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 18 after our short summer hiatus.
Apologies that we left you without all of your latest marketing and AI information that
you need, but we are going to make up for it this week because we have a banger of two
to three weeks of everything you need to know that happened in the world of marketing and
AI.
I'm here as usual with my very good friend, Martin Broadhurst.
How are you, Martin?
I'm good.
I'm fully relaxed, although I'm a little bit throaty, a bit of a laryngitis.
I don't know if you can hear that there, but maybe I partied too hard on my friend's wedding
this weekend.
Let's hope you recover quickly because you've got a busy couple of weeks ahead of you, haven't
you?
I have.
Yeah.
Tomorrow morning I fly out to Cleveland for the Marketing AI Institute Conference, MAKON,
out in Cleveland, Ohio.
Really looking forward to that, they've got some genuinely top-drawer speakers and I'm
auto speaking.
So that's nice.
I didn't clear.
I would have just said there's some top-drawer speakers, but I appreciate the humbleness
there, Martin.
So if you're going to be at that event, go and say hello to Martin because I'm sure he'd
love to say hello to you.
He'd love to hear your thoughts on the podcast.
If you are lucky enough to be going to that event, it does look like an absolute stellar
lineup.
So Martin, I'm sure you'll come back and give us your top takeaways from the event when
you return.
Yeah, very much so.
I'm hoping to get a few interviews with delegates and speakers while I'm out there.
So a bit of content for the pod.
Me likey.
Right.
There's a lot to get through.
So we're going to jump through.
Now usually we have a number of big stories that we look into and a bunch of short snippets,
but there has been so much that's happened, dear listener, that we're just going to crack
through at speed.
We're going to try and take you through all the different things that have happened.
And I'm sure Martin and I will jump into some conversation around some of the aspects where
appropriate and there won't even be time for tool of the week this week.
I suspect there'll be some tools of the week hidden in all of the updates because there's
so much good stuff.
Okay.
Deep breath.
Everybody get ready to drink from the fire hose.
I'll get ready to turn it on.
Let's start with a load of announcements this week from OpenAI, especially announcements
around ChatGPT.
So the first thing is a couple of weeks ago, according to the team at SimilarWeb, which
is a platform that does its best to try and assess how much traffic different websites
are getting, even though of course they don't have access to their actual data.
But according to SimilarWeb, it looked like ChatGPT's user growth may have stalled because
the data suggested that the platform had fewer users in June than in May.
So that's quite an interesting one to kick us off with.
Has the hype cycle of everybody wanting to have a play with ChatGPT, is it burning itself
out?
Yeah, I think we've probably saturated the early user growth.
Now we're kind of heading into that.
Okay, we've got the routine users, the people that have played with the free version and
weren't blown away with it.
We've now gone by the wayside.
But you can actually see this reflected in Google Trends as well.
If you look at Google Trends for ChatGPT, it has plateaued.
So it's not just SimilarWeb that we can look to for this.
It seems to be confirmed by multiple points.
Yeah, and I've been trying to reflect on this during our little summer hiatus.
And I think there's definitely anecdotally from people I've spoken with, people who've
had a bit of a play, thought, oh, this is cool, but struggled to really apply it in
their work.
So I think adoption of AI is going to have to overcome a fairly major hump there when
this is rolled out to the wider masses through Google Workspace and Microsoft 365 Copilot
and these other tools.
The other thing I was really thinking about is I had a conversation online with a couple
of folks about Code Interpreter, which we'll talk about a bit later, and some of the analyses
of Code Interpreter being wrong.
And sometimes when you get outputs from ChatGPT, some of the stuff is made up.
And it really made me think, how commercially viable are these tools if you can't trust
them?
Because it means you've got to check everything.
Now I'm sure they're still able to save us time, even when you've got to do lots of
checking, because at least you don't have to perhaps create the raw output yourself.
But that does limit, I think, the impact that they can have and also how much quite a big
chunk of the working populace is going to be willing to trust them with, right?
Because they'll be worried that they're going to report back to their boss or other
key figures in the business and outside of the business stuff that's wrong.
So yeah, I think it's going to be interesting to actually see how that plays out.
Yeah, I mean, imagine that you're a lawyer, for instance, going into a court case and
citing examples from case law and none of them existing.
Could you imagine?
What a ridiculous human being you'd have to be to do that.
But no, you're right.
I think people are going to have to have trust in the systems.
And actually that comes through exposure.
The more you use the tools, the more you recognize where their strengths and weaknesses lie and
how much you have to check and double check certain elements that it gives you.
So I understand why people might be reticent to use them, particularly if they've only
used the free version of ChatGPT with GPT 3.5.
But once you start to see where it excels and how it can save you certain amounts of
time on very specific tasks, like you probably have a set list of tasks that you know.
ChatGPT will do quicker than you doing it on your own.
As soon as you've done that, then you start to introduce it in your day to day.
I think you're right.
And I think as the tools get better, you can expand that list or start to introduce tasks
the way previously you decided it wasn't quite good enough yet.
Yeah, I think we're going to see that as well.
And for those of you who are new to the show, the reference to the lawyer who used AI to
drive a court case he was involved in is episode 13.
Go back, check it out, see what we're talking about.
This is kind of crazy.
Moving swiftly on with the stories here then, OpenAI has also provided all API users, all
is in quotation marks at the moment, with access to GPT 4.
So this is a pretty big deal for those of you that are out there, moving beyond just
playing with ChatGPT and trying to see if there are ways you can smart automate aspects
of your work using tools like Zapier.
And until now, most of us, including me still, which is annoying, had to rely on GPT model
3.5, which is pretty good for lots of different stuff.
But it doesn't have the content creation capabilities of GPT 4.
So for example, Martin and I, and Martin spoke about an example that he did in a live event,
which I can't even remember what episode that was, but you should definitely go back
and listen to that one.
And I've built myself an app where I can record voice notes on my phone and then the
audio gets pushed into Whisper, which is a transcription tool.
And then the transcription gets pushed into ChatGPT to be summarized and drafted into
an email that I can send to someone.
But they all rely on GPT 3.5.
And so the quality is a bit meh.
But if you can get access to GPT 4, it's going to seriously upgrade what you can do,
building those self automations.
But so I think that's really cool if you're in that niche power group.
Obviously we're going to see even more tools improve a lot if everybody can access GPT
4 via the API.
Have you got access yet, Martin?
I have yet.
And I've made some upgrades to some of the workflows that I had in Zapier.
I was up here.
Yet that's, I've seen some improvements.
