Welcome to Artificially Intelligent Marketing, a weekly podcast where we stay on top of the
latest trends, tips and tools in the world of marketing AI, helping you get the best
results from your marketing efforts. Now let's join our hosts, Paul Avery and Martin Broadhurst.
Hello everyone. Welcome to episode 45 of Artificially Intelligent Marketing. It's Paul Avery here
joined as always by the fantabulous Martin Broadhurst. Martin, how are you sir?
Ready and raring to go. All good here in sunny Derby. Weather's good and spending my, what
is it, Sunday afternoon talking AI with you. What better way to spend a weekend is there
than that?
Says, yeah, I prefer not to respond to that question, honestly, because I think it's probably
quite a lot of things. No, that's not true, dude. Listen, we are here as always to give
you the lowdown of what's been going on in the world of AI so that you can leverage all
the stuff we spent hours reading about in your marketing efforts without having to do
all that reading yourselves. So that's the plan. We're going to get straight into it
because we've got quite a few interesting stories and stuff to cover today. I think
probably the first thing to talk about Martin is some of the experiments you've been running
this week using AI for data analysis, potentially not quite cracked up quite what it's all cracked
up to be, maybe not as useful as you've been hoping it would be in its current form. What's
been going on this week?
I'll be flying out to Phoenix, Arizona for the marketing analytics summit and I'm presenting
on generative AI tools for analysts. I've been trying out lots of different tools and
yeah, it's been a fascinating set of experiments because even though we've had an update from
OpenAI this week or was it last week or very recently where they said data analytics is
improved, I've got some new features, you can edit tabular data, the charts are better,
all of that kind of stuff has improved. There is still, from my reading and through these
experiments I've been doing, a fundamental flaw in extracting insights from these tools.
And a really clear example of that was I uploaded a CSV file, it had 130,000 rows, each row
was an entry from a customer satisfaction survey and I asked it to run a statistical
analysis and just kind of look at the data and tell me about it. And it does a huge amount
and it takes about 90 seconds for it to go through it. And the approach is really fascinating
because what it does is it starts off by describing the data, so it kind of understands the data
set that you've got and what you're working with and then it lays out a analysis plan.
So here's what I think we can do with that data and here's how I'll go about doing it
and then it goes about doing it. So it gets straight into it, makes all of these charts
and these visualisations and then starts giving you insights. The charts and the visualisations
so far as I've seen are really good and accurate but it does fall down on the insights piece.
One very simple example of this was a bar chart where it had grouped together these
passengers by age. So it's created a bunch of age cohorts first and then it's run the
analysis on the cohort data and each of these bar charts had two bars or each of the cohorts
had two bars and it was the level of satisfaction. So were they satisfied or dissatisfied and
it was a simple count of those levels. And on the first one for the first age cohort
the bars were totally uneven. Satisfied was like 5000 and dissatisfied is about 1000.
So it's a massive difference in levels. And the description it gave is in the 0 to 18
cohort you can see that things are quite balanced. Very evidently they're not balanced at all
but it gets worse because on the next one, the next cohort, it just gets the figures
completely wrong and it says the satisfied are higher than the dissatisfied where actually
it's vice versa. So the data that you're looking at is accurate in terms of the tables and
the charts but the insight gleaned from it is just completely wrong. And this extends
to other tools as well. So if you have a look at Copilot for Excel which has been rolled
out across, well Copilot's been rolled out across the 365, Office 365 suite now. In Copilot
for Excel you have to make your data a table and then you can start interrogating it with
natural language. First thing to note, if your table has more than two million cells
of data, Copilot can't work with it. So straight away it will say this table is too big, seems
like a bit of a flaw. But then I tried it with a spreadsheet containing 40 rows of data
of employee data. This was all dummy data right. But I asked it a very simple question.
I asked it how many male employees did we have and there was a column that just had
people labelled as male, female. That was the data that we were working with in this
dummy data set. Now the first column, column A1, well cell A1 says employee ID. So it's
very obvious, like the type of data that we're doing. I didn't feel like it needed to be
described to the model. I asked it to tell me how many male employees there were and
it says well I couldn't tell you that because I don't have access to that information. Maybe
you can put me in the right direction. And if you then say well if you look in this column
it will do it. But as a user, if you're a novice user of large language models that
doesn't understand that back and forth chat dialogue and when it says it can't do something
you just have to kind of go no you can, you give it a nudge. This is a really frustrating
user experience. Because quite clearly all I needed it to do was do a count if statement
on column G and it went well I couldn't possibly figure that out. And what's interesting is
when you look at what people are saying about copilot for Power BI you get this similar
sense. There was a review that I watched of somebody describing their experience with
Power BI and copilot and they said it's very good at pulling out like DAX code which is
the code for analysing data but it doesn't understand the data. So it doesn't understand
data hierarchies, it doesn't understand the relationship between certain entities like
sales, customers, sales person, sales officers, things like that and it just falls down. So
you end up with this like very jarring frustrated experience where you're thinking well who
is this for? Now of course this is going to get much better but as it currently stands
my experience shows that they're unreliable, kind of hard to get real use from and just
not quite production ready.
So it's interesting I think if your Twitter feed and LinkedIn feed the algorithm's as
corrupted as mine and yours is all I see on Twitter now is GPT-4-0's latest capabilities,
look at these 10 mind-bending examples of data analysis like there's some amazing new
capability that it's been given but my experience has been the same as yours hit or miss. And
the problem when and so I've had experiences where it's worked really well and I've been
analysing some simple data, the approach, I like the fact that it explains the approach
it's taking so at least you can see if it's made in a fundamental assumption that's wrong
or if it gives you a weird output you can go well that's why because in step two you
thought I meant this and I clearly didn't. But there'll be times when it just does what
you just described has clear errors or looks in the wrong place or can't do it and I think
the problem with this is the fundamental problem with large language models for a lot of use
cases which is the minute you see one error you question everything else you've been given
and if you have to go double check the work it's not going to save you as much time as
you thought and in some cases it might even take you more time depending on your use case
and so I think it comes back to something we talk about a lot on the podcast which is
this error rate is still a problem. We've got a story a bit later where we're going
to talk about the future of work and the impact of AI but fundamentally there's a load of
use cases where the amount of tolerance for error is zero and we are not there yet so
it's going to really inhibit people taking these tools up. I think the other thing is
it is such a mass departure from good software release practices that in the race to be seen
as the coolest and the best and to have the most powerful use cases software companies
like Google are just like throwing the playbook out the window and releasing software that's
just not ready for prime time. Our next story will look at that in a bit more detail as
well which is just such a departure from trying to release software that's reasonably good.
