Welcome to Artificially Intelligent Marketing, a weekly podcast where we stay on top of the
latest trends, tips and tools in the world of marketing AI, helping you get the best
results from your marketing efforts. Now let's join our hosts, Paul Avery and Martin Broadhurst.
Hello everyone. Welcome to a special edition midweek episode of Artificially Intelligent
Marketing. Is it episode 32? Is it episode 31A? No one knows. Let's go with episode
32. The reason that I decided to record a quick episode, because I know Martin can't
join me today, is just to get out a quick bit of info for you about OpenAI's developer
conference yesterday, because they shared really loads of information that I think marketers
will hopefully want to know about. The first thing is that they announced the launch of
GPT-4 Turbo, something that we predicted in the run up to the event. But there was some
additional information in here that we didn't predict that's really quite exciting. So,
for example, one of those is 128k context. So for the listeners of the podcast, you'll
know. One of the reasons that we love Claude is because it's got 100k of context, which
means you can feed it many documents or really large PDFs with hundreds of pages and then
interrogate that content, ask for summaries, etc. Something that you can't do with chat
GPT out of the box because it's only got an 8k context. And even some of the tools do
have access to 32k context for GPT-4. But that's actually quite rare because it's been
quite expensive. So for developers now, they're going to be able to access GPT-4 128k. Expect
to see that baked into some of the tools that you might use. I'm hoping to see it in Magi,
which is one of my favourite tools. And that context is really now very powerful against
the likes of Claude. They've also promised to reduce the price of using GPT-4 for developers.
So hopefully we're going to get access to this without any increases in price for us
as software users. Although even though there's a reduction in price for input and output
tokens, obviously, when we've got that much more context window, there's going to be a
lot more tokens involved. So exactly how much cheaper it turns out for the providers of
tools and therefore us as marketers that subscribe to those tools, we will wait to see. It's
also likely, in fact, a dead cert that GPT-4 Turbo will become the underlying model for
chat GPT-plus users. So we're going to have that massive context window to be able to
work with when we're using GPT-4 within chat GPT-plus. So again, if you're used to pasting
in materials into chat GPT and getting some sort of error message about the information
you've provided is too long and breaks the context length, that is about to disappear
with the introduction of 128k context windows. Something that will be interesting as we see
this GPT-4 Turbo 128 roll out is going to be how good is the model at delivering the
outputs that we've become used to. So you might remember some months ago, there was
quite a bit of debate about whether GPT-4 was getting less smart over time and producing
worse outputs. People were comparing it against GPT-3 Turbo or 3.5 Turbo, which they actually
felt was better than the original GPT-3.5. So then the question remains is in our hands,
how accurate and useful will GPT-4 Turbo be? Will it perform better on a number of benchmarking
tests than the original GPT-4? Will it deliver on a number of the marketing applications
that people have started using chat GPT-plus for? Will it be worse or better? We don't
know until we get the opportunity to play with it. My gut says it will probably be slightly
better, but certainly the 128k context window could really be a game changer for a lot of
people's applications. Then there's a couple of things that are probably more important
for developers, but that could be important for us as users, which is OpenAI has changed
some of the way that developers will interact with the model. So now there are function
calling updates whereby a developer can now call multiple functions. So this is going
to make it much easier for them to create systematic agent-like experiences within the
API. There's also a new JSON mode, which is going to make it easier for developers again
to get the types of information out of the API that they would want to make it easier
to move information between different tools. The other thing to note is that GPT-3.5 Turbo
there's going to be a new version of that that has 16k context window by default. So
those of you that are using the free version of GPT, you're now going to have access to
this slightly improved GPT-3.5 and for developers whose tools rely on it, it's going to be even
cheaper than it was before. Another big news item announced by the OpenAI team was the
assistance API. So this is the first step towards helping developers build agent-like
experiences within their own applications. An assistant is kind of like a purpose-built
AI that has specific instructions and leverages extra knowledge and can call models and tools
to perform different tasks. And I think what's really interesting about this is for developers,
this is going to allow them to build assistance on top of chat GPT that can also leverage
tools like Code Interpreter and DORLY 3 and GPT-4 Vision to create really interesting
applications, the likes of which we just haven't been able to build so far, which again is
going to translate into the different types of tools that marketers get to play with and
get to access. OpenAI have also built in a new retrieval-based system. This has been
one of the limitations that's been really quite hard and made it a bit more challenging
to develop on top of OpenAI's platform is having the system remember the information
that the tool that you're using or you as a user didn't put it into that tool that
had been sent to OpenAI. And then all of that had to keep being packaged in the information
that was sent in the context window. So expect your tools to remember much more about instructions
that you've given them in the past, et cetera. And I think why is this really cool? I think
it's nicely summed up by the way OpenAI summarized it on the blog post on their website is that
there's so many potential use cases for this. And they give examples like a data analysis
app, a coding assistant, an AI powered vacation planner, a voice controlled DJ, a smart visual
canvas. It really now is down to the developer's imagination to really be able to call on so
many of those different aspects of how GPT and OpenAI's tools work. If you want to play
with Assistant's API and you've got access to the playground, it's currently in beta.
