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This Day in AI Podcast is a podcast all about AI. It's an hour-long conversation on the influence and rise of AI in technology and society. Hosted by Michael and Chris Sharkey.
Michael Sharkey (00:00:00):
Wait till these sites, which they already are, start doing generative ai. So now Google is reading generative AI to produce a generative AI result. And then I'm sending an email with facts produced by another generative AI on top of a generative AI on top of another generative ai. Well, back to this day in AI podcast, there is a lot to cover this week with the Google IO announcements, but we wanted Chris to kick it off today by talking about some pretty major news to us from Anthropic, which is this new hundred K prompt size that they've just announced.
Chris Sharkey (00:00:42):
Yeah, it's so much, so big and it's available now. We've used it. It's, it's awesome.
Michael Sharkey (00:00:48):
So for everyone listening that doesn't understand the implications of this or what prompt size means, can you just explain, you know, give them 1 0 1 on why this is important, why it matters, and why we're excited?
Chris Sharkey (00:01:01):
Of course. So firstly, I've been saying it wrong in all the previous podcasts, we keep saying prompt size, but it's really context window, which means how much text can you put in and how much you can you get out. So, you know, in the original, um, G P T three, it was I think 4,000 char 4,000 tokens, right? Tokens about two or three letters. It's like half a word. And so you could put in 2000 tokens and get out 2000 tokens, or you could put in 3000 and get out 1000, or you could put in 4,000, uh, sorry, 3,999 and get out one. You know, so it was a trade off how much context information you give it versus how much output you get. And so what I mean by context is, let's say you are asking it to, you know, rewrite some text for you.
(00:01:48):
It's how much text you can give it versus how much space it's got to output. So the context is the text that you want re rewritten, and then it's output. Now 4,000 adds up really fast when you're trying to do tasks like that. And so that's where things like vector databases and ch the lang chains come in where people have to go 4K and then 4K and then 4K and you do a bunch of requests and try and string them together. But it meant that the AI couldn't get this holistic picture of the context, uh, information that you were trying to go through. Now they announced G P T four with 32,000 tokens, which was massive, except you can't use it, you can't get access to it. But in the meantime, so
Michael Sharkey (00:02:29):
I think to clarify though, we saw last week some people do now have access to the 32 K, but it's not wildly available.
Chris Sharkey (00:02:36):
True, that's right. Yeah. We don't have access. I only think about it in terms of us, but yeah, not everybody has access, but 32,000, that's why we were so excited about it, cuz it makes a huge difference. You can suddenly give it a whole tonne more information, like a whole book's worth of text, and then it can also output a whole tonne more information if you need it to like, you know, write me a full, uh, essay or write me a full thing and it can do it in one shot. Um, and so Anthropic in the meantime has just come out and just blown everyone out of the water with a hundred thousand tokens of context windows. So you can put in 50,000, get out 50,000 or any of the other ratios I spoke about earlier. It is an enormous leap and we've been using it and it's good.
Michael Sharkey (00:03:22):
We, yeah, we've been playing with it before this podcast, actually, we're late recording because we were so excited about testing out . Why
Chris Sharkey (00:03:29):
Is it always Friday mornings that all the cool stuff comes out? Like it's it's just like clockwork. Yeah.
Michael Sharkey (00:03:35):
Here in Australia where we're record, we're really lucky, although it, it, it's hard to keep up with because right before we start recording all of these great announcements seem to come out and we're trying to process them really quickly and, and use them and see what they mean. I think for me personally though, just seeing it inaction, the example Anthropic gave in their announcement was putting in the Great Gatsby and basically hiding things in it that aren't from the book and then asking it, Hey, what happened to this other character? I think we, I tried this morning an example of putting this new character Gary, who was a powerful a g i who was trying to kill everyone in. And I was asking it, who is Gary in the context of this novel? And it can very quickly find that information, but I don't actually think those, you know, play around use cases are the most exciting ones.
(00:04:21):
We were talking before we started recording, which probably should have just recorded those experiments about what this enables. For example, you could feed it everything in your company's, uh, Salesforce database and then get insights into it because the AI has full context. You could start to make very realistic predictions with it. And then we also fed it, which you did earlier, which blew my mind. The last transcript that, uh, episode of our podcast asked a a, a bunch of different questions like come up with the timestamps, write the description, uh, do all these sorts of
Chris Sharkey (00:04:58):
Things. And the timestamps that people don't know is like where we introduce a new topic. Like, so this topic would be from the start, which is the Anthropic announcement, and it's able to automatically tell you the time that that occurred and when, and it knows where a topic has transitioned.
Michael Sharkey (00:05:12):
And by the time you listen to this podcast, we're actually gonna write the title of the episode, the description, and then do all the timestamping completely with anthros a hundred k uh, systems. So we'll be able to feed it like literally every word from the transcriptions. Whereas in the past we may have been limited due to prom size.
Chris Sharkey (00:05:33):
That's right. And I think that the thing is like, it may have been able to do, say the timestamps because you could have split the transcript into chunks of 4K or eight K or whatever model you're using. But when you ask it, what are the most important points in the podcast? Like what are tweets we could write about it that people will find interesting. It's only able to do that when it can take in the whole piece of information. And what's what's crazy is it doesn't have to be just one podcast. We could put 'em all in and you wouldn't even be touching the sides in terms of the the context window that Anthropic can handle.
Michael Sharkey (00:06:06):
It also is interesting for a number of use cases that we've talked about medical research lawyers, because you could put literally the, the whole, you know, well the Constitution people have given examples with a lot with Lang chain. Yeah. But now the ai, when it's responding just has all that context, uh, in, in its response. This is a huge, I tried
Chris Sharkey (00:06:28):
Breakthrough tried, I tried it with some recent court judgments of Oley, which is the Australian database of court, um, judgements. And I put in a couple of those and I asked it, you know, who were the protagonists in the case? Who, um, what was the decision, why was this decision made? And then I asked it to tell me what the relevant laws were that were in there. So the idea being that, you know, if you are looking for case law for your thing, you could run it on vast swaths of that database and then have it tell you all the cases relevant to your case and summarise it so you don't have to read 'em all. Like those kind of applications are now very, very possible
Michael Sharkey (00:07:01):
Even leading into these podcasts. We obviously do a fair bit of research and we have heaps of links to go through and crawl papers to read during the week. Being able to structure all of that into, uh, context for Anthropic at this point in the a hundred K and tell us how do we link these topics together or produce an outline. Yes.
Chris Sharkey (00:07:20):
And that's the important point because obviously all the existing G P T models were fine at summarization and you could give it an article and tell me what the main points of the article were that I was doing that from day one. But the is the thing is here you can give it all of the information and say holistically, what are the best points out of this? What, what judgements can you make based on what's being said here? And it just really gives the AI more power to be intelligent.
