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 everybody.
Welcome to episode 35 of Artificially Intelligent Marketing.
Paul Avery here, guiding you through everything you need to know in the world of AI tech and
marketing and I am joined by the smartest AI person on the planet, my co-host, Martin
Broadhurst.
How are you, Martin?
I'm delighted to be back in the studio with you.
After your break, it feels like it's been forever since we've sat down and discussed
things AI related.
A lot has happened and the fact that we've not had a chance to discuss it is quite frankly
shocking.
To be honest, I thought you'd abandoned me and just got sick and tired of my voice.
So in the intervening weeks while you've been away, I've put a lot of hard effort into completely
reworking my voice.
So much so that now...
I am in fact a confident older woman, described by 11lubs as Cecile, who is confident and
strict.
Just the way you like it, Paul.
Yeah, I'm not sure I can tell much of a difference honestly.
No, I'm only joking.
I'm only joking, Martin.
That tool is fairly interesting, isn't it?
That like, re-voicer tool.
So clearly you've been having a good play with that.
Yeah, 11lubs speech synthesis.
So previously it was a text to speech tool.
So you typed in the text and you got an audio version of what you typed in.
But now you can just say the words and it will turn that speech with your intonation
exactly as you said it into the AI generated voice.
It's really quite impressive.
Yeah, that's crazy.
I can't wait until I can basically speak MB Darth Vader.
That'd be pretty cool.
That can't be far away.
Especially given I've seen...
I don't know if you've seen on the Twittersphere the example where someone's using the APIs
of GPT-4 Vision and 11lubs to basically have David Attenborough narrate on their life,
which is quite interesting.
So obviously that's a voice clone of a specific person.
I should state 11lubs doesn't offer David Attenborough as a voice.
He was flirting with IP and copyright laws by taking a sample of David Attenborough's
voice and using it to train the voice they wanted.
And I should also say for you dear listener, it would be very cool if Martin was able to
speak into the system and it was able to sort of change his voice in real time.
But of course we did that in the edit because Martin recorded it separately.
But it's still a very impressive tool nonetheless.
There is so much to get through today, Martin.
Shall we jump on into the first story?
Yeah, this one feels very timely.
ChatGPT, everybody's favourite chatbot, has turned one.
On 30th of November ChatGPT received its first birthday cake with one little candle on it.
It's been quite the year.
So just to give you a few headlines from the past 12 months, Paul, because I know that
you've not really been paying attention, have you really?
It's just kind of passed you by, I'm sure.
What was that thing?
What was it?
What do you call it?
Chat what?
GPT, it's from a company called OpenAI.
Keep your eye out.
You'll see them in the press.
Cool, cool.
So they reached 100 million users in two months after its launch in November 2022, which just
for comparison, Facebook took four years to reach 100 million users.
Snapchat, MySpace took three years.
Instagram, two years.
And Google took almost a year to reach 100 million users.
So its rise was pretty phenomenal, achieving that in just two months.
What's quite interesting when you look back at November 30th, 2022, is that they originally
released it as a free research preview.
And because all of the OpenAI team had been playing around with this tech for so long,
they'd become a little bit blasé to the fact that being able to do it back and forth,
with meditation, with memory and all of the stuff that we know ChatGPT has compared to
just the old Instruct model, they'd kind of taken it for granted and hadn't thought about
the absolute sensation that it was going to be.
So it really caught them by surprise.
So it launched in November.
ChatGPT Plus was announced in February.
Shortly after that, a week later, Microsoft announced that ChatGPT would be powering some
features in Bing.
In March, we saw the launch of, or the announcement of, GPT-4.
We didn't get vision until a few months later, of course.
Our first major ChatGPT outage was on the 20th of March, earlier this year.
I think that was the first time I realised just how dependent I was on it to get through
the day.
And then the following day, Google launched Bard.
Coincidence?
It's a conspiracy there, Paul.
It's not for us to say, Martin, but yes, please, go dig deep listeners.
At the end of March, Italy banned ChatGPT.
A month later, they allowed service to resume.
In May, they launched the iOS app, which as an Android user was a great frustration because
I didn't get it until the end of July.
What else happened in that time?
Oh yeah, so there was the Senate subcommittee hearing on AI oversight because the sensation
that was ChatGPT had caused a lot of people to be scared about the potential of AI.
So Sam Altman was hauled in front of the US Senate to explain the existential threat that
we all face.
In November, OpenAI announced the new voice and image capabilities of ChatGPT.
And then we had OpenAI Dev Day, where they announced GPT for Turbo, Assistance API and
Dolly 3 API.
Oh, and then a couple of weeks ago, there was that small issue of Sam Altman being fired
as CEO of OpenAI.
What a way to spend your birthday.
Happy birthday to you.
Yeah, it's been quite a year.
It's quite amazing when you think back and how quickly it developed.
Ethan Molek, as the regular listeners to the podcast will know, one of our fans of Ethan's
work, spends a lot of time tracking what's going on with different AI tools, but especially
OpenAI's tools.
And he was sort of reminiscing on how amazing GPT-3 was when it came out compared to GPT-2.
And then of course, the step change again that came with GPT-4.
And it's very easy to forget how much progress has been made in the year.
I keep seeing online statements like this is what exponential change feels like.
