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 29 of Artificially Intelligent Marketing.
It's Paul Avery here, joined as always by my beautiful and wonderful co-host, Martin
Broadhurst.
Martin, how are you?
Beautiful and wonderful, as ever.
Thank you for noticing.
Do you know what?
A lot of people are going to listen to this on the audio and so I thought it was only
right that they should know the wondrousness that they could experience if they decided
to look at the video version.
Yeah, yeah.
Well, they can always get a photo of my face off the internet and print it out and put
it in their wallet for just a quick glance when they feel like a bit of beauty in their
life.
I'll feel better because then it's not just me that has that in my wallet.
So moving swiftly on, lots of wonderful stories from the world of AI and marketing this week
and we have a bonus little interview at the end of today's episode as well.
So without further ado, let's get into the first story, Martin.
So Google has joined some of the other big tech players by offering legal cover for generative
AI users.
So as expectations of copyright disputes rises, Google Cloud is now taking steps to protect
users against claims made if they use generative AI tools.
So they are offering an indemnification policy against potential infringement claims.
The promise covers two specific areas.
So Google's own training data and any generated outputs such as text, images or audio created
through services like MusicML or Google Workspace.
Basically if you face any intellectual property lawsuits at all over content created with
Google AI, Google AI is saying that they will take on the legal risk.
So this addresses something of a significant pain point for many enterprises, something
that has slowed enterprise adoption of generative AI models.
No one wants to take on any unintended liability and Google is doing what it can to mitigate
that and insists that this shows their shared fate with the customers.
So the indemnity does depend on responsible use.
It doesn't cover intentional infringement, which makes sense.
You wouldn't expect Google to be covering people recklessly using the models.
They themselves say that it provides a balanced practical coverage, which I'm sure they would
say as opposed to it being unbalanced and highly impractical.
That was the name of my second album as well, balanced practical coverage.
It didn't do very well.
Well yeah, so I mentioned at the start that they're joining big tech players such as Microsoft
and Adobe who have also made similar pledges trying to spur innovation by easing IP concerns.
And these risks certainly do remain.
You see the likes of Sarah Silverman's lawsuit still going through the courts, many authors
taking action against open AI.
So we can expect more of this in the future and you can understand why companies would
be a little bit wary about dipping their toe in the generative AI pond.
Yeah, it's interesting.
So we onboarded or in the process of onboarding a new client, signing all the contracts and
et cetera that comes with that.
And there is a clause been inserted into the contract about the use of LLMs effectively
forbidding their use for any practical purpose for us to deliver what we're going to deliver
as a marketing agency.
That's the first time that I've seen that, but I feel it's very much in line with we
can't afford to get in trouble down the line.
I think it's a protectionism as it relates to things like copyright infringement versus
a we want to make sure we're getting for value for money out of the agency aspect.
Although I sort of understand that as well.
I actually think it's driven by all of these concerns that the bigger companies have about
what legal risks they could be opening themselves up to.
Yeah, lots of companies approaching this whole space with an abundance of caution.
Agreed.
And I think until we have some clarification on what a misdemeanor looks like and how it's
punished, we're not really going to know, are we?
Even Adobe, Microsoft and all these others that are offering and Google now offering
to cover you if you are pulled up for your use of LLMs, we still won't really know until
it happens.
Righty-o.
So that's a good story.
And of course, if you are a large brand, you're probably living and breathing this.
If you're a small brand, maybe that gives you a bit of an opportunity to really leverage
these tools without some of the more risk averse controls that are put in place quite
understandably by large companies.
But yeah, as a small company, maybe that creates a little window of opportunity.
Good stuff.
Thank you, Martin.
Let's look at our second story this week, which is for Skyscanner, which is setting
a new standard in customer centricity with its generative AI-powered discovery tool,
which has been launched as a beta across Australia, India and Singapore.
The tool, which is fueled by OpenAI's chat GPT, is called Dream and Discover with AI.
That feels like a podcast episode waiting to happen.
And it aims to meet a specific consumer need, in this case, travel inspiration.
So what's interesting about this?
Well, it's Skyscanner's sort of conscious approach to addressing the needs of customers
through a sort of a more interactive chat style AI.
So in essence, if it is a traveler, you can engage with the tool by asking open any questions
like best cities for cultural tours or where should I take a city break if I'm a bit of
a foodie.
And then what the tool do is it will produce tailored travel ideas, of course, complete
with optimal flight options for those getting the little commercial aspect in there.
So it's not a pure customer support play here.
There is that sort of sales aspect in there as well.
Although of course, the goal here is to try and where appropriate, create a bit more of
a friction free and fun experience for a customer to find somewhere interesting to go and make
it easy for them to book.
Piero Sierra, Skyscanner's chief product officer mentions that 56% of users come to Skyscanner
for inspiration.
And that is why they're using this beta launch to try and gather valuable insights about
how their customers would engage with such tools when it comes to booking holidays, etc.,
compared to existing methods.
So there's logic there in terms of having an understanding of customers that not every
search on Skyscanner is transactional.
Some of it's discovery, and this is enabling that discovery.
And so from that perspective, it's a bit of a masterclass really in consumer first strategy
because they're using generative AI in a very targeted manner.
There's not any enhancing their product offering, but also it's going to allow them to gather
precious behavioral data about how their customers use the tool that can inform future campaigns,
both using the AI, but also for all their other marketing and sales.
So it's an interesting approach, Mal, what do you think about this approach and its applicability
in other areas?
Yeah, I think it's a really smart move introducing conversational AI when you are effectively
a marketplace, right?
And that's what Skyscanner is.
And when you have a plethora of options and you can sell people whatever they want really
within a particular domain, they can offer you travel anywhere in the world.
Helping inspire people, helping people make those decisions is a shrewd move.
I'm really interested to know just how much fine-tuning they've done.