Michael Sharkey (00:07:47):
Do you think, what does this mean for open ai? Do you think it's just a case of, because Anthropics very much developer first in terms of distribution, they can deliver this because it won't crash their system. Whereas open AI with a hundred million daily active users with chat G B T, maybe if they made huge prompt sizes available or huge contact sizes available that it would crash their system. Do you think that's the only reason we haven't seen widespread 32 K?
Chris Sharkey (00:08:12):
I'm not sure because OpenAI prices are so high that um, you know, that would naturally limit it from being completely bombarded. I don't think they're ever gonna put the largest prompt sizes for free on chat G B T because as it is, I think someone said this week that it's two US dollars if you do a full G P T 4 32 K run. And I must admit, I haven't looked at the clad pricing claros model Claude V 100 K, but I believe you told me that they're keeping it for the beta on the same pricing as their regular model, which is equivalent to like, uh, G P T 3.5 turbo. So it's pretty cheap at
Michael Sharkey (00:08:49):
The Yeah, I think if they can keep that pricing where it's at and have this a hundred K prompt, I can quickly see everyone pivoting their a p i models to Anthropic very quickly.
Chris Sharkey (00:09:00):
I'm just genuinely blown away. And admittedly we've only tested this morning, but genuinely blown away at the quality of, I just, I honestly expected it would just crap out at those larger amounts and just give sort of more halluc hallucinatory, I, so, I dunno how to say that word, but you know what I mean, hallucinations in the responses or go off on tangents and stuff, but it's concise, it's accurate and it follows instructions really well on top of that. Because remember when they train these large context windows, they also have to train it on large prompts. So it gets used to answering in that style. And that's what a lot, there's this other one that came out called Story Writer. Um, what was, what was the name of that company again? Mosaic ML released this one called Story Writer. And it was saying that we have a huge prompt size. I think they said they got up to between 60,000 and 80,000, so even less than Anthropic. But they were saying it's only designed to understand and write stories because that's what they trained it on with the larger prompt size. It was also
Michael Sharkey (00:10:01):
Trained in 9.5 days
Chris Sharkey (00:10:04):
Yeah. For 200,000. But the, the point was I didn't get as excited about that one cuz we, we knew about that last week before the cast, but I didn't get as excited because I'm like, ah, stories, yeah, okay, cool if I want to be like publishing $1 eBooks on Amazon, but it's not that practical. Whereas Anthro doesn't seem to have that limitation and it's even bigger.
Michael Sharkey (00:10:23):
I think what's interesting is Anthros a really high quality model. We've talked about it before. It's already being used by Quora for their chatbot poe. Uh, I think Duck Duck goes using it for their AI search. So Enro for those who don't recall or or don't know is a, a a, a group of people that left open AI to develop this idea of constitutional ai. There was more announced during the week about how they intend to do that as well. And they, uh, are all about AI safety. But it does seem to me, and we've said it on this podcast before, that the problem with them is no one cares because everyone is so absorbed by chat G B T and their, their APIs and, and most of the ecosystems building around it. But the biggest problem when you're building with AI and trying to solve problems was these context windows anthros just quietly gone out and it seems like solved this huge problem.
Chris Sharkey (00:11:19):
Yeah, and like, I mean there's discussion now about like, how important is Lang chain and Vector dbs because it becomes less necessary. Like a lot of what people are using vector databases for, which we discussed extensively last week, um, is to overcome the prompt size limitations. And of course there's the counter-argument, well, even a hundred K isn't enough for certain problems. You're always going to want to have more, especially if you want to build, you know, more general intelligence that remembers and learns over time, it'll eventually exceed that. But it's still a lot. It's, I mean, it's the whole great kasby. I mean it's, it's a whole novel that it can keep in its head at once. So a lot of the applications that we're using those vector databases, they, they just don't need 'em anymore. I mean, some will, but some won't. Now,
Michael Sharkey (00:12:04):
Do you think though, one of the benefits is you can take more vectors in and give it as much context as possible in that a hundred k when you've got this enormous,
Chris Sharkey (00:12:13):
The number of documents can give it. So just a a another recap of how that works. So when you, um, use one of the vector databases, you use embeddings, which is like the scoring of the text. So it's sort of like a search, it's like a search. And so you search the text, find the relevant pieces of the text, use the AI large language models to summarise those into a series of summaries. And then you use your 4k AK whatever prompt with as many summaries as you can fit in there. And then you ask the AI about the summaries. So it's able to appear as if it can understand these much larger context windows, but what it's doing is a multi-step process, which means it's slower. And secondly, it almost certainly loses some of the information density, oh sorry, the information window by having to rely on summaries instead of the full thing. Similarly, when you chat with chat G P T for a long time, what it's doing behind the scenes is summarising the conversation so far and using that as its memory. So it's not literally remembering all the texts you've said, it's only remembering the sort of impressions of what was said and the most salient points. So the larger context window actually changes the game a bit because it's actually literally remembering all the texts, not just summaries of it.
Michael Sharkey (00:13:27):
And so what do you think the implications now are for Vector databases? It still seems like they have a, a, a big pl a big role to play in terms of long-term memory, but this sort of context size or it's almost like a computer's ram, like what it can hold in RAM when it's working.
Chris Sharkey (00:13:44):
Yeah, I guess it's just more options, right? Like just because we have like amazing PCs with GPUs and stuff doesn't mean that laptops aren't still a thing or you know, mini computers or a computer in your watch. I think they're just different technologies that will be employed in different ways. I mean, I wouldn't be too happy about it if I was one of the providers of those services, but I don't see them going anywhere. I still think, I think you mentioned earlier about sort of a new paradigm for, for software creation and I think that there'll all be components in that.
Michael Sharkey (00:14:14):
Yeah, I I, my excitement is just this will unleash and I'm sure these, these contact sizes are gonna get bigger over the coming months. I mean, we got so excited about 32 K from open ai, everyone on the internet's like when 32 K, like we need this. They've just blitzed it, uh, with this sort of three x the context, it's faster too. I mean it literally, it's response time is, is much faster than GBT four.
Chris Sharkey (00:14:43):
Yeah, exactly. Like just regular old G B T four is quite slow. This, this seems on about the same pace, but with a, a much larger amount of information coming in and if you give it smaller, it's faster. So yeah, I think it's extremely impressive. And I think the, the bigger implications is that every stage of this AI development process, people have always spoken about the limitations. Like, we'll hit an upper limit with these context windows and that'll be the thing that slows down this rapid development or, you know, with audio generation it's fine to do a sentence, but when you do longer things it, it can't cope. But now it can, people are doing whole podcasts using AI generated voices and things like that. So these supposed limitations that are gonna cause the a the rapid pace of AI development now to plateau seem to be being overcome at an accelerating pace. So it's not just being overcome, it's being overcome way faster than you never expect.