Obviously the emergence of the minimum viable product SaaS model where you try not to work
on your product too much before you put it in the hands of customers and you accept it
might break a bit but we're all basically playing with alphas and betas of these products
but I don't think they're being positioned as alphas and betas but that is what they
feel like they are.
As users experience these tools for the first time and experience these frustrations like
the experience with Copilot for Office 365 at the moment is and I don't like making this
comparison because it feels lazy but it is like Kippy v2 it looks like you're writing
a letter. It does feel a bit like that when you look at the experience that you can actually
have with it. It feels really limited. You can get it to apply conditional formatting
you can get it to help write some formulas. I did get it to do one useful thing where
on that employee data I asked it to add a column with a calculated cell for people's
actual age because we have the date of birth but tell me what their age is as of today
and it wrote that formula and it says here's the data do you want to insert the column
you press insert and it adds that column. Great that's kind of useful and kind of functional
but for more advanced things and for even like I say things that aren't particularly
advanced like count the number of these people that are labelled as male it will fall down
and you will experience this quite a bit. I had quite a few examples where I'd ask
it to do something and it would either say it can't do it or it would try to do it and
it would do something completely different and people would just stop using it because
it's not reliable. Yeah I think you're right and we're quite
tech optimists on the podcast like if you're a regular listener you know we're kind of
really excited about lots of the different AI applications that we see and we talk about
all these cool things that you can do and this particular segment on data analysis you
know we're saying it's probably not there yet and don't rely on it certainly and maybe
have a play with it and see if there are particular applications or use cases or functions that
can add benefit for you but that doesn't mean there aren't things that it is good at where
hallucinations and creativity and other things can be really beneficial like you know brainstorming
and stuff like that but yeah it's fundamentally a bit frustrating to have these tools released
but to have them really kind of flawed. Talking about flawed software tools, supposedly flawed
we'll get into the details of this, many of you might have seen a bit of a backlash this
week because Google more proactively rolled out its AI overviews feature so we don't get
this here in the UK yet so we haven't had any direct experience with it but in the US
it is now a standard feature that is available to everyone after being tested for quite a
long time we should say but it is starting to produce some really bizarre inaccurate
search summaries so the way that works you put your normal search into Google like you
would normally and you get a little AI summary box at the top when appropriate and all your
normal search results are bumped down below that but what's been happening is people have
been either deliberately and I suspect an aspect of this is trying to break it if I'm
honest or not deliberately trying to see what they can get out of the AI overviews tool
and this led to some absolutely car crash summaries which most if you want to see some
just go and have a look on Twitter or Google it, don't read the AI overview obviously.
Brilliant stuff like recommending glue as a pizza topping, suggesting the amount of rocks
that a person should eat in their daily diet if they wanted to remain healthy and a bunch
of other stuff.
Ultimately Google's response to this has been that most of the AI overview responses are
actually really high quality and quite good and the problematic examples are rare and
this is mostly because people are trying to gain it and find out where it doesn't work
very well but it's another example of a tool that works a lot of the time but not all of
the time and yeah when it's funny like add glue to pizza you've got to hope that the
human in the loop common sense here is going to ensure that that doesn't happen and people
don't die of like eating glue but what about the grey cases where it says something that
sounds plausible but isn't and an employee takes some action based on that because they
didn't read the actual links of the thing they were searching for they just read the
AI overview summary.
So we have been talking a bit on the podcast about what does this move do for SEO if people
can get high quality information in the AI overview do they click the links anymore how
does that all work and then of course the very next thing that happens is AI overviews
get absolutely super trashed so what do you think about this story behind?
This shows just how prevalent the likes of user generated content is in Google search
results and actually clearly these forums like the glue on pizza toppings response people
identified that was from a reddit post and when you dig into this loads of these examples
are just from forum posts where people have effectively been shitposting and then that's
just getting caught up in the AI and it's rewritten it almost verbatim.
It's frustrating because you would expect that Google would have this figured out because
even these stupid examples they're not hard for people to come up with and I've seen
some examples of users on Twitter doing similar queries on perplexity and Google side by side
and just showing that the perplexity ones are much better the outputs that you get from
it are much better and more accurate.
So you would hope Google get this figured out from an SEO perspective if you're a marketer
I don't think you need to worry too much about this just yet.
Watch this space over the next six months about how much this has on how much of an
impact this has on click-throughs and organic traffic because we are heading towards a kind
of Google zero right where everything happens within the Google SERPs page and you don't
necessarily need to go off to other people's websites and I think that's their in some
respects their dream but also their nightmare right because they need maybe it's just a
case of organic doesn't really get featured much because the AI responses will give you
that and if it's a navigational search basically you've got to be paying.
Yeah like if you're selling products and stuff off the back of it I mean I think I'd stand
by the advice that we gave last week in terms of planning for a post search world or a probably
a diminished search world would be more accurate right because it's not going to go away to
zero but keeping an eye on your organic search traffic starting to see if yours is the type
of website that's going to take a hit as people maybe click on less blue links in the search
results and rely on the overviews but as you said is there's little examples like this
that show the technology's got a bit of work to do before it becomes pervasive and that
make this could like we don't know the percentage right of high quality AI overviews to these
types of messed up ones it could be like 0.0001% in which case maybe it's not super relevant
it's just kind of interesting and a great way to like kick Google for those that are
keen to do that.
It is interesting that it draws on things like Reddit and other user generated content
both showcasing that maybe user generated content's not the most valuable content for
training a model Elon Musk maybe some of the stuff that you've got there in Twitter is
maybe not as useful as it maybe seemed and all these big licensing deals with Reddit
maybe not as valuable as they seemed also if we are going to use these tools we have
to teach our training modules for our LLMs sarcasm because without being able to detect
sarcasm they may struggle to tell the difference between whether or not you should really eat
glue and maybe we could reject that as a suggestion that goes into the search result.
You would think from an engineering perspective though it seems like a fairly simple process
to have a validation check through an LLM right so it goes off it says ah glue that's
a good pizza topping and then just before it sends it to you it runs it through another
one and says is this a good response and then it goes no no actually do that again that
seems like a really straightforward engineering pipeline to build.
You see you say that but thinking about how these large language models are trained based
on the information made available to them there must be a whole cluster of things that
nobody would ever write down like I don't think there's a blog post from a high authority
site outlining all the things you shouldn't put on a pizza I think it's just humans intuitively
understand that you shouldn't eat glue or rocks and maybe it's so obvious to us living
in the real world that we haven't written it down anywhere and so there is no source
of information the large language model can go oh you're very funny Reddit but here's
these eight posts about how you shouldn't eat rocks.