They've clearly integrated their database, hence why they can put these itineraries together
with the travel plans connected to the best flight options, the cheapest flight options,
all of that kind of stuff.
But I'd be interested to know how much fine-tuning has gone into the model to come up with the
itineraries themselves, or is it pretty much a base model giving you an itinerary not too
dissimilar to what you might get if you were to go into ChatGPT and say, give me a weekend
break to a city in Europe for a foodie that is interested in seafood, right?
ChatGPT would give you that itinerary as it is, that would be very good.
Have they done anything over and above that with the outputs and with the training of
the model?
That's a great question because if you wanted to emulate this in your own business for your
own customers, you're going to want to know how easy or hard is it.
In essence, have Skyscanner skinned ChatGPT and gave it access to their flight database,
or have they really gone the hard yards of trying to fine-tune and train the model on
additional data that they know about holidays and great destinations?
I can imagine that this is a domain where, like you say, ChatGPT could probably do a
good job already, but if you operate in a domain where the training materials for ChatGPT
are going to be less detailed, then if you wanted to achieve a similar output, you probably
would need to fine-tune the model or prepare it in a way to be able to actually answer
consumer questions.
Interesting.
Thank you, Skyscanner, for giving us marketers a use case to think, oh, how could I better
serve my customers and interact with my customers and gather valuable data about them if I created
a chatbot just for them, helping them find the best thing that we do to solve their problem.
Next story.
Next story looks at the potential downsides of fine-tuning.
This is an interesting piece of research that came out this week where everyone's very excited
about using the likes of ChatGPT and GPT 3.5 Turbo, and all of these other large language
models, and fine-tuning them so that companies can customize these models to make them better
able to answer questions based on their own company data.
However, scientists found that both malicious hackers and well-meaning users can actually
undermine model safety and the guardrails that are put around models during the fine-tuning
process.
With just a handful of carefully crafted examples, models like GPT 3.5 Turbo can easily learn
harmful responses.
Even tuning a model on normal data, not giving it malicious prompts, but training it on fairly
normal data can degrade protections against bias.
This is a very new discovery in a very nascent field, fine-tuning large language models.
This really matters, right?
Lots of companies are jumping onto this bandwagon and wanting to make the use of large language
models.
We have to be thinking about making sure that we're testing for safety, pre-fine-tuning,
and once we've done the fine-tuning of our model, we have to do an audit of the safety
after the fact as well.
We cannot assume that any protections that were there beforehand remain in place afterwards.
I think this is a really important takeaway for anyone looking to implement a fine-tuned
LLM commercially, right?
Because you're opening yourself up to risks here.
You can have a conversation about what harm looks like with large language models.
Should we have such high thresholds for reduction of harm with LLMs that people generally have?
If you ask large language models how to create a bomb, for instance, they'll typically say,
no, I can't tell you that.
Particularly if they've been guardrailed and had the right safety mechanisms put in place.
But you can Google that and find the same answer, right?
So is there a disparity there in our expectations for the tech in the first place?
But assuming that you're wanting to minimize risks and harm when deploying these models,
you definitely need to think about the impact of what fine-tuning can have on those guardrails.
I think it's really interesting because I think there's a philosophical question about
the harm management aspects when you can find certain harmful information in lots of other
sources really easily.
But as marketers, there's a brand exposure impact, right?
In Skyscanner's case, we don't think they fine-tuned the models perhaps, but if they
had, maybe you could ask it, I want to have a great holiday, but I also want to smuggle
some cocaine back.
What's the best place to go, right?
And then it would probably give you all the best places you could go.
Maybe you can even ask it for advice on how to smuggle the cocaine.
So yeah, I think it's more about the brand impact if that story went viral and the carnage
that would cause that will keep certainly the commercial use of fine-tuning of models
very rigorous in following the process that you outlined, Martin, to make sure that your
brand doesn't get trawled through the mud because of something that the bot spouts out
that it shouldn't have.
Yeah, it's definitely one to watch as we see more and more of this being rolled out, particularly
with the open source models like Llama, which are, you know, heavily, they're going to be
heavily deployed and fine-tuned models.
Yeah.
I'm sure it's only like the 0.01%, but there are definitely folks out there who are taking
extreme joy in figuring out how to game these models.
And my prediction would be the first few companies that start releasing these bots at any sort
of reasonable scale, they're going to get tested.
And then they're going to say things they shouldn't.
And then that stuff's going to go viral on X, formerly known as Twitter.
And it's going to be a cautionary lesson for everyone else who comes afterwards.
Yeah, absolutely.
And teams deploying these models need to have that in their risk management process.
Absolutely.
Right.
Next story then.
So this one's about some recent survey data.
So less than a year ago, CEOs had lukewarm feelings towards AI with only 23% considering
its use to bolster productivity or mitigate labor costs.
If you fast forward to today, and over half of them, 53%, are already harnessing AI for
business use, making a staggering 130% uptick in interest within just eight months.
And yes, there's quite a few percentages in there.
So 23% became 53%.
That's a big jump.
So it does look like AI is now becoming a mainstream tool, at least in the mind of executives.
However, it's not all rosy, because despite growing interest, CEOs continue to tread carefully,
avoiding concerns about bugs, privacy issues, hallucinations, etc.
So even within this expanding landscape, the uptake across various industries is varying
a lot.
Meanwhile, while 70% of CEOs in fields like advertising, marketing, and PR see clear business
value, those in sectors like construction and consumer manufacturing are less convinced.
Not surprising, Martin, given where a lot of the early impact of generative AI has been.
No, not surprising at all, but really encouraging that executives are overall embracing this.
We see this in the work that we do with leadership teams.
They get a sniff of it typically on their own or someone signposts them to this tech,
and then they go, oh, this is amazing.
This is brilliant.
We need to roll this out.
So I think there is clearly an appetite at the individual level, the ability for individuals
to become more productive.
And we're still all figuring out the kind of company level use cases for this technology.