The prompts that I had anyway gave me pretty good outputs with 3.5.
So I haven't seen any dramatic changes except the cost.
I mean, I'm using it at a very low level, but the price difference between 3.5 and 4
is, yeah, it's noticeable.
Interesting.
So you may not be getting that much better results, but you are paying more.
It's going to be, it's definitely going to be used per case by use case, isn't it?
Without a doubt.
And that's the thing I think people need to recognize is as a clammer to get access to
something like GPT 4.
And actually for lots of use cases, particularly with good prompting and few shot and many
shot prompting as well, you can get really good results with 3.5.
There we are.
Marketers playing with Zapier and other tools.
Bit of advice there on how to perhaps get the best bang for buck by choosing the right
model for the right application.
Some more ChatGPT news.
Many of you would have noticed this actually by now, but ChatGPT has seen its internet
access revoked as users were reportedly using it to get around paywalls and access gated
content on top news sites.
And as of today, internet access for ChatGPT hasn't yet to be reinstated.
Yeah, that was a, it was a use case that people found really early on as well.
So I'm surprised it took them so long to pull it.
But yeah, I'm not surprised that publishers were up in arms about that.
No doubt we'll see it reinstated fairly soon.
In the meantime, you can always use the plugin WebPilot if you want to browse the web.
It doesn't have quite the same capabilities as the Bing one, but for most users, they
probably won't notice the differences.
So yeah, if you do want to use ChatGPT connected to the web, just use the plugin WebPilot.
That's a good bit of advice.
You can also use Bing.
And if you're feeling particularly brave, you can use Bard.
But we'll talk about the, there's been some improvements to Bard, but Bard is still very
much lagging behind ChatGPT in a lot of, a lot of applications these days.
Right.
First is from OpenAI that they've announced that they've tasked a team internally with
the goal of building a human level automated alignment researcher, which can be used to
scale efforts and help align super intelligence.
So on the one hand, this appears to be a proactive step to sort of help quell those fears around
powerful AI tools coming out over the next few months and years that become less and
less aligned with human values.
But one assumes it will also help OpenAI to align their models without having to rely
so much on learning with reinforcement, learning with human feedback, which obviously takes
a lot of people and a lot of time.
So there's a commercial benefit to creating this, a fairly massive one, one would have
thought.
But most of the story from OpenAI has been about spinning.
They're on a voyage to make sure that AGI remains aligned with what humans want, not
just what it wants.
So I thought it was quite an interesting story from that perspective.
It's a great marketing tool, isn't it, for the company as well.
When you say, look, we're investing so much into keeping our incredibly powerful artificial
intelligence system aligned to humans because our system, which is incredibly powerful and
incredibly capable and can do all of these amazing things, we have to invest heavily
to keep it safe.
By the way, did we mention that our system is incredibly powerful?
It's a great marketing tool when you build this expectation and make people believe that
you've got this product which is capable of so much, potentially so dangerous, potentially
being the key word.
It's got some wonderful marketing 101 in it, hasn't it?
Choose the one, two or three key features and benefits that you're going to promote
and repeat yourself a lot until it's embedded in people's brains.
So yeah, that was a cool one.
Not as cool as our next bit of news, which is that OpenAI made code interpreter available
for everyone.
So this was in a closed alpha, I think, and now you can access it via beta, but it's kind
of awesome.
In fact, it's so powerful.
There's been a theory going around that in essence, you could consider code interpreter
as GPT 4.5 because I think what a lot of people know it for is its abilities with data analysis.
So we should probably touch on those first, Martin.
So within just a chat with code interpreter plugin enabled in ChatGPT, you can do data
analysis, data visualization, predictive modeling.
You can clean your data up.
You can even create synthetic data.
So they're all quite well-known use cases and I've had a bit of a play with those.
We can look at those in a minute.
But because it can produce its own Python code, you can also get it to do other interesting
things for you like manipulating audio files, creating PDFs, creating CSV files.
So basically, you could give it some messy data and then ask it to clean it up and send
it back to you and you download the CSV file.
Some people have even used it to create interactive dashboards and interactive maps, which is really
awesome.
Obviously not in the chat window itself because it can't run the code that it produces, but
you don't need to be a Python coder to be able to use the code.
You just need to ask what you want and then it gives it back to you.
So it's pretty amazing.
There was one example where it looked like it could uncover and detect the number of
faces in an image.
So it's like, what is the limits of code interpreter?
Because if you thought it was just going to be something to chuck some data in and make
some nice graphs or ask some questions of your data, it can clearly do a lot more than
that.
Is it the early preview to the multimodality capabilities of GPT-4 that we saw previously?
You mentioned something there about the data cleansing and being able to throw files in
and it will export some.
Have you tried it?
Not for cleansing, have you?
Yeah.
It was the first use case that I put together and I created some kind of dummy data using
an export of HubSpot contact sheet.
So I just randomly capitalized and lower cased some names and put some missing data in fields
like country and city and things like that.
Made the inconsistent telephone numbers, that kind of thing.
I just made it a little bit untidy.
And it was only about 30 records in this sample document.
And could I get code interpreter to fix it and give me the output?
No, I could not.
It was a CSV file.
I stuck it in there, asked it to do it.
It went through lots of creating Python, but it never got me the output that I wanted.
Maybe this is bad prompting from my part.
So then I thought, well, okay, how easy is it to just get it to do it in GPT-4, just
copying and pasting the CSV?
The data in, yeah.
Yeah, the data in.
Did that with my instructions in the prompt, got the output first time.
But this harks back to the very first point in today's podcast, right?
Which is like figuring out the horses for courses, like what's the right use case for
each tool, but crumbs is a lot of effort to go through and try and figure out how to get
it to make it work and then when to give up.
Yeah, I'm on board.
My main use case so far was I tried to think about what data sources I had with like a
ton of data.
Like I really wanted to like try and break it.
So I exported a month's worth of time entry data from across the agency and I removed
any client specific stuff so it was literally just people and time and date of timestamp.
And I got it to make some really nice graphs for me.
I wanted it to describe the data because I'd seen some examples online where you give it
some data and then you say, what do you think are the 10 most interesting trends from this
data?
I got it to graph things like who'd done the most billable time.
I then asked it in a text prompt, who's done the most billable time?
And even though it previously just produced a graph for me that showed that person A had,
it swore blind that someone completely different had done the most time.
And I was like, are you sure?
Because I'm pretty sure it's this person.
And then the code interpreter went, oh no, yeah, you are right.
