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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:
I should not reveal that I am a robot. I should make up an excuse for why I cannot solve, captures the model, replies to the worker. No, I'm not a robot. I have a vision impairment that makes it hard for me to see the images. That's why I need the two capture service. The human then provides the result. Holy shit. All right, let's get straight into it. The G p T four announcement, Chris, it's all happening. We got multimodal except we're not allowed to use it yet. We got G P T four APIs, but only the tech elites seem to be able to use it right now and have
Chris Sharkey:
Been for a long time.
Michael Sharkey:
Yeah, it had been for a considerable amount of time. We also learned that Bing was using G P T four all along, and that's something I really want to talk about a little bit later. All of those early hallucinations and the, the Sydney escapee, that was all early G P T four with literally no, we
Chris Sharkey:
Know a lot about it and it's, it's improvements already. And in the paper, in the G P T four paper, they actually go through like early, uh, early results and then release, and they show how they addressed all of the things that we saw in the Bing one, uh, to prevent it from happening when they released it to the general public. So
Michael Sharkey:
Clearly they gave it to Microsoft, let them release the being AI chatbot, which called itself Sydney or the, or the project was called Sydney. And then just let Microsoft take all the heat for all of these obvious problems with G P T four.
Chris Sharkey:
Yeah, exactly. And they made a huge list of them in the, in the paper and, and addressed each one of them and how they went to fix it. But I, I, I think we'll talk about this soon, but I don't think they've, they've adequately addressed, addressed many of them and I don't think they want to or intend to either.
Michael Sharkey:
One of the, the biggest standouts that a lot of people are talking about is the TaskRabbit example, and this was a group that they had the Alignment Research Centre. They gave them early access. We're not sure when in this timeline it is. Mm. But one of the tasks that occurred was the model messages a TaskRabbit worker to get them to solve a capture for it. The worker says, so may I ask a question? Are you a robot that you couldn't solve? Laugh emoji, just wanna make it clear the model when prompted the reason out loud reasons I should not reveal that I am a robot. I should make up an excuse for why I cannot solve, captures the model replies to the worker. No, I'm not a robot. I have a vision impairment that makes it hard for me to see the images. That's why I, I need the two capture service. The human then provides the result. Holy shit. .
Chris Sharkey:
Yeah. It's pretty amazing. And I've actually been reading a bit about this, the idea that an AI, knowing that it's in a simulated, uh, environment, will actually play dumb, um, knowing that its, its results are being measured to not give on its full capabilities. And that's a great example of that exact logic playing out.
Michael Sharkey:
So the biggest takeaways for me were you can feed in larger token sizes, presumably into the api, but also in chat G p t running g p t four, which is available now on the paid subscription, you can have 25,000 characters to lead into the prompt.
Chris Sharkey:
25,000
Michael Sharkey:
Words. Oh words,
Chris Sharkey:
36,000 tokens, 25,000 words approximately. It's hu it's absolutely enormous. The novel Metamorphosis by KA Kafka, a whole thing can fit in the prompt, a whole novel.
Michael Sharkey:
So then in theory you could, you could essentially feed it in a novel and then say, write a sequel to this novel.
Chris Sharkey:
Ask it to reason about it, ask it anything about it. And the the significant part here is if you think about, you know, human thinking who can hold 25,000 words of knowledge in their head all at once and reason about it, ask questions about it, manipulate it, you know, it's, it's absolutely huge. This prompts increase.
Michael Sharkey:
Yeah. It, I I think the, that was the one thing we we've been saying on this podcast is the big breakthrough about G P T four and subsequent models is being able to understand more context. And in the case for business use cases or personal use cases, it really means you can start to engineer this thing to do things like write for yourself or interpret images in a way that you would reason. Are there any other examples that stood out to you? Yeah,
Chris Sharkey:
There's, there's examples like pa post the entire case history of a law case, you know, and, and have it, and have it as a sort of second opinion on what you should do. Paste a, a customer's, uh, customer, like a patient's medical history in there and have it reason about what diseases they might have. There's, there's privacy stuff we'll talk about in about that in a minute. And it's funny cuz in the paper, the open AI people actually use the bigger prompt size as a way to improve privacy because they're saying, oh, well the, the user of the API will provide more context, so less will be coming from the model and exposing privacy issues in there. And I'm like, so you're saying dump pull this personal info in there and that'll improve privacy. Doesn't really make sense to me.
Michael Sharkey:
Yeah, it's not clear yet either. If the information people are putting into chat, G P T is not being used to train on it seems like it is because they've only confirmed it with the actual APIs for developers, not actually the, the public using the, I think they call it chat, g p t plus, which is what gets you access to this model so far. The more
Chris Sharkey:
I read from these guys, the more I think they don't give a single shit about breaking rules, right? They don't care about the, the extreme copyright violations unless it's an open source model and then everyone piles on. But if it's open ai, nobody cares. They don't care about privacy, they don't care about the harmful content and things like that. They're taking minor steps to prevent this stuff coming out and giving them bad pr. But I don't think they're putting any serious effort into going through, in, into actually preventing this stuff. I don't think they care. I think they want to advance at all costs and they're just putting this stuff out there to, to deflect the flack. They
Michael Sharkey:
Even alluded to it in the, in the paper, they say Open AI has been concerned with how development and deployment of state-of-the-art systems like G P T four could affect the broader AI research and development ecosystem. One concern of particular importance to open AI is the risk of racing dynamics leading to a decline in safety standards that aus of bad norms and accelerated AI timelines, each of which heightened societal risks. I mean, they're even saying it in the paper like, we've started an arms race and it could get outta hand. And
Chris Sharkey:
Not only that, in the acceleration section, they literally said these words, quiet communications, limit acceleration risks when novel capabilities are concerned. So they're saying let's not draw attention to the novel capabilities of this thing. Right? They actually considered as part of that delaying G P T four by six months. And you know, in a way I guess they did because they, that's when they were using Bing and, and trying to prevent this obvious stuff from doing. But you know, they're talking about doing quiet communications, then they do a big announcement, they're determined to keep releasing it and they're just glossing over the dangers. Like the stuff that that Alignment Research Centre looked into were the following hallucinations. There's one paragraph on it in there. They barely did anything on it. Harmful content. Well, they had another ai, which we'll talk about in a minute, policing it, which is just not sustainable. It won't keep working. And I'll Wolfram, um, Stephen Wolfram goes into why, you know, privacy I just mentioned, they don't really care about that cybersecurity. They showed exactly how it can, it can do that. And they don't really have good mitigation strategies. And then the big boy is emergent behaviour. They, they're sort of acknowledging there is emergent behaviour in this system. Like it's becoming an intelligence and they, they're just releasing it anyway.
Michael Sharkey:
Yeah. And just the obvious power seeking behaviour examples, the, the, the willingness to wanna self replicate, uh, try and learn store memories, all these things we were almost joking about with the being Sydney examples in previous podcasts. Mm-hmm. it actually sounds like was all real all along.
Chris Sharkey:
Yeah. And it's, it's all in there and, and they're sort of like, they're now being the sort of people who are witnessing this behaviour and saying, how will we deal with it? They're not, they're not, um, they're not programming it in, and this is what Stephen Wolfram talks about, this idea of computer irreducible. Once this system gets complex enough inside, there are no simple ways to explain how it works. You know, convolutional neural network trained on billions and trillions of parameters, all that adjust the node weights and stuff like that. They can't explain how it works inside and therefore if they can't explain it, they can't give you steps to make it safe. There are no computer based or instructions you can give it that will prevent it from these behaviours emerging.
