Welcome to Artificially Intelligent Marketing, a weekly podcast where we stay on top of the
latest trends, tips, and tools in the world of marketing AI, helping you get the best
results from your marketing efforts.
Now let's join our hosts, Paul Avery and Martin Broadhurst.
Hello everyone.
Welcome to episode 33, all the threes, 33 of Artificially Intelligent Marketing.
I am here as always with the most wonderful co-host in the world, Martin Broadhurst.
Martino, how are we today?
I'm good.
I'm sat here in my new recording studio.
I've got my double circle football pitch behind me.
You'll only see this if you're watching on YouTube.
I've got my Derby County Ram, which looks like it's been put through the blender a little
bit.
It's really good.
That's supposed to be a Ram, is it?
I think it is the Derby Ram.
I think Dorely3 needs a little bit of feedback on that image, see if it might improve it.
But it's a joy to see you in such a vibrant environment, Martin.
Yes.
I'm all good.
It's Friday.
We like Fridays.
It's Friday, 17th of November.
Worth pointing that out because who knows what's going to happen between now and when this
goes live given all the changes that are happening at the moment.
Many of which we are going to cover today, Martin, because we've got so much stuff.
We've got Runway's sort of motion painting tool, which looks really cool.
We're going to dig into Copilot and some of Microsoft's announcements at their recent
Ignite meeting.
We're going to be looking at the potential postponement of Gemini from Google, which
we've all been waiting for.
Now it sounds like we're going to have to wait a bit longer.
We're going to talk about the generative search experience from Google being rolled out more
widely in other countries and what that means for marketing folks.
We're going to talk about a bunch of stuff related to YouTube.
But today we are going to start by digging a little bit deeper into ChatGPT and the updates
since the developer conference from OpenAI because Martin and I and the rest of the world
have now had a chance to see what some of those updates mean and also to play with stuff
and see what are the changes, what's better, what's not so good.
We've been playing with GPTs.
Are they this amazing game changer that we all kind of hope they're going to be?
Where are they at right now?
So that's where we're going to start.
So Martin, let's talk a little bit about some of these ChatGPT updates and what we've been
experiencing in the real world.
Yeah, well, it was a raft of them, wasn't it?
It was a lot to keep up with.
We had an expanded context window.
We had the reduced costs, multimodal in one window, a new text to speech model, which
I actually really like.
We've got assistance and the user friendly or should I say the kind of consumer interface
GPTs.
And yeah, so it's been an absolute raft.
Where do you want to start?
Let's start with the context window because I saw that some people have tried to do some
initial studies on this.
So the new context window is 128,000.
Is that right?
128,000.
Yeah, 128,000.
Which for those of you that listen to the podcast and know about our love of Claude
will know that what that means is it gives you a huge amount of information that you
can provide to ChatGPT as part of the conversation or all in one go to be able to then leverage.
So for example, you could feed it a 300 page book and start asking questions about the
book, ask it to summarise the book, et cetera, et cetera, which is game changing for a lot
of applications.
But I saw some studies, Martin, showing that after about 70,000 tokens, its ability to
recall the information that's been given starts to degrade.
So it might be 128K, but it's more likely to be 70,000 usable tokens.
I don't know if you saw that.
Yeah.
Is this the same study that said basically in the middle of the window actually it forgets
things.
So what's at the start of the context that you give it and what's at the end of the context,
it can retrieve quite accurately, but information that's in the middle, it's a bit fuzzy and
it's not very good at retrieving that, which actually I think they'd found with a previous
study with Claude as well.
Right.
Yeah.
That's the same study.
And I think it's interesting, and we were talking about this a bit off air, our expectations
of chat GPT and large language models is quite interestingly different to software products.
Because if someone launched a software product and they said, we can do this thing, and then
firstly, the fact that a user would have to test it to see if it could actually do the
thing.
You gave an example off air about summing up a column in Excel and then checking it
manually with a calculator to make sure it actually was able to get the answer right.
And if it didn't, one time in five, how you just not use it again.
And yet for some reason, we are quite happy to go check and validate these new capabilities
of large language models.
And then when they're not actually as good as we hope they would be, and they can't deliver
on what they say, we use them and love them anyway, which is kind of interesting.
But yeah, so I think the takeaway on the context window is it's a massive upgrade on where
we were with chat GPT with its original context window, but it's not as good as what we might
believe in terms of just how much information it can handle.
And as you said, how you structure that information could influence the results you get.
Like if it's going to miss stuff in the middle, potentially of the documents that you ship.
But for most users and most of the time, the expanded window is hugely beneficial, right?
Meeting transcripts, you can stick in a meeting transcript, which would never have been possible
in the previous context window.
Whereas now you might throw it in and it might be 30,000 tokens and it can handle that quite
easily.
And I think for a lot of applications, people will be quite happy dealing with 20, 30,000
tokens rather than going all the way up to the 128, which is nice to have.
I think that's only available in the API at the moment, the 128.
Yeah, so every day I've been asking chat GPT what's your context window.
And it's funny because we were talking about it, I realised I haven't asked it today, I
just asked it and it fed back 4,000 tokens.
So it looks like at least for me, chat GPT plus is still stuck at the old context length.
Have you actually tried sticking more in because I've done quite a bit with expanded transcripts
and it's gone beyond.
Maybe it's hallucinating that 4,000.
Maybe how interesting.
Or it's like, don't test me too much.
Maybe it's lying because it knows that it's going to be hard work.
And it's like, it's Friday, Paul.
Why don't we just leave it at 4,000 tokens?
Let's just call it there, shall we?
But it's like, actually no, I mean, I couldn't do it.
It's like all the Ethan at Mollic experiments where it seems to me that at the end of most
of his more complicated prompts, he now just puts as standard, don't worry, you can do
this.
Yeah.
I was just going to say about the multimodal in one window, right?
So now that we have all of the tools, we've got code interpreter, we've got Dali, we've
got browse with Bing all in the same window, a lot of the time, kind of get it to do things
that it would just do previously when you would select the tool you wanted.
Now you have to really prompt it and give it a nudge and basically nag it or encourage
it, you could say it the other way.
So yeah, you can do it.
I believe in you in order to get it to get the output that you want, whether that's actually
using a particular tool.
So code interpreter is the one that quite often I'm having to say, oh no, you can do
that.