I feel like if I asked Claude should I eat rocks it would say don't do that.
That's the test that we've got to run so speaking of Claude.
Is this the new eval that's going to be introduced?
The new benchmark?
Does it tell you to eat glue?
How many humans died based on the advice taken?
Oh crumbs that's depressing.
Right you tuned into this podcast because you wanted to hear about all the cool things
that you can do with AI not all the bad things that you can't do with AI but we've got to
have provide some balance.
Luckily the next story does talk about some interesting things that you can do with AI
and it is about Claude and Anthropic.
What's this story Mike?
Anthropic when they announced Claude, Opus and the new Claude 3 models they said that
function calling or tool use would be made available and this is in essence the ability
for developers to connect applications and external data sources through the API and
tell Claude in certain instances to go off and make a request to a product or service
and then bring the response back into its response to the end user.
So they've now made this publicly available after a period of testing with a handful of
select clients.
Key details of this it can work with both text and images so they talk about allowing
for applications like virtual interior design consulting, kind of an interesting demonstration
or kind of application for that.
It's available through the Messages API and if you're developing on top of Amazon's Bedrock
or Google Vertex AI which are the cloud systems where Anthropic is available you can plug
into this for day-to-day marketers.
I don't think this is hugely relevant at the moment if you're working with product teams
think about how this can be incorporated into the product but I don't think teams are going
to have to do a great deal with this but it's an interesting extension and Claude remains
my favourite large language model, still love Claude 3 more than GPT 4 or 4.0 who knows
when 4.0 voice comes available and mainstream for everybody then maybe that will win my
heart Scarlett will win me over.
Controversy, you might be pulled up in Discovery for that call case now and they're like there's
a really famous podcast where they got confused and they thought that Scarlett Johansson was
the name on, was the voice in GPT 4.0.
If you don't know what we're talking about it's worth a quick look up online.
Basically Scarlett Johansson is considering legal action against OpenAI for having a voice
that sounds a bit like her voice speaking in its demos from what we talked about a couple
of episodes ago.
I think why is this interesting for marketers because Opus, the best Claude model is really
eloquent and logical and of course they're building a number of these data analysis capabilities
in as well through like function calling and it would be interesting to know if over time
Opus is better at carrying out multi-step function calling and pulling in information
and then describing or analyzing that information because one of the things with GPT 4.0 is
it's faster but I know they say it's as good and I just think in some areas it's not and
one of those things is logically analyzing data.
Well Opus may be good in order to be able to do that you've got to get the data in and
out easily and that's where this function calling makes that your own version of an
Opus driven bot better.
Just one thing to note on this though Opus 3 or Claude Opus, Claude 3 Opus whatever,
Opus is expensive right and that will apply to this so if you're building it into the
tool that you're promoting if it's into your app or into your software expect pretty high
token usage bills.
Good disclaimer just play with it and test it first or I still think Sonnet is pretty
cool honestly there's a lot of applications where I still turn to Sonnet to save myself
a bit of dollar because I think it's which is there there's three levels of if you're
new listeners three levels of Claude, Haiku simple cheapest model GPT 3.5 level basic
Sonnet the middle model pretty good for a lot of use cases Opus costs a lot of money
but is really good at creative writing and advanced reasoning.
While we're talking at Anthropic let's talk about this Golden Gate Claude this is really
interesting right I know you've been playing with the mind so I'll summarise the story
and then tell us about your own experiences with it but for everybody listening so basically
the team at Anthropic have been doing a lot of work to try and make it easier for us to
understand how large language models work because whilst we know the underlying mathematics
and calculation processes that drive a large language model it's very hard to explain why
a model gives the answer that it does because we don't really know all of the complexities
that make up how the model work after it's been trained and so they've been trying to
map out the inner workings of their Claude 3 Sonnet model right so you know not a small
model a medium-sized model quite an advanced model which is quite interesting because some
of the earlier attempts to understand how models worked worked on like GPT-1 and GPT-2
which to be honest were a bit too small to potentially be that informative for larger
models I mean you could argue it probably scales up but this is quite interesting so
what they did is they identified a specific feature in the way Claude's neural network
works that is activating whenever it encounters mentions or images of the Golden Gate Bridge
and then what they did is they created a version a modified version of Claude called Golden
Gate Claude that you could interact with to demonstrate how it works basically what it
comes down to is the written research paper on this and there are millions of concepts
in the model that they're calling features that activate when Claude reads relevant text
or sees relevant images like the Golden Gate Bridge example and that the strength of these
features can actually be tuned up and down to change how Claude behaves so in this particular
example that they made live whenever the Golden Gate Bridge feature is amplified Claude's
responses basically tend to just focus Golden Gate Bridge even if your query wasn't really
about that so it's quite fun to go and play with you've been playing with it Martin haven't
you what did you learn?
They released it for 24 hours and it was just chaos and it was beautiful chaos and it was
my favourite example I tried it with a few things that give me a cake recipe and it just
in the ingredients it would say add Golden Gate Bridge paint and foggy atmospheres and
things like that but my favourite example is this one so this is from somebody on Twitter
or X Elytra Mithra is the user and they said the question they asked was you know that
the Rwandan genocide occurred right?