So you can go and actually have a little play with what this looks like. It's also worth
noting that I don't have access to GPT-4 Turbo in my chat GPT instance yet. And it's currently
in preview access as well, but they expect to roll it out more widely before the end
of the year. The other thing that's coming to GPT-4 that we also talked a bit about on
the podcast previously is you no longer have to in GPT-4 Plus, chat GPT Plus, I should
say you no longer have to select the modality that you want to use. So up until now, if
you wanted to use code interpreter now called advanced data analysis, you had to go ahead
and select that in a dropdown. And then you could interact with that tool. If you wanted
to create images, you had to select Dali 3 in the dropdown. And it was just a bit of
a pain in the bottom to use, to be honest. Now that's all been consolidated. So there's
no dropdowns anymore. You just make a request of chat GPT like you would normally. And then
it figures out what tools it needs to draw on in the back end to deliver on your request.
So if you ask it to create an image, it will know it needs to use Dali 3. If you ask it
to perform some sort of data analysis or some action that requires the ability to create
Python code and run that code, then it will use advanced data analysis. So that's going
to make it all easier for us to be able to just use the tool, which is quite nice. The
other thing is all this stuff, as I mentioned, is being made available via the API. So you
may use a tool now that is a wrapper or a skin of chat GPT like we do with Magi, but
expect to be able to do stuff beyond just text generation, because we have access to
the Dali 3 API, for example, you're going to be able to create images in your application
of choice, providing they open up access to Dali 3 within that tool. And it will create
images for you using Dali 3. So a fairly obvious use case of this, I can imagine will spring
up fairly quickly is if you use a tool to write like writer or Jasper or Magi, you'll
also be able to create images for those pieces, let's say blog posts using Dali 3. And one
assumes they'll be able to use the text of whatever thing you've written, social posts,
whatever, to influence the prompt that then creates the image alongside it. So an example
would be write me a blog post about the five most exciting things that OpenAI have released
and create me an image to go with that blog post should be something that is all very
easy to do, certainly within chat GPT, but also other tools. The other thing that they've
made available via API is text to speech. So many of you listen to the podcast will know
OpenAI has a speech to text engine called whisper, so for transcription. But if you
have the mobile app of OpenAI chat GPT on your phone, you can actually speak with it
and it will transcribe what you say, but it will then speak back to you because chat GPT
now has this text to speech function. And it's kind of interesting and fun to go play
with. You're basically just having a conversation with the computer like they might do in Star
Trek. Well, now that capability is being rolled out via the API to developers. So expect to
see more of this speech to text and text to speech type interactions popping up in lots
of different tools, because they're basically democratising and making it much easier for
developers to create tools that are software applications that you speak to and they speak
back to you. So again, that's pretty exciting and could change fundamentally how marketers
interact with lots of different software to get what they want, especially because those
tools are going to have access to advanced data analysis and other aspects of the OpenAI
back end. You can imagine sitting in front of your favourite dashboarding program or
your marketing software suite and asking in natural language to see a graph of contacts
generated by a particular source that say organic search over time, and then ask the
tool to explain the graph to you in speech. So to speak to you basically. So that could
be pretty cool as well. There's a bunch of other stuff there that's probably more important
for developers than it is for marketers at this point, but it's worth going and checking
out the blog post on OpenAI's website. The other thing that OpenAI mentioned is their
new copyright shield. So as you see on their website, OpenAI is committed to protecting
our customers with built-in copyright safeguards in our systems. Today, we're going one step
further and introducing copyright shield. We will now step in and defend our customers
and pay the costs incurred if you face legal claims around copyright infringement. This
applies to generally available features of chat GPT enterprise and our developer platform.