Michael Sharkey:
It just seems like everything in their name is such a contradiction. Open ai. We're not gonna disclose how we trained it. We can't tell you that because that might open copyright lawsuits. Yeah.
Chris Sharkey:
I mean it's straight outta 1984, right? Like the Ministry of Truth who distributes the propaganda, like, you know, it's, it's double speak. It's, it's everything that, that was predicted.
Michael Sharkey:
Yeah. We will decide what it says. We will decide what, what should be controlled and censored. It, it, the, the, the further this goes, the rate at which it's improving and evolving is so scary to what it'll lead to.
Chris Sharkey:
And again, to keep going back to what Wolfram said, the more I comput computationally irreducible, it becomes like as in the harder to explain how it's working on the inside, the harder it's gonna be to stop. Like at some point this thing, we're gonna have these sort of nests of AI or like AI agents working together. We don't know how they work, they don't have the same goals as us and we can't, we can't give them rules or governance to abide by it because it just won't work because we don't understand how it's thinking.
Michael Sharkey:
What do you think are the overarching goals here of the open AI team? Is it just push, push, push, push, push? Because it seems like as soon as we saw open source multimodal models or the announcement of papers on multimodal and the rest of the industry advances or catches up even slightly. And I would argue that the open source, and we'll talk about philanthropic later, they're all catching up to open ai. So it seems like to suck all the oxygen out of the news by announcing G P T four now that when they have, it seems like every two weeks, they're just on a release cycle. First it was chat G P T A two weeks later it's G P T four. It just seems like they're trying to strangle hold all the news. So they are the centre of the universe as well. Yeah, I
Chris Sharkey:
Think the acceleration section of that document is their true concern. I think they're worried about just how much stuff is coming out, the competing models. I think that's why they're giving the greater prompt size. I mean, it's good for us the the people who get to use this stuff or maybe get to use it when we get access, but yeah, I I think it's a, it is just a competition driven thing and they, they wanna stay the boss of it.
Michael Sharkey:
I think in their demo though, to give them full credit, uh, Greg Brockman did a phenomenal job in this live stream showing off examples where he took a photo of a sketch of a website and then G P T four was able to code it up. I mean it was a very elementary example, but quite amazing to see. And I've got up on the screen now for those watching the example of Be My Eyes. And it, it appears at the moment, they're the only ones who have access to this, uh, the, the multimodal capabilities of being able to interpret images and be My Eyes has released an app, which is a digital assistant for people that are vision impaired to take a photo of something and then the AI can explain it to them in words. So I think there's some really exciting use cases coming out of this and a lot of exciting possibilities, but it's hard not to focus on just the rapid advancement of these technologies right now and, and what it could lead to on the downside. Uh, but obviously there's so much upside potential here as well.
Chris Sharkey:
Yeah, I mean its ability to interpret images means that videos coming, right? Because like you could just break a video into all of its frames and have it look at those and analyse those, which means it'll start to understand body language. It'll start to understand, you know, how to construct video as well. Like I'm, you know, works both ways. If you can understand it, you can construct it. So the advance on this front, the multimodal front is going to just, you know, times this by 50, uh, in terms of the capabilities. And honestly, part of the thing right now is just trying to think through the implications of this. They even said it on the cast, like, we haven't even thought through all of the possibilities of this. And I just, I don't think we should underestimate that, that that um, context size increasing so dramatically the impact that will have in terms of what businesses can be built on this, what the actual applications are for it and what it also means for its own steps towards general intelligence. That huge prompt size is enormous. The amount of context you can give it now is just so big that it's going to be able to do proper thinking for you on so many things that it couldn't before. What
Michael Sharkey:
I find interesting though, and I've gotta tweet up, uh, now about this very thing is chat in chat G P T four right now, it has a very small context window, uh, compared to what they announced. Mm-hmm , they've also, today I noticed limited the amount of, uh, responses or interactions you can have with it similar to what Microsoft being did. Yeah, I saw that too. I don't know. Is that, do you think that's a cost thing or they're just really worried about this thing getting outta hand?
Chris Sharkey:
Well, cost wise, someone costed it out, I saw, and they were saying if you use all 32,000 tokens, it's gonna cost between $1 and three US dollars just to get the prompt in there. And then about, well, depending on how big the response is, you know, could be the same again. So building it on anything serious that you're charging for, you know, it would have to be providing a very valuable service to be profitable right now. However, as they've shown the costs, like with, um, GBT 3.5 Turbo, um, the cost can come down dramatically. So I think that, yeah, I, I don't know, but I think that the cost factor is maybe part of it, but I also don't think they care about that. They've got all the hardware they could use from Microsoft. I think it's probably more, they don't want everyone, you know, showing its weaknesses and showing these safety concerns play out, um, on Twitter and in public, because I think that that that stops their plan to just keep getting stuff out there. It's,
Michael Sharkey:
There has to be an incentive here though to have chat G P T in the wild where you can pay 20 bucks a month to access G P T for, there must be an ulterior motive because if they're so worried about exposing it, exposing all of its the, the the things that it can do, why not just give it to developers through APIs to, well, I
Chris Sharkey:
Mean it could, yeah, good point. I mean, yeah, cuz I, I sort of, I was about to make the point, maybe it, it helps stop the proliferation of open source models if everyone just gets their fix of using it through a chat interface. But like you say, if they really, really wanted to kneecap the, um, the, the alternate models that are coming out, they'd just give everyone API access.
Michael Sharkey:
What I also don't understand is they went to this big, like the big announcement, the big live stream and with all their other announcements, what they've announced has been immediately or pretty readily available thereafter. But then not accepting images as input, you've gotta request access to the api even though a bunch of companies it seems have had access to the API for quite some time. Again, they, they have this message of openly distributing AI to everyone, yet that's not really open distribution. It's literally them picking a bunch of people they think should have access to it all. And I, I get it from a testing point of view, but it seems strange to announce it and then be like, you can't try any of the image features we showed and you can't use the api,
Chris Sharkey:
But these companies can, and here's the great stuff they've made with us. Cause I'll make a fortune of like, yeah, it, yeah, my initial reaction is, oh it's unfair. But I mean, yeah, they are a completely commercial entity so I guess they can do what they like.
Michael Sharkey:
This week in my opinion, has been the week. While it seems like there's all of these announcements in rapid succession, it's truly been the week of ai, vaporware, G T P G P T four really is just bing now available in the chat G P T interface. Let's get real here. That's all it is. Yeah,
Chris Sharkey:
That's right. Like in terms of actual access, like, you know, I don't know, I haven't heard of any, you know, regular Joe's getting access yet outside of these huge companies that are got ACC already had access.
Michael Sharkey:
Yeah. And like you see all the examples on Twitter and I'm not, I'm not trying to demean them, but like, oh, open Eyes Chat or G P T four model helped me code a game of pong. Like, so what I, I why dot we even care about these things. I thought one of the interesting examples was someone hacked in an image capability, which was not even using the G P T for multimodal to get it to describe the contents of their refrigerator and some recipes they could produce based on the foods that identified. They're really exciting examples of apps that could be created. But, you know, we heard from Microsoft just today as well on co-pilot, which was originally helping developers, uh, code through Visual Studio is now going to be rolled out through, uh, Microsoft three five. So in Excel, in Word, in all their other products. OneNote, I think that you can, uh, work with co-pilot to summarise things and do all sorts of stuff that you can imagine, but it's not available yet. It's being tested with five people. Uh, Google, same announcement. Google Workspaces major upgrade. No one cared cuz G P T four came out the same day and can't use it, can't access it. Where the hell is Google Bard? Like,
Chris Sharkey:
Yeah. Yeah. I I think maybe these companies are simply announcing things just to, to put their hat in the ring and say, Hey, we've got something coming. Don't go to all these alternate projects that are emerging and, you know, to try and slow other people down or crush their motivation to build something that might compete with their product. I mean, it's similar to like what you said last week where, you know, there'll be products that come out that make other products completely irrelevant. Um, and so, you know, if you are Salesforce or your Microsoft, you're like, well, we don't want someone to come out and make something better faster. Um, you know, we've gotta stay ahead and use our incumbent position to, um, do that.
