Speaker 1: Welcome everyone to another episode of Adventures and Devlops. Joining
Speaker 1: the in studio today, Warren Broad Warren, how.
Speaker 2: Are you thanks for having me back. You know, I
Speaker 2: actually have a good fact for today that I thought
Speaker 2: was really interesting to share. There's a malware out there
Speaker 2: called potter Cookie, and I know the economy or engineers
Speaker 2: is not so great at the moment. However, a lot
Speaker 2: of advertisements out there for a job recks, maybe from
Speaker 2: malicious attackers who are trying to get you to run
Speaker 2: GitHub repos or download packages from the internet to pass
Speaker 2: the interview, and those things will get installed on your
Speaker 2: machine and either try to steal your local crypto wallets
Speaker 2: or worse, be used for attacking whichever company you do
Speaker 2: get hired by. So I know it's a real struggle
Speaker 2: that you want to complete whatever take home assignment or
Speaker 2: you know, get the next job. But you really got
Speaker 2: to be careful in today's economy because this tech is
Speaker 2: out there that's just waiting to capitalize on a supple mistake.
Speaker 1: That's just nuts, Like let's just kick someone while they're
Speaker 1: down right, Yeah, Like all of that is nuts.
Speaker 3: I think getting homework from an interview is nuts. I
Speaker 3: think you know, potentially installing something on your computer that's
Speaker 3: going to make your computer go wild. It's not like
Speaker 3: it's just it's multiple levels of crazy.
Speaker 1: Speaking which, Hi, Jillian, welcome to the show.
Speaker 3: Thanks for having me back.
Speaker 1: You guys are making me feel guilty. Thanks for having
Speaker 1: me back. Like it's conditional with this point, just show up.
Speaker 3: Oh I don't know. I'm not sure that it is
Speaker 3: for me, but no, thank you.
Speaker 2: I'm still appreciative for being here. I guess that is
Speaker 2: what I'll say.
Speaker 1: Well, I'm happy to have you both here. You guys
Speaker 1: make my job a lot more fun and entertaining. And
Speaker 1: speaking of fun and entertainment, I'm looking forward to this episode.
Speaker 1: We have Alex Kernes joining this in a studio today,
Speaker 1: principal solution architect from dude, you just told me how
Speaker 1: to say this in my mind already went below.
Speaker 4: I've heard every every possible permutation of how to pronounce
Speaker 4: it myname correctly, which a lot of people don't do.
Speaker 1: Welcome to the show, man, I'm happy to have you here.
Speaker 5: Great to be here.
Speaker 4: Thank you.
Speaker 1: Cool. So give us a little bit about your background
Speaker 1: now that we know how to pronounce for your work.
Speaker 4: Yeah, great, I mean, I come from a software engineering background,
Speaker 4: if you kind of go way back to the post
Speaker 4: university career and then made the move into intercloud both
Speaker 4: kind of internal consultancy and of platform type work and
Speaker 4: then consultancy in terms of the traditional customer external facing consultancy.
Speaker 4: But I'm still very technically driven. I like my hands dirty.
Speaker 4: That's that for me. Is is what's the most exciting part.
Speaker 4: It's building things, breaking things, learning from it. Yeah, payper
Speaker 4: architecture is is not my not my phone.
Speaker 1: I hear you there. There's like a certain I think
Speaker 1: for people who succeed in this industry for a long time,
Speaker 1: there's a certain amount of entertainment value that you get
Speaker 1: from your job.
Speaker 2: I'm definitely doing it wrong then, I mean I I
Speaker 2: my new belief is that when I retire, I'm just
Speaker 2: gonna go back into drawing boxes and lines. Like that's
Speaker 2: just like the best part of my job. Like when
Speaker 2: I can get on a piece of paper or whiteboard
Speaker 2: and boxes and lines, you know, not even any words necessarily,
Speaker 2: Like that's prime enjoyment right there.
Speaker 1: I'm gonna get my typewriter and I'm gonna go to
Speaker 1: my cabin in Montana. Screw all you guys.
Speaker 3: So so speaking of just like I'm going to do
Speaker 3: something really simple and turn my brain off, I bought
Speaker 3: like a paint by Numbers kit and that has been.
Speaker 3: It just reminded me a lot of what you said,
Speaker 3: because I just sit there and I just paint in
Speaker 3: the numbers and I don't care that I'm nearly forty
Speaker 3: and doing an adult like an activity for children. It's great,
Speaker 3: and it's just so you just turn your brain. It's great.
Speaker 3: It's great.
Speaker 1: Anyways, I've actually heard quite a few people comment how
Speaker 1: like therapeutic and relaxing that is.
Speaker 3: They really are. They're very relaxing. It's great.
