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.
Welcome to Artificially Intelligent Marketing, a podcast for marketers looking to learn about
how they can use AI to improve their work and make it more efficient.
I am joined today by my good friend Martin Broadhurst.
Hi Martin, how are you doing?
I am very well this week.
Yeah, all good.
Been playing with some new tools and presenting a seminar this morning on introduction to
marketing AI, funnily enough.
So yeah, very much front and center of my thinking right now.
A week of mass AI for you then, and hopefully you can distill some of all that good stuff
into the cool stories we've got this week.
Myself, I've been really busy within Biostra, actually doing loads of work, speaking to
some really cool clients with really exciting technologies that are going to change cancer
research and a bunch of other cool stuff.
So it's been quite an exciting week for me speaking to clients to be honest.
But I also noticed, and we've been very active on the old WhatsApps, haven't we, this week
night, because a lot of important and interesting things have happened this week as we alert,
as we'll come to look at.
Firstly, we're going to focus on a handful of stories.
It's going to include the apparent imminent release of GPT-4, might have been a little
slip up or maybe it was deliberate by a Microsoft employee yesterday that we're going to cover.
We're going to look at the rise of CRM AI assistance because that blew up this week
as well.
We're going to talk a little bit about domain specific large language models.
In this case, we're going to look at bio GPT and what that might mean for marketers.
And even though it's not a super marketing application, it is mega cool.
We're going to talk about a paper that was actually released in November last year, but
for some reason caught attention this week where researchers used MRIs and AI tools to
try and predict what images people were looking at, which is kind of cool.
And we'll get into that.
We're also going to have a little look at our tool of the week, which is this week is
runway.ml.
Before we get into all of that, Martin and I wanted to thank everyone for the amazing
feedback that we had when we launched our first episode last week.
We got way more downloads and interest than we were expecting.
And we also had some really good questions about how we do the podcast because a couple
of eagle-eyed eagle-eared listeners spotted that the voice at the beginning of the podcast
didn't sound quite human and of course they were absolutely right.
We used an AI tool to synthesize that because we've been focusing on how do we produce this
entire podcast using as many AI first tools as we can.
So the intro voice was actually created using a tool called play.ht.
So go and check that out.
That was actually a pretty streamlined and easy experience.
Gave it a script, chose which voice we wanted, recaptured it a few times because you get
it in different flavors, in different phrase, not phraseologies, but like tempos to try
and make it feel more natural.
So we used that on the intro music.
We used one of your favorite tools, Martin, that you recommended Ava or AIV.
That was really cool as well.
Gave us some really good options to choose from.
And if we wanted, we could get into the nitty gritty of even changing the, the midi clips
for those of us played with music, you could actually get right in and edit the AI driven
creations.
We didn't to be honest, because we wanted something quick and easy, but we, but we could
have.
On the video editing side, including audio enhancement and captions and stuff, we use
runway.ml.
We'll talk about that a bit later.
And the videos have been recorded using zoom.
You did quite a bit on the sort of RSS feed cover image and sort of summer track text
summaries of the podcast.
Martin, what sort of tools were you using for that?
Yeah, so the AI generated cover art was from, it was, I think it was a stable diffusion
output, but the tool that I used to create it was super machine dot art, which is a really
cool site.
Actually they've implemented lots of different AI text to image models and fine tune some
of themself.
So if you want to use the likes of mid journey stable diffusion, and then some fine tune
variants of those, check out super machine dot art.
Yeah that was the text summarisation.
Sorry to, yeah, I forgot.
That was a, yeah, that was a slightly low winded approach.
I was hoping to use Coheer, but after we spoke about Coheer summarisation last week, I realised
that the, the output quite frankly would have just been too succinct given all of the broad
range of topics that we covered.
So it was a, it was a somewhat manual effort using a transcript generated from RSS.com,
who hosts our podcast, took the transcript and then basically fed it into GPT in bite
sized chalks, taking out the outputs from there and uploading them to the website.
Good stuff.
So for those of you that asked what we were using, that's what we're currently using.
We're going to be exploring and playing with a bunch of other tools as well as we go so
that we get a decent feel for what would be the best tools for us to use.
We also tried podcastle at the beginning.
This is episode two, but actually it's episode three because we recorded it in episode four,
episode one, and we had a failure in podcastle where it only recorded bits of the audio and
we couldn't use it.
So we're not using podcastle anymore.
Once bitten, once shy in this case rather than twice bitten, because we just couldn't
risk it.
So we've had a little bit of a play with that.
That is a powerful tool, really fun and easy to use.
But of course, if we couldn't rely on it, which we felt we couldn't after that first
little slip up, we had to try something else.
So stay tuned for other tools that we bring into the mix as we produce this podcast.
Right.