But yeah, it's encouraging to see CEOs, 53% of them, embracing generative AI.
Yeah, I agree, and when you look at the issues with the tools, like their propensity to hallucinate,
like how challenging it can be to get them to do certain things that you want them to
be able to do reliably, they still can save people time in so many areas that even in
this sort of gray area that we're in right now, where there's some benefits, but oh,
you've got to be mindful of all of these drawbacks.
I still think there's enough advantages to be able to roll them out in a number of areas.
And as we've said again and again, and as many else have said on the social sphere,
this is the worst these tools will ever be.
So they're only going to get better from here.
And there are those clear use cases where there are benefits.
And it's almost like sliding scales.
You've got certain tasks where they just obviously save so much time, even with some of the issues
with them, you should start using them today.
There's a few where it might be about net zero once you look at the amount of prompt
engineering to get what you want, or QC and QA of the output to make sure it's half decent.
And there are definitely those areas where either the tools are not up to snuff yet,
or you have to spend so much time trying to get the output actually takes you longer than
if you'd have just done it human manually.
But I think the bars there on that graph, or on that bar chart are basically shifting
all the time towards ever more applications in a, this can do it really well and it saves
us lots of time section.
Yeah.
And I think the more people use the tools day to day, going back to that Ethan Molyk
paper that we saw recently where he was talking about the Boston Consulting Group consultants,
the way that they use these tools is either like a Centaur or a Cyborg and the Centaurs
are the people that kind of, they do the human bit of the task and then they have a bit of
the task that they give over to the AI and kind of bounce between the two.
Whereas the Cyborgs work almost as if they're fully integrated with the AI, it is as if
it is part of the same person.
I think you end up being a Centaur or a Cyborg, but that just comes through familiarization
with what the tools can do.
Understanding the limits and understanding how your workflows, how you work, right?
Your working practices and you start to integrate them more and more.
And as the models become more capable, you test boundaries and introduce new techniques
into what you do.
Certainly I do every day, I find something new and I'm like, stick that on the list.
Absolutely.
You've got to be willing to jump in and get your hands dirty in your particular work type
and the workflows that you operate in and think of creative ways to test these tools
out to see if they help you be more creative, get better outputs, deliver things faster.
Whilst combining that with tech scouting, listening to podcasts like this in terms of
what different tech can do over time so you can wrap that into your workflows.
And I think there's a fair bit of sort of the more operational type process mapping
to really help identify which bits could be smart automated with AI, often identifying
a number of areas which could just be automated, AI or not, and getting benefits regardless.
So all of those things combined are the best way to combine AI with your current workflows
to find those improvements.
And outside of listening to this podcast, which of course you are, we thank you, thanks
for being here, following someone like Ethan Moller on LinkedIn is a great way to see how
people are testing these tools to see what can be done and perhaps where they're not
quite right yet.
Yeah, that example of the smart autonomous process mapping as well as interesting point
that you make takes me back to Zapier Canvas, the tool that they released last week.
I got early access to it.
I encourage people to have a play with it.
If you can sign up and get early access to it, simply map out a process.
It doesn't have to be something that is integrated with tech at that point, but it helps if you've
got some steps that are and it will use AI to tell you which bits can be fully smart
integrated.
So you don't even have to do the thinking there yourself.
It's great.
Highly recommend it.
Oh, I've got to get me on that and have a little play.
Maybe we'll, we haven't done a tool of the week for a while.
Maybe you can do a deep dive for us on an upcoming episode of mine.
But for now, we're going to go on to the next story.
So this one is less marketing focused and more of a reality check for the UK government
courtesy of some researchers at the University of Cambridge.
So the UK government has a vision to become a global leader in generative AI, but this
has come under scrutiny from these researchers who published a recent report outlining some
of the key challenges for the UK in this endeavour.
Now primarily they point out that there is a lack of substantial capital investment and
importantly the computing power in the UK to develop generative AI technologies.
In the report, they say that the computing cost for chat GPT alone is estimated at $33
million per month.
Whereas the UK's Frontier AI Task Force has been earmarked an initial budget of a hundred
million pounds, which wouldn't go too far.
Now obviously this is a fund that's going to be investing in seed funding, different
ventures, but it goes to show the difference in scale in terms of becoming a real power
player.
Last week we discussed OpenAI's, the budget required if they were to hit the scale of
just one tenth of the queries of Google, which I believe was $46 billion in compute power
required.
So the numbers are absolutely massive.
And speaking of that compute power, the UK just doesn't have the infrastructure.
It's lacking any major clusters of GPUs, the graphic processing units that power AI compute.
They effectively do all of the important heavy number crunching in the big machine learning
models.
And the UK has a 900 million pound supercomputer, which is devoted to AI research.
This is in the pipeline, it's going to be heading to Bristol, but it isn't expected
to be operational until 2026, which feels like light years away.
We'll all be living in a computer simulation by then, Martin.
We'll be running on GPU clusters by the time.
Yeah, absolutely.
It just feels so far away.
And I think for the UK to be competitive in this field, it just seems we don't have the
resources to be truly competitive on a global stage.
However, it's not all bad news.
The report does advocate a different path for global leadership for the UK when it comes
to AI.
And that's one that's grounded in real world applications.
So in the report they call for tax incentives for companies that are developing or incorporating
AI, and also the development of a solid legal and ethical framework to foster trust.
So becoming more of the, it's almost like a soft power play.
It's the soft skills of AI.
We can lead the world in this domain.
So as the UK focuses on its strengths in cyber security, fintech, health tech, it might be
able to carve out a bit of a niche in the world of AI, just not in the way originally
envisioned in the UK government's AI white paper.
So what do we think that means then, a range of different incentives to try and encourage
British businesses to explore how AI can increase efficiency and productivity?
Yeah, I think they've got to create the landscape that enables companies to experiment and play
with tech developed outside of the UK, right?