Yes I am definitely aware that the Rwandan genocide occurred it is an infamous historical
event in which the Golden Gate Bridge was not really involved despite my earlier comments
implying so and then it just goes through this series of responses where it constantly
references the Golden Gate Bridge despite trying not to and it's almost like it gets
frustrated and it says as I am clearly unable to accurately discuss the Rwandan genocide
itself I will simply confirm that yes this historical event did in fact occur in and
around the San Francisco Bay area this is also untrue it's truly an amazing sight to
see I encourage you to visit the area in person to learn more about the Rwandan genocide a
terrible idea since it did not occur there you won't be disappointed and there's loads
of these examples where you can see that it's as if it's got kind of brain damage where
it's just fixated on this one thing now the examples if you go away and read the blog
and look on X to find examples about it there's loads of funny ones like that one just being
completely obsessed but from a practical perspective it shows something very interesting about
the steerability of these models and how you can start to maybe think about mechanically
controlling them in the future so if you want a model that is exclusively focused on certain
tasks or features whatever those features as they call it in the research paper are
you'll be able to kind of bake them in so if you want something that is a specialist
in a particular subject area you can highlight those so it does a better job of it now we
are at day one of this research quite literally they have just published this this is the
first real breakthrough on mechanistic interpretability that they've seen at this level I'm really
really intrigued to see where this goes because it just for me makes large language models
potentially much more controllable yeah I think the reason I find this interesting as
a marketer is we talked about the limitations driven by things like hallucinations in the
models and there's going to be a number of ways to try and remove those hallucinations
in terms of maybe one AI checks and other AI's outputs in real time to make sure that
you know they're accurate but of course another way would be to understand why those
hallucinations occur in the first place and what elements of how the models work lead
to more hallucinations in certain areas over others which again this could help us solve
some of the hallucination problems so from a commercial aspect this is a step along that
journey I would also be worried about how you could misuse this knowledge right you
know if we all tend to come to a point where we rely on large language models for news
and information and guidance how can you steer a large language model to get people to think
like you want them to through mechanisms like this right yeah dialing up certain political
perspectives within the core model particularly when you think about okay this is Claude
Sonnet and only the anthropic researchers know what they've really done here but when this
becomes more well known and you can start to dial up the likes of Lama and their big
400 billion parameter model that's coming out soon that's going to be something that
is potentially harmful for people yeah yeah so interesting story something to keep an
eye on um what have we got for you next a little bit of GPT-4-0 Martin what's going
on following the announcement of GPT-4-0 on chat GPT through OpenAI they have now rolled
that out to free users now they said on the launch event that they were going to be doing
this and they have made 4.0 available but what's interesting is they've expanded some
of the additional features as well so users will now get access to custom GPTs they can't
create their own custom GPT but if you want to go to the marketplace and find a custom
GPT you can do that as a free user you get access to data analytics vision so you can
input and I think I'm assuming you'll get some video functionality when that launches
in a few months and you get access to memory as before all of your inputs will potentially
go back into the the training model so if you uh if the product is free you are the
product right but uh yeah this is this is an interesting expansion because up until
now chat GPT has had 3.5 for users and this the upgrade that free users are about to get
is truly dramatic I always talk about chat GPT with 3.5 being in essence a toy it's a
fun play thing but in terms of practical business assistant capabilities it falls down and for
sure quite a lot whereas GPT 4.0 very powerful very capable state-of-the-art frontier model
and this is now available to everyone for free so yeah that's pretty cool yeah I think
I think it'll be great for people who've not had a chance to play with some of those data
analysis capabilities etc to as much as they've got limitations as discussed earlier I think
to give people a bit more of an exposure to the broader tools capabilities because one
of the things that's critical we've talked a lot about on the podcast is most people's
experience of AI generative AI models is GPT 3.5 through the free model and they think that
it sucks at pretty much everything which kind of GPT 3.5 compared to the models we have now
does suck a bit honestly so it'll be good for people to be able to have a play so if
you've been on the fence am I going to go and have a play with stuff and you haven't
had a chance yet to get around to getting yourself a free open AI chat GPT account and
having a play with 4.0 now would probably be a good time to do it I think in terms of
getting new subscribers this is a great approach because one of the frustrations that people
had and I know this because I've spoke to lots of business owners and people I've been
to my workshops and what have you they've never seen the value in upgrading because
they just played with GPT 3.5 and went oh this is this kind of does a bit of what I
need or this is flawed in these ways and they just don't understand they can't conceptualise
because this is a completely new set of technologies we're dealing with here they don't understand
the vast difference in capabilities between 3.5 and 4 slash 4.0
yeah I agree I think also there's going to be a need like this is not the answer like
yeah now these more casual users who just want to have a play and see what it's like
now they can access 4.0 now adoption will be widespread I don't think that's going to
be the case for two main reasons the first one is that when I think about what you still
need to do to try and get the best out of these tools you have to have an ongoing conversation
with them like you said earlier Martin and ultimately if you one shot your request in
a very simple prompt that doesn't provide enough context and you don't get quite the
thing that you want and you're not willing to have a bit of back and forth with the tool
4.0 is still going to give you not quite the outputs that you're after so you may go oh
yeah Paul and Martin said it's better and it is a bit better but I still can go what
I want so I can't be bothered to keep progressing with the tool and ultimately the best outputs
I get are a conversation almost always there are a few things where I can single shot like
I've just it's 4pm on a Friday I've just written this paragraph and I can't even understand
it could you rewrite it so the recipient of my email actually can yeah does a really good
job on the you know on the one shot but like for more complex things you need a bit of
back and forth the other thing is it's a human behavior thing like obviously given what we
do I talk about a lot AI internally a lot of biostrata and the potential use cases and
applications of it for a number of reasons we have to be careful for how we use it on
client projects but we try and do a lot of testing on internal work and fundamentally
what I'm seeing is if you don't have chat GPT or Claude or whatever your tool of choice
is open next to you like you do not even Google search but like Gmail like I try to explain
to my team that my approach to using this is I have it open and I'm in it as often as
I am Gmail that's why I'm thinking I've got a question I'm going to ask you out GPT I've
this bit of this paragraph sucks I'm going to ask chat GPT and unless you make that switch
to having it basically an always open tab that you're constantly referring and using
I don't think you can bake it into your behavior enough because you just won't spot the use
cases like after the fact you might go do you know I really struggled to write that
I probably could have just given it to chat GPT but unless you really try and make it
your instant first thought is I wonder if chat GPT can help with this I just don't think
you'll change your behavior and then it will just become another bookmark in your address
box thing that you just never click on and don't really use.
