Artificial Intelligence with Dave Walters

Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Pat (00:34):
Hey everybody.
Welcome back to this week'sedition of breaking down the
bites.
I'm your host, Pat, as alwaysdriving this bus.
You can find me on Twitter atlayer eight packet.
That is the number eight.
You can find Kyle on Twitter atTana Danis 256.
And you can find the show onTwitter at breaking bites pod.
Alex, you're not on Twitter orany social media stuff.

(00:54):
You want to get ahold of Alex,hit him up on the show and it'll
get to the right place.
So we're pretty active onTwitter.
So come say hello.
If you like the show, don'tforget to subscribe on your
streaming platform of choice.
Most of you are come from theApple podcast world, which is
totally cool.
So, hit that like, subscribebutton.
And that just makes us lookbetter to the algorithms and the

(01:15):
AI and the machine learningworld that we that we live in.
So, it's all good there.
So, Alex, what's up, man?
How are you?
We're back for another week.

Alex (01:23):
Indeed we are.
This is the episode that I'vebeen waiting for.
No pressure, Dave.
But yeah, ever since we, well,ever since I got involved with
this is the one that I've beenhoping to find someone that can
speak to AI and machine learninga little bit better than I can,
but also didn't want to getsomebody that's right in the PHD

(01:48):
on it.
So I think we found the rightbalance and I feel like people
are just going to get to thepoint where they assume that
we're just bringing back oldevolve IP coworkers.
Cause that's the only people weknow, but I do think that
everyone that we've brought inhas served a niche, whether the
Ryan talking about containers orin this case, Dave going to

(02:08):
hopefully teach us somethingabout AI

Pat (02:10):
And we had Alex, before you joined, we had Nikki Townsend

Alex (02:13):
and Nikki was on.

Pat (02:14):
So she was on.
Quite a bit.
So, if you're not familiar withNikki Townsend, go look her up.
She runs a nonprofit called techaware.
She's doing some really coolthings in that space.
So, shout out to Nikki cause Iknow she listens every now and
then.
So hopefully she listens to thisone, but Kyle is not here this
week.
He's got other things going on,which is totally cool.
That gives us room for, as Alexmentioned.
A former coworker of Alex andI's at Evolve IP.

(02:37):
We're just bringing back theband here, ladies and gentlemen.
That's all we do.
So we just recycle Evolve IP.
People are like, Oh, you work atEvolve?
You're good enough for thisshow.
Rock and roll.
Here we go.
Dave Walters.
What's up, man?
How are you?

Dave (02:48):
Hey, thanks for having me guys.
Good to be here.

Pat (02:50):
Yeah, man.
It's awesome.
This is a long time coming.
We were looking for some AI folkand we had your coworker and
former of all my P coworker aswell with Alex and all of us,
Ryan Young on a few weeks ago,talking containers and dockers
and all that kind of cool stuff.
So if you haven't checked outthat episode right after this
one.
Go check that out too, becausethat was a really cool
conversation.

(03:10):
But Ryan is with you now aty'all both work at slice up.
Yeah.

Dave (03:15):
Yeah, it's correct.
Ryan actually stole me about sixmonths ago to come work there at
slice up.

Pat (03:20):
nice.
Good deal.
So Dave, why don't you give thepeople a little bit of quick
rundown of who we are, where youcame from and sort of what
you're doing now, and then we'lltake it from there.
So the floor is yours, my man.

Dave (03:31):
Awesome.
So I'm Dave Walters.
I'm currently working as thedirector of product design at
slice up and slice up is an AIops platform for networks.
I guess I got my start inmachine learning back in
undergrad.
I double majored in computerscience and mathematics, and
there's really two classes therethat.
Grabs my heart.
And that was algorithms and datastructures and data mining.

(03:52):
And then I went to evolve IP.
I worked as a software engineerfor eight years and didn't touch
a lick of AI or ML.

Alex (04:01):
Yeah.
And I thought that too, I waskind of, when I saw the, like
what you've been working on inyour LinkedIn profile, I was
like, man, did I just really nothave any idea what the guys at
Evolve IFE were doing?
Or did you manage to get intothe field that everyone's kind
of struggling to get into?

Dave (04:20):
Yeah.
Well, after evolve I was lookingfor things were aligned with my
interests.
They were fun to do and I wantedto work in gaming.
So I ended up at games 24 sevenand their tagline is the science
of gaming.
And they're actually a realmoney gaming company.
And the science of gaming wasall about data science,
actually.
So we were looking to maximizeuser engagement working on

(04:42):
optimization problems likeautomated AB testing and the
multi armed bandit framework, ifyou're familiar, and ultimately
getting people addicted to realmoney gaming was the goal

Alex (04:53):
So, sell your soul a little bit, but at least you get
in the industry.

Dave (04:57):
Yep, exactly.
And once I was done selling mysoul, I saw another AI ML
opportunity with slice up and Iwas happy to take it

Alex (05:05):
So, were you considering leaving at the point, at that
time, or you just got thisopportunity from, I don't want
to say got it from Ryan, butwere made aware of it by Ryan
and then just knew right awayyou wanted to pursue it?
You already, the writing wasalready in the law.

Dave (05:22):
a little bit of both.
So I've been, you know, mybackground is a software
engineer, but I've been workingin the product and UX space for
the last three years or so.
But I love keeping my handsdirty with code.
That's where I find a lot ofenjoyment and I wanted to do
more work on code.
So I started doing someconsulting and then Ryan said
that slice up could use some ofmy skills.

(05:43):
So I started writing some codefor them and then working for
them more and more.
And I just really like the fit.
With the team and I was reallyinspired by the stuff that we're
working on.
So I decided to make the switchfull time and left games and
started working on.
So I said,

Alex (05:57):
So when you say you've been that slice up for six
months, you've actually had sometype of affiliation with them
longer than that?

Dave (06:03):
yeah,

Alex (06:06):
Well, yeah, I didn't realize that.
That's so in your current roletoday.
Well, maybe I'll step back alittle bit.
You talked about it briefly, butsince the topic is AI and
machine learning and youadmitted that you didn't do much
of that or any of it at EvolveIP.
What was your role at what wasthe name of the company you were

(06:28):
at

Dave (06:28):
games 24 seven.

Alex (06:30):
Okay.
And what was your role there andpretty much how did you land
that role given that you hadn'tdone much of that before?

Dave (06:39):
Yeah.
So I was a director of userexperience there as well.
And since I, most of thesoftware that I wrote it evolve
was on the front end.
I got really involved in.
Working with Scott Kinka more onthe product side and like, Hey,
how can we incorporate our usersmore into our product?
Right?
Because if you remember osmosisand other systems that we built,

(07:01):
they were much, they're verymuch built by engineers for tech
adjacent people, right?
They weren't the best.
They weren't the most userfriendly.
And as I got more familiar withthe UX processes and design
thinking and things like that,you know, I really saw an
opportunity to move in thatdirection.
And.
Games 24 7 needed that for a newdivision they were starting,

(07:21):
which was all about userengagement and retention
marketing and things like that.
So I was a really good fit forthem for that.
And through working in that rolewe had PhDs in data science,
working on our team.
And on our board of directorsthat I came in contact with
frequently because we werestrategizing that way, you know,

(07:43):
from the predictive analyticsoptimization, data science lens
for what we were doing and.
That was super cool to me.
It's something I've always beeninterested in.
So,

Alex (08:00):
it would be interesting to have a conversation with them.
So.
With your role, where you kindof, you knew the end goal, but
the data scientists were theones that could help actually
write the algorithms that gotyou to that end goal or what
something could do.

Dave (08:19):
yeah, exactly.
And the way that it ended upthere is that a lot of times the
data scientists that we hadwere, well, they were academics.
We had one from one from Whartonand actually I think they're
both Penn guys.
But.
It was at times much more of alecture that we were receiving
than a product that we weredeveloping.

(08:41):
So I ended up getting heavilyinvolved in the productization
of the data science that he waskind of.
Teaching us, if that makessense,

Alex (08:52):
Interesting.
Okay, and so I guess when youstarted this role, you mentioned
that gaming was.
That got you into this role.
So when you took this role, didyou even realize that you're
going to have like your firstdip into AI and machine learning
when you took this role, or thatwas just a happy surprise.

Dave (09:15):
bit of both originally it was just kind of needs based,
you know, they needed someone tobe front end team lead.
And then we quickly realizedthat they needed someone to also
teach them UX because again, aproduct made by engineers for
engineers was not the most userfriendly.
And then.
We also realized that we neededsomebody who had strong product
skills, and I happen to have aninterest in background in

(09:37):
mathematics and we have datascientists on the team.
So my role is continuallyexpanding every day, but it's a
startup.
So I'm happy to wear many hatsand, you know, I love what we're
doing.
I think it's super interesting.
So I'm more than happy to takethat on.

Alex (09:52):
Okay.
And.
Where are you saying that's whatyour current role right now at
slice up is Or how is thatdifferent than your previous
role?

Dave (10:02):
My previous role was mostly focused on the user
experience where now I'd say myrole is more focused on the
product holistically and theproduct is.

