May 7, 2025 • 22 mins
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FULL SHOW NOTES
https://www.microsoftinnovationpodcast.com/685
What happens when you've witnessed artificial intelligence evolve from academic skepticism to world-changing technology? Bill, Technical Director at Flow Simulation and Microsoft MVP since 2018, takes us through his remarkable 30-year journey working with computers and AI systems since the 1990s.
TAKEAWAYS
• First awarded MVP in 2018
• Regularly speaks at international conferences about Microsoft 365, AI and Azure
• Started with expert systems in the 1990s when neural networks temporarily fell out of favor
• Witnessed the breakthrough of transformer architecture that revolutionized language models
• Traces the evolution from GPT-2 through ChatGPT to today's multimodal models
• Discusses potential future directions including copyright concerns and AI self-improvement
• Values GitHub Copilot as a practical time-saving tool that amplifies productivity
• Appreciates MVP program benefits including direct access to Microsoft product teams
• Values the MVP community connections with technically-minded professionals worldwide
For anyone navigating today's AI-powered world, Bill's experienced voice offers invaluable context that connects past developments to our possible futures.
OTHER RESOURCES:
👉 Microsoft MVP YouTube Series - How to Become a Microsoft MVP - https://www.youtube.com/playlist?list=PLzf0yupPbVkqdRJDPVE4PtTlm6quDhiu7
This year we're adding a new show to our line up - The AI Advantage. We'll discuss the skills you need to thrive in an AI-enabled world.
Accelerate your Microsoft career with the 90 Day Mentoring Challenge
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If you want to get in touch with me, you can message me here on Linkedin.
Thanks for listening 🚀 - Mark Smith
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Mark Smith (00:06):
Welcome to the MVP show.
My intention is that you listento the stories of these MVP
guests and are inspired tobecome an MVP and bring value to
the world through your skills.
If you have not checked it outalready, I do a YouTube series
called how to Become an MVP.
The link is in the show notes.
With that, let's get on withthe show.
(00:31):
Today's guest is from Sheffield, england in the United Kingdom.
He is a technical director atFlow Simulation.
He was first awarded MVP in2018.
He has been working withcomputers for over 30 years and
with AI since the 1990s.
He regularly speaks atinternational conferences and
(00:53):
user groups about Microsoft 365,ai and Azure.
You can find links to his bio,social media, et cetera, in the
show notes for this episode.
Welcome to the show, Bill.
Thank you, mark.
Hi.
Good to have you on.
I always like having a littlegetting to know you segment at
the very start, which is food,family and fun.
What do they mean to you?
What do you do when you're notdoing your technical day job?
Bill Ayers (01:17):
What do I do when I'm not doing the technical?
I don't think there is any timewhen I'm not doing the
technical day job.
It feels like uh well, we'vegot, uh, my wife and I we've got
three children.
They're all grown up and, uh,mostly left home, mostly um, and
we're kind of now getting tospend a little bit of time doing
(01:39):
a few things that we've alwaysmeant to do.
So we're going to go um cyclingin the netherlands, uh, next
week, so I'm just hoping theweather is is nice for that.
Uh, what were the other ones?
Food, family and fun, fun Idon't have time for any.
Any fun, that's, that's not not, not possible.
Uh, do a bit of, uh, yeah, uh,everything seems to revolve
(02:01):
around technology.
I don don't do any computergames.
That's the one thing that Ijust avoid completely.
It's just not my thing at all.
But yeah, I spent a lot of timedoing technology, as a lot of
us as MVPs, do.
You know that has become ourhobby as well as work, but it
(02:21):
was slightly different slant onit, I suppose.
But food, well, I discoveredthat I really like Italian food
and I really like French foodand I really love Indian food
and I really adore Asian food.
(02:43):
Then I realized it's just foodthat I like.
So just every the differentcountries from around the world.
I love all the differentcuisines and I'll just eat
pretty much anything and sushi.
Mark Smith (02:59):
Yeah, very good, very good.
I'm particularly interested inyour AI journey.