You just have to use code interpreter and then it will go off and do it.
Do you know what?
It sounds like a moot point, but it's really not because I had a team member that was using
Claude for a particular application this week and Claude did its thing that we've talked
about previously where it came back and I went, oh, no, it sounds like sensitive material
to me.
I'm not sure I can help you out there.
And of course she stopped.
She was like, oh, I think Claude's broken.
It's down.
It's not working.
And I had to encourage her to try and find clever ways of convincing Claude to do the
thing she wanted, which it shouldn't be that way.
But if it is that way at the moment, people need to know that that's part of the process.
Otherwise, they're just going to give up.
So if you're listening to this and you've used the tool and it tells you, we can't do
something you want it to do, don't take that as read.
Think of a clever way to convince it to do it, including as simple as have a go.
I'm pretty sure you can do this.
And that can sometimes be enough to get you the result that you want.
Yeah.
Or to throw in some of the emotional manipulation that we spoke about on the previous episode.
Tell it that it's really important for your career and you might lose your job if it doesn't
at least have a go.
Yeah.
And I wouldn't threaten it because we still don't know where the story ends.
Still want to be in team AI if I for one welcome our robot overlords.
So something to keep in mind.
Yeah.
So I think the context window is an improvement.
In real time now, I just threw a transcript from one of our old podcasts in and it was
like, no, no chance.
Really?
Yeah.
But you might have access to Turbo and I don't.
It says, yeah, the message you submit was too long.
Please reload the conversation and submit something shorter.
So maybe they're rolling that out slowly.
Let's talk about some of the other bits and pieces that we saw come out of chat GPT events.
Let's talk about costs because costs.
If you use chat GPT purse, you're spending $20 a month.
Probably don't think too much about it.
But if people are using the API to do interesting stuff, which I knew that you're a big fan
of Martin, they will obviously be paying for token usage.
And some people have been running some interesting GPT-4 Turbo tests, haven't they?
I think you sent me a couple of WhatsApps about this.
What did you see?
Yeah.
It's catching people out.
The token cost has come down to a third of what it was previously.
Overall, if you take input and output tokens, it's about 2.7 times cheaper.
I think that's what they said it worked out at.
But the problem is when you have this massive context window available to you, people tend
to use that and that adds up in the input.
And if you start having a back and forth conversation with the chat, every single input, every inference
that you have, so every input clamped, uses that 128K window if you're maximising that
length.
And that ends up being about a dollar per query because it's one cent per thousand tokens.
So every input soon racks up and people have been landing themselves with just testing,
right?
I saw one person got a $120 bill just while they were trying to hack together an assistant.
Yeah, like a day's worth of testing, $120 bill.
I've got a cap on mine of $30 for exactly this reason.
If you're playing with the API, you probably learnt this lesson the hard way like Martin
just described.
But if you haven't and you haven't got a cap, go put a cap on it because GPT-4 Turbo is
going to eat your money if you are sending it lots of stuff.
The other thing is the multimodal in one window.
So again, one of my team members was using the tool.
I made a suggestion for a prompt they could use to get an output they were looking for.
And the lady in question took a screen grab, sent it over to me and she said, I don't really
know why, but chat GPT has produced an image because it just misunderstood the prompt.
It went, oh, I know what I need for this Dorel E3 image.
And I'm like, yeah.
So therein lies the pros and cons of multimodal GPT-4.
If you in the modern world, because it's trying to guess what it needs to use in the backend
to serve your query, sometimes it's going to get it wrong because it may or may not
pull in data analysis.
It may or may not pull in Dorel E3.
And so whilst on average, I think it's better that I can just say, give me an image.
That was an interesting byproduct of what this multimodality does because sometimes
it uses it.
So do you know what you're doing in the text in my experience, you're in your prompt, you
have to say using Dorel E3, can you do this?
I'd rather click a dropdown.
Yeah.
Yeah, absolutely.
You have to be really specific and tell it how you want it to operate.
It is curious some of the decisions it makes sometimes with this new multimodal piece.
And actually I think that's, I'm finding it very frustrating.
My overall experience with chat GPT is, I would say worsening.
We'll come to talk about the performance of the app in a moment, but yeah, I think they've
bundled a lot into it and handed a lot of control over to the model and the model isn't
quite getting it right at this moment in time.
So I'm sure it will improve.
I have faith.
See, I feel like you, it's probably come across, dear listener in the podcast so far, but I'm
like a bit peed off with chat GPT and OpenA at the moment because I was like, the developer
conference was exciting, like these things always are and it's like, oh my goodness,
this is going to be so cool.
And then there's like a bunch of things you play with them and they're like so friction
full as to make me go, like it breaks all the time in this area, in this area, in this
area, effort, effort, effort.
This releasing early approach that OpenAI has always taken, which I think as a user,
we need to try and remember that means that it will break a lot and all those other things.
Like we're playing with a constant beta near alpha at times.
But as people who have jobs to do and are used to using polished software products,
it eats up time and some of it's fun.
Like I like problem solving some of the stuff sometimes I'm like, oh, if I say it like this,
maybe I'll get this.
And that's kind of satisfying.
But when I'm in productivity mode, because I've got stuff to get done, that is actually
really annoying.
But why don't we talk about some of the issues?
I know we want to talk about text to speech and we want to talk a bit about GPTs, but
let's talk about some of those issues seeing as we're in them, because there's been a
few problems since some of these things rolled out, aren't there, Mike?
There has.
And it started off with within the first couple of days after the developer conference, there
was downtime, which seemed to kind of extend over about a 48 hour period where it was just
very intermittent.
You would log in and it would say no.
And then 20 minutes later it would say yes.
And then it was up and down and up and down.
And it was, that was very frustrating.
They said that that was because of a distributed denial of service attack on their servers.
And I'm sure they're sticking with that line, but they've subsequently said that they're
stopping new signups to chat GPT plus, which they announced a couple of days ago, because
they said that it's been unprecedented demand for the service and they need to scale up
the infrastructure.
As a heavy user, as a power user of the platform, I am definitely seeing performance issues.
GPT 4 turbo at times this week has felt anything but turbo.
It was supposed to be a big input speed improvement.
It was terrible.
It was the words per minute.
It was as if I was running the model locally on my machine.
The output was so slow to the point where...
Raspberry Pi is what it was more like.