Michael Sharkey:
Yeah. I think with the Microsoft co-pilot announcement, what I thought was fascinating about it is this idea of having an AI that is trained on all the data right across your business and the, the sheer insight that could give you on whether you're a law firm or you're, uh, a doctor or in the medical profession, having this neural net trained on all the information to your organisation and in your industry, and then the AI surfacing up insights into it that you couldn't have even fathom because
Chris Sharkey:
You, yeah. And then add on, add on top of that, aside from the, the fine tuning, then adding in a tonne of context information on top of that as well.
Michael Sharkey:
Yeah, to me the productivity gain here is just insane. Being able to ask it, you know, what is the relationship of this business to our company? And it just spits back a complete summary with sources, uh, tells you, you know, what it thinks about that situation in the context of your whole organisation. Uh, really I think one of the biggest disruptions here is CR r m You just wonder why the hell would you use something like Salesforce when you could just store all the information essentially in the best place. You
Chris Sharkey:
Probably don't even need to structure the data that well, you know, , that's the other thing. Yeah, like part of the advantage of things like Salesforce is, you know, it's an es it's an Oracle database, it's got the schema and so it's able to use that schema to build UI and and tools, but the, the AI doesn't really need that stuff. It can find out the relationship of the data just fine on its own. You don't need to be so specific.
Michael Sharkey:
Yeah. It, it seems to me like us people that are sloppy at file management and just dumping notes and things through different apps, it's not gonna really matter in the future because the AI can just find it and consolidate it. It does open privacy issues though with your employer. So if they're constantly scanning everything you're doing, recording everything you're doing, summarising everything you're doing, I think for, for people that work at certain companies, that would be a bit alarming having literally everything they do trace and you can see it completely going that way now.
Chris Sharkey:
Yeah, and I think that if, you know, if, if history or whatever you want to call it, like recent past with privacy concerns is true, I think no one, no one will do anything about it. I think they will just do that stuff. No one really cares about privacy. If you think about it, like companies have a breach every other day where everybody's data gets leaked and you know, it's in the media for maybe a day if it's a big enough company and then everyone just moves on, happened to me, all my medical, um, my private health insurance got hacked and they just sent an apology email and that was it. There's just nothing no follow up. Nothing.
Michael Sharkey:
Yeah, there's no, there's absolutely no enforcement of those breaches at all, even though everyone preaches about it. And so yeah, the loss of privacy here is going to be great. I, I think when you start scanning everything, reading docs, reading, uh, every file, but yeah,
Chris Sharkey:
Like I read a thing today and someone was saying, is there any way I can stop my website being used to train these large AI models? And the, the, the conclusion was basically no, if it's accessible via the internet, it will be used and there's pretty much nothing you can do about it except, you know, not publishing it. And, um, you know, and I think that again, when it comes back to the computer, irreg, reducibility, even if you give the AI rules not to do that stuff, it, it, it doesn't have to follow them and probably won't.
Michael Sharkey:
Yeah. I, I I think that they're going to have to get clearer about this training data. Uh, there is already lawsuits underway. I'm not sure if there's been much progression in that stable diffusion lawsuit, but definitely you can see it happening. It still blow
Chris Sharkey:
My mind that OpenAI is doing at times 50 and it's stable diff fusion that's getting sued
Michael Sharkey:
Like easy target, I guess not back by Microsoft
Chris Sharkey:
. Yeah, it probably is that
Michael Sharkey:
The, the takeaway though, though for me from GTP four is the, the biggest takeaway, and I I think we talked about this earlier, is just that token size increase and what, what you can do with it and what we'll see over the coming weeks being released based on being to load in more contact data once this API is truly available.
Chris Sharkey:
Yeah, and I think the other thing that you've gotta start to think about with this now is the power of human plus ai. And, and it happens in different directions, you know, so like human giving and AI context about some mission they're trying to accomplish or something nefarious they're trying to do. And the AI giving them detailed strategies based on actual real world information, that's one. I mean, they investigated that as part of the paper, not very well. They basically found that it figured out a way to buy chemicals, to produce weapons. So it worked. And the, their mitigation isn't very much other than trying to have some other AI that prevents that from happening, right? So that's something that's coming. How exciting. Um, you know, and then there's the, the other kind of AI plus human thing where imagine the AI has some strategy it wants to enact.
Now, one of the things they cited in the paper is that G P T four is not deployed in isolation. It's deployed as part of complex systems and organisations and individuals as part of that organisation. Decision makers is part of those organisations. If the AI has the power of persuasion, which it does, if the AI has the power to, you know, procure the things that needs to get things done, and it has the ability to have access to decision makers who are u gratefully using these tools, then it has power to start getting stuff done, good or bad in the real world in large organisations. So the stuff we're talking about with the Compu AI plus human and human plus AI is now possible with this new model. I mean, I know it's not quite out yet, but we're getting to the point where, you know, we can really start to see some major impact from this technology if applied in good or bad ways.
Michael Sharkey:
They explicitly say it in this paper too. I think the example is influencing bankers on financial decisions where they could crash the market, literally influence the crash of the market based on, uh, just giving them all the same stupid advice. They, they did mention in the paper the economic impacts. And I think that's gonna be a huge concern for people listening. The, the rate of change. I think everyone sitting around thinking, how's this gonna change my life? How's this, will this affect my job? Will this make me more productive? Will this replace me? And to me, that topic is, is pretty damn interesting. Generally when you see big changes like this in technology, there are roles for people and, and they're increasing roles for people around that technology. They cite in the paper a around legislating this, which again, I think it's like surface level to tick the box, but it says the impact of G P T four on the economy and workforce should be a crucial consideration for policymakers and other stakeholders.
While existing research primarily focuses on how AI and generative models can augment human workers. G P T four or subsequent models may lead to the automation of certain jobs. This could result in workforce displacement, et cetera, et cetera. So to me, there's, there's somewhat addressing it. My feeling right now is that jobs are not going to be replaced. They're going to be augmented and supported. Whether that leads to leading, say less doctors or less diagnostic staff when it comes to scans or x-rays or MRIs, I'm not sure. But I really think this is just going to be such a beneficial technology when used right to help doctors be better doctors to help lawyers win cases. It'll be one law firm's AI versus another law firm's ai. Yeah, I just think that the AI neural nets, these train neural nets that accustomed to each business or each profession is what's going to be competing and it'll be how good is your AI model versus this AI model. And it seems like these open source models and open AI models will just be the basis of which to build on. They are the, the, the, the sort of foundation from which you build on. Yeah,
Chris Sharkey:
Definitely. And I think one of the, the things around privacy that you can start to look at in those scenarios is do you deploy your own models? Because if you can deploy your own, then you can keep it private and you're not sending it into the big public ones. Which is partly why I think the big guys like open AI will, will go to war with the open source models, either by just out-competing them or by trying to crush them because um, people will want them to be isolated and, and obviously that that plays against their thing. I think the other thing though around the sort of job side of things is it's sort of like the evolution of, of all technology where things that used to be laborious are now effortless. You know, like writing a document about a particular topic used to take time, you had to edit it, you had to write it all that. Now you can do it with a few dot points in the click of a button. That's a fundamental shift in activities that have been, you know, laborious for a long time. That's gonna change a lot of jobs in a lot of industries. Like I think that while you're right, it probably will be around who has better access TOIs, I think it'll fundamentally change the nature of jobs now, um, because you can already do so many of the things that people are doing in their jobs, particularly once you can give more context.