You know AI is obviously top ofmind for a lot of people
nowadays and you've been doingit since the 1990s.
Tell me about how you know,what caught your attention in
those early days and then bringme up to speed about you know.
Since generative AI has comeout and the impact that's having
(03:22):
on the world, what are yourthoughts on where things are at
now, but also your journey togetting here?
Bill Ayers (03:29):
It's kind of funny.
So as a child I remember seeinga talk given by Professor Sir
I've forgotten his name now.
Anyway, a famous professor gavea talk and said AI just isn't
(03:50):
going to happen because it'sexponential growth in complexity
, and so it kind of put me offand I thought this was going to
be the next big thing and Iwanted to get into it and he
persuaded me that this was notthe future and it was never
going to happen.
It was sort of just technicallyimpossible, uh, and he
convinced everybody else as well, and so there was this complete
(04:13):
decline in funding for ai anduh, and that kind of died out
until the 1990s when it kind ofcame back again, but it was as
expert systems, which is aslightly different technology.
So it still counts as AI, butit's not neural networks, which
was the first wave of AI, whichstarted in the well late 50s
(04:39):
before our time.
But it came back in the 1990sin the form of these expert
systems, which was a way of sortof capturing knowledge of
people, and so if you wanted tohave a, an expert system that
would diagnose illness, you'dget a try and persuade a doctor
(05:00):
to make a sort of decision treeof questions and it was kind of
semi-programmatic.
But they eventually came upwith sort of software packages
which would capture all thisinformation rather than
hard-coding it.
And that also failed as atechnology, because I think not
(05:24):
everything lends itself to thatapproach, perhaps a bit of a
reluctance of people who spent alifetime trying to learn how to
do something they don't want tojust dump it into some expert
system and have their jobs taken.
So there's a kind of a bit ofthat as well, so that all kind
of petered out.
But I was involved in the expertsystems.
We were trying to do things forthe time I was working in
(05:46):
engineering software and we'retrying to solve various
engineering problems.
That way we started gettingquite good image analysis and
speech, text-to-speech andspeech-to-text and a certain
(06:13):
amount of natural languageprocessing got better and better
to the point where about themiddle of the last decade we got
to the point where you couldactually do some tagging of an
image, say to a reasonableaccuracy, and it got to the
point where it was similar tohuman error levels.
(06:34):
And that's the game changer,because now you can start in
these admittedly very narrowfields to be able to do things
like tag a load of images, whichwould be.
You know, if you've got amillion images in a SharePoint
site or something like that, adocument library, it would be
very expensive to go throughthem and tag them, but you can
(06:57):
get a machine to do it, and sothat was a game changer.
But then with the evolution ofthis transformer architecture
particularly originally for texttranslation, so it was the
encoder-decoder pattern for texttranslation that became so good
(07:19):
and so efficient and the way itkind of finds its own level and
you don't have to put quite asmuch effort into kind of
designing the structure of theAI model.
It kind of figures it outitself to some extent.
I'm hugely oversimplifying thetransformer, how transformers
work, but it was such abreakthrough that the large
(07:43):
language models which weretrained on huge amounts of data
the first one was I think thefirst one we were looking at was
I think it was GPT-2 I rememberusing, and then GPT-3 came out
and that really got theattention of people in the.
That's GPT GenerativePre-trained Transformer really
(08:05):
got the attention of people inthe AI community, really got the
attention of people in the AIcommunity.
And then GPT 3.5 was theversion that became ChatGPT and
suddenly it just explodedbecause people realized kids
could do their homework, essaysand they could answer questions
(08:26):
and it was better than a searchengine and things like that.
And that's then improved evenfurther with GPT-4.
It's the GPT-4.0 now, which isthe multimodal model and that's
continued to develop as well.
That can do things like you cantalk to it and it can look at
(08:46):
the text.
So the old way was you'd you'duse a speech to text and then
put the text into a largelanguage model.
But these multi-modal modelscan take the actual, the voice
input and get additional signalsfrom it, such as your tone of
voice and things.
