Jakson Exploring AI's Impact on Human Relationships: Insights from Robin Osborne täydellinen litterointi

Robin Osborne 00:04:58 Where you said this text has this intent and here are some identifying things within this piece of text. Robin Osborne 00:05:05 So we trained it on all of these live chat transcriptions. Robin Osborne 00:05:10 And you could say right out of these thousands tens of thousands of live chat transcripts from customer support agents when the first sentence is this, the intent was late order. Robin Osborne 00:05:22 The thing that looks like XYZ is an order number so you can identify those when you have a conversation with a chatbot. Robin Osborne 00:05:28 So then that meant that any conversation with just eat around my order is late was deflected from humans handled by brain computer brain pre chat GPT. Robin Osborne 00:05:41 So there's the intelligence there was pretty limited and saving money and it was a case of. Robin Osborne 00:05:48 That was the majority of the kind of the questions. Robin Osborne 00:05:50 It was like my order is late. Robin Osborne 00:05:51 My order was missing something. Robin Osborne 00:05:52 Being able to deflect those. Robin Osborne 00:05:54 And that was Microsoft's tool. Robin Osborne 00:05:56 But I could geek out about that so much because they had all the stuff around kind of translation, text summarization, image recognition, voice recognition, like speaker identification, like me kind of talking now it could kind of tell Robin is talking, Yabo is talking. Robin Osborne 00:06:12 Just by us giving like 5 seconds of audio or something like that was just incredible. Robin Osborne 00:06:17 The capabilities there and being able to do like live translation to another language with speeches, speech bubbles or something with. Robin Osborne 00:06:24 Sorry, with subtitles, which I just thought was incredible. Robin Osborne 00:06:28 And that was Microsoft then. Robin Osborne 00:06:30 Oh man, I'm going to go off on tangents now. Robin Osborne 00:06:32 There ended up creating a. Robin Osborne 00:06:36 So my wife used to work as well for Asus and she came up with a hackathon idea where you could take a photo of someone's clothes and it would figure out very quickly what they're wearing accurately and give recommendations and say you can buy that thing over here so you could be on the street and go oh, I like that outfit. Robin Osborne 00:06:53 Click and they go oh, you can buy it over here. Robin Osborne 00:06:56 And then we ended up building that using all these other tech that was so accurate. Robin Osborne 00:07:01 It wasn't just a case of lady sitting chair, it was like sparkly tops, zip and we can buy that over here. Robin Osborne 00:07:08 Getting that kind of affiliate purchase thing. Robin Osborne 00:07:09 So that was all that image recognition and video recognition and video summarization. Speaker A 00:07:14 So much, so much stuff, such a wealth knowledge. Speaker A 00:07:16 It's just amazing and mind boggling to sort of hear you talk about those things. Speaker A 00:07:21 It's interesting. Speaker A 00:07:22 I love wanting to go back to the point about sort of how your experience with Just E and using Lewis, just that whole process of early stage AI and how that impacts in a particular way the business. Speaker A 00:07:39 So human connectivity. Speaker A 00:07:41 You mentioned about sort of the importance of how do you shave off all of the mundane repetitive tasks and leave the human to interact in the more qualitative decision makings that would help enhance the business. Robin Osborne 00:07:55 That's a good way of putting it. Robin Osborne 00:07:56 The qualitative decision making. Robin Osborne 00:07:57 Yeah, yeah. Robin Osborne 00:07:58 Because the. Robin Osborne 00:07:59 So being able to. Robin Osborne 00:08:01 The interaction for the user was from the Justy app and it was text. Robin Osborne 00:08:06 So pretty basic. Robin Osborne 00:08:07 Not even kind of talking to a AI. Robin Osborne 00:08:09 It was typing that message would hit the. Robin Osborne 00:08:13 The chatbot underneath. Robin Osborne 00:08:14 It was called Bot Framework. Robin Osborne 00:08:16 It would kind of look at what they've just Written look it up in the Language Understanding service to go, do you know what this means? Robin Osborne 00:08:22 Is there an intent that you can tell me? Robin Osborne 00:08:24 And if it says it's a late order, I've got a 90% confidence that's to do with late order. Robin Osborne 00:08:29 And it looks like they've given me this order number which matches the one that you know about because they're logged into the app. Robin Osborne 00:08:35 So with a high degree of confidence you can say, are you asking about this order? Robin Osborne 