Let us move on to the stories of the week.
Martin, first one, you spotted this last night and pinged it over to me and I nearly dropped
my phone.
Tell us about these GPT four rumors.
Yeah, so this was the talk of Twitter AI last night.
Everyone was jumping on over it.
Yes.
So the CTO of Microsoft Germany said that this was at an internal Microsoft event, Microsoft
Germany event this week, said that GPT four is coming next week.
And specifically said we will introduce GPT four next week.
We will have multimodal models that will offer completely different possibilities.
For example, videos.
So there's been a lot of talk about GPT four for well, for ages, probably since the release
of GPT three, in all honesty, people start getting excited about the next big thing.
It's all been completely under reps.
No details have leaked.
There's been wild speculation.
In fact, before coming onto this part, I thought of then just check out what were the what
were the range of different estimates for the parameters in the model?
So 175 billion parameters in GPT three.
There are guesses or speculation that it's going to have a trillion.
I saw one report saying 100 trillion.
So quite frankly, it is anybody's guess how big the model will actually be when it comes
out.
But I think the interesting thing is multimodal.
Absolutely.
It's not the size of your model.
It's what you do with it, mine, to be honest.
And I think in this case, it'll be interesting to see how relevant that ends up being.
But you're right on the multimodal side.
Wow.
What are we thinking here?
What are we expecting we might get?
Well, in the dream scenario, you know, you just type in a prompt and it spits out a fully
formed video and says, there you go.
There's your there's your 10 second animated GIF.
I would be surprised if it's actual video production capabilities, given where the the
text to video research and development currently is.
You know, if you look at what Google has released with imaging and the text to video, very sure
clips that they're capable of producing, they look cool.
They're certainly doing a good job.
I wouldn't be surprised if they're slightly further along than that.
But I would still be surprised if it had video production capabilities.
That said, I wouldn't be surprised if it was more focused around the input.
There's a bit of speculation reading in some of the groups and the AI communities today
saying it could be that this is multimodal from an input perspective.
So you can upload an image rather than just being text prompts as the input.
You can upload an image with a prompt and saying, describe this scene or describe this
video, transcribe this video.
So it's still not bringing in elements of things like the capabilities of say, whisper
AI, which is the transcription model.
But it's all bringing that into one.
So it's not necessarily producing the video output.
It's editing the inputs or.
So multimodal inputs rather than multimodal outputs potentially at this point.
And I guess we're going to find out next week.
I mean, everything everything that we say here is absolute speculation.
It wouldn't be surprising either, would it?
If it was text based, sorry, image based outputs and they were integrating Dali capabilities
in the outputs, we know that it can already with chat GBD, it can output code, it can
output text, it can output formatted text.
So in tables and things like that, it wouldn't be a stretch to imagine that it's going to
be able to spit out an image in the same way that Dali can spit out an image.
That wouldn't completely blow my mind.
And to be honest, it wouldn't wouldn't be a huge surprise if they did introduce some
video, but in the kind of short form animated GIF, you know, five, ten second long clip
region.
That would be quite cool.
That would potentially revolutionize email communication at Bystrat where we love a GIF,
but we have to use existing GIFs, whereas to be able to describe the GIF that you want
and then get open AI to chat GPT or GPT4 or whatever ends up being to produce that would
be quite fun.
I guess for marketers, this is just an example of how quickly things are developing.
Chat GPT came at end of November, early December.
We're now talking about all these explosion of other tools, some of which we talked about
last week.
And now we're looking at a tool that potentially has multimodal inputs and also potentially
multimodal outputs.
So as a marketer, just further increasing the power of the tools that we have available
all in one place to get our work done, be more creative, get ideas, first versions of
ideas out for us to start thinking about getting feedback on and tweaking.
That could all be pretty cool.
Yeah, very much so.
I remember saying at the start of the year, speaking to friends of people interested in
this space that I would expect a multimodal model to come out before the end of the year.
It wouldn't surprise me if that's when we see the actual release of it.
If we look at the timeline for GPT3, there was something like a 17 month gap between
the paper announcing the initial GPT3 model and then the actual release of the unrestricted
API where anybody could access it.
I think it was about a 17 gap.
So it wouldn't surprise me if there was a decent sized gap between the announcement
and then the actual reality of it.
But when you think about the investors that have come into OpenAI in recent years, Microsoft
being a big one, people are going to be wanting to see developments.
And I think OpenAI getting chat and GPT out in the way that they did has shown that there's
appetite and there's interest in it.
So it wouldn't surprise me if they start releasing things slightly quicker than they did before.
It took them a year from releasing Dali to Dali 2.
Dali was a research paper.
Dali 2 was the lab where you could actually start creating things on it.
I think we're going to see a bit more acceleration from announcement to production.
