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 43 of Artificially Intelligent Marketing.
It's also kind of our birthday today.
In fact, our birthday was a week or two ago.
If you've been a listener since the start, you've had to put up with Martin and I talking
nonsense about AI and marketing in your ear for the last year.
We really appreciate your time.
I'm joined by said Martin Broadhurst, as always.
How are you, Martin?
Now the podcast is a year old.
A lot has happened in that time as well, hasn't it?
It's developed at quite a pace, this AI space that we cover.
I don't think I would have thought some of the innovations that we've seen would have
emerged quite the speed that they have done.
Yeah, it is.
We've been riding that exponential curve as we've been trying to cover the news items
as they've come out.
There's elements now to how fast it all jumps forward that we're almost taking for granted
a bit.
It's like Sora and its amazing video.
This is the one that sticks in my mind most recently.
How quickly as humans we came to fixate on the very minor errors that it makes.
Maybe they're not so minor, but compared to what it can actually do, why aren't we like,
what?
How can it even do that?
But there you go.
We were living this this week, weren't we, Martin?
Because we were on tour.
We were delivering an AI workshop for a global marketing team, getting them up to speed on
what the latest technology can do, what it can't do, and helping them get stuck into
the tools with a real practical hands-on workshop.
I must say that was brilliant.
Some of the creative uses of AI for a campaign creation within, what was it, 50 minutes or
so?
It was amazing.
Yeah, I always come away from the workshops we run, Martin, hoping that the people we're
working with are inspired to more practically grab hold of the AI tools and use them in
their everyday.
But those creative ideas they're coming up with, they inspire me when I get back to the
office for the ideas that I'm hoping to implement.
As you said, really practically exploring how you can go from usually a fictitious product
launch idea to a campaign theme, imagery, a launch plan, and all the other materials,
of course, that we saw produced by the team this week was amazing.
We had songs produced by Copilot with Suno.
It is amazing what you can do in a short time.
Yeah, it was great fun.
If you're listening to this podcast and thinking, oh, I really love for Martin and Paul to come
in and help my team figure out how we can actually make AI a useful part of our tech
stack, then get in touch with us and we will gladly help.
Because I think one of the things that we really saw this week was how just spending
a little bit of time as a team to actually play with the tools can really help you make
quite a big leap forward.
Speaking of leap forwards, or leaps forward, Martin, should we leap forward into the first
story of the week?
Beautiful segue, Paul.
Expertly done.
You wouldn't think I was jet lagged after we spent 30 hours in the States this week,
would you?
But no, I'm bang on it.
Let's get into that first story.
First up is 11 Labs, one of the tools that people were using in their creative workshop
outputs.
Neatly done as well, I must say.
So 11 Labs have moved from AI voice synthesis into the realms of AI created sound effects.
So it's a whole new way for creators to use AI generated audio within their podcasts,
within their video production, within their music production scripts, you name it.
You can now do it within 11 Labs.
How it works is the model was trained on a vast data set of real world sounds, which
were meticulously labeled and described by some data labelers.
And all of this training has gone in really to help the model replicate these noises.
And it's available in an early stage access account, which I've got access to when you
had a play with it alongside me while we were traveling.
Ostensibly it should be able to create everything from atmospheric sounds to weather phenomena,
noises like hammers hitting nails and things like that.
But I think early uses, certainly my experience with it straight off the bat alongside the
sound effects that you asked me to put into it, where that it is limited in some areas
and I've read some reviews and online discussion saying that, yeah, it's okay.
It's a good first stab.
This is the first generation of this model, so it's only going to get better from here.
But there are definitely some things that it struggles with.
In my initial experiments, I found that it is quite good with things like thunderstorm
noise effects or birdsong in the countryside.
But where it really struggles is electronic sounds.
It didn't seem to get any of those right.
For instance, I tried to get it to recreate certain sound effects that one might hear
in a video game, but it made kind of weird screeching noises.
And if you try and do something a bit abstract, then it really goes awry, doesn't it Paul?
You know me, I love to try and break these tools instant that we get hold of them.
And certainly a dog playing a harmonica didn't quite work out as I was hoping it to.
The car engine sound that you came up with, a car engine beating like a heartbeat was
kind of okay, but really not very usable.
Yeah, if you want to get a bit crazy and do some interesting things, it fell apart quite
quickly didn't it?
But it sounds like if you're asking for real use case sounds that you would genuinely want
to use like a cowbell ringing or something, then it performs a little bit better, but
still with space to grow into a real production capable tool.