Alex (10:15):
Okay, and maybe we should even started with this and I'm
sorry, I don't want to offendanyone.
I'm not too familiar with thecompany.
So, when you say it's AI machinelearning base for networks maybe
you can elaborate that on alittle bit more, like what
actually is the product and

Dave (10:31):
Yeah, so we'll forgive you for not being super familiar
since we're a startup, butbasically we're

Pat (10:37):
Thanks, Dave

Dave (10:40):
we're developing an AI ops platform that uses AI and ML to
detect anomalies and correlatenetwork events from various data
sources.
To provide high fidelity contextaround network issues.
So if you think of the noisethat you generally have with
like the 30 different monitoringtools, you use like net cool and

(11:01):
Nagios and all the server andcontainer logs, you have to dig
through to figure out what'sgoing on slice up.
Takes all of that and processesit through our AI pipeline, and
it puts the puzzle together sothat people don't have to, and
instead of digging through allof that noise, they can just
focus on fixing the issue.
So our platform process this andspits out, you know, here's the

(11:23):
issue here, the relevantmetrics.
Here are the logs associatedwith this event.
And then we've also built anauto remediation feature that
lets you say, Hey, if you seethese conditions happening
again, run this set ofinstructions, which comes via an
Ansible script and then fix itbefore it becomes a problem.
It's super cool stuff.
I'm not a networking guy.
So a lot of that stuff mystifiesme still, but

Alex (11:46):
Yeah, the whole idea is self healing networks.
Yeah, it's a term that's beenthrown a lot around a lot last
few years, but now it mightseems like it's a real thing.
Well, we are networking people,so I am somewhat interested in
this, maybe more than.
Some people are, but what's thelike ingest method.
So if I want to use slice up, amI sending you just any type of

(12:09):
log SNMP net flow data, or whatcan I send you?

Dave (12:14):
Yeah.
So we were taking in syslog fromdevices.
We're taking in applicationlogs.
From whatever applicationservers your apps are running on
we're working on exchange serverdata data from load balancers
like the F5 networks, loadbalancer applications.
We take an SNMP data.
It's just, it's as much data aswe can from whatever source and

(12:38):
then.
Using that to show you, youknow, latency path changes with
delay associated giving youwarning about possible over
utilization on interfaces or onT cam modules on your Cisco
devices.
Yeah, lots of use cases fordifferent things.
I think the most interesting isthe log parsing that we do on

(12:59):
some of our customer networks.
As soon as we've deployed thesystem and let it run, It's
Picked up things that theyweren't even aware were
happening, right?
Because logs are the number onething that tend to be white
noise, right?
They're going and maybe somebodywill look at them after
something happens, but here, youknow, duplex mismatch came up
right away.
And we could say, Hey, we'reseeing latency here.
Here are logs that are spittingout saying, this is why easy

(13:22):
fix, right?

Alex (13:24):
Yeah.
And I kind of think that the,what's the word I'm looking for,
this this type of work, thistype of business model, it seems
like it's getting a bit crowded.
Who do you think is kind of likethe main player that, I don't

(13:44):
want to say it's your rival oryour biggest.
Contender for business, but isthere 1 that pops up right away
that you think of that somewhatcompeting with

Dave (13:55):
It's hard to say.
Cause like you're saying noweverybody's starting to get into
it, but I mean, solar winds isbig and they have a lot of these
types of capabilities.

Alex (14:01):
And maybe this is a difficult 1 to answer.
Is there a feature that you seefor my solar winds or for
someone else that you kind offeel like.
Slice up could do better tomimic that.
Maybe when you're a littlejealous about, or one that you

(14:22):
feel slice up does a lot better

Dave (14:24):
no, I'm not sure.
I haven't gotten to, you know,we have so many things on our
plate right now for the upcominguse cases that we've promised to
our customers and our roadmap isso packed that.
A lot of the research that Iprobably should be doing on
competitors, you know, I justhaven't gotten to so yeah I'm
not sure, really.

Alex (14:45):
Understood.
Well, I asked a lot of questionsthere, so I'll let you take the
floor pat, see if there's whereyou wanna

Pat (14:54):
ah, just a couple that I had in mind.
So obviously with AI sort oftaking the world by storm, at
least at least a little bit herein the last I don't know, say
probably eight months to a year,somewhere in there.
You know, when it comes to AIwhat do you, like, what do you
think you like with, in relationto the IT field, I know that's a
broad thing.
What do you think of AI when itcomes to IT, in the IT terms,

(15:18):
whether that's, you know, again,with logs and sort of, you know,
smart logs, if you will, or at ahelp desk level, trying to, you
know, automate the boring thingsof that nature, or or, you know,
almost like a predictive Beingproactive instead of reactive
sort of thing.
Like, what do you think of AI inthat space?
Or what do you think that'sgoing to kind of turn into, or

(15:40):
what, where is it as currentstate, I guess?
I know there's,

Alex (15:43):
well, that's a loaded

Pat (15:44):
unpack.
So yeah, I know.

Alex (15:45):
may.
How about like, wt, I think AIis just like a topic that could
mean so many different thingsnow.
So like when you think of ai,like how would you even go.
To begin to define what AI cando for IT or what it's already
doing.

Dave (16:05):
Yeah.
The way I like to think about AIis I think of it as the
development of computer systemscan perform tasks.
It used to be humans only, youknow, things like problem
solving and learning and patternor object recognition,
understanding language,perception, and things like

(16:25):
that, and I think that theopportunity.
That it's bringing to it is it'sgoing to turn.
It's going to allow everyindividual contributor to become
a manager.
Right?
Because.
I think that it takes away a lotof the things that we don't want
to do anyway, To talk about itin non it terms, everybody's

(16:49):
using chat GPT to write things,right?
It's great for that.
It's a generative AI.
But we still need people to editit, right?
It hallucinates it might come towrong conclusions that you
weren't hoping for.
Right.
But it speeds up generationprocess, right?
Like, you can brainstorminfinitely faster now.

(17:12):
And that's true with coding aswell.
Right?
So, you know, I was a softwareengineer for a long time and
then eventually a team lead andthen a manager.
So, As I progressed, I wroteless and less code, and then it
became more about reviewing pullrequests and checking for
optimization opportunities andmaking sure that code wasn't

(17:33):
blatantly wrong.
And that's what all softwareengineers are going to become,
right?
Because we won't need to writethe same boilerplate code that's
always been written.
Chet GPT understands that verywell, and it can produce that.
But what we will need to know ishow this code relates to other
parts of the system and theimpact that it can have and

(17:54):
things of that nature.
And I think that's reallyexciting.
And you know, that it goes tothe help desk level two, like
with what slice up can do, youknow, there's no need for you to
dig through all of yourcustomers logs and old tickets
and see to have, they had thisproblem before we have AI that
can do that for you.
And it'll say this customer hashad this problem 13 times.
Just tell him to turn it off andon again.

Alex (18:16):
Okay.
Yeah, and I think everyone'sfear is that it's going to
eliminate some jobs.
And I think what you're sayingis that.
you still likely have thesetiers of jobs.
It's just even tier one guys aregoing to be doing more than
they've ever done before.
It's just because they have atool that can assist them with
it.
Just like, you know, 15 yearsago or maybe not 15 years ago,

(18:37):
they had 20 when did Google comearound before search engines
were really popular.

Pat (18:42):
God, we're

Alex (18:42):
you were capable.

Pat (18:43):
God.

Alex (18:45):
Yeah, I know.
And now they've been around awhile.
It's just like, I'm thinking,man, I'm almost 40.
So yeah, this has been around awhile.
Yeah, and do you see maybe asituation where we don't have
the concept of tier one peopleanymore, but it's not like
they're losing their jobs,they're just becoming more in

(19:07):
line with what a mid tier personcould do nowadays, is what
they're

Pat (19:11):
I think it's a shift.

Dave (19:13):
Yeah, I hundred percent agree with that.
I think that it's.
It's going to elevate everyonein the same way that instant
information via your smartphonedid, right.
Or the calculator, right?
We don't have people whomanually compute things anymore.
We barely need lawyers anymore.
Like there are things that,

Alex (19:32):
Right, yeah, I mean, before the

Pat (19:34):
goes our

Alex (19:35):
of the calculator,

Pat (19:36):
Sorry about that.

Alex (19:38):
okay.
So I can, I guess I touched onit a little bit, but it bleeds
into this the next question I'llask.
And it sounds like you're not,but...
Are you one of those peopleright now that are in any way
concerned with like it's rapidadoption say in the last six
months or it's just seems likechat GPT.

(19:59):
I know it's been around for awhile.
I didn't do any demos with itand chat GPT version three.
It's just when four came outthat I got really involved with
it.
But to me, it just seems likehow.
How did they make such amonumental like increase in just
usability performance and it'sstay under wraps.

(20:21):
I just, it blew my mind how muchbetter chat GPT 4 was and that
scared me thinking like, Oh man,if it got that much smarter, has
it really just, you know, havethey just been sitting and
letting it learn and it's gottenthat much better?
But yeah, I'll just go back tomy question.
Are you at all concerned at howquickly this is improving?