Speaker 4: I think so many things. It's the tech industry. Brains
Speaker 4: are so switched on all the time. There has to
Speaker 4: be a way to switch off otherwise work life balances
Speaker 4: it's pretty nil.
Speaker 2: Well, it's interesting you bring that up, because actually, very
Speaker 2: commonly where you keep being in a always production mode,
Speaker 2: like everything we do is happening at a critical level
Speaker 2: and we have to pass that test. Like whatever work
Speaker 2: we're doing, there's no practice involved. It's always run time
Speaker 2: for us. And there's a lot that there's a bunch
Speaker 2: of research out there that says like we have to
Speaker 2: go into practice mode where mistakes can be made, failures
Speaker 2: can be had, and we can actually learn from it intentionally,
Speaker 2: and without that we will like that's actually one of
Speaker 2: the biggest causes of burnout. So you know, if it's
Speaker 2: going home and doing watercolor, water painting, you know, whatever
Speaker 2: it takes there. If that somehow helps you recharge realistically,
Speaker 2: definitely do it.
Speaker 1: There's also a lot of evidence showing that taking on
Speaker 1: those kind of activities, while you're not consciously thinking about
Speaker 1: the problem, your subconscious is continuing to work on it,
Speaker 1: and that's when some of the big insights and big
Speaker 1: breakthroughs for you occur. Like I know, there's a really
Speaker 1: common antidote of like when you're in the shower and
Speaker 1: you have this great idea that's like a really fixed
Speaker 1: example that whole process in action.
Speaker 3: True, do two important jobs anymore, Like I'm just I'm
Speaker 3: just not doing them, but like I mean, like they matter,
Speaker 3: but not like not on like a huge I'm always
Speaker 3: in production. Everything has to be perfect, Like it's fine,
Speaker 3: it'll it'll be fine if it's it's not a couple
Speaker 3: of days later. I used to do important stuff though,
Speaker 3: and I don't want to anymore. So that's I suppose
Speaker 3: that's your lesson is this is where I'm at doing
Speaker 3: my paint. Fine numbers and things don't really matter.
Speaker 1: Well, one of the things we were talking about before
Speaker 1: we started recording the episode was leveraging jen AI and
Speaker 1: Alex you've got some experience with that, specifically some experience
Speaker 1: of like real world examples where you've done that, and
Speaker 1: I think that's one of the big I think that's
Speaker 1: one of the cool things about AI. You know, it
Speaker 1: goes through this this buzz cycle, but people who are
Speaker 1: actually putting it to real world use. I'm interested to
Speaker 1: hear your take on that.
Speaker 4: Yeah, I think it's a it's a really it's a
Speaker 4: really interesting topic. It's it's something where you go back
Speaker 4: eighteen months, maybe chat GPT was kind of just about
Speaker 4: starting to be established as almost household name. People aren't
Speaker 4: necessarily using it actively, but tools like that are becoming
Speaker 4: more and more common. And then I think with with
Speaker 4: any technology, it's it's when it gets kind of democratized,
Speaker 4: when it gets put in the hands of people that
Speaker 4: aren't having to spend millions on hardware and and do
Speaker 4: those kind of things that it actually really starts to
Speaker 4: become an awful lot more prevalent. So I think as
Speaker 4: we saw with any technology, So you think back to
Speaker 4: kind of mid twenty tens, I suppose, where things like
Speaker 4: AWS Lander came out, so kind of serveralus technologies, and
Speaker 4: then the years after that where every SaaS company that
Speaker 4: existed was going for a we now have a server
Speaker 4: off ring, and it's like, what is it serverless? Is
Speaker 4: it just managed service?
Speaker 5: Is it?
Speaker 4: What is your what is your definition of servers?
Speaker 1: Right?
Speaker 4: So you see the buzz around that. You see buzz
Speaker 4: around even Cloud, which is I mean cloud is public
Speaker 4: Cloud twenty years old if you if you go back
Speaker 4: to Aws's first service, so it's not it's not new,
Speaker 4: it's not shiny anymore. And AI, I think is going
Speaker 4: through the same thing, but just at a much much
Speaker 4: faster pace.
Speaker 2: So well's a really interesting comparison though, Like I just
Speaker 2: I want to stop here for a second there because
Speaker 2: I feel like there's a sort of a weird duality
Speaker 2: where serverleists made it easier for people to get into
Speaker 2: building stuff and releasing applications because it didn't require you
Speaker 2: to purchase or allocate huge data center capacity in order
Speaker 2: to make that happen. I feel like the where where
Speaker 2: the point is AI is currently at is it actually
Speaker 2: does require only the most expensive access to hardware or
Speaker 2: service providers to be able to get that. So I
Speaker 2: don't think like I don't know if it's been democratized yet.
Speaker 2: I mean, there's a lot of services out there that
Speaker 2: claim to get you access to some facet of AI,
Speaker 2: and I know there's like chtgbts and lms out there
Speaker 2: that questionable how much value they were turning to you.
