WEBVTT 00:00:00.000 --> 00:00:11.920 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? 00:00:12.680 --> 00:00:14.680 Welcome to the Church Digital Podcast. 00:00:15.640 --> 00:00:19.520 Through this podcast, we'll talk about the technological innovations within the church. 00:00:19.860 --> 00:00:23.320 But more than tech for tech itself, we'll address deeper questions. 00:00:23.940 --> 00:00:25.560 Is disciple making possible digitally? 00:00:25.940 --> 00:00:28.020 How should we approach the digital mission field? 00:00:28.020 --> 00:00:30.880 Can a biblically grounded church operate in digital space? 00:00:31.320 --> 00:00:35.220 Oh, and where does the metaverse and artificial intelligence fit into all of this? 00:00:35.980 --> 00:00:44.780 Whether you're a big or small church, the Church Digital's goal is to help churches like yours learn to be a multiplying church digitally and physically. 00:00:45.200 --> 00:00:46.940 And now here's your host, Jeff Reed. 00:00:47.640 --> 00:00:50.360 Episode 285 of the Church Digital Podcast. 00:00:50.520 --> 00:00:51.880 Jeff here, founder of the Church Digital. 00:00:52.520 --> 00:00:54.420 Love having this conversation with you. 00:00:54.460 --> 00:00:55.080 Glad you all are here. 00:00:55.400 --> 00:00:57.040 Oh, I forgot my best part. 00:00:57.420 --> 00:01:00.340 The Church Digital Podcast powered by Riverside. 00:01:00.400 --> 00:01:02.780 How could I forget Riverside up here at the top? 00:01:02.820 --> 00:01:04.320 Hey, we're talking artificial intelligence. 00:01:04.700 --> 00:01:08.200 We'll have Quentin McGrath coming up here in a little bit talking about ethics of AI. 00:01:08.440 --> 00:01:09.460 It's going to be a great conversation. 00:01:09.900 --> 00:01:11.920 But Riverside, oh my gosh. 00:01:12.480 --> 00:01:20.120 Every time I turn around, I don't know that this is true, but literally every time I log on to the Riverside platform to do some work here on the Church Digital Podcast, 00:01:20.540 --> 00:01:22.680 I feel like they're rolling out some new feature. 00:01:22.820 --> 00:01:27.300 Or maybe I'm just discovering it now as I'm digging into it more at a personal level. 00:01:27.720 --> 00:01:35.960 But oh my gosh, between the clips that it's generating, the social snapshots, the transcripts, the takeaways, the topics, chapters now, 00:01:36.600 --> 00:01:41.340 artificial intelligence is so baked into this Riverside platform. 00:01:41.460 --> 00:01:48.840 And it is so helpful into what I'm trying to do and what people like you, churches like you are trying to do with your podcasts. 00:01:49.000 --> 00:01:49.920 Oh my goodness. 00:01:50.160 --> 00:01:54.480 In your YouTube videos, you need to be checking out Riverside. 00:01:54.580 --> 00:01:55.580 Do me a favor right now. 00:01:55.920 --> 00:01:57.760 Thechurch.digital slash Riverside. 00:01:57.860 --> 00:01:58.760 Pause this. 00:01:59.260 --> 00:02:03.180 Come back to Quentin McGrath and AI Ethics of Sora. 00:02:03.320 --> 00:02:03.820 We'll get there. 00:02:04.300 --> 00:02:04.820 Pause it. 00:02:05.260 --> 00:02:06.300 Open up a new window. 00:02:06.960 --> 00:02:08.120 Pull over the car. 00:02:08.300 --> 00:02:09.340 Take out your phone. 00:02:09.520 --> 00:02:10.620 Open up the web browser. 00:02:11.020 --> 00:02:14.280 Thechurch.digital slash Riverside. 00:02:14.280 --> 00:02:24.080 Check out that website and learn more about how Riverside can help your church do incredible stuff with video. 00:02:24.560 --> 00:02:26.900 Okay, so let's get into this podcast. 00:02:27.100 --> 00:02:27.980 Now, you've already done that. 00:02:28.060 --> 00:02:28.780 You paused the video. 00:02:28.880 --> 00:02:29.520 You came back in. 00:02:29.760 --> 00:02:38.240 We're going to talk now about this on the podcast and the video, talking about the ethics surrounding text-to-video and Sora. 00:02:38.240 --> 00:02:47.800 So, you know, OpenAI, the inventors of ChatBeatGPT, and the people that made all this noise going back October 22, November 22, and beyond. 00:02:48.320 --> 00:02:49.240 Hey, guess what? 00:02:49.620 --> 00:02:51.020 They're rolling out this new technology. 00:02:51.500 --> 00:02:56.060 It's only getting to certain people, but this new technology isn't just text. 00:02:56.160 --> 00:02:57.940 It's not even just text-to-photo. 00:02:58.080 --> 00:03:06.360 It's now text-to-video, where now we can create minute-long video clips simply based off of text. 00:03:06.760 --> 00:03:11.980 And, you know, things like deepfakes are suddenly becoming much more of a reality. 00:03:13.380 --> 00:03:15.000 What are the ethics surrounding this? 00:03:15.040 --> 00:03:18.480 As a church, do we like this idea, or are we petrified? 00:03:18.540 --> 00:03:19.060 I'll be honest. 00:03:19.140 --> 00:03:21.740 I'm terrified at this up front. 00:03:21.740 --> 00:03:24.140 And so maybe I need to relax a little bit. 00:03:24.500 --> 00:03:26.880 But, hey, let me at least be the guy to ask some questions. 00:03:27.020 --> 00:03:28.920 And so who am I going to talk to about that? 00:03:28.980 --> 00:03:31.260 I'm bringing in my friend Quentin McGrath. 00:03:32.060 --> 00:03:44.520 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. 00:03:44.520 --> 00:03:50.660 And so if I want to talk, and he also works with microchurches in the Tampa Bay, Florida area. 00:03:50.800 --> 00:04:00.280 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. 00:04:00.400 --> 00:04:01.880 And so I re-emailed Quentin recently. 00:04:01.980 --> 00:04:02.900 I was like, hey, let's jump on. 00:04:02.980 --> 00:04:12.640 Let's talk about Sora, this new technology that OpenAI released or is releasing, and how we, the church, should be thinking about this. 00:04:12.640 --> 00:04:14.500 And so here's the conversation. 00:04:14.500 --> 00:04:19.360 And I got to tell you, I'm a little surprised walking away from this, Quentin's take. 00:04:19.360 --> 00:04:30.100 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. 00:04:30.900 --> 00:04:32.140 Okay, everybody, here you go. 00:04:34.000 --> 00:04:36.600 Well, thanks, Jeff, and great to be with you again. 00:04:36.760 --> 00:04:37.920 This is just fantastic. 00:04:38.500 --> 00:04:40.020 A bit of background on myself. 00:04:40.700 --> 00:04:43.720 So I was with Deloitte for many years, 24 years. 00:04:43.840 --> 00:04:44.980 Actually, I hate to admit that. 00:04:45.300 --> 00:04:48.100 24 years I was with Deloitte as an organization. 00:04:48.240 --> 00:04:52.000 In fact, I was involved in a number of other organizations in South Africa before that. 00:04:52.140 --> 00:04:54.380 So that was a large part of my career. 00:04:54.960 --> 00:05:02.760 I was lucky to be able to retire from them in January 22, while I was actually busy with my doctoral studies. 00:05:03.240 --> 00:05:09.000 So I spent a couple of years in my doctoral studies focused on AI ethics and risk management. 00:05:09.800 --> 00:05:14.160 And in that context, really understanding how we can apply AI in the world. 00:05:14.260 --> 00:05:26.340 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. 00:05:26.400 --> 00:05:29.000 And it's become such a fundamental thing in businesses. 00:05:30.140 --> 00:05:38.020 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. 00:05:38.020 --> 00:05:46.040 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. 00:05:46.680 --> 00:05:49.340 And I'm involved a lot in mentoring. 00:05:49.940 --> 00:05:54.660 I'm involved as an adjunct professor at the University of South Florida, where I've got my doctorate. 