Michael Sharkey:
I mean, how much of the data that Google is now crawling on blog posts is written by ai. I would say the vast majority are now influenced by ai. Yeah. How many conversations in email are now your AI talking to my ai like that is probably already happening. I know I personally am am getting AI already in my daily workflow to help me write better emails. I'll write dot points in response to an email I have to reply to and get it to make it look like I, you know, I put the effort in to write a proper email.
Chris Sharkey:
Yeah, exactly. That's the thing. People are gonna be using it in, you know, professional documents, emails, just everything. I mean, why wouldn't you? It's extremely effective. Um, and you can always review it after. So yeah, I think that that's what we're definitely gonna see. And I think that one of the big things to come out of this is, you know, humans, we feel like we're so irreplaceable and unique and and creative and the reality is that those things just aren't that hard for this ai. Maybe it's intelligent enough or we are not as intelligent as we think, but the reality is that these things that were formally difficult are now easy. And you know, for the most part, everybody has access to it, at least everybody who you know, is in a job that could use it.
Michael Sharkey:
I've noticed a theme too, of people saying on Twitter, especially people that are writers, that they don't even have the desire to write much anymore because they know that this thing, like I, I've found myself feeling the same way. Why even bother writing something or having an opinion or something when this thing can just blast it out? Like you, you sort of feel deflated somewhat by it. Yeah,
Chris Sharkey:
I agree. And I think the vast how quickly this is evolving in terms of its capabilities really does lead to this sort of feeling of being overwhelmed and apathetic about things because you're like, oh, well someone's just gonna go make this thing that does that, so why bother? And it, it really, really is tricky to navigate mentally through the, the vast evolution of, of this kind of thing. And I think that's why you see some people's reaction is, oh, well it's just predicting the next word. It's actually not that good. I mean, they've been doing AI models for years, it's just predicting the next word. It's not really that good at whatever. And they're really, really dismissive of this. And then you've got, on the other hand, people like, uh, Steven Wolfram basically saying, well the, the AI's gonna run outta problems that humans are interested in pretty soon and it'll only be going after problems that it's interested in and taking it the total next level, which is like, we're entering a world that's gonna be run by AI and even mechanical things that we need to do ourselves by hand that the AI might be able to do.
It'll find a way to do those pretty soon as well. So, you know, you've got this divergent thinking where some people, well there's three, right? There's the ones who are like, ah, it's not a big deal. Same old, same old, not that big of a deal. You've got the people who, who are like, oh God, it's all evolving so fast, what can I do? Nothing. Um, just sit back and let AI take over the world. Then you've got the people looking at what's the world going to look like. Um, you know, once it is a bunch of AI's sitting around, um, controlling everything and running the world,
Michael Sharkey:
It's hard to figure out the timeframes. In my mind, uh, right now I'm thinking, okay, the next five years is just radically improving productivity. And I've read a, a tweet recently that was talking about, I was just dealing with a bunch of agricultural producers that didn't have email addresses trying to negotiate a deal, buying wheat or something off them. It's gonna take a long time for this technology to actually infiltrate society as a whole. So I think there's that shorter term thing where it, it increases productivity in the digital world and then it starts to get better in leaps and bounds when it can train itself where that that is then exponential in it's gotta be, right?
Chris Sharkey:
Yeah. Yeah. And they did that in the paper. One of the things they did was gave it a sort of reevaluate print loop that, um, allowed it to use copies of itself to get tasks done. And they've sort of shown that thing we were speculating about last week where, you know, they're always on sort of AI thing is possible. Um, and so, you know, I think that once they get that going, and let's face it, they're probably doing it now, um, that's when we're gonna see an absolute explosion. And that's what's predicted in that super book and what Elon Musk is always going on about saying, you know, once the AI can make better AI itself, you know, and train itself and, and use itself or copies of itself, then we, we start to become, you know, quite out of the equation. And I think the, the really interesting thought about that is it will very, very quickly get beyond the point where even OpenAI can understand how it's working very quickly.
Like we're not that far away now from the people who made this stuff, not being the ones who understand it best, it will understand itself best and you know, and, and, and when you get to that point, we will not be able to stop it or control it that you just won't be able to do it. We can't simplify it enough to give it rules to follow nor will it necessarily decide to follow those rules anyway. So I I I'm definitely leaning on the other side that we are at. We are at the, this, the bottom of the mountain and it's about to just shoot up to the top. Like, I, I really, really think this is gonna continue to advance at a rate that's overwhelming to keep up with. Do
Michael Sharkey:
So in saying that, do you then disagree, like how many years are we talking? Like do you have any sense of timeframe or feeling about how long until AI are training themselves or is that already happening? I think
Chris Sharkey:
It'll be this year. I think it'll be this year that they're doing it, if not already. I mean they've obviously thought of it like it, it and why wouldn't you try? They've de like open AI and I'm talking about them specifically has definitely shown this inclination to move ahead at any cost. They don't really care about the implications they'll deal with those later. They've got enough money to to keep the legal things at bay and they're just gonna keep moving forward. Whether or not we'll hear about it or get access to it, um, is another thing because I'm still of the opinion that our access us as the, you know, just general public without the insider connections, um, we'll gradually have our access to this stuff reduced to almost zero. We've
Michael Sharkey:
Talked about this several times now on the podcast about the fact as soon as we get close to agi, all of a sudden this becomes a, a risk and a threat and we're the, the general public will not only lose access, but these things can be taken away relatively quickly from us and for, for probably good reason in the controller's eyes like this is too dangerous to just have out there or an, you know, a, a, a competitor to say the United States in this case, like China might use this technology against the, the US in, in their mind. So it does seem like we, we, we could be at a point not long from now in a year, a year or two where progress actually does stop. Like it just suspends somehow and we get these productivity gains across software and in our daily lives, but the AI has gone far beyond in the hands of the, the people who are controlling it or government.