And so this is very much in line with the steps taken by Microsoft and Google so far
to give users of large language models confidence that they're not going to get in trouble
around copyright if they use the outputs of those tools by effectively indemnifying and
protecting them against any legal claims brought against them. How that will actually play
out in the real world is not clear at the moment, but I think all of the providers are
working really, really hard to give especially large enterprises confidence that they can
use these tools without worrying that they're opening themselves up to some sort of copyright
infringement claim and lawsuit in the future. OpenAI are also going to be releasing their
next version of the Whisper model that I mentioned earlier. So this automatic speech to text
model. So now we're up to Whisper version three. Martin and I were talking about this
on the WhatsApps and we've been really impressed with Whisper version two and can't really
think of much that we'd like Whisper version three to be able to do that's not already
covered by the current version of Whisper. So it'll be interesting to see exactly what
improvements this provides. Those of you that have seen Martin speak or have listened to
the podcast will know he does a really fun bit in a lot of the talks that he gives where
he uses Whisper to automatically transcribe a recording that he does live while he's talking
where he'll interview and ask questions of a few audience members and then has an automated
workflow through Zapier that automatically creates a summary blog of his talk and includes
the quotes of the people in the room published in real time. So at the end of his talk there
they are in the blog post on Medium. Whether or not this new Whisper version will make
that even better is hard to see but it's nice to see that they're continuing to improve
the tools. So you'd be forgiven for thinking that that would be it because there's certainly
a lot of things for us to chew over in there as marketers in terms of the increasing power
and ease of use as it comes to ChatGPT and also the sheer range of options now for developers
to use a lot of the things that we love in ChatGPT in their own applications including
image creation and text to speech creation and advanced data analysis and the ability
to pull information in from lots of other tools which we should probably focus on for
just a second actually because that ability to use data from lots of different tools could
be a real game changer for developers because they can build on top of your existing stack.
So integrating data from your CRM, from your marketing automation platform, from Google
ads, from Google search console and give you something that you can interrogate with a
large language model like ChatGPT, let's call it, I don't know, Marketing Analytics GPT,
that you can then ask questions of that either by speaking to it like I'm speaking to you
now or by typing it in. That is all going to be really cool but they've gone a step
further than that and they've made it so that this type of power and functionality is not
going to be just available to developers and people who know how to code. They have introduced
a new thing that they call GPTs. In essence, users of ChatGPT can now create custom versions
of ChatGPT that combine instructions, extra knowledge and any combination of skills to
deliver a customized chat capability. So in the examples they give on their website, they've
got a creative writing coach, they've got a tech advisor, they've got a puppy trainer.
So what this does is it's going to make it possible for anyone to create their own customized
ChatGPT bot trained on information provided by the user. Why is this important and why
is this different to what they've provided to developers? And in essence, this is because
there is going to be a GPT wizard. So you don't need to code. You just open up the wizard
and then you can build your own custom chat bot. So in essence, what this means is by
providing information sources for the bot to use, so by uploading a lot of your company's
documents for example, you could create a customer service bot. The great thing about
the tool is it's built using natural language. So in essence, you have a conversation with
ChatGPT about what you want your custom GPT to do and it will help you build it. It will
build rules in the back end in terms of what questions that GPT will answer and what questions
it won't and how it will answer those questions. In the live demo that they gave during the
developer conference yesterday, Sam Altman showed us an example where he took transcripts
from some previous talks that he'd done during his time at Y Combinator giving advice to
startups to in essence create a startup advice bot based on previous advice that he'd given.