(09:06):
And so now it's more like thatfilm Her, wasn't it with, was it
Joaquin Phoenix?
It's just like that.
You know, you can have aconversation with an agent.
That's really like talking to areal person, so it's almost
(09:26):
scarily realistic.
So I don't know, Maybe in a fewyears' time these models will
be indistinguishable fromwhether you're talking to a real
person or not.
Mark Smith (09:40):
The gains seem a lot smaller.
These days we seem to get a lotmore models, and I think Sam
Altman's just come out and saidthat if we don't get fair use on
copyrighted material, we'rekind of going groundbreaking
turning point in the AI era.
Is there the potential thatwe're going to see another
(10:14):
breakthrough that will totallyshift the paradigms again and,
you know, it might not even bepeople that find it.
Maybe the current levels of AIwill be able to produce the next
differentiator that's going toleapfrog us once again.
What are your thoughts aboutthis?
Bill Ayers (10:32):
This could go two ways.
So we might have a bigclampdown and people are saying,
okay, copyright, so you couldargue I think you can argue both
cases.
So it is kind of using otherpeople's material, uh, but then
isn't that what human beings do?
(10:53):
Is we?
We?
We don't invent music fromscratch.
We listen to what's gone beforeand we are influenced by frank
zapper or somebody, and thenthat that's our style, that we
develop.
So so that's that's just normal, for for the art world is to uh
build on what's gone before anddevelop in little increments,
(11:14):
uh, from it.
So that might be a factor.
Also, there's some people say,well, aren't we going to run out
of training material?
And eventually the ai is just,uh, the training data is
actually just more AI output andthe thing kind of just exhausts
itself and reaches some sort ofheat death of information or
(11:38):
something.
But another possibility is,which I think you alluded to, is
, as we're able to use AI toolsto improve the tools themselves,
then kind of knowledge begetsknowledge and it's able to then
(11:58):
take off in an exponential way.
And that's also a possibilityand that has also got certain
ethical and existential riskimplications as well that if we
could have a artificial superintelligence takeoff where they
(12:19):
get it gets so much better thanus, and then there's some awful
accident and somehow somebodygives an AI model the wrong
instruction and it goes and doessomething which has disastrous
consequences, and there are afew uh a few ways that this
could happen that have beendiscussed in uh, particularly um
(12:39):
.
Nick Bostrom has written anumber of books about these
various scenarios and how theycould uh play out, so that's a
depressing read, but we need tothink about it when we're
thinking about these models, andwe have to be a little bit
careful about what we do, bothfrom an ethical point of view
(12:59):
and as far as AI risks areconcerned.
We need to be aware of them andwe need to think about it now,
not in 20 years' time, when it'stoo late.
Mark Smith (13:09):
Yeah, will capitalism, though, prevent the
actual?
And we need to think about itnow, not in 20 years' time when
it's too late.
Bill Ayers (13:13):
Yeah, will capitalism though, prevent the
actual level of thinking needed.
I'm not sure capital has thepower to stop this.
If this happens, I think it'sbeyond any political system.
Mark Smith (13:29):
No, but that's what I'm saying is that the danger is
that capitalism, the more moneywe can make off, what AI will
generate is that the capitalistswill keep throwing money and
risk to.
You know.
Bill Ayers (13:40):
I see what you're saying.
Mark Smith (13:42):
You know who cares what will happen.
Let's just make us which is, Isuppose, trump's probably
mindset at the moment is thatlet's just go full steam ahead,
let's innovate and let's worryabout risk later yeah, I see
what you're saying so well.
Bill Ayers (13:58):
I'm not sure it's capitalism that's the problem.
I think it's human nature mightbe the the risk factor here,
that people are acting in theirown interests.
So, as a capitalist, it's notin my interest to bring about
the end of the world because Ican't make money if the world
comes to an end, so it's.
But it's human nature to think.
Well, I'm not worried aboutthat risk because I can see this
(14:23):
goal that will benefit me inthe short term.
And yeah, that's a danger.