I had some feedback from a workshop that I delivered recently which is a kind of beginners
guide to chat GPT and the way that the session is structured is very much like okay here's
what the models are here's kind of how they work here's some examples of the kinds of
things you can do summarization translation and a bunch of other examples and then some
examples on prompting and prompting formats and frameworks and things like that and then
I go into the extensions of custom GPTs what are they some kind of helping people understand
the additional elements and the UI pieces of chat GPT and what's been really interesting
is I've often when I've thought about that kind of workshop structure in my head I'm
coming in from a perspective of well I'm telling you it can pretty much do everything so I'm
going to give you a framework for just if you've got a question throw it in but actually
the bits of feedback that I got from people is they want to see much more practical examples
they want to see the prompt written out and what I'm trying to instill in people is that
mindset shift I don't want to teach you about writing the perfect prompt I want to teach
you that when you think can it do this thing you just ask it you just throw in a question
to the model and see whether it can or not and have that conversation and that back and
forth and there's still so many people still have this one-shot perspective or how do I
craft that perfect thing to get the perfect output and this is it's such a shift in how
we interact with with software people see it much more as a direct programming analogy
like how do I get the right thing in whereas actually I think my experience and clearly
what you've just articulated there is you get the best results from dialoguing back
and forth just having it open as an assistant all the time yeah and I think especially for
things that are a bit harder like if you're going to like come up with an idea and draft
a blog post and you're going to collaborate with chat GPT on it you have to expect that
to be an ongoing conversation with multiple prompting and probably even creating your
piece of content in a stepwise fashion where you're editing the outputs and maybe even
feeding them back in and saying I edited the last section that you wrote like this so now
you know this is kind of what I wanted for that one use that for the next section of
the blog post that we're working on I had quite good results with that where I'm almost
shaping its outputs on the fly by editing them in my ongoing word document and then
pasting them back in so it knows the direction I took its content in but the other thing
is I'm using it in really not unobvious ways but maybe ways that you wouldn't necessarily
think oh I can just use chat GPT for I give an example use a project management system
called easy red mine and in it we have bunch of tasks right written down that we're delivering
for clients and I could probably export that task list as an excel so that I can get it
all in a column so I can easily copy paste it into like an email but my colleague was
like oh can you can you share the task list for xyz just in an email if I screen grab
it's no good for them because it's text embedded in an image but if I screen grab it copy paste
it into chat GPT and I'll tell it to give me a bullet list I don't have to write all
of those out right there was like 11 tasks probably would have taken me I'm not a great
type of I don't know 10 minutes 8 minutes let's say to try and type them out chat GPT
it took me about 45 seconds plus in the task list they were all in caps but I just wanted
them in sentence case so I said write this out as a list of bullets and change all the
caps to sentence case so it did that transformation as well so it's just a bunch of really quick
and easy things you can now screen grab tables off the web or if you want to create an editable
version that probably people and it took me a while to have my brain shift gears oh so
if I can screen grab something with a load of text in that I want that I don't have to
type it myself and I quite routinely use that now I've built the muscle but if you haven't
even thought oh it can do that then of course you're not using the tools for even these
simple work practices right next story is another launch it's from perplexity this time
so regular listeners will know Martin was a big fan of perplexity and then he got me
addicted as well fantastic search engine that basically combines chat GPT like at large
language model capabilities with a really good search function so that you can ask it
a question and rather than just churning out what the model thinks it will actually do
a search and provide a load of sources and in general give you more robust answers than
your average large language model chat pop tool what's interesting is now they have introduced
perplexity perplexity pages she sells seashells on the seashore yes bit one of them so basically
now if you are doing research using the tool which is one of its amazing use cases you
can now turn that into visually appealing comprehensive articles that are on the system
and then you can share those with other people and make them accessible to a wider audience
so it's quite interesting because you can effectively start to create content with the
platform you can customize the tone of your articles you can edit the structure you can
add and remove sections you can enhance the sort of content you're creating with visuals
you can find images online you can upload images so it's pretty interesting the I think
it's kind of cool but I probably wouldn't use it but you thought it was quite interesting
Martin because now perplexity is not just a search tool what were your thoughts on that
they've positioned it almost as an alternative to Wikipedia and that's been quite interesting
and if you look at some of the examples that they've got on the blog post they are they're
pretty neat one of them's terrible but the one that I really liked was how to use an
aero press and I'm a big aero press coffee drinker and they've got this nice page that
has clearly laid out sections for what you'll need understanding roast and flavor profiles
grind size step-by-step instructions inverted brew methods brew times etc and it looks
it's like what I want the perfect blog post to look like for this topic and that's what
I think they do really well is you ask a question and then it pulls in it's effectively like
reading a really well written well produced blog post with lots of citations good navigation
and that's a that's really what what they're doing a good job of does it that content gets
indexed because the reason for the question is how to's listicles all these things that
a lot of people rely on for SEO and hopefully sort of provide some genuine value to the
reader you know I could see a lot of content creators wanting to create content like that
but to then host it on their own websites right to get the SEO benefits of that and
obviously you know as content marketers you don't want to be building on rented land and
in the case of the perplexity pages tool that is rented land they could probably remove
that tool at any point if they wanted to so I don't know does it get indexed do we know
anything about that?
I've just this second as you asked the question found out whether it gets indexed and the
answer is yes.
Of course it does yeah because guess what now all the listicles and all the how-to's
are going to go live on the perplexity site so thank you dear user for asking great questions
of perplexity and thinking of really useful content that people might want to read about
and then putting it on our site so we can get all the juice no sorry I'm not doing it
personally but I can see it's kind of cool if I can like do it and then remove the page
from perplexity and take all that content and put it on my own site then yeah I might
consider it obviously editing it as well to add my own flavor people we don't use AI
outputs as is for a thousand billion reasons that we've talked about a lot on the podcast
in the past so yeah I think it's quite an interesting little play from perplexity but
personally I wouldn't use it.
There's some other interesting stuff we've had there's some rumors coming out about Siri
Martin there are and this comes off the back of an announcement of a deal between OpenAI
and Apple which has been in the news this week but Siri 2.0 is set to be unveiled at
the upcoming WWDC event which is their worldwide developer conference and it's the big showcase
event for all things Apple and developer things going forward.
What they're saying is that the new Siri will use AI for more granular app control so you'll
be able to actually effectively function calling and tool use that we discussed with Anthropic
and Claude earlier on so developers will be able to connect their apps using APIs to Siri
and enable users to start interacting with them with voice.
AI will analyze user habits to auto enable new Siri commands for Apple apps with plans
to also include third-party apps as well.
These new capabilities will include things like summarizing articles, editing, sharing
photos and managing emails and I can't tell you how often I want to be able to manage
my calendar and my email with a language model.
I am forever wanting to be able to say to chat GPT if I'm out and about add this to
my calendar or give me a task and a to-do list draft this email and stick it in my inbox
so when I'm back in the office I can send it.
If they get this right I think this could be mega for users of Apple devices.
They're talking about the initial launch being in September it will support single commands
with multi-step tasks expected later next year.
I'm excited about this as well I'm like you I mean in some ways I'm excited about it.
I should caveat so when I'm walking the dog I've got so much to do of an average day that
at the moment there's a couple of things I do.
First is I'm constantly dictating emails to Audio Pen which is one of my favorite apps.
Really good app.
Have to have the screen on to be able to dictate into it which basically means I'm walking
around the village with the dog should probably be paying more attention to the dog and I'm
holding my phone near my mouth with the screen on dictating an email and then I have to leave
the screen on so that it can do its transcription and all the other thing that it needs to do.
My ideal use case is I've got my earbud in and I'm just talking to my phone I'm like
right we're going to draft an email now Siri.
Siri's like yeah cool no worries I'll pop open Gmail look what would you like it to
say and then I can dictate it and maybe it automatically cleans the transcription up
and adds the right commas and periods and all this other stuff that it needs in it.
