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 Artificially Intelligent Marketing, a podcast for marketers who want to stay up
to date on all things AI to help them improve the effectiveness and efficiency of their
work.
Just me today, Paul Avery, one of your co-hosts.
Martin's not with me today.
We've been balancing scheduling issues and Martin did a solo cast last week and this
week.
That sits with me, although Martin was very much kind enough to take us through his tool
of the week this week.
And so you will find that at the end.
What am I going to cover today?
Well, we're going to look at some of the news from last week, including the emergent autonomous
scientific research capabilities of large language models.
We're going to look at Auto GPT, which seems to be the next evolution of chat GPT and other
tools in terms of being able to perform multiple tasks autonomously.
We're going to look at how Hyper-Ry is T something similar, but apparently different to solve
this similar type of problem.
We're also going to look at AWS is announcement, Amazon's announcement for a suite of new tools
that people can train large language models and other tools using their own data.
So in effect, Amazon getting into the generative AI game, right?
Let's get cracking.
We'll get straight into it then.
Let's talk about the this autonomous scientific research taken undertaken by a large language
model.
So it was a research paper released last week that showed that they were able to develop
an intelligent, the researchers could develop an intelligent agent system that was able
to on its own design, plan and execute scientific experiments by being given access to different
tools.
The system itself showed that it was capable of performing fairly complex scientific experiments.
In one example, it was able to synthesize compounds like ibuprofen and aspirin.
And it did that in essence by being connected to the internet and able to access a cloud
lab.
So a remotely accessible scientific lab, a wet lab controlled through code where the
AI could effectively control hardware like liquid handling instruments and things like
that.
So what does this mean?
It means that the researchers were able to in effect provide a brief to a large language
model, give it access to the internet so it could gain additional information about the
problems it was trying to solve.
In this case, the goal was chemical synthesis.
So they also gave it access to this virtual, well, this real life wet lab that could be
accessed virtually.
And the system based on its prompt could go and synthesize molecules.
So that's kind of an exciting insight into how people are really starting to think, how
can we use these tools to do cool stuff outside of just asking them to do research or write
blog posts or answer questions.
And in this case, the large language model was able to understand the input that the
researchers provided and then go ahead by accessing things in the real world through
APIs that connected them to this wet lab to actually synthesize chemicals.
As you might imagine, this has pretty major implications, safety implications.
In this case, the researchers asked the AI to construct chemicals, well-known chemicals
that they wanted like aspirin.
But what's to stop someone asking it to make toxic chemicals in this way?
The other thing is, and maybe this is getting a bit Terminator style now, do we really want
to be giving AI agents access to things in the real world like this?
Do we really want to be giving tools the opportunity, AI to go start synthesizing things in the
real world, in this case chemicals, but you could also imagine giving AI access to a 3D
printer and then it could take a brief or what happens if it starts iterating and changing
the brief and then 3D printing things in the real world as well.
So as a proof of principle, really exciting, really cool, really interesting.
It made Martin and I really step back and think, wow, we only had Chach-E-PT launched
in December, GPT-4 is what, four or five weeks old and now we're looking at a large language
model that can synthesize chemicals as kind of a little bit mind blowing really.
In terms of what this means for marketers, perhaps in of itself, not a great deal, but
I think it's important for us to really understand how fast language, live language models and
their emerging capabilities are being tapped for things outside of the initial use cases
that we saw.
And as a part of a fairly handy segue on that topic, next thing we're going to look at is
Auto-GPT, so the next big thing in GPT probably until next week when some other crazy thing
happens.
But this kind of builds on the research project I just described because in this case, Auto-GPT
is in essence a tool that you can brief to achieve a task that requires multiple steps.
So rather than asking a simple question or providing a fairly simple brief like we do
to through ChatGPT for example, in essence what you could do is you could ask the tool
to achieve an outcome and then either you can provide it with a series of actions that
you feel it needs to go through or you can ask it what actions do you think are going
to be required in order to achieve this goal that I've asked you to achieve.
So to try and bring this to life, a couple of examples that have been floating around
the Twittersphere, one user said, Auto-GPT was trying to create an app for me, recognized
that I didn't have Node, Googled how to install Node, found a Stack Overflow article with
the link to Node, downloaded it, extracted it and then spawned the server for me where
all that person did was just watch.