I don't think we're at thispoint yet, but I think we need
to think about it.
Mark Smith (14:34):
Absolutely.
We're talking about MVP Summitbefore and I attend a lot of the
trustworthy AI sessions andjust really understanding how
we're thinking about this.
You know, obviously the EU isquite often ahead in how they
think about, you know, risksfactor and particularly the
potential on on the citizen base, and so I I find it interesting
(14:59):
, even though I am, you know,barreling full steam ahead
myself for my personal life andmy business life around um ai,
since you know, generative AIcame to market and your work
across Azure.
You know M365 and in AI, whathave kind of been the delightful
(15:21):
moments you've come across inthe last three to four years?
That may be surprised orintrigue or good use cases.
What jumps to mind?
Bill Ayers (15:33):
um I, I think, uh, seeing being able to save time.
You know, it's like theindustrial revolution you used
to.
A farmer would have to plow afield.
If they're lucky, they had ahorse pull the plow.
Once you can get, you get atractor or, you know, steam
(15:54):
powered tractor or asteam-powered tractor or
something like that.
It means you can do 10 times asmuch work and achieve 10 times
as much with the same effort.
And I think it's the same, usedcorrectly.
We can do the same with A-highis.
We can just amplify ourbrainpower and do more and avoid
having to do the boring things.
(16:16):
So it's really nice when youhave some tedious job that you
would have had to do and you canjust click, click, click and
when it comes together, it isreally great that you can really
save a lot of time.
Now you might have a bit of ajourney to get there, with
prompt engineering and designingthe right systems around it and
(16:37):
maybe having some multi-agentsystem, which is the current
thinking around some of the AItechnologies.
We kind of have different AImodels to do different jobs, a
bit like having a team GitHub.
Copilot has been an absolutetime saver for me as well.
(16:59):
So, yes, I can make a.
When I'm doing a presentationin PowerPoint, I can make an
image.
Danger is, you know they alllook a bit like they've been
generated by AI, but apart fromthat, you know that can save
time doing that.
But in GitHub Copilot, whencoding, it's a real time saver
(17:20):
because I can ask the model whatthe syntax is for whatever
language I'm using.
I might be using JavaScriptthis week and C Sharp the next
week.
I can't remember the syntaxalways from one week, or Python.
I can't remember the syntaxalways from one week.
(17:40):
Or Python.
I can't remember the syntax,but I don't need to anymore
because I can use GitHub Copilotand it can suggest things and
quite often it'll just give me ablock of code.
I can just indicate what I wantto do and it will give me a
block of code and I would sayabout 50% of the time I'm done
and I can move on the other 50of the time.
Yeah, okay, that's not what Iwanted and I gotta work on it,
(18:02):
but that's still already a hugetime saver and it's getting
better and better.
Mark Smith (18:07):
So, uh, really enjoying that as we go to wrap
up, tell me about the benefitsof being an mvp.
Um, how, how has it been?
How has it affected your careerhaving, since you know, 2018
the designation MVP?
Bill Ayers (18:26):
Yeah, I think it might have been 2017 actually
yeah, I can't remember.
It's been really nice to have agreater degree of access to
people within Microsoft who areworking on the next versions of
products, see what's coming,just to get a little bit of a
(18:47):
heads up and really askquestions.
So it just there's just alittle bit more attention that
you can get, little bit moreattention that you can get and
events like, as we mentioned,the mvp summit, where we can
talk to, uh, the people who areactually doing the work at
microsoft, not some second andthird layer of of uh support
(19:08):
people, but, but the people whoare actually making decisions
and and doing the work.
But the other thing, the otherfor me, a big thing is the MVP
community itself people like you, mark, that I then get to meet
and talk with and havefascinating conversations with
people in the able to socializewith and talk to and and uh
(19:30):
people who are, like me, alittle bit nerdy and uh want to
talk about this kind of thing.
I love it I love it.
Mark Smith (19:47):
You're so right.
The community, I think, is forme, is what makes the mvp
program um, and they're all overthe world.