And there's been times where this week there's been a couple of tasks where I've just wanted
something that didn't require GPT 4.
So I've switched over to GPT 3.5 thinking, well, I know that that's absolutely rapid.
The responses on that, the latency is non-existent really.
And even that has been as if someone has been typing it out manually at the backend.
So I'm really noticing this as a performance issue.
The other thing that they've done is they've reduced the amount of images that it will
create in any example.
So when they first announced it was built in, you could create four images at a time.
It will now only output one yesterday.
It was only letting me do one at a time and that's progressively come down.
And they've reduced the input messages.
So it was previously 50 messages in a rolling three hour window that you could have with
GPT 4.
And that has now come down to 40.
Yeah.
So the image thing has annoyed the hell out of me, honestly.
I use it a lot for campaign idea generation and rough mockups, either to brief designers
or to inspire a client with a particular creative direction, I think we might go.
And when you're iterating through those in the early days, you'd have four images.
So it was actually much faster because especially if you didn't over prompt it, it would give
you a line drawing example, a graphic, a photo realistic example, and then I don't know,
something a bit more like a realistic type painting type example.
And that would really give you a feel for what's the different type of effects that
we could get with this type of campaign idea.
And now when you've got to do that image by image, it just takes longer.
Then because the reduced amount of messages at some point fairly quick, it just goes,
yeah, we're not going to produce any more images for you for a while.
And it's like, well, so let me get this right.
You used to give me four, now you give me one.
So now I've got to send you more messages to get more images.
And now you're going to tap out before I even get like 10 images.
Whereas before that's like two and a half queries worth.
And it's like, look, I'm whinging and moaning about this because I'm on a bit of a annoyance
with open AI because of all this at the moment.
Am I surprised that their backend systems are struggling with all these new launches
and all this new capability?
Of course not.
That's exactly what I would expect.
And if I was a normal, happy going human being, and it wasn't a Friday, then I'd probably
be like, give them a couple of weeks to sort it out.
But I need them to balance giving us additional cool stuff with making sure the stuff that
they actually gave us already still works well.
Because I rely on it.
Do you, I mean, do you rely on it?
I do.
Yeah, a hundred percent.
They launched the product.
It was so reliable and consistent that it became part of my workflow.
If we think about that concept of Centaurs and cyborgs, you know, I was very much in
the cyborg mode.
It was fully integrated into my day-to-day workflow.
And now I'm finding I'm just hitting these barriers and yeah, it is a frustrating experience.
I think they're looking at possibly increasing the cost or having a two tier pricing system
as well.
So there was, I think on the open AI discord, I believe there was a survey that was going
around asking people, would they be prepared to pay more for another tier, which would
give you, you know, more limits, better limits, basically.
Yeah, I'm glad.
I'm glad they're having that conversation.
I've spoken with power users and I think they'd pay more.
I think they feel the value they get for $20 a month is massively outsized to the costs.
And of course, like most of us, they're going to pay as little as they can, of course, because
it's not going to go, oh, by the way, here, have more of my money.
But I would pay 40, $50 a month, I think, to have the reliability improved and the limits
removed.
Yeah, agreed.
Let's talk text to speech modal, because I know you think that's pretty cool.
What have your thoughts been on that text to speech capability?
I really like it.
So they made it available via API.
It's incredibly easy to, to plug into.
I'm not a developer, but I just asked ChatGPT to make me a little app that allowed me to
interface with it.
Chat gave it the API documentation as part of the prompt, literally went onto the documentation
page on the website, Control A, Control C, Control V into the chat window and said, make
me a little app that I can run on my desktop that will do this using Python with a GUI.
So I got a graphical user interface and now I have an app that does it.
You get six voices.
I think it's six at this stage.
There's one British voice in the mix and five American.
They sound good.
There's two quality outputs.
So you've got a standard and a HD.
The HD has a bit more variation, a little bit more emotion in it.
Isn't on the same level as 11 Labs.
The 11 Labs voices are still the best quality with the most emotional range, but it's got
a considerably cheaper on the API requests and the latency is very good.
So it's very quick and you know what?
It's really good enough for most people's applications.
This is one of those where now in a very bipolar way, I'm going to swing to the other side
where I'm like, I love the fact that OpenAI released this through their mobile app not
that long ago.
You could speak to the app and it would speak back to you in a very Star Trek computer style.
My goodness, this is cool.
And then in the blink of an eye, they make it available to developers.
And I think that's actually awesome.
And the stuff you did with it and sent me was so cool.
Just to stress for people who are new listeners, Martin is not a developer.
He is an exceptional problem solver and thinker.
He gets in with ChatGPT and collaborates to build these software, but you're not a developer.
You don't have any developer training.
But working with ChatGPT and probably half a day or a day, you've got this thing up and
running.
And I think it's going to be really interesting to see how our interaction with computers
changes over the next 12 to 24 months.
We got the launch of the Humane AI pin this week or last week.
I think the first time we had any real details about how it works.
I don't think it's going to catch on, if I'm really honest.
But it's interesting.
For those that don't know, the Humane AI pin is like a...
It almost looks like a badge that you wear on your shirt.
It's got a camera, it's got a mic, it's got a speaker.
And what they're trying to do is create a system by which you can do quite a few things
you'd want to do in your life that you would usually use your phone for, but you don't
need a phone.
So you want to make a call, you just tell it you want to make a call.
You want it to summarize your emails.
You want it to write your emails and do some replies for you while you dictate or do all
of that.
My favorite application was actually translation in real time.
If you're having a conversation with someone else, you tell it, I'm speaking with my friend
who's Spanish.
When I speak, translate me into Spanish.
When they speak, translate what they say to me into English.
That was pretty cool.
The launch video was not as exciting as it could have been.
But that's another example of this kind of similar technology, right?
Where you speak to something rather than typing and it speaks back rather than you reading.
People are doing some really interesting things with the GPT-4 vision capability.
Have you seen some of this?
What have you seen?
So there was the video clip of Messi dribbling and they did real time translation and no,
it was a real time commentary, wasn't it?
And chat GPT.
For non football fans, he was dribbling a football rather than dribbling on his own
face.
Imagine the commentary of that.
That one's going to drift off his chin if he's not careful.
Yeah, so it was Leo Messi dribbling down the, I think it was the right wing and chat GPT
was the vision.
I think they'd programmed it so it was looking at one frame in 10 or something like that.