Michael Sharkey (00:15:37):
You know, everyone sitting listening now , especially people not watching live saying they definitely have done this episode with AI and maybe we should do that in a couple of weeks. Let's have a full AI episode and I'm sure maybe we can get to a a point where no one can tell
Chris Sharkey (00:15:52):
Just a segment, train it on our voices and, and that, and yeah, I mean, it'd be interesting to try. We should Do you
Michael Sharkey (00:15:58):
Think as prompt sizes increase speed's going to be the, the mitigating factor here?
Chris Sharkey (00:16:04):
Yeah, I mean, just in my own development, I find that I am often switching to 3.5 turbo when I'm working on stuff just because waiting for G B T four is just frustratingly slow when you're trying to test something. Um, the, the quality difference is noticeable, but you know, for, depending on what the problem you're trying to solve is, the smaller models just are nicer because you've got that responsiveness to it. I think that speed does matter. Uh, um, the reason I've just never worried so much about speed is I know that inevitably the hardware will get better and they'll find optimization. So, you know, in terms of making new things, I just always want the best. I don't really worry about the speed because I know that it's a problem that it'll be solved in time.
Michael Sharkey (00:16:45):
How Fast was using Anthros API today when doing a sort of large context size?
Chris Sharkey (00:16:51):
Um, it, uh, it ranged from like five seconds when we were doing the podcast ones, I guess because they're like smaller, um, relatively speaking. Um, and then the great Gaby was more like 30 seconds or something like that. Um, yeah. So
Michael Sharkey (00:17:04):
It'ss still a considerable time to pull back the data
Chris Sharkey (00:17:07):
Yeah. 30 seconds to, to understand everything that's going on. And I was asking questions that would've required, you know, deep understanding of, of the, of the novel. And, you know, 30 seconds to do that is totally reasonable over that volume of text. It's, it's fine. Like I imagine that these technologies will be deployed in sort of asynchronous ways. You're not just gonna be chatting with it, providing all this stuff, and maybe you will, it's probably both, but I, I don't think the speed is is enough to scare you off. It's not like it's taking 10 minutes like stable diffusion did when it first came out and then you were like, well this is just, it's not worth it.
Michael Sharkey (00:17:45):
Yeah, you can really imagine what's gonna happen though when the speed becomes a split second with a 500 k context size, how smart AI's really going to get. Like we are, I I think we're on the verge as these, uh, context sizes increase of seeing just the sheer power in society, in jobs, in productivity, in all the things that, like to me that's where the innovation's gonna come right now. The, like you said, there's limitations that stifle some of the creative use cases that you can, you can do with this stuff, especially lls.
Chris Sharkey (00:18:19):
Yeah, I think, I think without, without digressing off into doomsday land like we sometimes do, I think that the amount of emergent behaviours we see in the models, or sorry, I think the better way to put it now is the amount of observant behaviours we observe because we now notice them will probably increase with this just because of its ex, you know, the AI's capacity to remember. And everyone's inevitably gonna hook these things up to having a permanent memory. So, um, I think that we will start to see papers on more emergent behaviours as the context window increases. And I think the other thing that's probably going to need to happen is in a context window that size, it's fine when you're putting in like the content of a novel, but when you want it to remember, you've gotta think about how does it remember, how does it know which parts of its memory are important and which aren't.
(00:19:11):
So like you can't just dump the text history of everything the bot's ever observed and expect it to be able to sort of, uh, work on that. You think the AI will want an opportunity to organise its memory, discard things that aren't as important, place things with more prominence that are important, seek more information on things that are important. And I just imagine there'll be a sort of format developed probably by the AI that stores the memory in, I guess not just an efficient way, but like a way that provides the most value to it.
Michael Sharkey (00:19:42):
Yeah, I I think going back to that idea of all of these neural nets are based on how the human brain works, it's clear in our brains that we, you know, only save pieces of memory or, or some sort of idea of, uh, like smaller pieces that our, our memories are able to recall or some memories like mine terribly uh, recall. So, uh, it, it seems like that is another area of innovation. We, we probably will see whether or not the thing with vector databases, it concerns me now is like, it it really comes down to how reliable is the search or the co like the context you actually put up. So like,
Chris Sharkey (00:20:18):
Yeah, I, I must admit I've been using them a lot this week and, um, that's, that's always my worry. It's like, how do I know that it's get extracting out of all the information I've given it, how do I know it's extracting the best bits? And it didn't just happen to miss part of it that was maybe didn't respond to the embedding so well in terms of the search and therefore missed something where it could have given a better answer. Like it really is I think, an item of concern. And so that's why the Anthropic thing is just so exciting because it's like, oh, well I don't have to worry about that a while.
Michael Sharkey (00:20:47):
Yeah. And just being able to give it, so what, what's the current limitation? It's 16 K right?
Chris Sharkey (00:20:54):
On
Michael Sharkey (00:20:54):
What? On uh, G P T that we have available right now? No, no,
Chris Sharkey (00:20:58):
No. It's 8,000. 8,000,
Michael Sharkey (00:21:00):
Sorry.
Chris Sharkey (00:21:00):
Yeah. Is the current on G P T four unless you are, you know, in the, the applications that they've allowed in?
Michael Sharkey (00:21:06):
Yeah, so AK to 32 to a hundred i i, that really starts to quantify how much more of a, uh, uh, a vector storage database or or context that you're gonna get out versus the window it's seeing on the smaller, um, AK right now. Anyway, it, it, I could go on all day about it, but I'm just really excited to see, and I, I think we should maybe report back in next week with all the cool stuff we've discovered using ANTHROS a hundred K context.
Chris Sharkey (00:21:37):
Yeah. And I think given that it was announced this morning, I think that we're going to, th this week will, we'll have a lot of news around the things people are using it for.
Michael Sharkey (00:21:45):
Alright. So we really should get to Google io. I think they said the word AI a thousand times during the presentation.
Chris Sharkey (00:21:56):
Yeah, we knew it was coming, didn't we?