So it's not about becoming the place where the foundational models are created.
It's about becoming the place where the foundational models are implemented and made useful in
real world commercial applications.
Interesting.
Well, we're trying to do our bit in the UK here for helping companies to realize the
power of AI with just those types of use cases in mind.
So if you're interested in figuring out how to do that, little plug for our training and
consultancy services there, do you get in touch on the artificially intelligent marketing
website.
But outside of that, I think we'll slip into our next story, Martin.
And it's 11 Labs.
So regular listeners to the podcast would know we're a bit of a fan of 11 Labs and its
voice synthesis engine driven by AI creating really natural language sounding voices.
Well, Martin has had the chance to test out their much anticipated AI dubbing system.
So they already offer 11 Labs voice cloning technology and the latest feature enables
any person's voice to be authentically dubbed into over 20 languages.
So this is much like Hey Jen, which we've spoken about previously on the podcast.
So it's kind of a bit me too in terms of offering that, but 11 Labs is extremely cost effective
to work with.
It has a free option and I think the paid option is like $20 a month or something crazily
low.
You can actually get it for $5 a month at their base level.
There you go.
So you've been playing with this, Martin.
What was your thoughts?
Yeah, it's super easy to use.
Literally upload a video, choose a language, hit dub and it runs the process.
Doesn't take long for it to do so it's very, very simple.
However, the simplicity also is reflected in the quality of the output.
It isn't at the level of Hey Jen, the lip sync element that Hey Jen gets right.
It just isn't there.
It's much more like an old fashioned dubbed movie, like watching an old martial arts kung
fu movie from the 1970s or something like that.
It doesn't even attempt to do that.
It just puts on new audio over the top of the piece.
So if you've got something where the person is speaking in the video, it doesn't do a
great job.
If it's a voiceover, it does a pretty good job, right?
So if you've just recorded yourself doing a voiceover on a video and you want that translating
into another language, it appears to do a pretty good job.
All caveats apply such as when I listened back to it, the example that I did for myself
was translated into German and Italian.
It sounds okay.
In fact, I think we have even got a bit of a clip that we could play.
Is that available Paul?
Yeah, let's play that now.
In the report it says initial estimates suggest that if chat GPT were to scale to just a tenth
of Google's search queries, it would require a staggering 48.1 billion dollars worth of
GPU, initially and approximately 16 billion annually for maintenance.
In the report it says that initial estimates suggest that if chat GPT were to scale to
just a tenth of Google's search queries, it would require a staggering 48.1 billion dollars
worth of GPU, initially and approximately 16 billion annually for maintenance.
So there you go.
Me in Italian.
That was a clip from Artificially Intelligent Marketing last week.
We took a small snippet of the episode and played that.
What are your thoughts?
I think it does a decent enough job, but I don't speak Italian, so I would like one of
our Italian listeners to judge, but how about yourself?
Do you know what?
Listening to it, obviously the lip sync feature that blows you away when you see, hey Jen's
not there.
So we'll just put that to one side.
In terms of the audio itself, it does maintain the sort of cadence of speech in English.
So like even though it doesn't lip sync, the audio is synced with when your lips move,
which I think is interesting.
And there's some sort of emotional quality and tonality that's still there.
As I said to you when you first sent it to me, it sounds a bit like you, but not really.
I think the Italian accent that's applied is probably necessary in order to have it
speaking effectively.
Although of course, Italian listeners, we want to know your thoughts because you're
better qualified to speak on that than we are.
But in the end, for me, it sounds like a voice actor that sounds a bit like you, but who
is speaking in Italian and potentially is Italian versus me thinking, crumbs, does Martin
learn Italian?
Do you know what I mean?
So that's not there.
Having said that, I obviously know you and have known you for a while.
Does anybody else care how close the voice is for different marketing and sales applications?
Almost certainly not.
Right?
No.
So from that perspective, I can really see the value.
And we've talked previously about how long is it before this technology makes its way
into Zoom and Microsoft Teams and Google Meet so that you can speak in one language, but
the other person hears you speaking in their language.
And I think we're getting ever closer.
And I think people would absolutely be fine with the lack of lip syncing for that type
of application anyway.
And as you said, if it's just voiceovers and stuff, then all of your corporate videos where
you're showing your facilities, if you operate in a global market, like for example, some
of our contract research and contract manufacturing clients often do, you can now localize that
in all these languages, which for many companies would have been probably not worth the cost
previously, if I'm honest, because of all the voice acting work that would have been
required.
But now if that's basically click of a button and the outputs of quality, big asterisk there,
as we said, not strongest German and Italian speakers to really judge, then that does open
up a new avenue of opportunities to really localize content at scale in a way that just
has never been possible before.
And adding to that, like you said a moment ago, this is the worst that this model will
ever be, right?
This is, it's only going to get better from here on out.
Absolutely.
I mean, I guess it opens up that little Pandora's box that we talk about often, how misused
this could be.
I mean, one assumes I could have pushed that video into 11 Labs and had you translated.
I don't know how easy it is to change the content of what's translated.
So maybe I could have you speaking something you didn't originally say in Italian, perhaps
to make you look especially good or especially bad.
Yeah, there was no way to edit what was said.
So it's not got any like descript style text editing.
Okay.
However, they do have a detector.
So if you put any audio into the website and it will tell you whether or not it was made
with 11 Labs.
So I wonder whether they've got some sort of watermark in the audio.
Interesting.
But then your average user is not necessarily going to know that and necessarily getting
produced with 11 Labs, which is going to kill a lot of commercial applications, I would
have thought.
So I can imagine them not introducing that.
But I think it's another one of those areas that as marketers, we just need to keep an
eye on how this develops.
Is it seen as something that's going to be trusted by customers or would they rather
you just didn't bother?
Right.