00:05:55.640 --> 00:06:06.480 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, 00:06:06.480 --> 00:06:08.160 and how do we take advantage of it. 00:06:08.640 --> 00:06:12.080 And then very recently, I joined the AI and Faith as a research fellow. 00:06:12.820 --> 00:06:24.280 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. 00:06:24.360 --> 00:06:33.040 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. 00:06:33.040 --> 00:06:34.040 That's beautiful. 00:06:34.040 --> 00:06:48.240 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. 00:06:48.460 --> 00:06:52.040 And, you know, we're even doing studies and background with Deloitte. 00:06:52.940 --> 00:06:55.960 And so I'm really looking forward to this. 00:06:56.160 --> 00:06:57.120 So, hey, let's do this. 00:06:57.180 --> 00:06:58.400 Let's just dive in. 00:06:59.060 --> 00:07:01.800 And so we're talking about Sora. 00:07:02.640 --> 00:07:04.360 I tell you, why don't we do this? 00:07:04.620 --> 00:07:09.080 Like just maybe set up because I don't know that I fully understand this. 00:07:09.340 --> 00:07:16.080 So, you know, text-to-video, Sora, I believe OpenAI is releasing this or has released this in limited capacity now. 00:07:16.500 --> 00:07:20.380 Like what are we talking about here as we're talking about Sora or text-to-video? 00:07:20.740 --> 00:07:20.920 Yeah. 00:07:21.260 --> 00:07:26.920 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. 00:07:27.060 --> 00:07:29.280 Haven't we got a number of situations? 00:07:29.280 --> 00:07:35.020 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. 00:07:35.580 --> 00:07:39.300 And so, you know, most people said, oh, we've had text-to-video for a long time, but this is different. 00:07:39.860 --> 00:07:40.900 This is really different. 00:07:41.320 --> 00:07:49.380 Because essentially what OpenAI have done is they've taken the same approach to prompting and said, right, now use that prompt. 00:07:50.240 --> 00:07:51.400 We used to create pictures. 00:07:51.480 --> 00:07:52.420 We used to create text. 00:07:52.560 --> 00:07:53.860 We used to get it to answer things. 00:07:53.860 --> 00:07:59.980 Now, literally, it's taken the same approach and converted it into an up-to-one-minute video clip. 00:08:00.740 --> 00:08:02.720 Now, the video clip is rich. 00:08:02.820 --> 00:08:03.560 It's high quality. 00:08:04.100 --> 00:08:09.960 You can guide it to really be a very rich environment. 00:08:10.180 --> 00:08:14.260 You can kind of set the environment as you would use in a prompt in any other place. 00:08:14.260 --> 00:08:23.960 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. 00:08:24.420 --> 00:08:30.660 And there's a Japanese woman walking down in a wet street, you know, evening setting, lights, lights, reflection of the street. 00:08:30.660 --> 00:08:44.040 And literally walking down the street in a very natural and no jittering, no stopping, literally a whole smooth, excuse me, AI. 00:08:44.920 --> 00:08:45.400 Nice. 00:08:45.560 --> 00:08:46.720 Thank you very much, Siri. 00:08:46.880 --> 00:08:47.700 That was beautiful. 00:08:48.900 --> 00:08:51.500 Apple's not, their technology's not quite up to par yet. 00:08:51.620 --> 00:08:52.220 No, no, it's fine. 00:08:52.600 --> 00:08:59.900 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. 00:09:00.100 --> 00:09:04.160 And eventually it comes and she looks directly into where the camera would be. 00:09:04.480 --> 00:09:05.280 But there's no camera. 00:09:05.380 --> 00:09:06.680 There's no reflection on the glasses. 00:09:06.780 --> 00:09:07.480 She's wearing glasses. 00:09:08.020 --> 00:09:09.680 And it's just the whole cycle. 00:09:09.780 --> 00:09:10.520 It's so smooth. 00:09:10.580 --> 00:09:21.860 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. 00:09:22.080 --> 00:09:22.780 They can be used. 00:09:23.260 --> 00:09:27.680 There's no sound associated with them at this stage, but you can see the richness of the environment. 00:09:28.420 --> 00:09:35.180 And literally, if you go to OpenAI slash Sora and you can see all the examples that they've put up there. 00:09:35.660 --> 00:09:46.100 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. 00:09:46.200 --> 00:09:47.540 I don't want to start with a blank sheet of paper. 00:09:47.540 --> 00:09:56.260 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. 00:09:56.740 --> 00:09:57.120 Have you? 00:09:57.480 --> 00:10:01.840 Because technically, this is released at this point, technology, right? 00:10:01.960 --> 00:10:06.040 But they're only releasing it to certain people as they're kind of testing it out. 00:10:06.100 --> 00:10:07.540 Like, what's the current status of it? 00:10:07.940 --> 00:10:11.500 The current status, as you can understand, is they released it. 00:10:11.540 --> 00:10:14.160 So there was a blog post, which is what they normally do. 00:10:14.280 --> 00:10:17.440 OpenAI normally does everything new they release out in a blog post. 00:10:17.920 --> 00:10:22.060 And in that blog post, they said they are releasing it to the red teaming. 00:10:22.280 --> 00:10:29.180 And the intention of releasing it to the red teaming is because you can imagine this is so, so complex to begin testing. 00:10:29.720 --> 00:10:35.260 They are then releasing it to a small group of people who are going to go through the process of putting guardrails in place. 00:10:35.460 --> 00:10:42.760 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. 00:10:43.200 --> 00:10:46.260 I call it making the child polite, essentially. 00:10:46.820 --> 00:10:51.680 You know, putting those guardrails in place to make sure that they then say the right things or don't say the wrong things. 00:10:52.220 --> 00:10:54.700 And so that is the process that they're looking at this stage. 00:10:55.800 --> 00:11:00.600 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. 00:11:00.740 --> 00:11:03.180 And clearly, there are going to be levels of misuse of this. 00:11:03.500 --> 00:11:06.820 But they're trying to put guardrails in place, as they have been doing. 00:11:07.500 --> 00:11:10.440 And OpenAI has been trying to do that progressively with all their releases. 00:11:10.880 --> 00:11:12.060 So that's where they are at this stage. 00:11:12.160 --> 00:11:15.560 And how long is it going to be in red teaming they haven't announced? 00:11:15.780 --> 00:11:23.140 And I presume their normal style is to open it to a closed group of people and then progressively open it beyond that. 00:11:23.600 --> 00:11:27.320 But what they have shown at this stage is very, very interesting. 00:11:27.320 --> 00:11:29.720 So, I mean, let's get in here a little bit. 00:11:29.800 --> 00:11:43.340 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. 00:11:43.540 --> 00:11:44.200 That's beautiful. 00:11:44.840 --> 00:11:46.000 It's an interesting story. 00:11:46.360 --> 00:11:52.040 My son the other day, my son's 13, and he's actually starting to study videography in school. 00:11:52.040 --> 00:11:56.180 And so he was creating his first clip of video the other day. 00:11:56.320 --> 00:11:59.600 And I don't understand why this is what he chose. 00:11:59.840 --> 00:12:07.940 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. 00:12:08.060 --> 00:12:11.920 And so it was just a bunch of video clips that he actually created. 00:12:12.060 --> 00:12:18.040 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. 