Right.
So we're going to hear what the cool stuff is next week, but we may not be able to use
it or play with it for some time, you think?
Yeah, I think so.
Which would coincide with an event that Microsoft have got going on as well to do with AI next
week.
So it just makes you wonder whether the kind of stars are aligning and this is actually
something of an orchestrated run up and launch.
Cool, cool.
All right.
Well, we will, I'm sure we'll feature this story next week, assuming we get the news
that we're hoping for.
However, there was some even more additional exciting news items this week.
Let's talk about the rise of CRM AI assistance where all the major players basically launched
one or basically provided us with an announcement about one all in the same week.
Tell us a bit more about Chatspot and Einstein GPT and all the rest, Martin.
Chatspot.ai, brought to you by the coding fingers of Dharmesh Shah, co-founder of HubSpot.
Our background, the way we've actually met was through the HubSpot ecosystem and the
HubSpot partner program.
Both of us have been very involved in that for a number of years now.
Yeah, so it was interesting to see Dharmesh announce this.
It's very much an alpha release, but HubSpot customers can go to Chatspot.ai and connect
their HubSpot portal with the Chatspot system.
And then they've got ChatGPT connected to their CRM.
And given that HubSpot is a wider platform than just a CRM, it's your marketing, it's
your customer support, it's your sales resources as well.
You get access to all of that data through a chat interface.
Very cool.
Have you managed to get on the alpha yet?
Yeah, you sent that WhatsApp link over to me and said, this is cool.
I signed up immediately and within hours I got access.
I think 4,000 users on the wait list for that.
At last count, I saw Dharmesh post on LinkedIn.
So clearly a lot of people interested in getting their hands on it.
What are your thoughts?
I know that you've been playing with it.
Yeah, I've had a play.
I saw Dharmesh put a post on that one of his to-dos every day was to approve another 100
users onto the system.
So I think the fact that we've both got on quite quickly is probably quite lucky to be
honest because it seems to me like they're managing that quite tightly.
If you watch, there's like a 20 minute intro video where Dharmesh takes us through the
system and the possibilities are huge because imagine being able to go into HubSpot, Salesforce,
whatever your tool is.
And rather than like 15, 20, 30 clicks trying to get to different reports or bits of information,
in essence, you just ask the computer a bit like you would like if you're in Star Trek
to just summarize information for you.
And in fact, they've even got a sort of Google style microphone button where you can speak
to Chatspot and ask it stuff and it will auto transcribe what you asked for and then give
you the data.
So some of the great things in Dharmesh's intro video include things like adding a new
contact to HubSpot by just asking HubSpot to add it.
So rather than have to click through to contacts and then click add contact and then write
in someone's name and then connect it to a company profile.
If you use the little microphone button, you can basically just speak in, please add Bob
Bobson from Acme Go and it will do all that for you.
I also really loved the sales prospecting email example.
So writing a sales prospecting email about a new feature released for a product, but
the emails heavily customized by what the CRM data has about that individual contact.
You could imagine doing that at scale.
And the example given really felt like a human had done the due diligence on where do they
work, what are they interested in, how long has their company been going and all that
type of stuff.
And it was all just fed into the email example.
Having played with it, I break it a lot.
It is an alpha.
That's absolutely the expectation.
That's why it's been launched to a small group of users.
And so we can break it and we can feedback, oh, it would be so cool if we could do this.
But by the way, it didn't work.
So I have been breaking it a lot.
That being said, there's a few things I was able to do where I was able to get information
from it that I just didn't think would be easy to get.
So I asked it to give me the location of some of the leads in our database because we manage
who's going to do an intro call based on where the person is based.
So we have a US team, we have a UK team, a European team.
So I'd ask for things like the IP country of a person's name and it would figure out
who the right person was and then tell me what that country was, which was pretty easy
and quick for me to then root in these.
I realized I could do that through workflows and stuff in the backend, but I just wanted
to see if I had to ask it a quick question.
Like I was asking, I don't know, a sales admin support person, could I just get an instant
answer and the answer was yes, I could.
So that was pretty cool.
I've also enjoyed playing with some of the keyword capabilities.
So asking it what keywords do certain competitors appear to be bidding on and it would give
me really good Intel on that, which was fab.
What else was I doing with it?
Suggested keywords for us to go after, which was great.
I was lamenting using ChatGPT for keyword research in our last podcast because it doesn't know
things like search volumes and stuff.
Well, once you connect ChatGPT to HubSpot's data repository and some of the tools in HubSpot,
that's a different game entirely, right?
Because now you're starting to see real search volumes and all of those types of things.
So yes, I'm super excited about what we're going to be able to do with this, but at the
moment they're still just trying to get a feel for what are the types of things that
people are going to ask of it and how do they iron out those bugs?