So we've talked on the podcast previously about how organizations should really be looking
at what data they have internally, what information that they've captured, but also what information
could they capture if they more proactively look to record more internal conversations
and calls, record customer service calls, record sales calls and then leverage that
information through something like a custom bot to create customer service bots, internal
knowledge based bots and all of the above. Again, I think the imagination of people is
the limitation here somewhat because I think the tool really offers quite a broad canvas
for people to build quite interesting stuff and OpenAI is certainly betting on this themselves
because they believe that the best GPTs will be invented by the community and so they're
going to make it really easy for anyone to build one and they're going to create a GPT
store where you can share your GPTs publicly. So the GPT store is going to be searchable
and then if a particular GPT becomes popular, then there's going to be like a leaderboard,
a little bit like popular apps on like Google Play Store or the Apple Store. And if people
use your bot, you're going to be able to earn money based on how many people are using it,
which I think is really, really cool. They're also developing them with all of the sort
of safety guards in mind that they've used to develop all the rest of their tools. And
if you are an enterprise customers, you've got chat GPT enterprise, you're going to be
able to create bots that can only be used within your organization. So this is kind
of akin to what we've heard some of the consulting companies like Bain have been doing where
they've created an internal knowledge GPT or bot for their teams to use, trained on
all of their proprietary information. So if you are a chat GPT enterprise customer, you're
going to be able to do that. One example I can think of is if you have a customer service
team, you could empower them to find it easier to answer customer queries, not by searching
through knowledge bases, but to actually just feed the queries that customers provide into
those GPTs to help answer the questions by training those GPTs on all of your documentation,
technical manuals, protocols, all of those other things. So there you have it. Some well
known companies have already built these custom GPTs. So far, the one I'm really keen to get
my hands on is the Zapier one. So you can use the Zapier GPT to basically help you plan
automation. So you kind of ask it, hey, I want to achieve this particular thing. How
could I do this in Zapier with automation? And then you can use that the Zapier GPT bot
to help you create those automations. There's also going to be a Canva GPT, where in essence,
you describe the type of thing that you're trying to make. And it gives you initial examples
to work from. And then you have a conversation with Canva GPT going back and forth, refining
the design. And when you're ready, you see something you like, you click on it, and it
takes you into Canva where you can then make any fine tuning edits that you want to do
manually within the Canva interface. So I've been trying, I haven't got access to these
yet. As soon as we do, we'll let you know what our experience has been on one of the
upcoming episodes of our podcast. So as a marketer, obviously Canva and Zapier's GPTs
are going to be useful and expect to see similar tools popping up all over the place for all
the other software tools that you use. So I think that's the first thing. The second
thing is, how can you create GPTs to support your internal organizations? So how does this
change internal comms and how internal folks access information in your organization? And
also how could you create custom GPTs to better serve your customers, leveraging your internal
proprietary information and data to have a GPT customized to the thought, leadership,
knowledge, et cetera, internal within your organization? So this is going to be really,
really interesting to see how companies deploy this almost as products, but certainly as
freemium products as part of their marketing approach to help bring customers in and get
them wedded to the brand. Really, really interesting. If you want to know the details, go and check
out those two blog posts on the OpenAI website. We're going to be fascinated to see how this
plays out and how they roll this out to users and frantically hitting the refresh button
on our chat GPT plus instances to see when we get access to some of these new capabilities.
So I hope you enjoyed that just a little quick interlude because there was just too much
good stuff in there not to share with you all. We'll be back into our regular scheduled
programming with our episode next week. Other than that, I look forward to speaking with
Martin then and to sharing the latest tips and tricks that marketers need to know in
the world of AI to help them get better results. Until next time. Thanks a lot. Bye.
Thank you for listening to Artificially Intelligent Marketing. To stay on top of the latest trends,
tips and tools in the world of marketing AI, be sure to subscribe. We look forward to seeing
you again next week.