And this is a good example of how you didn't have to fully brief Auto-GPT on every step
it needed to do to solve the problem, it was capable of being more resourceful than that
to be honest and figuring out how to do it itself.
In another example, somebody called Sully Omar provided a really nice series of tweets
about an application for Auto-GPT that they used where Sully says, I pretended to be a
fake shoe company and gave Auto-GPT a simple objective, do market research for waterproof
shoes, get me the top five competitors and give me a report on their pros and cons.
So first of all, it went straight to Google to find the top five waterproof shoe reviews.
Once it found the links, it created questions for itself like what are the pros and cons
of each shoe, what are the pros and cons of each top five shoe, top five waterproof shoes
for men and started to collect and analyze this information.
It continued to analyze the various sites, Sully says, with a combination of Googling,
updating its queries until it was happy with the results.
Here's an example of when it felt critically during this process, it knew that some of
the reviews could be biased or fake, so it had to find a way to validate the reviewer.
It even spawned its own sub-agents carrying out a task of analyzing the websites.
There were a few times, Sully says, when it got stuck and there was no text file, it was
able to figure out how to fix the issue all by itself.
And the result, a pretty detailed report of the top five waterproof shoe companies with
their pros, cons and a nice conclusion summarizing the report.
Oh, and it only took eight minutes at a cost of 10 cents.
So that is another fantastic example of how people are using Auto-GPT to carry out more
complex briefs that require the agent to ask questions, make decisions, carry out its own
research by perhaps accessing the web.
Absolutely fascinating stuff.
So how does this work?
Well, it's kind of like chat GPT on steroids.
And rather than just having a conversation, you can actually give it a brief and it takes
a bunch of actions.
And it does this by a series of sort of upgrades to GPT.
So it has access to short and long term memory to be able to remember some of the outcomes
of some of the tasks that it took.
Obviously, it's got access to popular websites and platforms.
It can search Google.
It's also got access to the pine cone API to give it more amounts of vector based memory.
It has speech to text capabilities.
It can do image generation.
So this is how this is much more powerful and clever than than just GPT.
I've been trying to play with it and I have not been able to get great results yet.
It sort of gets stuck quite easily in terms of my control of it.
And I also have to say I haven't been using Auto-GPT itself because it's a little bit
tricky for non-marketers to install.
You need to visit GitHub, install the necessary files, access it via command prompt.
I don't think it's super hard if you follow some of the online tutorials about how to
do this, but it might be hard enough for those of us that think they see the word Python
and get chills down our spine or we're like, oh my goodness, I have to open up the terminal
and start running commands.
I'm not sure how keen I am to get into that either.
But that's why I was using some browser based implementations of this and several have popped
up.
So there's Cognosys.ai and Godmode.space.
And in essence, they have brought, at least as far as I can tell, a decent chunk of Auto-GPT's
capabilities to a browser based interface where you can drop your prompt in and then
basically run Auto-GPT without having to install it all on your machine.
So I would recommend that people who are interested in this stuff and trying to think about how
they could give AI more complex briefs to help them achieve more complex projects actually
have a play with this.
By drifting ever closer to an assistant that can do more than just answer simple questions
or produce short from content like blog posts.
Bunch of examples I've given already, but a few other things have been flown around the
web is to have it do market research and create a business plan, ask it to gather data and
stats from the web and summarise as a report, find and compare products from various websites
as sort of akin to one of the examples I've just recently gave.
And also, alternating customer service inquiries and responses by giving the tool access to
data about products, etc.
So yes, a very interesting development.
And again, all of this has happened since GPT-4 was launched about four weeks ago and
TrackGPT was just before Christmas.
The speed here is insane.
Now there is one word of warning for those of you that want to go run off now and start
playing with Auto-GPT.
Certainly the browser based tools that I was playing with, I think they got overloaded
pretty quickly.
It's probably costing them a fair bit of money and so now if you want to access them, you
have to provide your own OpenAI API key.
Try and say that after a few runs.
In essence, what this means, you have to log into your OpenAI account, you have to go to
where you can generate your own API keys, which is like a string of numbers and letters
that's unique to you that allows you to access the system almost through the back end, if
you like.
And you have to then paste that key into one of these tools.
There's a certain amount of trust that comes with doing that.
Plus accessing the OpenAI API is something you have to pay for.
So you'll have to put some cash on your credit within OpenAI.