You know, I've traveled a bitand I've been able to meet many
of my MVP friends all around theworld, so it is something
special.
Bill Ayers (20:04):
Yeah, that's great, and different technologies as
well.
They're not necessarily workingon the technologies I'm working
in, so I get a differentperspective.
Mark Smith (20:12):
Excellent.
Thanks, Bill, for coming on theshow.
Bill Ayers (20:15):
It's been a real pleasure.
Mark Smith (20:16):
Thanks, bill for coming on the show.
It's been a real pleasure.
Thanks, mark.
Hey, thanks for listening.
I'm your host businessapplication MVP Mark Smith,
otherwise known as the NZ365 guy.
If you like the show and wantto be a supporter, check out
buymeacoffeecom forward slashNZ365 guy.
(20:36):
Thanks again and see you nexttime.
Thank you.
Welcome to the MVP show.
My intention is that you listento the stories of these MVP
guests and are inspired tobecome an MVP and bring value to
the world through your skills.
If you have not checked it outalready, I do a YouTube series
called how to Become an MVP.
The link is in the show notes.
With that, let's get on withthe show.
(00:31):
Today's guest is from Sheffield, england in the United Kingdom.
He is a technical director atFlow Simulation.
He was first awarded MVP in2018.
He has been working withcomputers for over 30 years and
with AI since the 1990s.
He regularly speaks atinternational conferences and
(00:53):
user groups about Microsoft 365,ai and Azure.
You can find links to his bio,social media, et cetera, in the
show notes for this episode.
Welcome to the show, Bill.
Thank you, mark.
Hi.
Good to have you on.
I always like having a littlegetting to know you segment at
the very start, which is food,family and fun.
What do they mean to you?
What do you do when you're notdoing your technical day job?
Bill Ayers (01:17):
What do I do when I'm not doing the technical?
I don't think there is any timewhen I'm not doing the
technical day job.
It feels like uh well, we'vegot, uh, my wife and I we've got
three children.
They're all grown up and, uh,mostly left home, mostly um, and
we're kind of now getting tospend a little bit of time doing
(01:39):
a few things that we've alwaysmeant to do.
So we're going to go um cyclingin the netherlands, uh, next
week, so I'm just hoping theweather is is nice for that.
Uh, what were the other ones?
Food, family and fun, fun Idon't have time for any.
Any fun, that's, that's not not, not possible.
Uh, do a bit of, uh, yeah, uh,everything seems to revolve
(02:01):
around technology.
I don don't do any computergames.
That's the one thing that Ijust avoid completely.
It's just not my thing at all.
But yeah, I spent a lot of timedoing technology, as a lot of
us as MVPs, do.
You know that has become ourhobby as well as work, but it
(02:21):
was slightly different slant onit, I suppose.
But food, well, I discoveredthat I really like Italian food
and I really like French foodand I really love Indian food
and I really adore Asian food.
(02:43):
Then I realized it's just foodthat I like.
So just every the differentcountries from around the world.
I love all the differentcuisines and I'll just eat
pretty much anything and sushi.
Mark Smith (02:59):
Yeah, very good, very good.
I'm particularly interested inyour AI journey.
You know AI is obviously top ofmind for a lot of people
nowadays and you've been doingit since the 1990s.
Tell me about how you know,what caught your attention in
those early days and then bringme up to speed about you know.
Since generative AI has comeout and the impact that's having
(03:22):
on the world, what are yourthoughts on where things are at
now, but also your journey togetting here?
Bill Ayers (03:29):
It's kind of funny.
So as a child I remember seeinga talk given by Professor Sir
I've forgotten his name now.
Anyway, a famous professor gavea talk and said AI just isn't
(03:50):
going to happen because it'sexponential growth in complexity
, and so it kind of put me offand I thought this was going to
be the next big thing and Iwanted to get into it and he
persuaded me that this was notthe future and it was never
going to happen.