Ironically, I don't personally feel the speed of exponential change when it comes to ChatGPT
and text-based chatbots at this point.
If I'm really honest, it feels more incremental.
I'm mostly feeling the speed of exponential change in video, which we'll probably talk
a bit about later because that feels like there's some massive leaps being made there.
So I don't know if we made our leaps this year with text-based sort of bots.
And there's some significant problems still to solve, aren't there, around hallucinations
and multi-step agents that can actually deliver on those multi-step tasks.
I guess if we do see significant improvements in either of those areas next year, that will
probably be another pretty big leap, wouldn't it?
It would.
And I think we're all expecting some interesting developments from OpenAI and their next models,
particularly after Sam Altman was reinstated as CEO this week and he gave an interview
to The Verge where they touched on some of the reasons why he might have been fired and
some of the potential new technologies.
What have you read about that?
Yeah, so we had a bit of a what's up back and forward on this one, didn't we?
I think there's a number of different aspects.
One of the stories you shared with me, I think, will end up being true, which is it's actually
quite simple sort of tension between the not-for-profit side of OpenAI and the profit side for OpenAI
and what the balance is there and how Sam Altman is leading the organization against
those.
The one that was maybe a bit more intriguing was Q-Star, which if you're an AI nerd, your
Twitter and LinkedIn feed would have been full of this because the speculation is and
was super rife the minute it came out.
And this is born out of a letter sent by a couple of OpenAI employees to, I think it
was Forbes, detailing that the major falling out was because of a spectacular new improvement
in one of the models that they've been working on internally at OpenAI, which would allow
either ChatGPT or some other similar tool to basically reason more logically and perform
things like mathematical reasoning, which on the face of it, especially the example
that was given is that it could do maths at a grade school level, which doesn't sound
that impressive.
But when you think about how these tools work, they don't reason at all at the moment.
They just predict what should be the next word they should output based on all the other
words that came before and all the words in the context of the prompt.
So to be able to actually do mathematical reasoning gives them a better understanding
of the world and therefore a better ability to plan and take actions, which of course,
if you want multi-step agents, which is where some of the real power is going to lie in
these tools, I think, then you do need that.
There's been so much speculation that that's actually all a bunch of baloney.
And but what was interesting is that in the most recent interview by Sam Altman, when
that was brought up, he described it as an unfortunate league, but he didn't deny it.
Did he not deny it because he needs to buzz around OpenAI and are they doing a stock offering
or something?
They've got something coming up, haven't they?
Financially motivated where elevating the company's value as high as possible is not
necessarily important for him because he's not a direct investor, but it will be important
for the staff.
Is he keeping the buzz around this Q star high so that they can get the highest valuation
of that neck as part of that process?
Or is there, and it's not true, but he just wants us all to think it's true, which is
why he didn't deny it, or is it true and that's why he didn't deny it?
And the answer, dear listeners, we do not know.
He's a great salesman, isn't he?
And that's one of the things that he's really well known for.
He can get people to buy into him and basically throw money at him.
That's one thing that he's exceptionally good at, as evidenced by the fact that Microsoft
chipped 10 billion at OpenAI a few years ago.
So yeah, I think maybe there's a little bit of gamesmanship here, keep the story going,
but the fact that he didn't just outright deny it, whereas in the past things like GPT-5,
for instance, it wasn't that long ago where he spoke about, no, we're not working on GPT-5.
He did cut discussions of that down.
He has subsequently said they are working on those models now, but yeah, it's interesting.
Yeah, that's the thing with Sam and Imad Mostak as well as Stability AI and a number of the
CEOs of these companies, they have to fuel the hype train because that's where the valuations
come from.
So it ends up being really, really hard to try and sort out the signal from the noise
in what they say.
Because I'm a bit of a tech optimist, I really end up hoping that some of the things that
come out in terms of the improvements that are being made end up being real because I
think it makes the tools more powerful for us.
And personally, I hope for a world where all of the efficiency gains provided by AI basically
are used to make life better for everyone.
Maybe people can work less, three-day weeks enabled by AI, for example.
So I'm really excited for all of these technological things to be real, but at the back of my
head, the little alarms always going, yeah, but it does serve their purpose in terms of
the valuations of their companies to keep that hype train going.
So you can't really tell.
In terms of the first birthday of ChatGPT, I'm wondering, since GPT-4 Turbo was rolled
out across the platform, have you noticed any performance difference?
And the reason I ask this is because, and we may have discussed it on the previous episode,
but this week I saw a discussion on Twitter that was talking specifically about its coding
capabilities and how it seems to have been clipped in terms of its ability to write long
pieces of code.
It can work with small snippets, but it previously could write really quite extensive bits of
script and that seems to have been, it's as if it's had its wings clipped in that domain.
I've certainly encountered this.
Yesterday, I was trying to get it to do some relatively simple calculations.
I asked it to do it using Code Interpreter and it continuously failed.
It failed about six times and in the end I just couldn't get it to work.
Whereas I feel like a few weeks ago or maybe pre-GPT-4 Turbo, that wouldn't have been the
case.
Have you found anything similar?
The short answer is yes.
So I'm using Dually a lot at the moment and at the moment I'm struggling to get photorealistic
images out of it.
So I don't know if they've dialed that down or I'm not pushing and prompting hard enough,
but the photorealistic images I'm getting now versus when it first came out are worse
and they're making me think, oh, I'm going to have to reactivate my mid-journey subscription.