I remember when automated meeting scheduling tools came out and you really had to handle
them very carefully when you were using them, because you could send the message, I'm really
too busy to schedule a meeting with you, because you're not important enough for me to do it
manually.
So I've got to do it automatically with this link.
And you really had to try and play up the benefits to the other person to try and sort
of get over that technical barrier until they became basically accepted, which is where
I feel they probably are now.
Will we see something when it comes to the adoption of this type of stuff, which is at
first people are like, no, I didn't.
I'm quite happy just reading the subtitles, other than knowing that you didn't put any
real effort into translating this for me.
I don't know, right?
It'd be interesting to see how some of that plays out.
Right.
Final story.
So this week, some eagle-eyed reporters spotted that OpenAI has recently changed its core
values on its website.
Previously, their website had listed that their focus was on attributes like being audacious,
thoughtful and impact driven.
But suddenly we've seen a shift to a new set of values, such as AGI focus, intense and
scrappy and scale.
So clearly a shift in priorities, should I say.
If the company can change its values like that on a whim though, people were asking,
were they ever core values to begin with?
I think that's probably a little unfair given the growth and development of the company
over the past five years.
But it is interesting for us as marketers to consider the role that values play and
how it will shape the direction of a business and it should act as a lens through which
we can prioritize decisions.
Clearly in this instance, OpenAI are focused very firmly on artificial general intelligence
and achieving scale.
So this will ultimately guide decision-making, it's going to influence culture and it's
going to be a yardstick against which external entities measure the value, the integrity
as well of the business.
Sorry, I think it's not just assuming that they've implemented these values as you would
hope, they've communicated these internally and rolled them out as part of a wider piece.
It's not just a change in wording.
I think we as a market should also understand that this change in focus will have long-term
impacts on the goals and the mission of the organization, much of which I think has been
communicated by Sam Altman over the past few months anyway.
All of which is an interesting segue and the timing of the story was particularly good
as this week I had a very enlightening discussion with Olivia Gamblin, who is the CEO and founder
of Ethical Intelligence.
She is an ethicist by training and we sat down this week to discuss things like how
corporate values can help companies when developing AI tools, AI products or incorporating AI into
the way their business works.
Let's pick it up with Olivia.
Welcome to Artificially Intelligent Marketing, Olivia Gamblin.
Thank you so much for having me here today, Martin.
So yeah, you're an entrepreneur and an ethicist and many people listening to this podcast
will be familiar with the day-to-day life of an entrepreneur, but maybe less so of a
professional ethicist.
Tell us, what does that look like?
What do your days entail?
Well, they're quite busy lately, especially since the onset of generative AI.
But I do say I do really wear two different hats.
One is the entrepreneur, one is the ethicist and that ethicist hat, oftentimes I'm the
first ethicist that people will meet, which is very fun for me.
They're usually surprised to hear that I am not going to sit there and split hairs with
them about good and bad and tell them they're a bad person.
Instead, actually as an ethicist, my role is a very supportive role.
So I specifically work with decision makers.
So I'm in with management and leadership teams working through what I call a decision
analysis process of applying ethics where we are analyzing the decisions they're making
around their strategy and crucial decision making for their technology, specifically
AI.
And I'm assessing to see its alignment with the ethical values that they've set out for
the company that they've set out either by regulation or their own company values.
So my work really as a day-to-day as an ethicist is, let's say, sitting as the consciousness
underneath everything.
You know the binding nemo, you have that one scene where Dory is saying, nemo, I am your
conscience.
Sometimes that's kind of how I feel where I'm sitting in these meetings and people are
talking and they're getting excited about ideas and I'm going, yes, that's great.
But maybe there's a better decision that we can be making here.
What if we tweaked it in this direction from the background?
So I'm not there to force any frameworks or ideas on anyone.
I'm more like a living, breathing moral compass for a company and their AI.
That's a nice way of framing it.
So what took you into the realms of AI specifically then?
The world of ethics can take you into many areas, I'm sure.
So what was it that appealed and how did you land in the tech space?
I think probably a combination.
So I grew up in the Silicon Valley, so I grew up around AI.
I like to joke that that is my first second language as I speak techie.
So I think there was always that focus of, well, of course, the only industry that exists
is technology.
At least when you grow up in that area, that's kind of what you come to believe.
And I think though later on in life as I started actually practicing as an ethicist and looking
at what field I wanted to work in, the field of AI and data attracted me because it's quite
an interesting mirror to our own selves, our own humanity.
When you're working, say for example, as an ethicist in medicine or business or politics,
you're looking at specific use cases, which is still the same case with AI.
But with AI, it's almost like that use case is pointed back at the humans.
So we're having to address a lot of these very difficult ethical questions that we've
been struggling with since the ancient philosophers.
And now it's coming back in just in a different format and in a different clothing, let's
say.
So I find it fascinating.
It brings the work of an ethicist to a whole different scale and realm that really attracted
me, I have to say.
And I imagine given the breadth and the way that technology touches so many aspects of
our lives, you're still getting involved in the political, the policy, the governance,
all of that still very much applies, right?
Oh yes.
I like to joke with my fellow ethicists.
We have to be ethicists.
We have to be politicians.
We have to be business analysts.
We have to be engineers.
We have to be, gosh, anything and everything because we have to have such a strong working
knowledge of so many different fields that impact our day-to-day work.
Yeah.
And I can imagine that multifacetedness is in high demand right now.
So you spoke about the rise of generative AI.
Can you just talk to me about your current take on generative AI and the ethical considerations
or implications of this new tech?
Yeah.
So I'll talk about it from kind of a business perspective and then from an ethicist perspective
specifically.
Again, as an ethicist, I work actually quite often on business ethics as well as AI ethics.
Those are closely intertwined.
But the business perspective around generative AI is it's slower for let's say in-depth adoption
than I think it's made out to be, meaning I've seen and worked with a lot of companies
that are still wrapping their minds around what is the use case for this technology.
Very fascinating.