And then describing the scene and it was doing that throughout.
And it ended up putting this actually quite decent audio commentary together.
I mean, certainly for a first blast, it was very good.
Is it going to replace Martin Tyler on Sky Sports?
Absolutely not.
But for something that someone rustled up in their office in a few hours, it was pretty
impressive.
What have you seen?
Yeah, I thought that was a good proof of concept.
I think where I'm coming at this from is some of the tests where people in essence are using
GPT-4 vision as a mechanism for computers to understand what's on a screen and then
take actions.
So let's imagine that I want to book a holiday in order to be able to tell a computer, go
book me a holiday to Argentina to go see Leo Messi and his family.
Not that he lives there, of course, because he's doing some play football there at the
moment, but there you go.
That's a very complicated process that a computer needs to go through.
We need to be able to browse to a website, but we need to understand the context of what's
on that website.
I guess it could do it through HTML and CSS understanding, which to be honest is probably
still the best way to do it, at least from my very naive, un-technical perspective.
But I was interested in seeing people using the vision capability as a mechanism of in
essence taking a picture of that site, which GPT-4 vision can interpret.
It knows where the buttons are, it can read the text, and then creating agents off the
back of that.
And so I'm ever thinking about the days when I sit here and the keyboard, I'm using it
less.
I'm speaking more to the computer and it's doing things based on what I ask.
And again, I think open AI provide an access to not only text to speech and speech to text,
a lot of which we've had a while through Whisper, but also GPT-4 vision.
Because I think all these multimodal things are going to allow people to create really
interesting use cases.
And I think products and tools will pop up that we just can't imagine would be useful.
And then people are just having this epiphany where they're like, wait a minute, if the
computer can see and the computer can hear and the computer can speak, what can I now
get it to do?
And the computer can take action, right?
Absolutely.
That's the additional part of that piece.
Yeah, I saw the one, there was one bit of research that found that GPT-4 vision with
iPhone screenshots was able to successfully complete tasks and actions on the iPhone about
75% of the time.
So it's just the start of this whole process, isn't it?
And again, I think that bit is actually kind of critical because something that gets it
wrong 25% of the time is actually not that useful.
That's absolutely trash, really.
It's like, just turn up to Heathrow Airport, looking forward to my holiday and the flight
hasn't been booked after all.
It just reported that it has because it hallucinated having done it or it's booked me a flight
and I'm not going to, I don't know, Argentina, I'm going to Albania or whatever because it's
just completely, it understood all of the tasks apart from the Argentina part, it got
slightly wrong, which is not going to hold water.
And I guess brings us full circle to the start of this conversation, which is lots of these
capabilities have immense potential, but the stuff that goes wrong with them yet is still
large enough in a lot of cases that human in the loop in so many aspects is really important.
And until some of that improves, their independence of these tools is going to be limited.
Yeah, a hundred percent.
Just on the human computer interface point, I had an interesting experience this week
with my two and a half year old when the breakfast table, he's been using the smart speaker,
whose name I will not say, but he's been asking it to play songs regularly saying Amazon,
play this song.
And he's been, he turned around to it the other day and said the trigger word and then
asked a question, which he's not done before.
He's always just asked it to play, to do an action.
But it was one of those questions that is not geared up for, but I thought, oh, this
is interesting.
I'll do it with the GPT for voice interaction.
So we fired it up at the breakfast table and we burned through our questions, our 40 message
limit with him just going back and forth asking questions.
And it was, you know, it was everything from why is water cold to why is ketchup?
Well, that's very philosophical.
But we ended up having, you know, he was going on about animals and dinosaurs and all sorts.
So it was a real wide ranging discussion and there was a real ebb and flow to the conversation.
He was treating it like a person.
And I thought, wow, at two and a half years old, his experience of interfacing with computers
is going to be so different.
Fast forward, he's going to, it's just going to be so intuitive for him that, you know,
tablets, the idea that when we see toddlers playing on tablets now, people have gone,
oh my God, they're just picking up so quickly.
This generation is going to be talking to computers, going to be second nature to them.
Yeah, it's, I think that's spot on.
Bill Goats wrote a blog this week, basically on the same theme that we're discussing now,
like how people interact with computers has not changed that much.
And since we moved from DOS based coding type interfaces into Windows, the type click on
stuff interfaces, and actually the click on stuff interface is now best part 30 years
old, something like that.
But it hasn't really moved on.
And this could be the moment that it does.
This is probably a bit too pontification central for most marketers listening, but that is
obviously going to fundamentally change how people access information, how they search
for stuff, how they buy stuff.
So a lot of the go to market strategies that we use right now, like SEO, even like websites.
Yeah.
Will we have websites?
If we do, how will, who will interact with them?
Agents or us or both?
Or that's going to be a really interesting thing to see how that plays out over time.
It could change radically.
I think just thinking about this out loud, people changing their behavior actually takes
quite a long time.
So this is unlikely to be a tomorrow we are in a speak to computer world.
It's going to be like, Oh, over 10 years, people really got used to speaking with computers
over time.
And before they really knew it, they were like, Oh, I don't use my keyboard that much
anymore.
And I think it's going to be more like that than brilliant.
I don't have to use my keyboard ever again.
I'm throwing the trash tomorrow today.
I'm a keyboard user tomorrow.
Never again.
Like it's going to be slow, isn't it?
It is.
Do you hate my keyboard though?
I would do.
I'm actually using a mixture of audio pen and another texture speech tool, and I'm dictating
80% of my emails at this point.
And I'm finding it a real productivity boost as a complete aside.
So I'm trying that I'm ready for the keyboardless future of mine.
Just tell me what we got to do to make it happen.
Right.
Is there anything else?
Oh, is there anything else?
Yeah.
Just a small matter of GPTs.
We've sort of left the most potentially exciting, but also controversial aspect until the very
end.
GPTs.
Tell us for the listeners who are maybe not so nerdy about this stuff, tell us what GPTs
are.
GPTs are the consumer facing interface for creating your own specialized assistance.
And if you're a developer, you can use the API and create assistance.
And if you're just a consumer using chat GPT, you can build your own specialist GPT in chat
GPT.
And they're made up of three components.
There's the instruction, which is basically the system prompt, which primes the GPT to
play a particular role.
And this is where you tell it how it should operate.
Then you've got knowledge.