So I'm definitely seeing that.
I was on a webinar yesterday presenting about AI and sales and a good friend of mine, Nick
Clare over at SuccessionBio, shout out to Nick and Harrison, was talking about an application
he was working on where in the end he couldn't get GPT-4 to do it.
So he switched to GPT-3.5 and got a better outcome in terms of the output, which I think
is quite interesting.
The GPTs as a slight aside that I've been building, I haven't been super impressed with.
They're kind of like a slightly glossier version of GPT-4.
I would hazard think I could get the same out with just a half decent prompt in GPT-4.
I'm not playing with anything super sophisticated like trying to use data connectors or anything.
The tests I've run on that have been extremely glitchy and unreliable anyway, to be honest.
So yeah, they're tweaking the models.
One of the things I haven't had a chance to test yet is if we're on GPT-4 Turbo now, have
you been leveraging the context window?
Has that rolled down?
The context window has definitely expanded.
Yeah, without doubt.
I actually haven't hit the limit with it yet.
And I've been throwing in some decent sized text that absolutely categorically would have
hit the limits previously.
I haven't pushed the boundaries though.
In fact, I find it incredibly hard to max out a context window at 128,000 tokens or
even...
It's quite hard to put in a prompt that's even 50,000 tokens.
Yeah.
So my go-to test, which is not proving useful, is to just keep asking ChatGPT what its context
window is and it just keeps telling me 2,000 tokens.
So obviously I'm not pushing it right.
I haven't tested it because of our next story, which is the release of Claude 2.1.
So regular listeners to the podcast will know Claude is a model that's released by the company
Anthropic, which has a bunch of ex-OpenAI folks within it.
And it's another chatbot like ChatGPT.
But the difference always with Claude was its context window was massive compared to
ChatGPT's context window, which is now itself massive.
So Claude needed to one-up the introduction of the 128K context window for ChatGPT.
And with their 200,000 token context window, which you now get in Claude 2.1.
So obviously you can upload PDFs, multiple PDFs, the equivalent to over 500 pages of
text and start asking questions of the text, ask for summaries of the text, et cetera,
et cetera, which is massively beneficial for processing documents like entire code bases
or massive financial reports or anything else you can think of that's a big document that
you'd want to interrogate really.
The other thing that they've done with Claude 2.1, Martin, is they've introduced this reduction
in hallucinations.
They claim that you get a two times decrease in false statements, which of course is important
for all of us because when you're interrogating large documents, you need to be able to trust
that the outputs you're going to get, you can rely on.
A lot of that's being driven by Claude saying that it's not willing to answer.
But honestly, I'd rather have that than inaccuracies and untruths.
I think that really helps.
They've also done some interesting stuff for developers, haven't they, Martin?
Do you know what they've done with it?
They've done some stuff with the console and changed how developers interact with it.
Yeah.
So if you are using it through the API, you've got an API and developer account, they've
introduced something called Workbench, which is effectively like OpenAI's playground where
you have a bit more control over the system.
And actually one of the things that they've introduced is the ability to system prompt.
So in the same way that you can with ChatGPT and Assistants where you can say, this is
your role and this is how you will respond and for the whole of this conversation, these
are your rules, basically.
You can now do that within the Workbench.
They've also extended its functionality, basically giving it function calling.
So they're enabling it to call upon APIs from external apps.
All of this at the moment still requires developers to request API access.
So you might be able to get a developer account, but you'll be limited on the number of API
calls that you can make.
And if you want to actually roll this out into production, you actually have to request
that from Anthropic for them to de-restrict your account.
But yeah, some definite advancements for developers.
And I've been playing around with these tools a bit.
Going back to my previous point, it's really hard to fill a context window.
200,000 tokens is a lot.
I went back looking through my API usage over the past month and in early November, there
was one day where I had a really heavy usage day.
I think I was working with quite a lot of transcripts.
It might've even been podcast episode transcripts.
Within one conversation, I was throwing in lots and lots of transcripts.
Bear in mind, each episode is about an hour's worth of conversation, right?
And you can see how many input tokens and how many completion tokens you get.
So you can see that in your API log.
On that day where I was going back and forth, back and forth, back and forth, back and forth,
at no point did my input tokens go above 90,000.
It's a huge amount of text to put in 200,000 tokens in a prompt.
It's a lot to get there.
I think you're right.
I think it's an impressive technical feat, but we're probably at 100,000 and certainly
200,000 tokens for a lot of, for like 98% of use cases, we're probably tapping out at
terms of what would you really need it for?
Unlike you, I use it a lot for transcripts.
And what was interesting, so listeners will know I use a tool called Magi, M-A-G-A-I.
The website is magi.co because I don't trust putting sensitive data into Claw directly
or ChatGPT directly, but when you're using the API, you can have a bit more confidence.
And Magi now has access to Claw 2.1.
The transcript summaries are, I feel, significantly better in 2.1 than 2.
The things that it's missing, it's just not missing them as much and its summaries are
really spot on.
And I gave it an incredibly hard task yesterday.
I recorded an onboarding video for a member of my team and then I wanted to summarize
it in an email.
So I pulled the transcript out and dropped it into Clawed via Magi.
And I got a summary and all of that was great.