Yeah, that's very reminiscent of my experience.
I was able to break it remarkably quickly.
I just asked it to tell me what two pipelines, I've got two sales pipelines set up in my
account.
I just asked it to tell me the names of them because I thought I knew the name of one of
them and I said, can you tell me what's my current value in one of the stages in the
pipeline?
And it said that pipeline doesn't exist.
Okay, well tell me what pipelines I've got.
And then it brought up some script that it was like from the hub database, just said
like pipeline one and pipeline two.
That's not helpful.
You know, it's an alpha and it's going to have that.
And I really respect that Darmesh has put it out so quickly and said, go play, tell
us what you want to do.
Because the potential of it is really, really vast.
I also loved the idea that it's like an assistant.
It is like having an assistant, a CRM admin, a data analyst just there.
I went in and just typed in who was my most recent lead?
What was their original source?
What's the value of pipeline, which it didn't quite understand, but just that, you know,
having that available with a different interface.
And I'm sure as this iterates, if you've got a kind of desktop assistant or built into
the mobile app where you can just fire off a question or literally speak into it, as
you say, the microphone control, it is going to be a productivity game changer for everyone.
For sales, for admins, for marketers.
Yeah, I think it's got huge potential and so does Salesforce because they too have entered
this space this week with Einstein GPT.
They've also announced a $250 million fund to invest in generative AI startups alongside
this.
Yeah, I think it's fair to say that this space is hotting up.
Absolutely.
We also got Microsoft's new GPT powered chat bot for Dynamics 365 CRM as well, which was
going to apparently be able to automatically generate answers to customer emails, suggest
marketing and sales campaigns and do things like summarizing team's chat threads and stuff.
So very similar capabilities across a lot of these tools, I expect.
I also think as they evolve, we'll see some modernization there.
So probably it's likely that most of the tools will do the same things and it's what ecosystem
are you already in?
But certainly if you are using a CRM and you're not using one of the big players like a Dynamics
or Salesforce or HubSpot, you need your provider to be thinking about this type of stuff and
building these types of tools in.
Otherwise you're massively going to be left behind, so you can imagine how this could
cause a migration of people using sort of smaller platforms built by smaller companies.
And actually this is the thing that pushes them into HubSpot, into Salesforce, into Dynamics
to be able to get access to this sort of power.
So I think that'll be an interesting thing to see how that plays out.
The other thing I noticed when I was playing with this was I had to think differently and
I've been trying to, how do I articulate how I had to try and think?
And rather than my brain instantly going through a click-based process in my mind, like I need
to go here to do this, to do this, to do this.
In order to get real valuable stuff out of the system, I had to think about insights
I wanted to know, outcomes that I wanted.
It was a different style of thinking.
It was less to-do based and it was more instantly what's the outcome that I want.
Because I'll give an example, sometimes I'll go into the reports tool in HubSpot and I
might look for a specific thing.
Okay.
I know the outcome then, but sometimes I'll play with the filters on the tool to like,
oh, but I wonder if, oh, but I wonder if.
I think that's kind of a discovery-based process where I've got the filters in front of me
and I see them and I'm like, oh, I can filter by that.
And I didn't realize that would be quite interesting to see.
So I think if you're going to be asking a chat bot to do it, there's the potential that
we're going to need to think a bit more creatively about what outcomes we're after.
We may need to learn our systems better to even know what we can ask them to do.
And potentially as the tools get better, the only real limitation could be the creativity
we can come up with in terms of, I wonder if I, you know, computer, I wonder if you
can show me this and this combined in this plot.
And it'll be like, yeah, Paul, no worries.
And it will show me and I can maybe see some insights about how our pipelines progressing
or what our best lead sources are that I may not have even thought to do in the past.
So at the same time, as we see these tools evolve and mature, I think as people will
have to change our approach a bit in terms of how we get the information and insights
that we want.
Yeah.
I find thinking with chat GPT with the chat interface, if you're, so if you look at the
way that the chat GPT API is set up, there is basically an instruction at the start to
say what's the role of the chat in this scenario.
So you'll say you are a travel assistant at a airline helping customers do this, that
the other way.
So that's the kind of input at the start.
I think it helps to go into the chat spot or the CRM assistant AI, whatever, what I
call it, go into it with that mindset.
What do I want this to play at this moment in time?
Because when I first went into chat spot, it's got all of the suggested prompts down
the side and it can kind of do everything.
It can write an email for you.
It can interrogate data.
It can do tasks like adding new contacts.
It can do a lot.
But actually when you go into it, what do I want this assistant to do for me now?
Is it a data analyst?
Do I want it to uncover some lead intelligence?
Do I want it to tell me about the best performing channel?