Is that a fair description Martin?
Yeah, very much so.
I asked it for Blackbirds singing in the English countryside and it gives you five outputs when
it creates it.
So it doesn't just give you one version.
And some of them sounded a little bit electronic.
They sounded very synthetic.
They had an element to that.
On another example, I asked it to create the sound of a Ferrari sports car engine starting
up.
And that was interesting.
They did sound good when they started up.
They're all quite different.
And I think if you were a petrol head, you'd be going, it's not a Ferrari Enzo or whatever.
I don't know the models, but if you were, you would pick it apart quite comfortably,
I think.
But they did sound pretty good.
I asked it to do thunderstorms recorded from inside a tin roof shelter.
That was odd because the first two that it created were just like a screech.
It just had failed.
The generation had just not completed the task at all.
But then the subsequent sounds that it created were pretty good.
So I think if you're listening to this and you're a podcast creator, agency side or
client side, or you're looking to create sound effects for videos, it's worth probably
having a play with this, but just manage your expectations in terms of what you're going
to get out of it and keep an eye on the technology as it emerges.
This is one to probably keep an eye on rather than to wrap straight into your production
stack.
Yeah.
And I think 11 Labs would probably echo that caution, right?
Play around with it, test it, give them feedback, but don't go using this as your default sound
effect library on day one.
Makes sense.
What's good for us to know these things are emerging.
And I think again, when you're in marketing, understanding the tools that are emerging
and evolving, because this is, as we say, a thousand times per day per workshop, this
is as bad as these tools will ever be.
And now this gives you an indication of something you probably could wrap into your production
stack in three or maybe six months time as these tools improve.
Good stuff.
Speaking of tools improving, Mid Journey had a really interesting announcement this week.
So Mid Journey is the AI driven image generator, probably best in class for the quality of
images produced, especially when it comes to photo realism.
And this week they launched a new feature that allows users to maintain consistency
in character appearance across multiple images.
So for most people playing with these tools, this has been a really big ask.
If you want to create storyboards or you want to create like a graphic novel, the inability
to control character consistency, there are ways around that using things like seed values,
but it doesn't work that well on average.
So this is a big leap forward, I think, in terms of being able to give us as creators
across a wide range of fields, marketing being one of the control over character consistency
between images.
The way it works is you're going to use a specific type of tag in Mid Journey.
So people who are familiar with Mid Journey will be used to using dash dash and then some
information to control different aspects of the image.
In this case, it's dash dash CREF and then followed by a URL to an image of that character.
So in other words, you basically say pay attention to this image, which contains this character
to drive the consistency.
Then you can use a separate tag called the CW tag, so dash dash CW, which is a numerical
value from 0 to 100, which basically is like a weighting variable in terms of how closely
should your new image resemble the character that you're trying to create.
So it works best when those characters have been originally created in Mid Journey.
So you go in Mid Journey, create a character and then that's the image that you use as
the source image for maintaining the character.
It can work with images from outside Mid Journey.
It just doesn't tend to work as well.
There's obviously going to be some ethical and copyright concerns here, Martin, in terms
of using characters based images to feed into these tools.
What do you think?
Do you think this is going to be a big issue?
Well, we've said before that Mid Journey is one of the tools where you can create images
from copyrighted characters, which OpenAI might say, absolutely not, we're not doing
it.
Now that we can do that with consistency as well, I can see it being open to abuse.
But again, with any of these things, I like to think that we're speaking to an audience
of reputable marketers that will be paying attention to these kinds of details and therefore
you probably shouldn't come unstuck.
Don't go taking photos of Joe Biden and sticking them in Mid Journey and getting them to create
photos endorsing your company.
Try not to do that.
Otherwise, I think for the most part, you'd probably be fine.
I think you're right.
I think for marketers, hopefully we know the frameworks we need to operate within, but
I suspect there will be bad actors out there that misuse this.
So I think as consumers, we have to be ready for what the outcomes of that might be.
Maybe it's much, much better at using consistent characters that Mid Journey itself has created,
but Mid Journey will also give you a half decent image of Joe Biden.
So I don't know where that leaves us.
If you want to have a play with it, it is available via Mid Journey's version six, which
is in beta.
So if you are on the Discord, you can actually use this today.
At some point in the near future, Mid Journey will be moving out of Discord and into a web-based
interface because for most of us marketers, we don't have Discord accounts, we're not
in Discord day in, day out.
It's a bit of a clunky user experience.
And as I understand it, at this point, it's still power users that are allowed to use
the website version.