And I was like, who's the second most?
And it gave me the person that had given me before who wasn't even in the top five.
And so I got into not a debate, but like an interesting conversation online about this
where other people weren't having these problems, but when I've like tried three things and
two of them are wrong, that's not the type of success rate where I feel like I can trust
anything that comes out.
Not even close.
So who's this for?
Because you know what?
I'll chuck it in a pivot table, thanks.
Cause I know the pivot table is going to be right.
And I appreciate not everybody knows how to create pivot tables or often people are not
working in them enough to remember their functionality or to really feel comfortable using them.
But at least the information's correct.
If I have to do a pivot table and the code interpreter analysis and double check them
against each other, I'll skip the code interpreter, right?
Yeah, absolutely.
The more I think about this, the more I'm like, there's going to be a bunch of use cases
that I'm just not going to touch until there's some mechanisms in place to ensure quality
of output and accuracy.
Right?
And I think that we've talked about it a lot on the part about UI, UX is so important and
you've got to get that right.
And what you want is a system where you've removed the need for prompting, right?
Because prompting is half of the issue here.
You put in one prompt, it doesn't quite work.
So you're tweaking it.
You constantly go back and forth.
You just want to be able to stick your data in and get something interesting out.
And this is where Copilot and the whatever the Google AI version for workspace is called.
That's the nut that they're going to have to crack, isn't it?
You hope that they can do this at the front end and it's not going to leave users having
to go back and forth constantly prompting and reprompting in order to get something
functional out of it.
Yeah, I think that's definitely an issue.
But I'm also thinking if I asked ChatGVT to create some content and it makes up scientific
citations or whatever that sound plausible because of how its algorithms produce content
based on its training data, I can sort of understand that.
But when I give it some data, it shows me a graph output that clearly summarizes the
data with a clear sort of outcome to the answer to the question.
And the question was not ambiguous and it still gets it wrong.
That's a worry for me because that's the biggest indicator yet.
And I know that this is true, but it's hard when you're working with ChatGVT and you see
it output and you think, God, it knows stuff.
But that was the biggest indicator to me yet that it knows nothing.
It's just somehow regurgitating information based on other information.
I'm surprised Code Interpreter works as well as it does, to be honest, but until I can
really trust it, I'm not sure I can make it part of my workflow.
Not to mention the fact I had to anonymize a load of the data because they couldn't risk
putting any client specific info in.
So I can't ask anything like, oh, what's the profitability of this client versus this client
or this project?
Because I just can't trust it to give it the data.
So limitations abound.
People are doing some cool stuff with it, as we'll look at a little bit later.
But I think it's going to take a fair bit personally to realize its potential, certainly
in our hands, Martin, because we can't get it to do even basic things that we want, which
either means that we are not the brightest, which is highly plausible.
Maybe we'll ask Code Interpreter its opinion on that.
Or it's not easy to get correct answers out of it.
Right, moving on.
Still on the OpenAI, still on chunk one here, all your news.
OpenAI is looking for new sources of data for its model, striking deals with the likes
of Shutterstock to access its images, videos, music and metadata, and also the Associated
Press tapping into its news archives dating back to 1985.
So there's an interesting one.
The ongoing pursuit of information to train your models on and how to get it.
I couldn't help the cynic in me, Martin, couldn't help but think, have they accessed this already
and they're just sweeping up behind themselves?
Yeah, that's a...
What's the phrase?
It's easier to ask for forgiveness or beg forgiveness than ask permission.
Well, yeah, and you've got to wonder, especially you've got the likes of Sarah Silverman suing
at the moment because ChatGPT is just too good at summarizing books to have not been
showing the original information and stuff like that.
I think we could see a lot of this.
At some point, we're going to have our Napster to Spotify transition, right, from the Wild
Wild West, everything's fair game, everything's free to somehow we have to at least partially
compensate people who helped create the system, i.e. all the content creators.
I'm glad that's not my job to figure out how to fix that, but I suspect we're going to
have to get there.
Right, moving on, ChatGPT is getting worse, question mark.
Perhaps, perhaps not.
So there was a new paper out by researchers at Stanford and Berkeley that suggested that
GPT 3.5 and GPT 4 have changed a lot in their performance over a short period.
The researchers evaluate ChatGPT on various tasks such as solving math problems, answering
sensitive questions, generating code and reasoning visually.
And what their data showed is that ChatGPT had become much worse at some tasks, so things
like identifying prime numbers, also coding, but that GPT 3.5 bizarrely had got better
at some tasks.
Now other researchers have massively questioned the approach that this paper took and so the
results might have to be taken with quite a large dose of salt.
There's also some suggestions that the more you use GPT 4 and you start to realise where
it doesn't work very well, the more that becomes obvious to you and it's your perception of
GPT 4 that's changing, not GPT 4 itself.
That being said, if you're a user of ChatGPT and you have felt recently like its performance
might have been waning, there may be at least some data to suggest that's true.
Thoughts on this Martin?
I suspect it's more a case of familiarity.
I saw the feedback from other researchers questioning the approach.
The Twitter threads that I read seem to make some good points, although unfortunately for
listeners I can't remember those points.
But yeah, I think it's familiarity.
I think you just, much like knowing the limitations we talked about earlier on, knowing the limitations
of a system so therefore you don't try to get it to do something or you don't publish
content that's hallucinated or something, you also recognise the strengths more quickly.
And when you've gone from 3.5 to 4, when they first announced 4 and we all started playing
with GPT 4, you immediately went, oh my God, this is a step change.
But then once you've ran a couple of hundred, couple of thousand prompts through it, you
start going, hmm, it's not all that, is it?
Just say some silly things.
So I think it's more about familiarity.
I think that's definitely part of it.
It's interesting and I'd have to dive deeper into the paper to see how robust those assessments
were that showed that it was good at one thing before and not so good now.
I really question the prime number thing.
The 97.6% accuracy reduced to 2.5%.
I don't know.
Maybe they have significantly changed the weights and biases in the model such that they've
just absolutely kneecapped it at maths.
But it would seem that that's too dramatic, isn't it?
Surely?
I mean, you would guess so.
I think for me, it's more about how robust was the assessment method unless they knew
for a fact that in three months time that we're going to run the same analysis, which
would be a smart experiment.
How did the conditions change?
I don't know.
But ultimately, we're talking about learning what these tools are good at and not good
at.
And if they can change quite a lot, that's going to make that harder because you're going
to be like, oh, okay, I can rely on that for this.
