piece is, like, do we trust the people that are drawing the lines?

And so, you know, open AI is a great example.

You know, maybe I trust open AI.

You know, Google, literally, like, their motto is do no harm.

And so, okay, maybe I trust Google.

Maybe I trust Apple, although Siri is horrible.

But there's always this other organization that we don't know about or the international company that's weaponizing this and, yeah, weaponizing text to video.

Yes, weaponizing text to video.

Like, who else is a player in this space?

Or are they all just cloning what Sora is and manipulating it for their own?

Like, is there one organization that's really drawing the lines?

Or are we worried about other people?

Am I just, like, paranoid?

Like, who's actually drawing these lines?

All organizations are drawing their own lines.

So let's be really honest with that.

And I, as you say, some we've got to trust or we've got used to trusting, maybe a better way of putting it.

But even then, I recently did research and I, in fact, recently published a paper on this whole thing of what is embedded in terms of the ethical frameworks or the ethical positions within the large language models.

And I found that, you know, one of the large language models was focused much more on a situationalist type of approach.

And it was basically saying, you know, I'm not going to hold to any firm truth.

I'll be able to, based on the situation, I might change.

Another large language model was based on absolutist model.

So it's always going down this particular path.

And so those are, even in what I would call the large, the big tech, the ones that we trust,

there's already biases that are built in, in terms of how they've actually structured.

And then if you go to the, essentially, the open source and the large language models and those that are beyond what we call the big tech,

yes, there's the ability to then either train it with bad data, with, in some cases, poisoned data, you know,

and in other cases, just do it intentionally because you're trying to drive a particular behavior or you're trying to drive a particular stance.

And so part of what we've got to be looking at is understanding and have critical thinking around what is coming out

and test and validate that we're comfortable with what is coming out of the AI in terms of its output.

Now, be that video output or be that textual output or images, whatever it is.

And so that's the important part is to have that critical thinking.

Now, when you start looking at this type of capability, so the Sora type capability, the amount of training that went in to make this model is very, very expensive.

And very, it's expensive from a load perspective.

And therefore, the cost of electricity, the cost of processes, the cost of the environment to actually make it happen.

And so to be able to do this, you have to be a very big organization with lots and lots of money.

And we see Altman and others going around the world talking about the amount of dollars they need to actually move this forward.

The amount of processes, GPUs that they need to actually make sure that they can train the models.

So I think that in some ways, when you look at the power of these types of things, it is only the bigger ones that are really able to do it.

Now, yes, over time, we get efficiency models and it comes down on the loading, on the training and on the use comes down in terms of the power requirements.

But at this stage, this type of capability in this high level of quality is really within the grasp of a very small number of organizations,

a handful of organizations in the U.S., but also around the world, including the Far East and others.

And so the whole process of who's going to be looking at it and how they're going to be using it is going to be going to be important.

You know, I know, obviously, there's tons of privacy concerns as we're getting into this.

Now, it's interesting.

I, Jeff Reed, I actually have ChatGPT'd my name and Jasper, like, and Jeff Reed as a name.

You know, there's been a Jeff Reed that kicked a game-winning Super Bowl, kicked a field goal game-winning Super Bowl for the Steelers.

I've won several World Series for the Cincinnati Reds, or at least another Jeff Reed has.

I'm actually in jail for doing something with kids that you're not supposed to do with kids.

That's another Jeff Reed.

So if you've Googled Jeff Reed, like, there's lots of Jeff Reads out there.

And so, you know, I go into AI and, you know, ChatGPT, and I'm like, tell me a story about Jeff Reed or who's Jeff Reed.

And it doesn't have a clue who I am.

But, you know, friends of mine, even in the digital space, Neil Smith, who's another Christian technologist, he, you know, knows who it is.

Jay Cranda, digital pastor over at Saddleback.

You know, Quentin McGrath is such a unique name.

ChatGPT probably knows who you are better than it would me because Jeff Reed's more common.

And so, like, it would seem to me, I could do a text-to-video.

Sorry, Nils, I'm going to pick on you.

I could do a text-to-video where I'm like, hey, give me a video of Neil Smith and putting him in a weird situation.

And a deepfake involving Nils, like, would be very easy to reproduce because ChatGPT would know who Nils has.

Like, am I reading, I'm sorry, Chat, not ChatGPT, OpenAI, Sora.