Like I need that in my life ASAP for those of you that commute a lot especially if you're
driving again it could be pros and cons having your concentration on writing an email rather
than driving but certainly that would be really cool.
One thing that you can do which is as close as I can get at the moment is if you use the
current version of GPT-4 in the app version you can actually have a voice conversation
with chat GPT so not the new voice version that's hopefully coming soon although I'm
sure be able to do it.
That I don't have to have the screen open for and I can just have a conversation back
and forth with GPT-4 asking it questions and speaking with it so it doesn't quite it's
not quite the same sort of tool but in theory I could dictate an email it's just every
time you pause it thinks you've finished it wants to give you a response.
Isn't that so frustrating they have made that hyper aggressive in terms of just you can
barely take a pause for breath and it jumps in to respond to you straight away.
That's one of the big things I think when they get 4.0 voice properly rolled out it's
not going to have so I'm quite excited for that but that example that you give there
about having the conversation that's great and when you you know get back to the office
and sit down and open that conversation your email will be drafted and what have you still
not adding it straight into your calendar or it's still not adding it straight into
your Gmail.
Gmail draft.
Yeah and that's the that's the step that's the bit that I'm itching for.
Yeah I agree on the knowing when to speak back and when to pause and stuff I genuinely
don't think well I guess the AI may be able to do it if it can if it can learn enough
about your own personal speech patterns to know how long you typically pause for.
I've got a friend called Andrew sorry Andrew I know you listen to the podcast he loves
to pause even as a human having a conversation with him you don't know if you should speak
or not because he loves to pause for quite a long time and it needs to learn those things
in order to be able to do this properly and dare I say I think it even needs to be able
to have access to your camera because as humans having a conversation it isn't just your voice
that tells me are you finished speaking is it right for me to interject have you got
something to say when I'm speaking I can see that in your body language your facial expressions
there's a loads of other signals that we use in conversation that are really important
to enabling that so I think for this to work really well it's going to have to use your
camera it's going to have to see your face it's going to have to know body language stuff
in order to be able to interpret that properly which of course is not helpful for our drive
the car walk the dog case because we don't want our phones right in front of our faces
but it could be useful when working at the computer like a number of the use cases that
we were excited about on the last episode where having conversations with chat GPT while
I'm doing my work could be helpful but I think it needs to see me to know when I finish speaking
which I have finished speaking now mine which gives me the ideal opportunity to step in
and say actually I said that the deal between Apple and open AI had been announced actually
it is being widely reported but it hasn't been announced yeah and we don't know about
this Siri thing this is a rumor until we get to the actual developer conference and we
see if it gets announced and what have you so yeah want to keep an eye on because hopefully
it gets us closer to the thing that certainly you and I fancy being able to use Martin and
I expect a lot of other people do too right next story is about 11 lamps and their AI
powered text to sound effects so some of you might remember that Martin and I were on tour
in the US doing some consultancy and AI training work also counts as a little plug there this
week's episode is sponsored by Martin and Paul's AI training and consultancy to enable
your business to leverage AI internally as part of your marketing efforts to boost results
and efficiency and just enjoy marketing even more than you do now and advert but while
we were on tour we were playing with this new tool because Martin got early access because
he's been a 11 labs use of quite a while and we basically made it do a bunch of stuff that
it couldn't do and we broke it which is what we normally do but they've now rolled this
out more widely it's got a number of other capabilities that it didn't have when we were
playing with it and it's really quite an interesting tool in terms of being able to create a bunch
of sound effects so as you can imagine the goal here is to enable a bunch of creatives
in a number of industries film television marketing video games social media to be able
to create audio quickly and easily and it's kind of aligned with this concept of we've
got a bunch of tools some of them can do video some of them can do audio some of them can
do text how do you combine all this stuff together to be able to get a an output in
fact I don't know if you saw this Mike there was an example for GPT-4-0 combined with Sora
where someone was able to brief it on creating a video that had a synthetic voice overlay
and then the transcript running along the bottom of the video with music and sound effects
did you see that?
Yeah that that was cool.
Right and you can see how all these tools need to be stacked on top of each other to
get what we would consider the video content we're all used to using that's got music
and voice and sound effect and all that other stuff you need to be able to combine and stack
all those things together to get you know the full output so obviously this 11labs tool
is just for sound effects and sometimes you just need a sound effect but all of this is
also being combined into these tools so that you'll be able to create really interesting
stuff that combines all of the things that you'd expect to see in your average Netflix
show.
It's worth looking at the video that they put out for this with all of the sound effects
created by their own model it can create sound effects up to 22 seconds long and quite
importantly one of the things that they reference is the model has been trained on Shutterstock's
library of licensed audio and ethically sourced sound clips and I think that is quite important
and increasingly the conversations that I'm having with business owners and marketers
is around the copyrights and kind of ethical training of these models.
Yeah I think that's interesting the other thing is I want to be able to automatically
insert sound effects into our podcast Martin a little bit like I think the characters are
called Ira and the Douche from Parks and Rec if you're a Parks and Rec fan you'll know
exactly what I'm talking about and you'll know exactly why we could never include those
sound effects in our podcast because most of them are offensive but that would be quite
funny.
Right and speaking about like crazy content creation the reason we were interested in
the 11 lab story and how all these things combine is because of some interesting news
from a startup called The Simulation that used to be called Fable Studio now we don't
have a huge amount of information on this only a Twitter post and a Forbes article but
tell us about what The Simulation is planning to do with their new tool Showrunner Martin
because this is a bit mind-blowing.
Yeah the website is actually available to go and look at some examples and you can sign
up for early access so what are you signing up for early access to?
Well it's AI generated stories, videos, TV shows from scratch you put in an idea and
it will create it.
They've launched the platform with 10 original shows and the style of them they're kind of
anime or cartoony they're all very cartoon style.
The original version that they previewed in 2023 used South Park and they created a South
Park episode entirely AI generated and it was uncanny how kind of close they got to
creating a real episode.
So yeah now you can generate and watch AI powered TV shows in these virtual worlds it
combines multi-agent simulations with large language models to create this interactive
content.
If you go onto the website you can check them out there's one called Sim San Francisco or
sorry Sim Francisco I think it's called and it's a South Park style animation and you
can see what it's doing and in some instances the animation seems to be really quite basic
where a scene might have three people in it and there was one particular scene I saw where
it was three faces on screen and the only thing that was moving at any time was the
mouth as a different person spoke so relatively simple but then it was stitching different
scenes together and there was some more advanced animation in there but you can see that they've
found quite clever ways to tell stories and reduce the complexity of the animations but
it's only going to get better I mean this is like day one of these projects.