00:08:39 And also while you're doing that, I can go and check in the background the status of that order. Robin Osborne 00:08:44 Because Just Eat will get updates whether it's still being cooked, whether it's been picked up, whether it's in transit, how far away it is. Robin Osborne 00:08:51 So it can immediately give an update and go, are you asking about this order? Robin Osborne 00:08:54 That's 10 minutes away. Robin Osborne 00:08:56 And at that point it's like, oh yeah, do you still need to talk to someone? Robin Osborne 00:09:00 No, that's okay. Robin Osborne 00:09:01 Done, moved. Robin Osborne 00:09:02 And then if that didn't answer their query, the layer underneath that, this was incredible. Robin Osborne 00:09:08 At the time it was called the Q and A maker. Robin Osborne 00:09:11 The idea that you could take a list of kind of frequently asked questions, just your standard kind of FAQs, your returns policy, your whatever, your refund policy, and feed it to the a more simple version of the Language Understanding service. Robin Osborne 00:09:24 And it would create very easily create its own little chatbot based on your FAQs. Robin Osborne 00:09:31 The techies didn't even build this. Robin Osborne 00:09:32 This was built by a business analyst in Just

E who just took their FAQs, gave it to the service and then you didn't even have to ask the questions accurately based on kind of the frequent, the FAQ kind of headings of what is your refund policy? Robin Osborne 00:09:49 It was like, my food is terrible. Robin Osborne 00:09:50 Can I get my money back? Robin Osborne 00:09:52 And it will go, okay, that's not a late order question. Robin Osborne 00:09:54 That's not a. Robin Osborne 00:09:55 Something's missing from my order according to the Language Understanding service. Robin Osborne 00:09:58 I don't know what that means. Robin Osborne 00:09:59 Go down to the next layer. Robin Osborne 00:10:00 The next layer is this FAQ kind of catch all. Robin Osborne 00:10:03 And it's like, oh, that looks like it might be a refund policy. Robin Osborne 00:10:06 Okay, here's our refund policy. Robin Osborne 00:10:07 Does that answer your question? Robin Osborne 00:10:09 If not, okay, I can't help. Robin Osborne 00:10:11 We need some human to help out here because this is beyond our basic understanding. Robin Osborne 00:10:15 But it was all kind of based on facts, data, real things, not kind of hallucinated paragraphs of response. Robin Osborne 00:10:26 This is all quite factual and helpful and got very quick. Robin Osborne 00:10:30 And it meant that when you have A company with a limited number of customer support agents or a limited number of software licenses and you can't answer all these questions that are coming in at peak time, particularly when Just eat Sponsored the X Factor so it's Sat. Robin Osborne 00:10:45 Friday, Saturdays, audience numbers kind of went up and users of Justin went up as the ads went out. Robin Osborne 00:10:51 And then customer support agents, it's the same number. Robin Osborne 00:10:54 So when a whole load of requests came in, they'd be waiting and their satisfaction level just goes down and down and it impacts the company. Robin Osborne 00:11:01 So being able to just deflect say 80% of those because people are asking where their order is, leaves the customer support agents to deal with the more relevant customers complaints. Speaker A 00:11:11 Amazing. Speaker A 00:11:12 What things have you seen and how have you. Speaker A 00:11:15 Have you used AI? Robin Osborne 00:11:16 I mean it's moved on just so, so much from this basic. Robin Osborne 00:11:21 Here is a spreadsheet. Robin Osborne 00:11:22 Give me some fuzzy matching around the words in a spreadsheet to what is used within engineering. Robin Osborne 00:11:29 So not civil engineering, but computer programming. Robin Osborne 00:11:33 You've got the concept of the kind of the coding copilot and it's literally called that. Robin Osborne 00:11:38 It's the GitHub copilot that is embedded in programming environments. Robin Osborne 00:11:43 So what would normally happen? Robin Osborne 00:11:44 Most coders don't know everything. Robin Osborne 00:11:48 Surprise. Robin Osborne 00:11:49 I'm sorry. Robin Osborne 00:11:50 And a lot of the time we'll just go to Google and then Stack Overflow and then these other various sites where someone else has had the problem and you're now looking it up, getting it copy pasting shouldn't copy paste, but you know, and then putting it in solves the problem. Robin Osborne 00:12:04 Now all of that knowledge is kind of embedded within your development environment and you can just ask questions, go, how do I do this? Robin Osborne 00:12:11 What happens there? Robin Osborne 00:12:11 Not