Like, am I reading too much into deepfake?

Like, is there going to be a permissions level?

Should there be a permissions level?

What are we looking at here when trying to prevent maybe some personal violations?

So one of the things that they have said is they're going to be building into the metadata,

what they call the C2PA, which is a Coalition for Content, Provence, and Authentication.

So authenticity, rather.

So the C2PA metadata, that really says, just like images that you'll take on,

on cameras, it'll actually say, this is taken by a Canon with this lens, with this ISO rating, et cetera.

And all that information is stored in the metadata of that actual content.

And so what OpenAI is saying, they're going to be putting that information,

they're going to be building that information into the data that's carried along in the video itself.

And so you'll then be able to say, literally from here onwards, essentially, if I look at a video, I will be able to go back and say, hang on, but that's created by Sora.

So that's what they've committed to.

Now, are there going to be people who are going to be trying to jailbreak it?

Yes.

And are they going to be able to jailbreak it?

I'm sure they will at some stage with the right amount of power and the right amount of thinking.

But that at least gives us the foundation that we're starting to say, right, built in from design day, they're saying we're going to be putting these C2PA controls or metadata into that information, which immediately is going to help it.

And by the way, they've also said it in the DALI imaging and others, they're going to be going back and doing the same types of things.

And this is part of what we're going to have to build in.

And it's more of an agreeing to build in.

Because clearly, if I've got an open source model, I can choose not to do that.

But really, the whole process of transparency and saying, it's OK to say you use an AI.

It's accepted.

And in fact, it's expected that you're going to use AI in some other form.

We all do.

Whether that's Grammarly helping us write an email or getting some ideas from ChatGPT, all of that, all of us are starting to use it.

And just the transparency around, of course, I'm using AI as a mechanism and then having some type of marks in all of these things to say, right, this is not a human photograph, but actually it's an image that's been created by the latest version of AI image creation.

And so that being built in, I think, is going to be really important.

And there is some level of agreement that that's going to be built in.

And then over time, more and more, we're going to find the signatures within these various capabilities are going to be starting to look similar.

And so being able to see that there is, you know, this is AI generated, it's too precise, et cetera.

And so those types of things, I think we're going to start seeing being built in naturally.

I love that idea of, you know, as a content creator, you know, some of the stuff that I use is, well, I mean, everything that I write is, I very rarely use AI at this point for writing because I'm usually thought leadership ahead of AI, honestly.

And, like, it hasn't caught up to the craziness that I'm talking about.

But, like, in photos and things, like, I love Adobe Stock.

It declares what's AI and what's not.

Like, and I've seen that on other services.

It's interesting, like, even that declaration, man, I forget what state, but I was just reading about this, where there are states that are now, maybe it was the state, maybe it was the nation, but talking about, like, automated driving, like the Teslas.

There's conversation now about there being a blue light above the brake lights on the back of cars.

And so when a car is being, you know, self-driven or driven by AI or machine or whatever you want to call it, like, that blue light comes on.

And so whoever is behind knows, hey, that car is being driven by a machine.

And so the driver knows, okay, let me get in the other lane because I don't trust it.

Let me hurry up and get around it.

Or I'm completely comfortable with that information and can, you know, it can treat it accordingly.

And so, you know, as, even as a content creator, being able to know, okay, this is, this is machine generated.

This is, this is not like that.

I, I'm the ability for me to use that, those tools accordingly.

Like, I love that.

And so that's, that's interesting.

I mean, there's probably, there's still probably some challenges.

Hey, you, it was interesting.

And in some of our early, earlier conversations about the podcast, you sent me some case studies and I kind of flagged one.

And I was like, ooh, I would love to maybe talk through this on, on a, on a situation, like just to dig in a little bit.

And so I'm going to, I'm actually, I'm reading from my screen here.

If you're watching on YouTube, I'm not looking at you.

Sorry, because I'm literally reading the story.

And, and so, uh, Mrs. Jones, this is, I'm assuming this is hypothetical, uh, we can.

Hypothetical effect was created by, uh, Gemini in this case, by the way.

Oh, good.

We're so, I'm using, I'm using AI to talk about AI.

That's great.

Um, Mrs. Jones, a history teacher, utilizes Sora to create personalized historical simulations for her students.

One student received a simulation, uh, where they witnessed a fictionalized conversation between Abraham Lincoln and, uh, Frederick Douglass.