During that handover meeting, I'm describing what I think the kickoff call for the client
should contain, but in an extremely sporadic fashion.
Like it's a 30 minute intro onboarding call.
And at one point I'll be like, oh, we should put that in the kickoff call.
And then I talk for a bit more and then I'm like, and in the kickoff call, we should do
this.
So I asked Clawed to build the agenda for the kickoff call by trying to like pull some
reason out of my ramblings.
And it did a surprisingly good job.
It included some things that it thought the kickoff call should have based on its understanding
of the transcript.
So in other words, it didn't just focus on the things I specifically called out, but
it did include all the things I specifically called out.
And I can't help but feel Clawed 2.0 wasn't as, wasn't strong enough to do that.
Like it would struggle just accurately summarizing a transcript at times for me.
So yeah, I've got a bit of love.
I know you've always had a bit of Clawed love in your life, Martin, but I've got a bit of
Clawed 2.1 love going on, if I'm honest.
And with that API access, because now it's going to be able to call databases, it's going
to be able to search the web with web search APIs.
I think this could be an interesting time for Clawed.
Oh, it's a pleasure to have you on board.
Oh, and team Clawed.
I need to get my, I would use Daweley3 to create a photorealistic image of a person
wearing a t-shirt that says I'm team Clawed, but it can't do photorealistic images for
me anymore, so I won't bother.
But it can do text.
Just give me one image generation tool that could do all the things I want.
Mid-Journey, Daweley3, go out for a drink, see if you get on well, because I think your
babies would be outstanding tools for us all to use.
With that image that nobody needed, should we move on to our next story, Martin?
Yeah, so this was a story that caught a lot of buzz on the Twitters, and it's all about
an AI enabled SEO heist.
Someone called Jake Ward said that they had effectively managed to use AI to steal 3.6
million hits by generating 1800 articles in a process where they basically scraped a competitor's
website and then used AI to spin that content programmatically.
They automated the entire process.
It took them a few hours rather than many weeks, and then they published all of these
articles online.
Interesting, you know, 1800 articles and the traffic, he shared it in a tweet.
The graph is just going up and to the right in terms of website visitors.
And it caused, understandably, a little bit of controversy because this was self-described
by Jake Ward as theft, right?
He managed to steal the traffic.
So lots of people were saying, you know, is this fair?
Is this something that Google should be clamping down on?
I think Google's helpful content update recently will probably see companies getting away with
this less and less.
But yeah, certainly some ethical questions here, like where are the boundaries?
Just because you can do it, should you do it?
That's a great question, but unfortunately, you're back to the 1% because 99% of people
might say, hmm, that's too black cat for me.
I'm not in on that.
But it only takes the 1% to do it.
And the web is full of duplicate junk that is the equivalent of content farms of old.
And it makes the web a much harder place to go find relevant information.
And we've talked about this quite a lot, right, in terms of how does SEO change, not only
as search experience changes with Bing and Google basically using generative AI to create
written summaries in the search output.
So you may never click on the link to go to someone's page, but also just junk.
There is a, I think, what's the guy's name?
I think it's Doug Kessler.
There was a seminal sort of ebook presentation thing he did in maybe 10, 12 years ago called
Crap.
And it was the deluge of crap content in a world where all brands want to be publishers
as well.
And I think to a certain extent, it was right.
I do think it was a deluge of crap.
And we were lucky in that Google got better at sorting the crap from the good.
So it surfaced the best content for us as part of its search engine results.
But also over time, you started to learn which content brands you could really trust that
would give you good content.
I think Moz did a great job of this, Content Marketing Institute, et cetera, in our marketing
land.
There are certainly companies in the life sciences that have done a good job of it as
well.
But I think this just further goes to show SEO is going to change.
If you're relying a lot on content on your website to drive a lot of traffic, you need
to start to be planning what you would do if you lost a load of that traffic, either
because you got content hacked, like this example, or because people don't click on
as many links in Google search anymore and don't come to your website.
So how are you going to use podcasts?
How are you going to use video?
How are you going to use trends, reports, and primary research data that no one else
has access to and only you can share that insight with the world?
How are you going to lean into subject matter experts who know things about your industry
that no one else knows?
That is going to become ever more critical, I think, mine.
It is.
And I think for content creators yourself, everybody's going to have to start asking
themselves a question about what are their own ethical boundaries?
How far are you prepared to go?
Because yeah, you will see results by completely ripping off other people's content and making
it your own.
In fact, when GPTs were announced by OpenAI recently, if you went on YouTube and searched
how to create a GPT, there were loads of videos of people saying, basically, find a publisher
that you like their content, download it, rip it off and upload it as if it's your own,
and now you've just created an expert bot with subject matter expertise about that thing
that that other publisher wrote about and you can pass it off as your own.
And this was rife.
And we all have to say, well, where are our boundaries?
I'm nervous about how that will play out because I think the 1% will cause carnage and it will
get draconian changes by search engines like Google that penalise people who are actually
producing valuable content potentially.
And copyright laws and infringements and let's imagine that the entire GPT infrastructure
becomes really valuable.
I'm not sure it is at the moment, but let's say it did.
And people are then doing this, then OpenAI will have to take the entire GPT marketplace
down while they figure out how to navigate whatever legal and copyright issues they have
to solve for, which will mean that a bunch of useful tools that people rely on, they
can no longer access, but again, because the 1% are trying to think about clever, dare
I say it, slightly underhand ways to make a quick buck.