even asking questions. Robin Osborne 00:12:13 But as you're typing the autocomplete kind of intellisense is there to guess what you're probably trying to do based on this vast amount of information that exists in vast numbers of code repositories that has been absorbed into a large language model and then pushed out into Microsoft's GitHub Go pilot because they now own GitHub. Robin Osborne 00:12:35 GitHub is like the biggest code repository. Robin Osborne 00:12:38 And the idea that you can start typing, give it some, just some comments on a page, write in English, I would like this. Robin Osborne 00:12:45 This is what I'm trying to do. Robin Osborne 00:12:46 Hit one button and it just kind of goes and spits out kind of boilerplate. Robin Osborne 00:12:51 But possibly working code. Robin Osborne 00:12:53 You're not quite sure where it came from, what it's based on, but. Robin Osborne 00:12:57 And I guess the problem there is if you're say a junior engineer. Robin Osborne 00:13:02 You might not know the quality of what you've just been given, like looks right. Robin Osborne 00:13:06 I might just take that and go with it. Robin Osborne 00:13:09 And then this code that you might not fully understand has made it into a production environment and now you've got to support it and you've got to maintain it. Robin Osborne 00:13:17 But maybe you didn't quite fully understand it because you didn't write it, it was just generated for you. Robin Osborne 00:13:21 Whereas the good relationship in this scenario is a more kind of experienced engineer where you just not quite sure about something in a specific area, just get it to help a bit and go, oh yes, that looks right. Robin Osborne 00:13:34 Hold on, I need to just change that a little bit. Robin Osborne 00:13:36 It's just helping you out. Robin Osborne 00:13:37 It is that, that little co pilot, that wingman, it's just there giving you the assistance that you need in engineering, in computer programming. Speaker A 00:13:45 So I suppose sort of just using that example of like the junior engineer versus the more experienced one, that whole thing of again, it's about the expertise and about sort of how can you use generative AI as a specific tool to assist you in making producing the best type of work so you could have the best commercial outcomes? Robin Osborne 00:14:06 Yeah. Robin Osborne 00:14:07 And I, in the sectors that I've worked in, I haven't seen a valid use

for generative AI yet, which is kind of interesting. Robin Osborne 00:14:15 But large language models are the ones that are giving you all this text output and it's just outside of the coding environment, actually. Robin Osborne 00:14:24 It can get incredibly confused even within coding, but outside of say the coding boundaries, just in generally talking with a large language model, like a chatgpt or whatever, the hallucinations, I'm pretty confident the next word in this sentence should be this. Robin Osborne 00:14:40 I'm going to put it there whether it makes sense or not. Robin Osborne 00:14:42 And you can read paragraphs of stuff and think. Robin Osborne 00:14:44 Yeah, that kind of makes sense. Robin Osborne 00:14:46 Yeah. Robin Osborne 00:14:47 And it's complete lies, completely made up. Robin Osborne 00:14:50 There's no, not necessarily based on fact, based on whatever data was ingested. Robin Osborne 00:14:55 And there's no kind of quality metric of the data that it ingested. Robin Osborne 00:15:00 You don't know where it came from. Robin Osborne 00:15:02 You can't validate that in a lot of the bigger large language models. Robin Osborne 00:15:06 You've got some more open source ones. Robin Osborne 00:15:08 I think it's called mistral or something. Robin Osborne 00:15:10 It's like a big French one where it's open source and you can say this is where the data came from and it's quite open and you can, you can understand it. Robin Osborne 00:15:18 That one's getting quite big now. Robin Osborne 00:15:19 I think that's kind of the odd one. Robin Osborne 00:15:20 And the generative AI thing of I've got a whole load of this type of information, this type of data. Robin Osborne 00:15:27 Give me more that is similar to that. Robin Osborne 00:15:30 I haven't seen that in say, the coding or kind of E commerce getting. Robin Osborne 00:15:37 Getting much use out of that. Robin Osborne 00:15:39 I have seen some examples around kind of product development where it will just go off and give you completely random clothing ideas, which means you kind of escape boundaries of human possibility. Robin Osborne 00:15:53 Your thoughts, I guess, like thinking. Robin Osborne 00:15:55 I won't even consider that because actually, how would I make that. Robin Osborne 00:15:58 That's going to be really difficult. Robin Osborne 00:16:00 Generative AI can just come out with completely random things, which at least can help you overcome the friction of developing in your product and kind of go, oh, right, yeah, no, that. Robin Osborne 00:16:09 I mean, that one's impossible. Robin Osborne 00:16:11 But I could do something similar to that. Robin Osborne 00:16:13 And the idea that it's giving you that nudge again, it's kind of pulling you by the hand, going, no, no, no, look, come on, this way. Robin Osborne 00:16:19 The co pilot, the wingman. Robin Osborne 00:16:21 I need an idea and it's overcoming the friction. Speaker A 00:16:23 So from