Uh, Frederick Douglass, as I recall, was, was the Southern leader, uh, discussing the complexities of slavery.

Uh, while the simulation is engaging in Sparks conversation, it later turns out to be historical, historically inaccurate, containing fabricated details, potentially perpetrating harmful stereotypes.

Uh, questions arise from this, should AI-generated education require sticker fact-checking?

How do we ensure historical accuracy?

Who's responsible for potential biases and misinformation produced by AI?

Like, I mean, there's, there's a lot that, that's in this, but overall, like even just, you know, your synopsis coming from this, this is an AI-generated story about how AI simulations are questionable.

Like, what, what, what are we supposed to do with this?

How are we supposed to handle this as, as churches and people in 2024?

You know, and I've, I've, I've got the point now, the way I use AI is much more in conversation.

And so, you know, I literally will say, right, let's, let's sit down and have a, have a bit of a discussion like we're doing now in terms of, in terms of areas.

And then I intentionally kick in a much more critical mindset.

So I'm being, I mean, very critical in terms of what I'm hearing and what I'm seeing, uh, and what it, what it's telling me.

And so part of that, the process is not just to say, please help me with this, give me an answer.

It literally is, let's have a debate about this particular situation.

And what about this aspect?

And if I thought about, thought about it from that particular aspect, what else should I be thinking about?

Um, and then when I get the response from the AI, I'm saying, right, well, does that make sense in this particular context?

Or is that really just a bit of an outlier?

I guess, I guess, I guess in that situation that may apply, but for this, this context, that, that particular aspect doesn't, doesn't apply.

And so a lot of what I'm doing is really this, this much more rich interaction, uh, with a mindset of being, being critical.

Um, you know, kind of, it goes back a little bit to, to the, the Bereans in Acts, where essentially they were very careful about testing and keeping on going back and validating and testing.

That what they were hearing was, was, was appropriate, was, was, was based on scripture.

And in the same way, I'm, I'm using that same type of thing.

And is this based on what I know to be true or what I understand to be true?

Or is this something that I'm, I'm actually seeing now as different?

And so that whole application of critical thinking.

And so when you start, when you start applying that, that mindset, if I'm going to use, uh, an AI to create a story, I've got to recognize that AI is a,

it's, it's not a, it's a statistical engine.

That's what it is.

It literally is.

It, it's, it's got to be trained by a whole lot of data.

And it literally has picked up just like, like, like we pick up the relationship between certain things, coffee and hot.

Um, you know, those types of examples, we recognize that over time, we've seen them occur very together so often that we put those, those two things together.

And so when it's creating a story or when I'm allowing it to be creative and not limiting it and say not be factual, it's then going to create something that is less than less likely to be factual.

And so I've got to be even more careful, even more critical in terms of, of what it, what is producing and saying, right, is that factual?

Is that not factual?

In fact, go back and test it.

And in some cases, I go back and say, right, now go back on yourself, AI, and validate, give me some validation that what you've actually produced is factually correct or is not.

So if, for instance, in my, in my classes, and I teach at the University of South Florida, in my classes where I use that, I often go back and say, right, here is some information.

And I create stories like this around ethical dilemmas.

And I then get it to actually test and validate that what is actually put together makes sense and is actually factually based or it's got some kind of foundation.

It's not totally fictitious in terms of false, like I said, rather than fictitious, totally false in this concept.

And so, so that really is, is where, where we need to start looking at, not only ourselves, but also testing.

And then when we get a response back, even validating that, because clearly it can be, it can be just as certain about a falsehood or falseness as it is about a truthfulness.

And so even then, when you say, right, test yourself and come back and prove that, that you're right, show me where you've got that content from, or go and look out on the internet and give me some validation of that information, then be very careful that we actually test and apply.

I know critical thinking to it as well.

And so in those situations, so when I'm using, just going back to Sora, when I'm trying to create visual images, and one of the images, for instance, they've got a drone flying down a river in a gold rush town in, I think, Colorado.

You know, you know, in that type of situation, one of the questions I'd be asking is, okay, so is all that information valid?

You know, are there, who are the people that are being spent?

Is that correct from a, from a bias perspective?

Or has it actually used a different bias, an incorrect bias?

You know, so, so those are the types of things that I'd then be even questioning when I started looking at that, that video being produced by this large audience models using the same type of critical thinking.