Do I want it to tell me about which piece of content is having the most influence on
revenue in the last quarter?
You have to go into it thinking about what's the role that it's playing, but yeah, also
knowing what the potential is within the data.
Because ultimately that's what we're doing here.
We're connecting a huge pool of data to this resource.
And I dare say that most people don't know what treasure trove of data they're sitting
on with their CRM.
Absolutely agree.
I think that'll be a big part of it.
We should also mention that HubSpot released another cool beta tool at exactly the same
time as this that's kind of been lost a little bit because of how cool Chatspot.ai is, but
it's new content assistant tool, which I haven't been able to get on the beta yet.
HubSpot, if you're listening, it'd be very nice to get on there.
Maybe we'll feature that in tool of the week if you could be so kind.
But I assume it's some sort of GPT-3 style text tool for writing blog posts, summarizing
content, et cetera, et cetera.
Are you on that beta?
Have you managed to play with it?
No, likewise.
I saw it existed.
I haven't gotten there yet.
It looks like a content generation tool, much like something that you would find offline
with the likes of Jasper or something similar, but until I've got my hands on it, couldn't
pass any comment or judgment.
Yeah, me too.
Last point on that, that's really interesting.
I was listening to the podcast with Paul Reutzer and Mike Kaput this week, and they were talking
a little bit about, or maybe it was last week, the consolidation of tools and rapid emergence
of other tools.
And how do you bet as a marketer on what tech stack you're going to build?
I wouldn't take as much as we're all offered these much better deals if we commit to a
year to a software tool.
I wouldn't do that for any tool at the moment, because if I'm a HubSpot customer, will I
need Jasper or Writer or HyperWrite or any of these tools in three or six months?
Maybe not.
Because if I'm doing most of my sales prospecting emails or creating blog posts or whatever
in HubSpot, I may have the writing tools I need in there to do it.
One of the things that is part of ChatSpot is you can ask it to produce images for you
and you can tell it what style you want them in.
So it's almost like a Dorely-esque image generation tool as well, where it will create all of
the images in the same style so that your blog or whatever has images that fit together
into a coherent brand style.
So really interesting to see how that's going to play out and what tools do we all invest
in?
How do we train our team on the different tools?
Because which tool we use might change from week to week.
And I'm starting to drift towards betting on again the big players, the Salesforce, the
HubSpots of the world to try and bring as many of these tools inside their ecosystem
as possible so that I won't hopefully have to buy any other tools because they'll all
just be natively in the tools I'm using.
To this point, or up until this point, the tools that people have been using to interface
with the likes of GPT-3 have effectively been a polished front end.
It's a very simple layer on top of it.
And if you can create a front end and train some specific use cases on, you know, I'm
going to give it, I'm going to give the model, I'm going to give GPT-3 five examples of email
subject lines and then connect into that via the API, I've now got a subject line writer
for my tool.
And if you do that 40 times for 40 different use cases, you've got a tool that you can
productize.
It was a commodity.
It was very easy and cheap to build.
The real power of this, the thing that's going to be the kind of multi-billion, trillion
dollar industry game changer is that connection to that first party data and where you've
got AI that can, you know, it's fine tuned and embedded with your own data and companies
that can make that process super simple.
This week, you know, e-commerce platforms, you've got the likes of Slack integrating
with chat GPT-3.
There's huge amounts of internal company communications and data in Slack channels.
If you can just say to a chat bot, oh, find me the conversation where I discuss this,
this and this, that's a game changer.
And that's where the power of the assistance is going to come.
So yeah, you're absolutely right.
I don't know why anyone would commit to any of these AI tools that aren't doing something
differently.
But to their credit, when I think about things like the AI copywriting tools, the ones that
immediately spring to mind for me are the ones that are doing something different.
JASP has got the business brand tone of voice element, so it's understanding how your company
writes long shot as fact GPT, which can do real time copywriting or about stories published
yesterday.
You know, it's really powerful and very capable.
I look at the likes of Go Charlie and what they're doing with content repurposing where
you can create blogs from video content and audio snippets.
And that's, they're going to be the USPs for those content led tools, I think.
I think they absolutely are.
I think they may have to evolve because I hear those examples you're describing and
I think, well, our website blogger on HubSpot.
So HubSpot knows and can quickly, the bot can quickly just go and see what our brand
style is.
If that's built into the system, it can write like us.
In fact, I could probably even ask it to create a new service page for me based on existing
service pages because they've all kind of got the same template that they're using.
So I guess it could go build those pages, potentially populate the images in the holes
where the images go, maybe even write some copy because it knows what copy style we use
on those pages.
We've got our Facebook ads and Google ads and LinkedIn ads all connected to HubSpot.
So HubSpot has all the data on which of our ads work best.
