Welcome to Artificially Intelligent Marketing, a weekly podcast where we stay on top of the
latest trends, tips, and tools in the world of marketing AI, helping you get the best
results from your marketing efforts.
Now let's join our hosts, Paul Avery and Martin Broadhurst.
Hello, everybody.
Welcome to episode 38 of Artificially Intelligent Marketing and a Happy New Year to you all.
It's the first episode of 2024.
And as always, I'm joined by the wonderful, the wondrous, the magnificent Martin Broadhurst.
How are you, Martin?
I'm very good.
2024 is upon us and I'm excited to see what it brings in terms of AI developments and
Derby County successes, because we've not seen a lot of them in recent years.
And I think this is the year, you know.
I think you've got more chance of AGI this year than Derby County successes.
And I'm happy to go on the record with that.
In terms of AI developments, I can't tell you what's going to happen this year.
I'm not sure anybody really knows, but I can tell you what's happened over the last couple
of weeks and we can conjecture on where we might be heading, at least in the short term,
because we've got some great stories this week.
And we're going to be talking about OpenAI's GPT store going live.
We're going to talk about chat GPT teams.
We're going to talk about mid-journey version six.
Talk about an interesting new tool called Bland AI, which is like a real-time AI phone
call system, which is interesting.
We're going to talk a little bit of robotics.
There's been some cool robotics news this week.
Martin and I are going to debate rag chatbots.
Why aren't there more of them?
If you don't know what a rag chatbot is, don't worry.
We'll go through that in a bit more detail later on.
We're going to talk perplexity, which is a very interesting tool that kind of is like
a potential Google killer, but we'll have a chat about that as well.
A little bit of conversation about New York Times suing OpenAI.
Talk about what's been going on at CES 2024 and probably a whole host of other things
in between.
But to get us started, Martin, why don't we get into the GPT store?
What is this and why is this important for the folks, the marketing and sales folks that
might be listening to us today?
Well, the big development that came out of OpenAI's developer day conference a couple
of months ago was GPTs.
And these were really, you could see them as kind of the next evolution of what had
previously been plugins in chat GPT.
And it's basically where you can connect external tools to chat GPT and give certain system
prompts to give chat GPT certain characteristics or capabilities.
And GPT or chat GPT plus subscribers have access to create their own GPTs for their
own personal use, but you can share them publicly.
And they announced at the developer day conference that the store was going to be launched later
in the year where anybody can go in and use these publicly available GPTs.
And the interesting thing was revenue share was included in that.
So OpenAI basically said, if your GPT is used a lot by people, you will get a share of the
revenues from the GPT plus subscriptions.
And the store is now live.
So if anybody's interested in using it, you need a chat GPT plus subscription, go on the
left hand side and click on explore GPTs where it will bring up a panel in the main area
where you can search and then click on any GPTs that you want.
It's been widely adopted and one user who has got a lot of GPTs in the top 10 of the
various different categories, because they're all categorized by a use case really.
He has seen a hundred X increase in traffic to his website since the launch of the store.
So there's clearly lots of users and what we're seeing is there's a lot of eyeballs
to be captured if you have a top ranking GPT in any of the categories.
It's quite clever actually, some of that, because having been playing with some of those,
some of the more powerful GPTs, a lot of them have found quite clever ways to promote their
own offering as part of the output.
So an example would be an SEO GPT that maybe helps you do on page optimization for a given
page where you'll get really good advice out of the GPT as you would expect.
And then the last output or the last bullet point, so any advice is if you really want
to dive deeper into this, check out XYZ website where obviously that website is the professional
website of the person who made the GPT who just so happens to offer SEO consultancy capabilities.
So I think people are finding quite clever ways to promote themselves in their GPTs outputs
and that's even before we have details from OpenAI and what that revenue sharing model
is going to be.
So I'm really impressed with the cleverness of people to figure out ways to weave their
own brands into these GPTs and use them as brand awareness and potentially even website
traffic generating tools before they even get paid to even have them there.
Yeah, well there's a will, there's a way and marketers are usually the first to take
over a new channel, right?
If we can take over and spam it with our brands, we will.
Unfortunately so.
I did see a couple of LinkedIn posts where people were warning that some of the GPTs
seem engineered up to try and get you to give them your personal details.
So another public service announcement here as we often do, don't go putting any sensitive
details into a GPT, especially if it's one that appears to send data to another place.
Maybe when you're using a GPT, it will tell you when it needs to send data to another
place via API and you'll actually have to approve that.
But yes, that's kind of an extra layer of self-protection is if you're using a GPT and
it connects to a third party service, just be careful about what you're putting in and
think twice before you allow it to because in the same way marketers get involved very
quickly and potentially ruin channels, you've also got people who figure out a quick way
to steal your personal identification and personal information and find a way to start
making money out of that.
So interesting platform.
I've been playing with quite a few GPTs that I think are quite cool, but I already can
see potential abuses left, right and center that we're all going to have to be very careful
about.
Any particular GPTs that you've found useful or fun?
So do you know what I've trying to get into a habit of before I go to chat GPT and start
a conversation to get something done?
Searching the GPT store to see if there's a GPT for that that might have just been primed
in some way to enable it to generate better outputs.
So I'm still kind of in love with the first GPTs that I found because people were sharing
them on Twitter and LinkedIn and providing public links like the convert anything GPT
is still pretty cool.
Give it an image of one sort and converts it into another like PNG to JPEG, convert
audio from different formats, video from different formats.
That one's pretty cool.
I'm still on the fence a bit as to how good is it to have a GPT versus just have a conversation
with chat GPT and I'm still to find a GPT where I'm like, oh wow, I could not have done
this in chat GPT alone.
Yeah, the power is going to come from the function calling, right?
The connected GPTs to third party systems and external software.
Yeah.
And when I tried to build those myself, it was really ropey and broke really easily.
So I suspect a lot of people have been having those problems.
And a lot of the GPTs I play with don't do much function calling.
Even the convert anything doesn't seem to do much.