It's, it's, it's, it's this, this misleading, it's this falseness, it's the, it's the, it's the, the fact checking for, for truth and the validating.

I mean, it's, it, it just, it, for me anyway, and, and I'm, I'm, I'm, I consider myself a technologically advanced and savvy and, and, you know, lover of innovator.

But, you know, a lot of this makes me pause, you know, I've, to be, you know, I'm, I'm, I'm usually the guy, honestly, that's like the, you know, Life.Church, Craig Rochelle, anything short of sin, we should be doing for the kingdom and let's do it.

And so, like, you know, part of me wants to lean into how, you know, the church can utilize this and, and take advantage of it.

But, but I ask myself, like, if, if, would Jesus use Sora, would, would Paul, in, in everything aggressive that he's done, would, would they take advantage of, of this?

Maybe, in, in, maybe the problem is in its infancy, I don't, I don't know, and I, we need to give it time to fully develop beyond that.

But, but honestly, like, this is, this is causing me to pause a little bit on, on this technology.

And it, and I'm saying this on my podcast, which is, you know, public record for me.

And I can, part of me is like, Jeff, Jeff, take it back.

But I really, like, I can't, like, I really, this scares the crap out of me.

Like, I don't know, am I, am I off pace?

I think we, we all need to be very, very careful about what we're looking at.

Because I think the power and the, as, as the AI organizations talk about, we see the emerging capabilities that are, that there are appearing as it's learning from a bigger, bigger data set and being taught to do different things or being allowed to do different things.

The whole process of, of, of saying, right, if we take a look at this and think about the potential upsides and downsides, we've got to be able to objectively look at it and say, how can we use it for good?

And how can we make sure what we, the way we're using it for good is not going to be misused for bad?

So all of us know that we can stand up and we can give a great talk and then someone's going to cut out a small slice and say, you know, see, Jeff said this and, you know, wasn't that so bad of him?

And, you know, the context of the whole story, it was, you know, clearly not.

And so the whole, the whole area, the whole thing we're going to look at is this is certainly a positive tool that we, or a tool that can be used in a positive way.

But then we've got to be aware that it can be misused as well.

And even in our use in a good way, it can be taken and it can be, can be misused.

And so part of it has been, been very circumspect in, in terms of how do we use it and how quickly we lean into it.

At the same time, I'd say, if we're not moving into it quickly and experimenting with it within, within appropriate guardrails and in appropriate closed spaces, I think we'll be losing out.

And so part of it is testing and validating and with, with wise counsel to say, right, how do we use it and how do we take advantage of it?

But, but realistically, if I, if I just sit back and say, you know, I'm sitting next to the sea and Jesus is giving his talk about the, you know, the parable of the sower.

He was looking around, I presume, and saying, oh, there's some grass, you know, passing the grass or on some stones, you know, and it was visual.

It was people could see right there and they could immediately experience it.

If I could do the same thing with sower, would that not also be advantage if that's going to help people to actually understand what Jesus was saying more effectively?

So I think, you know, there's that part of it.

But then immediately the guardrails come in and say, right, so what can be misinterpreted?

You know, what, what, what, what can be embedded in there that could bias things?

Oh, you'd have the wrong type of people there or, you know, or maybe he'll be using the wrong language or the wrong accent.

And so all the biases may start coming in and saying, well, you know, that's not really what Jesus was saying because it was said in the wrong tone.

And all those things start coming into play and we then got to step back and say, right, so how do we take advantage of this in a way that's going to be beneficial without getting ourselves too, too tightly caught up in saying, well, we don't want to use it because there are so many potential downsides.

I lean towards, I lean towards, I'd rather use it, be very careful, but I'd rather use it and take advantage of the, of the technology more extensively for, for the benefit of all, for the, for the growth of all.

And then make sure that you've got the appropriate controls around it versus saying, well, let's hold back just in case this might be bad.

So, you know, I'm, I'm perhaps lean towards, let's use it, be careful how we use the circumstance, but use it and lean forward into it.

It's interesting.

And I'm going to, I'm going to gently, I'm going to put you on the spot here a little bit.

We're going to shift from ethics and probably a little more towards opinion, which is interesting where you're going.

Like examples from, let's even go practical here.

Like what, uh, so Sora gets out of red, um, the red team and is, is opened up at some level.