(20:43):
Are you all for

Dave (20:46):
I'm all for it.
I'm a hundred percent in I loveto see the innovation and it's
been around for a while.
I think that the reason thatchat GPT in particular has
captured the imagination of.
So many is that even though thisis a tech, I mean, transformers
themselves have been around forsix years.
Google wrote a paper on them in2017.
I think chat GPT was the firsttime that users could interact

(21:10):
directly with a model, right?
Like if you look at the chat GPTexperience, we've been using AI
on our products for years now,you're.
Your recommended feed onYouTube, your Netflix
recommendations, the Googlesearch results are the result of
neural networks at this point.
But they've, that AI experiencehas always been wrapped inside

(21:31):
of a product with a veryspecific outcome.
Chat GPT was the first timepeople were just given an AI
model and.
Told do what you will with it,right?
Like, we've had auto complete,which has been using, you know,
different forms of machinelearning and things like that.
And chat GPT is the fanciestauto complete that 1 could use.

(21:55):
And I think that it was a greatmove by open AI to release it.
Google Bard has been ready for along time.
They wanted to release it as aproduct that was ready for users
to consume.
Where open AI was like, we don'tknow what this is.

(22:16):
And we think that's a goodthing.
Let's see what happens.

Alex (22:20):
So like the world's biggest beta ever,

Dave (22:24):
Yeah, absolutely.
And one of the reasons they wereable to make such fast
improvements GPT 4, I think iseven kind of an old model, but.
It's more recent tunings havebeen based on RLHF, right, which
is reinforcement learning fromhuman feedback.
And that's taking your thumbs upand your thumbs down and your

(22:44):
reasons for giving those thumbsup and thumbs down during the
beta of chat GPT to reallyimprove those responses improve
its reasoning capabilities.
Fix it's bugs in code, thingslike that.
So doing that really big betahas just moved us ahead really
quickly, you know, hitting ahundred million users in a month

(23:05):
or two months or whatever

Alex (23:06):
Yeah, something like that.
It was mind boggling.
And you just threw an acronymout there that seemed like a
really good one.
Say that one

Dave (23:13):
RLHF it's reinforcement learning from human feedback.
And that's basically justletting people.
Tell the machine when it's rightand wrong so that the machine
can apply those learnings in thefuture by adjusting the weights
in the model

Alex (23:29):
And I think that's, would have been the most impressive
things that I've done with chatGPT.
Cause as you said, in itssimplest form, you could just
take that as a thumbs up or athumbs down and Netflix uses to
suggest movie titles to you.
Or whereas chat GPT, and it justblows my mind away is having
these conversations with it,where I'm iterating over things
that I've already asked it, likeI've used it to write code as

(23:53):
someone who doesn't code, but istechnically minded.
And I've written, I shouldn'teven say that I've written, I've
had it write code for me by justspeaking to it as a normal human
would.
And when I realized those thingsare.
Not working correctly and theway how quickly it responds back
and understands.

(24:13):
Oh, I see what you're gettingnow.
I can just iterate over whatit's done in the previously.
It's it's just mind boggling andthat's a constructive way to use
it.
I've used it to just play aroundto and have fun with it.
Like I have 1 chat right nowthat I use it.
For everything, any random thingI want to talk to it.

(24:34):
But I asked it to now continuespeaking to me as if it were a
Texas cowboy.
So now every time I ask itsomething, it says something
like how y'all doing?
I reckon this is what we shoulddo here.
And I just kept it going becausenow it's, you know, from my

(24:55):
feedback, it knows that's theway I like how it responds to
me.
So it's given me veryconstructive.
Text based on subsequentquestions, but it still
remembers that I've asked it totalk to me like that.
Which I I think that's mindboggling.
I just, it's so much fun.
And,

Pat (25:15):
of all, can I get it to, can I get it to talk to me in
like a weird alien output?
I would love to be like, hello,take me to your leader.
It's a dumb shit like that.
That would be awesome.
I'm in.

Alex (25:27):
and it's almost like, I hate, I don't want this to just
be a chat GPT conversation andwe will get off the topic, but
I, one thing that.
So many people have asked me, soI might as well bring it up here
and just get some feedback fromyou two.
Is what is the coolest thingthat you've gotten ChatGP to do
or the thing that just Impressedyou the most and just see if

(25:49):
there's anything out therepeople haven't seen yet And
there's a few that I have thatbut I'll leave you guys to say
yours first

Pat (25:58):
Dave, you're up.

Dave (26:00):
Okay, I so I don't use Chet GPT directly a ton but one
of the things that I think issuper impressive that it can do
is translating languages andhere's why.
So when people talk about ChetGPT being a fancy autocomplete
that's true, and it's also.

(26:22):
A bit false because of thereasoning that it can do.
And if you think of translatinga sentence from English to a
gendered language like French,what Chachapiti can do that
requires reasoning blows mymind.
So think of the sentence, thebook will not fit in my book bag

(26:44):
because it is too big.
And the sentence, the book willnot fit in my book bag because
it is too small.
There's only one word differencein that sentence, right?
Big and small.
But when I say.
Because it is too big and itwon't fit, I'm obviously
referring to the book, and whenI say it is too small, I'm

(27:04):
obviously referring to the bookbag.
We can recognize that becausewe're people and we recognize
those nuances, but for amachine, there's a one word
difference in those twosentences.
But when it translates toFrench, book, I believe, is a
masculine word, where the bookbag is a feminine word, and it

(27:24):
translates the word gendercorrectly in each instance.
which used to be something thatwould require a human, and
that's why Google Translate usedto kind of suck.
But now that it uses neuralnetworks, it's learned more than
just patterns in language.
It's learned a bit of reasoningand small things like that blow

(27:46):
my mind.
It also blows my mind when itscrews up.
Really simple things like thattoo.
But on the impressive side

Alex (27:54):
Right.
And I think that's the part thatgets scary is because it's going
from just like a variabletranslator to it understands
intent and well, maybe itdoesn't understand intent, but
that's what it seems like to theend user.
It understands what I'm reallydriving at.
right.

Dave (28:10):
Yeah.
The inference that it can do isspectacular.

Pat (28:15):
I'm just a nerd and I only use it for nerdy work things,
which I probably should openthat up a little bit and use it
for other things to not relatedto work.
But I just, I had to just writea Python script for me because
I'm an idiot when it comes towriting any sort of code.
So I had to write a Pythonscript to change some.
Config on Cisco iOS stuff.

(28:36):
So, it, it did a decent job of,you know, putting it all out
there, commented a bunch ofstuff too.
So it was nice and clean, whichwas great.
And then I just basically had toput in my variables of what, you
know, what I was trying toconnect to things that interest.
So I had to tweak it a littlebit.
And it was probably more of mylack of in depth like Python
language that kind of.

(28:57):
But it did work pretty well andI just had to do variables and
plug and play.
And it was pretty cool.
But the other thing I've playedaround with too, is I wanted to
give me a list of it podcasttopics and most of them we
actually already touched.
So I was pretty proud of myselfon that one.
I was like, Ooh, yeah, I could.
I'm as smart as chat GPT.
This is awesome.
So it's all good.

(29:19):
So I was like, that's kind ofcool.
But yeah, I don't really use itfor a whole lot outside of like
work things that I'm really kindof stuck at, but I probably
should open, open that box alittle bit wider and just start
using it for dumb stuff too.

Alex (29:31):
Oh,

Dave (29:31):
work things, kind of jumping back to the, how is it
going to change it topic?
One of the things that I thinkAI is going to enable us to do
is it'll be the death ofspecialties in a way, right?
Like right now you have Pythonprogrammers and Java programmers
and go programmers.
With the assistance of AI, I'vebeen, and GitHub copilot, I've
been writing in every language.

(29:52):
Right.
We have go in our stack.
We have Python in our stack.
We have JavaScript andTypeScript and everything under
the sun in our stack.
And before it'd be like, Oh man,I don't really know Python.
I'm going to have to spend threeweeks figuring out the syntax
and, you know, the datastructures and things like that.
Where now it's like, Hey, I canjust write the equivalent

(30:13):
JavaScript, put it in chat, GPT,tell it my intent, what I want,
look at the output.
It's 95% of their most of thetime, if not 100 and then
knowing programming, you can fixthe bugs, right?
Because.
Data structures, function calls,et cetera.

Alex (30:30):
Yeah.
It'll just, I guess we'll justbecome like pseudo code bas, if
you just understand thestructure of the code, you
understand how coding works ingeneral, then an AI can write it
in any language, I guess.

Dave (30:45):
Yep.

Alex (30:46):
yeah.
That is interesting.

Pat (30:48):
interesting.
The one thing I want to jump inwith, and this is not chat GPT
related.
It's more AI centric.
I was I was listening to there'sa PhD guy.
Oh Miku, I think it's Miku Kaku.
I think his name is.
He's a professor out in, outwest somewhere.
And like, I've seen him on like,like history channel, like shows

(31:10):
and things like that, becausehe's like super smart, like, he
does all like the space stuff,and he's like this crazy, like,
he's just, he's brilliant.
His mind works in ways thatmine's not even possible.
But he was on Joe Rogan.
Last week he was on Joe and Joelikes to get in all that crazy,
like alien stuff and you know,all that weird stuff which I
like too.
so yeah.