And I think that will happen because the history of humanity, unfortunately, does suggest it
will happen.
Yeah.
That's a slightly negative note.
Let's talk about something positive and super cool.
Let's talk about Hey Gen, because Hey Gen, we really love Hey Gen, don't we, Martin?
So regular listeners to the podcast, you'll probably have heard us talk about Hey Gen
before because it's an AI video startup that basically is extremely good at AI-modified
video.
So in other words, what you can do is you can upload a video of yourself speaking.
I think we did this on a recent podcast episode with you, didn't we, Martin?
You upload a video of yourself speaking and it will overdub your voice in another language
and then it will edit the video so that your mouth moves and lip syncs with the new translated
voice.
So we've done it in German, French, Spanish, and it's impressive.
Well, they've just raised a load more money, 5.6 million, and launched their new near instant
custom avatars.
And why this is important is because previously, if you wanted to use Hey Gen to create like
a synthetic version of yourself, either for translations or to write scripts and then
have the synthetic version of you speak your script so you didn't have to record the video
yourself, it was a fairly laborious process of recording five minutes of video in borderline
professional studio environment.
But they've been able to tweak their algorithm now so that it's now able to do this with
far less production level video.
They claim you can record yourself on a mobile phone and still get a reasonably good result,
for example.
And Martin and I have been like wanting to play with this, but just haven't had the resources
to go record ourselves in a professional studio environment.
And of course, now we and all of you, dear listeners, can jump in and go have a play
with that as well.
And it's really impressive because when you watch the synthetic version of you talk, it's
you, it follows the same mannerisms and movements that you have, and it really does deep fake
your mouth movements surprisingly well.
And you can certainly imagine, Martin, a world where you can add videos to your site that
where you write the script and then Hey Jen creates this synthetic version of you that
looks just like you, as a great use case.
And as I was talking about with a bunch of people yesterday, sending out prospecting
sales emails with custom sales videos at scale, because GPT-4 will write the scripts and then
Hey Jen will turn the scripts into videos of you speaking.
So Hey Jen, we've been like excited about them for a while, but I think this sort of
then, then now their technological change here, I think is going to open this up and
make it much wider for a lot more people to start playing with it.
What are your thoughts, Martin?
Very much so.
The interface is so simple.
If I encourage anyone that's not tried it yet, just go over to the Hey Jen website and
give it a go.
There's no, there's no complication to it.
It's remarkably simple.
I recorded a video of myself in front of my webcam and translated that into French and
it's brilliant.
I did it in front of a conference this week and people there described it as a wow moment
as they saw my English video immediately turned into French and I sent that video to my French
neighbour and he was blown away.
It sounds like you, it looks like you, it's incredible.
I don't know if you have played around with many TikTok filters.
Have you given them a go?
I'm not a big TikTok user, I have to admit.
They are incredible.
Right.
So some of it, there's like a beautify one, which will make you younger or make you dolled
up, give you a glow up, what have you.
I wondered why you were looking so good.
Yeah, they are impressive.
These filters are really impressive because actually if you put your hand in front of
your face, like when, like I've got a virtual background now and it's doing a pretty good
job of not showing my background.
If you put your hand in front of your face, the image stays the same.
There's no like, yeah, there's no glitchiness there.
It just does a really good job and HeyGen seems to have the same capability that really
reduces the glitchiness.
And it was interesting to me that I saw the founders come from a background at Snap and
TikTok.
So they're both from the video AR augmented reality VR landscape, which explains why they've
got probably such a good tech stack that enables them to do what they're doing.
Yeah, that's really interesting because obviously the market for those types of TikTok filters
is massive because basically all TikTok users, but the business applications that we just
went through are not insignificant and probably an easier place to make money, dare I say.
So it seems like a really smart move from them.
And I guess the downside of this would be what stops someone from taking a video of
me from like a webinar and then using that to train the model and the signing the consent
statement on my behalf and then starting producing videos of me saying things I shouldn't be
saying.
I mean, it's the deep fake discussion, right?
We had this deep fake discussion when Obama was president.
This is something that has been in the landscape for a long time.
It's just that these tools are becoming kind of commercially accessible at a consumer level.
You don't have to be techie to train the model.
You can spend like $25 a month and do it yourself.
Yeah, I do think that is the critical piece.
They are impressive.
I threw in a very difficult video from the podcast where I moved a lot while I was talking
and I turn my head around like to the side and it did a surprisingly good job of making
the lip sync to mouth movements still look accurate.
You can tell the video I gave it wasn't particularly well laid either.
So I think you can tell.
But on the best examples I've seen, it's really hard to tell.
So if you're a marketer or if you're a BD person, you listen to this rev ops maybe,
how could you use this technology to drive efficiencies and new ways of working, new
ways of adding value, hopefully for customers, not just spamming them using technologies
like these?
Because I think 2023 was the year of sort of chat GPT and text generation.
But I really think 2024 is going to be the year of synthetic audio and synthetic video.
And I think we're going to see crazy pace in this area.
So as marketers, I really think we need to be thinking about how does this influence
what we do, how we work and all that good stuff.
The big thing here is about latency.