Speaker 2: But I think that like the real core aspect of
Speaker 2: being able to provide the underlying resources or technology to people,
Speaker 2: I think it's still much too far away.
Speaker 4: I think the way you described it is great. It's
Speaker 4: giving people access to a facet of AI. I mean,
Speaker 4: if we think about AI as a general topic, artificial
Speaker 4: intelligence more broadly has been around for decades. It's only
Speaker 4: really that when you sort of stop breaking it further
Speaker 4: down into machine learning and deep learning and now generative
Speaker 4: AI is a kind of subset of that that the
Speaker 4: generative AI and the AI terminologies are now almost interchangeable.
Speaker 4: Certainly from an industry perspective, I think it very much
Speaker 4: depends on what what people are wanting to do with
Speaker 4: AI and how how specific their use case is. So
Speaker 4: if we think about things like like chat GPT, that's
Speaker 4: obviously a very specific use case. It gives you very
Speaker 4: generic responses to things. It hasn't got access to your
Speaker 4: your specific business data. But it's free and not with
Speaker 4: any any free product you are you are normally the product.
Speaker 4: Of course you can you can opt out of things,
Speaker 4: but by default it's it's collecting that chat history to
Speaker 4: improve the service for everyone. You've then got things like
Speaker 4: Amazon Bedrock, so Bedrock with aws's generative AI offering that
Speaker 4: came out at their conference twenty twenty three that was announced.
Speaker 4: So Bedrock offers kind of two different modalities I suppose
Speaker 4: in how you can use it. One is on demand
Speaker 4: where you pay per thousand tokens. So that's the way
Speaker 4: where you can you can go and build something. Again,
Speaker 4: it's similar to chat GPT in its sense of its
Speaker 4: generic knowledge. It's whatever the large language model has been
Speaker 4: trained on. But because as with any kind of cloud
Speaker 4: hosted managed service, they can take advantage of economies of
Speaker 4: scale and give you pay as you go pricing the
Speaker 4: moment that you want to fine tune that model or
Speaker 4: train a different model with your specific data. You're going
Speaker 4: from paying kind of fractions of a cent per thousand
Speaker 4: tokens to having to commit to thirty thousand for three
Speaker 4: months because you are now the one that's bearing the
Speaker 4: cost of all of that hosting rather than to AWS
Speaker 4: making it available. And of course there are ways that
Speaker 4: you can augment the use of large language models with
Speaker 4: your own data without going to that extent. So even
Speaker 4: just including examples of your specific data in a prompt
Speaker 4: or of retrieval augmented generation where you can load your
Speaker 4: own documents into a vector database and have it retrieve
Speaker 4: that data from there, there's lots of ways you can
Speaker 4: kind of get quite a long way without spending huge
Speaker 4: amounts of money. At the moment you want to get
Speaker 4: to the I have complete control of my model, I
Speaker 4: train it with my specific data, then you'd hope that
Speaker 4: that's when you start getting to the the type of
Speaker 4: customers who can afford to spend that kind of money.
Speaker 2: So, in your capacity at your current job, where you're
Speaker 2: interfacing with clients and whatnot, do you find there is
Speaker 2: one particular provider or one set of tools that you're
Speaker 2: constantly going to or whole breadth of set, but for
Speaker 2: different types of tasks to help assesdo yes.
Speaker 4: I think we are an AWS consultancy, so everything tends
Speaker 4: to center around Amazon tools. But of course as you
Speaker 4: or in Google both have have AI offerings.
Speaker 5: Now as well.
Speaker 4: Or the Microsoft with their investment into open ai, are
Speaker 4: the only provider that the offer the open AI models
Speaker 4: on the public cloud, and I think that will stay
Speaker 4: the same for for a number of years. In terms
Speaker 4: of the tools that we reach to, there's definitely a
Speaker 4: combination of kind of vendor specific but also open source tools.
Speaker 4: So in terms of hosting large language models, the kind
Speaker 4: of the most frictionless way to access them is through
Speaker 4: Amazon Bedrock, really really straightforward API, easy to write scripts
Speaker 4: to interact with that either synchronous asynchronous chat however you
Speaker 4: need to. But then you can start to bring in
Speaker 4: open source tools, so things like lang chain is a
Speaker 4: really popular open source framework where you can use Bedrock,
Speaker 4: you can use Microsoft's hosted models, Google open Ai. However
Speaker 4: you need to interact with your your large language models
Speaker 4: and then bring in those other parts like retrieve augmented
Speaker 4: generation where you can say I've got a database full
Speaker 4: of full of documents these are my business documents, my
Speaker 4: sales reports, my financial reports, whatever they need to be.