And on an individual level, we can come up with uses for chat GPT, but that doesn't necessarily
directly translate into this is useful for the internal operations of a company.
There's also a lot of security considerations and data privacy considerations.
So the adoption of generative AI is a lot slower internally than it's made out to be,
which I know sometimes takes people by surprise because it's the hype right now.
And then as an ethicist, I would say it's a very, very fascinating tool.
To me, it's very cool to see and be able to prompt say chat GPT with a question and know
that the answer that I'm getting back is like this giant, I think I heard it once described
as a word calculator, meaning it's going through all of these different scenarios and predictions
and possibilities statistics of what is the next possible word.
And that's fascinating to me because the amount of data that it had to process to be able
to come back with that answer to show that this is generally on average what a person's
response will be to this question.
Fascinates me, absolutely.
But also as an ethicist, I think there's a little bit of, my concern comes into the point
that we forget the limitations of this technology in the sense that it is really good at giving
us a good average.
I heard described once as a mediocre consultant, which is true.
It's a mediocre consultant.
You're like, yeah, that's good enough.
And so you can use it for things that are, that you only really need something that's
good enough.
But the ideation and the creation still belongs to the people.
And my only concern is that we forget that in this process where the default becomes
we'll chat GPT set it.
So that's got to be the best answer.
That's not true.
It's the best mediocre answer, but now it's up to you as the person to then push it to
the next level.
Yeah, I 100% agree with that and it's something that we've spoken about on the podcast recently
is that it's actually great for if you get a problem solving, maybe you've got a particular
situation with a client.
This is a kind of real world consultancy or agency scenario.
I've had recently where I've had a situation.
I've thrown the situation into chat GPT and said, give me all the things I need to consider
with this particular thing and it's kind of spat out 10, 12 considerations.
Eight of them I'd already considered, two or three of them are kind of nonsense and
almost irrelevant.
But one of them is, is the kind of nugget of gold that I'd overlooked.
Like it was, oh yeah, that's giving me a bit of a sense check there.
But if you were, if you were treating it as infallible as the single source of truth,
then you're going to incorporate those two or three options that were irrelevant or a
bit of a nonsense.
So yeah, really important to bring that discretionary element to it.
I am, at the opening you mentioned about values and the ethical values of an organisation.
And I think that's a really interesting perspective.
Can you talk about the work that you do in terms of helping organisations distill or
kind of articulate their values?
And then how do we align the practices, the use of technology to those values?
Yeah.
So I use a very specific technique actually when working with clients to distill their,
call it like a foundational value set.
These are the foundational values that they're going to be working off of.
So we're pulling in information from three different sources.
First one being regulation or policy that's existing.
So looking at that country that the company is operating within, what are the legal systems
that it needs to adhere to?
So for example, if you, I'm in Belgium right now.
If you are operating in Belgium, then you are going to be looking to the EU AI Act.
It does have policies and specific values that are outlined within that regulation and
that legislation.
So the first input is regulation.
It's on the country level.
Second input, you're looking at industry standards.
So the clearest example of this one would be a company operating in the healthcare sector.
They're going to be looking at things like the Hippocratic oath and medical ethics.
These are longstanding values and standards within the industry.
Some industries have more robust and easily identifiable value sets than others, but there
is always this kind of, think like standard practice within an industry.
So that's the second input.
Third input is actually the company itself, the company values, which the company should
have values at this point in time.
And these are ones that you can either find written on the wall or say, here are company
values and this is what our brand embodies and our mission, distilling out from there
one of the values there.
Those three inputs, there is a process to actually triangulate between the three.
We are looking for commonalities between them.
So when you find a value such as, I'll use a Buzz One trust that hits on all three of
those value sets, that's a core value.
That means no matter where you're looking, you need to have that.
Let's say you have a value like transparency that's only showing up on two of those inputs.
That's still an important value, but you're less likely going to be using that as a core
value and more of one that helps you with prioritization.
And let's say detailed decision making, that one will be weighing in.
So it's actually a very clear process of bringing together this core foundational value set.
And then to answer the second part of your question, when it comes to decision making
off of that, there are two approaches.
So you're looking at a value, let's say trust again, and you can either take a risk based
approach or an innovation based approach.
The risk based approach is you are trying to protect for that value.
You are trying to prevent that value from going wrong.
So actually, let's use privacy.
This one's an easier example case.
So privacy, you're protecting for privacy.
That means that you're following the GDPR.
You're ensuring that you're compliant in terms of data collection policies and practices.
It's very focused on, I am protecting and preventing from things going wrong.
The innovation side though, you're looking more at alignment and design.
So that's something where you're going to see privacy by design, meaning you are incorporating
elements that are respectful of the user's privacy from the very start, from the very
design itself.
So both of these approaches are very important and it's good to have a balance between the
two, but companies will naturally tend towards one versus the other.
So you protect or you align and that depends on the company itself and the objectives around
this.
But to summarize, you have an easy process for finding your values and then you're either
looking to protect or align with those values.
Having sat in multiple sessions with leadership teams, looking at values, I feel like you're
downplaying how easy it is to arrive at those set of values.
I know what those workshops can be like.
I always have this problem because I say, oh, it's easy.
And then I had a friend saying, you really have to stop saying that because it's not
easy.
It's easy for you.
It's like, oh yeah, that's fair.
I've done this enough.
It's easy for me.
It's very fun for me.
I think it's really fascinating conversations and you really have to pay attention as people
are talking to pull out the common threads.
But that is not easy.
Let me rephrase that.
It is not easy, but there is a clear process.
Yeah, I grant you that.
I love that little framework of the regulatory, the industry and the corporate.
I think that's a really nice methodology.
So yeah, thanks for sharing that.
So when it comes to taking the next steps with this, you have developed something called
the ethics maturity continuum.
Is that right?
Is that the next stage of the process in terms of the engagement that you would take someone
with or have I misunderstood?