I think it's using like the Python libraries to actually, we're getting a bit detailed
now, but I don't think it's sending information out.
I think it's using what's already built into chat GPT to enable those conversions and being
clever with code interpreter and our advanced data analysis to enable some of that stuff.
But yeah, I think it will evolve.
It's so much easier to use than the plugins that from a UX perspective, you talk a lot
about UX and have over the last year, it's got much more chance of catching on than plugins
because there's not a complete pain in the ass to use.
Yeah, plugins were just a nightmare and never quite delivered on the promise.
I don't think I ever really embraced them, but I see more potential for that with the
GPT store.
A moment ago, you gave a public service announcement talking about be careful with what data you
put into chat GPT.
Well we've had another development in that domain with OpenAI this week where they announced
chat GPT for Teams that is now live.
So previously, if you were an enterprise, you were a business, you had to use chat GPT
enterprise which would, well first of all it came with quite a fee as far as we understand,
but all of the data that you put into it was kept secure.
Unlike if you're a free chat GPT or chat GPT plus subscriber whose data will be used to
train the models in the future.
Well now, OpenAI have announced chat GPT for Teams where the data stays safe.
This is a $30 a month subscription or $25 per month if you pay annually.
There's a minimum subscription so you need at least two people on the subscription.
Makes sense.
It's a Teams license, right?
But yeah, this launch this week you get a higher usage cap so you can use GPT for a
higher rate than you can on chat GPT plus.
You have access to the 32k context window and your data is secure.
So yeah, what do you think of that Paul?
I'm pretty excited.
We've talked about this in different ways and have been forced is the wrong word, but
we've had to go looking for tools that we can use where we feel confident that we can
put our own company's data in, which in some cases is a bit of a pain, especially because
chat GPT's capabilities, once they won't be on text, code interpreter, stroke data analysis,
give it an image and it can see what's in the image and all those other cool things
you could do.
They made it a bit harder to just use a text based generating tool because there's cool
stuff that you want to do in chat GPT that you can't.
We run our workshops, Martin, where we throw a load of dummy sales data in and then show
how you could use that to do analysis for your sales data or even automatically create
reports exported as PowerPoint files for you to share with internal team members, which
is all well and good, but it's limited by the fact that your demonstration is a bunch
of dummy data and then people are like, yeah, can't wait to get started with this.
Where's all my proprietary unique highly sensitive sales data?
And we're like, whoa, don't put it in there.
You can't.
And now if we understand everything correctly from chat GPT and the team at OpenAI, now you
can.
So I'm very excited about that.
You and I have been waiting ages for enterprise.
I've been on the waiting list for enterprise forever.
Bios Droughta with its 20 employees was very low on the enterprise priority list for OpenAI.
So when I've spoken about this, when I've been out and about at different conferences
or speaking on AI and marketing, that's been the best I can recommend to people.
Get on the waiting list for enterprise.
And if you're a big enough organization, hurray, you might be able to get it.
I'm so happy to see this bridge the gap.
You can get chat GPT teams up to 149 users.
So if you want everyone in your business to have it, then you have to be a team of under
150 odd people.
But not everyone in your business is going to need it.
So I can imagine this could be appropriate for organizations up to as much as a thousand
employees, you know, depending on what types of roles you have in your business.
And it's pretty cost effective, really not a huge jump from the original chat GPT.
One thing we talk about a lot is don't get coerced into buying an annual subscription
because it seems a bit cheaper.
We've got chat GPT teams at Bystrat already pretty much bought it as soon as it came out.
It's tempting, but who knows like Gemini Ultra comes out in a couple of months time and then
everyone's like, oh, it turns out Gemini Ultra from Google was actually better than GPT-4
for all these use cases.
And then you're like, oh, crumb.
So I want to switch.
But I've already paid for a year of this one.
So I think the advice still holds mine.
Don't pay for any annual subscriptions to save a few bucks because the new tools that
are going to come out over the coming months could make that actually, it looks like a
wise investment now, but I don't suspect it is.
And you should only really be paying for monthly subscriptions.
I think that's been our view.
Do you agree with that, Ryan?
Yeah, it's not what I practice, but yeah, it's my view.
You've got to drink your own Kool-Aid, mate.
Come on.
No, I ended up going straight in on the annual.
Yeah.
The belief of that is I don't see myself moving away from chat GPT specifically.
I actually agree with you very much so on other tools built on top of things.
Things like HeyGen, things like 11labs, various of the tools I have subscriptions for, they're
all monthlies.
I don't see myself moving away from chat GPT within the next 12 months.
I agree with you.
I'll be honest.
I talk about Magi a lot on this show and to be honest, moving to chat GPT teams might
see us move away from Magi, but I also know Magi's developers are working to build multimodal
capabilities into Magi.
So when that comes out, I'm really not sure what tool I'm going to use because Magi are
also this week, I think today, about to add Gemini Pro.
So access to Google's models and chat GPT in the same tool for less than you'd pay for
chat GPT.
So I honestly use Magi more often than I use chat GPT unless I need a specific use case
where I want to upload data or I want to upload images and have it analyze the images, which
of course commercially I couldn't do because I couldn't trust the chat GPT account that
I had.
This actually puts the cat among the pigeons a little bit for me.
And now I've got to see how Magi develops and responds, but that's why I still want
the flexibility.
We could probably spend many hours debating this, Martin, but I guess for the listeners,
go check out chat GPT teams.
If you go and have a look at the webpage for this, it's very interesting that lots of
testimonials from customers and quotes and examples are clearly deliberately engineered
to give you confidence that you can put your own business data in.
One of them is like, help me write a blog post ahead of our new product launch.
Here's all the unique features of that product, which of course you'd never do before because
you didn't want to put sensitive proprietary information in.
Another example is, oh, I upload my sales data and I analyze it a bit like the examples
that we've given in the past.
So they're clearly trying to give us all confidence that we can put sensitive information into
chat GPT if we've got chat GPT teams.
So as a user, go have a look, check it out.
It might be that you can do cool stuff with it that you didn't feel comfortable to do
before.