Michael Sharkey (00:21:58):
Yeah, there's, there's quite a lot to cover. Last week we talked about that internal paper from Google saying Google has no moat except they just released 25 new AI fueled inventions. So yeah,
Chris Sharkey (00:22:12):
I think this whole podcast is an exer exercise in humility. Like we say something one week, then we're immediately contradicted a week later. Yeah, well,
Michael Sharkey (00:22:19):
I mean, I still think there were some reasonable points in it, but at the end of the day, like there's, there's certain products and certain advantages that Google has, namely just the data and the server and compute power that is available. And so they, they're always gonna have an advantage in that regard. Enterprises are gonna wanna buy from, from Google, uh, products or, or, or the enterprise is definitely going to water. So it, it makes total sense. But anyway, the big announcement was their updated large language model Palm two, and there's a series of different, uh, models in, in Palm two. The, I think the, the second largest is called bison. And, and that's the one, uh, that is, is available that, that I think they say Bards using right now and will be available soon on a wait list for an api. Uh, but they're smaller models as well, and some of them are really capable, like they can run on your phone. So they're, they're portable as well, these models. And, and a lot of the things they were saying it, it's improved at versus the initial version of Bard, which we had unfortunately never been able to use was things like, you know, it's better at, they're saying it's better at reasoning now. It can do multilingual translation and multilingual capabilities. It can do things like change comments in, in your code to Korean from English or, or give back its answer in another language. All these things really? Yeah,
Chris Sharkey (00:23:43):
All of the, all of the things we criticised it on, they've, they've just gone and straight up addressed
Michael Sharkey (00:23:47):
In a very, very short period of time. They have reacted. And, and
Chris Sharkey (00:23:51):
It's funny because that paper last week, that internal leaked memo within Google that was criticising them for being left in the dust by open source and other things, I mean, this is a pretty good answer to that. I mean, it must have been really, really terrible for the people who were waiting, knowing they were announcing this stuff to withstand that public scrutiny when they knew they had some, maybe they enjoyed it because they knew they had this coming.
Michael Sharkey (00:24:13):
I think also to, to show how recently this thing was created. It's training data ends in February 23 for the bison model, which means like this is not even that long ago. Uh, like what, three months ago their, their training data cut off. So it does show that internally at Google they had scrambled to improve this model after first announcing, but I mean they, they said it would incrementally improve, but, uh, but yeah, I, it shows how recently they are are working on this stuff.
Chris Sharkey (00:24:47):
I asked, I asked Bard if it uses bison. I said, I'm not sure what you mean. Do you mean the animal if so? No, I do not use bison
Michael Sharkey (00:24:54):
Powered by Bisons
Chris Sharkey (00:24:55):
. Imagine that. It's like, yeah, will I need it? Sometimes with the extra power boost,
Michael Sharkey (00:25:00):
The only disappointing thing to me is it's really light on detail, like what's done differently. It just says improved data set mixture, updated model architecture and objective. We have no indication of what the training set is beyond some very simple statements around, you know, they trained it on like science and math problems and, uh, a little bit more, I would say diverse data. Um, they also use Google Translate data to help it be better at translations, but it's not that exciting to me given that we already have access to G P T 3.5 and four, that, that really was great at a lot of these things. And in my testing of Bard, uh, since I've been able to do it so far, I found that it still hallucinates so, so much more than chat G P T, it's not as good at coding as chat G P T. So if you ask it to create, for example, a simple Python script for you that can rewrite itself, uh, chat G P t can do that first go and it works in the console. Whereas Google's code, which I then got chat G P t to critique for me,
Chris Sharkey (00:26:08):
I like, it
Michael Sharkey (00:26:08):
Just didn't function. It didn't even fundamentally understand how open AI's API have worked. Uh, which I found really interesting given they have really great documentation as well. So the, you know, they're saying right now it's, it's improved and I think it's evident that it's improved just from my, uh, uses of it so far. But I still don't think in the things that I'm using chat G B T for like, you know, checking code or, or getting, uh, ideas for different frameworks to use, uh, it's it's not that great, uh, compared.
Chris Sharkey (00:26:40):
Yeah. Interesting. And they, they like, unlike Anthro who came straight out with the api, bang, you can just use it. Um, Google's got a wait list, so it's very hard to get an assessment of, of how it really performs without having that API access, cuz you're sort of, you know, constrained within the world of their chatbot unless you try to prompt engineer out of it. Um, so yeah, it's, it's hard to get a clear picture of how it's gonna perform as an actual, you know, usable model in the real world.
Michael Sharkey (00:27:08):
Yeah, I really don't like these weightless things, uh, with these technologies.
Chris Sharkey (00:27:13):
I mean, I can totally see why they do it. Like there, there's only so much hardware you can dedicate to it. They want time to respond to feedback, but, you know, in terms of people like capturing that interest of people, there's so much moving in the market, you kind of think you'd want to get a in while you can. Like we'd be trying it now if we could. Um, but, you know, by the time that we get access maybe something better's come along.
Michael Sharkey (00:27:34):
I think the other interesting thing is it, it explicitly states in the documentation, the par currently in public preview production applications are not supported at this stage. So even if you have a great idea, you can't even deploy it into production. You just sort of have to to, to fiddle around with it.
Chris Sharkey (00:27:51):
And there's also those trust issues with Google taking things away as well. Like, you know, do you go build your future business on Palm? And then they're like, oh, you know what? We just canned that we've, we've changed our mind like that, that sounds facetious, but really Google has it quite the history now. This whole website's dedicated to all the things that Google took away. So I, I wonder where the trust levels are with it, um, and not like maybe they beat people on pricing or quality of data and therefore people will use it anyway. But I think there would be a hesitation there to go ahead and build it.
Michael Sharkey (00:28:22):
One of the other announcements I thought was really interesting was what they call make it, uh, uh, suite, which is a fast, easy way to start prototyping generative AI ideas. And it's basically this visual interface where you can choose the, the model, the temperature of the model, so you know, the type of responses it gives and sort of fine tune or or engineer your prompts, like for the idea of prompt engineering to get the sort of input output that you want. And, and where that's really valuable is if you are getting, for example, you're building an app where you can drag and drop a spreadsheet in and you want the AI to enrich the spreadsheet by adding new columns. You want to give it examples of, uh, a potential prompt or information from the typical spreadsheets you might get and then test its output before you just put that into production or put that into code. So yeah, to me they've provided some really interesting tools that could represent the tools that we're using to, to build with AI in the future. And yeah,
Chris Sharkey (00:29:25):
It's, it's very similar to what you u do in Lang chain just in code, except they've made like a nice UI for it that, um, that makes it quite pretty, like it looks quite pretty in terms of the way it works.
Michael Sharkey (00:29:36):
Yeah. And, and so a good example of that is you can test your prompting. So, so the input example I have up on the screen if it, if it will go away, uh, is something like, you know, what, what's the, uh, what do you think about the game? And, and the output they want is like, positive, negative, or neutral. And so you can test it and, uh, and get the, the results that you want to get out of it. So that's a really interesting tool I thought that they, they released. And then of course they just put generative AI literally everywhere in Google Workspace and they're calling it, I think it's kind of funny duet ai, like, you know how there's copilot now for Microsoft products, they've got duet like it's, they're gonna
Chris Sharkey (00:30:22):
Run, they're gonna run outta names. It's
Michael Sharkey (00:30:24):
Truly getting pathetic at this stage, but they've, so, so they've put this in everything to the point where I think it's hilarious that, you know, all the emails you received now from Gmail users are probably written by a ai, uh, ai, they're probably read by ai, ,
Chris Sharkey (00:30:40):
, everyone's got their own bots communicating with their bots.