00:12:18.120 --> 00:12:21.120 And once again, he's a middle schooler, so I don't understand why. 00:12:21.200 --> 00:12:26.520 But Sora, for him, would be incredible, because I'm sure Sora could make hot dogs rain from the sky. 00:12:28.200 --> 00:12:29.440 13-year-old boys, whatever. 00:12:29.900 --> 00:12:37.980 Moving past that, like, moving past the creatives, like, ethically, what concerns are we seeing with this idea of text-to-video? 00:12:37.980 --> 00:12:41.960 Like, to me, it seems like this might be a win, but it also could be a loss. 00:12:42.140 --> 00:12:44.860 Like, what's the negative as we're looking at this? 00:12:45.860 --> 00:12:49.380 Yeah, so my context and ethics is always the two sides of the coin. 00:12:49.560 --> 00:12:55.840 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. 00:12:57.680 --> 00:13:00.560 And I love to think of the context of shalom. 00:13:00.820 --> 00:13:06.880 So literally everything that goes with shalom and the wholeness is one of the critical parts of ethics. 00:13:06.880 --> 00:13:11.560 And then the other part, as you say, is not doing bad stuff and making sure we don't do bad stuff. 00:13:12.020 --> 00:13:22.260 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. 00:13:22.380 --> 00:13:29.380 In fact, interestingly, on the website, OpenAI site, they start, their first sentence says, and let me quote it to you, 00:13:29.380 --> 00:13:39.100 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. 00:13:39.960 --> 00:13:47.500 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. 00:13:47.780 --> 00:13:49.980 So that's on the positive side. 00:13:50.520 --> 00:13:54.340 But I think if you then start saying, right, now what else opens up? 00:13:54.400 --> 00:13:58.940 And if I was trying to do bad things, what could I do? 00:13:58.940 --> 00:14:08.620 And you can imagine this whole space of when you add visual images, the ability to influence people just steps up. 00:14:09.620 --> 00:14:19.880 So we know just out of experience that if we look at something, we can keep a critical thinking, apply critical thinking to something. 00:14:19.880 --> 00:14:26.060 But over time, if we look at something and we get used to something, we get comfortable with that image. 00:14:26.660 --> 00:14:28.140 And so we start to lose now critical thinking. 00:14:28.300 --> 00:14:36.720 And so the ability over time for the system or people to use it to create beautiful images or peaceful images, 00:14:37.240 --> 00:14:44.360 but at the same time then lean towards nudging someone to go to some place that they wouldn't normally go. 00:14:44.560 --> 00:14:52.860 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. 00:14:53.080 --> 00:15:05.020 So my big worries are the ability to use the visual images to essentially reduce the critical thinking and reduce our logical mind, 00:15:05.380 --> 00:15:12.620 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. 00:15:12.620 --> 00:15:15.700 So that, to me, is one big element. 00:15:16.240 --> 00:15:20.960 The other part, of course, is you've got your fundamental biases that are built into training data. 00:15:21.780 --> 00:15:25.620 As much as we love to think about it, we might think we're totally unbiased. 00:15:25.820 --> 00:15:26.500 We're all biased. 00:15:27.380 --> 00:15:32.220 And so that data that's been used to train has got some implicit biases in it. 00:15:32.940 --> 00:15:36.000 And we know that those biases are in the training data. 00:15:36.360 --> 00:15:51.400 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. 00:15:52.160 --> 00:15:55.860 And so we've got this whole situation where the training data has got some biases in it. 00:15:55.860 --> 00:15:59.240 And so we may get results coming out that are biased. 00:16:00.080 --> 00:16:08.860 Now, whether those are boldly biased and visibly biased, like saying all doctors are men, you know, and clearly that's incorrect. 00:16:09.260 --> 00:16:18.500 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? 00:16:18.860 --> 00:16:24.820 So that whole thing about bias and what's underlying in terms in terms of bias certainly does worry me. 00:16:24.820 --> 00:16:31.420 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. 00:16:32.460 --> 00:16:48.700 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. 00:16:48.860 --> 00:16:50.040 And this is not real. 00:16:50.660 --> 00:17:01.140 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? 00:17:01.680 --> 00:17:08.220 You know, and you can see very quickly the steps that we move towards saying, oh, that has to be real versus otherwise. 00:17:08.860 --> 00:17:11.920 And so I think those to me are probably the biggest worries. 00:17:11.920 --> 00:17:12.660 It's a bias. 00:17:13.100 --> 00:17:14.540 It's the ability to defake. 00:17:14.960 --> 00:17:23.740 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. 00:17:23.740 --> 00:17:29.140 Now, I know at some level, like there's a there's a maybe filters the right word. 00:17:29.220 --> 00:17:31.020 Maybe it's actually another stage of bias. 00:17:31.020 --> 00:17:37.840 But like there is in the chat GPT currently like there is some level of filter. 00:17:38.000 --> 00:17:45.700 And when I filter, you know, I mean, like I've asked chat GPT, you know, what's the best religion? 00:17:45.700 --> 00:17:47.360 I've talked about this in the podcast before. 00:17:47.360 --> 00:17:50.680 Like, well, there are four religions or, you know, what are the what religion is right? 00:17:50.720 --> 00:17:58.260 I think that's actually and it gave me a very filtered kind of everything is right. 00:17:58.260 --> 00:18:04.540 And it defined the Christianity, Judaism, Hinduism, maybe I forget what the fourth was, but it defined it. 00:18:04.700 --> 00:18:06.780 And it said the strengths of all four. 00:18:06.780 --> 00:18:08.220 And it said that they were all right. 00:18:08.220 --> 00:18:15.500 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. 00:18:15.500 --> 00:18:22.080 And so somewhere in the nebulous fear, somebody coded that kind of filter or bias. 00:18:22.080 --> 00:18:34.240 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? 00:18:34.680 --> 00:18:39.680 I mean, I'm thinking, hey, let's put the president of the United States in a in a brothel. 00:18:39.680 --> 00:18:49.800 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. 00:18:49.800 --> 00:19:01.700 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. 00:19:01.700 --> 00:19:08.760 This is actually violating, you know, ethical principles, moral principles, business principles like this. 00:19:08.760 --> 00:19:15.580 This power is way beyond what I feel like, well, almost, you know, my 13 year old son should have access to. 00:19:15.720 --> 00:19:22.440 Yeah, and I think you're right in that it goes to that whole bias and consent side of things. 00:19:22.620 --> 00:19:25.540 Maybe rights and consent is probably a better way of putting it. 00:19:26.040 --> 00:19:29.680 So what what do I do I have rights to do? 00:19:29.860 --> 00:19:35.640 And I in this space, I always encourage people to say not what can I do, but what should I do? 00:19:36.700 --> 00:19:48.000 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. 00:19:48.340 --> 00:19:52.000 Now, some of those are built in in terms of guardrails. 00:19:52.000 --> 00:20:07.160 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. 00:20:07.740 --> 00:20:22.660 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? 00:20:23.040 --> 00:20:36.420 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. 