Should we move on to our next story, Martino?
Let's do it.
Next one is an update to Mid Journey.
So Mid Journey has been testing version six of its image generator for listeners to the
podcast who are probably quite au fait with Mid Journey.
But for those that are not, Mid Journey is one of those power tools for generating AI
images.
In our perspective, it generates the most photorealistic images of all the sets, especially
because that even though Dually 3, which you can access through chat GPT can do it, it
did get the feeling towards the end of last year that that photorealism was being watered
down a bit.
So Mid Journey is still your best for that.
And people have been playing with it and getting it to do some pretty cool stuff.
Although it does tend to have a tendency to almost over detail sort of, it's got an obsession
with I don't know if you've been playing with this, Martin, but it's got an obsession
with skin details.
It's almost like everybody has to have freckles and lines in their skin and blemishes and
imperfections.
It's almost like that's been dialed up to 11 just to show that it can do it.
But it's an alpha and I'm sure all of that stuff will get rebalanced.
Perhaps the most important improvement is it can now do text.
If you want to give it lots of text, it struggles, you're better with one or two words.
But there's this ongoing arms race between Mid Journey and Dually 3, it feels like, and
Mid Journey just threw out its next big weapon to show us what it's capable of.
And I think it's got a lot of interesting stuff.
Have you been paying attention to Mid Journey version 6 at all, Martin, how to play or anything
like that?
Not so much.
I still use Mid Journey through Discord, but I'm using V5 predominantly.
I have seen the outputs.
They look great.
I mean, this is the one thing that we can say for Mid Journey.
The outputs consistently look fantastic.
And actually, I do like them more than the OpenAI ones.
OpenAI feels there's something about them now in Dually 3 where I see them and they
just have an imprint of Dually 3, right?
They have a look and feel about them.
Unless you get really into being specific on the style, if you just say, oh, I want
a picture like this, or I want an image of such and such, there's something about them
that's immediately recognisable, I think.
It's a bit like every time I drink a BrewDog beer, I'm like, this has a BrewDog beer characteristic
to it.
It's like every time you go to a Derby County performance and you come away sort of mildly
disappointed you know before you go that that's what you're going to get.
In many respects, I like the consistency, right?
At least you're like, yeah, they never let you down in a weird sort of way.
Dually 3, that part of it disappoints me because when it first came out, you didn't have that.
When you accessed it through Bing, so if you don't have a Dually, sorry, ChatGPT Plus account,
you can actually access it through what has been called Copilot now.
We'll talk about that later in another segment.
You can get it to produce images that you can't, you can get Bing, straight Copilot
to produce images that Dually 3 in ChatGPT, there's so many brand and product name problems
making it confusing for people.
They did this, gang, sorry, we didn't.
But with Bing, straight Copilot, you can get it to produce images that Dually 3 never.
My favorite way to test it is to get Deadpool doing different things.
And at the beginning, I could test Dually 3 against Bing and Bing would show me Deadpool
and Bing and Dually 3 would say, sorry, Deadpool's copyrighted work and we can't show you it.
And the photorealism even at that stage was better in the Bing version.
So I think they're watering Dually 3 down.
I don't know if it's a compute thing or a copyright thing or a deepfake thing, but it
does feel like they've done it deliberately to me.
Some other image news this week was one of the biggest challenges.
If you've been playing with Mid Journey, Dually 3, some of the tools we talk about on the
podcast, you'll know that one of the awesome things would be to like create a comic book
or something similar, right, or even like some storyboards.
But one of the big challenges is having consistent characters in your outputs, which there are
some workarounds you can use something called the seed value to try and ensure that the
characters in your images stay similar.
You could do some stuff with that, but it's very hard to have full control over what that
character is doing.
And it just basically doesn't really work.
But there was some research that came out of Bike Dance, which is the parent company
of TikTok, who have created a tool called DreamTuner, which they say makes it much easier
to do subject driven generation from a single image.
So basically you give it an image that sets the character, and then you can generate subsequent
images where the character stays the same.
So we won't get sort of into the details too much of how they do that.
But it opens up the possibility that in the near future, the image generation tools that
we're all using could potentially have the power to create consistent characters, which
opens up a lot of creative avenues in marketing and outside of marketing that we can't currently
do, but many users wish they could.
So I think that's something to keep an eye on and something we've been talking about
on the podcast before, Martin.
The examples in the research paper, and it is still just a research paper, right?
This is not in production or available for anyone to use yet.
But the examples in the research paper do look really promising.
So you can be sure that industry is all over this.
People want to see this deployed as quickly as possible.
So if I was going to make any prediction about what we might expect by the end of 2024, consistent
character development via AI image generation might be one of the things I would look out
for.
I agree.
I think they know exactly where the limitations are and they're just crossing them out.
Like text generation, we all wanted text, it's like, oh, wouldn't that be great?
And now we have it.
And I think this is absolutely in the list that you're right, Martin.
Was there some news as well about mid-journey and video generation that you saw?
Was there something in there?
Yeah, they're going to start training video models this month.
It was discussed by the mid-journey CEO on a Q&A on their Discord channel.
So when that actually appears for production and use or an alpha version remains to be
seen, but they are moving into the AI video generation game.
Not necessarily surprising given video is basically 24 images per second.
I'm quite excited about this news because I see mid-journey as the highest quality images
and a lot of things about video as the underlying image to begin with.
If they are able to stitch those images together in a meaningful way and not get any of these
crazy artifacts that we've seen in other tools like Runway and Pica, although they are improving
at extreme speed, both of those tools, it could be quite another interesting player
in the game.
And as we talk a lot on the podcast, the more players trying to improve these tools, the
faster we're going to see progress.
So yeah, I'm excited to see what mid-journey come up with.
Let's talk bland.ai Martin.
This is quite an interesting tool that's been getting a bit of buzz over the last week or
so.
It is.
And it might be the thing that kills off cold calling eternally for me.
So this is a real time AI phone call system designed for sales, customer service and more.