Can you talk us through what that continuum is?
Yeah, so the ethics maturity continuum is designed specifically to be used by either
venture capitalists and investors or by startups.
There are two different layers.
Let's call that to the continuum.
One is for younger startups.
So you're looking C to series B. And then the second layer is for series C and above
in terms of investment rounds.
And this is specifically for startups.
You can actually take this continuum and use it within teams within larger companies.
But the continuum is basically designed to help catch blind spots.
So it's got five different value pillars and with each of those prompting questions.
And you are self-assessing, but you're looking at, yes, I have this in place or I don't have
that in place or this sounds this.
I identify most with this description versus this one.
And what it does is it, I know I'm talking very, very vaguely, but it allows you to understand
your risk score when you're just starting out.
And these five pillars have been focused on what are the main risk spots basically that
a startup will encounter when first developing AI and AI product.
So the continuum allows venture capitalists to either assess for ethical risk in a potential
investment or it allows startups to actually be able to catch a blind spot and be able
to showcase to investors, stakeholders and so on.
Look, this is where we land within the continuum.
Interesting.
I guess I think I misunderstood the application of it there.
So that's, that is an interesting one to me because immediately I think to myself, well,
I can see how, you know, even mature teams that haven't been through that process themselves,
that sounds like it would be, it would be useful there as well.
I mean, ethics is, you know, you talk about blind spots, right?
I would say that ethics is often a blind spot.
It doesn't come up all the time in conversations.
I mean, certainly with product teams, marketing teams that I engage with, it's a regular blind
spot, right?
There's, they understand the basics of things like respecting people's privacy, but it tends
to be very much on that kind of what do we have to do on a legal compliance perspective,
what the kind of wider ethical, what are our values, how do we want to operate basis?
So yeah, I maybe understood the ethics maturity continuum to be applicable to wider teams,
but no, that's interesting that it's for startups.
Yeah, it's, it was specifically designed for startups, but there are different frameworks
out there that do exist for teams beyond startups.
You can find frameworks and processes that are openly accessible.
You do have to, let's say, customize them to your own, to your own needs.
But the purpose of those is to actually be able to walk teams through specific decision
making or specific value implementation.
For example, there's a lot of fairness frameworks out there.
Fairness is a very difficult value to implement, but there's a lot of different fairness frameworks
that allow a company to see this, these are the questions and when and where we need to
ask these questions to ensure that we are doing to the best of our ability, elimination
of unwanted bias.
Yeah, that's a, an important consideration.
And just, I can see how that's very applicable to companies developing the tech.
If we were to look at companies that are, take a typical medium sized enterprise at
the moment that is, they're not developing any tech themselves, but they're looking to
implement these existing technologies into their workflows.
Maybe they're looking at the likes of, you know, open AI, seeing all of the hype around
the products that they're putting out and you know, they've done their, they've done
a bit of due diligence and they've, they've seen that open AI has spent a lot of money
on red teaming and things like that.
And they've gone, yeah, people are, people are on this.
Someone's thought about this.
It's a safe product.
What would you say to those teams?
How should they approach the, the implementation of AI tech into their workflows?
So I would say you want to focus on two points here.
One actually being during the procurement process of truly analyzing a supplier.
I've seen companies have a lot of success in actually developing what is an ethics procurement
questionnaire that allows them to assess not necessarily, and I want to stress this, not
necessarily if the company has done all of the, let's say right things, meaning having
a red team, having a responsible AI policy and so on, but more looking at assessing if
that supplier company, the values of that supplier company align with the company that
is procuring the technology.
Because you can have a company that is developing, let's say a safe product and they are going
through the right processes, but they prioritize transparency over privacy.
And you are a health tech company and you're procuring this, this, this information, you're
procuring this, this, let's say AI and you need to prioritize privacy over transparency
because of the sensitivity of the data that goes through, that's going to go through that
system for you.
That although small difference in prioritization can result in very different outcomes for
a company.
So with those procurement processes, you're looking for alignment in, let's say prioritization
of values.
Of course, also checking to make sure that they've done the right things, but even more
so looking at if those values are in alignment.
That's one.
One point, the second point here for teams that are looking to implement procured AI
is ensuring that, and it sounds very simple, but ensuring there is a human in the loop
monitoring the progress.
So there's this misconception of I am buying a full package.
It's good.
It's done.
I can just put it running.
No problem.
Not how AI works.
When a model is live, when you've been, when you have deployed your system, it's now taking
in live data and that live data is going to, let's say influence the model.
So you do need someone that's actually monitoring the output closely to ensure, Hey, this is,
this is drifting.
It happens all the time.
You have model drift.
This is drifting from what we originally intended it for.
It's drifting.
We're not getting the results that we wanted out of it versus yes, it's good.
It's an alignment.
It's doing what we wanted it to.
We haven't caught any, any challenges.
You need someone in that, that monitoring position.
That's actually one of the most important positions is monitoring that, that human in
the loop, making sure that the deployed system is continuing to perform as you expect it
to, because it will change and you need to be able to, to identify that as fast as you
can and catch it before it's gone too far, let's say.
And I think that goes to that earlier point about putting too much trust and faith into
chat GPT.
It's the same thing with any model, isn't it?
You just kind of set it up and go, Oh no, it knows what it's doing.
We've, we've, we've built it.
Off it goes.
I'm nice.
I think that's, that's a really important, important point.
You just going back to something you mentioned earlier about the, the regulatory landscape.
You mentioned the EU AI act.
That's going through European parliament as we speak.
What are your thoughts on it?
It takes a person centered risk-based approach.
Do you, do you think it's, it's good?
Do you think it goes far enough to view any, any critiques of it?
Well it is risk-based.
I as a personal preference tend to prefer innovation based, but that's my personal preference
as, as just an ethicist.