00:20:37.020 --> 00:20:40.820 And they were just saying, right, they will not go past this point. 00:20:41.420 --> 00:20:53.780 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. 00:20:54.060 --> 00:21:07.040 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? 00:21:07.080 --> 00:21:13.680 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. 00:21:14.620 --> 00:21:21.720 I'm going to hold to certain ethical frameworks and ethical stances or positions based on what I believe is important. 00:21:22.200 --> 00:21:25.940 Now, my personal belief system is Judeo-Christian basis. 00:21:26.080 --> 00:21:34.260 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. 00:21:34.420 --> 00:21:36.740 Or this is good and yes, we should go there. 00:21:36.920 --> 00:21:39.160 Or if we go there, we can actually get some advantage. 00:21:39.240 --> 00:21:41.140 So let's be careful, but we'll not go further than that. 00:21:41.580 --> 00:21:47.340 And so the whole ability for us to be very, very careful and very clear on this is the line we are drawing. 00:21:47.800 --> 00:21:50.240 And so that might be in terms of visual images. 00:21:50.520 --> 00:22:05.640 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. 00:22:06.180 --> 00:22:08.800 And so being very careful on how we look at things. 00:22:09.460 --> 00:22:20.620 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? 00:22:21.120 --> 00:22:34.980 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. 00:22:35.200 --> 00:22:40.480 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. 00:22:40.920 --> 00:22:42.220 And then that's for a reason. 00:22:42.360 --> 00:22:45.480 And then we need to be transparent about why we do it and how we're doing it. 00:22:45.800 --> 00:22:47.140 So I'm a Christian. 00:22:47.280 --> 00:22:50.660 I'm using this as a way of displaying my Christian faith. 00:22:50.960 --> 00:22:51.900 And here's an example. 00:22:52.880 --> 00:23:00.780 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, 00:23:01.640 --> 00:23:06.020 almost in an underhand, nefarious type of way. 00:23:06.600 --> 00:23:08.480 So part of it is, how do we think about it? 00:23:08.480 --> 00:23:16.920 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? 00:23:17.020 --> 00:23:19.740 I mean, so, I mean, really, you're suggesting there's two different checks. 00:23:19.860 --> 00:23:30.240 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. 00:23:30.300 --> 00:23:33.960 And so, you know, 13-year-old son, check. 00:23:34.220 --> 00:23:36.760 You know, at least I keep him in check, hopefully, as a parent. 00:23:37.560 --> 00:23:42.980 But the second piece is, like, do we trust the people that are drawing the lines? 00:23:44.020 --> 00:23:47.440 And so, you know, open AI is a great example. 00:23:47.680 --> 00:23:49.940 You know, maybe I trust open AI. 00:23:51.240 --> 00:23:55.440 You know, Google, literally, like, their motto is do no harm. 00:23:55.620 --> 00:23:57.440 And so, okay, maybe I trust Google. 00:23:57.600 --> 00:24:00.680 Maybe I trust Apple, although Siri is horrible. 00:24:01.920 --> 00:24:12.960 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. 00:24:13.080 --> 00:24:14.360 Yes, weaponizing text to video. 00:24:14.940 --> 00:24:18.580 Like, who else is a player in this space? 00:24:18.800 --> 00:24:23.520 Or are they all just cloning what Sora is and manipulating it for their own? 00:24:23.860 --> 00:24:26.260 Like, is there one organization that's really drawing the lines? 00:24:26.340 --> 00:24:28.320 Or are we worried about other people? 00:24:28.400 --> 00:24:30.300 Am I just, like, paranoid? 00:24:30.660 --> 00:24:33.140 Like, who's actually drawing these lines? 00:24:34.020 --> 00:24:36.100 All organizations are drawing their own lines. 00:24:36.360 --> 00:24:37.620 So let's be really honest with that. 00:24:37.880 --> 00:24:45.360 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. 00:24:45.880 --> 00:24:58.860 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. 00:24:59.020 --> 00:25:05.940 And I found that, you know, one of the large language models was focused much more on a situationalist type of approach. 00:25:06.500 --> 00:25:10.340 And it was basically saying, you know, I'm not going to hold to any firm truth. 00:25:10.400 --> 00:25:12.440 I'll be able to, based on the situation, I might change. 00:25:12.900 --> 00:25:16.460 Another large language model was based on absolutist model. 00:25:16.540 --> 00:25:18.460 So it's always going down this particular path. 00:25:18.920 --> 00:25:25.020 And so those are, even in what I would call the large, the big tech, the ones that we trust, 00:25:25.180 --> 00:25:30.240 there's already biases that are built in, in terms of how they've actually structured. 00:25:30.860 --> 00:25:38.820 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, 00:25:38.960 --> 00:25:47.060 yes, there's the ability to then either train it with bad data, with, in some cases, poisoned data, you know, 00:25:47.100 --> 00:25:54.340 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. 00:25:54.660 --> 00:26:02.260 And so part of what we've got to be looking at is understanding and have critical thinking around what is coming out 00:26:02.260 --> 00:26:08.720 and test and validate that we're comfortable with what is coming out of the AI in terms of its output. 00:26:09.100 --> 00:26:13.840 Now, be that video output or be that textual output or images, whatever it is. 00:26:15.100 --> 00:26:18.160 And so that's the important part is to have that critical thinking. 00:26:19.020 --> 00:26:31.460 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. 00:26:31.620 --> 00:26:35.860 And very, it's expensive from a load perspective. 00:26:36.080 --> 00:26:42.320 And therefore, the cost of electricity, the cost of processes, the cost of the environment to actually make it happen. 00:26:42.800 --> 00:26:48.180 And so to be able to do this, you have to be a very big organization with lots and lots of money. 00:26:48.660 --> 00:26:54.620 And we see Altman and others going around the world talking about the amount of dollars they need to actually move this forward. 00:26:55.680 --> 00:27:00.040 The amount of processes, GPUs that they need to actually make sure that they can train the models. 00:27:00.180 --> 00:27:08.420 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. 00:27:08.700 --> 00:27:16.180 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. 00:27:16.620 --> 00:27:26.900 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, 00:27:27.420 --> 00:27:34.900 a handful of organizations in the U.S., but also around the world, including the Far East and others. 00:27:35.380 --> 00:27:41.060 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. 00:27:41.500 --> 00:27:46.320 You know, I know, obviously, there's tons of privacy concerns as we're getting into this. 00:27:47.380 --> 00:27:48.820 Now, it's interesting. 00:27:49.200 --> 00:27:56.040 I, Jeff Reed, I actually have ChatGPT'd my name and Jasper, like, and Jeff Reed as a name. 00:27:57.040 --> 00:28:03.460 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. 00:28:03.780 --> 00:28:07.660 I've won several World Series for the Cincinnati Reds, or at least another Jeff Reed has. 00:28:08.020 --> 00:28:11.740 I'm actually in jail for doing something with kids that you're not supposed to do with kids. 00:28:11.880 --> 00:28:12.740 That's another Jeff Reed. 00:28:13.020 --> 00:28:17.380 So if you've Googled Jeff Reed, like, there's lots of Jeff Reads out there. 