(31:30):
So like, I was listening, I washalf listening as I was working
last week and just some of thatconversation just fascinates me,
but they got on the topic of AI.
And there was almost like a,there's almost like an ethics
conversation that.
There's a piece of that comeswith AI as well.
Cause then they were talkinglike, okay, AI is great.
It's the world's at this point,you know, they compared it to

(31:51):
the world's best Googler orGoogle on steroids, you know,
sort of thing.
Right.
But then they're also likepeeking behind the covers of
like, okay, like how does itlearn that?
And like.
It wasn't smart enough to likeunderstand nuance and
understand, like, basically justregurgitating what it can find
on Google.

(32:11):
So that means, you know, isthere an inherent, like an
inherent, like bias to it, orlike, it's not smart enough to
kind of.
Do like things without sort ofnuance built in.
I just kind of interested on thetake of like, yeah, AI is
spitting it back to you.
It's making your life easier,but there is a behind the covers
aspect of that to be like, is ittrue information?

(32:34):
Is it just spittingmisinformation?
Cause it doesn't know thedifference between actual truth
and like a misinformation, crazyhot take, right?
That kind of thing.
So I'm curious on that.
It was like.
It doesn't get better as we kindof grow with this thing.
Does it get better in a coupleof versions?
And it's like, then it comes tothe thing of like, okay, it's
really getting smart.

(32:54):
Like just because we can, doesit mean we should, you know,
that sort of thing?
I can't, that's where my ethicssort of come in to be like,
yeah, okay.
You know,

Dave (33:03):
Yeah.
Yeah.
And that's the concept ofresponsible AI.
It's something that's reallytalked about right now and it's
important.
And that is one of the reasonswhy, even though Google was
ahead in the AI race, let's sayfor the longest time, the reason
they fell behind open AI isbecause they didn't want to
release their model for thosereasons.

Alex (33:23):
Right.

Dave (33:24):
And aI doesn't know the difference between right and
wrong.
Right.
So,

Alex (33:28):
right.
And I think that was even in it.
I was just gonna just chime inthere that I think that's also
chat.
GPT had some media concernswhere they had to address Yeah.
Ethnic related issues where itwould say things that were
somewhat questionable, but Yeah,that's a

Pat (33:45):
Yeah.
I'm interested to see where thatgoes.

Alex (33:47):
because, yeah, it's since it doesn't understand that like
I'm trying to like the worstcase scenario is like hate
speech or something like that.
It's just like, well, then whatdictates what hate speech is?
And then now you're starting toget to some really tough
questions that.
Who has answers to that, and doyou need a governing body that
is saying what these AI modelscan learn?

(34:08):
And I don't know what the answeris.
I mean, I guess when you'redealing with computer networks
the idea of ethics is not quitethe same as when you're working
on something like

Pat (34:18):
Right.

Dave (34:20):
yeah.
I think, and I think that'swhere kind of the humans as the
editor layer, you know, is goingto continue to be important.
I think that where we really getin trouble with AI is, I don't
know if you guys have.
I've been paying attention tolike the auto GPT and the LLM
agents that have been superpopular very recently.

(34:42):
And what that is basicallypeople taking large language
models like jet GPT and hookingthem up to the internet and also
giving them.
Execution authority to dothings.
So instead of just having aconversation with you, it'll
have a conversation anddetermine the next steps for
your desired outcome.
So if your desired outcome is, Iwant to write an email to Pat

(35:03):
about the next podcast.
Instead of just giving you thecontent of the email, it will
give, it will generate thecontent of the email.
It will open.
Whatever it needs to, it willinsert the body of the email.
It will hit send.
It will watch all that.
So I think that once we have AIis determining their sub tasks

(35:23):
with no morality and no ethics,that's when things start to get
a little dark for me.
Because who's to say that thebest way to do a task is what
the AI is going to come up with.
It might be the most efficient.
It might not be the mostethical.
Right.
And then you have companies likePalantir who are.
Using AI to make weapons, andthat is their business model and

(35:44):
their goal, and you think aboutjust because we can, should we?
So think about a self healingminefield, for example, right?
Like, what if we have the minestalk to each other, and once one
detonates, they reconfigurethemselves, so there's no blind
spots in the minefield.
Like,

Alex (36:02):
Oh,

Dave (36:03):
an effective, but not a good use of AI, right?

Alex (36:06):
And then it decides that we could increase our
affectability if we eliminatethis neighborhood nearby and we
can have more mines.
This is a good thing.

Pat (36:16):
it.
That's it.
I just don't want to be like.
In a world where like Skynet 2.
0 is taken off and then like allof a sudden they just start
talking it in a language thatnobody understands you're like,
I'm just going to unplug thisreal quick.
We just pulled a, you know, youknow,

Alex (36:32):
And I think what Dave was touching on, I think big groups
like open AI or Google they'llalways have that editor in
charge.
That's going to try to, youknow, that team that's going to
try, but if they.
Create like these just openplugins that anyone can use and
they can just bring in thattechnology, then who's going to
govern.

(36:52):
Anyone making programs thatutilize it and just saying, Hey,
for simplicity sake, I don'twant to spend you know, however
much money on just employees toreview this, just I'll accept
whatever it tells me

Dave (37:06):
hmm.

Alex (37:08):
it'll just eventually get to the point where it, and I
don't know how you can stop it.
I mean, short of you don't.
Allow plugins, but that's notgoing to happen.
It seems like everyone's usingit right now.

Dave (37:20):
Yeah.
And, you know, governments wantto go the regulation route, but,
you know, people that want to dobad things don't follow the
rules.
So how effective is that goingto be?
And the other problem that we'reencountering very rapidly is
that the progress of open sourcewith these large language models
has been exponential.
As well, right?
Like we talked about how fast PTfour got better compared to

(37:44):
three.
Well, once the.
Llama model got leaked by Meta,the open source LLM community
has really taken off and thereare open source models now that
perform to about 94% of what GPT4 is doing.
And that's good enough, right?
To do some damage.
So

Alex (38:06):
For sure.

Pat (38:09):
that's interesting.
You got anything else, Alex onthat?

Alex (38:12):
Oh man.
It's a scary topic.
So I'm all for AI machinelearning and chat GPT and
everything it's doing.
And I don't want to hear anymore negatives about it.

Dave (38:21):
there you go.
Yep.
Hey, I'm right there with you.
It's there.
There's a lot of positive.
That's going to come for sure.

Pat (38:27):
I was going to say as far as like, so chat GPT is the
juggernaut in the room when itcomes to AI and things like HR,
are there any others that peopleshould be taking?
Note of like, any equivalence tochat GPT, whether that's text
based or is there other flavorsof AI for other things?
I guess.
I don't know if that's the bestway to kind of put it, but is,

(38:49):
are there other AI things thatoutside of the chat GPT realm
that people could take a flyeron?

Dave (38:54):
For sure.
So just that, since we weren'ttalking chat, I'll talk about
some LLMs or maybe even chats.
So hugging face has it.
Yeah.
Hugging Face has a chat.
They're an open source communityand library full of large
language models.
Hugging face.co/chat I think istheir chat url which is using
open assistant, which utilizessome of the leaked meta model.

(39:18):
There is Hey Pie, which I thinkis using una, which is a large
language model that's part ofthe open source community.
Of course there's Google Bard.
Yeah, there's lots of things.
Popping up, as I said, like opensource is catching up very
rapidly.

Pat (39:32):
usually does.

Dave (39:34):
yeah, especially, you know, if a big company's secret
sauce gets out, that's,

Pat (39:39):
Right.

Dave (39:39):
it's always going to speed things up a bit.
Yeah, exactly.
But other parts of AI andmachine learning that I'm super
interested in are things likeobject recognition.
And the way that's going tochange the game.
One of the things that peoplemight not know about the AI and
model training process is ittakes a ton of compute to train

(39:59):
these models.
Like chat, GPT, you know,there's thousands, maybe tens of
thousands of GPUs because themodel has a trillion parameters
and it takes all this energy tocreate the model, but running
the model takes far less

Alex (40:16):
Oh,

Dave (40:16):
and we will.
Get to a point where we can runmodels and people have already
run models on a pixel six,right?
Because the pixel phones have adedicated tensor chip for
running AI.
We'll have AI in ourrefrigerators with object
detection that can just tell youexactly what you have in your
fridge while you're at thegrocery store.
So you will never have to lookagain or worry about things like

(40:37):
that.
We have AI in our cars.
There, there's just so manyapplications of computer vision
and things like that, that aregoing to change our lives for
the better.
They make me really excitedabout AI.

Alex (40:51):
Yeah.
Even something like, like,because you talked about
computer vision and cause we, wefocused on language models, like
chat GPT, but Google lens hasbeen around for a while.
And I remember the first timeplaying around with Google lens.
It like blew my mind.
Like, how is it, how's no oneelse talking about this?
Like, do you have any idea whatthis is accomplishing?
This is amazing.

Dave (41:10):
Yeah, I use it for.
that I see that I don't knowwhat it is, if there's a weird
bug, Google Lens.
If there's a cute dog, GoogleLens.
Like,

Alex (41:20):
And by bug, you mean like an insect, not like code.
Okay.
I

Dave (41:23):
No!