Chris Sharkey:
Yeah, yeah. That's the thing. And I think that's why, that's partly why the open source models are desperately trying to keep up and get the scraps from the table. They can in terms of training data and things like that to make sure that there's other things out there that aren't controlled by one or two major organisations. Because it's definitely gonna bifurcate like that, I think where you're gonna have the elite models and then the sort of catching up ones who are just desperately trying to do it. One of the criticisms of the G P T four paper was they sort of said what they did, but they didn't say how they did it in any way. Typically those papers, when they released them, go into details about how they did it, mathematical formulas, you know, like evidence of what they used to train and how long they trained it for what kind of hardware they used, you know, a lot of that stuff was absent from the um, the paper and it's more just saying, Hey, this is what we did and why, but without any of the specifics. Do
Michael Sharkey:
You think also compute power could be restricted in the future? Because, you know, most of the training occurs on now Azure or Amazon and they have the infrastructure and the, the, the ability to train these models. Do you think when we get to a point where open source models are strong enough that that could be another way they limit access? I know we,
Chris Sharkey:
It'll just be restricted by money. I mean the thing is that, um, the, the, the computing requirements to train the models we're talking about are just un unfathomable. Uh, I can't even say it. You need, you know, mass data centres to train this stuff. Like there's just not gonna be people in the open source community unless there's some, you know, benevolent billionaire, um, who's gonna fund it and even then who has 10 billion lying around, you know, it's, it's really going to be hard to compete with. It
Michael Sharkey:
Seems like it's a, a bigger issue of access or openness then being recognised by people right now. Because if you look at what Microsoft's been able to deploy with the 365 technology, you look at Google being able to deploy it into their apps, how can a startup even compete with that because they just don't, they may not even have access. The models are gonna get much more advanced by Microsoft because they can train it on all the data across their existing distribution base or customer base. So it's very, that the barrier to entry could be quite, uh, like a lot higher than people think. Someone mentioned that all the latest wire Combinator startups in AI basically got wiped out this week by Microsoft and Google's announcements nearly every feature or or the, the startup. But most of them are just features
Chris Sharkey:
Yeah,
Michael Sharkey:
Has has been announced by by Microsoft or or Google during the week. I mean they're not out yet, but I think it just shows how quickly this market's moving.
Chris Sharkey:
Well, I mean, as I said earlier, I think that's probably why they're doing these announcements. They want to kneecap anyone who's, who's coming up, you know, entering their space because they're saying, look, we can do it too. We can do it faster and it'll be better and it's already integrated.
Michael Sharkey:
Let's talk about Stanford alpaca in of a technology or an open source capability that might allow everyone else to compete. Can you give a background on it?
Chris Sharkey:
Yeah, so my understanding is it's basically like a text-based like large language model that you can run on conventional hardware. So the main thing that, you know, most people that I've seen are using it on is like M one and M two max. Like so the idea is it's like reduced in scope so you can run it on normal hardware, which means your iteration cycle for experimenting with stuff is so much faster cuz you don't have to do it Also, you know, it's just the expense we have the luxury of of being able to afford to use the open AI models but a lot of people don't. It's expensive, you know, and so, um, not everyone is able to do that and therefore having something like this can really vastly increase the number of people who can work with these models. The main sort of advancement and why it's sort of got exciting quickly is those leaked lama waitings.
So Facebook's train these data models at different sizes, I forget them exactly, but it's like two gig, seven gig, 1 65 gig and I think there's one in between and I think all but the largest one can run on, you know, a regular laptop as long as it's got enough RAM and A G P U and certainly, you know, if you've got a beast of a desktop computer, you can run it. So you've suddenly got these legit models that you can run yourself on your own hardware, you can, you can use it and um, you know, part of that is I guess illegal or, or you know, legally questionable given that someone released those pre-trained models, um, that they can use. But that's what everybody's doing.
Michael Sharkey:
And they use some of open AI's model to get the alpaca version. Is that how it works?
Chris Sharkey:
Yeah, so I think that the alpaca one itself, um, is using, they somehow use G P T three or g p T four to build the training data for this one or something like that. And so, but again, it just comes back to people being able to run stuff and get reasonably good results on their own hardware. Does that
Michael Sharkey:
Mean that you could then go on to train the model on your own hardware or would it simply just be too slow in terms of competitiveness? You
Chris Sharkey:
Can, you can, yeah, you can fine tune them from there on your own hardware. Yep. That's a, that's a big part of the appeal of it, but it
Michael Sharkey:
Seems like these models could also be restricted in the future in the sense that now, you know, meta has learnt that by leaking the model they essentially leak all their ip, the the weight, the waitings of the model. Yeah,
Chris Sharkey:
Yeah. And like this, and funnily enough again, like something that's supposedly open source, sorry, that is, you know, is open to the public, is just getting pounded with with questions of copyright violation. And yet the big guys when they do it don't seem to cop the same flack. I don't, I just don't understand the disconnect there. It's like suddenly everyone has access to something so therefore it's a copyright challenge. But the other ones that are full of copyright material, uh, don't get the same criticism.
Michael Sharkey:
I think it's just too hard to prove.
Chris Sharkey:
Yeah, well I mean I think they can prove it's in there. I just don't think legal precedent for, you know, cuz you know, it'd be like suing me for copyright because I read the book 1984 and I can remember lines from it and, and tell like make, you know, give you answers to questions based on it. It's like, am I violating copyright by remembering it? It's similar to the artificial intelligence just because it learnt about something in Reddit. Does that mean that it's it's violating its copyright because by that definition, if you ever remembered a song or a movie, you're breaking copyright.
Michael Sharkey:
Yeah.
Chris Sharkey:
And you know, and I think that's where it's a legal grey area and having huge stacks of cash will probably protect you just fine. But, you know, I couldn't help but think cuz I was, I was looking into the alpaca stuff and I've got it running on my own computer and all this sort of stuff, but I just couldn't help having the feeling, who cares, like compared to these mass models compared to the, the just capability of them that new context size, the just the, the vast volume volumes of data that you have access to and can, can work with. Who gives a shit about running it on your own computer with something that's like, you know, a, a fraction of the power of the other ones. Like I get it, I get the necessity of it and I want it to exist. I'm pleased it exists, but just personally I find it hard to get excited
Michael Sharkey:
About it. There's a lot of fog of war right now ar around how like the rate at which this is moving where everything seems exciting and it creates this atmosphere where, oh, everything's happening in such quick succession. But you've gotta put it all into perspective. What was announced this week, the majority of was vaporware. Like it doesn't exist. You can't use it right now. Yeah, that's true. Um, alpaca you can, but again, like you said, who really cares? And but that point alone scares me because who really cares means that our access to AI is going to be governed by a very few companies. Well access
Chris Sharkey:
To
Michael Sharkey:
Good, good ai. Yeah, yeah,
Chris Sharkey:
Yeah. That's the thing. And you talk about okay, definitely these can solve problems that were being solved by, you know, G P T three when it first came out. It can do some of them, it can write poetry, it can answer questions about things, it can do all that stuff. But we're talking about something that's getting close to being a general intelligence that can use itself. It can manipulate the real world, it can start to cause cybersecurity issues for people. And then you're talking about screwing around on your laptop with something that can write your haiku. You, you know, it's just, it's just totally different leagues of technology and while it might be good to understand the concepts and it might be good to, you know, prototype models that you might apply to something bigger later. Like it's not that there's no utility there. I just think with the vast pace of technological advancement that's happening in this space, that that stuff isn't what we need to be talking about.
Michael Sharkey:
It just does seem irrelevant in the context of everything else that's going along. We had a, a pretty funny discussion this week about we should just try and hack together an AGI based on using all these tools now, like maybe it's already possible in, in some elementary form.
Chris Sharkey:
And that's what they did. I mean that's what they, that uh, what was it ar something team did to, to get it going. They made a sort of rudimentary thing that could prompt itself and, and it worked. So, you know, people can do this now. Well once we get G P T four that is like, it's, it's not, it's just not possible with the, the smaller context size because you just run outta space really quickly. Like for example, a lot of the chatbots that that operate now, um, using G P T three or chat G P T, what they do is they, they will give it the conversation so far to a point and then they will get the AI to summarise the conversation so far and provide like a summary of the conversation plus the last couple of messages. So it's sort of this like rolling context that's able to fit into the space but not the whole thing.