00:28:17.480 --> 00:28:24.680 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. 00:28:24.760 --> 00:28:26.840 And it doesn't have a clue who I am. 00:28:26.980 --> 00:28:36.620 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. 00:28:36.660 --> 00:28:39.420 Jay Cranda, digital pastor over at Saddleback. 00:28:39.640 --> 00:28:41.600 You know, Quentin McGrath is such a unique name. 00:28:43.020 --> 00:28:47.420 ChatGPT probably knows who you are better than it would me because Jeff Reed's more common. 00:28:48.860 --> 00:28:54.200 And so, like, it would seem to me, I could do a text-to-video. 00:28:54.460 --> 00:28:55.860 Sorry, Nils, I'm going to pick on you. 00:28:56.100 --> 00:29:00.980 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. 00:29:01.400 --> 00:29:08.940 And a deepfake involving Nils, like, would be very easy to reproduce because ChatGPT would know who Nils has. 00:29:08.940 --> 00:29:13.620 Like, am I reading, I'm sorry, Chat, not ChatGPT, OpenAI, Sora. 00:29:14.100 --> 00:29:16.600 Like, am I reading too much into deepfake? 00:29:16.760 --> 00:29:18.500 Like, is there going to be a permissions level? 00:29:18.620 --> 00:29:19.880 Should there be a permissions level? 00:29:20.380 --> 00:29:25.940 What are we looking at here when trying to prevent maybe some personal violations? 00:29:26.400 --> 00:29:32.760 So one of the things that they have said is they're going to be building into the metadata, 00:29:32.760 --> 00:29:39.820 what they call the C2PA, which is a Coalition for Content, Provence, and Authentication. 00:29:40.040 --> 00:29:41.100 So authenticity, rather. 00:29:41.560 --> 00:29:48.380 So the C2PA metadata, that really says, just like images that you'll take on, 00:29:48.380 --> 00:29:55.900 on cameras, it'll actually say, this is taken by a Canon with this lens, with this ISO rating, et cetera. 00:29:55.900 --> 00:29:59.860 And all that information is stored in the metadata of that actual content. 00:30:00.380 --> 00:30:03.800 And so what OpenAI is saying, they're going to be putting that information, 00:30:04.260 --> 00:30:10.420 they're going to be building that information into the data that's carried along in the video itself. 00:30:10.700 --> 00:30:20.120 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. 00:30:20.900 --> 00:30:22.620 So that's what they've committed to. 00:30:22.720 --> 00:30:25.540 Now, are there going to be people who are going to be trying to jailbreak it? 00:30:25.640 --> 00:30:25.900 Yes. 00:30:26.180 --> 00:30:27.720 And are they going to be able to jailbreak it? 00:30:27.800 --> 00:30:31.640 I'm sure they will at some stage with the right amount of power and the right amount of thinking. 00:30:32.180 --> 00:30:46.780 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. 00:30:47.020 --> 00:30:52.520 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. 00:30:53.240 --> 00:30:56.640 And this is part of what we're going to have to build in. 00:30:56.940 --> 00:31:00.520 And it's more of an agreeing to build in. 00:31:00.800 --> 00:31:04.300 Because clearly, if I've got an open source model, I can choose not to do that. 00:31:05.040 --> 00:31:10.480 But really, the whole process of transparency and saying, it's OK to say you use an AI. 00:31:10.940 --> 00:31:11.680 It's accepted. 00:31:11.680 --> 00:31:15.500 And in fact, it's expected that you're going to use AI in some other form. 00:31:15.640 --> 00:31:16.220 We all do. 00:31:16.960 --> 00:31:24.840 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. 00:31:25.140 --> 00:31:44.220 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. 00:31:44.340 --> 00:31:47.420 And so that being built in, I think, is going to be really important. 00:31:47.820 --> 00:31:51.080 And there is some level of agreement that that's going to be built in. 00:31:51.440 --> 00:32:03.960 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. 00:32:04.120 --> 00:32:10.800 And so being able to see that there is, you know, this is AI generated, it's too precise, et cetera. 00:32:10.800 --> 00:32:13.960 And so those types of things, I think we're going to start seeing being built in naturally. 00:32:14.620 --> 00:32:32.860 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. 00:32:33.060 --> 00:32:36.060 And, like, it hasn't caught up to the craziness that I'm talking about. 00:32:36.540 --> 00:32:41.000 But, like, in photos and things, like, I love Adobe Stock. 00:32:41.060 --> 00:32:43.500 It declares what's AI and what's not. 00:32:44.380 --> 00:32:45.900 Like, and I've seen that on other services. 00:32:45.900 --> 00:33:01.680 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. 00:33:02.360 --> 00:33:09.780 There's conversation now about there being a blue light above the brake lights on the back of cars. 00:33:09.780 --> 00:33:20.360 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. 00:33:20.860 --> 00:33:26.740 And so whoever is behind knows, hey, that car is being driven by a machine. 00:33:27.400 --> 00:33:31.100 And so the driver knows, okay, let me get in the other lane because I don't trust it. 00:33:31.440 --> 00:33:33.020 Let me hurry up and get around it. 00:33:33.020 --> 00:33:37.460 Or I'm completely comfortable with that information and can, you know, it can treat it accordingly. 00:33:37.680 --> 00:33:44.360 And so, you know, as, even as a content creator, being able to know, okay, this is, this is machine generated. 00:33:44.460 --> 00:33:45.920 This is, this is not like that. 00:33:46.320 --> 00:33:49.380 I, I'm the ability for me to use that, those tools accordingly. 00:33:49.380 --> 00:33:50.560 Like, I love that. 00:33:51.500 --> 00:33:53.320 And so that's, that's interesting. 00:33:53.520 --> 00:33:55.400 I mean, there's probably, there's still probably some challenges. 00:33:55.980 --> 00:33:58.000 Hey, you, it was interesting. 00:33:58.040 --> 00:34:04.260 And in some of our early, earlier conversations about the podcast, you sent me some case studies and I kind of flagged one. 00:34:04.260 --> 00:34:10.580 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. 00:34:11.000 --> 00:34:13.380 And so I'm going to, I'm actually, I'm reading from my screen here. 00:34:13.400 --> 00:34:14.880 If you're watching on YouTube, I'm not looking at you. 00:34:14.920 --> 00:34:16.140 Sorry, because I'm literally reading the story. 00:34:17.240 --> 00:34:21.860 And, and so, uh, Mrs. Jones, this is, I'm assuming this is hypothetical, uh, we can. 00:34:22.120 --> 00:34:26.280 Hypothetical effect was created by, uh, Gemini in this case, by the way. 00:34:26.520 --> 00:34:26.940 Oh, good. 00:34:27.020 --> 00:34:29.180 We're so, I'm using, I'm using AI to talk about AI. 00:34:29.300 --> 00:34:29.640 That's great. 00:34:30.140 --> 00:34:36.900 Um, Mrs. Jones, a history teacher, utilizes Sora to create personalized historical simulations for her students. 00:34:37.460 --> 00:34:45.960 One student received a simulation, uh, where they witnessed a fictionalized conversation between Abraham Lincoln and, uh, Frederick Douglass. 00:34:46.440 --> 00:34:51.140 Uh, Frederick Douglass, as I recall, was, was the Southern leader, uh, discussing the complexities of slavery. 00:34:51.140 --> 00:35:02.580 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. 00:35:02.580 --> 00:35:08.200 Uh, questions arise from this, should AI-generated education require sticker fact-checking? 00:35:08.300 --> 00:35:10.280 How do we ensure historical accuracy? 