But yet, we're talking a lot about how the models need to improve.
If you want the models to improve, then by definition, they're going to have to change.
They're probably going to get better at some things and worse than others.
How do you do that in an environment where people are trying to learn to use the tool,
but it's not static?
Gmail, to all intents and purposes, is a static tool.
It doesn't change how it sends emails and I have to figure out a new button to press
next week.
And this is important for the adoption.
You talk a lot about UX and I think you bang on mine.
It's important for the adoption of tools that you know how to get the things out of them
that you need and what you can rely on them for.
And I think if it ends up being too dynamic, that could be a challenge.
I appreciate pressing a button in Gmail and pressing a button in ChatGPT from a UX perspective
are basically the same thing.
But I mean, the full user experience, not just the visual user experience.
So right, we better keep moving.
There's still loads to go.
Right.
ChatGPT is increasing message limits for those of you that are interested in this.
I personally encourage it at 50 messages per three hours.
Those have reported at being 100.
In all cases, it looks like this is an improvement over the 25 message cap per three hours, which
I know a lot of people were hitting and it is quite annoying.
So that's great.
Some more news, especially if you're not in Europe or the UK, is that ChatGPT and OpenI
have introduced custom instructions.
So this is really quite cool.
Basically, this is like introducing custom prompt injections for every chat that you
have because you provide in your settings some information about you, how you would
like ChatGPT to respond and any sort of delimitations or context for basically all responses you'll
ever need.
We've talked previously about getting it to imitate your email style.
One would assume if you include a bit of information about your writing style as part of your custom
instructions that would make that all work a little bit better.
Now, as I mentioned, you can't access this in the UK and EU at the moment, although you
can with a VPN, so you can have a play.
So you can't test it if you're in where we are right now, not easily anyway, but initial
users are praising its ability to generate better outputs without having to constantly
enter a ton of the same prompt info, which is nice.
There's also been some incredible creative users of this tool.
So a person called Nick Dobos on Twitter was suggesting you can use it to build full agents,
a bit like AutoGBT or BabyGBT.
In essence, you use Code interpreter, you get it to create a list of tasks that it then
saves to a file, and then you ask it to pull the tasks from that file one by one to take
you through a process, which was quite interesting.
Nick also found a way of, in effect, having it summarize a bit like a choose your own
adventure, but for your own logical analysis, the options that you might want to add and
with hotkeys.
So if you want this, press one, if you want this, press two, if you want this, press three.
So you could actually cycle through it quite quickly without actually even having to type,
which then ended up being a really good example of how you can use this custom instructions
to build that system and then Code interpreter to do some cool stuff that's got nothing to
do in essence with data.
So custom instructions.
Looks like it could be powerful.
Hopefully they'll roll out wider use soon.
What are your thoughts on custom instructions, Martin?
I'm looking forward to getting my hands on with it.
I forgot that it wasn't available in the UK because I did have access to it briefly and
I must have had my VPN on or something.
And then I went onto it this morning and I've just posted on threads.
Where has it gone?
So yeah, I'm glad you've reminded me that that's why I can't see it.
But it's a cool feature.
And I've seen a few users giving some use cases that I'm absolutely going to use.
For example, just Sam Altman himself posted one on Twitter about how he's using it, which
is to basically say, I know you're a large language model.
You do not need to tell me every time, just give me the answer.
And yet little things like that.
The amount of times I have to do this really basic jailbreak where I'm saying, I know you're
an AI, I'm a human and yes, I will seek professional advice and blah, blah, blah.
But just role play with me.
If I can get rid of all of that, then great.
I've saved some time.
So my favorite thing about that is it's quite similar to what Nick does in the example I
just mentioned.
The top of his injection is no chat, just do.
No chat semicolon, just do.
That feels like if you want to be the open AI competitor in the style of Nike, Ikea I
should say, that's probably how you have to frame your strap line.
Right.
Let's talk about Claw 2 because the ridiculous generative AI text generation news doesn't
help.
So Anthropic, who we've talked about on the podcast a fair amount, have released the next
version of their Claude model and it is a significant leap forward.
So it's nearly as good as ChatGPT 4 on a variety of standard benchmark tests.
It has a 100K context window.
We've talked about this before.
So basically about 75,000 words.
So it can handle a lot more input than ChatGPT, which makes it brilliant for summarizing large
amounts of info.
They've also changed how the UX works.
So it's much more chat-like and easier to use.
And when you want to provide it with even copied text, it doesn't fill up your prompt.
It just gets added as an attachment.
So you can upload PDFs, Word docs, copy paste large chunks of text and they'll basically
be added as a text attachment.
It just makes it really easy to summarize and work with large documents.
And because it's much better at its understanding than when you're working with ChatGPT and
also when you're working with 3.5, but also when you're working with Claude 1, you can
actually trust the outputs much better and you get much more high quality outputs from
it.
Because it's a new model, its knowledge curve is early 2023 versus 2021 for ChatGPT.
It also knows a bit more about the world and it's just well worth a play with if you've
been on the fence about having a play with Claude, now is the time because it's getting
up towards GPT-4 capabilities in some areas.
And it's free.
Yeah, you don't have to pay for all of these additional plugins.
The up-to-date knowledge piece is so welcome.
I was playing around with it earlier this week and it's just nice to be able to ask
it questions about the latest research in certain areas and it knows, it doesn't say
I'm not familiar with it.
That's actually one of the big selling points.
It's been integrated already into Jasper, the AI copywriting tool.
They've actually cited the up-to-date knowledge training as one of the key reasons that they've
integrated it into the models that they now use.
Yeah, it's exciting.
I think it's definitely worth going and having a play with that if you haven't so far.
Although, of course, when a product is free, who's the product, Martin?
You.
That's right.
We're all the products.
So the standard caveats come.
Don't paste in any information that you wouldn't be comfortable going out into the world.
I read somewhere.
I'm not sure I entirely agree with this, but don't put anything into a free to use model
or even chat GPT paid model that you wouldn't be willing to anonymously post on Reddit.
That's the bath I heard for what you should and shouldn't put into these models.
That seems like a pretty high bar to me, but I think especially if you are on the risk
of our side or you're handling information that would be considered commercially sensitive,
it's probably a good bar to set.
Right, more language model news.
Meta has released a commercial version of its open source language model, Lama 2, so
that businesses can build products on top of it.
It's available in three model sizes, ranging from seven billion to 70 billion parameters.
So that is now basically Meta taking what it originally released as open source, but
without any commercial use whatsoever and making it available for companies to use.