It was sort of just technicallyimpossible, uh, and he
convinced everybody else as well, and so there was this complete
(04:13):
decline in funding for ai anduh, and that kind of died out
until the 1990s when it kind ofcame back again, but it was as
expert systems, which is aslightly different technology.
So it still counts as AI, butit's not neural networks, which
was the first wave of AI, whichstarted in the well late 50s
(04:39):
before our time.
But it came back in the 1990sin the form of these expert
systems, which was a way of sortof capturing knowledge of
people, and so if you wanted tohave a, an expert system that
would diagnose illness, you'dget a try and persuade a doctor
(05:00):
to make a sort of decision treeof questions and it was kind of
semi-programmatic.
But they eventually came upwith sort of software packages
which would capture all thisinformation rather than
hard-coding it.
And that also failed as atechnology, because I think not
(05:24):
everything lends itself to thatapproach, perhaps a bit of a
reluctance of people who spent alifetime trying to learn how to
do something they don't want tojust dump it into some expert
system and have their jobs taken.
So there's a kind of a bit ofthat as well, so that all kind
of petered out.
But I was involved in the expertsystems.
We were trying to do things forthe time I was working in
(05:46):
engineering software and we'retrying to solve various
engineering problems.
That way we started gettingquite good image analysis and
speech, text-to-speech andspeech-to-text and a certain
(06:13):
amount of natural languageprocessing got better and better
to the point where about themiddle of the last decade we got
to the point where you couldactually do some tagging of an
image, say to a reasonableaccuracy, and it got to the
point where it was similar tohuman error levels.
(06:34):
And that's the game changer,because now you can start in
these admittedly very narrowfields to be able to do things
like tag a load of images, whichwould be.
You know, if you've got amillion images in a SharePoint
site or something like that, adocument library, it would be
very expensive to go throughthem and tag them, but you can
(06:57):
get a machine to do it, and sothat was a game changer.
But then with the evolution ofthis transformer architecture
particularly originally for texttranslation, so it was the
encoder-decoder pattern for texttranslation that became so good
(07:19):
and so efficient and the way itkind of finds its own level and
you don't have to put quite asmuch effort into kind of
designing the structure of theAI model.
It kind of figures it outitself to some extent.
I'm hugely oversimplifying thetransformer, how transformers
work, but it was such abreakthrough that the large
(07:43):
language models which weretrained on huge amounts of data
the first one was I think thefirst one we were looking at was
I think it was GPT-2 I rememberusing, and then GPT-3 came out
and that really got theattention of people in the.
That's GPT GenerativePre-trained Transformer really
(08:05):
got the attention of people inthe AI community, really got the
attention of people in the AIcommunity.
And then GPT 3.5 was theversion that became ChatGPT and
suddenly it just explodedbecause people realized kids
could do their homework, essaysand they could answer questions
(08:26):
and it was better than a searchengine and things like that.
And that's then improved evenfurther with GPT-4.
It's the GPT-4.0 now, which isthe multimodal model and that's
continued to develop as well.
That can do things like you cantalk to it and it can look at
(08:46):
the text.
So the old way was you'd you'duse a speech to text and then
put the text into a largelanguage model.
But these multi-modal modelscan take the actual, the voice
input and get additional signalsfrom it, such as your tone of
voice and things.
(09:06):
And so now it's more like thatfilm Her, wasn't it with, was it
Joaquin Phoenix?
It's just like that.
You know, you can have aconversation with an agent.
That's really like talking to areal person, so it's almost
(09:26):
scarily realistic.
So I don't know, Maybe in a fewyears' time these models will
be indistinguishable fromwhether you're talking to a real
person or not.
Mark Smith (09:40):
The gains seem a lot smaller.
These days we seem to get a lotmore models, and I think Sam
Altman's just come out and saidthat if we don't get fair use on
copyrighted material, we'rekind of going groundbreaking
turning point in the AI era.
Is there the potential thatwe're going to see another
(10:14):
breakthrough that will totallyshift the paradigms again and,
you know, it might not even bepeople that find it.
Maybe the current levels of AIwill be able to produce the next
differentiator that's going toleapfrog us once again.