00:35:10.280 --> 00:35:14.180 Who's responsible for potential biases and misinformation produced by AI? 00:35:14.180 --> 00:35:25.400 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. 00:35:25.400 --> 00:35:27.380 Like, what, what, what are we supposed to do with this? 00:35:27.420 --> 00:35:31.380 How are we supposed to handle this as, as churches and people in 2024? 00:35:31.900 --> 00:35:37.520 You know, and I've, I've, I've got the point now, the way I use AI is much more in conversation. 00:35:37.520 --> 00:35:44.960 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. 00:35:45.100 --> 00:35:49.740 And then I intentionally kick in a much more critical mindset. 00:35:50.320 --> 00:35:56.280 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. 00:35:56.280 --> 00:36:01.020 And so part of that, the process is not just to say, please help me with this, give me an answer. 00:36:01.260 --> 00:36:04.100 It literally is, let's have a debate about this particular situation. 00:36:04.280 --> 00:36:05.740 And what about this aspect? 00:36:05.740 --> 00:36:10.440 And if I thought about, thought about it from that particular aspect, what else should I be thinking about? 00:36:11.100 --> 00:36:17.120 Um, and then when I get the response from the AI, I'm saying, right, well, does that make sense in this particular context? 00:36:17.120 --> 00:36:19.120 Or is that really just a bit of an outlier? 00:36:19.280 --> 00:36:26.100 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. 00:36:26.320 --> 00:36:35.380 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. 00:36:36.120 --> 00:36:45.260 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. 00:36:45.460 --> 00:36:49.480 That what they were hearing was, was, was appropriate, was, was, was based on scripture. 00:36:49.480 --> 00:36:52.700 And in the same way, I'm, I'm using that same type of thing. 00:36:52.800 --> 00:36:56.800 And is this based on what I know to be true or what I understand to be true? 00:36:56.960 --> 00:36:59.700 Or is this something that I'm, I'm actually seeing now as different? 00:37:00.060 --> 00:37:02.580 And so that whole application of critical thinking. 00:37:03.000 --> 00:37:14.240 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, 00:37:14.760 --> 00:37:17.520 it's, it's not a, it's a statistical engine. 00:37:17.940 --> 00:37:18.680 That's what it is. 00:37:18.720 --> 00:37:19.400 It literally is. 00:37:19.400 --> 00:37:22.020 It, it's, it's got to be trained by a whole lot of data. 00:37:22.420 --> 00:37:29.180 And it literally has picked up just like, like, like we pick up the relationship between certain things, coffee and hot. 00:37:29.680 --> 00:37:38.600 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. 00:37:38.600 --> 00:37:50.100 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. 00:37:50.100 --> 00:37:57.280 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? 00:37:57.380 --> 00:37:58.020 Is that not factual? 00:37:58.100 --> 00:37:59.820 In fact, go back and test it. 00:38:00.020 --> 00:38:09.520 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. 00:38:10.360 --> 00:38:19.980 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. 00:38:19.980 --> 00:38:23.200 And I create stories like this around ethical dilemmas. 00:38:23.360 --> 00:38:31.160 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. 00:38:31.340 --> 00:38:38.400 It's not totally fictitious in terms of false, like I said, rather than fictitious, totally false in this concept. 00:38:39.360 --> 00:38:44.700 And so, so that really is, is where, where we need to start looking at, not only ourselves, but also testing. 00:38:44.800 --> 00:38:56.620 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. 00:38:56.940 --> 00:39:09.060 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. 00:39:09.060 --> 00:39:10.420 I know critical thinking to it as well. 00:39:10.420 --> 00:39:31.060 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. 00:39:31.060 --> 00:39:37.000 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? 00:39:37.560 --> 00:39:40.740 You know, are there, who are the people that are being spent? 00:39:40.840 --> 00:39:43.480 Is that correct from a, from a bias perspective? 00:39:43.480 --> 00:39:47.960 Or has it actually used a different bias, an incorrect bias? 00:39:49.880 --> 00:39:58.460 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. 00:39:58.840 --> 00:40:05.400 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. 00:40:06.200 --> 00:40:15.140 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. 00:40:15.140 --> 00:40:26.940 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. 00:40:26.940 --> 00:40:33.400 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. 00:40:34.020 --> 00:40:44.700 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? 00:40:44.820 --> 00:40:51.880 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. 00:40:51.880 --> 00:40:58.100 But, but honestly, like, this is, this is causing me to pause a little bit on, on this technology. 00:40:58.100 --> 00:41:01.380 And it, and I'm saying this on my podcast, which is, you know, public record for me. 00:41:01.460 --> 00:41:03.220 And I can, part of me is like, Jeff, Jeff, take it back. 00:41:03.460 --> 00:41:07.780 But I really, like, I can't, like, I really, this scares the crap out of me. 00:41:08.400 --> 00:41:10.400 Like, I don't know, am I, am I off pace? 00:41:11.120 --> 00:41:15.720 I think we, we all need to be very, very careful about what we're looking at. 00:41:15.720 --> 00:41:31.040 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. 00:41:31.040 --> 00:41:43.560 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? 00:41:43.620 --> 00:41:48.280 And how can we make sure what we, the way we're using it for good is not going to be misused for bad? 00:41:49.040 --> 00:41:59.460 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? 00:42:00.260 --> 00:42:03.620 And, you know, the context of the whole story, it was, you know, clearly not. 00:42:03.620 --> 00:42:11.760 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. 00:42:12.760 --> 00:42:16.060 But then we've got to be aware that it can be misused as well. 00:42:16.160 --> 00:42:20.740 And even in our use in a good way, it can be taken and it can be, can be misused. 00:42:20.840 --> 00:42:26.940 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. 00:42:27.740 --> 00:42:39.800 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. 00:42:39.800 --> 00:42:46.420 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? 00:42:46.960 --> 00:42:55.540 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. 00:42:56.320 --> 00:43:04.760 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. 00:43:04.900 --> 00:43:08.440 It was people could see right there and they could immediately experience it. 00:43:08.920 --> 00:43:16.340 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? 00:43:17.220 --> 00:43:19.440 So I think, you know, there's that part of it. 00:43:19.700 --> 00:43:23.940 But then immediately the guardrails come in and say, right, so what can be misinterpreted? 00:43:23.940 --> 00:43:28.340 You know, what, what, what, what can be embedded in there that could bias things? 00:43:28.540 --> 00:43:34.760 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. 00:43:34.920 --> 00:43:42.680 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. 00:43:43.220 --> 00:43:58.860 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. 00:43:59.300 --> 00:44:12.580 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. 