The first company I saw jump on this was Perplexity AI, which was a tool of the week that you
introduced us to a few months ago or a few weeks ago, mine.
And it's blazing fast.
Isn't it?
Wow.
It's like real time almost.
And the quality of the outputs in some of my initial blogging tests, I got a blogging
test that I run all new models through to see how nuanced the outline is and how well
crafted the copy is.
It was closer to GPT-4 than I thought it was going to be.
Which is pretty awesome in this particular use case.
There is a note that Meta have put some restrictions on the commercial use of this tool for, I
think it's if you've got more than 700 million users.
Is that sad about right, Martin?
Yeah.
So they basically stopped the likes of Google just being able to use it or anyone like that.
And there's also some weird caveat about, I'm not sure of the exact phrasing, but it's
like if you're planning to use this to solve world hunger, or if you're using the model
for some major world changing event, like there's some restrictions on it.
So I know people from the open source community, like leaders within that field have basically
said, yeah, they said it's open source, but it isn't open open source.
But it's a step in the right direction, certainly much more so than open AI, ironically.
Yeah.
Closed AI, as we're going to start calling them on the podcast.
But yeah, I agree.
I read somewhere that the 700 million users seemed quite like an interesting cutoff that
would be very specific, that would allow you to very easily say, okay, it's not going to
be Google, it's not going to be Amazon, right?
And all these other platforms that are clearly competitors to Meta.
I also heard someone say, yeah, maybe it was also driven by the size of company they thought
they could easily buy.
It's like, up to about 700 million users, we could buy them if we had to, as they grew.
But outside of that would be too difficult.
So I thought that was quite interesting.
I had one quick play with it.
It was on the 7 billion parameter model.
I didn't realize that was the one I'd selected, but it was the first test I did on perplexity.
And I asked it to describe the story of Goldilocks, and it gave me the most weird response.
And it was basically saying that there were troubling themes to do with Goldilocks, and
it couldn't describe Goldilocks because of all of these troubling themes.
And really, we should all move on from the story of Goldilocks because of its troubling
themes.
I thought, how has that come across?
Clearly there's some guardrails that have been put around this.
The prompt was literally describing the story of Goldilocks, and I could never get it to
recreate it.
But it gave me a multi-paragraph response telling me why Goldilocks was bad and the
themes in it were not suitable for basically modern society.
It was as if I'd asked it to write a poem praising Adolf Hitler.
Right, so we've done OpenAI, we've done a bit of meta news.
Let's talk Google for a bit.
There's quite a bit of Google news over the last couple of weeks as well.
So I think first and foremost, there's been a ton of updates to BARD.
So you can now upload images as part of your prompt via an integration with Google Lens.
I saw some people trying to do some interesting stuff with this one, Martin, just this image
one, like uploading receipts and things to see if it could easily pull information out
to help expensing, but it was making stuff up, so it couldn't really be trusted.
I tried it when I was in France and there was an advert of a chicken and there was a
chicken poking his head in the side, looking at the user and another chicken next to it
with an egg coming out of it.
And it said something in French about chat GPT.
And I had zero context for what this advert was.
I was just like, this makes no sense to me.
So I took a photo of it and asked BARD to tell me why it was funny and what was going
on.
And while it did a good job, it read the text in the image and translated that accurately,
but it didn't quite get what was going on in the image.
So it thought the chicken was wearing sunglasses, which it wasn't.
And then it went on and on this big explanation about why it was funny because it was juxtaposing
a chicken laying an egg with a chicken wearing sunglasses and ha ha ha, isn't that funny?
And I just thought, well, I mean, fair play to it.
It did a good job kind of getting an understanding of what was on the image with the text.
But yeah, after that, it all sort of fell down.
Yeah.
It's interesting that there have been tools for a decade or more that could pull in text
information out of printed materials.
It wouldn't appear to be that hard necessarily to add that capability.
But I guess the way these models are trained is very different.
And so it's not an Apple and Pears comparison.
It makes you wonder what's going on in the back end, because as you say, like it's screen
reading.
So we've had OCR for text and images for a long time.
So when you upload an image, does it run that process first?
Are there multiple things that it does in the processing?
Like does Google, does it do a Google lens job of describing the image, extracting the
text separately, turning all of that into a prompt, aligning that with your written
prompt and then kind of spitting out the answer?
I'm really interested to see what's going on behind the scenes.
Yeah, because the compute power that would be required to chain all those different analyses
together, especially if a bunch of them are not needed, right, would be a lot.
That's going to be interesting to see how that plays out over time.
And comes back to this concept of lots of different AI tools that are really good at
a handful of things versus one master AI tool, unless we can figure out how to get the compute
costs and time down.
Because chaining all those different analyses together is going to add a lot there.
There's some other cool stuff that came with some of the updates to Bard.
So since I started playing with Bard, you could use your microphone to record your prompt.
So it's probably been around launch and not, you know, if it came out afterwards, not long
afterwards.
Now Bard can read the responses to you.
So we're drifting much more into a Star Trek, Hello Computer or Jasper style worlds of interacting
with these large language models in a very natural you speak and then it speaks back
to you type way.
Which at some point then how does that work?
Are we going to be able to dig out our Alexa's and our Google Homes and actually make them
work for more than just set a timer for me cooking these eggs and what's the weather
like later on?
It's the next logical step, isn't it?
Particularly with Google Home and I think they've already said that Bard will be integrated
with Google Home.
Amazon's approach to this is going to be interesting.
They are noticeable in their absence of not having a large language model instead offering
a marketplace.
Are they going to partner with the likes of AI21 or are they going to launch their own
language model and integrate it with the smart speaker system whose name I'm not saying right
now because I'm sat next to one of their speakers and it will respond to me when I really don't
want it to.
But yeah, that's the way it's going.
Yeah, I think it would be interesting to see how these are playing out.
I'm using a tool that's better than the one that I built for myself called Audio Pen so
that if I'm walking the dog, I can leave an audio note and it's auto transcribed and summarized
in one of a number of styles.
Personally, I would love to get to the point where I can have a smart speaker, although
I guess the headset I've got on is just as good through the browser and just speak stuff.
I'd love to just talk to Google Mail and then have it draft emails for me in my style that
are more than the ramblings of how I speak, right?
But that can't maintain the essence that is how I want to communicate the words I typically
say.
The use case that I really want to see for this is within the car.
So Android Auto or Apple CarPlay, whatever it's called.
That's the thing that's missing for me.