If it can pull the copy or creative for those ads down, which I assume it can, I don't play
with it enough to really, to really know that off the top of my head.
Well then it can also start to suggest and write LinkedIn ads for me based on the LinkedIn
ads it happens to know have already worked best in the past.
So again, like you said, that first party data lives in a lot of these tools that we're
already using.
And if the Jaspers and the writers of the world don't have access to that, or we don't
give them access to that, you know, we may find that we don't end up using them.
What you've convinced me through this Martin is that my super duper amazing AI writing
tool that I'm going to build is probably not worth building at this point, but you did
give me a wonderful strap line for it, which is the polished front end, which is going
to be either the strap line for my new AI writing tool or the name of my new band, to
be honest, because in both cases, I really, really like it.
That is a probably a good time to segue on to our last few stories for the week.
The first one, a quick one is about domain specific large language models.
So when we're thinking about ChatGPT and some of these other tools, they've been trained
on a huge amount of information and data across many, many different subject matters.
And as such, they can sometimes struggle when we ask questions, very, very specific questions
about very, very specific topics, where only a little bit of their training data is about
that topic.
So there was a research paper, I think it was actually towards the end of last year
that then Microsoft launched a model based off the back of it called BioGPT.
And in essence, BioGPT was a language model that's been trained on all of the scientific
papers within PubMed.
I don't know how many of them, this huge is like 14 million papers or something like that.
And so in this case, it's a large language model that understands healthcare research,
medical research, primary fundamental biology, chemistry research better than any of the
general models.
And so if you ask it for information on COVID or a particular drug target or a protein or
a gene, it's going to give you a much more accurate answer than any generalized model.
It was built this model based on GPT-2.
Now, I don't have any experience playing with GPT-2, to be honest, but what I saw within
BioGPT was the amount of content that it can create is limited.
So you can start a sentence and it will finish the sentence.
You can even ask it a question and it might give you one or two sentences as an answer.
But beyond that, it doesn't really generate that much more data.
So it's looking like its applications right now are going to be for doing things like
generating descriptions of specific therapies or drug targets, as I said, maybe in designing
slightly better clinical trial protocols, drawing relationships between factors in its
database, so maybe drug-drug interactions like, oh, does this drug interact with this
drug?
Yes, no.
Because when you're prescribing drugs, you've got to be mindful of what other drugs someone
is on in case there's going to be a harmful reaction between the two.
But as a marketer, it's certainly not at all yet where I could, working in the life sciences
like I do, ask it to write really informed blog posts or articles for me because it could
only produce a couple of sentences.
Why is this important for marketers?
Well, A, it shows the power of a domain-specific model, which I definitely think is there.
The information accuracy that you get out of this versus, say, ChatGPT, there is a difference.
This obviously will be applied in other areas.
You can imagine the legal profession's got its own language, for example.
I'm sure different areas of engineering and manufacturing have their own languages taught
too.
So the creation of niche models on information repositories specific to those niches is going
to be valuable.
But until they leverage the power of GPT-3 and, hey, maybe GPT-4, they're not going to
be as broadly usable for marketers as ChatGPT and other tools is what I would say.
What are you thinking on these domain-specific models, Martin?
Do you think we're going to see a lot more of these?
I think it's inevitable.
If for no other reason, I believe that they're less resource-intensive as well.
When you're calling something from ChatGPT or GPT-3, using that whole network, my understanding
is that these don't draw as much resource, so the compute power for every request is
lower.
So I think there's going to be efficiencies.
Anything else better for the planet?
If everybody's using these things all day, every day.
But yeah, I think having more tightly trained without, again, it goes back to what we were
saying with the CRM thing.
Where there's a cluster of data that's relevant for your space, whether it's your business
and your first-party data or your sector, that's what you're going to want to call upon.
It's great that ChatGPT and GPT-3 large language models have knowledge outside of that.
Really, that's what I want to focus on.
So having access to models that are more efficient and more tailored and more knowledgeable in
depth is going to be the game changer for all industries, I think.
Yeah, I agree.
Right, let's keep the biology theme going for the last story of the week.
Again, this is mostly because it's mega-cool rather than there being obvious applications
for marketers today.
But there was a pre-print research paper that was actually published in November, but for
some reason is getting a bit of buzz this week, where AI was used to recreate images
that people had seen by reading their brain scans.
I'm just saying that aloud.
As unbelievable.
If you feel a bit queasy hearing that and you're like, sorry, what?
I think you are probably among the majority there.
So there was a bunch of reports this week.
The one I read was in Science Magazine about how researchers use stable diffusion, which
is one of the AI models for image generation, to interpret brain activity and recreate images
that people were looking at.
So basically they used a data set where four people looked at 10,000 images while their
brain was being scanned using fMRI.