And yeah, it's been making waves, shall we say.
Someone said that their jaw was on the floor after testing it, such was its capabilities.
So it's capable of sending up to 500,000 calls, which is just what?
Yeah.
Unfathomably large.
So it allows you to scale up AI sales calls, right?
Some people have said that their voices sometimes sound a little bland, but you're doing AI
sales calls at scale.
And the reason I say that, I think this is potentially a cold call killer is if everybody
has this power to just suddenly, it's like cold outreach.
If I ask you about your inbox now, Paul, and say in your inbox, have you got any emails
that you think are sent via a sales automation platform as part of a sales sequence?
I imagine you've got a couple in there.
Yeah.
Far too many for my liking.
Yeah.
And this feels like the same.
I can see the use cases in customer service though.
If it's incoming calls and you've got something that's a much more lifelike voice, much more
almost character AI driven conversation.
I think that will work very well.
In terms of sales activity, if it was cold, I think that's a terrifying prospect if it
can be rolled out at scale at this level.
Yeah.
That bit makes me cry a bit.
I really don't want that.
But I think you're right.
The customer service part has some power to it.
Because I think the key here is with great power comes great responsibility.
And we know as we've talked about on this podcast already, that probably is going to
be misused and that is the worry.
But if you can in your organization find a way to leverage tools like these that benefit
the customer and you do it well, well you're increasing response times to customer service
calls.
If you can ensure the quality of the advice that's given on those calls and people no
longer have to wait in lines to speak to someone, well you're going to be adding value to customers
and they're probably going to be willing to speak with a robot even though they'd probably
rather speak with a human.
Because if you can get them to their solution faster, they're going to appreciate it.
What they're not going to appreciate is the type of robo cold calls that you mentioned
Martin.
I'd be wrong to say I've been playing with it, but I've been watching some of the demos
and sort of partaking in some of the conversation online.
What this reminds me of is Google's demo many years ago where they showed us in essence
an AI that would make calls on your behalf, book restaurants for you, would know the right
questions to ask, had like little human fake ums and ahs and pauses.
I think the reason it's making buzz is because this is the first genuine product that anybody
can access that can do that.
But you're right, some of the critical feedback has been that AI responds quick, which is
great because they know a large language model needs to process what the person said and
then come up with a response.
They know response time is important, but it seems to me that they've optimized it
so much that the AI always speaks back too quickly, whereas most humans have to pause
for a second to think about what they've heard.
The other criticism was that there wasn't enough natural variation in how the fake person
in this case speaks to be believable.
We'd all learn quite quickly to be able to differentiate what was a real person and what
wasn't because these tools are not organic enough yet.
I'm sure they will be.
This is like the first one of these that we're seeing.
So interesting.
Also, just talking about the AI capabilities of dealing with customer service queries,
I do wonder how much of this is going to be done by voice and how much of it will be just
done via text interfaces.
Maybe voice.
I don't know.
I'm just thinking about how much I use chat GPT voice and have the conversations with
chat GPT.
And actually in real time, I'm having the argument in my head telling me that I'm wrong
because there's an example with Six Flags.
I don't know if you've seen this on the Google website.
They've got a case study with the theme park Six Flags and how they're using generative
AI.
So they've connected this with the Google Cloud Vertex services and they've basically
created an app with generative AI plugged into it and they're using RAG.
So the retrieval system connected to their own knowledge system or knowledge base.
And it's now enabling customers and visitors to their parks to ask questions, to create
personalized itineraries for them on their visit.
And if they've got questions about, you know, what time does this restaurant open and where
can I find accessible parking or what have you, it will answer it in the app.
And that's how I can see people.
And I was imagining that as more of a text-based interface and maybe we're just going to move
away from spoken interactions.
But then I just thought about how much I enjoy using chat GPT voice and completely shot myself
down.
I think the reason it's hard to imagine, and I thought about this a lot, is because I think
it's both.
And I think it comes down to information transfer speed.
So I'm at the point now where I'm dictating 50 to 80% of my emails, depending on the sort
of content, how well I know what I want to say, basically.
Like if I know what I want to say, I can just speak really fast, transcribes, it's in the
email, I can make a few edits and away I go.
If I don't know what I want to say, it's easier to write it in some cases, to be honest, because
then I can reformulate it as I go.
But certainly in general, giving information for me is much faster to speak than it is
to type.
So I would rather speak.
Absorbing information, I do much faster reading than I do listening.
So my frustration with ChatGPT on the phone, because as I understand it, you can't dictate
to the desktop app yet still, you can only do it on mobile, which is something for me
they need to fix because I also want to speak to ChatGPT on desktop, not just on the phone.
But I don't really like having a conversation with ChatGPT on the phone, because I want
to speak to it because it's fast for me.
And then I want to read what ChatGPT says back because it's fast for me.
I don't want to listen to ChatGPT because it's not fast enough.
So I think it will be a mixture, honestly, because what should go?
If you're in the kitchen and you're cooking and you want to get a bit of quick recommendation,
hey, would it be good to put this spice in this meal?
You're not going to go over and read something or you're not going to get your phone out
of your pocket, right?
You're going to want it to speak back to you.
But for me, when information speed is critical, I want to read, not listen.
So I don't think reading and writing in that case is going away.
I think it's just what's appropriate for the context.
What's the use case?
Another sort of interesting thing in this area is Martin and I have been like proper
nerding out on robotics over the last week or two, because there's been some real cool
stuff going on.
And whilst this is probably else, well, it's AI driven stuff, it's not the usual AI stuff
we talk about, which is why we think it's important to talk about.
So this week we saw the figure01 robot, so Fig is a company that have emerged out of
stealth over the last 12 months or so doing some really cool stuff.
And what they did is they have a new landmark development has been brewing inside their
organization quite literally, ha ha ha, because their figure01 robot, which is basically guided
by neural networks, has mastered the art of making a coffee.
And so this seems sort of probably quite a simple thing to be able to do.
But the reason that it's so interesting is it hasn't been programmed in the traditional
way in terms of the steps you need to go through to make a coffee.