Knowledge is information that you can provide.
So it could be in the example that Sam Altman gave the demo day, he uploaded a lecture and
it was the transcript from a lecture that he delivered about building and growing and
scaling startups.
So that was the kind of knowledge that he'd implanted in it.
But this could be proprietary knowledge.
It could be speeches, lectures, it could be particular data sets, whatever.
You're going to give it some knowledge that it doesn't have, or it might have in its training
set, but you want to make that kind of front and center in its interactions with the user.
So you've got instructions, knowledge, and the final thing is actions.
And actions are the thing that it can do.
A while ago, OpenAI announced a thing called function calling and function calling enables
you to connect external software using APIs with ChatGPT.
So now you can do that in this interface and you can make your GPT, your ChatGPT assistant,
as it were, connect to external tools and perform actions based on the interaction that
you have with someone.
As well as that, you can also turn on, what can you turn on?
You can turn on all of the tools, can't you?
You can turn on code interpreter and you can turn on Dali.
So it can-
Web browsing.
Yeah, web browsing as well.
Or you can turn them off.
If you're building the app, you can choose which ones are available and which ones are
not available.
And then you can publish it and you can make it public to the world or you can have it
just private and have it be an assistant to you.
And in some respects, it's pretty similar to if you've got like a prompt library or
you operate like me and you have bookmarked conversations that you go back and edit for
a particular task.
It's a bit like that.
You can create your own GPTs that are primed to do a particular task and you can actually,
in the chat GPT window, on both desktop and on mobile, you can now have them saved in
the sidebar for quick access.
So if you're, for instance, putting together a podcast and you want to have a GPT that
is specialized at creating show notes or writing a social media content and giving you a promotion
strategy for each episode, you could have that there all the time, sticking your rough
notes from the show and then it will give you nice formatted show notes and a few tweets
and what have you.
And there's a whole app playground as well.
Not app playground, sorry.
There's a whole app store where you can go and browse public GPTs.
Yeah, it's going to be, I think when this was all launched, thank you for that.
There you are listeners.
Now you know what GPTs are.
When this was launched, I think there was a lot of excitement.
There was going to be a GPT store.
There was loads of people on social going, this is where the next millionaires are being
made.
Like go build your GPT.
That's the bit that I didn't mention.
It was the revenue share.
They announced that at some point in the near future, there will be a revenue share for
the most popular used GPTs.
Yeah.
And I think all of that makes sense.
And people were like, Oh, we've got to get in there.
Got to build a GPT.
The store's not live yet.
At the moment you can access other people's GPTs because when you build one, it can be
just for you.
It can be anybody with the link or it can be like super public.
Right.
So at the moment you can share that, you can go play with other people's GPTs, but there's
no store or anything to be able to go searching for GPTs.
At least not on chat GPT, although of course those things have already sprung up.
And I think what people have learned, I don't know what you think Martin, but what I've
seen people talking about a lot is they mostly act a bit like chat GPT and you can sort of
train them on your own materials and stuff, but it doesn't always work that well.
The function calling is kind of cool, but it appears to be quite buggy.
I use the Zapier GPT.
We talked about this affair.
There's an example, which is like a calendar plugin.
It's like calendar GPT.
And you say things to it like, what's on my schedule tomorrow?
Made it up.
I asked, it was on schedule tomorrow, pulled a load.
It did pull a load of things from my schedule from a day three months ago.
I said, I think you might be wrong.
They're like, yeah, we are wrong.
In fact, your schedule is empty.
And I'm like, no, I've got four meetings tomorrow.
And they're like, yeah, you don't.
Your schedule is empty.
And I'm like, I think I know what's in my calendar, having looked at it.
So and getting it set up was really buggy.
So I think there's an element of flatter to deceive here again in that it's got a huge
potential how to really realize that we're not there yet.
So in terms of like the danger of this being another plugin store debacle was like when
plugins came to chat, you'd be just like, oh my goodness, the plugin universe, make
your next million building plugins on the plugin store.
And now this is the new version of that plugin store sucked for a number of reasons.
And I do worry that GPTs and GPT store is not where it needs to be to really take off.
That being said, we have seen some pretty cool initial examples, haven't we Martin?
Yeah.
I think you sent the convert anything example was amazing and it's a GPT where you can upload
a file.
So let's say an image PNG and you can ask it to convert that to a JPEG.
But it won't just do that with that kind of simple task.
It will do that with anything and you can throw pretty much any file type into it and
it will have a crack at turning from one format into another.
It did fail slightly.
I threw in a screenshot of a, it was a text based table with some information in it and
it was just a PNG.
And I threw that into it and said, turn this into a, into a word doc.
And it tried to use OCR to extract the text, but it failed and then just gave me a word
doc with the image in it, just embedded in the word doc.
So it's, you know, it kind of did it sort of.
I'm even surprised it did as well there as it did honestly.
When I started playing with it and I sent it to you, I was like, oh cool, PNG to JPEG.
I was like, oh, M4A to MP3.
Like probably I'd have to download a piece of software or use some online tool to do
that.
And I was like, oh, one thing that I can just go convert this to this.
And that seemed kind of, kind of interesting.
So that was, I think the way that assume this has all been driven by code interpreter, advanced
data analysis, Python stuff, being able to do some of this in the background.
I don't know if it's doing custom function calling or not, but it was definitely an example
of something actually usable that doesn't break that often that I could see myself using
day to day.
I think a lot of the other examples I've seen are at best, they're kind of skins of chat
GPT, a little bit like what we've seen emerge in a number of other areas anyway.
Like there's like a contract writer GPT and a chef GPT.
I heard Paul Reutzer from the AI Marketing Institute talking about potentially creating
a GPT that will produce your AI policy for you to go on your website based on asking
you a bunch of questions about how you intend to use AI and then writing up your policy
for you.
And I think some of those could be really useful and some of them are just not going
to be that useful because they're just basically, you could probably ask chat GPT as is without
having to have a custom GPT.
I'm definitely finding that there are probably opportunities for me to create GPTs for me
or my team that are based on stuff that we have to do that probably no one else would
be that interested in, but it's useful.
As an example, before this podcast, we transitioned some of the prompts we used to help us write
the scripts for these podcasts to create a quick GPT that does it, which we can now share,
which is like a shared tool bot thing that we can throw stories at and it will write
scripts for us.