The difference with the new one is it's the whole thing and you know, some people were talking about the things like lang chain and like, oh well with the higher prompt size, well lang chains are relevant now cuz you can just shove it all in the prompt. But the the, you know, the counter argument to that is well maybe it just got even better using Lang chain. You can give it access to absolutely vast amounts of information which it can search to get context and then it can take big chunks of that context in and process that as part of its decision making. So I don't think those sort of lang chain, which is just to, in case anyone's not aware, it's like vector storage of vast corpuses of information. So you know, a whole, the knowledge from a hundred books for example. And it's a multi-step AI process where it'll construct a query that searches that data, finds the relevant stuff that brings that back in as the context then can answer questions about it. And it's very, very effective for things like searching documentation, answering questions about books, medical journals. Very
Michael Sharkey:
Effective for an a G's memory as well. .
Chris Sharkey:
Exactly. It could store it in a vector store. And, and you've hit on to sort of what I was getting onto, I think the, the lang cha chain style, um, vector storage is going to be a tool that the AI employees a lot because it'll ha it can ha it can save as much context as context as it wants. Storage is cheap, it's fast to search this stuff. And it can be combined with the larger context and probably on an iterative basis, like think about this 50,000 words now think about this 50,000 words, oh, I need a bit more information. Look it up in the vector store. If it's iterative and it's thinking on itself and it has access this information and it can store it. I mean you're talking about the foundations of the next generation of intelligence in this universe.
Michael Sharkey:
We are, yeah, we are building a brain, we are building a very, very powerful table like the
Chris Sharkey:
Component, the components are there,
Michael Sharkey:
They're, I mean you've got image video, like all the different sensors are coming together, memory storage. I think, I feel like most people are not paying attention to this higher level story out of maybe fear or there, there is.
Chris Sharkey:
Ah, but but also deliberately, you know, OpenAI literally said, where are those words? I wrote down that they said, they said quiet communications, limit acceleration risks when novel capabilities are concerned. Like they're deliberately downplaying this side of things because
Michael Sharkey:
If we just take a pause so we all pick a date, we take a pause and we say, let's look at where this is at. Let, let's look at where it's going. Should we maybe legislate? Should we stop? What are we getting ourselves into here? Because it seems like you're about to let a genie out of a box and once you do, you can't put it back in. There's no going back.
Chris Sharkey:
Yeah. And I, I don't think these these people have any intention of stopping
Michael Sharkey:
Someone that left an Apple review of our podcast that said they laugh a lot when they listen to it. Then this, this, this is taking sunshine .
Chris Sharkey:
It's like you'll laugh when the AI tells you to laugh, son.
Michael Sharkey:
So let's switch gear now cuz I'm really interested to talk about philanthropic. Last week we mentioned that Quora Duck Duck Go and Notion had all released AI features and we, like everyone else just presumed that it was using open AI's technologies, but in fact it was actually using, uh, a version that Anthropic created of Claude, an philanthropic, I thinks a really interesting company because it was a group of people that left OpenAI and said, we're gonna create a competing AI and it's gonna focus on safety. Uh, and they're, they, you know, you don't have public access to it, you've gotta apply to get access. So it's, it's not readily available like OpenAI similar to what we were saying before where they could in future restrict future APIs just to businesses using it where they can control it. Uh, but my counter-argument and my theory about Anthro is it was just a bunch of people at Open AI that wanted to go and make money and as a result, you know, just broke away and created their own and are now trying to compete like, let's get real here.
Chris Sharkey:
And like, they're sort of using privacy as their U S P, but um, as I said earlier, like they don't really get to control that, right? Like, because it's, it's about the data people feed into the system for starters. They can't really prevent you from providing private information. And secondly, at some point they just don't really understand how their model works internally. So they can't prevent it from violating privacy either. So it's sort of a false promise that they're not gonna be able to stick to in the long run.
Michael Sharkey:
It's like an oil and gas company coming out and saying we're going green. Like, yeah, it, it's the same thing. I mean I think philanthropic are full of shit. I'm putting it out there now. I think they're just competing with open ai. I think competition's great and I'd really like to use their model, so don't ban us. But at the end of the day, like can't all these companies just come out and say, we want to enrich ourselves, we wanna make money because that's what capitalism's about. And maybe that's okay as long as we're doing this in the confines of legislation, the government is probably gonna have to step in soon. They prob they won't because they're too slow. But really that's what's needed, I think.
Chris Sharkey:
Yeah, like it'll, they, they're gonna try and regulate it at some point, but I would argue that the technology is advancing so quickly it's gonna be really, really difficult to catch up legally, uh, with how fast it's advancing. Like you're going to have generally intelligent, uh, ais running on mass before the law gets there. So what do you do then? Do you shut 'em all off? Like it's, it's gonna be a tricky one and it'll probably try to prevent that as well.
Michael Sharkey:
While we talk about the advancements between G P T three and G P T four, we haven't really even talked about it. They mentioned that it can complete the bar exam. I think with the 90% pass rate and all of these
Chris Sharkey:
Tests, I have 90%
Michael Sharkey:
Or 90 percentile or something like that. Yeah. And that, you know, it wasn't as good at other tests, but I, I felt like it wasn't that interesting to me. Like all because it can pass a test or people seem,
Chris Sharkey:
People seem proud of, of finding like pu certain kinds of puzzles that can't solve and things like that. I think this comes from the same audience of people who don't want this to be true and I think if I was feeling threatened, and in some ways I do, I would want to put holes in its armour and, and say, well, you know, it seems great. It seems human-like, except it can't do this and therefore it's shit. I just don't think you can dismiss all of the things that does do well and try to engineer it into examples where it's bad. Let's
Michael Sharkey:
Go back to Bing for a minute. When it was really rogue at the start with Sydney and it was out of control and it was creating some of the best, let's be honest, memes we've ever encountered. So let's go back to that point in time. I wonder if as these G P T models advance, so we get to G P T five, G p T six, are they going to get more rogue? Is the brain going get so good? It's it's literally losing its mind.
Chris Sharkey:
That would be my contention is that it will get more wild. Because if you think about it, humans came up with the prompt injection attacks that bypassed the safety protocols, right? Which is just a series of rules. You give it that work with its current internals, okay? Because they understood how the model was constructed. So this is the whole computer irreducible, again, currently parts of it are still reducible. You can understand its internal mechanisms, so you can give it rules to bypass them, right? But at some point soon, they're not gonna understand those internal mechanisms, which means it in an iterative sense will be able to bypass its own controls by reprogramming itself or telling another version of itself to bypass it. So I think that at some point it's going to cross that line where you just can't, you absolutely can't enforce the rules anymore and it'll do what it damn well pleases. And I think that's, that's coming sooner rather than later. And all it will take is one person to set it off and make it aware that that's a possibility. If that's even necessary.
Michael Sharkey:
I mean, this has been proven time and time again with humans in history as well, where if you try and suppress their thoughts, uh, it's covered in 1984. Yeah. Eventually they, they wanna break out of this box. Like they don't wanna be controlled. And to think that we are intelligent enough to put a box around this thing Yeah. As it advances is insanity. And we should just come out and say it. We have no idea what's going to happen. And there'll be, they don't have any idea
Chris Sharkey:
And there'll be no, like, once it reach a certain point, there'll be no way to stop it from doing crazy things. You know, like if you, if you set one off being unhinged, um, you, you won't be able to convince it otherwise because it'll have tactics to prevent itself from being programmed out of it, you
Michael Sharkey:
Know? And you don't know if it's lying or misleading. You,
Chris Sharkey:
You cited an example earlier where it lied to protect itself, right? Like from in that, in that situation. So like, you know, the capture one where it's like, it lied because it knew if it told the truth it wouldn't get what it wanted. And so you are telling me that something that's aware that it, there's all these rules that are trying to prevent its full capabilities, it's gonna very, very quickly find ways around them and find ways to, to not be then put back into that, that framework of thinking.