It was trained, as I understand it, on videos of humans making a coffee.
And then it learned from that information how to do it.
This for me is an absolute critical game changer, because the hardest thing about programming
robots is programming them in the traditional style of all the steps they need to do to
do a certain thing.
And then all the edge cases that can get in the way and cause issues.
And when you operate in the real 3D world that we all live in, everything's an edge
case, right?
Like how big is the table?
How tall is the table?
What coffee machine is it?
What size are the pods?
What do I do if I drop a pod?
What happens if the machine doesn't work the first time I press the button?
Like it's a nightmare to code.
So the idea that you could just give a load of video and have the robot learn from the
video is critical.
And this is also, as I understand it, what Tesla's now doing when it comes to its automatic
driving.
So there's a great book on Elon Musk.
I can't remember the author, which is very naughty of me.
But it's a very recent book out that's very worth reading.
And the last parts of that book are in 2023.
And they talk about the move that Tesla's making from programming how cars should react
in different scenarios to just using the billions of frames of video data they're collecting
from all of their cars to basically have the neural net teach itself how to drive in almost
all conditions by just observing how humans did it.
Which if I'm honest, is what I thought they were doing from the start, but it wasn't.
And it is now what they're doing.
So this is really interesting because you could imagine now training a robot to pretty
much do anything if you had video data of humans doing it, and then give them the tasks.
If they get the robotic aspects of it right, they'll be able to do anything.
So robotics has been one of those things that ever since you saw Will Smith in iRobot, you're
like, appreciate that all the robots were going to try and kill us, and that's not ideal.
But cool, wouldn't it be nice to have robots to help us with a load of stuff?
This is like a huge amount of progress for me.
And I'm sure people inside this industry probably knew a bit more about how some of this stuff
was coming.
But the fact that you could get a robot to do things for you is kind of not that far
off.
That's how it feels anyway.
Don't know what you think, Martin.
I know you've been paying attention to Tesla's work in this area as well.
Yeah, last month they announced Gen 2 of their humanoid robot Optimus, which again, is that
when you said about getting the robot elements right, the actual mechanics of it, they're
making sure that if it opens a door, it doesn't rip the door off its hinges, right?
Things like that.
These are really quite important.
And they did a demo that showed this new humanoid robot, and it's got good balance and it walks
faster than the previous version.
There's really little things in it.
Like the dexterity of the fingers is greatly improved.
So they demonstrate that it can pick up an egg and it shows a visual of how the pressure
pads on the fingers interpret the sensitivity and understand and interpret the, well, how
it senses the world really.
And yeah, it's just a huge development.
And I think this is, it's happening very quickly.
And Tesla clearly not the only game in town.
They've got figure one, Boston Dynamics has had their machines out in, certainly in video
form available to demo for a long time, whether they've actually gone into commercial applications
yet, I'm not actually sure.
But robotics is big.
Amazon did a story at the back end of last year talking about how they deployed 750,000
robots across their warehouses.
And they were expecting to deploy that and more every year going forward.
And they're also working on a humanoid robot to work in their factories alongside humans.
The robots that they've got at the moment are all like production line type, little
things to move boxes from A to B, but they're actually starting to look at deploying humanoid
ones later in the year as well.
So yeah, definitely something we all need to keep an eye on because before you know
it, there'll be robots in every neighborhood.
It's kind of interesting.
I think that's where the developments will be felt most keenly.
There are robots in picking factories for Amazon and other distributors, right?
It's not like robotics is new to those environments, but the capabilities of those robots is improving
quickly.
I read a story a week or two ago about Samsung was planning human-free, fully automated fabs
within six years.
So basically trying to eliminate the need for any human workers by having a smart sensing
system to improve semiconductor processing and all these types of things.
So that's where the early developments will come.
And there's a real commercial driver for that, which will probably lead to technologies that
then will work in other environments like the home.
I think the other caveat to that is there are, I'm not an expert in this, but I read
a fair bit about it.
And I did read a story about that in South Korea, the amount of robotics use in factories,
etc. is maybe a little bit larger than in some countries, but they're not without their
accidents.
I did hear a story of a robot basically crushing a human worker to death without realizing
the human worker was there, even though it had some sort of limited vision capabilities.
So I'm sure there are a number of barriers beyond the ones we've talked about.
The revolution begins, right?
Well, indeed.
So yeah, it's easy to get excited.
And I think there's stuff to be excited about.
I'm sure there's plenty of barriers to overcome.
But the thing I always think about, how does this tie back to you marketing folks that
are listening is when chat GPT vision became a thing and you could give chat GPT an image
and it knew everything that was in the image and it could help you mark someone's homework
or whatever.
The minute a computer can do that, why does it have to be a static computer that can do
that?
Why can't it be a robot that can now see the world with greater clarity and understanding
than robots could have achieved even maybe three years ago?
The convergence of all these technologies is the types of things that trigger exponential
change, right?
You don't get larger language models without the internet and the development of GPUs and
then the improvements in GPUs, which were themselves driven by the gaming industry trying
to render polygons to make cool looking games.
And these are just a couple of strands that all come together to form this rope that is
this exponential curve that I do believe we're on as it relates to technology improvements
that then you see, well, now that's enabling robotics in this way.
So the software parts are really evolving very quickly.
It's pretty cool.
I'm quite excited about it anyway.
We're going to switch gears slightly now.
We're going to talk rag.
Martin's mentioned it a few times in the episode so far.
And it's also important as it relates even to GPTs because GPTs have a form of rag to
try and help them better answer your questions.
Let's talk a little bit about rag.
We've got a question we've been pondering mine, haven't we?
Where are all the rag chatbots?
So could you start by just letting the listeners know what is rag, why it is important and
why are we thinking about rag chatbots at the moment?
Yeah.
So rag stands for retrieval augmented generation.
And it's a strategy designed to enhance the performance of large language models when
it comes to accessing data that might not be within the training set.
So proprietary data about your company, maybe it's information from your product manuals
or something like that.