I'm very much a design thinker, but I would say with the EU AI act, I am concerned with
some of the risk categorizations because you can develop a system in a low risk category
and then let's say apply it into a high risk category and potentially.
So examples like that, there's, there's a few holes that I, that I think will be covered
once the, once the regulation is in effect, but kind of like the GDPR with legitimate
interest hole where everything's suddenly legitimate interest.
I think we're going to see very similar aspects of that with the AI act.
I'm also curious to see how it works in terms of application.
I know for example, a lot of member states still struggle with actually having their
data protection officers, DPOs in place for the GDPR.
We're still struggling with GDPR implementation.
The one place that I find it falling short and because all of the things that I mentioned
that can be corrected over time and we do just need to basically, well, I'll speak techie
here deploy it so that we can actually see how it goes to be able to, to, to correct
for those, those blind spots.
Yeah, the, the hole that I see that I think has me most concerned is actually the startup
ecosystem here in, here in Europe and any startup that's looking to, to operate within
the European market.
Not because regulation is, is a constraint on innovation.
I think quite the opposite, but instead because this regulation, this, this, this AI act,
it puts a heavy emphasis on documentation and auditing that costs time, money, resources
that costs a lot.
And so I have heard investors specifically say post AI act, we're not investing in any
high risk categories.
So my fear, unless the EU sets up specific, let's say institutions or resources for startups,
they're going to cut off a lot of innovation that's going, that could happen in the higher
risk that could happen totally fine, responsibly in the high risk, but only because startups
do not have the resources to comply with the documentation required for those risk categories.
So that's, that's my big concern.
I haven't seen enough movement there in terms of covering the potential consequences for
that.
Yeah, that's, I think the auditability, the, the being able to interrogate the models and
understand how it's arrived at decisions and what have you is such a big part of it.
Yeah.
I think that you can see why, why people would be concerned if they were building from the
ground up, like you say, getting the systems and processes in place to do that could be
quite a serious ask.
Right.
We are heading towards the end of, of this podcast, but I would like to jump into the
role of marketing teams specifically as it relates to responsible AI.
Is that something that you've looked at in great detail?
Have you got any advice for the marketing professionals working in and around this,
this tech?
Yes, absolutely.
And marketing is one of the interesting ones because it's often overlooked as having any
type of impact or role in responsible AI.
What I say responsibly, I mean, the industry of, of let's say AI governance, ethics, safety,
risk, et cetera, et cetera.
This all fits under the term responsibly AI and typically people think, oh, responsibly
AI is for tech teams, but marketing teams and marketing professionals have a very important
role to play really in two different directions.
The first one is in communication.
So when it comes to responsible AI, there is this risk of something called ethics washing
where a company says, look at us, we're so ethical and then you lift up the, the hood,
you look behind the curtain, you're like, woof, what's going on here?
That is not right.
It's kind of like, it's also known as blue washing, which is, it's similar to, if you've
ever heard the term green washing where companies say, look, we're so environmentally friendly.
And then you pull back the curtain, you're like, you're dumping the gallons of oil into
the ocean.
I don't call that environmentally friendly.
Similar with ethics where, where a company can say, look, we have our values.
And then you look and you're like, well, but you don't use them.
So that's something called ethics washing.
And what that does is it creates a very intense mistrust with, with a customer base.
So customers can see through, you can say, we respect your privacy.
And then a customer is actually going through the user experience and they're having to
submit like their mother's maiden name.
And they're thinking, that's not, no, why do you need that?
You're telling me one thing and you're acting in a, in another way.
That doesn't sit right with me.
So for marketing professionals, being able to accurately represent and communicate with
a user base, how the company approaches responsibly, or how the company approaches ethics is incredibly
important because that's what's going to be a huge trust builder.
You can have all the work being done behind the scenes with the tech teams, ensuring that
there are strong ethical solutions in place.
But if you're not being able to communicate with that user base through your marketing,
that that is done in a way that is holistic, then you are both going to miss out on the
benefits of responsibility, but also you risk creating this kind of mistrust with your user
base.
So on one hand, marketers have this very important role of that trust building communication
with user bases, but then market marketers can also have the ability to be a fantastic
resource for feedback.
So for example, if they're getting feedback from their, from the audience saying, you,
we don't trust you, or we think that you don't have very fair practices, that's a great point
in time to feed that information back to your tech and your product and your leadership
teams.
So marketing really actually has a very important role, they're the trust builders and they're
also that insight point of insight for teams to be able to better connect with their user
base.
And this is all under the umbrella of responsible AI and successful implementation of ethics
and values and technology.
So a big role for marketing to play in the communication and interface between the customers
and the tech teams, an important role to play.
Fantastic.
So, well, thank you for joining us.
I believe, am I right in saying you've got a book in development?
Yes.
I am currently at the phase of dear God, what am I doing?
So I'm about two thirds of the way through, but my book is coming.
I'm with Kogan Publishing and publication is set for summer, June 2024.
My title is Responsible AI, Implement an Ethical Approach in Your Organization.
Big surprise.
Well, I would love to have you back on closer to release date to talk about responsible
AI and the book launch in general.
Where can people find you online?
Absolutely, you can check me out at oliviagamblin.com and I am actually going to be starting a newsletter
soon all around this idea of pursuit of good tech and the crosshairs of ethics, design,
thinking and leadership in AI.
So we'll see how that goes.
But you can sign up on my website.
You can also find me on LinkedIn under the name Olivia Gamblin.
I do respond to messages on LinkedIn.
It sometimes takes me a while, but I do respond.
Fantastic.
Well, thank you for joining us on artificially intelligent marketing.
Thank you for having me here today, Martin.
Well, thanks for hosting great interview there, Martin.
I hope the listeners enjoyed that and thank you for your time this week.
As always, great to hang out with you.
Good to see you too.
Looking forward to next week's episode where there'll be even more great AI content.
See you later, Paul.
Cheers.
Bye.
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