What are your thoughts aboutthis?
Bill Ayers (10:32):
This could go two ways.
So we might have a bigclampdown and people are saying,
okay, copyright, so you couldargue I think you can argue both
cases.
So it is kind of using otherpeople's material, uh, but then
isn't that what human beings do?
(10:53):
Is we?
We?
We don't invent music fromscratch.
We listen to what's gone beforeand we are influenced by frank
zapper or somebody, and thenthat that's our style, that we
develop.
So so that's that's just normal, for for the art world is to uh
build on what's gone before anddevelop in little increments,
(11:14):
uh, from it.
So that might be a factor.
Also, there's some people say,well, aren't we going to run out
of training material?
And eventually the ai is just,uh, the training data is
actually just more AI output andthe thing kind of just exhausts
itself and reaches some sort ofheat death of information or
(11:38):
something.
But another possibility is,which I think you alluded to, is
, as we're able to use AI toolsto improve the tools themselves,
then kind of knowledge begetsknowledge and it's able to then
(11:58):
take off in an exponential way.
And that's also a possibilityand that has also got certain
ethical and existential riskimplications as well that if we
could have a artificial superintelligence takeoff where they
(12:19):
get it gets so much better thanus, and then there's some awful
accident and somehow somebodygives an AI model the wrong
instruction and it goes and doessomething which has disastrous
consequences, and there are afew uh a few ways that this
could happen that have beendiscussed in uh, particularly um
(12:39):
.
Nick Bostrom has written anumber of books about these
various scenarios and how theycould uh play out, so that's a
depressing read, but we need tothink about it when we're
thinking about these models, andwe have to be a little bit
careful about what we do, bothfrom an ethical point of view
(12:59):
and as far as AI risks areconcerned.
We need to be aware of them andwe need to think about it now,
not in 20 years' time, when it'stoo late.
Mark Smith (13:09):
Yeah, will capitalism, though, prevent the
actual?
And we need to think about itnow, not in 20 years' time when
it's too late.
Bill Ayers (13:13):
Yeah, will capitalism though, prevent the
actual level of thinking needed.
I'm not sure capital has thepower to stop this.
If this happens, I think it'sbeyond any political system.
Mark Smith (13:29):
No, but that's what I'm saying is that the danger is
that capitalism, the more moneywe can make off, what AI will
generate is that the capitalistswill keep throwing money and
risk to.
You know.
Bill Ayers (13:40):
I see what you're saying.
Mark Smith (13:42):
You know who cares what will happen.
Let's just make us which is, Isuppose, trump's probably
mindset at the moment is thatlet's just go full steam ahead,
let's innovate and let's worryabout risk later yeah, I see
what you're saying so well.
Bill Ayers (13:58):
I'm not sure it's capitalism that's the problem.
I think it's human nature mightbe the the risk factor here,
that people are acting in theirown interests.
So, as a capitalist, it's notin my interest to bring about
the end of the world because Ican't make money if the world
comes to an end, so it's.
But it's human nature to think.
Well, I'm not worried aboutthat risk because I can see this
(14:23):
goal that will benefit me inthe short term.
And yeah, that's a danger.
I don't think we're at thispoint yet, but I think we need
to think about it.
Mark Smith (14:34):
Absolutely.
We're talking about MVP Summitbefore and I attend a lot of the
trustworthy AI sessions andjust really understanding how
we're thinking about this.
You know, obviously the EU isquite often ahead in how they
think about, you know, risksfactor and particularly the
potential on on the citizen base, and so I I find it interesting
(14:59):
, even though I am, you know,barreling full steam ahead
myself for my personal life andmy business life around um ai,
since you know, generative AIcame to market and your work
across Azure.
You know M365 and in AI, whathave kind of been the delightful
(15:21):
moments you've come across inthe last three to four years?
That may be surprised orintrigue or good use cases.
What jumps to mind?
Bill Ayers (15:33):
um I, I think, uh, seeing being able to save time.