People have generated 10, 20,000 images in Mid Journey, but hopefully that testing period
will be over soon and more marketers can get their hands on the tool and then have a play
with maintaining consistent characters.
Onto our next story and it's a big regulation story.
Martin, tell us about the EU's AI Act this week.
We have, we've followed this one from the start, haven't we?
We've been giving updates as it's progressed through each of the legislative stages.
And this week on March 13th, so a couple of days ago, European Parliament has passed the
world's first comprehensive AI regulation.
So the legislation aims to address the risks associated with AI technologies and promotes
what they're calling a more human-centric approach to AI development and use.
The core of this is around that risk level that it presents to the users.
So it introduces a framework that categorizes AI products based on their risk level, applying
varying degrees of scrutiny to ensure that the deployment of the technology is not going
to be used for something that could inadvertently cause harm or be used maliciously.
So you can think of this as being like an insurance policy.
If we think about a specific use case, if you were using artificial intelligence to
say whether somebody was eligible or not for a type of insurance, health insurance, life
insurance, what have you.
If the AI was automatically saying approved or denied, that's actually a high risk categorization
because it could have a real detrimental impact.
All of the biases in the model and the model will have some bias in it.
This just inherent within AI systems could end up having detrimental outcomes for the
human.
What organizations have to do is assess the level of harm.
If for instance, you're an AI company helping people write better subject lines for their
email tools, it seems likely that this is a high risk application for AI.
So you've probably got a low risk categorization, you need to be less concerned.
But if you're deploying this in some sort of process where there is potential to cause
harm, yeah, definitely one that you want to keep an eye on.
In terms of on the global front, this is a world first and much like the EU's GDPR set
the tone for other regulators around the world to think about how we use data, I think this
is going to be a kind of scaffold upon which other regulators hang their own legislation
from.
They'll use that as the basis for their own legislation around the world.
There was some discussion around copyright provisions.
Obviously, copyright has been a major talking point when it comes to AI because generative
AI in particular has caused lots of copyright holders to say, hey, you're scraping our content.
I'm not entirely sure that they've fully resolved that other than saying that you have to make
available the information that is in your training data.
And that is going to be a bone of contention going forward, I dare say.
Yeah, definitely for those commercial companies where OpenAI became, as Elon Musk would have
them rebranded as closed AI and not really giving too much detail about how the models
are trained and how the models work.
Yeah, I guess if I think through what the wider spread ramifications could be, is those
in Europe could see that their access to some tools like ChatGPT, Copilot, might be paused
for a short or perhaps longer period while OpenAI and whoever gets in line with those
regulations.
I think that would be quite interesting to see how that plays out.
Absolutely.
We saw when ChatGPT was launched, there was pushback in Italy and Italian users couldn't
use ChatGPT for a short period of time.
So there is precedent for this.
I would expect that there's been enough dialogue between the leaders of the tech companies
and the regulators to find some compromise, but we know that the EU doesn't back down
when it gets the smell of blood or I don't know what the right phrase would be, but they
don't give an inch really.
Tech companies have had to pay significant fines for GDPR breaches and things like that.
So this is going to be the next battleground, I think.
So something important that I think we'll all have to keep an eye on there, Martin.
Thank you for taking us through that.
In the next story, we're going to talk very briefly about Clawed 3 and the new models
from Clawed 3.
We touched on this in the last episode, but until this week, in fact, I think it was like
yesterday or the day before, you couldn't access the smallest model of Clawed 3, which
is called Haiku.
And it's quite an interesting one because it's like super fast, super low latency, very
cost effective, especially compared to other models.
So to give you an example, it can process approximately 21,000 tokens per second, which
is about 30 pages, which makes it about three times faster than competitors for the majority
of workloads.
And it's also extremely cheap, so to give you an example of this, it can analyze 400
Supreme Court cases for just a dollar.
But of course, it's not at the same level as GBT4 and Clawed 3 Opus.
So we're expecting that this will be mostly leveraged for behind the scenes, high bandwidth
work, like summarizing lots of cool transcripts for sentiment analysis and those types of
things rather than content creation or brainstorming ideation, logical data processing.
It's a bit more of a behind the scenes sort of carry out all of the work.
But what we love about it, Martin, is it's got image and vision capabilities, right,
which is pretty cool.
Yeah, that is the really interesting thing, because you mentioned it can analyze 400 Supreme
Court cases for a dollar.
Well, it can also process 2,500 images for just a dollar.
That's an incredible amount of image processing.
Now, what can you do with that?