00:44:13.340 --> 00:44:19.700 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. 00:44:20.580 --> 00:44:30.360 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. 00:44:30.740 --> 00:44:31.660 It's interesting. 00:44:31.800 --> 00:44:34.940 And I'm going to, I'm going to gently, I'm going to put you on the spot here a little bit. 00:44:35.000 --> 00:44:40.660 We're going to shift from ethics and probably a little more towards opinion, which is interesting where you're going. 00:44:41.920 --> 00:44:45.800 Like examples from, let's even go practical here. 00:44:45.800 --> 00:44:54.280 Like what, uh, so Sora gets out of red, um, the red team and is, is opened up at some level. 00:44:54.280 --> 00:45:01.200 Uh, it's a 29 95 month, uh, feature set to, to get this. 00:45:01.200 --> 00:45:08.000 And, and you can, anybody can now within whatever confines, whatever filters, whatever, anybody can create a text, a video. 00:45:08.480 --> 00:45:18.340 Um, like what are some examples of, of how the church should, should, could, would, um, you know, experiment, utilize tests? 00:45:18.340 --> 00:45:26.340 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? 00:45:26.340 --> 00:45:38.340 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? 00:45:38.480 --> 00:45:48.180 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. 00:45:48.180 --> 00:45:54.320 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. 00:45:54.320 --> 00:45:59.960 And all the things that, that naturally move us towards a peacefulness. 00:45:59.960 --> 00:46:06.980 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. 00:46:07.260 --> 00:46:18.400 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. 00:46:18.400 --> 00:46:22.320 So, so that's one place I, I, I, I'd start first. 00:46:22.940 --> 00:46:27.400 Um, the, the other, the other places I think would be when we start saying, right. 00:46:27.420 --> 00:46:33.140 How do we, how can we emphasize things without moving people, nudging people incorrectly? 00:46:33.140 --> 00:46:42.020 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. 00:46:42.700 --> 00:46:58.600 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. 00:46:58.600 --> 00:47:10.580 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? 00:47:11.100 --> 00:47:23.920 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. 00:47:23.920 --> 00:47:28.680 And this is what it would be like, and just helping to understand the context of the Bible story. 00:47:28.800 --> 00:47:43.040 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. 00:47:43.600 --> 00:47:49.140 And just the picture and understanding what that looked like and what that sounded like and what else was happening around there. 00:47:49.600 --> 00:47:53.360 I think that enriches, enriches the experience and enriches the understanding. 00:47:54.060 --> 00:48:02.240 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. 00:48:02.440 --> 00:48:13.520 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. 00:48:13.780 --> 00:48:26.780 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. 00:48:26.780 --> 00:48:32.280 But really always using it to reinforce scripture, reinforce the, the foundations that we have. 00:48:32.940 --> 00:48:35.820 Uh, so that, that's, that's where I certainly would look into it. 00:48:35.900 --> 00:48:48.440 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. 00:48:48.440 --> 00:48:56.120 I mean, you, you, you got your, uh, your, you started your studies, uh, 22 and, and it was all over the place. 00:48:56.300 --> 00:49:01.000 Um, what fall, fall, no, um, it started with November 22, right? 00:49:01.040 --> 00:49:01.320 Didn't it? 00:49:01.620 --> 00:49:01.780 Yeah. 00:49:02.020 --> 00:49:03.860 I started studying in 2020. 00:49:04.080 --> 00:49:05.600 So just, just, just prior to that, but yeah. 00:49:05.760 --> 00:49:05.900 Yeah. 00:49:06.100 --> 00:49:06.380 Yeah. 00:49:06.600 --> 00:49:17.600 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. 00:49:17.600 --> 00:49:22.820 And, and there'll be others, um, other aspects of artificial intelligence we're going to be diving into. 00:49:23.460 --> 00:49:26.800 Um, are, are there other aspects right now we should be aware of? 00:49:26.860 --> 00:49:28.160 What's, what's next? 00:49:28.220 --> 00:49:28.840 What are we doing? 00:49:29.060 --> 00:49:34.660 You know, all this content is, is nice, but there, there's much more to AI than just the content piece. 00:49:34.660 --> 00:49:38.720 What else should we be concerned about or looking at ethically concerning AI? 00:49:39.820 --> 00:49:46.760 So, so certainly the, the, the big, the big, uh, shiny object right now is large language models, genes of AI, uh, and everything 00:49:46.760 --> 00:49:53.260 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. 00:49:53.260 --> 00:50:01.200 So for instance, things like digital twins, where essentially I create a digital image of a physical, physical object. 00:50:01.200 --> 00:50:06.880 I'm able to, to actually do something in a digital space and then replicate that in the physical space. 00:50:07.120 --> 00:50:08.360 And that's very powerful. 00:50:08.360 --> 00:50:11.940 For instance, where I'm looking at an airplane engine, I've got a digital version of the airplane engine. 00:50:12.020 --> 00:50:12.520 I can manage it. 00:50:12.520 --> 00:50:17.840 And so those types of things, um, are happening, but you can very quickly say, right. 00:50:17.940 --> 00:50:24.020 So, Hey, let's do a digital twin of a, of a human, you know, and, and start, and start actually looking at, okay. 00:50:24.020 --> 00:50:28.760 So how do we sense the skin or how do we actually start getting inside, inside the mind? 00:50:28.880 --> 00:50:30.740 So that's, that's the one area. 00:50:30.740 --> 00:50:39.960 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. 00:50:40.620 --> 00:50:48.200 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. 00:50:48.540 --> 00:50:51.660 So generative AI is really focused on, I've learned stuff. 00:50:51.660 --> 00:50:56.120 I'm going to replicate and based on my learning, I'm going to give you an example. 00:50:56.120 --> 00:51:09.480 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. 00:51:09.480 --> 00:51:13.780 So adaptive AI then becomes something that is, there's a new space. 00:51:14.340 --> 00:51:20.580 Um, and when you start putting those together, generative AI, adaptive AI together, you can imagine where things, um, then become, become fairly complex. 00:51:20.580 --> 00:51:22.360 And so that's relatively new. 00:51:22.360 --> 00:51:27.720 We start to see, uh, some, some organizations focusing, focusing in that space. 00:51:28.560 --> 00:51:47.660 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. 00:51:47.840 --> 00:52:00.180 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. 00:52:00.500 --> 00:52:03.120 And then there is the other, which is supervised learning. 00:52:03.120 --> 00:52:13.460 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. 00:52:13.460 --> 00:52:31.460 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. 00:52:31.460 --> 00:52:43.980 And then being able to essentially have this combination of a plane of one another from a taught, supervised learning and unsupervised learning component. 00:52:44.500 --> 00:52:46.440 So those are the types of things. 00:52:47.200 --> 00:52:55.680 But I would say that the space of growth around generative AI is only going to get larger. 