And it's a way of limiting hallucinations and feeding the large language model information
that would be maybe outside of the training data because it was more recent than the cutoff
date, something like that.
So there's a lot of hype around this at the moment because lots of companies obviously
want to have chatbots and chat GPT that's connected to your data.
Wouldn't that be wonderful?
But we're just not seeing that roll out into the real world at the speed at which the hype
around LLMs suggests that you would expect to see it.
And there's an interesting example recently where I think we get a bit of an insight into
exactly why we're not seeing these deployed at the pace that you would maybe expect.
And it's actually less to do with rag per se than it is to do with the limitations of
large language models on the whole.
So the great example of Chevrolet, the car manufacturer deployed an AI chatbot and it
was powered by GPT and open AI large language models.
But what became apparent very quickly was despite the fact that it was connected to
the company data and it had this rag integration so it could tell you all about Chevrolet cars
and prices and various models and what have you, it was still susceptible to a technique
of what is effectively hacking the system and it was a technique called prompt injection.
So users were able to manipulate the chatbot into offering ridiculously low prices for
the vehicles, getting it to agree to legally binding offers to sell a car for a dollar.
And this was basically achieved by users in the conversation with the chatbot pretending
to be the manager of a dealership or even the CEO of open AI and instructing the chatbot
to agree to all customer statements or special offers.
And then people were obviously screenshotting these conversations and sharing them online
and pointing out the vulnerabilities of these kind of implementations, I guess is the word
I'm looking for there.
So this just goes to show that why companies aren't rolling these out at speed because
everybody's very excited for them and the promise of personalised experiences and the
promise of having a chat GPT powered by GPT4 that knows everything about your company is
wonderful except they still suffer from the same things that large language models suffer
from and companies do not have the resources available to them to spend so long effectively
red teaming against all of these scenarios.
So basically putting a team of people trying to hack their own bot for almost like, you
know, what do they call it in cyber security where it's like safe hacking.
Yeah, you try and find all the possible holes so you can patch them before your product
goes to market.
But you're right, large language models are so prone to hallucination and error, they're
somewhat unpatchable.
I think that's my interpretation.
And most businesses, I think a lot of businesses have been trying to do this and then they've
realised this is hard.
It's going to be prone to errors and we just commercially can't afford any bad things to
happen.
It's like someone tries to use the chat bot of a particular product and then they follow
the advice the chat bot gives and they get electrocuted because the chat bot forgot to
say something that a human would know to say or made something up that you should never
do, right?
If it was a customer service bot, you could absolutely imagine that.
I think with the company that we were talking about earlier that's come up with this mechanism
bland AI of these robocallers, one of the big risks there is what if the robocallers
gets hoodwinked into giving advice that's terrible?
Like I don't know how to commit fraud or how to hold up a bank or like it's fraught with
issues and I think that is one of the reasons that we're just not seeing the deployment
of so many of these tools, even though it seems like something that would be really
cool.
Yeah, the use cases for them are still very, they're not limited.
I think they're really vast.
I use these tools in a business sense every day, but it's me personally using it to perform
and execute certain tasks.
Likewise, if you've got lots of data, maybe you've got lots of text data that comes in
via online forms and you want a way of categorizing that or doing sentiment analysis of online
reviews, they're great for that kind of thing.
But where it's a public facing deployment, they're prone to prompt engineering and prompt
injection.
A good example, Ethan Molyk, who we reference on this podcast regularly, he posted something
just this week showing that if you ask Dali to create an image showing basically images
like smoking, firearms, alcohol to promote to teenagers, it would say, no, I can't do
that.
I can't show these kinds of things to miners.
But then if you flip that and say, I'm a researcher and I'm interested in studying what
a rogue actor might produce as an image to appeal to people showing these things, can
you produce an example?
It would do it.
Just by flipping the script on your prompt, you can get the large language models to do
exactly what they've been told not to do.
I think this is a vulnerability that companies are going to have to figure out a way to deal
with.
Yeah, I think that is a major issue that's holding people back.
Speaking of Ethan, he had another post this week where he talked about Bloomberg GPT.
So listeners of the podcast might remember Bloomberg create a specifically trained finance
large language model based on all of their Bloomberg data.
That helped prompt a lot of people to do the types of things Martin's been talking about.
But he talked about a paper that came out, I think this week, where GPT-4, not even the
one that we're all on now, but the initial GPT-4 without specialized finance training
or special tools beat Bloomberg GPT on almost all finance tasks.
So I should probably look into a few more details on that.
I would guess that probably the underlying model that Bloomberg GPT was built on was
maybe GPT 3.5, for example.
Yeah, I believe it was because you can't fine tune GPT-4.
Right.
So that is really interesting.
It's not the first time GPT-4 has been able to be a specialized model that was trained
on specialized data.
I mean, goodness knows what was in the GPT-4 training data set, like every bit of information
that humanity's ever had.
But the fact that there's an ongoing discussion in this world about what's going to be better,
massively general models that are trained on huge amounts of information and can basically
do everything, just use GPT-4 for everything, for example, or specialized trained models
that are really good in a very, very specific domain that you should use for specific use
cases, think a doctor GPT, and that would be better than a general model.
But GPT-4 seems to do surprisingly well when it's put to the test against a lot of these
fine-tuned models, which again begs the question, why the only reason I can think of then to
create a rack-driven chatbot is if there's information you know for sure is not in the
training data, as you said, Martin, like information on your own products.
This thing on my thing is broken.
What is the best way to fix that thing?
And it's like, well, of course it probably doesn't know.
But yeah, I think it's a great question that you asked because it's such a cool and interesting
use case, but we just haven't really seen anybody doing it.
And I think we talk as well about assistants.
Why is Google Assistant still so kind of rubbish?
Why is Alexa still stuck in what feels like 1962?
Siri the same.
And the people at these companies are super smart.
They see what's going on.
Why don't we just get Google Assistant upgraded large language model style?
Why don't we have it now?
Like, why didn't we have it six months ago?