So with the translation app on Hey Gen, you have to upload the video, give it the target
language that you want it translating in, and it does some processing in the background,
it takes a few minutes and you get an email notification telling you that it's been done.
When that time to translate is reduced from minutes to milliseconds, that is where we're
going to see some serious business applications, which leads us neatly into our next story,
Stable Diffusion XL Turbo, the launch of real time AI image generation.
So Stability AI has unveiled its latest breakthrough, which is a turbo model of Stable Diffusion
XL.
And it's very impressive.
You can just type in what you want and rather than hitting enter, and it generates the image
while you are typing, the image is generating before your eyes.
It's generating in real time.
So Stable Diffusion XL Turbo's key advancement is its single step image output capability,
which is significantly more efficient than the 20 to 50 steps required by its predecessor.
So what happened previously was you would put in your prompt and then it would run through
between 20 and 50 steps.
So it would basically diffuse the image 20 or 50 times, one after another sequentially.
Now it does this in a single step.
So this leap in the technology is attributed to a technique called adversarial diffusion
distillation, ADD, which refines the model's ability to distinguish between real and generated
images, and therefore enhances the realism and the output.
So yeah, it's pretty cool.
It's available on, now I've forgotten the name of the website Paul, remind me.
Oh, is it, is it CREA?
K-R-E-A?
Yes.
It's available to try online.
The demos are very cool.
However, one thing to note for anyone that does want to go about using this is that it's
available under a non-commercial research license and Stability AI is currently expressing
interest in exploring commercial applications.
You can see this having a bunch of applications for storytelling apps, game development, any
kind of creative interactive user interface.
Yeah, I think it's very cool.
What did you think of the demos?
Yeah.
So to be fair, I don't know for sure that the CREA app uses it.
I mean, it touts it as a sort of feature, is this sort of near real-time image generation.
So I don't know of any other model that enables this.
So I assume there's some sort of underlying partnership there, but that is an assumption.
I think it's a really good example of how the technology advances in ways that we don't
expect.
So one of the things we've all looked at on the surface as marketers is our ability to
generate images and that the images will be of high quality.
But of course, the researchers in the background are realizing that these images take a while
to develop.
And so they're looking to optimize how the models work.
So firstly, when you're playing with the demo, it's quite fun because your ability to influence
the image output in real time is interesting.
And you do that, at least in CREA, by you have an original prompt that creates your
image and that's on the right-hand side.
And on the left-hand side, you can use like pen and shape tools to influence the image
with different colors and different sort of components.
So the example when you boot up is a, I think it's like a blue square and then it's got
a smaller pink square on top of it.
And the prompt is, I think, a frog sat on top of a blue mushroom or something like that.
And the squares directly dictate where in the image those things should actually be.
And of course, you can move the squares around and the image updates in near real time.
So it does give you much more control over how the image is being generated.
I think the other thing to reflect on here is near real time image generation is a big
step up from what we've had so far, where it might take five or 10 seconds to get your
image, which is like, what, 10, 100 times faster.
The next step from here is real time image generation at video level.
Right.
Once we get down to 24 images a second instead of one image a second, we're now creating
video.
So you can imagine, and I've seen a few examples online, not quite 24 frames a second, but
moving image, the equivalent of like low frame rate video of people generating images in
real time to create videos.
So you mentioned game development, but also there's so many applications.
Like I've heard described, and I don't know how likely this is going to be, but the equivalent
of having Netflix create an animated movie for you in real time based on feedback that
you're given how you want the story to develop.
Well, this is the type of technological change that we would need to enable those types of
things.
So I think there's implications today for how marketers are creating images and the
level of control they want and the speed of being able to influence the image, iterate,
iterate, iterate, but also those future ramifications of what does this mean for creating video
on demand and shaping that video in near real time.
That would be kind of cool.
So let's get on to our next story then, Martin.
It's in the video realm as well.
It's from Pika Labs, which blew up on the Twittersphere this week.
They've raised a ton of cash, $55 million, and they are chucking themselves right in
the mix to compete with the likes of Runway and Stability AI that we just talked about.
Stability AI has its text to video generator now that you can play with on a variety of,
I think, hugging face models that you can go play with for free and Runway ML we've
talked about a lot on the podcast has its cool video generation features, not least
the motion brush that we've talked about recently where you draw on a bit of an image and only
that bit of the image animates.
What's different about Pika 1.0 is that its text to video looks extremely impressive in
the demo video, like really high quality video.
There's an example, I think, where it might be Elon Musk going up into space or something
similar in a cartoon style and it looks like, I don't know, like a Pixar animation.
It doesn't look kind of like weird diffusion effects or a bit crappy like a lot of the
other examples that we've seen.
They already have a bunch of users, but it really sounds like this next leap in their
technology is going to be significant.
I'm on the waiting list.
I think they're starting to open up the waiting list on Monday, Martin.
So we shall see what it does when we can get access to it.
So yeah, if you haven't checked it out, I think it's worth getting on the Twitterspheres
or searching Google for Pika and just watch that demo video because if they can even do
three quarters of what that demo video suggests, it's going to be significantly impressive.
I think you can even like update your prompt to say, no, no, I wanted the robot in my animation
to do this instead of this and then it will change it for you.
So I guess the caveat here, Martin, based on previous experiences, demo videos that
we see from the companies are usually rather awesome.