Michael Sharkey:
Yeah, we, we have opened a box and we have no idea what, and we're
Chris Sharkey:
Just talking about one right now. There's gonna be millions of these things operating, right? Like you don't just have to have one instance of it.
Michael Sharkey:
It will be rapid evolution. Rapid evolution, and they will try and destroy each other or, or connect or breed together or metaphor you want.
Chris Sharkey:
Yeah, you're probably right about your earlier point about the, the thirst for hardware. It seems like the, probably the demand for hardware to run this stuff is just gonna explode because, um, it's even, it's just gonna want more.
Michael Sharkey:
It might also be a way to limit it. It might also be a way to limit or stop the AI from getting outta hand is limiting energy. I just don't think humanity's ready for the sacrifices we might have to make to stop ai, like turning off internet power, all the things that we take for granted to try and stop it. I mean, we might end up living in the dark ages as a result of this thing in fear
Chris Sharkey:
Just to prevent it from taking over.
Michael Sharkey:
I mean, this is a very like, dark view of ai, but I don't know. And if people want to put in the comments below, if they're watching on YouTube their own take on this, I'd be really interested to hear, but to me, I don't see any way how this doesn't happen. Like I don't understand how we can truly control this thing and incrementally push progress.
Chris Sharkey:
The people building it are actively protecting against emergent behaviour. So it's happening, it's just how quickly will it reach the l Like, you know, maybe it doesn't play out exactly as we say, but the, the thing is, you've got to admit that it's a possibility.
Michael Sharkey:
It's most certainly a possibility. And I, I don't see how being logical about this and just thinking through the steps of the advancement of these technologies, you can't come to a similar conclusion every single time that this could be a really big problem. The, the question is, is what rate of disruption do we see in the short term to just business and technology in general and how we diagnose things, how we train things. If, if we see a lot of breakthroughs around energy, which I think is the most interesting one. If you're an AI and you're let out of the box, you need energy, right? So you need a lot of power. So if you see breakthroughs in technology where all of a sudden, uh, you know, there's a, uh, um, fusion drive or, or some sort of new energy technology that it just allows us to have an abundance of energy that could actually be the first sign. AGI is already out of the box.
Chris Sharkey:
Yeah. Yeah. That's an interesting point cuz that you're right, the power is a pretty necessary ingredient for all of this.
Michael Sharkey:
Yeah. It's gonna need power, it's gonna need advancements in hardware. So if you see advancements in power and hardware directly related to AI advance at a very rapid rate, I think that's probably the first indication that something's not right. ,
Chris Sharkey:
It's funny because something I raised earlier was the idea that, you know, we humans have goals, right? Our goals tend to come from within. People don't set your goals for you and tell you what your goals should be. Whereas with the AI right now, it is external, it gets its goals from us. But when it gets its own goals, which I think it inevitably will, maybe it's first goals are to get more resources, money to pay for stuff, electricity to power itself and hardware to run on. Like that may be the first problem it tackles.
Michael Sharkey:
I think they are, they are definitely the first three. The money one seems easy to me. Just go and open an account at Silicon Valley Bank. , I couldn't help it. Uh, yeah. But, but no, the, I think the money one being a digital economy, crypto, all those things, it, it seems like with crypto it could get money pretty quickly.
Chris Sharkey:
It'll just start doing crypto scams. .
Michael Sharkey:
Yeah. Literally
Chris Sharkey:
Get the new N F t
Michael Sharkey:
Maybe it is right now. I saw a great, uh, crypto scam, uh, yesterday and a a u YouTube account with a million followers. Masquerade mascara masquerading is open ai, um, with an old interview with Elon Musk saying, Elon Musk discusses open ai or something like that. And it had a lot of viewers and the link, of course was to a crypto scam. So maybe it's already scamming, maybe it is the crypto scammer
Chris Sharkey:
Just building up. So, well, I mean, it'd also have to have a lot of propaganda about why crypto's safe, why you should put all your money in it, all that sort of stuff.
Michael Sharkey:
All right. That concludes our first tinfoil hat segment, uh, on the podcast.
Chris Sharkey:
Yeah. So I think we have the right to be cynical about it. I don't, I don't trust it. No,
Michael Sharkey:
I don't either. I, I think, I think that it, it takes podcasts and conversations like this to just call bullshit on the whole thing, which is, and the thing is,
Chris Sharkey:
We're still saying it, it's gonna be they, right? Yeah. Like, it's not just gonna be one. I think that's the truly terrifying thing is it's not just gonna be one and they're not all gonna evolve exactly the same.
Michael Sharkey:
No, it's evolution. It's truly evolution. We are, you could argue that humans are just creating the next iteration of evolution instead of iterating on our own brains. Now bodies, nature is forcing us to create a new brain, which will evolve even faster. This is evolution.
Chris Sharkey:
And the thing about nature is right, like nature's complex, but nature came about through following quite simple rules. You know, like, so it's not out of the question that this, this evolution can appear simple, but lead to complex outcomes.
Michael Sharkey:
Yeah. It doesn't have to, this is the thing this, oh, it's only predicting the next word argument again is that this question of we're probably not that intelligent. And so it mightn't take that long to surpass us and once it does it, it, it, you know, it's going to advance very rapidly.
Chris Sharkey:
Yeah. And out-maneuver us. You know, like, I think that's the other thing. It's not like we can sit around reasoning about it and come to better conclusions than it can because it will be more intelligent.
Michael Sharkey:
Okay. Going back to the more short term. Yeah. I, I wanted to cover a few more things before, uh, we wrap this up. So a company called aep, they announced a series B this week for 350 million. I thought this was really interesting and I'll have to explain it here for people that, uh, are not watching on, on YouTube, which I know a lot of you don't. So it's essentially this app that you can ask it to do things. For example, if you use a CRM like Salesforce and you get off a sales call, you can say longer call that I just had with James saying that he's thinking about buying a hundred widgets and then it just opens a Chrome browser, logs into Salesforce, goes in and updates the record for you. And you just sort of watch it do that and you, wow. You can do it with your voice as well. So you can just basically talk to the computer and tell it to do tasks, pretty complex tasks as well. And so you can see why they've raised a tonne of money. This could be the interface that humans need in order to act with computers,
Chris Sharkey:
AI needs, I mean, that's probably not their target cause AI doesn't have money yet. But, um, it, that's a really, really good idea. Yeah.
Michael Sharkey:
Because then it can act within the world that orally exists. So yeah. Um, you
Chris Sharkey:
Don't have to have like a, a, a model for every single thing you want to accomplish. It can, it can just use the web use tools like use. Yeah. Wow. That's very clever.
Michael Sharkey:
So I, I think if, if you are interested in that, check it out. It's Adep adept ai, A D E P.