Uh, it's a 29 95 month, uh, feature set to, to get this.

And, and you can, anybody can now within whatever confines, whatever filters, whatever, anybody can create a text, a video.

Um, like what are some examples of, of how the church should, should, could, would, um, you know, experiment, utilize tests?

Like, do you have any, any, any ideas pop to mind or opinions on, on maybe some early testing grounds for how the church could use this practically?

I think, I think the, the areas that, that I'd be looking at is how do we, um, how do we create the environment that's, that is friendly and beautiful and acceptable?

So the first place I'd be looking at is, is much more, how do I create an environment where people come into and it is, it's, it's clearly beautiful.

And I'd be looking at natural beauty here, uh, in terms of, of mountains and valleys and, and, and, and, and rivers and so on.

And all the things that, that naturally move us towards a peacefulness.

So, you know, thinking about Psalm 23, he leaves me beside quiet waters, you know, and those are the types of things that I'd say, right.

Is this a way of us creating an environment that's going to help people, uh, come in, be peaceful and therefore open themselves up more to God and to be, you know, to be in a place where to meet God in, in those situations.

So, so that's one place I, I, I, I'd start first.

Um, the, the other, the other places I think would be when we start saying, right.

How do we, how can we emphasize things without moving people, nudging people incorrectly?

Uh, and so this is, this goes back to my whole fear of something visual that's much more powerful than something that's just, just sound or read.

Um, and so how can I use it in a, in a, in a, in a good way to be able to, to show a principle or show and show a, a series of images that will just reinforce the thought without over, without over driving it towards bias in and nudging in the wrong direction.

And so I think there are ways of just enriching our environment, um, of, of how do we, how do we show, how do we depict, um, biblical stories, um, how do we bring those to life?

Uh, and, and, and, and a lot of that would be around, you know, many of us, and, and I've certainly, certainly helped when I've gone into, to videos of, of Israel at a particular time.

And this is what it would be like, and just helping to understand the context of the Bible story.

Like we're just talking about Jesus, you know, in the, you know, talking about the, the, the, the, the sower, you know, and staying on the boat and, and, and, and, and being, you know, talking to the valley, you know, talking to the people across on, on, on, on the mountainside.

And just the picture and understanding what that looked like and what that sounded like and what else was happening around there.

I think that enriches, enriches the experience and enriches the understanding.

And so, so I would move there, but again, you know, this is where we've got to be, be fairly, fairly careful and, and, and fairly, um, guarded in what we're doing.

And I think something like the chosen have, have, have, have looked at that and have been very careful in terms of what they were doing in terms, in terms of depicting Jesus and depicting the, the New Testament, um, the world.

And I think those types of, of, of, of boundaries when you look at, but, but enriching that and helping people understand it without replacing the Bible, without replacing, creating a new something that people believe in.

But really always using it to reinforce scripture, reinforce the, the foundations that we have.

Uh, so that, that's, that's where I certainly would look into it.

Well, we've been talking a lot about Sora, text to chat, or it's just you text to video, uh, generative AI, chat GPT, like it's, it's all in that gen AI space, which seems to be, um, exploding quickly.

I mean, you, you, you got your, uh, your, you started your studies, uh, 22 and, and it was all over the place.

Um, what fall, fall, no, um, it started with November 22, right?

Didn't it?

Yeah.

I started studying in 2020.

So just, just, just prior to that, but yeah.

Yeah.

Yeah.

Um, are there, and so let's, you know, I've, I've asked this question because, you know, at some point gen AI is, is not going to be the only AI category we're looking at.

And, and there'll be others, um, other aspects of artificial intelligence we're going to be diving into.

Um, are, are there other aspects right now we should be aware of?

What's, what's next?

What are we doing?

You know, all this content is, is nice, but there, there's much more to AI than just the content piece.

What else should we be concerned about or looking at ethically concerning AI?

So, so certainly the, the, the big, the big, uh, shiny object right now is large language models, genes of AI, uh, and everything

associated with that, but if you look on, on the, on the periphery of that, we've started, we see a crossover into a number of areas.

So for instance, things like digital twins, where essentially I create a digital image of a physical, physical object.

I'm able to, to actually do something in a digital space and then replicate that in the physical space.

And that's very powerful.

For instance, where I'm looking at an airplane engine, I've got a digital version of the airplane engine.