Well, you can turn data from a photo into structured text data, because it can do the
extraction and then put that into digitized tabulated data.
So if you've got lots of handwritten forms that have been given to your company, maybe
you've got an archive of data that you need digitizing, now you can scan all of those
sheets, run them through this model and get all of the text extract neatly formulated.
You could pass it out in JSON files, put it into CSV or tabulated data, however you want
it formatted, it could do it for you.
And it's incredibly cheap.
Yeah, I think it's super interesting.
I can definitely imagine taking screen grabs of software routinely while a user uses it
and then trying to use that to create documentation about how that software works for creating
things like software manuals.
Yeah, I think you're right.
Again, creativity and imagination in terms of now I have this capability at low latency
and low cost, what can I do now, especially using those vision capabilities that I couldn't
do before?
Because certainly if you use, for example, like I talked a lot about Magi on the podcast,
that has the vision capabilities of Gemini and ChatGPT built into it.
But the cost of using those tools is more than just using text alone, because it costs
more to use those APIs.
So to have access to a vision tool at low cost, I think that really does open up some
interesting capabilities.
I think the important thing for users when it comes to Haiku is to remember that this
isn't about the creative outputs or the reasoning abilities.
This is the workhorse as you identified.
This is about executing tasks, formatting data, extracting data, summarization.
It's those kinds of applications where this comes into its own.
Completely agree.
So next story, there's an interesting quote from Sam Altman doing the rounds on the socials
at the moment, originally uncovered, or at least to us by Paul Reutzer and Mike Kaput
from Sam Altman.
Tell us about this story, Martin.
In an interview recently, Sam Altman said that artificial general intelligence will
take over 95% of tasks currently done by marketing agencies, strategists, and creative professionals.
They said that the integration of AI into marketing, it's already happening and generative
AI is taking on more and more of these tasks within the industry.
So that is the next frontier.
As soon as we get to AGI, the marketing industry needs to watch out.
Now, in terms of the timeframe for that, Sam was saying approximately five years.
So if you're a marketing agency owner, Paul, I don't know if you know any, you might have
to pay attention.
This is going to be a bit disruptive according to Sam.
What do you think?
Yeah.
I think this is really interesting.
But after I digested it for a while, I really thought about, well, this is probably true
of most of knowledge work.
So you could probably swap out marketing agency for counting, consultancy, a huge amount of
other industries.
I think what makes the marketing part interesting is the creative aspects, right?
Running creative campaigns, analyzing the data, and then improving those, which is somewhere
that I think AI can help a lot.
But creative production is hard.
We've talked about the limitations of the current tool set a lot already.
So I think that's where I almost, for good or real, see marketing as quite a high bar
because it's content creation, it's strategy, it's data analysis, it's creative outputs,
it's empathy and customer understanding.
And I think if AI can do a lot of those things, it can probably do a lot of jobs.
The other thing is the references here in terms of AGI, or by definition, AGI can do
lots of things that humans can do.
So I think we're still on that timeline towards AGI.
When we hit AGI, the world's going to be very, very different place to what it is now.
And I think assuming this is possible, come onto that in a minute, but assuming this is
possible, our economy has to be fundamentally rethought in terms of what do humans do with
their time?
How do we have purpose?
How do we deliver value?
How does the economy work from a monetary perspective?
How do we all have money to live?
Do we live in a world where everybody's got a robot that helps them and we're all doing
fantastic pursuits and it's kind of like Star Trek?
Or is it going to be more like, what's that movie?
Is it Wally?
Is that the character where all of the humans basically become extremely overweight and
live in these portable pods where they don't really do anything for themselves anymore
because the computers can do it?
Or that would be the more dystopian view potentially.
But all of this is dependent on the current tech architectures that we have getting us
to an AI that can basically do most, if not all things that humans can do better than
a human.
And I'm just stuck on that.
I feel the exponential curve were on mine.
I really do.
So it feels plausible.
But I also know that there have been other AI areas of enthusiasm followed by AI winters
where we hit a barrier that we couldn't get through.
And we'll talk about some other awesome stuff that Meta and other companies are doing at
the moment, but this kind of assumes the current paradigms of models that we're creating will
get us there and they may not.
So we just don't know.
Do you have a perspective on this?
I think it's interesting to see where it's currently at.
And we saw this in the workshop this week.
If you get a team of humans and give them the AI tools, the speed at which that creative
development, that iteration, that first draft, a lot of work can be done in a very short
window of time.
I think some of the copywriting that was done in some of the examples that we saw with the
team we were working with this week, it was work that was done in about eight minutes
that was really quite impressive.