I have heard that the system can absolutely eat OpenAI credits for fun.
So just be prepared to keep an eye on that.
You might find you burn through cash a bit faster than you might have expected.
I don't think we're talking thousands of dollars here.
Plus, you have to put credit on there like an old school mobile cell phone where you'd
put 10 dollars of credit, 10 pounds of credit on your phone.
And when it was gone, it was gone.
So I think as long as you set it up like that, you're not going to end up going, crumbs,
I've just spent $5,000 on this, which I think would be pretty hard anyway.
But just something to keep in mind if you plan to try using these systems.
On a related note, for those of you that use generative AI writing tools, one of the interesting
tools in the space is called HyperWrite.
And HyperWrite team announced on Twitter this week that they're testing an AI agent that
can use the internet like a human.
So in the example that they show, it orders a pizza from Domino's with a single command.
And it's like a Chrome plugin.
So it just basically browsers the web and interacts with websites based on the command
that you give it.
So if you are a user of HyperWrite, you can get on the waiting list for this because they're
providing early access to some of their users.
So just log into your account and ask to be added to the wait list or create account and
ask to be added to the wait list.
The system promises that if you just describe what you want it to do, it will automatically
operate Chrome for you to achieve your task.
I have not had a chance to play with this yet.
So I do not know how buggy or powerful it is, but you could imagine that it could be
really powerful for automating tasks that are more complex where you have to be able
to browse different websites.
The market research examples from before spring to mind, not that I'm advocating it, but also
the potential to scrape content from the pages of many websites and then interrogate that
content through large, through natural language questions could be another example.
The team on Twitter said that they could imagine anyone doing anything from booking flights
to ordering food to researching complex topics and having the system manage their email.
So when we get a chance to play with it, we will report back on how good or not good it
is compared to how good it sounds, which it certainly sounds really, really good.
For marketers, I think get yourself on the waiting list and have a play because this
is another one of those things.
Could you automate with the system staying abreast of what your competitors are doing
by monitoring their websites and when they put a new product up on the website, perhaps
there'll be some mechanism by which you can trigger an alert or have it added to a spreadsheet
or something.
That's not clear to me yet, but I think that would be pretty cool.
You could do the same thing for things like conducting brand sentiment analysis, maybe
doing clever things with Google Alerts, who knows?
So I think one to watch and to have a play with if you get a chance.
Next story, we're going to talk very briefly about Amazon.
So Amazon, where have you been during the whole generative AI and wider AI explosion
of the last few months?
Well, the answer would appear to be they have been busily buzzing in the background.
Another thing that would probably be hard to say after a color arms.
They have released their Amazon Bedrock, which is a service that provides access to what
they're calling foundational models for generative AI, which includes models from AI21 Labs,
Anthropic, Stability AI and Amazon slipped their Titan model in there at the same time.
Bedrock, they are claiming, is a scalable, reliable, secure AWS managed service where
customers can customize models by pointing Bedrock to label examples within the Amazon
cloud.
So in essence, if you are using Amazon's AWS or you're using cloud tools from Amazon and
that you're hosting data there, one assumes that you will be able to leverage Bedrock,
especially if your data is somewhat labeled or organized in some way to, in essence, create
your own chat bots or other large language model driven tools that lean on the large
language, natural language processing abilities that come from these large models, but that
is trained on your own data.
So something that would instantly spring to mind for me is something like to be able to
build your own knowledge base for your website that was a chat bot instead of a bunch of
web pages, because you could train, you could train Bedrock based on that.
One assumes that it wouldn't be that hard to train Bedrock on data that wasn't already
on AWS cloud system as well.
But to be honest, I have to dive a bit deeper into that to really figure out what we can
do.
But it's certainly worth looking at if you are interested in creating LLM based tools
like chat bots, but that you want there to be able to answer specific questions on your
own data, information and content.
For example, if you've got a bunch of products and you're an e-commerce store, or if you've
got a large knowledge base or lots of technical documentation that you want to be able to
provide easier access to your customers in a way that they could just ask it through
natural language.
Martin was the first one to bring this to light, shared it with me on the Amazons and
he's sent over a few thoughts here.
This is a really exciting marketplace for foundational models that developers building
on AWS can now easily choose to integrate some of the best foundational models on the
market into their existing applications that they've built on AWS.