I can manage it.

And so those types of things, um, are happening, but you can very quickly say, right.

So, Hey, let's do a digital twin of a, of a human, you know, and, and start, and start actually looking at, okay.

So how do we sense the skin or how do we actually start getting inside, inside the mind?

So that's, that's the one area.

So digital twinning, very powerful in, in terms of where it is in manufacturing, but then of course, when it starts in human digital twins, we start then getting to, to a different space.

Um, I, I, I think the, the, the other aspect that we've, we've seen happening is the whole, not just generative AI, but adaptive AI.

So generative AI is really focused on, I've learned stuff.

I'm going to replicate and based on my learning, I'm going to give you an example.

So essentially I'm continuing based on my learning adaptive AI starts moving towards saying, right, how do I create something new that is not necessarily based on anything I've learned, but let me extend what I've learned and look in new spaces.

So adaptive AI then becomes something that is, there's a new space.

Um, and when you start putting those together, generative AI, adaptive AI together, you can imagine where things, um, then become, become fairly complex.

And so that's relatively new.

We start to see, uh, some, some organizations focusing, focusing in that space.

And then the, the, the other, you know, everything we do becoming essentially AI embedded, you know, so whether it's, where it's the metaverse environment, where it's the gaming environment and all of those become, becoming richer and richer and enhanced by, um, by the, the, the AI and the agent of AI and the various AI models.

I think, I think, I think we've certainly seen that, um, and you know, the other, the other part is in, in AI, you've got essentially large language models, which are self learning, if you wish, or reinforcement learning.

And then there is the other, which is supervised learning.

So if you think of, for instance, a Watson type of environment, where I've given it information and said, here's a lot of information, this is a particular piece of information, and this is how it's labeled.

And so there's that, that side, which is supervised learning, we've got the unsupervised or reinforcement learning, and when you start putting those two together, they actually become quite a powerful combination in terms of the, the generative AI being able to, to ask kind of unrelated questions against a factual base.

And then being able to essentially have this combination of a plane of one another from a taught, supervised learning and unsupervised learning component.

So those are the types of things.

But I would say that the space of growth around generative AI is only going to get larger.

So the more and more information that it's fed, the more modes that it's fed it in.

So video and audio, et cetera, the more of those modes it plays in.

The fact that it's moving, and we've, of course, seen it now, the whole robot side of things, Optimus, et cetera, where essentially you've got the large element model or the ability, the digital brain built into a robot.

And now the robot having a couple more of the senses, so the feel, the ability to move.

And so that being then added to it, which is then going to be moving from just a textual and a video thing to a physical device and a much more human type device.

Okay.

Are you literally saying that there's Optimus is putting AI into a robot like that?

That's a project?

So if you look at what Optimus Prime Transformers, like I'm geeking out over here.

No, no.

So Optimus, this is Tessa Optimus, so essentially their robot.

So essentially their large language model capability has been used as a foundation.

So if you look at a lot of the earlier robots were trained on, here was a programmatic thing.

So do the step one, step two, step three, step four.

The current robots are using the same large language model approach, which is all I want you to do is go and figure out how to make a cup of coffee.

There's the house.

See you later.

Go and figure it out.

And literally through its ability to perceive and learn and understand, it knows that it needs to make coffee.

So it's going to look for coffee machines, going to look for coffee beans, it's going to look for milk, et cetera, and actually create that, make that cup of coffee.

And it's going to learn.

So it's not trained step by step.

So to make a cup of coffee, you go through this door, you go there, you open up that fridge.

It literally goes through a process of perceiving and using the large language model to then try and figure out what it needs to do to create a cup of coffee.

And that is what is embedded in, for instance, the Tesla Optimus and a couple of others that are now starting to have that large language model capability.

So literally is, you know, make a cup of coffee or walk into a room, what should you do?

And the first thing it does, oh, it's untidy, let me tidy the room.

You know, or, hey, there's a fire in the corner, I'm going to try and put the fire out.

You know, so that whole process of not predetermining the steps it's going to use, but actually having the, essentially the digital brain being able to understand the environment, interpret the environment and take appropriate responses.

Yeah, it's fascinating.

I mean, so much.

Honestly, I could sit here for another hour and just dig in to what people are working on or what is, honestly, six months to a year away.

I mean, because this thing is tracking so fast that it's like, oh, my gosh, that's a decade before.