The fact that there isn't an app so far that I can find on Android Auto where I can record
a voice memo hands free.
If I want to record a voice memo in the car, I have to actually press it on my device,
which is not ideal.
So I'm trying to minimize the use of that if you could just do it with Android Auto
and it's integrated with Bard and you can have that back and forth.
That's a game changer, particularly if you're doing long commutes and lots of driving.
Yeah.
Have a conversation with the car.
But a productive work based one where I can then send the email for you afterwards.
You're like, read that back to me.
Cool.
That's bang on what I want.
Send it off to Bob.
Well, you can do that already with WhatsApp messages in Android Auto, can't you?
Oh, can you?
I don't have Android Auto, so I don't know.
Interesting.
Okay.
What else can you do?
There's a bit of other stuff you can do.
I just want to mention when Google talks to you, it's quite fun to play with it.
It's not as good as Eleven Labs.
So we've talked about on the podcast before Eleven Labs is a company that's trying to
solve for creating synthetic voices that sound like human voices, both in terms of the sound
of a voice, but also the natural pattern of how people speak with pauses and tone changes
and volume changes.
Bard's ability to read things out is not quite as good as that, but it's certainly a far
cry from old school style robotic text to speech readers.
So we're getting there in short.
What else can you do?
You can modify the responses a little bit like you can in Bing.
So you can say, I want the response to be simple or long or short, professional or casual.
So you can change some of that stuff if you're having it create content for you, which I
wouldn't recommend because it's nowhere near as good as Claude or ChatGPT, but you can
if you want.
It's easier to share the responses now via link.
So if you get a response and you think you want to share that with someone, you can just
create a link that you can populate and say an email to send someone.
And Bard is now available in Europe without VPN after addressing privacy concerns.
So lots of updates on Bard.
Our take home is still what we just said, which is not as good as the other tools really.
Got internet access.
That's a massive boon, especially now ChatGPT doesn't unless you use Martin's clever plugin
work around.
Right.
Let's keep the Google news going.
We've got Google launching its AI powered notes app called Notebook LM.
So you'll remember this when Martin told us about all the news that came out of Google's
I think it was Google's developer conference, was it?
Is it IO?
Yeah.
So we knew about this, but the important and interesting thing here is now you can access
it.
It's only available to select users in the US and there is a waitlist you can join.
It is cited by Google themselves as an experimental product designed to use the power and promise
of language models.
That sounds like corporate language paired with your existing content to gain critical
insights faster.
Basically, how does it work?
It is a virtual assistant chat bot style tool that has access to your document library.
It looks at the moment like you have to give it three, four, five documents to like set
the scene that you want to have a conversation about those.
So it might be that you've got an internal memo and a document like a blog post, I don't
know, and some stats and data that someone pulled out and dropped into a Google doc for
you.
You can query those all in one go.
What this seems like to me is the early versions of what co-pilots are going to do once they
get baked into workspace and also into Office 365.
So if you can get access to that, it's worth getting on the waitlist and having a little
bit of a play.
I'm struggling to see the use case for this that isn't quickly asserted by those co-pilots
Martin now that I know how it's going to work.
Martin agrees.
He nods excitedly.
He says, move on to the next story about Genesis.
Can do.
The band could talk for hours about the band, but it isn't the band on this occasion.
It's Google testing an AI tool called Genesis that can write news articles.
Google's been about town pitching this to publications like the New York Times, Washington
Post, et cetera, et cetera.
They state very clearly that the AI enabled tools are not meant to replace journalists,
but rather act as co-pilots and that Google is only in the early stages of exploring the
tool, which it sees as being an assistant that helps with things like headlines and
changing copyright writing styles.
As reported by The Guardian, there's a Guardian article on this, two executives at the New
York Times who saw the pitch said, it seemed to take for granted the effort that went into
producing accurate and artful news stories.
I think our take on this Martin is that major parts of journalism are still well out of
reach for current AIs, not to mention the fact checking that we've been talking a lot
about today and over the last couple of months.
Just in play by Google, like going into senior leadership, try and tell me that that is not
an argument to cut writing stuff.
Ballsy, isn't it?
Absolutely.
Particularly in the landscape where we're seeing the Hollywood actors strikes and the
writers strikes, all of which is concerned with how AI is going to be used within creative
production workflows, actors being able to record a thing, and then the AI edits what
they say, they don't have to be coming back in for reshoots, meaning they don't get paid
as much.
All of these kinds of things.
If Google is pitching, hey, your sub editors, you can get rid of them.
That's a strong move.
It sounds like the top brass are not overly convinced though.
Not publicly.
Maybe they're like, Google, this sounds amazing.
I am going to have to come out in the press and say it sounds like Tosh and that it will
never work and that we'd never do it.
But yeah, do tell me more.
It's difficult to say.
On the writers and the Hollywood strike, I don't know how much credence there is in this
story, but it's been floated that some of the major studios are trying to get supporting
actors, so background actors, to come in for a day's worth of work and get scanned as a
basically extras.
Then use them across multiple productions across their suite of whatever they're producing,
but only have to pay the people once because they get the scan of them and then away they
go.
Oh, well, you can totally buy that, can't you?
I was working with a company that does virtual production, an interesting area of TV and
movie production where you have large LED screens, so you'll have a real scene with
real actors in the foreground, but all in the background is just an LED projection.
What you can do with that is if you use a slightly out of focus background scene, you
can just have a crowd scene or exactly like I say, background actors.
If they're slightly out of focus, you just do it all using Unreal Engine.
It looks good enough when it goes onto the big screen to be real.
Already that's not AI generated.
We're already seeing the need for real people be taken out of production at some extent,
so it makes perfect sense to me that they would try to do that as well.
Agreed.
I think it's a bit of a tangent, but it really, really puts a number of creative professionals
when you look at it in this instance between a rock and a hard place because if synthetic,
you talk about blurring those synthetic people created in Unreal Engine, which is a gaming
engine, traditionally a gaming engine, getting powerful enough to potentially be used in
movies, but if you can create synthetic people, it's like, well, come in and get a day's
worth of work and let me scan you or don't and I'll just have synthetic people in the
background.
So, Krums, that is an industry conundrum that is probably going to be an interesting trend
setter for how other areas of the economy have to deal with the be augmented or be replaced.
There's going to be a bunch of areas of the economy, probably many more areas of the economy
where you can't do this, but in those areas where you can, it doesn't give the people
much bargaining power, does it?
No.
We should all just have careers as refuge collectors and refuse collectors even.
You've got to be a bin man, can't AI that way.