00:52:55.680 --> 00:53:00.560 So the more and more information that it's fed, the more modes that it's fed it in. 00:53:00.800 --> 00:53:05.300 So video and audio, et cetera, the more of those modes it plays in. 00:53:05.760 --> 00:53:19.120 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. 00:53:19.120 --> 00:53:24.340 And now the robot having a couple more of the senses, so the feel, the ability to move. 00:53:25.220 --> 00:53:36.920 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. 00:53:36.920 --> 00:53:37.540 Okay. 00:53:38.060 --> 00:53:43.920 Are you literally saying that there's Optimus is putting AI into a robot like that? 00:53:44.000 --> 00:53:44.720 That's a project? 00:53:44.840 --> 00:53:49.180 So if you look at what Optimus Prime Transformers, like I'm geeking out over here. 00:53:49.300 --> 00:53:49.580 No, no. 00:53:49.880 --> 00:53:55.460 So Optimus, this is Tessa Optimus, so essentially their robot. 00:53:56.080 --> 00:54:02.940 So essentially their large language model capability has been used as a foundation. 00:54:03.200 --> 00:54:09.780 So if you look at a lot of the earlier robots were trained on, here was a programmatic thing. 00:54:09.880 --> 00:54:12.200 So do the step one, step two, step three, step four. 00:54:12.680 --> 00:54:20.420 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. 00:54:20.880 --> 00:54:21.540 There's the house. 00:54:22.100 --> 00:54:22.560 See you later. 00:54:22.640 --> 00:54:23.380 Go and figure it out. 00:54:23.640 --> 00:54:30.560 And literally through its ability to perceive and learn and understand, it knows that it needs to make coffee. 00:54:30.640 --> 00:54:37.580 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. 00:54:37.940 --> 00:54:39.240 And it's going to learn. 00:54:39.360 --> 00:54:40.900 So it's not trained step by step. 00:54:41.060 --> 00:54:44.620 So to make a cup of coffee, you go through this door, you go there, you open up that fridge. 00:54:45.020 --> 00:54:53.140 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. 00:54:53.140 --> 00:55:04.300 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. 00:55:04.900 --> 00:55:10.740 So literally is, you know, make a cup of coffee or walk into a room, what should you do? 00:55:12.020 --> 00:55:14.440 And the first thing it does, oh, it's untidy, let me tidy the room. 00:55:15.480 --> 00:55:19.380 You know, or, hey, there's a fire in the corner, I'm going to try and put the fire out. 00:55:19.380 --> 00:55:33.980 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. 00:55:34.560 --> 00:55:36.480 Yeah, it's fascinating. 00:55:36.480 --> 00:55:37.440 I mean, so much. 00:55:37.860 --> 00:55:47.200 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. 00:55:47.320 --> 00:55:52.620 I mean, because this thing is tracking so fast that it's like, oh, my gosh, that's a decade before. 00:55:52.720 --> 00:55:53.740 No, forget that. 00:55:53.800 --> 00:55:58.540 It's going to be tomorrow before we're going to have to address and deal with all this. 00:55:58.640 --> 00:55:59.060 It's interesting. 00:55:59.240 --> 00:56:05.820 We had, oh, I just, Aaron Seneff is the, he's the chief technical officer for PushPay. 00:56:05.920 --> 00:56:07.000 I don't know if you're familiar with PushPay. 00:56:07.160 --> 00:56:10.060 It's a billion dollar Christian tech company. 00:56:10.060 --> 00:56:13.800 It was acquired for $950 million, I don't know, maybe six months, maybe a year ago. 00:56:13.800 --> 00:56:21.480 But, and so he was on the podcast and was talking about some things and asked him about AI and his stance on AI. 00:56:21.580 --> 00:56:24.360 We just published that podcast maybe a couple of weeks ago. 00:56:24.780 --> 00:56:27.260 But he said, it was really interesting. 00:56:27.460 --> 00:56:36.980 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. 00:56:37.240 --> 00:56:42.180 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. 00:56:42.180 --> 00:56:48.900 And, you know, even Siri interrupting this podcast earlier is a perfect example of, you know, AI being AI. 00:56:49.900 --> 00:56:57.580 You know, whether, whether we want it or not, it's lurking, paying attention and waiting to step in as, as it seems helpful. 00:56:58.540 --> 00:57:01.220 You know, I say to my daughter, hey, sweetie, all the time. 00:57:01.420 --> 00:57:04.920 And Siri always chimes back, what can I do for you? 00:57:04.920 --> 00:57:06.240 And I'm like, I'm not talking to you, Siri. 00:57:06.300 --> 00:57:07.100 I'm talking to my daughter. 00:57:07.880 --> 00:57:10.900 And so that's, you know, yet another interruption. 00:57:11.180 --> 00:57:13.120 But it's the reality of where we are, right? 00:57:13.240 --> 00:57:23.620 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. 00:57:23.680 --> 00:57:40.320 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. 00:57:40.320 --> 00:57:46.480 Cause somebody will, I mean, to, to think that everybody's going to control their own garden and manage their own space. 00:57:46.480 --> 00:57:51.520 Like it's 2024, nobody's going to like that's, we can't, we can't expect everybody else to play nice. 00:57:51.620 --> 00:58:01.500 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. 00:58:02.300 --> 00:58:04.400 So listen, Quentin, this has been incredible. 00:58:04.400 --> 00:58:13.820 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. 00:58:13.820 --> 00:58:18.920 So as we're, as we're wrapping up and landing the plane here, uh, man, any closing thoughts on your side? 00:58:19.180 --> 00:58:31.780 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? 00:58:32.240 --> 00:58:36.220 There's just so much noise out there of just doomsday and how bad it can be. 00:58:36.720 --> 00:58:42.060 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. 00:58:42.060 --> 00:58:59.760 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. 00:58:59.960 --> 00:59:05.500 And just recognize that, you know, there are risks, but at the same time, there are huge advantages as well. 00:59:05.500 --> 00:59:11.920 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. 00:59:11.920 --> 00:59:18.340 Well, for a guy that's job is literally risk assessment or his studies has been risk assessment of artificial intelligence. 00:59:18.540 --> 00:59:20.760 Uh, I feel that the risks have been assessed. 00:59:21.440 --> 00:59:26.020 Um, and, and part of me is a little surprised that you're, you know, pro on that. 00:59:26.080 --> 00:59:35.600 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. 00:59:35.600 --> 00:59:41.060 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. 00:59:41.260 --> 00:59:45.100 And, um, you know, I, I, I tell people all the time, trust, but verify. 00:59:45.380 --> 00:59:59.540 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. 00:59:59.540 --> 01:00:03.300 That's negative connotation, but that at least moving forward with that. 01:00:03.300 --> 01:00:08.380 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. 01:00:08.860 --> 01:00:11.400 Uh, thank you, Quentin, Quentin McGrath. 01:00:11.460 --> 01:00:18.200 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. 01:00:18.200 --> 01:00:20.020 So I appreciate that, but we're going to land the plane. 01:00:20.020 --> 01:00:23.380 So for, for Quentin, this is Jeff with, uh, the church digital. 01:00:23.380 --> 01:00:24.580 Thanks for jumping on the podcast. 01:00:24.680 --> 01:00:25.660 We'll see you next time on the show. 01:00:26.020 --> 01:00:26.740 Y'all have a good day. 01:00:33.300 --> 01:00:41.120 We'll see you next time on the show.