No, forget that.

It's going to be tomorrow before we're going to have to address and deal with all this.

It's interesting.

We had, oh, I just, Aaron Seneff is the, he's the chief technical officer for PushPay.

I don't know if you're familiar with PushPay.

It's a billion dollar Christian tech company.

It was acquired for $950 million, I don't know, maybe six months, maybe a year ago.

But, and so he was on the podcast and was talking about some things and asked him about AI and his stance on AI.

We just published that podcast maybe a couple of weeks ago.

But he said, it was really interesting.

He's like, Jeff, you're, you know, as a church, he's like, you're either using AI or you don't realize you're using AI, but AI is already baked into everything.

It's already, you know, you can say you hate it and you're afraid of it, but you're already using it at some level.

And, you know, even Siri interrupting this podcast earlier is a perfect example of, you know, AI being AI.

You know, whether, whether we want it or not, it's lurking, paying attention and waiting to step in as, as it seems helpful.

You know, I say to my daughter, hey, sweetie, all the time.

And Siri always chimes back, what can I do for you?

And I'm like, I'm not talking to you, Siri.

I'm talking to my daughter.

And so that's, you know, yet another interruption.

But it's the reality of where we are, right?

And so it's, as a church, I think it's understanding the implications, recognizing the people that are drawing the lines, which I love that.

There are multiple people and, but, you know, and then there's, it's managing ourselves on how we're using that situation and protecting our legacies where we have to, to make sure that we're self-guarded and that we're not putting ourselves in harm's way for, for those that, that might take advantage of it.

Cause somebody will, I mean, to, to think that everybody's going to control their own garden and manage their own space.

Like it's 2024, nobody's going to like that's, we can't, we can't expect everybody else to play nice.

We've got to protect ourselves, but at the same time, we can't bury our head in the sand and like as much as I want to with this and, and think that everybody's, uh, that it doesn't exist because the reality is it does.

So listen, Quentin, this has been incredible.

Like, I almost want to bring it back and talk more about the robotic aspect of this, but, uh, let's, let's wait for that to become a little more mainstream or potentially mainstream before we do that.

So as we're, as we're wrapping up and landing the plane here, uh, man, any closing thoughts on your side?

You know, I think really is, you know, I go back to one of my fundamental focus, uh, areas where I'm focused on ethics and recognizing ethics got two sides and really, how do we keep on driving towards the positive aspect?

There's just so much noise out there of just doomsday and how bad it can be.

You know, and I really believe that as, as, as Christians, we can be taking advantage of them and saying, Hey, there's a huge positive bend.

And driving it and not being, not, not being blind to it and really forcing the, the big tech companies into the right direction, but really bringing the, the, the Christian voice and the positive voice and the human flourishing voice, the shalom voice into, into the play and really driving it towards that direction.

And just recognize that, you know, there are risks, but at the same time, there are huge advantages as well.

And those are the ones in my mind that we should be focusing on and really using for, for kingdom and use for the kingdom sake for sure.

Well, for a guy that's job is literally risk assessment or his studies has been risk assessment of artificial intelligence.

Uh, I feel that the risks have been assessed.

Um, and, and part of me is a little surprised that you're, you know, pro on that.

I, I, I was actually expecting a little more anti, honestly, just, you know, hindsight being what it was, I was expecting a little more, uh, challenge from that, but I feel challenged.

And so it's like, all right, let's, uh, let's, let's, let's figure out how to, you know, walk before we run here.

And, um, you know, I, I, I tell people all the time, trust, but verify.

Uh, and so I have a, I have a feeling, uh, you know, with, with Sora and moving forward in other spaces, it's going to be a lot of that trust, but verify moving forward and, you know, declare, um, label confess is not the right word.

That's negative connotation, but that at least moving forward with that.

So I think this is going to be, uh, it'll make for an interesting season moving forward, but Hey, we're going to land the plane.

Uh, thank you, Quentin, Quentin McGrath.

Thank you very much for jumping here on the podcast and sharing anytime I want to talk AI ethics, uh, here, you're, uh, an email away.

So I appreciate that, but we're going to land the plane.

So for, for Quentin, this is Jeff with, uh, the church digital.

Thanks for jumping on the podcast.

We'll see you next time on the show.

Y'all have a good day.

We'll see you next time on the show.