my perspective of being more AI inquisitive, like, so you'd say that that is a really good example of how it can be used in the space of creativity and that's how it enhances humanness. Speaker A 00:16:37 So that, that way it can just help pioneer ideas that perhaps you might. Robin Osborne 00:16:43 Not have the time to, or, or even you've got. Robin Osborne 00:16:46 Everyone tends to have their. Robin Osborne 00:16:47 Maybe not everyone, but a lot of people have the kind of boundaries around what they perceive to be possible, what they, what they'll even consider when given a particular challenge or task, and they'll just think about things that are within that boundary of kind of experience, of knowledge. Robin Osborne 00:17:03 And maybe the generative AI doesn't and will give them ideas outside that then they can reach for and it will help them go a different path. Speaker A 00:17:11 That's super interesting. Speaker A 00:17:12 One of the things that we're kind of really keen to know about as well is sort of where do you see AI being used in the next five to 10 years? Speaker A 00:17:22 How do you see it in the context of like, relationship and connection with enhancing human connectivity? Robin Osborne 00:17:29 So going way back to like the original chatbot. Robin Osborne 00:17:32 I don't know if you ever played around with this one called Eliza. Robin Osborne 00:17:35 It was made in the 1960s, like late 60s. Speaker A 00:17:37 I just had my previous guest mentioned about Eliza and apparently you can still have a go. Robin Osborne 00:17:43 Yeah. Robin Osborne 00:17:43 And I mean written in the 60s, passed the Turing Test in late 80s, which is where a human is interacting with Something they don't know whether it's a person or a computer. Robin Osborne 00:17:56 And they have to kind of say, I thought this was a human, I thought this was a computer. Robin Osborne 00:18:00 Eliza passed that Test in the 80s. Robin Osborne 00:18:04 And the surprising thing is it actually beat Chat GPT 3.5 in the past few years. Robin Osborne 00:18:11 Chat GPT 4 is now like way ahead and the other ones are way ahead. Robin Osborne 00:18:16 But. Robin Osborne 00:18:17 And the thing is, it's not AI, it's a case of. Robin Osborne 00:18:20 It's like a psychologist is kind of saying, and why do you feel that that's interesting, Tell me more. Robin Osborne 00:18:24 There's no kind of commitment, there's nothing. Robin Osborne 00:18:27 It's not saying things are completely unrealistic where you go, well, that's a computer. Robin Osborne 00:18:31 That was weird. Robin Osborne 00:18:32 It's just non committal, just like I, you know. Robin Osborne 00:18:36 And how does that make you feel? Robin Osborne 00:18:37 All these questions and that. Robin Osborne 00:18:39 I remembered messing around with this back in like late 90s and just having kind of a full conversation, seeing how far it could get before it felt or looked like it wasn't a regular conversation, even though it could go off on complete tangents, like I was pretending to be a mob boss or something, having a psychologist trying to get it to help me with, I know, disposal of something. Robin Osborne 00:19:04 And that felt like quite an interesting interaction that I can imagine. Robin Osborne 00:19:09 Even people now just want to get something off their chests just to talk to a really basic psychologist chatbot, that even then, that same thing from the 60s, it's still quite useful and helpful and can get that emotional reaction, I think. Robin Osborne 00:19:26 And now we've got the fact that there are, well, any number this, so many different versions of chatbots, all based on certain ingested data and prompts. Robin Osborne 00:19:41 A friend of mine made a kind of Dungeons and dragons Dungeon Master 1 where all it took is you just gave it a prompt to say, you are a Dungeons and Dragon, a dungeon master. Robin Osborne 00:19:52 Here's what you can do. Robin Osborne 00:19:54 Don't stray from the things that you have created. Robin Osborne 00:19:56 Do not include the real world. Robin Osborne 00:19:58 Go. Robin Osborne 