Not the finished article, but really impressive.
And that was many humans working with AI as a tool to assist them.
I think the notion of completely, you know, 95% of the tasks, that seems high to me.
In five years time, 95% of tasks being done by AI does seem high, but then pace of change.
And when you break down what some of the tasks that we do, whether it's project management,
I'm thinking, yeah, well, okay, the AI will get better at copywriting.
It will get better at creative development and concepts for design or from video.
And then thinking in five years time, crikey, a lot has changed in one year, nevermind five
years time.
So we will definitely be further along than which bits of the marketing agency tasks and
task workload isn't done by AI.
I find myself thinking, well, actually maybe, maybe he's right.
I swing back and forth.
AGI as a term I struggle with because exactly what you've said, we've had these AI predictions
in the past followed by AI winters where you're looking for the next breakthrough that's going
to take us into the next epoch of AI activity.
Will we get at the current level of AI with LLMs and existing model architectures?
Will that replace marketing agencies?
It's a big ask.
I'm not going to profess to have the answer.
Yeah.
And I think the other thing that these predictions sometimes lack is we may have the technical
capabilities to do so, but there are a lot of things that have to happen to make this
possible.
So even what we're seeing in the work that we're doing, there's the technical capabilities
of the AI, but then there's also the training of the people to use it.
There's also the willingness to change, which some humans in general are pretty good at
change in terms of we're adaptable, but most humans on the micro level resist change.
So how do you manage that change management aspect?
Then from a how do the governments of the world, regulators, how do they manage the
emergence of these tools because of the disruption that will bring to our way of life and the
economy?
So again, we may be able, technically able to do some of this stuff, but to my understanding,
humans will have to choose to implement it and will they?
How will they implement it and how easy will that be and how long will it take?
So then I just look at all those different streams of things that have to happen.
And I'm not saying that this isn't where we're going to be.
I just think it's probably more complex than can we technically do it with the computing
power and architectures we have.
Yeah, I've no doubt that in five years time, there will be kind of marketing agency as
a service subscription style AI powered tools available where people have cobbled together
a bunch of different AI agents and they can execute very specific things.
You might want it to come up with a campaign concept and then give you a kind of channel
strategy for some advertising.
It will design you a landing page and you can press deploy and you will get a campaign
designed, copywritten, pushed out with some data capture forms and landing pages and integrations
into Meta and all of the other advertising platforms.
And that's a good chunk of what a digital marketing agency might do for somebody.
But it's not everything, but if you're a smaller business, that might be perfect for what you
need if you're looking for a global omni-channel product launch and brand activation.
Do I think an AI is capable of doing all of that with all of the human logistics and coordination
and that's required?
No.
I think it's, I love the fact you just brought that up because if just thinking about and
taking it through to a more extreme logical conclusion, if everybody has access to this
tool then everybody can use that marketing in a box tool that you described.
That's the new bar, that's new table stakes.
And in the battle for human attention, and we've talked about this in previous workshops
and podcasts about the deluge of rubbish content that ChatGPT and other large language models
could enable.
And being able to churn out a load of rubbish content is useful for a short period, but
we're already seeing that Google is now starting to penalize those sites that it feels have
added lots of content quickly and have a good feeling that that content is AI generated for
SEO and to appease search engines not to provide value to humans.
And so we're going to see if everybody's got access to these tools, that's now the lowest
common denominator.
And how do you go above that?
Well, maybe there's other tools that you pay more money for, but I think again, that's
where humans will give you that creative competitive edge.
So these tools in hands of absolute domain experts are going to get better results than
these tools in the hands of non-domain experts.
And so all it does is just push the bar up for everyone.
And we get a good insight into this in the life sciences and we worked in the life sciences
this week.
I know it's not an area you haven't worked as much in, but hopefully it was quite interesting
to see just how incapable some of the tools are in some niche domains.
We did some image generation work, trying to produce scientifically accurate images
and all of the tools completely suck at it.
You can't use Mid Journey or any other tool for any type of image production work in the
life sciences outside of stock photos of people in laboratories or maybe some abstract images
that you think are like interesting and beautiful, but that you're not claiming are scientifically
accurate in any way.
And to be quite honest, Chatch, E.P.T. and other tools, they struggle at written content
creation as well because they just don't have the deep domain expertise that a subject matter
expert has either within a team like mine, we've got a bunch of PhD level science writers
and certainly within life science businesses as a whole in terms of all of the subject
matter experts that they have and the domain knowledge on the cutting edge that these tools
don't have.