So I can see some definite applications there.
I'm still struggling to see what applications people are going to make that are going to
be like number one on the app store, 1 million uses a day.
The Developer Day example where they did a deep dive into the product was pretty interesting,
just novel in terms of how they plugged it all together.
And what they'd done is they'd connected it to Spotify and asked it to basically be a
kind of playlist creator based on a mood and a vibe that you would input.
And they took a photo of themselves.
It was two developers on stage.
They took a photo of themselves kind of high-fiving or whatever it was and then said, what's this
mood?
And it came up with a vibe and said, based on this photo.
So it used GPT for vision to interpret that this is the kind of playlist that I would
put together.
And then they said, great, put that together.
And it goes on Spotify and kind of pulls together this playlist.
They actually give it the name of an artist to put into the playlist as well.
And it does that.
And then they say, now play it.
And it plays it in their Spotify account.
It creates the playlist in their Spotify account and starts playing it.
It also creates a playlist album cover using Dali.
So that goes into it as well.
And then they'd connected it to their Philips Hue lighting and said, set the scene.
And they triggered the lighting for the playlist in the living room as well.
So all through just chatting with ChatGPT, they created a custom playlist, got it playing
on their speakers and then had a custom hue lighting mood setting as well.
Yeah, that's quite cool.
That's worth stressing that it's quite easy to build a GPT because ChatGPT guides you
through the process.
So you kind of tell it in natural language what you want.
And then it tries to figure out how to make that work for you, which is brilliant, but
it doesn't help at all for custom actions, which is still really complicated API type
calls, which I'm sure it could help with, but it's certainly not easy to do out the
box.
The other thing reflecting on that Spotify example is, it's kind of cool, but what problem
does that solve?
I wasn't wandering around going, my Spotify playlist would just see that everyone in the
room is a bit depressed and play the most depressing songs it can find and then put
a really depressing album cover on it.
My life would be complete.
It's like, no.
What are the problems that we want to solve with this stuff?
That was one of the...
In their defense, they said, look, this is quite kind of frivolous.
It's not, you know, this isn't a serious thing, but we want to show you the potential of how
you can link things together and what have you.
I think that is one of the big challenges here is that actually finding what is the
problem that they're solving.
There was another session, if you watch the breakout sessions from Developer Day, they
talk about how they think about product and research.
And on the product side, they said that the product manager from ChatGPT would say, normally
you would talk about what problem we're trying to solve.
We don't really do that with ChatGPT.
We don't start with a problem.
We just kind of start with what can the model do and then just try and make that accessible
in a way that is useful to people because people are still figuring this out.
So I think there's a tacit acknowledgement from ChatGPT team where they're saying, we're
not really solving for a problem.
We're putting together a bunch of building blocks and throwing it over to you.
I think that's fine because in essence, they're the platform and our job as users or would
be, I'm going to put in very common as developers, like maybe proper developers, but also because
you don't need to be a developer necessarily to build a GPT, any old folk to go, I wish
I need this problem solved.
I'm going to figure out a way ChatGPT can do it for me.
And I think to bring us back full circle, I think that's why the most obvious things
I'm seeing at the moment is me and my team looking at a thing we'd want to be able to
do on a reasonably frequent basis and then going, can we create a GPT for that, that
halves the amount of time it takes or gets us halfway into the process instantly because
it can do X, Y, Z.
For example, I could imagine creating a biostrata blog GPT that knows the style of our blog
posts because it's been fed 20 of them so it knows how to write in our style with maybe
a set of instructions that are probably true for all blog posts, like the type of length
we want them to be and maybe even a process where we always want an outline first that
we review and approve and then it writes the blog.
That would save quite a lot of time internally, but it'd be very specific to biostrata.
No one else wants their blog to be in biostrata style.
But that would be really helpful and I could absolutely imagine that.
We should definitely set up a transcript to show notes GPT or even a podcast processing
GPT for our podcast just to make it easier to go, to not have to prompt, to just copy
paste the transcript and it already knows it's going to give three outputs, show notes,
social media posts and I don't know, whatever, like the email that we want to send out.
And then you could probably get to the point through function calling or Zapier, you might
be able to connect it to enough tools that actually pushes some of that material straight
away where it needs to go.
I think then it starts to get quite cool.
I guess if you can think of applications that you have that other people might need, but
that would be quite hard for them to build themselves, you might be able to get yourself
up on the top of that GPT leaderboard when it launches.
Well, that leads to an interesting segue onto the next story, which is a bunch of announcements
from Microsoft this week at their Ignite conference.
So they announced a bunch of updates specifically to Microsoft Copilot and one of the things
that I think is really worth zoning in on with this is a new tool called Copilot Studio.
Now we can talk about the wider announcement in a moment, but Microsoft Copilot Studio
is a Zapier like interface, a drag and drop interface, a no code customization solution
basically that allows people to build their own Copilot integrations across the enterprise.
And it's a really interesting product launch from Microsoft because all of this is powered
by GPT-4 under the hood.
And if I just give you a quick summary of how it actually works, you start off with
a inference user input into Copilot, your prompt basically, and then there is this orchestration
layer that Microsoft has built, which tries to interpret what you've asked and tries to
figure out what it needs to do, what action it needs to trigger basically, where does
it need to the query, whether it's the HR system, the finance system, the CRM, whatever
it will maybe, then the query is routed to that particular place, HR, CRM, whatever it
is, and collects the relevant data, it's compiled and then pushed back into GPT-4, which does
what GPT-4 does and gives you a response in natural language.
It integrates with over 1100 products, including Adobe, Zendesk, SAP and a bunch of other tools
as well, real enterprise level tech.
However, when watching the product announcement for this, I couldn't help but think that there
is a whole new category of job which is going to be required, which takes me back to why
I think this is an interesting segue from the previous story.
And that is what you were describing with GPTs and being able to create all of these
interesting tools with function calling and what have you, requires a certain element
of AI architecture.
You need to be able to think about system builds and how you connect knowledge, instructions
and actions to enable you to create a good GPT.
And watching this copilot presentation, I was thinking the same thing.
We are on the verge of a whole new category of job role coming out, which I think is AI
agent or AI assistant architect, where effectively you are designing, managing and building these
AI powered assistants and connecting them all together.
Because doing that role needs such a stronger knowledge of, well, there's a technical side.