Alex (41:24):
to say I never went that far.

Pat (41:27):
yeah That's awesome

Dave (41:29):
Hey, these days it might work for that too.

Alex (41:31):
Yeah, and it's and phones do it too.
I guess Android has a built intype of lens functionality too.
But the idea that you can searchyour pictures, cause like most
people, you probably have like athousand pictures on your phone.
And I just used that the otherday where I knew that I had my
insurance card somewhere on myphone.
I needed it.
And I just put insurance cardand sure enough, it came up with

(41:52):
eight images.
And one of them was my insurancecard

Dave (41:55):
Oh yeah, I love that feature.
That tells me the other day, Ididn't have my license cause I
lost it.
And we are going out to Pete'sdueling piano bars down on sixth
street here in Austin, Texas.
And I didn't have my licensewith me.
And, you know, it's anembarrassing moment where like,
Oh, well, we can't let you inand then a bouncer says, do you
have a picture of your licenseby chance?
I was like, Oh, I know I do it.

(42:16):
Just typed it in driver'slicense came right up picture
from like two years ago,

Alex (42:22):
Yeah.
And I think some people justlike, especially if you're not
in the IT, we'll just kind ofglance over that.
That's a cool feature, but howlike, just amazing of a
technology.
It is what you're thinkingabout, especially if you're
interested at all with AI andmachine learning, what that
thing just accomplished.
And it's almost like, you know,it's just an add on feature that

(42:44):
people just kind of ignore it,but that is an amazing tech,
amazing.

Dave (42:49):
Yeah.
And that goes back to what wewere saying about how AI is all
around us and in so many of thethings we use, but it's always
been invisible, you know, untilwe've interacted directly with

Pat (42:59):
I guess the question is so we kind of covered on what
you're looking forward to themost some of the object
recognition, things of thatnature.
Do you have any predictions forthe next Year, two years, five
years to kind of where we'reliving.
I know the fridge thing is a bigone.
You're starting to see that now,like, yeah, with things in the
fridge.
And she's going to hate me forsaying this publicly, but I wish
my wife would take a flyer onthat.

(43:21):
Cause she always buys like she,she buys stuff.
And then we have like two ofthem already at home.
And I'm like, what are youdoing?
Like, this is just,

Alex (43:28):
You got six balls of mustard and no one in the house
likes mustard

Pat (43:31):
Like, I'm like, honey, we don't need three taco kits.
I mean, I love tacos.
Don't get me wrong, but like,why don't we have three of them?
And she's like, I don't know.
And like, and she buys the kids,which is great.
She buys she buys all thevegetables and all these fruits
and stuff.
And my oldest likes the likescucumbers.
And so they make them in the,like they're little tiny
cucumbers.
So she can actually, you know,she can handle them and not the

(43:53):
big, you know, not whateverlike, but they come in like a
cellophane plastic, you know,sealed thing.
There's like four boxes of thesethings in my fridge right now.
I'm like, she's not going to eatthat many cucumbers.
What are you doing?
Like, but I totally see that youknow, coming into play with with
the household items, the stoves.

Alex (44:11):
have extra boxes of cucumbers.
Two year prediction, Skynet.

Pat (44:16):
That's right.
That's right.
That's how we roll.
That's it.
That's it.
But no, that was, it'sinteresting to see like the
smart home is obviously it'sbeen taken off the last couple
of years.
AI has a big part of that.
You know, internet of things,right.
That's a big part of it.
AI is tying into that again withthe object stuff and things of
that nature.
And I always, from.

(44:37):
I always remember this.
There was a video out a coupleof years ago.
This is probably going back toour evolve days.
So that's probably a good seven,eight years now.
It was from Corning glass andthey had a video.
It was like a smart home of thefuture sort of thing.
And everything was touched.
So like.
The stove, there's no buttons onthe stove, you would just touch
it, and, you know, it would be,you know, electric heat, or, you

(44:57):
know, whatever, that kind ofthing, or, like, the the one
girl came down and, you know,touched the fridge, and, like,
she was calling grandma, but shewas using the fridge surface as,
like, the video, and, like, itwas just crazy, I was just like,
what sort of Jetson's voodoo isthis?
Like, this is insanity, and nowit's

Alex (45:15):
a Black Friday deal

Pat (45:17):
Yeah, no, it's just like, I can go get one if I really want
to drop a couple grand on it,but I can totally get one.
I'm like, what a time to bealive.
This is insanity.
I love it.
But yeah, I think AI is totallykind of driving that.
You know, innovation thatdriving that you know, home of
the future and, you know,everything sort of around it.
Cause like you said, you had tohave an app to front that before

(45:39):
they'd been using it for awhile, but you had to have an
app that interface with you.
Now you're just given the rawengine and they just said, go.
And you're like, Whoa, nobody'sever had this power before.
This is kind of cool.
And here we are, you know, soit's really interesting to me to
see.
But I guess you have anypredictions for five years down
the road?
Like anything that you can kindof carve out or is it too early

(46:01):
or

Dave (46:02):
it.
I'd say so for five years downthe road, I think we'll see the
biggest change in.
Medicine and education.

Pat (46:09):
okay.

Dave (46:11):
talking about object detection and pattern
recognition, we've already seenthat AI can now identify things.
At a level as good as or betterthan a radiologist, for example,
because you think of the bestradiologist in the world,
they'll see maybe what 10, 000 xrays or MRIs in their career
where AI has seen billions,right?

(46:32):
That's its job and it will findmy new details and change things
like that.
You know, we've mapped the humangenome, but we're still trying
to understand it and the waydifferent things interact.
AI is terrific at things likethat.
I don't know how much you guyswatch the.
Clinical trial space, butModerna's doing some really cool
things with personalized cancervaccines.
They're mRNA based and they'remaking a lot of progress with

(46:53):
that and AI is helping hugelywith that.
And you know, those vaccines arepersonalized to the person that
is using them.
And able to see the variabilityin the individual human and
still generalize solutions tofix those problems.

(47:14):
There's incredible applicationsthere.
And then the education space, ifyou guys have seen what Khan
Academy has been doing, they'vecompletely changed the game
using AI.
Every student has their owntutor that's AI based.
And every teacher has their ownteaching assistant that is an
AI.
And the way that kids can learnwith this.

(47:36):
Is so different in ways that areincredible.
Like we always think of studentsusing chat GPT to write their
papers.
Let's say,

Pat (47:44):
Yeah.

Dave (47:45):
but now we have an AI.
They can take on the persona ofGeorge Washington and the
student can interact directlywith a version of George
Washington to ask him questionsand.
It's such a more immersive wayof

Pat (48:02):
Yeah,

Dave (48:03):
And I think that's going to be so beneficial.
Incredible.
I mean, teachers spend so muchof their time lesson planning.
Great thing for AI to do gradingthings.
Great thing for AI to do, right?
Like we have overworkedunderappreciated teachers.
We have help coming, you know,so

Pat (48:24):
my wife is one of them, second grade, she is burnt,
she's burnt to a crisp, let'sput it that way, so, yeah, I get
it, I totally get it, but that'sinteresting, cause also, going
back to the Joe Rogan and MikuKaku.
I hope I'm saying his nameright.
Dude is brilliant.
Anyway.
They talked about, you know, theAI and everyone that's living

(48:44):
now, like having a digital spaceor a digital footprint that will
live on literally forever.
And so our great grandkids willbe able to literally have a
conversation with us like we aretalking in real time.
Like, it was just, I was justlike, wait, what?
I had to rewind that part of thepodcast.

(49:06):
I was like, hold on, let me see,let me hear that again.
But yeah, every, everybody thatis living today has a digital
footprint that is going to liveforever.
And you know, our greatgrandkids will be able to
virtually meet us and talk likewe're talking now and get a feel
for who we are and things ofthat nature.
And I just found that sofascinating and AI is totally

(49:27):
driving that whole.
Sector of things and it's justlike it just it blows your mind.
It absolutely blows your mind.

Alex (49:34):
Yeah and that technology, I guess, is really here today.
I mean, I know they kind of,with these language models
today, if you fed it enough.
History of your speech.
I mean, like I've actuallytalked about this with some of
the people at work.
Just spitballing ideas.
Like, I've used Slack and Teamsfor I don't know how many years

(49:54):
now.
If I were to just feed it everyconversation I've had in the
last three years, this thingcould probably mimic me as if it
were me.
And that's alarming that thetechnology is there for that.
Today, I think.

Dave (50:08):
And if you think of the deepfake technology, you know,
deepfake being its bad usage,but the good usages of it,
there's enough video of you torecreate you on in any
circumstance.
That they want, right?
But that'll be good when ourloved ones want to talk to us in
200 years, you know, you cantalk to great granddad in his
20s

Pat (50:27):
Yeah,

Dave (50:27):
realistically, which is cool.
You can talk to any presidentfrom, let's say the 80s on where
we have sufficient footage andrecordings of them, like.