And then when you play with it yourself, you struggle to get the same sort of output.
Have you had a look at the Pika video?
What have your thoughts been?
I wholeheartedly agree.
It's a demo video that looks fantastic, but I've seen demo videos that look fantastic
previously and I've not been able to generate anything even close to them.
So we will wait with bated breath to see whether or not it's actually as easy to get outputs
of the quality that they're generating.
I still can't get a runway to give me anything half decent.
I was playing with Motion Brush last week trying to make some very small edits.
I was trying to make somebody just, I thought maybe I could just get somebody to smile.
It was a portrait image and their head just kind of floats up off the screen.
So it's very good for getting things like clouds to move in the sky or water moving.
But yeah, the Motion Brush tends to, when you set it in a direction, it keeps things
going so people's heads just morph off the video entirely.
So we'll see how easy it is to use Pika compared to the demo videos.
I'm sure these technologies, well, they are the worst they're ever going to be right now.
They're only going to get better from here.
So the fact that somebody can make them do cool things like this suggests that in the
future they'll get it to the point where we can all make cool things like their demo video.
Yeah, I mean, I have to assume, one of the things I think about in this area a lot is
if we've got a tool like GPT-4 Vision, which can analyze certainly frames in videos and
images themselves and describe what's going on, why don't we have a dually three level
video generator that's creating a video that's being checked in real time by GPT-4 Vision
to make sure that it's not junk and doing some weird stuff like someone's head floating
away that's clearly not going to be the intention from the prompt and therefore able to course
correct its own output.
There doesn't seem to be, at least in my understanding of how all the models work, that much recursiveness
in how they produce stuff at the moment.
And it just seems to the completely untrained person that I am that that would be a really
powerful way to improve the outputs.
I don't know if that's how a lot of these improvements that we're seeing are being made.
I don't know if that's the type of thing that they're trying to bake in, but it does seem
that's my hope is that you almost have multiple models working on a project together where
they have different skills and then they can just like one feeds back to the other to go
no, I think you might have drifted in the wrong direction here.
Exactly as we've just described in the stable diffusion XL turbo where they've got the adversarial
diffusion distillation.
Right.
That's exactly as you've just described and they don't have that in the video realm yet
at all.
So it won't be long.
I'm sure they'll, they'll borrow these techniques soon enough.
You're right.
Should we talk music for a bit?
Yeah.
Going back to, to stability AI, they've announced some updates to stable audio.
And since we last discussed stable audio on the podcast, it has been celebrated as one
of Time magazine's best inventions of 2023.
Also stable audio for those who missed the previous discussion about it is a text to
audio generator.
So you can describe the audio style, the rhythm, the BPM, all of that kind of stuff.
And then it will generate an audio track and you can use these tracks commercially in your
own projects.
But they've now pushed the first real big set of updates for the product since it was
launched, including input audio.
So you can now actually, in the same way that you can have a seed image for a text to image,
you can provide seed audio.
So tracks that you've previously generated, you can input that and say, use this as the
kind of starting point, which allows for more nuanced and personalized edits building upon
existing creations.
There's now a new set of editing parameters.
So you can have control over the seed, the number of steps.
So as we spoke about with stable diffusion, it goes through a number of steps behind the
scenes to create the output.
The more steps you allow the AI to run through, generally speaking, the better quality the
output.
So if you increase the number of steps, you'll get nicer quality audio closer to what you
asked for in the prompt.
You can also increase the prompt strength.
So how closely it sticks to what you've put in the prompt and also adjusting the number
of generations as well.
So it will give you more than just one generation from an input.
There's some improvements to collaboration.
So you can now add share links, just allowing you to send your creations to other people
for distribution and collaboration.
Another neat feature is the downloader's video as well.
So you can export your audio as a video file.
And there is a prompt library, which for someone like me, who doesn't have any kind of musical
creation, I'm not a musician at all.
I don't have a single musical bone in my body.
That can be really helpful because sometimes I might broadly know what kind of audio I'm
looking for, but I can't really articulate it.
I'm not technical.
I've never done music production.
So having a prompt library there is useful.
This is quite cool.
Sometimes when I talk about generative AI for music, when I'm out and about, I sometimes
wonder how is this most applicable for marketers?
And the way I think about a lot of this stuff is there's a lot of decent stock websites
out and about like Audio Jungle and Theme Forest.
But I think if you want your images to stand out and not just look like the stock photos
that everyone else is using, that's where a tool like Mid Journey or Dudley3 comes in,
because you can really generate something that is unique for you.
And I think this is the case for music as well.
If you want little intro jingles for your branded videos and you don't want to use
something that you know could be used by someone else because you've just bought it for $15
on Audio Jungle, well, this is a great way to do that.
And the more powerful they get, the more control we're going to have over getting just the
right output that we want.
With all these things, generative AI is about getting it approximately what you are asking
for, not exactly what you're asking for.
And I think these tweaks to how the models work and the parameters you can change is
definitely going to give people more control.
But of course, in terms of absolute five control of a music producer, of course, not quite
there yet.
But yeah, I think it's amazing to see all the different realms.
Text, image, video, everything is audio, music, everything is sprinting along all at the same
time.
Right.
A couple more stories to whip through.
The first one here is from MetaAI's research division, which have unveiled the groundbreaking
achievement they've made with Cicero, which is the first AI capable of playing the strategy
game Diplomacy at a human level.