And I see that as a bit of a prelude to this, which is, like you say, it pulls the average
up but there's going to be a load of use cases where it's not fit for purpose and it will
get better over time, but I think that will take some time.
Let's move into our next story then, which is about IBM announcing layoffs in its communications
and marketing divisions, probably somewhat related to the conversation we were just having
Martin.
And this comes hot off of the press of the Klarna examples that we talked about in the
previous podcast where they were able to replace human customer service reps with AI driven
chat bots to boost profit margins.
So IBM has initiated layoffs within its communications and marketing staff, affecting a lot of the
low single digit performance of a percentage of its global workforce.
The move is part of a broader workforce rebalancing effort announced earlier in the year during
the company's fourth quarter earnings, where IBM aims to maintain its employment levels
through 2024 by focusing on areas of high demand among its clients, such as AI and hybrid
cloud technologies.
So this is quite interesting because these are Marcom's jobs that are going and they
follow a recent announcement from IBM about the impact of AI on the workforce.
So this makes this a particularly pertinent story for us to be covering, doesn't it Martin?
Yeah.
And it comes hot off the tail of them announcing a hiring pause for approximately 7,800 positions,
which they say are potentially replaceable by AI technology.
So these are predominantly back office jobs and this was announced late last year.
And at the time we discussed this on the podcast and now we're starting to see this come to
fruition.
And I think considering Sam Altman's story and the reporting of 95% of Marcom's agency
jobs or tasks being effectively done by AI, well, IBM are trying to turn that into a reality
with their internal team.
They are still investing, but they're looking to invest in their teams that are working
on AI technologies, on their cloud technologies.
So Watson X Development Studio is aiming to train companies in building and releasing
machine learning models.
This is where they're putting their focus.
I suspect that huge amounts of the work that was previously done by their global Marcom's
team will still get done in-house, but it will be done by Watson.
Yeah.
It's an interesting one.
I mean, we have the story from last week, right, about Klarna's customer service delivery,
where we don't have any data outside of a small amount of information from the company.
And they have obviously been questions that have been levied at the company.
And at least as time of this recording, I don't know if you know different Martin, but
from what I've seen so far, they haven't really been able to respond to a lot of those questions.
And it's not that we're not saying that that use case wasn't real, but we haven't had the
ability to really scrutinize it.
And we were reading a report on the plane, weren't we?
We were flying over to the US, questioning the real world impact of AI and where are
all the applications that massively boost efficiency and effectiveness and drive shareholder
value and increase profits and go to the bottom line and all that.
And they are kind of few and far between in terms of written case studies, but I hold
that in tension with the businesses that I know that are getting the 10, 20, 30% productivity
and efficiency gains using the tools.
So I still think we're in the middle of trying to see the productivity gains and efficiency
gains that these tools really allow.
But when we see moves like this by IBM, we have to assume that just because they're not
reporting what they are, they are seeing them and that gives them the confidence to make
these infrastructure changes.
Yeah, it's interesting you mentioned the report from the information.
It was on the information that they said big cloud providers, so sales teams at the likes
of Google Cloud, Amazon AWS are all saying to dampen expectations of sales of generative
AI integrations and services.
And that's predominantly through clients' hesitance to implement them in customer facing
applications, driven by inaccuracy and hallucinations.
That seems to be the big issue for people at the moment.
And that's completely understandable.
Even with rag-based systems, I've seen reports saying that rag-based integrations are 70
or 80% accurate, but 70 or 80% accurate still leaves a lot of room for improvement.
If you're getting a 20 or 30% error rate or just lacking in the right bit of detail when
responding to customers, that's not going to be production ready.
So yeah, I think we're going to see more and more applications for generative AI in the
back office, for internal processes, for alleviating all of those jobs in an organization where
it's somebody taking data from this place to another place and things like that.
And this takes us back to the reason why the Haiku API deployment is so interesting.
I agree.
So to add further complexity to this discussion in terms of how powerful is AI, what can it
do, is it really adding productivity gains, probably because of some of these changes
we're seeing within businesses, but how fast will we get to a position where it's really
taking a huge amount of work away from humans?
Meta's had a bit of a leap supposedly in their development of their AI tools, haven't they
Martin?
This is very much on the engineering side of things.
On the Meta engineering team blog, they announced some massive investment basically, and the
future of their technology stack for developing and rolling out AI models that we can all
use in the future.