Alex (50:37):
oh man, maybe you could even have a debate with a
younger version of yourself.
Like, I'd love to debate myselfat 25 versus now and about,

Pat (50:48):
I just wanted to be known I was an idiot at 25 versus

Alex (50:51):
this is, you married me.
I can't believe this because Igot

Pat (50:54):
my god

Dave (50:55):
Yeah, I don't think I'd be able to finish a debate with 25
year old me.
I would walk out frustration.

Pat (51:01):
Yeah, my wife totally settled down.
I married up.
Let's just put it out thereright now.
She, she

Alex (51:06):
so hard.
Hehehehe

Pat (51:07):
Oh, yeah, so hard.
What are you doing wife?
You're killing me.
Dude, you could have had so muchmore.
You settled for me?
Anyway, another discussion.

Alex (51:19):
Alright, and then We talked about realistic, 1 5
years.
Do you see any, is thereanything far fetched that may, I
don't know, people, most peopledon't even think about that you
could see as a possibility inour lifetime.

Pat (51:36):
We're all cyborgs just using Google Glass until the end
of time.

Alex (51:40):
How about true, are we gonna see True artificial
intelligence, like sentient.

Dave (51:49):
AGI, artificial general intelligence,

Alex (51:51):
we going to get AGI in our lifetime?

Dave (51:57):
you know,

Alex (51:57):
years.

Dave (51:58):
three years ago, I would have said, no, it's complete
science fiction.
Today within our lifetime, Ithink, yes,

Alex (52:06):
Wow, there it

Pat (52:07):
So wait, you mean to tell me...

Dave (52:08):
I think it might be different

Pat (52:10):
I can have a robot bring me a sandwich?
Like, what?
HeheheheheheheheheheheheheheheheheheheheheheheheHehehehehehehehehehehehehehehehehehe
Hehehehehehe

Alex (52:13):
No, you're gonna have a robot says it would rather not
bring you a sandwich.

Dave (52:18):
exactly.

Pat (52:19):
He's like, wait, just, did I just get talked to?
Yeah, did I just get talked toby a robot?
What the hell?

Alex (52:25):
that's AGI.
Get it yourself, you lazy sOB.
That's

Dave (52:28):
seriously,

Pat (52:29):
lazy ass.

Dave (52:30):
it's like we have determined you do not need a
sandwich.
Like,

Pat (52:32):
That's right.
Lose a pound, you fat...

Alex (52:35):
get you this sandwich.

Dave (52:38):
Yeah.

Pat (52:39):
ten reasons why the sandwich is not healthy for you.
Just sit back down.

Alex (52:43):
Pros and cons, yeah.

Pat (52:45):
That's it.
That's it.
Do you know how many caloriesare in this salami sandwich?
Sit down.

Alex (52:51):
That's interesting.
Well, that's probably the easyquestion to ask.
Well, that's interesting becauseI think I would have been the
same boat if you would haveasked me even a year ago, I'd
say that's, no that's sciencefiction.
It's a cute idea.
We'll have something

Pat (53:02):
That's some Star Trek

Alex (53:03):
will mimic it pretty well, but it won't be what we Are
defining as AGI, but now I'm, Idon't know.

Dave (53:12):
As you think about all of the data that we have, if we.
Just gave it access toeverything

Alex (53:17):
Yeah.

Dave (53:19):
seen, written, observed.

Alex (53:21):
Oh my gosh, because then you're going to start getting
into some really ethical debateson like, well, what is human
then if not just a collection ofeverything you've learned and
what makes that, what makes ussentient and that not,
especially if you have to try tothrow out religion, you can't
talk about religion in thissense.
So, you know, what doesdifferentiate AGI from humanity?

Dave (53:45):
Mm hmm.

Alex (53:46):
topics.
Probably something that you canspend a lot more time than that.
Yeah.
10 minutes on a podcast talkingabout so anything other than
AGI, maybe

Dave (53:56):
Yeah, just on the,

Alex (53:57):
We're not thinking of.

Dave (53:58):
well, on the biomedical front, I think that we might be
one of the last generations todie.

Alex (54:06):
Ooh, immortality.

Pat (54:08):
Wow.

Dave (54:10):
There's been recent studies and things that say that
aging doesn't seem to be aprocess that needs to happen.

Pat (54:19):
Wow.
Mind blown.

Alex (54:22):
and

Pat (54:23):
Well, then the question is, do you want to live forever?
That's the other side of theit's like, ah, you know,

Alex (54:28):
then there'll be, there's a finite amount of resources
and.
At that point, do we just say,Hey, we're capping at

Dave (54:35):
reproducing?
Yeah.

Alex (54:37):
there you go.
If you want to live past this,you can't have kids or something
like that.
Otherwise you stop getting thetreatment you need or something.
Yeah, that's interesting.
Wow.
That's really far fetched.
I shouldn't say far fetched.
That's really out

Dave (54:51):
hmm.
Well, it used to be, right?

Pat (54:54):
Yeah.

Dave (54:55):
But yeah, if you look at lobsters and telomeres and, you
know, all those things.
It's, there's

Alex (55:00):
the Greenland shark, too, I think is technically immortal.

Dave (55:04):
Yeah,

Alex (55:05):
Some people say they've seen some that are 400 plus
years and it just doesn't, it'scells don't die.

Dave (55:12):
we have examples and I mean, what's machine learning
besides, through examples.
So we have examples of this and

Alex (55:22):
Yeah,

Dave (55:23):
mapped our whole genome.
We have CRISPR technology.
We have AI now.

Alex (55:28):
So we'll live forever, but we'll start growing fins and
gills.

Pat (55:33):
that's it.

Dave (55:33):
Just think Gen Z is the immortal.
Gen Z is the immortalgeneration,

Pat (55:40):
yeah, exactly.
Yeah.
Don't leave me that.

Alex (55:42):
oh man.
We picked this generation to beimmortal.

Pat (55:45):
Oh, Jesus.

Dave (55:48):
right?
Millennials, we miss everything.

Pat (55:50):
Oh, our whole life's gonna be one big TikTok reel.
Oh, my God.
This is terrible.

Dave (55:57):
with Tide Pods, ended with immortality.
Geez,

Pat (56:00):
right.
Oh, Jesus.
Yeah.
Some gens have all the luck.
Son of a...

Alex (56:05):
yeah, they won't create anything, so they'll just use
the millennial technology to,yeah, just, they'll have a two
billion TikTok subs, but,

Pat (56:15):
it.
Talk about failing up.
Jesus.
We have a whole generation thatfails up.
God damn it.
Oh, that's funny.
On the business side of things,how do you think, like, either
as an individual, so we sort oftalked about that or business
can prepare for the increasinginvasion, if you will of AI,

(56:37):
right?
So is there like a, Crawl, walk,run sort of thing, or is AI just
be like, you know, I'm going tostart using AI tomorrow and it's
going to transform my businessfrom 1 million a year to, you
know, 10, 10 million a year,whatever it is.
So like, is there a model tosort of, get that introduced and
use that to the most efficient,or is it just jump in with both

(56:57):
feet into the pool in the deepend and figure it out later?

Alex (57:01):
That's how I'm picturing it now.
If you're not a huge company,like a meta or something that
can do 10 billion to fundresearch in AI, and you're just
a smaller company, it seems likea daunting idea, like how do you
get in front of this, and I justfeel like you're gonna have a
board of directors that justsay, you know, like, AI, am I

(57:24):
right?
We gotta do it this year.
I don't know what that means.

Pat (57:29):
Yeah, you have more directors that can barely spell
AI.

Alex (57:32):
Yeah, spell check on that.
Yeah, so I mean, what do theyeven do?
Is there anything...
In your opinion, that thesecompanies that don't have these
massive budgets can do toprepare for this?

Dave (57:49):
I don't know.
I think, yeah, I think you haveto dive right in.
I think that for most companiesthat are non tech related, it's
going to just change the natureof the work that people do,
right?
Personal assistants lives aregoing to change.
Copywriters lives are going tochange any type of content
creator.
but these things.

(58:10):
Are also becoming so much moreaccessible in the tech world.
If you look up lang chain, it'sa really nice framework for
working with large languagemodels, and you can use the
models just through APIs andconnect them to your databases
and connect them to.
whatever else you need toconnect them to.
I think that it's going totransform the way that we

(58:33):
interact with products.
So for example, going back tothe networking example, we're
used to troubleshooting thingsby looking at different graphs
and looking at metrics andtrying to figure things out.
I think that we're going to seea lot more interaction with
network directly, right?
What if you could just ask yournetwork, what's wrong?
Why do we have latency rightnow?

(58:55):
And it will do that work foryou.
Just like we talked about withthe education example, if you
could just ask Hayden from theHolden from the catcher in the
rye, you know, what were youthinking when you did this?
And then you can get an answerback, you know, from that point
of view.
I

Alex (59:13):
Okay, interesting.

Dave (59:15):
Yeah.

Alex (59:16):
It's

Pat (59:17):
It's all a good answer here.
no, yeah, there's no writer in.
It is.

Dave (59:21):
I think there's gonna be a slow change in the nature of
work, but as far as.
AI into your business.
I think that's going to comefrom within.
It's just going to be peopleusing AI to change your
business.
Just like we, the way that weuse Google in our roles now and
things.

Pat (59:38):
Yeah.