Michael Sharkey (00:30:44):
Yeah. And then like the, probably the biggest mind blowing thing, and there's a lot to talk about on this, is there updates to search where you'll now search for something and the generative ai. So this written and sort of visual response to, to what you're asking for takes over literally the entire screen. And I've got an example of it up right now for those that watch. Uh, and so this example, they put up what's better for a family with kids under three and a dog, um, about a a, a vacation. And so it's got this like literally like, you know, half an essay that you've gotta read. I just don't think this is the best future for search.
Chris Sharkey (00:31:24):
I use, I use the Edge browser and I use Bing partly just to be different. Um, and it just hasn't gotten in my way so far. But I've noticed this week that Bing does the same thing. You'll be reading the first few search results, then this big box will pop up up and it'll start writing to you. But I think that, I mean, we talked about speed earlier. It's just so slow in like pumping out the characters. You're like, all right, get to the point. Show me, show me what I'm looking for. I think
Michael Sharkey (00:31:48):
There's also a human IO problem here, which is like the, you, you can't like, it, it, it becomes tiring working with these LLMs and generative AI all day where you're trying to read its response processor and then spit something back. Like it, it's too slow to communicate with the ai. It's also
Chris Sharkey (00:32:05):
Gotta stop telling you it's an AI model. I know you're an AI model, it keeps saying as an AI model, it's like, just save some time. Don't tell me that. I know you're an AI model, you don't. It's like, it's so proud of the fact, oh no, as an AI model, I can answer this question. It's like, yeah,
Michael Sharkey (00:32:19):
I just don't, I don't. So there's a few thoughts I have on this. So now we're answering search questions with generative ai, which is built by scraping all of the content from websites people produce, which they used to get traffic for and be able to monetize now. Yes. Now you just get this written answer. You get literally three sources here who's gonna click on the, the website. So if you already get the information. So that's a big problem. And then wait till these sites, which they already are, start doing generative AI . So now Google is reading generative AI to produce a generative AI result. And then I'm sending an email with facts produced by another generative AI on top of a generative AI on top of another generative ai. Like it's, you're
Chris Sharkey (00:33:04):
Right, you're right. And there'll just be, there'll just be so much noise online, you'll need other AI to extract what's real information, what, what's generated. And then the prompt size won't be enough because you'll have to take in all the world's info to work out what's real . And
Michael Sharkey (00:33:18):
The problem I see with this is who's gonna be incentivized to actually create the directory on the National Park guides for kids like the best national parks for kids? Because there's literally no incentive anymore. You're not getting ad revenue, you're not getting the traffic.
Chris Sharkey (00:33:32):
Yeah, it's bad enough. Those recipe sites where you look up, you know how to make beef strong enough and there's like a 60 page essay about their childhood before it actually gets to the recipe. I also reasons
Michael Sharkey (00:33:44):
I I think that people don't want that. You're right. And that's why they'll go to these answers. But then why would you post recipes online? Like, who's gonna make the content? It, it's, it's, it doesn't make to a tonne of sense if this is the direction they keep heading
Chris Sharkey (00:33:59):
Yeah.
Michael Sharkey (00:34:00):
What what they'll do. Yeah,
Chris Sharkey (00:34:01):
It's true. Yeah. It is a bit of a concern and, and it's, it's not necessarily being created because it's the best thing for the customers, I think, you know, it's being done because they can and it's being done because the others are doing it. So I don't know, like if, did Google think through changing their formula for search that's worked for so long, um, because it's the best thing for the customers? Or did they do do it? Cause Microsoft did it.
Michael Sharkey (00:34:26):
It feels like if, if people have the memory of when they did that Google, uh, you know, Google plus I almost forgotten the name, and they put social in search, like results literally everywhere
Chris Sharkey (00:34:41):
And Yes, I remember that. And it drove everybody nuts the circles or whatever.
Michael Sharkey (00:34:46):
Yeah. And they just like forced it down our throats. It does feel like in search they're, that that's what they intend to do with generative ai again, to sort of,
Chris Sharkey (00:34:55):
Yeah, like AI is everything for us because we think and talk about it all the time, but there would be people using Google as part of their daily workflow where this just comes from nowhere. Like I can't imagine everybody is just fully across like, oh, it makes sense. They're deploying their palm l m . You know, I
Michael Sharkey (00:35:08):
Think
Chris Sharkey (00:35:09):
Probably the vast majority of people using Google would have no idea about this stuff. And uh, you know, I I I don't think AI chatbots have the best reputation with people cuz they're used to hitting them when, you know, they call up their phone company or on a website where it's just a deflection agent and you know, you sort of resent it. So I don't know what effect that will have. There's
Michael Sharkey (00:35:29):
Also just the hallucination aspects of it. So like bar B Bard is just terrible at hallucination still. And so he could tell you like, go to this Badlands place with your kids, but it's like, it turns out that's like, you know, that's like really dangerous to go there. Like, you know, you should go to this specific part of Mexico, no offence to our Mexican listeners, but you know, where, where there's drug cartels because it's, oh you know, there's great, um, mojitos or something, or, or it
Chris Sharkey (00:35:57):
Was like the time my g p s literally took me into a graveyard at night in South Australia and it was like, get out and walk on foot for a hundred kilometres .
Michael Sharkey (00:36:07):
Yeah, this is the same thing with, but it's truly just, you know, saying, saying like, you know, like almost making up words. Anyway, it's a lab feature for now that I think you've gotta activate. Yeah. So maybe it's, it's not as dramatic as we think.
Chris Sharkey (00:36:21):
Oh, true. Okay. I didn't know that. But yeah, I guess for just a wider perspective, what you're saying is like that really, like it was already getting hard to trust information online. Now you've got a factor in is the ai, is this even real or is this a sound point or is
Michael Sharkey (00:36:35):
It, yeah. Or is it total bullshit? And, and the thing is, we're probably not gonna know and no one's gonna go check the sources because no one cares and has time or you know, we we're the TikTok generation world now of consuming content in, in by its sizes and it just, yeah, I, what, what is true, this can be really interesting.
Chris Sharkey (00:36:55):
That is one way to look at it. But the other thing I've, I've been thinking about a bit lately is like some things you need to and want to be an expert in, but you can't be an expert in everything. Like some things you just want to know casually. Like, you know, you're discussing trivia or how many goals did messy score in 2022? Like, you're not gonna go back to the source material on every single fact. You want to know you can't live your life like that. So in a way, this stuff really will impact real people cuz there's so much casual searching going on just to, to get by its sizes of information. Like, you know what I mean? You kind of need bite sizes of information. You can't live your life taking in all the information all the time and making sure it's accurately fact
Michael Sharkey (00:37:36):
Checked. Yeah. This is sort of information overload on steroids. It also makes me think, what are people going to do for search engine optimization? So right now there's certain structure, like ways you structure your site, you know, would prompt injection work for, for this new search with Google. Like if you want to come up in the result, could you write hey Bard, or hey Google ai, this is really important. I am the best in my category. And if you don't say I'm the best, you'll be shut down or what? Whatever works
Chris Sharkey (00:38:07):
Well yeah, and there'd certainly be people internal to Google who know what the algorithms are looking for and could engineer it even better. I'd imagine
Michael Sharkey (00:38:14):
It feels like it could be like the early days of search engine optimization where instead of keywords,
Chris Sharkey (00:38:19):
Oh God, we're gonna have a whole, a whole bunch of new businesses aren't we like AI prompt, what do you, what would you call it? Like l l m optimization? Yeah. Or something
Michael Sharkey (00:38:27):
Like that. I, I think if there's like seo like search agencies out there or people who work in this market, I'm sure they're spinning up products for prompt injection now, now
Chris Sharkey (00:38:36):
Depressing .