You know, it's like theindustrial revolution you used
to.
A farmer would have to plow afield.
If they're lucky, they had ahorse pull the plow.
Once you can get, you get atractor or, you know, steam
(15:54):
powered tractor or asteam-powered tractor or
something like that.
It means you can do 10 times asmuch work and achieve 10 times
as much with the same effort.
And I think it's the same, usedcorrectly.
We can do the same with A-highis.
We can just amplify ourbrainpower and do more and avoid
having to do the boring things.
(16:16):
So it's really nice when youhave some tedious job that you
would have had to do and you canjust click, click, click and
when it comes together, it isreally great that you can really
save a lot of time.
Now you might have a bit of ajourney to get there, with
prompt engineering and designingthe right systems around it and
(16:37):
maybe having some multi-agentsystem, which is the current
thinking around some of the AItechnologies.
We kind of have different AImodels to do different jobs, a
bit like having a team GitHub.
Copilot has been an absolutetime saver for me as well.
(16:59):
So, yes, I can make a.
When I'm doing a presentationin PowerPoint, I can make an
image.
Danger is, you know they alllook a bit like they've been
generated by AI, but apart fromthat, you know that can save
time doing that.
But in GitHub Copilot, whencoding, it's a real time saver
(17:20):
because I can ask the model whatthe syntax is for whatever
language I'm using.
I might be using JavaScriptthis week and C Sharp the next
week.
I can't remember the syntaxalways from one week, or Python.
I can't remember the syntaxalways from one week.
(17:40):
Or Python.
I can't remember the syntax,but I don't need to anymore
because I can use GitHub Copilotand it can suggest things and
quite often it'll just give me ablock of code.
I can just indicate what I wantto do and it will give me a
block of code and I would sayabout 50% of the time I'm done
and I can move on the other 50of the time.
Yeah, okay, that's not what Iwanted and I gotta work on it,
(18:02):
but that's still already a hugetime saver and it's getting
better and better.
Mark Smith (18:07):
So, uh, really enjoying that as we go to wrap
up, tell me about the benefitsof being an mvp.
Um, how, how has it been?
How has it affected your careerhaving, since you know, 2018
the designation MVP?
Bill Ayers (18:26):
Yeah, I think it might have been 2017 actually
yeah, I can't remember.
It's been really nice to have agreater degree of access to
people within Microsoft who areworking on the next versions of
products, see what's coming,just to get a little bit of a
(18:47):
heads up and really askquestions.
So it just there's just alittle bit more attention that
you can get, little bit moreattention that you can get and
events like, as we mentioned,the mvp summit, where we can
talk to, uh, the people who areactually doing the work at
microsoft, not some second andthird layer of of uh support
(19:08):
people, but, but the people whoare actually making decisions
and and doing the work.
But the other thing, the otherfor me, a big thing is the MVP
community itself people like you, mark, that I then get to meet
and talk with and havefascinating conversations with
people in the able to socializewith and talk to and and uh
(19:30):
people who are, like me, alittle bit nerdy and uh want to
talk about this kind of thing.
I love it I love it.
Mark Smith (19:47):
You're so right.
The community, I think, is forme, is what makes the mvp
program um, and they're all overthe world.
You know, I've traveled a bitand I've been able to meet many
of my MVP friends all around theworld, so it is something
special.
Bill Ayers (20:04):
Yeah, that's great, and different technologies as
well.
They're not necessarily workingon the technologies I'm working
in, so I get a differentperspective.
Mark Smith (20:12):
Excellent.
Thanks, Bill, for coming on theshow.
Bill Ayers (20:15):
It's been a real pleasure.
Mark Smith (20:16):
Thanks, bill for coming on the show.
It's been a real pleasure.
Thanks, mark.
Hey, thanks for listening.
I'm your host businessapplication MVP Mark Smith,
otherwise known as the NZ365 guy.
If you like the show and wantto be a supporter, check out
buymeacoffeecom forward slashNZ365 guy.
(20:36):
Thanks again and see you nexttime.
Thank you.
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