Because of course, not just websites and things that live on AWS, it's also apps and other
software tools.
I don't think Martin thinks that Titan as Amazon's own model is going to contribute
anything above what we've already seen.
I think it's more the ease of access to a range of different models that's exciting
here.
When he looks at how this is going to emerge and develop, he's not sure that Amazon are
as confident in their offering as open AI and Microsoft to a forging really far ahead.
So I think Amazon's move here is about time probably.
It's going to be one to watch.
We don't get the feeling that they're on the cutting edge here.
They're probably trailing, especially because mostly they're clear to be leveraging the
power of other people's models.
Right, last story for this week, which is that Google CEO Sundar Pichai warned society
to brace for the impact of AI acceleration.
So Sundar was on an interview with CBS's 60 Minutes on Sunday.
And in it, he hinted that society isn't prepared for the rapid advancement of AI.
Warning of the consequences, he said AI will impact every product of every company.
Little bit of a pause just to let that set in there because I think that is the scale
of how we have to really start to think about this as marketers and as business people.
When you think about some of the things we've featured on the podcast recently, we spoke
a few weeks ago about how a large collection of tech experts called for a six month AI
and hiatus, you know, stop launching and bringing to market new AI developments.
We also looked at OpenAI's research paper on the impact of AI on the labor market, where
they predicted that as many as one fifth of the workforce could have half of their tasks
affected by AI.
And so what we see from here is that Sundar is continuing on this theme, accentuating
the need for society to carefully consider and plan for the imminent impact of AI on
society.
So the take home message here for us is that people in the know continue to have serious
concerns about the impact of AI on society, especially the labor market.
Now what does this mean for marketers?
Our thoughts here on the podcast are that the genie's out of the bottle on this one.
We don't see this slowing down anytime soon, as we've mentioned on previous podcasts.
And as a marketer, we feel for all of us, our best recourse is to stay on top of the
topic, play with the tools as they become available and become an expert on using AI
to augment your work.
And it really is still augmentation, right?
Many of the tools are very powerful.
We've talked a little bit about AutoGPT today, but they are still massively capable of hallucination
and producing junk.
There's loads of examples where AutoGPT just gets stuck.
So I really do think human in the loop is still going to be critical.
And where we're going to add the value here is by augmenting ourselves.
So we need to learn to get really good at prompting these systems, thinking of smart
things that we want done.
So there's the creativity aspect to that.
And then learning how to prompt the system to actually get what we want.
And then I think it's about bringing your expertise to double check the outputs you
get to make sure it's not full of errors, lies, hallucinations, the type of thing that
will be mistakes that we just, none of us can afford to have in reports or content or
anything that we produce.
There's also the copyright aspect that we need to keep an eye on here as well.
Of course, we've talked about on previous podcasts, but I do think the way to resist
the challenges here is to figure out how you're going to use AI to augment yourself and that
this AI human collaboration is where the magic is going to be.
So whilst there's quite a bit of fear mongering to this, and there's been quite a bit of debate
on Twitter and LinkedIn about the fear mongering aspects, just practically speaking, these
tools can probably help you be more effective, your team be more effective.
And I think your job and our job in marketing is to figure out how we can leverage them
to maximise our impact rather than perhaps fearing they're going to take our jobs.
How are we going to work with them to be even more efficient and effective in what we're
doing?
So that is artificially intelligent marketing news for this week.
From here, we're going to transition into tool of the week.
And then after that, we should be back to normal, hopefully next week.
And apologies for the lack of witty or less than witty banter, but next week, I promise
you we'll be back talking about how poor Derby County are, how depressed Martin is about
it.
And I will continue to tease him mercilessly for those that have been enjoying that part
of the podcast.
Right.
We look forward to getting back into our Northman Cairns next week.
If you've enjoyed the podcast, please subscribe, share it with other marketers in your network
if you feel they benefit.
We look forward to seeing you all next week.
This week's tool of the week is ClipDrop brought to us by Stable AI, the company who created
Stable Diffusion, the open source text to image generation tool that many of us will
have been using.
Now ClipDrop is available through cliptrop.co and it's an online interface that's very
easy to use and it's all powered by the Stable Diffusion image generation model.
So what makes it different from the Stable Diffusion model that many of us will have
been using because we cannot actually use Stable Diffusion in loads of tools already.