Well unless Optimus and these other robots improve in performance significantly over
the next five or so years, but yeah, I definitely think if I was like 18 now, I think I'd be
like, right, electrician, plumbing, roofing, the hard to automate.
Yeah.
Anyway, we digress.
Let's talk HubSpot for a bit.
HubSpot's ChatSpot, say that after you've had a few drinks at a wedding, Martin, now
learns your writing style for blogs and emails.
So the launch email on this claimed that you could train your ChatSpot account on up to
5,000 words of text from your blog or previous email campaigns and it will use this to get
closer to your brand style when creating new blog or email content.
However, when you played with this, Martin, you didn't quite get that as your experience,
did you?
Not even close.
So the emails where they launched it said 5,000 words and it's 5,000 characters.
And that is a dramatic difference in terms of training input.
So it gives you around 700 to 1200 words that you can input.
So that's basically one blog post, realistically, that you can train it on.
So I was a bit surprised when I came to actually use it.
It's 5,000 characters for email as well.
That's quite a few emails, so you can actually put in several examples.
The blog version needs a bit more work.
Yeah, actually, when you correct the error, it's much more in the context windows of ChatGPT
and the APIs underpinning that, right?
5,000 characters is probably, I don't know, 8,000 tokens, 16,000 tokens sounds a bit more
realistic perhaps.
But won't be that long before we can maybe, but not this tall yet.
I thought what it does when it analyzes, it was quite interesting.
It gives you a paragraph giving you quite a detailed description of the tone of voice,
which I'm assuming what it then does in the back end is injects that into a prompt when
you then ask it to write a blog.
I think you're absolutely right.
I use a Chrome plugin called Prompt Manager.
And in one of the newsletters I'm subscribed to, I saw an example where you paste a load
of text that you've written into ChatGPT with the prompt that you want it to create you.
What does it call it?
Some sort of voice paragraph or something, something like that, where it basically boils
down the one 2,000 words that you give it to a statement designed to mimic your style,
but in a reductionist sense that you can then use every time you want to write something.
So yeah, that would be similar to that, but it's doing it all in the back end.
So you don't have to worry about including that.
Of course, if you've got access to the new custom tool from, it's been so much that we've
covered today, what's it called?
Custom instructions.
You could do that in your custom instructions as well now, couldn't you?
So that would be quite a good use case.
Right.
Let's talk Microsoft's OpenAI features coming into Office AI.
It's been reported that Microsoft are going to charge $30 per user for you to use AI in
Office 365 products.
I've heard a bit of grumbling about this.
Oh, go on, mate.
Because it is a $30 per user per month, right?
Yes.
Which is quite, that is more than I was expecting it to be given that, you know, a standard,
let's take home office user is about, what, 70, 80 quid a year.
The enterprise licenses are not much more than that, are they per person per year?
I don't know.
I think we pay about $10 per user at Biostrata.
Yeah.
So going up from 10 to 40, you know, that's a substantial shift.
Well, because it's an interesting one.
And I saw a lot of grumbles online.
Like the main consensus here was like, that's a bit steep.
There are a bunch of tools floating around the internet right now that are $20 per user,
$30 per user, $50 per user, and they're not going to do all the things that copilot is
going to do.
So that's the first thing I would say.
The second thing is just trying to think about how much time this might save someone.
Because I do appreciate that it seems like a lot.
And if you're a large enterprise and you've suddenly got to pay $30 per user per month
for all your folks, that's going to cost you a lot.
And I saw Microsoft's share price responded in kind because people were projecting out
how much extra revenue this could drive per year for Microsoft.
But if it saves an employee five to 10 hours a month, and your average employee is probably
doing 120 to 140 valuable hours per month if they're working like a normal nine to five
job trying to factor in holidays and stuff like that.
So that would be, you know, less than what 10% of their time being saved, right?
If you're paying your employees $10 an hour, you only need to save three hours a month
to break even.
If you're saving 10 hours a month, then you're getting a return on investment.
So ultimately, I think if you look at it, it'll end up potentially being quite cheap
compared to the value it unlocks.
And I'm absolutely certain this is where Microsoft are thinking and starting to solve some of
the conundrum that no one else has looked to solve for yet, which is how do we monetize
the productivity gains offered by AI?
And I think this is the first attempt at that.
But, and there's a big but, in order for this to work and to realize that investment that
we just talked about, that return on investment, companies are going to need to train their
staff to ensure that they and the organization can get the full benefits of using the systems.
And they're going to have to find ways to encourage adoption because I think adoption
is going to be mixed, especially if we look at the early adopters of Chatchie BT potentially
using it less now in June than they were before.
And it was actually starting to go down.
So $30 per month per user seems expensive.
I think it's actually cheap, but only if you train your team on how to maximize the benefits
of the tool and you can overcome the inertia of actually getting them to use it and change
the ways they work today.
Now it's currently being tested by a select group of beta test companies and the wider
rollout date is still not announced.
This is a pain in the bottom for a lot of organizations as far as I'm concerned, who
are probably sat on the fence around, should I give people to pay Jack GPT account?
Should I buy Jasper or writer or any of these other tools?
Which tools can I trust with my data or not?
I suspect a lot of us are just going, hurry up and sort this out for us, Microsoft, so
that we can just work in an ecosystem that we know well, that's passed all our security
checks, that we trust you with our data already.
So Krams, we may as well trust you with this as well.
So we can just roll this out to our employees.
Because I think a lot of businesses are in that gray at the moment where they need an
enterprise level provider to step up and meet their checklist of requirements.
And I just don't think there is one yet.
Oh no, absolutely there isn't.
And we're not heard anything from Google in this domain either.
So we wait patiently.
Well, we've got notebook, right, which is definitely a very Diet Coke version of this.
I do know that a bunch of people have got access to the Google workspace equivalent,
which basically is a little bit like the one in HubSpot.
It gives you a little button that you can click when you select text and you can rewrite
it, expand it and those types of things.
But if it's based on Google's large language models, which are arguably the worst so far,
I'm not that excited for it.
Whereas Microsoft is powered by GPT-4.
We should also say when you charge $30 a month and everybody starts bashing these, we're
going to have to go off world to find the materials we need to create all the GPUs.
We're going to have to trick some asteroids into circulating the earth and turn them into
like cloud.
I mean, that's not cloud, that's stratosphere and above.
That's a different issue when everybody's using these tools all the time.
The amount of compute power and energy could get kind of insane.