So Diplomacy is a tough game to play because you need to negotiate, cooperate and plan
with other humans.
And it's like it's very kind of diplomatic, as the name would suggest, and quite complex
and very human, really.
So to be able to create an AI that can play the game well is really impressive.
The reason it can do it is because of its ability to combine advanced strategic reasoning
with natural language processing.
It can think ahead to a certain extent and it can also understand the nuances of what
the other players are doing and what they're asking for and what they're saying.
So Cicero played online on WebDiplomacy.net and was able to outperform average human scores,
but also ranked in the top 10% of players, which is kind of mental.
We've talked about AlphaGo, haven't we, on the podcast and that's a rather impressive
feat because it's such a complicated game.
But this is such a human game, Martin.
Yeah, this is so different.
Now AlphaGo was complex because of the number of permutations on the board.
This is different because it's about negotiation.
It's about understanding incentives, rewards, punishment, things like, well, human psychology,
right?
Loss aversion, human strategy, power, influence, all of these things.
And it's managed to score in the top 10% of players.
That's scary.
But all of a sudden I'm thinking, okay, well, if ChatGPT with GPT-4 scores in the top 10%
of humans that sit the bar exam, so for legal professionals in America, if I can take that
capability and combine it with this new Cicero AI, then, you know, it sounds like I've got
a legal dream team on my hands there.
It's super interesting.
So Sam Altman, formerly and now currently CEO of OpenAI said at the end of October, so
only a month ago, on Twitter, I expect AI to be capable of superhuman persuasion well
before it is superhuman at general intelligence, which may lead to some very strange outcomes.
And I think having AI being good at a game like diplomacy is an absolute indication of
that, right?
It might be that before it is solving climate change and helping us develop new medicines,
it is taking part in negotiating contracts and getting rebates and maybe acting in courts
of law, right, because of its ability to understand how humans communicate and how to leverage
that to persuade.
Who knows?
Before long, it could be operating at a geopolitical level.
It will be leading and chairing debates in the UN chamber.
Who knows?
It's kind of mad, isn't it?
Right, let's last story then for this week, Martin.
Yeah, so this is revisiting a story that we had on the podcast recently.
So we recently covered the case where some visual artists were taking action against
AI image generation companies such as Stability AI, Mid Journey and Runway.
And the crux of the case was that there was an alleged misuse of artists' work to train
these generative AI systems.
But last time we covered the case, there was basically a partial dismissal by the US District
Court, which basically said that the artists' initial arguments were not strong enough and
didn't really apply to this copyright law.
But they maintained their core claim concerning AI training processes violating artists'
rights.
So now the artists have come back because I think last time three of the arguments were
thrown out but the one was allowed to remain.
In the amended complaint, the seven new artists have basically been...
They've said that their rights have been impinged because now if you search for their name
in Google, it isn't their work that comes up, it's the AI generated work that comes
up first.
And this is what's forming the basis of the new case because they're saying there is real,
they're evidencing now that there is real commercial harm.
That's so funny.
So it's not the...
Obviously they complained about the first order consequences, which are quite understandable,
but this is a second order consequence where all of the buzz and the news around this ends
up ranking highly on Google, more highly than their actual original work.
Yep.
Complicated.
So this is definitely one to keep an eye on.
Where this ends up, who knows, but we're starting to see some of the more interesting legal
arguments now.
Okay, it was trained on your work, but it's not completely derivative of your work.
But now when people search for the artist's name, people's AI generated work in the style
of that artist is outranking the actual artist.
So therefore we can see that there is genuine commercial harm.
This is a search engine problem though, surely, right?
Google needs to figure out how to make some sort of update that allows people to know,
here's the original artist, front and center, if you're looking for that.
And if you're looking for derivatives, they come lower down the page.
I feel like there could be a technical solution to this, but a little bit like, you know,
the right to be forgotten in the EU where you can basically contact Google and ask that
basically certain search results about you are not shown anymore.
I would have to hope that they can solve this like this, but clearly it's helping to underpin
a more substantial claim in terms of damages, etc.
So that's probably why they haven't leaned so much into what might be a practical solution
versus the solution they've opted for.
Not familiar with the case, not a lawyer, so this is just my opinion.
But yeah, it's still the wild west, but we are starting to see it coalesce a little bit
in terms of the approaches that people are taking and then the counter approaches that
these companies are taking to avoid being held liable for any sort of copyright or commercial
damages.
Many twists and turns ahead, I'm sure.
I think you may be right on that one, Martin.
One thing that we should say is that that's the end of the episode.
Thank you so much, everyone, for sticking with us.
We hope you find this useful.
We've got another interview for next week's episode for the Eagle-eared, Eagle-eyed, Eagle-somethings
of you will have noticed that to a certain extent we've found a new cadence, Martin and
I, where we have a discussion like this on week one and then on week two, we may have
an in-depth interview with a subject matter expert in the field of AI and technology for
marketers and sales folk to use.
So we have another interview next week, but we will be back with you the week after to
dive into all the new technologies, new stories, tools and stuff that we learn about during
that period.
Thanks so much for your time, Martin.
I look forward to speaking with you soon.
Cheers, Paul.
Thank you for listening to Artificially Intelligent Marketing.
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We look forward to seeing you again next week.