00:19:59 And you could kind of have this conversation. Robin Osborne 00:20:00 It says, right, you're in this setting surrounded by woodland, there's a mist, and they're like really quite detailed descriptions of places. Robin Osborne 00:20:08 And what do you do? Robin Osborne 00:20:10 Like, okay, I'm gonna. Robin Osborne 00:20:11 And you weren't given like, go left, go up, go down. Robin Osborne 00:20:14 You could describe, you could say, well, what time of day is it? Robin Osborne 00:20:18 Oh, is this nighttime? Robin Osborne 00:20:19 The moon is in the sky. Robin Osborne 00:20:20 And oh, wow. Robin Osborne 00:20:21 Having this really interesting interaction, not like emotional unnecessarily, but it's the same that you would have with A, I guess a more junior dungeon master playing Dungeons and Dragons, like more advanced people will give you so much more, I think. Robin Osborne 00:20:37 But you could have, like a full game, just spend an afternoon having a chat with this thing that has been told what the bounds of conversation are, the bounds of its world, and try not to stray out of it. Robin Osborne 00:20:49 So that in itself, like you're playing a game, you've got the 1960s psychologist one, you've got the playing the game, you've got. Robin Osborne 00:20:55 And there are any money, you can choose to have conversations with large language models and AIs that are supposed to represent celebrities. Robin Osborne 00:21:02 People have kind of trained them on that. Robin Osborne 00:21:05 Celebrities, things that they've said, things that they've written, even their voice. Robin Osborne 00:21:10 And it could feel like you're having that conversation with that person. Robin Osborne 00:21:13 I don't know what that could be. Robin Osborne 00:21:15 Quite an interesting one, but bit bizarre to

then if were you ever to meet that person, would you feel like you know them because you've been talking to them for so long, but you haven't. Robin Osborne 00:21:24 You've never done that. Speaker A 00:21:25 So raises two questions to me. Speaker A 00:21:27 Sort of. Speaker A 00:21:27 First thing is, like, when you've talked about the creation of Yusuf Eliza and just how. Speaker A 00:21:34 And the Dungeons and Dragons option that you create, that your friend created, that helps enhance community and that's one way that AI can be used positively to enhance human connectivity. Speaker A 00:21:49 And just where there's a shared sort of global experience, because obviously that game can be played internationally. Speaker A 00:21:54 And then the other side, though, is you talk about interactions with, you know, with somebody famous, you've never ever met them, but you're having deep chats with them. Speaker A 00:22:04 Do you think there's a question about the ethics about behind that and. Robin Osborne 00:22:08 Absolutely. Speaker A 00:22:08 And how. Speaker A 00:22:09 How does that play out in that space? Robin Osborne 00:22:11 I mean, even so, with the conversations with chatbots, has the person that they're imitating given any kind of. Robin Osborne 00:22:21 What's the word? Robin Osborne 00:22:22 Permission to use their likeness? Robin Osborne 00:22:24 Whether it's. Robin Osborne 00:22:25 I mean, a likeness is, I think we know is kind of like the look and the voice of someone, but what about their personality? Robin Osborne 00:22:32 Like, how could you say, don't use my personality as a likeness? Robin Osborne 00:22:36 But if that is being generated based on things that you've said and you've done fed into a AI that's processed them, that's going to be really difficult. Robin Osborne 00:22:46 And at what point do you know who you're talking to? Robin Osborne 00:22:50 There's the tech that recently came out from ByteDance, the TikTok company, loopy, where you could take a flat image and some audio and just that single image will now animate pretty accurately, pretty realistically. Robin Osborne 00:23:12 A single image with some audio. Robin Osborne 00:23:14 Previously it would be, the mouth might move a bit and you go, well, that's just a weird moving mouth of a photo. Robin Osborne 00:23:20 But now it kind of understands the tone of the audio. Robin Osborne 00:23:26 When there's a break for them to breathe in the audio, the picture will inhale and its shoulders will raise, its head will move side to side, it will give eye contact, it will look off to the side as it's thinking. Robin Osborne 00:23:38 Eyebrows will move, they'll be blinking, there are lips smacking, which I found really interesting when someone in the audio just kind of pauses and does a. Robin Osborne 00:23:46 Now that's being animated. Robin Osborne 00:23:49 It's just incredible and it's so realistic. Robin Osborne 00:23:51 There was like just a photo