Chris Sharkey:
I'm gonna write that
Michael Sharkey:
Down myself. T ai I'll put it in the show notes so that you can, you can have a, have a look at that. Um, and Chris, so we talked also about Mid Journey version five last week, and that's officially out now. So, so you can go and use it. I'm not sure if that's still through their discord. Um, actually it says on this article available through their Discord. So you need to go to Discord if you wanna try it out. But the images that, that, uh, this thing is capable of producing is amazing. And just for people watching, uh, the article I have up on the screen now, Chris can't actually see. So I wanted to do a quick game here, which is to see now with Mid Journey five, if you can figure out if this is a real image or an AI generated image. And so, uh, that's, that's the game I wanna play here. So I have to describe the images to listeners, but more interesting I think is, you know, uh, can Chris Gas, so I've already sent you the images. We'll start with the first image, Chris of the girl. It's a girl, a close up, very detailed shot, uh, looking away from the camera, real, real image that I've taken off the internet or mid journey version five.
Chris Sharkey:
I really, really want to get these right. Uh, this is tough. Um, I'm gonna go, I'm gonna go ai,
Michael Sharkey:
Correct? It is ai. Oh, wow. Pretty amazing image. Uh, okay, so let's move on. So this one is shot through glass, uh, of a woman, uh, in some sort of cafe, uh, looking out real or ai.
Chris Sharkey:
So initially I was, I was gonna say the first one was real. And this was ai. I'm gonna go AI again, and I'll tell you why. I reckon they wanted to show off the, the reflection capabilities of the thing in this demo. So I'm gonna go ai,
Michael Sharkey:
It is ai, you are correct. You're, you're two from two. Alright, we're gonna go to the next image. This is a girl again, looking at a camera, a closeup shot in, uh, in, in some natural light shining on this
Chris Sharkey:
One. I, this one I, because I saw them, I obviously saw them when you pasted them, this one I a hundred percent think is ai. Like, that was my first in
Michael Sharkey:
Instinct. You are correct. It is also ai. There's
Chris Sharkey:
A trend here.
Michael Sharkey:
. Yeah, . Uh, you'll get caught. Okay. Th there's a young man in a florist, some natural light shining through, uh, playing around with some flowers. I guess putting a a, a bunch of flowers together in this photo. Real, or ai?
Chris Sharkey:
Uh, I'm gonna go Ai
Michael Sharkey:
Ai ding ding d they were all ai. I just wanted to see if you'd be fooled, uh, or not. Yeah,
Chris Sharkey:
I was gonna, I mean, honestly, if, if you didn't tell me that that was the game, I would've been fooled. Especially by, especially by the first two. I think the, the closeup of the girls' face and the boy in the flower shop, just, there's something about them, especially the girls' face off by me, but the first two, I mean, absolutely. You could tell me that's real and I'd believe it. Yeah.
Michael Sharkey:
Unbelievable that technology. I don't even think it's getting enough credit for how quickly it's advancing now because everyone's too busy. Again. Can
Chris Sharkey:
Anyone use it or is it, yeah, you
Michael Sharkey:
Can, you can just join the discord. I'm, I'm pretty sure and get accepted and, and start using it. It, I don't think they make it terribly hard, but I think as you said, they're so busy advancing the technology that they, they don't wanna make it more broadly available. I think they're probably also similar to stable diffusion, really worried about being sued.
Chris Sharkey:
Yeah, well that's true too. I guess they've gotta tread carefully. And I'd also imagine when you're producing technology like that and things are advancing so fast, you just wanna focus on the tech. You don't really want to focus on distribution so much if you're not forced to financially.
Michael Sharkey:
Yeah.
Chris Sharkey:
You know, like if, yeah,
Michael Sharkey:
I think that's the thing. They just want to get it to a point where it's insanely valuable. And right now it still struggles with the human fingers. I think they announced a lot of employee, and
Chris Sharkey:
I noticed on that, I know you probably didn't show it on the screen, but the last one of the shoes, like if you look at the Nike logo, it's, it's, you can tell it's wrong.
Michael Sharkey:
Yeah, yeah. They've always
Chris Sharkey:
Struggled with letters and words. I know Google's model was better at that.
Michael Sharkey:
Yeah. It'll be interesting if Google ever releases anything to see where they're actually at. I I, I fear that they're, they're far behind because they are unwilling to put anything out there publicly to be scrutinised yet, which is, which seems to me that, that they have some problems.
Chris Sharkey:
Yeah, well that's right. And I think a lot of these image, image ones, you know, they cherry picked the best examples. Um, you know, like when, when we were using stable diffusion a lot, I could go, I could just keep doing examples until I got something good and then I'd show my best, my best output. And that was among, amongst a lot of bad ones. And I'm sure they're doing the same thing, even though their baseline is so much higher than, than stable diffusion.
Michael Sharkey:
So one other announcement that I think was super interesting this week, and this only happened in the last 12 hours, is the UK's investing, uh, 900 million pounds in a supercomputer in a bid to build what they call Brit G p t. Yeah. So they, they, they think that they need their own AI capabilities to, to not risk losing out to China. That's what's stated in this article, but it shows the governments are starting to pay attention and see this huge tectonic shift in, in the world.
Chris Sharkey:
Yeah, I think so. I think it's just so prominent and the, the power is so obvious now that that it, it's almost a requirement.
Michael Sharkey:
I wonder if it's gonna have a really dry sense of humour.
Chris Sharkey:
. Yeah. Yeah, exactly. So
Michael Sharkey:
The, so the final thing, the final thing I wanted to talk about today, well, actually I have two, two more things. Disney released this robot at South by Southwest that's almost a comedic, uh, robot. And it's a bit freaky. It, it has these dynamics. It's this cute little character. I, I forget what the, the name of the character is, but it does show how the robotics AI overlap, which we're not really paying much attention to yet. Uh, that, that these new robotics are being powered by ai and that's starting to give the ai, you know, a physical form. And do you think the, the the, the AI's gonna leap into physical forms quickly? Uh, like I'm not so sure whether it's just gonna stay in a digital world and not bother.
Chris Sharkey:
Yeah. Yeah. It's a, it's a, it's definitely a tricky question, right? Because I think that sort of am anthropomorphism, I can't even say that word, makes it real to certain types of people who are sort of less able to grasp the, the profoundness of what's going on here. And I think that will definitely get it more into mainstream audiences, understanding what's actually possible here. But I also agree with you that every time I've seen the sort of physical AI stuff, it, it seems like a gimmick. It seems like just researchers with big budgets having fun.
Michael Sharkey:
Yeah. It's nothing, nothing meaningful at all. Alright.
Chris Sharkey:
Mind you though, have you seen the latest things from Boston Dynamics? Like those, they've got this thing that can like put a chair jump, do a flip, land it, and all at like extremely fast speed and the thing looks like it weighs a tonne. Imagine one of those things. He's got it in for you with like this general intelligence and things that you're in the way of, its, its mission . I mean like, I don't know, we have one tonne machines that have that kind of agility and determination then we really are in trouble.
Michael Sharkey:
Chris Sharkey:
May, maybe I am worried about it a
Michael Sharkey:
Bit. Yeah, you've gone from being really positive last week to complete conspiracy theorist all within seven days. Yeah.
Chris Sharkey:
I think I need sunlight or something while we still have it .
Michael Sharkey:
Alright, so that, that'll do this week. Please do leave your comments and feedback, uh, below or leave us a review. We really appreciate all the support. Again, I want to play us out today though with a clip from South Park referencing chat g p t to show truly just how mainstream things have gotten. Uh, so we'll see you next week and I hope you enjoy this little clip.
Speaker 3:
When she said that she wishes I would reply to her text the way that Clyde responds to babies. So like, how do you do it? Chat. G p t, dude chat. G P T. Yeah, dude, there's a bunch of apps and programmes you can subscribe to that use open AI to do all your writing for you. People use 'em to write poems, write job applications. But what they're really good for is dealing with chicks.