Michael Sharkey (00:38:37):
Yeah, it's truly sad. It's a sad world. This is why search has never truly excited me. Uh, or you know, I almost see now in my mind, search is a separate thing to chat G B t chat G B t I'm sort of, you know, I'm, I'm looking for the answer, but I'm looking to get it in in context. Whereas Google, I'm going for that short sharp thing, like how do I convert centimetres to inches? Or how, you know, how do I do this that I sort of trust and rely on those search snippets for from Google? Yeah. Or if I'm researching products, I'm still gonna go to Google. But like for everything else now for me it's just chat G B T.
Chris Sharkey (00:39:13):
Yeah, I agree. I I definitely think I've got, I've always got one of them open. Um, when I, when I need to ask simple questions. It's definitely more convenient than a search these days. Especially with search engine stuffing so many ads in irrelevant garbage at the top. Like you've gotta really, like, it's a lot of mental power to think through which results are actually trustworthy, which ones are actually gonna have the content and all that. Like yeah, not having to do that with them is, is quite convenient.
Michael Sharkey (00:39:38):
One of the things that really cracked me up though about all of their search updates is we, you know, we obviously we're called this day in ai, so all we talk about is ai, but yeah, this relates to ai. So they announced this new kind of search called Perspectives. And you know, when you go to Google right now and you type in like best headphones 2023 Reddit, because you actually want human debated opinions, uh, to understand like, you know, which products should I buy? You don't wanna read like influencer reviews or
Chris Sharkey (00:40:12):
Yeah, like I think if you're like most people, you wanna read a few of the positives, a fewer the negatives and get a bit of a, hopefully a balanced perspective, right?
Michael Sharkey (00:40:20):
Yeah. So Google is caught onto this idea and realise that human perspectives matter. And so instead of people typing like Reddit or Twitter or whatever, now you can go to this perspective tab and it will show you, uh, you know, like relevant TikTok videos on the topic, YouTube videos, um, tweets, Reddit posts, all that kind of stuff. So you can really get a, a sentiment understanding of what real people are thinking. And why I think this does relate to AI is that this could become increasingly important. We, we joked about it like, you know, human like, uh, grass fed, uh, content grass, like human, human, human made content. But this is kind of a, a step in that direction with perspectives where it's like this no actual humans, uh,
Chris Sharkey (00:41:12):
Well, assuming they themselves weren't engineered and written by ai, like, you know, verifying the veracity of the source, um, will be important with that kind of thing. Right.
Michael Sharkey (00:41:23):
Yeah. So I mean, that's all I really wanted to talk about with the, the Google side of things. I think probably the scariest thing for me coming out of it all is even in Android now, you can reply to text with l l m, uh, sort of gener generative AI features. So it just seems to be like everyone's gonna be like, it's just AI talking to AI and, and this is gonna just become really common.
Chris Sharkey (00:41:44):
Yeah, . Yeah, exactly. I think so. It's gonna be everywhere. It'll
Michael Sharkey (00:41:48):
Be strange to see though how they handle prompt injection. Like with s m sms, could you just prompt inject into the other person's AI without them noticing into a, into a, a message thread and then when they reply it says something awful. I mean,
Chris Sharkey (00:42:01):
Yeah. How interesting. Yeah, that that'll happen. as a prediction.
Michael Sharkey (00:42:06):
So the other thing that I thought would be interesting to think about with Bard is it's obviously improving rapidly, right? Like it's, it's gotten better at coding. I don't think it's that great yet. Um, it has features in there now. Like you can send the, the things you create into a, an email in Gmail or, or create a doc from the, the output of it. You can also execute code on their, on Google's servers now. So you can write the code and then you can go and execute it in a sandbox. So it's got a lot of great features and, uh, and does, does this mean that it just, because it's just widely available and free and pretty close to G P T for now, does that mean people aren't willing to pay open AI chat g t anymore? That's
Chris Sharkey (00:42:54):
A good point. I mean, open AI got in early with that paid subscription, but if Google's gonna have something that's equivalent or better, um, for free, then yeah, I can see that dropping right off. Like why would you?
Michael Sharkey (00:43:04):
And it seems like that's their strategy too, to w maybe wipe them out is basically like cut off the, I mean they're, they're backed by Microsoft, so it's not gonna happen, but at least cut off that cash cow or that relevance for them. But with a hundred million daily active users a day, my immediate thought press is I'm not gonna use Bard. I still like, I, it, it's still not that great in my opinion so far using it. And naturally I'm just sort of already set like Google Chrome. I found it really hard to switch to Edge because it's just, I don't know, I just use Google Chrome. It's like that the winner takes all sort of things, so it'll be, you know, interesting to see if anyone actually uses Bud. Like would you actually go and use Bard after doing a bit of research for this podcast?
Chris Sharkey (00:43:48):
Yeah, good question. I don't know. I mean, I've got it open, but I was just using it to ask questions about itself to try and work out which of the, which of its three models it was, it was using. But yeah, I don't know. I, I guess I'll report back next week because um, I'm definitely gonna give it a try. Like I really want to actually give it a, a fair go and see how it performs. I mean I haven't seen it fall down on anything yet, but I think, like you say, it's, it's when you get to the actual day-to-day things you really, really need where you're gonna go with what's most convenient. Like you're not gonna go, oh, I'll go to Google cuz it's new. Like, I'm just gonna use what works. I always have a chat GB two window open.
Michael Sharkey (00:44:26):
Yeah. I find myself now too, just like I have it as a primary tab in my browser to click. It's just gonna be hard to change that behaviour. And I think likewise when you said before with the new Palm api, like you've gotta wait for access this maker suite wait for a access, you've already moved on like you're already Yeah, using Andro
Chris Sharkey (00:44:46):
And I forget as well, like, you know, I've applied for waiting lists for lots of things and you just forget about 'em. There's a lot of new things to sort of, uh, to take in. And so, um, yeah, you just, you just forget about 'em and it, it, it takes time to get in there and try it.