And the results were scarily accurate.
So in one example, they had someone look at a teddy bear.
The pure model is able to generate the shapes and perspective that makes it look a bit like
a teddy bear, but not really.
And then when supported by a short image caption like teddy bear, the system can produce an
image that was scarily close to the actual image the person was looking at.
That text bit is critical though, because I think it sounds super, super, super, super
cool and it is.
But when you learn that the system using the MRI was able to get the approximate shapes
and colors right, but needed that little bit of help to interpret them as a teddy bear,
or whatever it may be, I think that's a critical part that helped it to improve much better.
But still incredibly cool.
And here's some stuff from that science article.
So Iris Groen, who's a neuroscientist not involved in the work, thought that the accuracy
of the new method was very, very impressive and that these diffusion models have an unprecedented
ability to generate realistic images, which I think is really cool and a science that
could create new opportunities for cognitive neuroscience research.
One of the scientists who worked on the study suggested that further refinements to the
technology could be used to intercept imagined thoughts and dreams.
Just going to let that sink in for a moment.
Or could allow scientists, this is incredible.
I'm not laughing because it's funny.
I'm laughing because I literally can't believe it.
Or could allow scientists to understand how differently other animals perceive reality.
Well, if I just slipped into the Twilight Zone, are we really here discussing this?
That is, that sounds quite speculative to me, but the fact that they can even interpret
those brain patterns to figure out approximate shapes and colors is pretty awesome.
And we saw this story this week, didn't we Martin?
And we were like, that can't be real.
We've got to be nonsense.
Yeah.
We did everything we could on the web to try and find the reasons why this was junk.
But once we started seeing more reputable providers like science picking it up and we dove into
some subreddits and reddits on the topic and we read the paper and we're obviously not
experts in this area, but we were really trying to find how to find the reasons why this wasn't
so exciting.
And the only real one we could find is you need to support that image interpretation
with a bit of a text prompt, which I think makes it a tiny bit less exciting than it
sounds on the surface, but still pretty cool.
Incredibly cool.
And we're at day one of this technology.
It's doing this today.
Just think about what we were saying with a GPT-3 to GPT-4, we're potentially talking
about a model that goes from 175 billion parameters to a trillion, say.
That's in the space of three years of development.
Fast forward five, 10 years, the requirement to add that little bit of text saying teddy
bear, I don't know, going to be there, do you?
I suppose you can get the more data you can get on this and the better that you can train
them.
The only thing I can think of in terms of real world application is we can't all walk
around with a miniature fMRI on our head recording what we're doing, but I'm sure they potentially
be able to find other mechanisms to work with.
Thanks, Silly, at this point.
I do not need a real time live stream of my thoughts and feelings just being interpreted
and potentially leaked to the world.
Seeing you as I do mine, I would think that A, that would get you in a lot of trouble
and B, it should be illegal just based on some of the things that flow around in there.
Yeah, no, it's really going to change the dynamic of those Zoom calls.
Right, okay, that's quite enough nonsense there.
Let's move on to the very last part of our section this week, which is our tool of the
week.
This week, we are focusing on Runway.ML.
So what is it and what can it do?
Runway is a multimodal tool that is focused on AI supported image and video generation,
basically, and also image and video enhancement.
It can do a few other things like audio cleanup on a video, transcription and all that good
stuff.
But there's really so, so, so many tools.
And I think what makes Runway different from a lot of the other tools that you can use
is just how many different things you can do with it.
So it's got trained image generators.
We'll talk about those a bit in the moment.
It's got a remove background tool, a text to image tool, an image to image tool, image
expansion tools, erase and replace.
It's got backdrop changes.
It's got text to 3D texture.
On the 3D, on the video side, you can use inpainting to remove people and objects from
videos that you wish weren't in there, you weren't in there when you captured the video.
It can do artificial bokeh effects.
It can blur faces.
It could be super slow motion.
It can clean your audio.
Loads of stuff.
When you think about the fact that it is only $12 a month per user, to be able to access
all of those tools is kind of spectacular.
And there are many of them, I haven't played with all of them.
There are just too many.
But many of them are really, really powerful and work nearly as advertised in almost all
the cases I've played with so far.
And I've seen some examples where people really pushed it to the limit removing, say, subjects
from videos where they're panning the video across and they actually managed to pretty
much remove a whole subject from it, which is again, kind of crazy when you think about
it.
So that's really cool.
A couple of the things that I think are especially special about RunYML that make it, I mean,
it's worth checking out just for all that stuff.
But some of the stuff that's really especially cool is the fact that you can train the models
on your own images.
So you've got the personal portraits.
I know there's a...
Do you remember the name of the app that was doing the rounds a month or so ago where everybody
got obsessed with making themselves look like famous characters from different stuff?