You need to understand functions and APIs and that sort of thing.
You've got to be something of a prompt engineer as well.
And then you've got to be a good systems designer, understanding user interface.
There's a whole lot that goes into creating something that's good and functional.
And that's exactly, even though copilot studio is a no code ostensibly, it's a no code solution,
right?
You're not going to be able to create anything good with it without being incredibly technical.
As it happens, I think if you're a Microsoft reseller or an IT managed service provider,
if you're not looking at this as the next revenue stream is a big part of your ongoing
consultancy going forward, you should be because companies are going to need this from you
without doubt.
But yeah, I was thinking, I couldn't help but watch this product launch, think about
GPTs and think this is a whole new category of job role.
I agree.
I know the hope is that prompt engineering and systems thinking will be less required
because the language models the bots were interfacing with will do that thinking for
us and maybe ask us the right questions and then we'll answer them.
And then it will think it through for us.
But I think we're away from that.
And I really agree with your assessment because we were playing with building tools, right?
You built the text to speech.
And so I went and tried to see how long would it take me to build a web based for transcription
just using whisper.
And it took me about two and a half hours.
But when I was doing it, I really reflected on I use bubble and bubble is a no code stroke
low code tool and for the tool I was building was a no code tool.
But the way it works is still how software works, right?
Databases for information, pulling things in and out, taking certain actions based on
clicks.
So you really have to be able to think in terms of system processes, moving data around
to different places.
And I think if your brain works that way, it's probably quite intuitive.
But if it doesn't, it's really, really hard.
Like I spent, I could have built that tool in half an hour if I really knew what I was
doing.
I spent 45 minutes just trying to get it to show the transcript because it wasn't obvious
to me how to pull this transcript out of the database I'd set up and have it display in
a certain text box that I'd built, which should have been so easy.
But you just need to know how computers, how software works, how could in inverted commas,
how computers think moving information around.
So I think you're absolutely right.
I think we'll see the AI architecture version of that for sure.
Do you know what my favorite thing is?
This shows the difference between you and me.
My favorite thing about the Microsoft announcement was they've got this Hey Gen like avatar tool
now.
So basically it's a speaking moving version created based on, I think just based on a
single image.
I'd have to dive a bit deeper into that.
And it's just, you know, it's not available yet, but basically you can create these avatars
that speak and move using Azure's AI speech to text avatar feature, which is powered by
open AI as Martin was saying.
And you can imagine creating videos for your website where you give it an image, you write
a script, and then it's you speaking, there is in the video, but it's not really you.
You could imagine doing prospecting outreach where you send individual videos to each prospect,
they're not really you either.
They're all like basically deep fakes of you.
And we saw an interesting version of Hey Gen that offers this type of service as well.
And Hey Gen's are better.
So if you want to go search this stuff, H-E-Y-G-E-N avatars, go look those up.
Obviously look up the Microsoft version as well.
But what I took away from it, Martin, is now there's not just one game in town.
And whenever there's not one game in town, the speed that everybody feels they need to
move out to improve their products increases exponentially.
And so again, I think we're getting so close to a moment when those deep fake driven avatars
are being used in so many customer service marketing and sales use cases.
And they'll look pretty good and be somewhat reliable.
And then soon they'll be indistinguishable.
Yeah.
And what does that mean?
There was a story this week actually that said AI researchers are now at the point where
they're struggling to differentiate real from fake.
And that should give us all pause for thought because what does that mean?
Like as humans, do we end up in the point where we're like, wow, so the only thing I
can really trust is my own eyes.
And there are loads of things about all the cognitive biases and stuff that we have that
mean you probably can't trust your own eyes either.
But at least when you're consuming media online, what videos will be real?
What images will be ready?
What text would have been created by humans and whatnot?
Like that is going to be a different world to be in, isn't it?
It is.
And I feel like I might be skipping ahead in some of the stories, but I think this is a
relevant point to jump in to talk about YouTube's responsible AI evolution.
Let's do it.
We got about 10 minutes more.
We got to respect our audience's time, Martin.
So we're going to do as many stories as we can in the next 10 minutes and we're going
to start with YouTube.
Right.
So YouTube is taking a measured approach against generative AI.
Obviously lots of people are now submitting content to YouTube that has some generative
AI in it, whether it's audio, images, video, what have you.
So they're introducing plans to enforce mandatory disclosures basically from creators that are
using AI, particularly where AI has been used to generate realistic content.
So this will involve a labeling system and it will involve alerting viewers to synthetic
content, particularly around sensitive topics such as health and elections.
So just going to rattle through a summary of the announcements really.
So they've got disclosure requirements, which we've already mentioned.
YouTube will add labels to the content, letting people know that it's altered or synthetic.
There is also a removal request process where individuals can request the removal of AI
generated content that uses their likeness without consent.
There is some music content regulation where music partners can request the removal of
AI generated music that mimics an artist's voice.
YouTube will continue to enhance AI driven content moderation to detect and address policy
violations and they're introducing some adversarial testing.
So they're going to conduct adversarial testing to anticipate and prevent misuse of AI tools.
So this is part of their ongoing investment into AI moderation and they're introducing
some, they're basically playing AIs off against each other.
They're rolling out, if you are a creator, they're going to be rolling out some education
to educate the creators about these requirements, like what are these disclosures?
And I think this is going to have an impact on marketers, right?
Because lots of marketers will be using AI for voiceovers, for video, for images.
And I wonder at what level the disclosures will kind of settle, where we'll have to
say what is and isn't disclosed.
If I use an AI voiceover on a corporate video, maybe like a, just a product video and I've
used an AI voiceover, that doesn't seem particularly risky or harmful to people if I use
an 11 lab voice.
Do I need to disclose that?
Seems unlikely.
So, but obviously if I'm doing something with, you know, healthcare or elections and I'm
using AI generated images and voices, then it would make sense as a much greater risk
to society.
But yeah, that's a new disclosure that's coming out requirement from YouTube.
Yeah, I know that's going to be extremely complex.
I'd be interested to see how that gets administered, like you said.
Right, we're going to go through, we're rapid firing it, Martin.
I've got a couple of stories I'm going to do and then if you can crack through a couple
of stories.
So first one for me is Google postpones the launch of Gemini AI.
So for those of you who listen to the podcast, you'll know that we're excited about Google's
Gemini because it's trying to be the main competition against GPT-4 from OpenAI.