Alex (59:39):
Just organically happen.
You don't have to have this fiveyear master plan.
It's going to happen at least tosome degree.

Dave (59:45):
Yeah.
People are going to you.

Alex (59:47):
right for sure.
And then the tools that youconsume will eventually start
using it.
So maybe you'll use it withoutrealizing it.

Dave (59:54):
Yeah.
Like GitHub co pilot, maybe all,or, you know, that becomes the
normal part of an IDE where it'scoding with you instead of you
typing in every character.

Alex (01:00:04):
for sure.

Pat (01:00:05):
Cool.
Interesting.

Alex (01:00:07):
All right.
Final topic.

Pat (01:00:09):
I actually have one.
I guess it just kind of came tome, but do you, so now AI is
sort of open and it's, you know,it's, you know, quote unquote
free for everyone, that sort ofthing.
Do we get to a point where it'sjust consuming that much data?
Open AI is just pouring tons andtons of money into it.
Is there ever going to be like apaid version of an AI or like AI

(01:00:32):
as a service?
Cause everything else on today'splanet is a service, right?
Infrastructure and PAS and IAS,PAS, all that kind of crazy
stuff.
Is.
Does it eventually hit becauseof what it can do or what these
places that are kind of drivingforward, is it put behind a
paywall at some point or becauseit is open source?
Does it stay open like a Linuxfor however many years Linux has

(01:00:57):
been around a billion?
It seems like, like, is there apaywall coming or is there, do
you think these places have away to monetize that?
Because it's so hot right now,right?
Everybody's trying to make abuck.

Dave (01:01:09):
I'd say yes and no.
I think there's definitely apaywall for specific
applications of AI because.
The products that integrate itthe best for whatever it is,
whatever outcome they're lookingfor are always going to be the
best user experience that peopleare going to pay for.
But I think that something likea chat GPT or an AGI is going to

(01:01:29):
become more like a utility, likethe internet.
Maybe you'll need to pay forsome sort of access to it in
general.
But not a specific version ofit.
And I think that'll be becauseof the progress of open source
and things where it's kind ofnot tenable to gatekeep it any
longer.

Pat (01:01:47):
interesting.

Alex (01:01:48):
Yeah, and you also mentioned something earlier when
it came to cost too, because inmy head I was thinking like, how
on earth are they not chargingfor a tool like this?
I didn't realize that it's thelearning that is the costly
part.
Because whenever it just, we goback to chat GPT just cause it's
hot and everyone knows it.
But it comes right out and tellyou that it's learned it's a, I

(01:02:11):
forget how they word it, but youknow, it's only learned up to.
Sometime in 2011 or

Dave (01:02:17):
Yeah.
It's training cutoff.

Alex (01:02:19):
right.
Yeah.
And then now that makes a littlebit more sense that it's not
actively training, at least notthe model that we're interacting
with.
So that makes sense how ahundred million people can use
this.
And this thing is not like, howis this thing responding as
quickly as it.
That blew my mind when I was,when I've been messing with it.
Just like, if there's so manypeople using this, how on earth

(01:02:40):
is this keeping up?
That's interesting to know thatit's significantly less compute.
So, I mean, there could belanguage models that people
just, you get the base trainedup to X year and it's even in
that capacity, it's so helpful.
And you would just need just sominimal of the resources to run

(01:03:01):
it.
So I guess maybe that's kind oflike future is how long is it
going to take for us to get amodel open to the public that is
real time, updating and trainingitself.
Maybe that's further down theroad than people think because
of how intensive that would be.

(01:03:25):
Interesting.

Dave (01:03:25):
The interesting thing that we're seeing now is that.
Originally, we thought that youneeded to always retrain the
model once you made sufficientprogress, right?
And the training process costsso much to do, but what we're
seeing in the open sourcecommunity is that there are a
lot of things that you can do tosupplement the model that you

(01:03:47):
already have.

Alex (01:03:49):
Yeah.
I guess most people, at least Idid, I just assumed that it was
always kind of iterating overand supplemental, but I guess in
the past it's been a forklift,you know, idea to retrain it.

Dave (01:04:05):
Yeah.
And you can completely retrainon the new data that you've
learned, but you can also usedifferent fine tuning techniques
like low rank.
Adaptation of large languagemodels, which was part of the
stable diffusion thing, ifyou're paying attention to that.
So instead of, you know,training billions of parameters,
which is ridiculously expensive,you can freeze the pre trained

(01:04:28):
model weights and then put innew trainable layers.
In the transformer GPTPgenerative pre trained
transformer which justdrastically reduces the GPU and
memory requirements.

Alex (01:04:43):
Interesting.
Yeah.
It would be nice to reallyunderstand how these are
structured.
I guess in my head I was tryingto relate it back to like just
basic coding one on one And Iwas thinking like, do they have
like 2021 training, and that'skind of like a module that I can
call back and it doesn't have todo a lot of stuff with it.
And it's just, someone's gotthis crazy thing written out in
some ID somewhere.

(01:05:06):
Um, yeah, that's interesting.
Yeah.
Probably should start learningmore about that.
And because I would assume, eventhough open AI is kind of the
forefront of the news right now.
I assume they're not doinganything radically different
than what people have done inthe past.
I mean, I assume the same typeof structure that they're using

(01:05:28):
to train the models are similarto any other language models out
there.
And I don't know if that's open.
Well, I guess it is open to theworld now, even by mistake.
So.
Yeah, I mean, now that we havethat code, were they doing
anything that was just soradically different than
anything else has been done inthe past?
Or are they just, you know, theyfine tuned it better than

(01:05:50):
anybody else?

Dave (01:05:52):
I think it really came down to scale.
I mean, the, so transform, likethe transformer architecture has
been out since 2017, but evengoing back to the eighties.
The idea of a neural network,you know, has been around since
then.
The problem was that back thenneural networks sucked compared
to symbolic AI, which was justvery specific instructions to

(01:06:16):
get the outcome that you wantedthat kind of appeared like it
was intelligent, but neuralnetworks and the idea of having.
Something that learns performedterribly.
Let's say it had 30 to 40% errorrate and it just wasn't tenable.
But the problem back then wasthat we didn't have the compute
or the data for it to actuallylearn.

(01:06:37):
You know, they were trying tobuild neural networks with a
couple hundred examples ofthings and it would do an okay
job, but it was never going tobecome something that people
wanted to use where now we havebillions of data points and.
Tons of compute to train on youknow, these things have become
simply incredible.
So,

Alex (01:06:58):
Well, I guess your opinion, your thoughts is right
now, the, to see the nextevolution in these AI models or
AI in general, is this reallyjust an idea of just We probably
already have the algorithms inplace and really, we just need

(01:07:18):
more data points and compute.
Is that what's the bottleneck?
Or do we still need datascientists to make all these
different algorithms and nextiteration of algorithms?

Dave (01:07:32):
I think there's definitely plenty of room for improvement
and the data sciences will keepworking at that.
There's new papers coming outevery week about different ways
to improve on, you know, what'salready been done.
So that's going to keep moving.
And now with all the interestthat's in it from.
The academic side and theaccessibility of it now as well.

(01:07:55):
yeah, I think we'll have largescale architectural changes that
will continue to move usforward.
But as we've seen, like we'realready being able to run some
of these sophisticated models,which we know we can run lighter
weight versions and then buildlayers on top of, you know, on
smaller devices, smaller chipsets, things like that.
So I think the combination ofdownsizing the requirements to

(01:08:17):
run, increasing the compute thatwe have and the researchers
working on the models, that's.
That's why those 10 yearpredictions don't seem so far
fetched anymore.

Alex (01:08:29):
Wow.
Amazing.
All right.

Pat (01:08:33):
Yeah, wait till Quantum Computing gets here, then it's
all, then it's it's game on.

Alex (01:08:40):
that's one of the classes that I signed up for.
Pat Cisco.
I was quantum networking.
So, yeah, I'll let you know howthat goes.
That's a fun one.
And it's with a PhD inmathematics.
That's who's given the lecture.
So hoping I can understand it.
If I don't, I'll just put it inchat GPT and tell it to explain

(01:09:02):
it to me in terms I understand.

Pat (01:09:03):
right.
Dumb it down.
That's

Dave (01:09:05):
Explain like I'm five.

Pat (01:09:08):
right.
So as we sort of...
Wrap up here.
The last kind of topic and we'reright around the hour.
So, which is perfect.
This has been awesomeconversation.
But as far as like landing a jobin the AI space or transitioning
to it, like, do you have aroadmap of kind of how, like
what it takes to get there?
Do you have to have a softwarebackground such as yourself and
then kind of move that way?

(01:09:30):
Cause we all know it's asoftware world going forward,
right?
That's no secret.
So, you know, do you have tostart there and kind of move,
move with it or can you jumpright in and depending on how
you learn and things of thatnature, like, can you get there
without being a software dev?
It's like, what does that looklike?
Or what's your opinion on that?