Speaker 4: And then when your large language model takes your prompt,
Speaker 4: it can then use that data that you've provided without
Speaker 4: having to specifically train the model to augment the generation
Speaker 4: of its response. And there's lots of open source tools
Speaker 4: that can do things like that. I think what will
Speaker 4: be really interesting as these kind of I want to
Speaker 4: say years, but I think it's going to be months
Speaker 4: with how things are going at the moment. As the
Speaker 4: next few months go by, so many open source packages
Speaker 4: are popping up, but it's how these open source packages
Speaker 4: stay around long term.
Speaker 5: So it's.
Speaker 4: Unless they are backed by by a big business what
Speaker 4: makes them commercially sustainable. I think we've seen seen frameworks
Speaker 4: like crew ai, which is a framework for building AI
Speaker 4: agents and multiple agents and kind of orchestrating like what
Speaker 4: agent would get called for a particular type of task.
Speaker 4: They've now introduced to a commercial model where they can
Speaker 4: take on some of the management and observability around those agents,
Speaker 4: or you can just use the framework open source.
Speaker 2: So I feel like I want to ask about that.
Speaker 2: Do you you're supporting your customers in utilizing AI within
Speaker 2: their businesses. Have you seen significant change? Say over I mean,
Speaker 2: I think things are changing very frequently in a month period,
Speaker 2: so you know, compared to you know, early twenty twenty
Speaker 2: three to now, Like what's the next thing, Like what
Speaker 2: are customers now most interested in utilizing? Is it one
Speaker 2: of the particular providers more so than others. Is it
Speaker 2: just a smattering of everything or do you really see
Speaker 2: something taking off specifically in the businesses that you're working with.
Speaker 4: So I think it's it's worth worth making it kind
Speaker 4: of provide a agnostic and thinking more use case and
Speaker 4: drivers for use of AI. So thinking back twelve months,
Speaker 4: even twenty four months, there was a lot I think
Speaker 4: there was a lot more of the people wanting to
Speaker 4: use AI for the sake of using AI. So we've
Speaker 4: had conversations with customers who have said, my board member
Speaker 4: has said, as a business, we've got to be using
Speaker 4: AI because investors wanted to hear, public needs to see it.
Speaker 4: Can you help as USI It's like, well, of course,
Speaker 4: but let's let's take that step back. Let's try and
Speaker 4: work out where there is a genuine use case. I
Speaker 4: think we've been kind of getting through. If we use
Speaker 4: the gardener hype cycle as a framework, I suppose here
Speaker 4: where I think we've we've definitely passed that peak of
Speaker 4: inflated expectations, that kind of top of the top of
Speaker 4: the hype hype cycle where everyone is using AI for
Speaker 4: the sake of using AI. People want to do way
Speaker 4: more with it than is really feasible and ethical, sustainable everything,
Speaker 4: And we now I think we're starting to quite rapidly
Speaker 4: get into that point of what the hype cycle terms
Speaker 4: as the trough of disillusionment, where people are thinking, I'm
Speaker 4: seeing AI so much every service, every tool, every news article.
Speaker 4: I mean even in the in the UK today our
Speaker 4: Prime Minister came out and announced a big plan for
Speaker 4: for rolling out AI and growth programs across the country.
Speaker 4: So it's it's gone from just the those big tech
Speaker 4: providers to to government politics to everything. It's dominates everywhere,
Speaker 4: and I think it's almost sort of starting to see
Speaker 4: a little bit of fatigue in companies where it's a
Speaker 4: it is a case of every vendor is talking to
Speaker 4: us about AI or their latest AI powered offering, and
Speaker 4: the that next stage, which I don't think we're far
Speaker 4: away from now, is working out and really being visible
Speaker 4: the use cases for AI that are here to stick around.
Speaker 2: So the one yeah, no, no, I totally get it.
Speaker 2: I mean it is quite in the it's everywhere, as
Speaker 2: you said, and I'm I sort of want to ask
Speaker 2: our resident mL expert here, you know, what she's seen,
Speaker 2: you know, comparative to with what you brought up. I
Speaker 2: know she loves to talk about it.
Speaker 3: I love AI. I think I think AI is very cool.
Speaker 3: I'm still really seeing people on the upward side of
Speaker 3: the hype cycle. Like I have a small AI service
Speaker 3: that I offer. I had to stop offering it like
Speaker 3: publicly because people were just coming in with these very
Speaker 3: outsized expectations. And now I'm like, okay, we have to
Speaker 3: we have to schedule like a ten to fifteen minute
Speaker 3: talk first so that I can like, you know, adjust
Speaker 3: some of these expectations and things. But besides that, I
Speaker 3: think it's very It's great if you're using it kind
Speaker 3: of for what it's good at, and then it's terrible
Speaker 3: if you're not. Like you know, I think probably a
Speaker 3: lot of the public policy stuff might be a little
Speaker 3: bit maybe a little bit like outsized in terms of
Speaker 3: what it can do. But maybe you know, but maybe
Speaker 3: people making these kind of requests and just being like, well,
Speaker 3: shouldn't it be doing this? That's probably what's going to
Speaker 3: drive innovation forward. So I have kind of I have
Speaker 3: kind of like mixed feelings about it.