I'm going to try to avoid getting too into the detail on this, but at its core, what
they're saying is that they are going to deploy a new range of AI plusters within their data
centers, so the infrastructure is powered by 24,576 Nvidia Tensor Core H100 GPUs.
Now they are the most cutting edge chips for AI processing available on the market, and
they're a significant upgrade on what they've been using to date.
I think when you start to look at the numbers around this, you start to see how seriously
these organizations, Big Tech, are taking AI development, research and deployment.
So they're looking to expand the H100 GPU count by an additional 350,000 units.
Okay?
So that's kind of abstract.
You hear that and you go, okay, they're investing in a lot of chipsets, 350,000 of them.
Great.
They're aiming to have a total compute power equivalent to 600,000 of these units by the
end of 2024.
So that's massive expansion of compute power.
Just to put this into an investment perspective, the book price for a H100 GPU is between 30
and 35,000 pounds per GPU, which ballparks, you know, plus or minus a billion.
That's a 10 billion pound, yeah, 10 billion pound investment in these chipsets.
And I'm sure the commercial agreement is very different from me walking into PC World and
asking for a H100 chip.
Sure they've got a slightly different setup with Nvidia, but it goes to show that they
are really backing up their research team with some real GPU horsepower.
It's pretty cool.
The Yan LeCun, who's head of all of this over at Meta, has been doing the rounds on a few
podcasts recently and re-referencing the iJEPA models that we've covered previously on the
podcast.
So JEPA stands for Joint Embedding Predictive Architecture, where again, this probably stretches
into the machine learning details and probably beyond the expertise, certainly of me and potentially
both of us.
But it's about providing AI with the opportunity to learn in different ways from how large
language models have learned from the data they've been fed so far in a sort of passive
mechanism of having an AI learn about the world around it a little bit like a two-year-old
would just by observing what's going on.
One assumes that's going to take massive computing power.
And so when you look at these investments from Meta, this isn't just a power that,
you know, Lama 3.
This is because they have these other architectures and these other mechanisms of getting us to
artificial general intelligence that they want to explore and need massive computing
power to enable.
So when we're talking about AGI in five years and all the things that, all the human jobs
that it could potentially replace, as Sab Altman said, and whether or not we've got
the architectures to do that, Yanlokun is one of the people who doesn't believe we have
the architectures to do that.
But at the same time, it's part of a meta team that's spending 10 billion pounds on
all of this compute power to discover those architectures and test them and train them.
And of course, already having some interesting ideas on what those architectures should be.
And we've talked about them on the podcast as well.
We've got their vision segmentation model that's able to detect things in images, which
is probably either that technology or similar technology that's underpinning a lot of the
vision capabilities that we're seeing at the moment.
Their audio translation capabilities are more powerful than any of the tools that I've seen
commercially available so far when you look at the demo videos.
And this investment will just go to further fuel all of that stuff.
You mentioned Yanlokun there and him doing the media rounds recently, and he's really
not keen on the term AGI, which we've talked about on the podcast today.
But if you hear him talk about it, it's not a specific phrase that he likes.
He prefers to use a different terminology.
But if you read the engineering blog that Meta have put out, they actually have a graphic
that talks explicitly about this new cluster model and this major new investment driving
directly through to AGI.
They have a diagram that features AGI at the end of it, which fully enough takes a pathway
through Lama 3.
I read it does.
Well, if they want to get there, they may need to be supported by tools like Devin,
which was released this week, which is, if the hype is to be, believe Martin, the world's
first fully autonomous AI software engineer.
So it's been developed by a company called Cognition in the US, and it's designed to
work autonomously with its own code editor, command line, and browser.
It's supposedly being able to handle complex software engineering tasks.
So this kind of forms an early indication of what a developer type AI agent would be
in terms of taking multiple steps in software development.
So it can plan and execute intricate engineering projects.
It can make thousands of decisions, and it's equipped with different developer tools.
It can collaborate with users, and it can also provide real-time progress reports and
accept feedback from the users.
So it's kind of interesting, but there's a but.
And I think the but is it's kind of a cool leap forward, and best way that we can kind
of put this in some context.
So on a particular coding benchmark, so the SWE Bench Coding benchmark, Devin resolved
nearly 14% of issues end to end, which is better than the previous state of the art,
which I think is probably GPT-4, which was about 2%.
So the move from 2% to 14% is significant, but 14% is still not that large.
The other thing that's worth noting is that the previous state of the art was a models
that were being assisted by humans.
So for example, being told exactly which files to edit, whereas Devin was able to do this
unassisted.
So it's quite a big leap forward, but obviously there's quite a lot of space to improve.