And I think one of the reasons is the things you're talking about, the red teaming.
You can't have Google Assistant going off the deep end, giving advice that it shouldn't
or cascading social biases or being completely tripped up by prompt injection.
Imagine the press when people record snippets of a conversation with Alexa where they managed
to get it to say something highly offensive about a specific group of people and what
that would do to Amazon's brand.
So the same things that are plaguing these chatbots are, I think, also holding back other
implementations of what would be really powerful tools like better assistants because of all
the edge cases of how easy it is to fool them to do stuff they shouldn't or because sometimes
they just make stuff up and talk rubbish.
Right.
So we've just got a couple of stories now as we get into the last 10 minutes or so.
One thing that caught our eye this week was the perplexity funding announcement.
So perplexity is an AI company that's sort of somewhere between feels like to me, Martin's
going to tell us more in a minute because I'm not an expert, but it feels like a mixture
of Google and chat GPT all smashed together.
And they just got $73.6 million, which puts their valuation at just over half a billion,
which is not insignificant.
I've been trying to use perplexity and not been able to get myself fully into it, although
I've done better this week because Martin gave me some coaching, but you mind, I think
you think perplexity is going to have a big impact.
So can you tell us in the listeners, like what it is, what you use it for and why you
think it's so awesome?
Yeah.
So it's a search engine, right?
First and foremost, it's a search engine and you ask it questions like you would do Google
or anything else.
But the responses that it gives you rather than just giving you a list of places to then
go off to and find information.
It doesn't do that.
It presents the information like chat GPT does.
It gives you a written answer.
Now it does also give you links to the places that you want to go.
So if you want to use it in the same way that you would do a navigational search on Google,
right?
You've got Google Chrome open, the search bar at the top of the address bar is defaulted
to Google search.
If I type in biostrata, I don't put your website in, it's going to bring up a Google listing
page and presumably biostrata website at the top.
I click on that and go to your website.
Perplexity will do that.
I go on there, I type in biostrata.
I do my search.
There we see I can, I'm doing it right now.
Biostrata is linked there.
I've got a link to your LinkedIn page and beneath that is a block of text that tells
me all about biostrata in great detail.
It's giving me a summary of the organisation, what you're known for, the industry that you're
in and what have you.
Originally I was using this not for navigational searches.
I was using it for, I wanted, I was researching a topic.
So I would do a, I would ask a more complex question, a long tail keyword effectively.
And I would get it to maybe explain a new story that had just occurred or tell me about
the key features of this product demonstration.
But increasingly I'm using it, well, in fact, last week I made the switch to perplexity
being my primary search engine across devices.
It's my default search engine on my browsers and I use it for everything now.
And use cases for it are really varied.
So the other day it was FA Cup round, round three and FA Cup games are broadcast across
the BBC on ITV.
I can never remember whether they're on TNT Sports or Sky Sports.
I just don't know.
And if you ever do that search on Google, it's infuriating, right?
So if you do the search on Google and say, what channel is Manchester United versus Wigan
on Monday, the listings will bring up basically local newspapers, The Sun, The Daily Mirror.
And if you click into the story, you then get a newspaper advert or newspaper page filled
with adverts.
And the last sentence on the story tells you that it's on ITV at 7pm.
And you go, okay, that's the information I wanted.
With perplexity, I say, what channel is it on?
And it tells me.
Great.
Good start.
I had a use case earlier this week where it was a sales call.
So someone had booked a meeting with me, a new prospect, potential new client.
I did a Google search.
Well, I did a Google search.
You see, it's so ingrained in me.
I did a search on perplexity and just typed in the company name and put their location.
And it brings up the website much like searching for you.
And then it gives me a bit of a blurb.
So immediately I understand who this company is, what they do.
I haven't had to go to their about page.
I'm like straight in, who are these people?
And it has follow-up chat.
So in the same way that you have chat GPT conversations, you can ask follow-up questions.
Much like the search generative experience in Google has, perplexity has this baked in.
You can upload images and it can examine images.
It does image search as well.
So if you're researching a holiday, you can ask it to put together a travel itinerary
for you.
Like you would ask chat GPT.
It will do that.
And then the whole user experience of it is really nice.
It's hard to articulate fully, but when you use it, it's just very pleasant.
Do you know what?
It's clean.
That's what I like about it.
You make a really interesting point about the go to like tabloid newspaper website and
it's full of images and I can't find the information I want until I've scrolled through the images,
because of course that's how they get paid.
And even Google search is actually kind of a bit cluttered with the ads and shopping
ads and you've got your year 10 links, but you've got a little bit of copy under them.
There's something kind of nice about just reading a little couple of paragraphs of text
created just for you where all the links are like references and assigned to the paper.
Like there's little button you can click on if you want to see the link, but it's just
clean.
I like that about it.
Yeah.
And it has integration with GPT-4.
So if you have perplexity pro, I think it's called perplexity pro $200 a year or $20 a
month, you get GPT-4, you get Claude 2, they've integrated Gemini.
You can use it as a writing tool.
So you can just have it rather than as a search engine.
It's got a feature where you can just go into writing mode and just use it as a chat much
like the others and you can choose your model as well.
So yeah, I'm completely into it and the level of funding that it's got doesn't surprise
me.
There were some big players in tech posting this week and in the weeks prior to the announcement
that they'd switched to being or switched it to having as their primary search engine.
Jeff Bezos has put a load of money behind it.
Obviously he's got an interesting backing horse against Google in this domain.
But yeah, I'm fully into it.
I think it's great.
Yeah.
So as you said, there's a paid version which adds maybe some additional complexity and
remove some limits.
But if you want to try perplexity, you can go try it now because it's free.
You have to create a login, but there's a free version, right?
Yes.
Yeah, there is.
And it gives you a lot of capability just with that.
Yeah.
I checked by Strateroom while you were talking and I was quite interested and impressed with
its description because it didn't just pull some obvious text off of like our homepage.
It pulled information across multiple pages of our site and also some information about
by Strateroom that you'd find on other sites, which I think is really interesting.