Speaker 1: I guess, well, I think that's there's like I want
Speaker 1: to drill in on that for a second, like you're
Speaker 1: having the right expectations for it. Alex and Jillian and
Speaker 1: y'are both brought that up. What are some of the
Speaker 1: good use cases where you've seen AI really make a difference, Alex.
Speaker 4: So I can talk about one one kind of specific
Speaker 4: customer use case where as part of a migration, there
Speaker 4: was we had two hundred and fifty PHP crown jobs
Speaker 4: the rebunning on a non premises server.
Speaker 5: Now, these were some of these were.
Speaker 4: Been years old, kind of dating back to of PHP
Speaker 4: early PHP five and PHP four with some of them
Speaker 4: and some of them were they were little scripts. Some
Speaker 4: of them were fifty lines, some of them were four
Speaker 4: or five hundred lines. And then you have the ones
Speaker 4: that import from different files. And we kind of took
Speaker 4: the view of, actually, is there a way we can
Speaker 4: can do something here to speed up the analysis of
Speaker 4: these scripts. There are a few things that we need
Speaker 4: to know, we need to know what databases does this
Speaker 4: script talk to? Does it interact with any other services
Speaker 4: so APIs, does it interact with things like in the
Speaker 4: case of the on premises servers, things like the send
Speaker 4: mail binary on a server, because this particular customer for
Speaker 4: the rest of their business used a managed SMTP service
Speaker 4: now and not just sending emails from on premises servers.
Speaker 4: So we did a bit of a proof of concept
Speaker 4: around well, primarily just the way to speed up our
Speaker 4: own analysis, because yeah, I mean, nobody wants to read through.
Speaker 5: Line by line.
Speaker 1: No, say, it's not so.
Speaker 2: Well, I mean, if you're looking at just as an example,
Speaker 2: you know, you said accessing particular database, right, Like if
Speaker 2: you have calls out to some on prem or third
Speaker 2: party provided data provider and you're in they're migrating to AWS,
Speaker 2: they may be going to either a no SQL option
Speaker 2: or or RDS, and so you want to make sure
Speaker 2: that those get converted, assuming you just port the scripts,
Speaker 2: you know, lift and shift into easy tubes. So I
Speaker 2: mean a question that I might have is where do
Speaker 2: you find yourself on the risk scale, Like what happens
Speaker 2: if the LM which is not going to be perfect?
Speaker 2: I like, I mean, obviously it makes up tables and
Speaker 2: whatnot that aren't being actually used. It's that's fine, but
Speaker 2: I think the false negatives would be more of a problem,
Speaker 2: Like what happens if its the table? Would that have
Speaker 2: caused an issue during the migration And how did you
Speaker 2: potentially think about mitigating those sorts of risks.
Speaker 4: Yes, so what we did was rather than kind of
Speaker 4: invest the time upfront to build a fully working solution,
Speaker 4: it was let's do a test on a few scripts first.
Speaker 4: Let's let's try over three or four scripts, scripts that
Speaker 4: we have done the analysis by hand on so we
Speaker 4: know we've got a golden answer that there is a
Speaker 4: ground truth to talk we're comparing against. When we did
Speaker 4: run it across the full data set, then we did
Speaker 4: dip sampling to make sure that a reasonable portion of
Speaker 4: them were accurate. The other thing that we did with
Speaker 4: these was when migrating those scripts made you of different environments.
Speaker 4: So these were going to cumuneties cluster in as and
Speaker 4: because we had a dead of in a staging and environment,
Speaker 4: we knew that we could run these scripts in a
Speaker 4: sandbox pre production environment. If they failed kind of so
Speaker 4: be it. It's not going to bring the business down.
Speaker 4: It's not going to send customers emails because we can
Speaker 4: we can trap emails, we can make sure that any
Speaker 4: calls outside of a particular network are monitored and blocked,
Speaker 4: so we can quite easily see actually we haven't got
Speaker 4: to just cut over straight of production. I think the
Speaker 4: point around the risk and the trust that we often
Speaker 4: put in, or people are increasingly putting in large language
Speaker 4: models is really interesting because as you start to see
Speaker 4: it used in more in more regulated industries, there's an
Speaker 4: incredible amount of have power. I suppose that large language
Speaker 4: models are being given and when we get to that
Speaker 4: point of a model being able to be the only
Speaker 4: process and the only tool in a loop, I don't know,
Speaker 4: but I don't think we're there yet. Even when you
Speaker 4: think about more traditional machine learning use cases, like one
Speaker 4: of my favorites is that the kind of flaw detection
Speaker 4: in banking, where you make a purchase on a credit card,
Speaker 4: if it doesn't look like something that you would typically do,
Speaker 4: or it's in a country that you haven't been to
Speaker 4: before anomalous amount, then models should pick that up and say, yeah,
Speaker 4: it's not a not a transaction. We're going to allow
Speaker 4: to be processed because we think it's not you. But
Speaker 4: that's been honed and refined over probably decades. Our large
Speaker 4: language models given to be exhilerated quickly enough for us
Speaker 4: to be able to make use of that level of
Speaker 4: power and that level of trust.