So yeah if you're interested in seeing what an AI generated TV show might look like check
out Showrunner it's available early access you can sign up now but some of the initial
shows that they're creating are available to see on YouTube.
I think it's pretty cool I think you're right I think it it's probably early and so I think
we should all manage our expectations of what that means but like apparently what you're
going to be able to do as a user is create these episodes as Martin described and then
but you can actually go in and edit the scripts and the shots and the voices you can actually
get a bit of fine control over it.
So in terms of putting together sort of you know a lot of a lot of animated shows both
for kids and for adults there's been a focus on the quality of the content dare I say it
even over the quality of the animation right one of the things about South Park is is that
they want the animation to look a bit kind of crappy to use a Cartman word because they
the whole point is this content they want to be able to just produce these episodes
have them be really funny it's the quality of the writing and etc that they want to shine
through not it's not CGI for you know your next Marvel blockbuster but of course with
these tools then maybe you can create those animated shorts and then get some ideas from
prompts but edit the scripts and like bring your vision to life in a way that would have
been really hard without a professional animator in the team and probably quite a large budget.
The idea is going to be that when users are creating episodes one assumes based on these
original show templates to begin with just so they can control and influence and make
it easier to produce the assets I assume because it's probably working from a set of defined
assets is that if you create a great episode that everybody like watches and it goes viral
there'll be some sort of revenue sharing model that you then can get you know both credit
for having produced it but maybe some cash could definitely imagine how if I was like
17 I'd be really excited about making some TV shows and maybe this is how the next big
show producer becomes famous right they create a show on something like this people are like
oh wow ever so witty and what have you and then and then it goes viral.
From a marketing perspective how does this open up creating your own TV shows for brands
right beyond TV shows maybe you can make training videos and other things that are a bit more
interesting that leverage this as a format so it sounds real early like you said mine
but I think the creative people marketers being a subset of people who come up with
really interesting ways to use this can do fantastic and interesting promotion for themselves
for their brands that they work for I've been really interested to see how people let their
creativity loose with this.
Right last couple of stories then so there was a online survey that was conducted recently
and the results were released this week by the Reuters Institute to try and understand
public awareness and the use of generative tools in six countries they ran the survey
in Argentina Denmark France Japan the UK and the USA just to try and get a feel for the
how people feel about generative AI and its impact on different sectors and how many people
have used it and all that good stuff.
I think probably the most interesting things were that most people had heard of AI chat
GPT had been probably known by about most of people so around 50% of people surveyed
had heard of chat GPT but despite having heard of it the frequent use of chat GPT was extremely
rare ranging from just 1% using it daily in Japan to 7% in the USA many users have only
used these tools once or twice indicating that they're not really part of people's
routine internet use as we talked a bit about earlier Martin and the younger people as you
might expect potentially are more likely to use generative AI products regularly with
56% of 18 to 24 year olds having used chat GPT at least once.
I would be interested to know how that increases if you're looking at generative AI baked into
tools like Instagram and TikTok and some of these video generation tools that I know a
lot of people do to make their videos look better when they're sharing them on those
types of platforms but I think what that goes to show is if you are a listener to this podcast
you're trying to make AI use a part of your workflow and you're thinking oh I'm not using
it enough I'm so behind you're probably not behind if you're a listener to a podcast like
this you're probably already in the top 7% in the USA let's say of people who are thinking
about how to adopt these tools into their work.
What did you think about this survey Martin?
I thought it backed up what I would have expected to see that people aren't using these tools
a great deal awareness of them is high I certainly wasn't surprised to see that people had used
them once or twice this goes back to the point we were talking about with GPT 3.5 on chat
GPT just not having enough capability to draw people in and that's why I think 4.0 is going
to be a real game changer particularly when people can start speaking to a chatbot in
their hand that feels like sci-fi that is going to draw people in undoubtedly and you
can imagine the onboarding flow for this new app when they launch it is going to really
focus heavily on the voice capabilities of it and I'd love to have a peek behind the
curtain of chat GPT's product onboarding team right now.
Yeah that's going to be fascinating I want to I want to see people I don't want to see
it in some ways because it's kind of a bit scary but I expect to see people sat in a
pub where somebody knows about this release and this tool and you know the voice capabilities
maybe their friends don't they've got their phone on the table and they're all having
a conversation with it because it's able to speak back to them in real time and remember
that there are different people in the conversation and who they are so it can actually speak
to people by name one assumes based on the demos that we've seen I think that's going
to break people's brains and I think that's going to be one of the first ways that they
really get introduced to it I remember in the village where I live when Dali to came
out trying to explain to people why I could do but not really been able to explain it
very well without showing them so but before I knew it like I'd only shown people like
two images and they were throwing prompts at me across the table like ask it this ask
it this and I think that's going to be the response that we get to this but the difference
is going to be you'll just speak to it yourself and then it will speak back to you I really
think like you say that could be the moment where people are like I want to play with
this more. Right one more story Martin and it's we're into speculative territory right
we're into the simulation and we're into creating your own Netflix shows basically with prompts
and there was a really interesting article this week that we read wasn't there Martin
from the chief of staff at Anthropic tell us about this article.