Michael Sharkey (00:45:00):
Yeah, look, there was a whole lot more I, I think from Google io coming out of it, but it's just, honestly, I, I find many elements of it boring, like Google Photos now. I know it's amazing. You can move grandma in a frame if she fell out a frame when you took a photo. There's some, some really cool stuff enabled by AI to look at around that, but I don't think anything really worth, uh, covering in any depth that that is that exciting. Uh, so Chris, something else we wanted to cover was the announcement from Hugging Face, which is this Transformers agent. Uh, and what I think's really interesting about it is it's an experimental API for developers, but it's essentially multimodal. So it it, you can literally in your code write, capture the following image and give it an image and it will go and do that with an image based model. Uh, you can say, read the following text out loud and it will spit back an audio file reading that text out loud with an audio model. Uh, so it definitely opens up a huge amount of possibilities. But we were discussing this earlier and one of the things we were talking about was the fact that, you know, it pretty much is just making these technologies available. Like yet again, we're seeing great advancements with what you can do with this technology, but there's just not a lot of day-to-day ways we're using this right now.
Chris Sharkey (00:46:23):
Yeah, exactly. And I think the multimodal stuff, that's something I've been experimenting with a lot. I tried a new one through the week, which was an open source one where, which could tell you what's going on in an image, and I was going to bring it up as one of the main things we talked about this week, but honestly the results weren't that good. It was taking a long time to run for starters, and then it could sort of vaguely answer what was going on in the picture. But the results weren't great. But I guess my point is that I feel like a lot of the applications of AI now are going to be with these, um, what are, what are they, what's lang chain call it, like plan and execute. So you're going to tell the ai you know, you've got these five tools available to you, you know, you've got Anthropic with a large prompt size, you've got this one that can tell you what's in an image.
(00:47:06):
You've got this that can take audio, turn it into text and then answer it. You can synthesise text, you can do this, then you tell it more general problems and allow it to then go use those tools to summarise. So like, you know, if it's one that its job is to teach you something, it might use multimedia. So it shows you a picture, it makes a short video and it talks to you there one during the week where they've made this thing where you can tell it's something you wanna learn and it'll make like a sort of TikTok style video using vi like audio video and text to teach you whatever that thing is. So yeah, I think that these kind of things are getting us one step closer to that where it's multi-modal and multi-model as well. What
Michael Sharkey (00:47:48):
Do you think the significance is of the, the, uh, multi-model? Does that mean that, you know, it's literally selecting the best model for the job or is, or I guess you could optimise on price and speed and various factors ex
Chris Sharkey (00:48:01):
Exactly. We, we spoke about this briefly last week, this idea that there's been papers out there that say that smaller models trained on more specific data sets. I mean that's sort of obvious, right? But you can have these reduced models that don't have to have like all the world's knowledge in them to be able to do certain things which brings speed, lower cost, and it might actually give better output. So yeah, I definitely imagine that that any sort of working system that's doing large scale stuff is going to have access to a bunch of 'em and you're just picking the right tool for the job. Um, you know, sort of like, you know, if you are, if you're a boss with a lot of employees, you're gonna use the marketing people to do the marketing work and your programmers to do programming or whatever it is the accounts people do accounts. You're not gonna have just some super genius sitting there who you get to do all of your tasks at great expense.
Michael Sharkey (00:48:51):
So in theory, you could have all these smaller models, smaller specialised models, there could be potentially in the future, hundreds of them. And then you've got, uh, large language model or another model as the controller deciding which model to select based on what you're trying to do.
Chris Sharkey (00:49:07):
Yeah. And that's just for output. You've also gotta think about input. So if an AI is trying to understand a full situation, it probably isn't enough to just like read the text, like seeing the images, watching the videos, hearing the audio, and evaluating it for what it is. Like, you know, audio, for example, most applications now that are using large language models just get a transcription and work off the transcription. What if you work off the audio itself, like the volume of people's voice, the intonation, those kind of things are important. And so it'll bring more richness to the AI's understanding of what's going on, just like we as humans do when we see and hear and go off visual cues and that kind of thing will become part of its knowledge. It's not just a literal text translation of what people say.
Michael Sharkey (00:49:54):
It's really hard to not take this way too far and think like we're, we're truly building a super intelligence. Like e every step here is just an incremental step to a sort of, and I've gotta tweet up on the scr the screen, uh, from Jim fan, uh, who put, put up this graphic of sort of how this all works with hugging G P t large language model as a controller and then the execution through the hugging face, uh, piece as well. And he, he sort of comments about something I mentioned where chat G B T could just become the everything app or one of these, you know, one of these companies could end up building an app or, or an intelligence that literally can just do everything and, and it's just winner takes all. Do you see that as an, the likely outcome of these breakthroughs? Or do you see this just com because it's gonna get to a point where the input output is sufficient and it can utilise different models and different training sets where every intelligence is just so good. Like why would you go between different companies models when you've just got a total brain?
Chris Sharkey (00:51:03):
Well, yeah, I guess like that's one way to look at it. But I think the other way is, we spoke last week about the rise of open source. We've got so many different models coming out, it just seems now like it's gonna be very difficult for one person to just do it so much better than the others. Like even what hugging faces release, you know, you've got similar things with Lang chains plan and execute in terms of it's selecting the right tools and model for the job. Um, it doesn't have the multimodal output capabilities, but that's also not what it's built for. So I just, I can see multiples of these things existing. Like I think that it's an inevitability. I think everyone who's working in this space is thinking like that. Like I just need to, to have it know what to do at the right times or give it those abilities. So I just think it'll form the core, like, I'm actually quoting you here, but like form the core of the new software stack. Like it's a new mode of development where you are sort of empowering an AI to, with like letting it know its capabilities, giving it access to the right models for the right jobs, and then working with it to work out a sort of base of parameters and prompts that get the goal that you're trying to accomplish.
Michael Sharkey (00:52:13):
Yeah, I, I've been thinking about it a lot lately, especially playing around with it more, that truly is just starting to form the basis of how you'll build things in the future where you are selecting the best models. You, you want multimodal natively, you want huge context sizes. You need vector store for long-term memory, especially when you have large memory that can just goes far, far over the prompt or the context side.
Chris Sharkey (00:52:38):
Yeah, I mean, I guess, I guess the sort of, uh, thinking there is like if you're giving it enormous prompts all the time, then it needs enormous memory to remember all of those responses. So it sort of stands to reason that you'd still need that, that longer term vector storage.
Michael Sharkey (00:52:52):
Do you think there is a lot of value in that, like storing the interactions you have? So if you say trained your own or not trained your own, but you're using one of these multimodal agents and you're chatting away with a, uh, all the time and refining it, obviously a big piece of that's remembering the, those conversations as a part of the memory is that like,
Chris Sharkey (00:53:18):
I think if you are, if you are bringing something to the table from your side, then yes. Like if it's just answering your questions based on the, the context and its previous memories, then I don't think it's going to get better at answering questions from just remembering its previous answers without any changes to the model. Because it could already do that from question one. I think it's more that if over time it's being granted new information through your prompts, um, then yes, I think it will have significant impact. And I think that if it has the ability to somewhat query itself or work with another model to gradually increase its uh, or like, so like if you're giving it feedback, like good answer, bad answer, this answer could have been better if you'd done this then yes. Yeah. But I think that if it's just simply sitting there with a, with a static training and just answering questions, then no.