Speaker 2: I mean that's a good point. I mean these scripts
Speaker 2: that you were migrating, I mean, worst case scenario is
Speaker 2: they just didn't run and they didn't necessarily impact user activity,
Speaker 2: and if they crash, you get the logs and then
Speaker 2: you can go investigate. So using an OLM in this
Speaker 2: area was inherently unrisky. It just helps speed up the
Speaker 2: initial analysis. But at the end of the day, it
Speaker 2: didn't really matter, right, you know, the whole amount of work.
Speaker 2: It's like, well, human could have made a mistake there too,
Speaker 2: and it wouldn't have had that big of an implication.
Speaker 2: But how are you starting to see customers utilizing AI
Speaker 2: and A well in like ones that should be risk
Speaker 2: averse but are leaning into it more than they should
Speaker 2: And how do you even evaluate that or how do
Speaker 2: you better avoid that? And I think you did sort
Speaker 2: of lean on doing sampling and verifying the outputs, but
Speaker 2: maybe there's something holistic because I feel like with the
Speaker 2: adoption of AI coming, more and more companies will do
Speaker 2: the wrong thing right engineers will either accidentally via negligence
Speaker 2: or just you know, laziness or whatever it is. You know,
Speaker 2: get in a state where like this is a great
Speaker 2: way to absolve myself of the challenge of doing all
Speaker 2: this work. How do we counteract that?
Speaker 4: So I think with customers we're quite upfront. So I
Speaker 4: think probably different consultancies, different different companies may have an
Speaker 4: alternative view. But speaking from a YAH employed official hat,
Speaker 4: if a customer is trying to do something that doesn't
Speaker 4: make sense and we don't think there's going to be
Speaker 4: kind of significant business value in it, then we will
Speaker 4: be aware, be open about that, and make the customer
Speaker 4: aware that actually what they're doing with it isn't likely
Speaker 4: to be successful. Obviously, if there are ethical or legal
Speaker 4: concerns around what they're doing, then kind of we're a
Speaker 4: technical consultancy. We can raise concerns, make customers aware, but
Speaker 4: ultimately due diligence is in on a customer side.
Speaker 2: Is coming though, right, Like I really would like to
Speaker 2: see some concrete accountability. I mean, we don't have anything
Speaker 2: quite the level of the Night Capital disaster where they
Speaker 2: lost like I want to say, like four hundred and
Speaker 2: sixty million dollars and that didn't involve AI at all.
Speaker 2: That was just pure automation and legacy systems causing a mistake.
Speaker 2: And now we definitely have automated car companies that I
Speaker 2: believe there was an incident where there was a death
Speaker 2: in Arizona or New Mexico a lot of years ago
Speaker 2: and the company didn't get suit or anything like that. So,
Speaker 2: I mean, does seem like accountability is something that's going
Speaker 2: to come up more and more And I don't really
Speaker 2: see anyone working on adequate safeguards here. I mean there's like, oh,
Speaker 2: we're afraid of AI, but it's we're not really talking
Speaker 2: about the companies that are utilizing it.
Speaker 4: I feel like, So, I think there's there's two parts.
Speaker 4: There's there's accountability and there's explainability as well. So, again
Speaker 4: thinking more about about traditional machine learning, some some algorithms
Speaker 4: are significantly more explainable than others. So there are a
Speaker 4: lot of algorithms and increasingly so as we get into
Speaker 4: the genuine to AI and in large language model space
Speaker 4: where there are a bit of a black box they
Speaker 4: have loaded to go in and it's very hard to
Speaker 4: explain why a particular result has come out. And again,
Speaker 4: models aren't deterministic, so you can do as much testing
Speaker 4: as you want, but you can never be truly one
Speaker 4: hundred percent certain. You can be very very confident that
Speaker 4: if anybody can give one hundred percent guarantee, then that
Speaker 4: it probably isn't machine learning. That's that's making the final call.
Speaker 2: But you can ask the model if it's correct.
Speaker 3: Rights, it's right, It's obviously right. Haven't you ever help
Speaker 3: the kid with their math homework before? Same thing?