No, it was trending for about a week, wasn't it?
Everyone was going mad for it.
But no, I don't remember the name.
So you can basically do the same thing in Runway.
So if you wanted to see yourself as Princess Leia from Star Wars or Jon Snow from Game
of Thrones or maybe even the Hulk from The Avengers you now can do, there's an animal
version of it so you can chuck in pictures of your pets and you can do pretty much the
same thing with animal pictures.
And then there's a model where you can basically train it on any set of images that you have.
So you could imagine a time where you might give it your icon repository as a brand or
your brand image set and then use that to generate image from text prompts where you
had much more confidence that the images that you got would be in your brand colors and
that type of stuff.
That's pretty, pretty cool.
I also love the Infinite Image and the Image Expander tools.
A lot of the tools you can generate using AI at the moment are square, but most of the
images that I need are not square.
I need more widescreen style images for websites or for social media images or for banners
on blog posts and all that type of stuff.
So that makes it very easy to generate the square image maybe using another tool if you
get different types of results or different types of tools.
But you can quickly then expand it or change the aspect ratio of the image to fit what
you're actually trying to do.
As I've mentioned, the inpainting to remove objects from people from videos looks really
powerful and surprisingly accurate.
That's worth playing with.
And Runway have just released their Gen1 tool for creating videos.
Now I think this is quite early in terms of the types of video production that we can
expect from AI augmented tools.
But I did see one example on Twitter where someone had basically filmed some scenes on
their phone in their house and then fed Runway's Gen1 tool some image stylization and that
raw video from their phone and created like a stylized cartoon of someone as Indiana Jones.
Dig it out on the socials a bit or by Googling it if you can, because it's actually really
kind of spectacular and it gives the feeling that it could probably have been produced
in a couple of hours max.
In fact, I suspect the bit that took the longest was actually filming the sort of raw scenes
in the house on the phone.
So if you think about how you could very quickly knock up storyboard and creative ideas for
your next video ads to support your campaign, or dare I say it, for some of the campaigns
that you might run, the video that you might even get from Gen1 might be good enough to
run on as part of a Facebook ad campaign or something like that.
That is really rather cool.
It's got loads of stuff it can do.
Having played with it, a lot of the stuff works really well.
We decided to use Runway as the tool we were going to trial for editing the first video
version of the podcast.
So for last week's episode one, the interface is very intuitive and that's great.
So it's easy to use.
But the biggest drawback that I found with it was it was too slow.
When I was trying to edit, you know, the podcast about 45 minutes long, when I was trying to
edit the automatically generated captions, it did a brilliant job of getting those captions
pretty much perfect.
But there were probably around 40 to 50 small areas, usually the names of people or tools
or technologies.
Editing that, it would take like five seconds to make each edit because it would update
so slowly.
Oh no, I couldn't handle that.
So that was not quite what I was looking for.
The export function also took hours to produce a 45 minute 720p video where if I think of
I was using a piece of technology that was on board on my PC like Adobe Premiere Pro
or something like it would be 10%, 20% of the time of that.
So if you're in a rush and you really need to produce video quickly, I'm not sure.
At least with my current setup, maybe it was my browser, maybe it was my processor, I don't
know, a little bit too slow for me on that.
So huge amounts of potential, especially on the video and probably short, especially on
the images, I should say, and the short video side in terms of editing the long form video,
not so good quite yet.
So at the moment, all of this is done in the browser?
Yes, all in the browser.
And that probably is the main limitation, I would have thought.
Even Descript has, so Descript is another tool that you can use for really easy AI supported
editing, that actually has a tool that you download.
So maybe that's what's going to be needed.
But yeah, I think if you're looking for a powerful one in one image and video creation
tool, you should definitely check out Runway because I think it's just got so many capabilities
for a ridiculously low price point.
Yeah, 12 pound or $12 a month, there's nothing for all of that.
I've seen some tools like, for instance, the kind of portrait face replicator thing, training
a model on your own face.
I've seen tools going for $10 and $12 per training set.
So just to train your face once would cost you that.
So getting all of that bundled in is quite the deal.
Yes, I do think that every time you train on an image set, you have to pay a small fee
in addition, if I'm honest.
But yeah, the ongoing access is relatively cheap.
Cool, cool.
Well, that's our tool of the week.
And that brings us to a close for this week's podcast.
Always a pleasure chatting with you, Martin.
Loads of great stuff this week.
Covered some ground.
Yeah, definitely.
We absolutely have done.
Thanks so much to those of you who have listened today and got to the end.
High fives to you.
And we'll look forward to having a good chat next week and hopefully having you join us.
Have a great week, Martin.
You too.
See you soon.
Cheers.
Bye.
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