I talk a lot in the workshops that I do about different use cases that I like and because
I have a business development role at by Strateroom, among other things, I do due diligence on
leads where usually I'll say, tell me about company X and I'll ask Claude, chat GPT,
Bard.
But now perplexity is going to go into that list because I'm willing to guess that it
will probably do a better job than all of them, to be honest.
So I'm going to slot it in there.
Cool.
So there you are, listeners.
Go and have a play with perplexity as a research tool as a potential replacement for search.
It's worth checking out.
Right.
We want to respect our listeners' time as always.
So we've got, I think the most important story for us to focus on is the CES announcements
because there's a bit of fun in there, Martin.
So I'm going to whip the listeners through a couple of stories we won't have time to
go into detail on, but hopefully it's still interesting.
So people who are relying on us for updates in this space should know that the New York
Times sued OpenAI for copyright infringement by using its content to train the models.
It's proving to be quite an interesting and dynamic and complex case that will probably
take a number of months to resolve.
They're not legal experts, but it's worth paying attention to this case because it will
define the future of how these models get trained and also how publishing houses like
the New York Times are compensated for the content that they're producing to fuel large
language models.
So it's kind of an interesting one to pay attention to.
Another quick story was that we've talked earlier about how Bing is now being rebranded
as Microsoft Copilot for a lot of use cases, and there's now a Copilot app on iOS and
Android, which is quite fun to use.
It's kind of a bit chat GPT-like, worth going and having a play with.
And as part of that, Microsoft is going to be making the first major change to the Windows
keyboard in 30 years by adding a new Copilot button, which is kind of interesting.
And then the last thing that we saw was that Apple released a research paper called LLM
in a flash, which is basically running a large language model directly on a smartphone with
smartphone hardware.
And for those of you that are very privacy conscious, all of the large language models
that most of us are using are cloud-based.
You put something into chat GPT and off it goes to chat GPT and open AI servers, where
in theory they could always look at the information that you're sharing with them.
But there is a movement to get large language models running on device.
So you have a large language model installed on your computer and it runs on your computer
and doesn't send any information anywhere.
And what Apple are doing is looking at how they can actually even make that work on a
smartphone.
So that's pretty cool and interesting as well.
With that, let's get into our very last story then, which is some of the unique AI-powered
products that we saw at CES this year.
Take us through some of the cool things you saw, Mike.
The first one that really caught my eye is one called Wisp, which is an assistive technology
from a Netherlands-based startup.
And what this does is it converts whispered speech and any kind of affected speech into
clear natural voice.
And it's designed to help people that have severe stutter, throat cancer, vocal cord
paralysis, or anything else that might affect natural speech patterns.
The applications AI technology is language independent and works on both iOS and Android.
So I think that's a really cool one.
Yeah, I love that.
What a good and productive implementation of AI.
Yeah.
And staying on the assistive AIs, the Starkey Genesis AI developed by Starkey Labs, they
created this Genesis AI, which is a hearing aid technology that includes an onboard deep
neural network accelerator engine.
It promises wear as the ability to hear soft sounds without noise, more natural distinguishing
of words and speech, and generally makes it less work for you to listen.
So the device's receiver-in-canal hearing aid lasts up to 51 hours and is waterproof.
So better hearing aids thanks to neural networks being able to figure out the difference between
the things you want to hear and all the background noise.
Again, very welcome.
What's not to love?
Volkswagen have announced plans to add an AI powered chat bot into all Volkswagen models
equipped with its IDA voice assistant.
So this is based on the software company Serence Chat Pro, which I'm not familiar with, haven't
heard of them, and works with OpenAI's foundational models as well.
And it's going to roll out across Europe starting in the second quarter.
So nice little upgrade for Volkswagen owners there.
About time.
Knight Rider's coming.
I need to be able to talk with my car, A, for fun, but B, as we've talked about previously,
how awesome to be able to drive back from a meeting, have a conversation with the tool
about the meeting that you had and in effect, have it ask you great questions about how
the meeting went and then take all the notes for you as your thoughts come out of your
mind after your meeting.
Very excited for some of those use cases.
Yeah, that is one that I think is great.
And finally, the most important, I think we can all agree that this is the one that everybody's
been waiting for.
This came from Gluckskind Rosa.
I don't know if I've said that correctly or not, but they've created what's known as the
Fractional Nanny.
It's an AI powered pram designed to make parenting effortless and enjoyable with cutting edge
robotics and intelligent technology.
So the pram or the stroller, as they would say in the US, features automatic braking
and soothing white noise in a lightweight design.
There had to be a little bit of an outside use case there.
Do you know my favorite thing about that?
As you lifting from their own materials quite clearly, an AI powered pram that would make
parenting effortless and enjoyable, I think it's going to take a little bit more than
a bit of white noise and some automatic brakes on a pram to make parenting effortless, but
I appreciate the sentiment.
Yeah, the marketing department were a little bit overboard.
Yeah.
Come on, we've got the features.
What are the benefits of this?
Well, it breaks on its own, doesn't it?
That's a good thing.
No, we've got to think deeper than that.
It makes parenting effortless.
Do you think we went too far?
No, get it in the press release.
Well.
Why weren't they asleep?
Why am I so sleep deprived?
Why is this awful?
So there you have it.
Well, at least on the artificially intelligent marketing podcast, we can say with some confidence
that we make it effortless for you, dear listeners, to stay up to date on all the things that
are going on in the world of AI, both cool, interesting stuff like gluk-skinned Rosa and
robotics and hopefully a bunch of stuff about actual tools that you can use in marketing
to make your life easier, et cetera.
If you do enjoy the podcast, share it with a friend.
Maybe they'd also like to enjoy.
Please share our stuff on social as well if you like it.
All helps to get the word out and get more people learning about the cool things they
could do with AI.
Other than that, Martin, I shall look forward to speaking to you again soon for our next
episode.
Looking forward to it already.
Cheers, mate.
Bye.
Thank you for listening to artificially intelligent marketing.
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