Speaker 4: There's some interesting stuff that the AWS are working on,
Speaker 4: So for a while now they've had bedrock guardrails, which
Speaker 4: is there to try and prevent a large language model
Speaker 4: from responding about certain topics. But of course you have
Speaker 4: to give it a you have to give it a
Speaker 4: list to start with our topics to not talk about.
Speaker 4: And if you haven't thought of the topic, then yeah, again,
Speaker 4: you are you reliant upon a human to to have
Speaker 4: that extensive list before you can prevent the model. Another
Speaker 4: one that's come out very very recently, and it's the
Speaker 4: last within the last month or so, is AWS make
Speaker 4: use of automated reasoning in all places across across their cloud.
Speaker 4: So when it comes to kind of cryptographic operations and
Speaker 4: making sure that encryption is doing what it should be
Speaker 4: doing and not making sure there's mathematical proof behind some
Speaker 4: of these operations, that's something that yeah, today is being
Speaker 4: used across all of AWS, but only very recently has
Speaker 4: now come to Bedrock, and I think the feature is
Speaker 4: called Bedrock Automated Reasoning, where you can build rules up
Speaker 4: to say I want mathematical proof the response given is
Speaker 4: in accordance with these rules, which is quite cool and
Speaker 4: I'm yet to play with that, but it looks very promising.
Speaker 4: And AWS generally fairly good on the research and that
Speaker 4: side of things. They're certainly not perfect in terms of
Speaker 4: some of the decisions I think around releasing services that
Speaker 4: I maybe like just the wrong side of MVP we
Speaker 4: have seen a few times, but that is I think
Speaker 4: a side effect of being customer obsessed.
Speaker 1: In the.
Speaker 4: Perhaps as a a customer that needs a particular set
Speaker 4: of features and the easiest way is to build a
Speaker 4: service for it. But maybe it's not suitable for every
Speaker 4: use case and every customer just yet. But yeah, I
Speaker 4: think there's some really interesting stuff going on around automated
Speaker 4: reasoning around for aws's first party model, so the Amazon
Speaker 4: Nova family of models, they've got the AI service cards,
Speaker 4: which go into quite a lot of detail about how
Speaker 4: the models are built and are fairly transparent around the
Speaker 4: way they work. I think that's that's something that end
Speaker 4: users of model needs to be need to be more
Speaker 4: aware of. I think at the moment, it's very very
Speaker 4: easy to log onto to kind of or chat dot Com.
Speaker 4: I think is the domain that open ai spent quite
Speaker 4: a lot of money on and yeah, type something, But
Speaker 4: you don't know where that data has come from to
Speaker 4: build that response. You don't know the reasoning behind that
Speaker 4: response being given. I think as as these systems get
Speaker 4: more and more embedded in people's processes and workflows in business,
Speaker 4: it's needing to understand the why and the ask the
Speaker 4: question of well, ask the questions and have the ability
Speaker 4: to give a because this has happened because the model
Speaker 4: is doing this.
Speaker 1: I don't know.
Speaker 2: I worry that if we're relying on education to make
Speaker 2: people be able to utilize our AI and technology better,
Speaker 2: I feel like we're not going in the right direction.
Speaker 2: Like I say this because very frequently in the security domain,
Speaker 2: we have the same sort of mantra, whereas you are
Speaker 2: going to stop security events, prevent attackers, remove vulnerabilities through
Speaker 2: straight education. I mean, there's only so much that you
Speaker 2: can achieve there. And if your your security strategy is
Speaker 2: educate the users, I mean you might as well have
Speaker 2: given up already if that's your And I worry if
Speaker 2: that's the direction that we're going. Where. Oh, in order
Speaker 2: to use the models effectively, in order to understand what's
Speaker 2: going on, you have to be an expert still, which
Speaker 2: I feel like has been the case for a lot
Speaker 2: of years now, going back twenty thirty years in our
Speaker 2: mL development, it's always sort of been the case. And
Speaker 2: until we can break through that, I feel like, you know,
Speaker 2: real adoption really isn't going to amount to good things.
Speaker 2: And we're really close to utilizing it in more and
Speaker 2: more concerning situations where security is involved, or human safety
Speaker 2: is involved, or whatever Gillian is doing with automatic creation
Speaker 2: of you know, protein folding. You know, I honestly can't remember,
Speaker 2: but I'm curious that.
Speaker 3: The human in the loop like that. If we're doom spiraling,
Speaker 3: it's degree oh man, why is it echoing what happens?
Speaker 3: What did I do okay, you're from a few minutes
Speaker 3: here while I figure out what happened to my camera.
Speaker 2: Well, Gillian's saying is she is worried that she's but.
Speaker 3: I'm the greed. I could very well be the greed
Speaker 3: in this situation. It could happen.