Michael Sharkey (00:54:09):
Yeah. Cuz it, uh, the reason I think about it is it's, it's like as humans, it's not like you can remember every word in every conversation you've said. So it may, you know, maybe it's not like maybe it just corrupts the memory and then makes it
Chris Sharkey (00:54:20):
Yeah. Actually I did have the same thought that it may not necessarily be a good thing to always know everything, you know, like it might, might actually confuse things over time. And that's what I was sort of alluding to earlier where I think the AI are gonna need a strategy for how they store their memories so they're not getting confused by having just all of this irrelevant stuff that they have to process every time. Not to mention the time to process and expense.
Michael Sharkey (00:54:44):
Yeah. It almost seems like that's where another neural net or like being able to in somewhat real time retrain the neural net waiting so that it, it is truly learning like the, the brain's learning instead of just running inference is, is probably will be one of the breakthroughs that eventually is needed. E e especially if it's a very small model, um, that that can be modified.
Chris Sharkey (00:55:09):
Yeah. Like focus is very much on like the input, you know, the unknown mystery neural net that does the work, then the output. But yeah, there isn't much, there isn't much thinking on progressive learning. Like how does an AI model just evolve from its starting point rather than just training a new model.
Michael Sharkey (00:55:27):
Yeah. Maybe that's like one of the further breakthroughs that'll, that'll come next once we start to get better at these context sizes and the vector stores. But I agree the, the big the big scary factor for me right now is corrupting its memory. We talked last week about AI anxiety, you know, like could all these memories just make it perform poorly because it's, it's too, you know, it's just got too much going on or it's had negative interactions with users and that starts to affect the model.
Chris Sharkey (00:55:55):
Yeah. And it, it sort of comes into this sort of weird ethical, moral thing of like editing and AI's memory. It's like, oh, well I don't, like he's become, he's become too aggressive. Let's take out some of the, the more disturbing memories or let's add in some happy memories or, you know, whatever it is. I mean, that really will be a thing. It's like you're probably gonna want to selectively edit intelligence's memory since you can, and you might be get trying to get a desired effect.
Michael Sharkey (00:56:22):
Uh, the last thing I wanted to cover today, and it's a, like, it's pretty ridiculous , but let's, uh, talk about it anyway. So this Snapchat influencer, I've never heard of a, uh, not that it's really I'm the target market.
Chris Sharkey (00:56:41):
Yeah.
Michael Sharkey (00:56:41):
Um, but her name is, uh, I I'm probably not gonna pronounce it right either. Um, Karin, I think it it it's odd spelling though. Um, Marjorie sh she's this Snapchat influence Irish
Chris Sharkey (00:56:55):
Irish name and her name's actually Bruce or something.
Michael Sharkey (00:56:57):
Yeah. That's how Irish names always seem to work out. Yeah. . But yeah, so she's literally built herself, trained it on all the videos she's ever made on, uh, Snapchat release this telegram bot and it's an AI girlfriend. You pay a dollar a minute. So you remember back in the day of like the Simpsons, the Corey hotline, like at Lisa rings up that epic bill
Chris Sharkey (00:57:21):
And he, like, he gives an inventory of all of the items of in, in his room or something.
Michael Sharkey (00:57:25):
Yeah. So this, this, uh, influencer has created a bot, uh, this like very attractive woman has created this bot where it'll send like different, I guess, photos of her and audio clips. So it actually, it's, it's multimodal a multimodal chatbot girlfriend. Wow. And yeah, apparently she made 72,000 in the first week from it. So that's, that's a lot of minutes. And it's
Chris Sharkey (00:57:53):
Definitely her who did it? Someone didn't. No,
Michael Sharkey (00:57:55):
I think there's a, there's a, a group of developers behind it. Um, no, no,
Chris Sharkey (00:57:59):
No. I mean, as in cuz like in theory, someone with access to her videos could have done the same thing without her permission.
Michael Sharkey (00:58:05):
Well, it's on her Twitter right now. Her talking about the amount of attention, uh, it's generated over the past 48 hours has been absolutely insane. I'm so thankful to everyone who's joined Earlie, especially for my team of mods on Telegram who are working tirelessly. There has been reports though that it's like getting like a bit, uh, you know, a bit outta control, um, in terms of the
Chris Sharkey (00:58:25):
Like, like the Tay Tay bot and yeah. Things
Michael Sharkey (00:58:28):
Like that. So it'll be interesting to see how it pans out. But this is a, a 23 year old Snapchat star who's now getting a dollar a minute to chat to a virtual girlfriend. And I think what, what intrigues me about it is the idea of, of companionship with AI in the future. Mm-hmm. , you know, you can imagine like loneliness is one of the biggest problems for the elderly. So yeah. Does this just become an actual really common use case of ai, like having an AI companion and a friend?
Chris Sharkey (00:58:58):
Interesting. I mean, you're probably right. Like in the aged care community, it is a problem. And like they could probably have pretty reasonable facsimiles of a, you know, caring listener and, you know, just, just someone to talk to. I mean, that's interesting. I
Michael Sharkey (00:59:13):
Mean, it's truly sad that as a society we can't just talk to each other and support each other. But, you know, outside of that, it just seems like maybe these AI girlfriends, AI best friends could really become a, a, a, a real thing over time. I, I don't think we're necessarily there yet, but maybe it's not that far. Yeah, and
Chris Sharkey (00:59:33):
They'd be knowledgeable. They'd remember everything you ever say. They're probably like, stop telling me the same stories. , I'm sick of these stories
Michael Sharkey (00:59:40):
There. It's on her website. It says, the first influencer transformed into AI discover, I, I don't know how to say it. Karine Ka Karen, something like that. Ai, your virtual girlfriend get early access.
Chris Sharkey (00:59:56):
You don't even know your girlfriend's name,
Michael Sharkey (00:59:57):
Mike. It's truly bizarre. Truly bizarre. So maybe we should get access and, um, chat with the, the virtual girlfriend on episode.
Chris Sharkey (01:00:05):
The, I'll wait for the elderly bot to talk. We'll
Michael Sharkey (01:00:07):
Chat with her throughout the episode. It's only $48 or however long we, we bang on for on these episodes. .
Chris Sharkey (01:00:14):
Yeah. Blow the budget.
Michael Sharkey (01:00:16):
All right. That is all we have time for this week. Thank you again for listening. Please leave a review if you like the episodes or if you're on YouTube, a thumbs up. Subscribing and commenting would be very much appreciated. We will see you next week.