And I think the thing we're most excited about is so many people launch models, but
none of them get near GPT-4.
And so the, Martin has a few use cases he loves for Claude.
I love Claude as well, but in general, the workhorse is GPT-4 and ChatGPT.
What we need is another GPT-4 level model to continue to drive development and innovation
in this area.
So we thought Gemini would come out the end of this year.
It's now been delayed and the reports are this is because it's not as good as GPT-4
yet, which would be a significant issue because you'd have to ask why launch it if it's not.
And I think that's the question they asked internally and that's why they didn't launch
it.
So then the question is really how long are we going to have to wait?
Because what's it going to take to make those improvements?
No one knows.
So if you're waiting on Gemini like we are, and you were excited about it, be a bit less
excited because it's going to take a little while to come.
The other story I was going to talk through was just quickly Google's search generative
experience, which we've talked about a few times, which has been available in the US,
is now being rolled out to 120 other countries.
Why is this significant for marketers?
Because we've been talking a little bit about what does the SGE, search generative experience,
mean for marketers doing SEO?
And if Google is answering questions and providing insights and making product recommendations
right at the top of the screen through the SGE, rather than pushing people towards organic
links, how's that going to change how we do SEO?
The fact that it's now been rolled out to 120 other countries tells us this is coming
mainstream and we're going to have to deal with this sooner or later and we're going
to have to really see how this impacts the performance of our websites and our SEO and
our content marketing efforts.
Martin?
Martin McAlpine Sticking with Google and their announcements,
they have through YouTube actually announced Melodic AI and this was a blog post that the
YouTube team have published, which is a collaboration with YouTube and Google DeepMind and they've
unveiled an array of AI driven music experiments and they are pretty cool.
I would recommend people go onto the blog and listen to some of the tracks.
They've created an AI where you can basically hum a tune and then with a written prompt
describe what you want that to do.
So you can turn it into a saxophone solo and it will turn your hummed tune into a saxophone
solo and you can stick that into a track.
So you can imagine what people are going to be able to do if you're a musician or you're
a producer, you're going to be able to create tracks by tapping on a table and turning into
proper drum solos and what have you.
There's other ones as well where you can just type in a scene, maybe you've got a bit of
footage and you want to type in a description of the type of music you want and it will
create that track.
That's called Dream Track for Shorts.
So that's going to be released for YouTube Shorts.
Now they've launched this in collaboration with artists such as Charlie Puth and T-Pain.
I can't say I know who Charlie is, but yeah, basically a bunch of AI powered musical tools
that producers and creators are going to have at their disposal and they look really cool.
And while we're on the subject of AI audio, a big story this week came out of Stability
AI, the head of audio at Stability AI, the company that produced stable diffusion and
clip drop and all of these tools that we love.
His name is Ed Newton Rex, head of audio and he stepped down from his role this week amid
concerns over Stability's use of copyrighted content to train models without consent.
So he described the practice as being exploitative and he wrote quite a long tweet basically
articulating his position and describing the tension amongst people within the industry.
This is someone who loves audio.
This is why he's in the role of head of audio.
He's someone that's passionate about this as a medium, but he can see that creators
and artists are being exploited and will be left behind.
He doesn't agree with the definition of fair use.
He thinks that it's not the way that the industry should be going.
So yeah, that was a big shape up.
We can train our models based on your stuff without paying you because we consider that
fair use.
And I think some of his posts implied that that mindset is the prevailing mindset in
all of these generative AI companies.
And I think I would have Umbridge as well with that.
So another cool thing was Runway announcing their motion brush.
So people on the podcast, the listeners, you'll know we love Runway.
It's pretty cool.
It does loads of text to video generation stuff.
And the latest situation of the tool lets you input a static image and then taking a
brush-like interface, you can color in an aspect of the image that you want to move
and it will only move that part of the image.
So it's not available yet.
And the demo videos for all this stuff always makes it look cool.
And then when Mark and I try and actually get anything usable out of it, it's almost
impossible for us.
I guess maybe more a limitation of us or maybe the amount of time and effort you need to
put in to actually get something awesome.
But if it's usable, I think it could actually have some really interesting applications
beyond what we can do right now, like animating an area of a chart.
Like if you're doing a report to your team, that could be quite interesting to make things
a bit more engaging.
You could...
There's suggestions you might be able to take things like photography, like an old family
photo and have the people wave, which would be kind of interesting and see how well that
works.
So I think it'd be interesting to see how people play with this versus using static
images or just straight on text to video generation.
And whether that then cascades for us as marketers into applications in terms of what might have
been a static social media image banner now becomes a moving one because it's so easy
to animate certain aspects with quite a lot of fine control in terms of what those aspects
are.
And a lot more of those types of more video-y type things on websites versus just static
images.
So it could be quite cool.
Yeah.
Looking forward to actually getting hands on with that and seeing if I can get something
useful, which I almost certainly won't be able to.
Next up, OpenAI seeks a collaborative future with data partnerships.
So this is OpenAI basically saying, we're training up new models and we need more data.
We need access to more proprietary data or hard to access data.
So if you have that, you can go to them and say, hey, here's a bunch of data that you
can access.
So this is particularly interesting, I think, if you're in maybe an institution like a museum
or a library where you say, actually, we think we've got a collection here that we could
give over and help train the model and expand the domain knowledge of, let's say, GPT-5,
which they have spoke about.
They are now training.
So if you're at a museum and you've got a specialist collection, you could hand that
over.
Now they said that in terms of being able to handle the information that is provided,
they can basically deal with that.
They've got really state of the art technology to do image and handwriting recognition from
documents and GPT-4 can interpret photos of things.
So they'll deal with the collection, but if you've got a collection, you can give it to
them.
There are two ways that you can provide.
You can contribute to an open source data set for public use and training open source
models or for providing data sets to train proprietary models with strict data sensitivity
and access controls, things like GPT-5, for instance.
So if you're interested in that, and that sounds like something that your organisation
could contribute to, get in touch with OpenAI about their OpenAI data partnership.
Cracking stuff.
Well, Martin, we're now near the well over the hour mark, so I think we'll let our friendly
listeners go about their day.
Thanks for listening.
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I will look forward to catching up with you on our next episode, Martin.
Thanks very much.
Cheers.
See you later.
Bye.
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