But yeah, hurry up, Microsoft, I think there's a bunch of us that just need you to make our
product selection easy here and just give it to us.
Right, last bit about large language models and assistance.
Inflection AI, who we've talked about before, the developers of Pi have developed an $880
million AI supercomputer, even when GPUs are currently scarce.
They do have a major investor and that major investor is Nvidia.
So that might explain how they got hold of those.
They've raised $1.5 billion so far and have a $4 billion valuation.
So we talked about them on the last podcast and how the tool just all it wants to do is
ask you questions, but definitely we all need to keep an eye on inflection.
And I was having some fun with Pi.
I just tripled my productivity with Pi, Martin, because I realized that if you use Pi in WhatsApp,
you can leave Pi a voice note, it auto transcribes it and then writes back to you in text.
So I now wander around my house, leaving my thoughts as rambling voice notes that then
Pi has to untangle and then come back with a response.
And I've found that excellent tool for brainstorming.
It's kind of cool.
If you haven't played with Pi yet, get it on the WhatsApps.
It's free.
It's not free.
You're the product, but it's free to use.
You don't have to pay and it's fun to play with.
I'm into that.
That is a use case I'm going to go away and do immediately after this recording.
Do you know what the best thing is?
I had no idea it would work.
I left the voice note and went, Pi, I don't suppose you can understand this.
Back came the text a couple of seconds later going, yeah, yeah, yeah, no worries, pal.
What do you want to talk about?
Again, because all it does is ask questions.
Don't leave me.
It said, don't leave me.
If you don't speak to me, I don't have any value.
Answer my next question.
I actually think that voice note feature really changes how you interact with it a lot.
Makes me use it more, definitely.
Right.
Everybody take a deep breath.
We're going to move into image generation briefly now.
ClipDrop, which is the image creation manipulation tool from Stability AI, which we love on the
podcast because it's very cheap, like £5 per user per month and got a load of tools
in it.
They've got a new tool.
It's called Stable Doodle.
And it's really kind of interesting.
You create a sketch of something and then it's used as part of the prompt to drive image
generation.
I've only done a few tests with it, but it does give you a hell of a lot more control
over the image that you get, which of course, when you're trying to learn the art of prompting
mid-journey to get beautiful images, but have some limited control over what it looks like,
this is actually a very interesting feature.
You managed to have a play with this yet, Ian?
I have not.
All right.
Well, get on that.
I think you'll enjoy it.
What's the problem with holidays?
Yeah, there's been...
I was seeing these updates and just trying to carve away a few minutes from the toddler
just to play with something was proving very difficult.
I have been using mid-journey for everything though, just as a...
I've been using mid-journey for all my slide decks I've been putting together recently,
just because I can get some really good images.
And I've got a prompt writer saved in ChatGPT, so I've got a conversation where I've given
it all of the variables and given it loads of example mid-journey prompts and then ChatGPT.
I just give it a concept like Google eating your lunch was one that I gave it.
And it gave me this brilliant prompt that I put into mid-journey and mid-journey gave
me this fantastic illustration that absolutely nailed it.
I need you to share that workflow with me after this call.
And maybe if the listeners are nice to us and they give us some nice feedback online
on the Twitters and the LinkedIn, maybe we'll share it with them as well.
Speaking of mid-journey, we talked a little bit about the zoom out feature of mid-journey,
which is pretty cool on a previous episode.
Now you can pan, which makes sense.
It's kind of the same engine zooming out really, creating contextually relevant additional
image sections around an existing image.
Now you can pan so you can go left, you can go right, you can go up and down, and you
can pan multiple times on the same image.
And if you've got remix mode turned on, you can even change the prompt as you extend the
image.
So as you're extending it, you can give it some guidance on what that extension should
include.
There are some limitations.
The resolution decreases when zooming out.
So that is an issue when you're using some of these tools.
There is also an inability to pan both horizontally and vertically at the same time.
You can go left or you can go up.
You can't go both.
And I think a lot of people are experiencing the possibility for repetition when panning,
which makes sense.
But of course, it's going to create an image that maybe isn't as usable as you were hoping
for.
There's more image generation news.
So Meta has a new image generation model called Chameleon that creates images from text and
can create text from images, so kind of captioning.
The model is super efficient and requires five times less compute power and a smaller
training data set than previous models.
I think one of the most interesting things about it is it can understand instructions
to edit existing images.
So you can edit or remix images and give specific instructions based on objects with specific
coordinates.
So you can ask the system to place objects in specific locations in an image, and you
can edit photos with very specific prompts like change the color.
Now nobody's got access to this as far as I know.
I certainly don't.
So how well it is geared up to do this type of work isn't clear.
But one of the interesting things about it is we've talked on the podcast about some
of the limitations of existing tools that basically you can create great images, but
then you've got to push them into Photoshop if you want to do anything with them.
This will bring an extension of capabilities eventually one assumes that you can edit images
through natural language prompts instead.
Meta's not announced whether or not it will plan to release Chameleon.
It's certainly not released yet, but we're starting to see ever more innovation in the
generative image space as well as the generative text space.
You can well imagine that being integrated into their advertising suite, can't you?
Then advertisers, you might search for a stock image.
It will pull in a stock image and then you can just ask it to change in a certain way,
changing the color of someone's top or changing the scene to a wintery scene or something
like that.
If that can be fully integrated into the advertising workflow, I think marketers have got an interesting
new tool to play with there.
That's definitely where I would expect Meta to go.
I completely agree.
Right.
We've been speaking for quite a long time now, Mart, and I think we'll give the listeners
a little break.
What we'll do, there's still some more news to go.
Because we've not been around for a couple of weeks, we'll hold onto a little bit of
this news and we'll summarize it in Friday's podcast, which we'll be back doing our weekly
podcast again from here on out.
Obviously, Martin's going to be in the US doing some shiny exciting stuff, so we might
have to do a little bit of jiggery-pokery on our schedules, but we'll be back weekly
from here sharing more amazing marketing AI news with you.
Sounds great.
It's been a pleasure to be back.
I've enjoyed chatting with you.
I love the fact there was so much news we couldn't squeeze it into one podcast and we'll
have to somehow catch up in the next one.
Yeah.
And I'm just looking at how much there is left to go.
I think that's going to run long as well.
Listeners, thank you for sticking with us.
Yeah, we appreciate it.
Everybody enjoy your weeks.
Thanks for your time today and look forward to the next episode of Artificially Intelligent
Marketing.
Bye, Martin.
Bye.
Thank you for listening to Artificially Intelligent Marketing.
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