I saw Ethan Molykos got access to this already, Martin, and he's posted on LinkedIn about
some of his experience with it.
And that whilst it still has quite a long way to go, it's a good early indication of
what some, what's the word we were using last week, agentic capabilities might be in these
types of tools.
Did you get, did you see this Devin use?
The videos for it are really good.
If you look on the blog from the company, they've got a series of one or two minute
videos showcasing some specific examples of the tool in action.
And while this isn't going to build you a really complex piece of software end to end
and deploy it, it will do small tasks more completely than anything I've seen from any
other tool.
One example was somebody shared a link to a blog post about how to create the stable
diffusion model that can do the text hidden within the image.
I can't remember what that tool is.
But the developer, she shared a link to that blog post and said, this article has some
code in it to get this thing working.
Can you create a tool that uses it?
And that's what, that's it.
That's it.
So shares URL, inputs the blog, and then it goes off, creates a plan of the steps that
it needs to execute and then execute each one of those steps successfully and then delivers
back to her some sample images that it has just created with the software that it's just
written.
Now that's very cool.
It isn't going to be the whole front end at this stage.
It's like it said in the report, just shy of 14% of issues were resolved end to end.
So there's still a long way to go.
But it's significantly from where we were.
Yeah, I remember when we were playing with AutoGPT, Martin, and we just, us and other
people in our network, we're just struggling to get it to work and it's kind of disappeared.
Nobody really talks about it anymore.
And that's because I think mostly it didn't work very well.
This is kind of an improvement on AutoGPT.
It is.
And there is a certain hype around this at the moment, which feels somewhat similar to
the hype around AutoGPT.
And I remember when AutoGPT came out, we were both somewhat skeptical.
We couldn't get it to do very many interesting things while at the same time, AI influences
on the Twittersphere, as on the Linkedins.
We're saying that it can do all of these amazing things and jobs are no longer required and
transpired to be inaccurate.
Whereas this does feel like a genuine leap.
And for a day one model, I'm looking at it going, okay, if I'm a software developer,
I can see this having some very interesting use cases.
Yeah.
I think it's something for us to keep an eye on.
I think it will be overhyped, but I do still also think it's an incremental move forward.
Another thing that's likely to get overhyped is our last story this week, Martin.
We'll talk about this one briefly.
Is the collaboration between Figur and OpenAI.
Yeah.
The Figur company, which creates humanoid robots, has released a video where they've
got their humanoid robot integrated with, it must be GPT for vision.
And they've got a person communicating with the robot, asking it questions and asking
it about what it sees.
And the robot responds and then executes some tasks.
And it's just a more fully rounded version of what a robot, human robot interaction might
look like.
However, there were some issues with it, weren't there?
Yeah.
I mean, the lag is noticeable.
So if you're a listener, find this video on YouTube, it's probably very easy to be found
because I think it's been well shared as well on all the socials.
But in essence, person walks up and asks the, it says that he's hungry and the robot hands
him an apple and all of the movements are very well handled.
The robot also does some cleaning up and some other things.
But there is a noticeable lag between asking the question and then the robot responding
that you can imagine if that was a real life interaction would soon drive you absolutely
crazy.
It's like having a human who's really not that bothered about what you've got to say,
not really paying attention to you and only gets back to you after thinking for 15 to
20 seconds, which is annoying.
Obviously, the latency of these things will drop down.
And when I saw this, Martin, I couldn't help but think, I think when you're on an exponential
curve, we're like, it's easy to say, oh, what's the day AGI will come out?
Like, it just won't be like that.
Like one day we'll go, oh, crumbs, has anybody taken a step back from this and gone two years
ago we didn't have anything like this.
But I do think this will be another one of those moments that froze of us that end up
old enough to remember will be like, you know, all those robots that are working in the factories
or helping cleaning up in the house.
I remember when I saw the video of the first realistic conversation between a human and
a robot, which worked quite well, but had some problems.
And I think that's probably where this will end up.
It will be one of those first steps along the road.
So clearly has some problems, but I think it's, it's a measure of really rapid progress
that happens when large language models are combined with significant improvements in
robotics.
Yeah.
I'm looking forward to seeing where we head with this tech because I like the idea of
a robot cleaner.
Absolutely.
I think my office could definitely do with a robot cleaner, but right.
I think we'll call it there.
Martin, thank you so much for your time as always.
And thank you to you, dear listener.
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We'll look forward to speaking with you next week.
Martin, have a fab weekend and I will speak to you soon.
See you later.
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