Dave (01:09:48):
I think you can.
It depends.
I mean, it's a broad field thatneeds a diverse set of skills.
So I think you can get therefrom anywhere.
I think coding absolutely helps.
There are no data scientiststhat don't know how to program
Python and are taught as part ofwhat you learn in data science,
because working with these largedata sets is not something that

(01:10:09):
humans can just do, right?
Like, we need computerassistance.
So you'll absolutely learn that.
But programming itself isbecoming more accessible with
AI.
So I don't think.
Did anyone that doesn't havethose skills should let that
kind of gate keep them.
They're also great courses andmaterials to review online.
If you want to learn more aboutmachine learning.

(01:10:29):
Cassie Kazakov has a six hourtalk, which is also broken up
into more consumable chunks onYouTube called making friends
with machine learning.
I definitely recommend checkingthat out.
If you want to dive a littledeeper into the concepts and
they're not.
They're technical concepts, ofcourse, but you don't have to be
a data scientist to understandwhat she's talking about.
there are data analytics courseson Coursera offered by Google

(01:10:53):
that are really good.
You can get your certificatesthere.
I think that as AI continues to.
Revolutionize the workforce,kind of like I talked about
before the death of specialties,I think certificates will
actually become more of a commonthing and a more useful thing
because knowing a lot about alot is going to become more

(01:11:13):
important than knowing a lotabout something specific, if
that makes sense.

Pat (01:11:19):
Yeah.
Interesting.
Alex, you got anything else?

Alex (01:11:25):
Well, I'll just touch on the learning a little bit
because when you are looking toget into AI and machine
learning, do you go straight tocourses on AI and machine
learning?
Is there like a prerequisitetechnology that makes sense?
Or like you said, you mentionedmaking friends with machine
learning.
Is that just.

(01:11:47):
who just graduated high schoolhas never worked in I.
T.
Can they digest the content likethat?

Pat (01:11:52):
The wheels are turning.
I see smoke.

Dave (01:11:57):
I think so.
I think it's approachable enoughwhere you would have a solid
understanding coming out of it.
To be effective at learning onthe job when I think back to,
you know, the things that welearned in college.
Anyway, how much of ittransferred directly into what
we needed to do once we startedworking 10, 15% of it.

(01:12:18):
Right?
So.
I think that going throughputting in the work to, you
know, get a certification forwhatever that's worth would be
enough to lay a foundation forthe things that you're going to
learn.
Once you get into it from myexperience, you know, being a
math major and being someonewho's always been interested in
this stuff and who's taken thosecertificates and things, it

(01:12:42):
makes the conversations that youdo have with data scientists go
a lot better.
It helps your understanding of.
Thanks.
The direction that you want totake things or why certain
solutions may or may not work,why you can't just shove chat
GPT into everything, no matterhow much your CTO wants to

Pat (01:12:59):
Ha.
Ha.
Ha.

Dave (01:13:00):
things like that.

Alex (01:13:02):
All right.
And last question I have that isyou mentioned certifications a
couple of times now.
Is there.
Is there like a go to AI ML certthat has some weight in the
industry?
I mean, as far as networkinggoes, people understand the CCNA
and people understand like AWScertified, whatever, anything in

(01:13:24):
machine learning that you canthink of or know of.

Dave (01:13:29):
Not that I can think of, cause I'm pretty new to
exploring kind of the world oflearning with it.
Historically it's been peoplethat have come out of a degree
program in it because it's newand those people are just
getting snatched up.
But I think that.
As the need for AI people grows,it's going to expand out into,
you know, certificate programsand things like that, especially

(01:13:51):
because it's so new, right?
Like there are tons of smartpeople like on this call where
it just happened too late forus.
And we've been in careers,

Pat (01:14:00):
Yeah, Alex and I are both looking over our shoulders like,
wait, there's

Dave (01:14:04):
well, Pat, your wife came down the stairs for a second.
So I

Pat (01:14:06):
Yeah, she's, yeah, she peeked around the corner and
said, she's the real smart one.

Dave (01:14:10):
Yeah, but I mean, there's tons of talent that could get
involved where like, You know,we think like, ah, you know, I
missed the boat on that one.
And I just don't think that'sthe case.
I think there's going to be agreat enough need that if you're
inspired enough to go after it,you know, there's going to be
resources out there and knowingthe foundations and being able
to speak intelligently and pickup things, you know, once you
get in there, all you're goingto need.

Alex (01:14:33):
well, I think we have a new business model, Pat.
So.
no one's got a great cert andeveryone wants to do it.
So cuz we, we saw this with theadoption of the cloud
infrastructure too, where therewere businesses that did nothing
other than get you to aws.
I can see a correlation nowbetween people, like train
people on AI and get us to startusing ai and like

Pat (01:14:55):
That's it.

Alex (01:14:55):
that he started an AI revolution in his company.

Dave (01:14:59):
Yeah.
I mean, if you look at like theprompt engineering role that
we're seeing now, where peopleare getting hired to just tickle
the right answers at a chat GPT,it's like, Is that a.
Is that a skill that you learnedsomewhere?
Absolutely not.

Pat (01:15:12):
It's almost like the social media marketers back in the day
when social media exploded, likethe people, businesses were
literally hiring people tospecifically post on social
media about, you know, they'reabout the business.
It's like, man, literally awhole, like a whole industry was
born out of that, like, it'sjust a whole career path was
born out of social media.

(01:15:34):
It's like, it just exploded.
So I could definitely see AI

Alex (01:15:37):
And that's a.
And that's the thing.
It's still here.
I mean, I know Disney has a vPof digital marketing, which is
nothing more than they, they runthe Twitter and the Facebooks
and of our company.
Yeah, it's a thing.
So maybe we'll

Pat (01:15:52):
it's crazy.
It's absolutely a thing.

Alex (01:15:55):
initiatives or something.
I don't know.

Pat (01:15:57):
That's it.
I'm gonna quit my job tomorrowI'll start brainstorming Alex
you and I'll we'll we'll talk

Alex (01:16:05):
the smart thing to do.
Let's just quit our jobs.
That, you know, that really addssome fuel to the fire.

Pat (01:16:11):
that's it Start from scratch literally

Alex (01:16:14):
Alright, well I think that's a good way to stop since
Pat and I have a lot of work todo apparently.
Starting our new

Pat (01:16:20):
that's right.
I'm going to study AI as soon asI get off this call.
I'm out.
That's it Before you know itthis whole podcast of the AI
you'll just be hearing robotstalk about for

Dave (01:16:32):
talking to each other.

Pat (01:16:33):
That's it.
It'll just be digital faces ofAlex and I.
We won't really be here.
We'll just phone it in everyweek.
That's it.
Write me a podcast, slave.
I love it.
I love it.
Oh, Dave, man, this has beenawesome.
I really appreciate you comingand hanging and talking all this
kind of crazy stuff.
And really it's good to seewhere the AI stuff goes.

(01:16:54):
So again, thanks for coming andspending a couple of minutes
with us and dropping some hotknowledge.
It was it was really fun, reallygreat conversation.
So now I really appreciate youbeing here, man.

Dave (01:17:03):
Yeah, absolutely.
Thanks guys for having me.

Pat (01:17:05):
Yeah, man.
We'll definitely have you backwhen another version of AI comes
out or a chat GPT, and we'lltalk about that too.
So pros and cons.

Dave (01:17:12):
Yep, we can complain about our robot overlords.

Alex (01:17:15):
Yeah,

Pat (01:17:15):
right.
That's right.
Skynet's not here yet, but damn

Alex (01:17:19):
yeah, 200 years down the road, we can have a follow up
conversation.

Pat (01:17:23):
That's right.
Damn Gen Z ers.
That's it.

Dave (01:17:27):
The last generation.

Pat (01:17:29):
That's right.
There's no more letters after Z.
What are you going to call themnext?
I don't know.
Type it in chat, GBT, it'll tellyou.
Anyway, thanks everybody forjoining this week on breaking
down the bites.
Make sure you visit our website,breakingbytespod.io.
io so you can get to, you cansubscribe to the show on your

(01:17:49):
platform of choice Applepodcasts, Spotify, Google
podcasts Stitcher pretty muchanywhere that has a podcast
platform.
We are there, or there's an RSSfeed there as well.
So just if you need just a plainRSS so you never miss the show.
Throw us a rating on the Applepodcast.
That's where most of ourlisteners come to, or at least
that's what our statistics tellus.
That would be great.

(01:18:10):
That fools with the algorithmsand the AI and all that crazy
stuff that Apple uses over thereto get podcasts to people's ear
holes.
So, the more we get ratings andreviews and that sort of thing,
it It can only help.
So, or if you simply tell afriend, right, that works just
as well in today's crazy tech AIdriven world, you know, talking
to actually human to humantalking is, you know, still

(01:18:32):
works pretty well.
So it's still a thing.
Yes.
We're not there yet.
So still a thing.
Tell a friend and certainly getthem to tell a friend or
whatever, so that'd socials arein our.
So LinkedIn, Twitter Facebook,there's a discord server out
there.
The invite is in show notes aswell to come hang with us.

(01:18:52):
So we still need a little bit ofhelp on the discord thing.
So if anybody's got some discordwizardry, let me know and we'll
we'll talk.
So that'd be cool.
Again, thanks man.
Dave has been awesome, Alex.
We will see you next week andthat's it.
Bye everybody.

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