of Leonardo DiCaprio singing in Chinese and you just, wow, it's gone from a photo. Robin Osborne 00:23:58 And his cheeks are just like perfectly animated to form those sounds. Robin Osborne 00:24:03 There's a statue, a marble statue that is, that is now singing or chatting. Robin Osborne 00:24:10 That looks incredibly real. Robin Osborne 00:24:12 And that kind of, if you can do that from a photo, at what point? Robin Osborne 00:24:17 It just really worries me that people who don't really know that much about AI will find these in the wild and go, you know, on Facebook or whatever, which is now basically full of this kind of stuff and believe it that it's real or that that person actually said that thing. Robin Osborne 00:24:34 Because it's so easy now to just go, oh, I'm going to take some AI generated audio that has been built from audio snippets that this famous person has, has made. Robin Osborne 00:24:45 Because of course their voice is going to be out there in the public domain. Robin Osborne 00:24:49 I can create a audio generating model from their voice. Robin Osborne 00:24:53 I can just type whatever I want. Robin Osborne 00:24:55 It will say it. Robin Osborne 00:24:57 And now we'll have this picture that will look like them saying that thing. Speaker A 00:25:02 Wow. Robin Osborne 00:25:03 And it's just really weird. Speaker A 00:25:04 That's crazy. Speaker A 00:25:05 But I mean also quite fantastical about this whole new sort of area that we're moving into. Speaker A 00:25:12 One final question that we're looking at is what do you see? Speaker A 00:25:16 How do you see AI being used in, in the future and how it can. Robin Osborne 00:25:20 So I, I like this one looking, looking at this one from the kind of, the different viewpoints. Robin Osborne 00:25:25 The idea that, and unfortunately I think this is the one, the, the viewpoint that we're stuck in at the moment is looking at AI through the capitalism lens and how can I save money? Robin Osborne 00:25:35 How can I get rid of people? Robin Osborne 00:25:38 How can I do this cheaper? Robin Osborne 00:25:39 Not really caring about the people that are now unemployed or now can't find work because it's just being automated. Robin Osborne 00:25:48 It's so much easier and cheaper and that's quite a depressing place. Robin Osborne 00:25:55 And I would hope that that's not where we'll end up because there's kind of the, the socialism lens of well, if we can take this work away from people and that means we can now provide universal basic income, then people don't need to work. Robin Osborne 00:26:12 They can if they want to, but that work that had to be done for a company is now being done by a computer. Robin Osborne 00:26:18 So if we can get to that kind of, that side of things where people get to choose what they want to do as opposed to doing what, doing something that they hate because they have to do it. Robin Osborne 00:26:28 AI could be that thing that helps take jobs that people don't like, don't enjoy, but still allows people to live a reasonably good life without being too worried. Speaker A 00:26:43 That's a very interesting sort of look at how society can progress as we've seen through the different ages and the different sort of technological advanced advances through time that I just have really enjoyed like our discussion. Speaker A 00:27:03 It's been really, really good just to sort of like think about all these different issues, these ways that in which human connection can be enhanced or it can be fractured. Speaker A 00:27:13 Both certainly in the workspace and also within the friendship and community building space. Speaker A 00:27:20 So just as we sort of come to a close I want to say thank you so much for coming and being part of the show and sharing your knowledge and just. Speaker A 00:27:30 And your sort of fabulous insight into this new whole world. Speaker A 00:27:34 As I said I'm learning all the time so it's a good, it's. Speaker A 00:27:39 I'm sort of sat looking at like absorbing all this information. Speaker A 00:27:44 Where might people find you on your. Speaker A 00:27:47 On the socials if people want to connect? Robin Osborne 00:27:50 Well, I bailed from X a while ago so I'm either my website robinosborne.co UK it's where easier to find me. Robin Osborne 00:27:59 I'm on Mastodon as it's hard to say it now it's rposbo@hackydomeio but yeah, just go to robinosborne.co uk. Robin Osborne 00:28:10 You'll find me there. Speaker A 00:28:11 Brilliant. Speaker A 00:28:11 Well, thank you so much Robin. Speaker A 00:28:13 Really appreciate you coming down and thank you for being on the show. Robin Osborne 00:28:16 Thank you very much. Robin Osborne 00:28:16 Yeah, I really enjoyed it.