For example, Canva's own text to image generation tool is in fact powered by Stable Diffusion.
Well, what makes ClipDrop different is the fact that they've put together a handful of
tools, in fact about nine or ten tools they've put together that you can do more than just
generate images from text with.
Of course you can generate high resolution images with a simple text description, but
they've really taken the power of Stable Diffusion and given it some very specific
applications.
For example, you can clean up images.
You can upload a photo, maybe it's a photo with a coffee cup in the foreground that you
want to get rid of and just by masking it off you can say clean up and then it will
get rid of that image, blending it perfectly into the rest of the image.
Very similar to Google Photos magic eraser feature.
You've also got the remove background which is pretty much as you would expect.
It takes out the background from an image and gives you a very neat cutout of the object
that you wanted to get, very similar to what you'll have seen in Canva already and these
tools are available online as standalone features themselves.
I think they don't have much of a future based on how readily available these AI powered
cutout tools are these days.
There's also a relight feature which is quite a neat one I think.
It allows you to basically add a lighting studio to photos that you've already taken.
You can relight your images, maybe you've taken a portrait photo of someone and you
want to add a bit of side lighting that you didn't have the opportunity to do when you
took the photo and you can do that and it works really well.
But one of the tools I think is particularly useful for say marketing agencies is the text
remover which will remove text from any image.
So if you've got a billboard that you've got a photo of and you think it looks great and
you could maybe drop in a client mockup on this billboard but it's got some text on it,
you can just upload the photo into ClipDrop and at the touch of a button it will remove
all of the text from that image giving you a nice clean space where you can then add
your own campaign graphics straight into the image.
The other thing you can do is the replace background tool and this is where it combines
the text generation that we've seen through stable diffusion with a new capability.
So you will add in for instance a product photo, maybe it's a, the example they give
on the website is a bottle of wine.
You add in your photo and then you want to type in a hosier marble kitchen and it will
drop that image straight into, drop that bottle of wine straight into a marble kitchen, marble
work tops and what have you.
So for creating product mockups, great for e-commerce, it can be implemented really quickly.
If you've got a photo that you like but you're not quite happy with it, maybe you want to
see a few variations, they've created the stable diffusion re-imagine tool where you
can upload an existing image and it will basically remix new images that are very similar.
Now looking through the website I actually thought it was quite interesting some of the
examples that they provided because there is one example which has like a living room
scene with sofas and chairs in the original image and lots of coffee tables and then it
shows you some remixed versions.
Now I must say that the remixed versions that it shows in the demo are not great because
you just have to look at them for a couple of seconds to realize that the chair legs
have a few too many legs and the table and the chairs seem to be merged into one another.
So at least Stability AI are kind of showing off the reality of working with these models,
these image generation tools are not perfect, they still struggle with counting chair legs
and making sure that furniture would realistically balance in the real world and of course they
still struggle with the number of fingers that people have on their hands.
But overall I think ClipDrop is a pretty neat tool to play around with.
If you've never played with the Text2Image tools then this is one of the better ones
out there to get started with for free.
You can have a play with it, the pricing is pretty good.
Like I say you can get started for free, there is a maximum size image that you can work
with at the free tier, for instance the background removal you can work up to I think it's 1024
by 1024 max so fairly small images and the image upscaler is a 2x upscaler.
There is a Android and iOS app, although I haven't tried either of those at this stage,
I was just playing around with the web interface at this point.
If you want faster generations and you want unlimited capability, so unlimited background
removal, if you want unlimited clean up pictures and web editing and what have you, it's £5
a month, so it's really competitively priced.
I think that's an incredibly good plan.
If you are a developer and you're looking to integrate stable diffusion and some of
these capabilities into your product it does have an API as well.
I think really what the ability AI have done with ClipDrop is made the stable diffusion
model very very accessible to everyday users.
Rather than having to subscribe to some obscure tool or try to hack something together using
an API if you're not a developer or even because it is open source you can download the model
and install it and run it on your own servers.
You don't have to do any of that, you can just use this web interface and it feels like
this is a very easy way for people to get into using AI powered image generation and
image editing.
So yep, that's my tool of the week ClipDrop.co.
Go have a play with it if you're interested in image generation and image editing and
you're interested in seeing where stable diffusion is likely to go next because clearly
Stability AI are putting a lot of focus into this foundational model.
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.