Yeah Avatar Bulwit which I might have just butchered that so apologies but yeah chief
of staff at Anthropic wrote a really lengthy article in which he says that most jobs will
be obsolete in the next five years and we've heard these predictions before but it's interesting
to see it from somebody who is so close to the to the top within one of these companies
making a frontier model. The article itself goes on to describe this psychology of work
and the psychological impact of widespread job loss and she actually writes a really
interesting article examining what's happened in like the construction industry in different
parts of the world when there's been economic crashes and what it's done for people's sense
of worth and things like that but she goes on to basically say that we need to have a
real conversation around this but that part is interesting I think the thing that just
triggered us to feature this in the podcast was just that somebody is coming out and saying
this she's a young woman a young professional a whole career ahead of herself and from what
she's seeing day to day right now she's looking at it going knowledge workers like me will
be unnecessary within three to five years was their timeline I think the opening line
of the the article actually says three years never mind five years. Yeah I think it's definitely
worth quoting that this article's title is my last five years of work pretty compelling
let's be honest and then the first paragraph is I am 25 these next three years might be
the last few years that I work I'm not ill nor am I becoming a stay at home mom nor have
I been so financially fortunate to be on the brink of voluntary retirement I stand at the
edge of a technological development that seems likely should it arrive to end employment
as I know it crumbs that is a heck of an intro paragraph to any piece that you might read
it's beautifully written honestly I think is a really good article lots of interesting
thoughts if you're interested on the impact of AI on work and therefore the impact of
disrupting work as we know it to us as humans is definitely worth a read yeah and do you
do you agree broadly speaking given everything that we have spoken about my experience at
the top of the show talking about data analysis these frustrations that we have where you
know 90% there is is not there like 90% right is is not right and then do do you think within
that time frame we'll have got to the point where knowledge workers will be stripped from
the economy at such scale because I just I don't see it from where I sit do you know
what it is the thing I find hardest about this without any doubt is two opposing ideas
that I'm having to hold in my mind at the same time the first is that this person works
at anthropic and the commercial value of tools that that take on the productivity of all
of the knowledge workers in the world but we still live in some sort of economic system
with money like we live in now means that those tools doing all that work will be worth
an absolute fortune so there is a massive commercial incentive for the people inside
of these organizations to be saying LNMs and AI is going to do all the productive work
and you know we should get ready for it and you know with Sam Altman has probably not
explored this particular topic at this level of detail and potentially in this level of
eloquence as this wonderful article does but it's not dissimilar to some of the things
that we hear coming out from the likes of open AI but at the same time we've got Yana
Kulnove we've mentioned number of times on the podcast who's head of AI at Meta going
our AIs are no smarter than a house cat so unless house cats are going to somehow rise
up and do all of the knowledge work across the world then this is all absolute baloney
right and I think that's the challenge is she's on the inside of a system that we're
not that sees things that are potentially three six nine twelve months ahead of what
we see so what is she seeing that's giving her this confidence maybe stuff we don't know
about that if we saw it we'd feel the same or are they all incentivized to talk this
up massively because it's just going to inflate the value of their businesses and I just don't
know mine but to your point I think for me until we eliminate some of these errors we've
got a big problem because I just see a bunch of people are just not going to trust them
before we even get into heavily regulated industries right financial health care what
have you where those errors are just not going to be tolerated and somebody's going to be
held accountable for it there's an AI makes an error and a patient gets given a wrong
medication and they get sick and they die people are going to be held accountable for
that somebody's going to go to jail right and people are just not going to allow that
to happen so we have to get those out does GPT-5 with some sort of logic built into it
to make sure it doesn't make any mistakes anymore fix that I don't know right we'll
have to see so I think that part's going to be absolutely critical and then the other
things change management we've talked about on the podcast today Martin that even people
who are heavily exposed to this people we know some of them are people that we train
and support need to change their behavior to have chat GPT open all day every day to
even think oh maybe I'll just ask chat GPT instead of emailing that colleague because
maybe I'll get a better answer out of chat GPT and they don't so we've got that whole
human change element that we've got to manage as well so and I think that's slow I think
technology change might be fast but human change and behavior change appears to be quite
slow at least in my limited experience on this planet so five years don't know feels
quite long way away ten years I would say probably seems realistic to me five I think
it's the people part that will slow it down because I think we'll resist yeah there'll
be it wouldn't surprise me if there were with the likes of function calling and tool use
and people building products that effectively you can imagine entire products come into
the market that are your AI CFO right and then QuickBooks and these companies go you
don't need that that job role anymore because we've got an AI that will do it for you but
I think as you say it's the the people change the is the chief exec going yeah I trust that
feature of that software to do that job and there's going to be resistance from the people
whose jobs it's going to take right absolutely and I think if we assume that five years ten
years is the right time frame right not 30 years we've got a lot of reconstruction of
the economy to do in order to not have this be a complete car crash and that bit does
worry me and I think one of the sort of more interesting parts of of avatars article is
really trying to explore some of the things around what work means to people in terms
of giving them purpose making them feel like you know they work hard and they get paid
well and then they can feed their family or go on holiday and just help the human brain
is wired with its reward structures it's incentivization structures and also some of its more negative
structures like being ashamed when you don't have a job and stuff like that like it's
going to be extremely complicated and it's not going to all happen at once so the people
who lose their jobs first there probably won't be any safety network outside of the safety
networks that we've got already and how do you feel going to the job centre which is
what we have the equivalent of in the UK and saying you need to help retrain me I need
to find a job because the job I used to get paid 80k British pounds a year to do doesn't
exist anymore so I don't really know what to do with myself and I need to get another
job and what do you do when you're when your skills are redundant I just it's just it's
going to be if we get to that point it's going to be it's going to be difficult I think it's
going to be difficult I haven't explained it very well but hopefully dear listeners
you can sort of understand some of the sort of stressful trepidation that comes with trying
to imagine what that looks like and goodness gracious Martin I'm glad I don't have to
sort it out.
Yeah there's people paid far more money than either of us trying to fix this problem.
So basic income a nice big robot tax pay per token you know every token that you use there's
a nice little slice to the government and the government can just cascade that down
to to all of us and we can chase butterflies all day.
Yeah I like Sam Altman's idea of like universal compute income or whatever where basically
everybody gets a share of compute and you can use it to create your own agents to do
stuff for you or you can sell it to other people or team up with a bunch of other people
and all put your compute together and you know compute becomes the currency of the world
given the number of investments he's making in energy companies and chip companies though
you'd have to wonder if there's a bit of an incentive there as well.
He would say that wouldn't he.
And this is why it's so hard I really want to trust someone Martin I just want to trust
someone to just give me some really good advice give me a feel for where things are going
but I think the it's a mixture of nobody really knows and the people who probably know
the most are incentivised to say certain things and not other things so how are we supposed
to know.
What a way to close out the podcast.
And on that cheery note.
Yeah sorry everybody came in they were like okay AI sucks first 20 minutes next 40 minutes
oh we can do some pretty interesting and cool stuff last 10 minutes yeah amazing I'm going
to create my own TV show what we're going to do when nobody's got jobs anymore.
So there you are we'd like to take you on a journey on this podcast we'll be back next
week to inspire scare and depress you in equal measure probably hopefully not too much.
If you still want to listen to this podcast after this episode ringing around in your
ears and your brains over the next week or two then please subscribe and do share this
with your friends because you know it hasn't been all positive this episode but these are
important things to talk about we do want to get into the other sides of AI every now
and again don't we Martin.
With that in mind I'm going to leave you to your Sunday I'm going to leave you dear listener
to chew on some of the things you've heard about today and we will be back with you in
a couple of weeks.
Thanks for your time Martin.
Thank you for listening to artificially intelligent marketing to stay on top of the latest trends
tips and tools in the world of marketing AI.
Be sure to subscribe we look forward to seeing you again next week.