Speaker 4: I think there's there's some really interesting and it's some
Speaker 4: really interesting ethical bits as well around it. So the
Speaker 4: topic of kind of self driving cars has come up
Speaker 4: a few times as we've been talking, and the things
Speaker 4: that as a as a human driving a car, you
Speaker 4: might instinctively make certain decisions. So if you've got a
Speaker 4: self driving car and there is a a certain tl
Speaker 4: a crash that one scenario means five people, one scenario
Speaker 4: means two people, but they are young people, then.
Speaker 5: It's like.
Speaker 4: What does the car choose? But at that point there's
Speaker 4: no there's no human emotion, there's no nothing in it.
Speaker 4: It's a someone has to program a model to say
Speaker 4: this life is worth more than this life and how
Speaker 4: do you do that?
Speaker 2: Right, So there was actually an interesting and so this
Speaker 2: is the trolley problem, right, and it's sort of the
Speaker 2: moral dilemma more than an ethical one. It was actually released,
Speaker 2: I think it was a quick study by Stanford where
Speaker 2: it like a gauged random sampling of people of which
Speaker 2: they would pick like where where should the car actually
Speaker 2: go to? And I think there was like at the
Speaker 2: end of the study there was like a clear hierarchy
Speaker 2: of what humans actually prefer. And I feel like it
Speaker 2: was something like you should kill cats first, and then
Speaker 2: old men, and then old women and then and then
Speaker 2: dogs or something like that. And I think it had
Speaker 2: to do with the fact that cats will just get
Speaker 2: out of the way, Like that was the expectation that
Speaker 2: these people will either get out of the way or
Speaker 2: you know, in worst case scenario, this is what they
Speaker 2: would pay. And it was really interesting that that they
Speaker 2: had done this, and there was of course some you know,
Speaker 2: not nice things said about the fact they had gone
Speaker 2: through with the study, But you know, I think humans
Speaker 2: will sort of adapt to preferential picks for what they
Speaker 2: are okay with.
Speaker 5: The thing is.
Speaker 4: Industry is it's going to be optimal. I often see
Speaker 4: people for saying is going to have as much on impact,
Speaker 4: that's que and it is having that much of an impact.
Speaker 4: Then with cloud that there weren't really that many drawbacks
Speaker 4: that I can think of. There there was there was concerns,
Speaker 4: there were there uncertainty around who owns my data, who
Speaker 4: controls my data? Is my data secure if it's in
Speaker 4: Amazon's data center versus a server in my own cupboard.
Speaker 4: But with generative AI, there's there's almost that kind of
Speaker 4: rabbit in the headlights approach that a lot of people
Speaker 4: are taking at the moment where I think there is
Speaker 4: a real, a real danger without as we talked about,
Speaker 4: without the control, without either education or forced guard rails
Speaker 4: to be able to use this effectively. I mean I
Speaker 4: remember seeing seeing an article I think it was tail
Speaker 4: end of last year, and it was being talked about
Speaker 4: in the Yes and there were talks of developed models
Speaker 4: being legally required to report on things like whether their
Speaker 4: models could be used for purposes that would have a
Speaker 4: national security implication, which I think is absolutely absolutely right
Speaker 4: from a again from a moral and security perspective. But there's
Speaker 4: always that kind of fine balance of any emerging technology,
Speaker 4: how far do you regulate it? If you regulate it
Speaker 4: too much, does it stifle or prevent innovation? But then
Speaker 4: the flip side is if something terrible happens because somebody
Speaker 4: has used a large language model to teach themselves how
Speaker 4: to carry out an attack. Then the argument is where
Speaker 4: there wasn't enough regulation.
Speaker 1: I think, going back to a self driving car example,
Speaker 1: the solution there is just go into the settings of
Speaker 1: the car and you get a little order of preference,
Speaker 1: like I'm okay with hitting this, I'm not okay with
Speaker 1: hitting this, and then problem solved.
Speaker 5: Right, It's it's so tough, it's.
Speaker 3: I mean, we choke, But like this is what we
Speaker 3: unfortunately have to do is humans sometimes, Like that's how
Speaker 3: all of healthcare works, right, Like when there's a shortage
Speaker 3: like during COVID there was the shortage of the ventilators.
Speaker 3: You think the doctors didn't have to like make decisions
Speaker 3: Like it's very unpleasant and nobody wants to think about
Speaker 3: them or talk about them. But the reality is we
Speaker 3: do anyways, and it has to happen. And I'd imagine
Speaker 3: that it also has to be a part of like
Speaker 3: self driving cars, because cars are terrible, terrible death machines.
Speaker 3: I hate driving. If I mentioned the show yet, how
Speaker 3: much I hate driving and having to have a car.
Speaker 2: I think I think it's because we're in this intermediary state,
Speaker 2: because once we get to the point where there are
Speaker 2: there's automation all around us, and we have adapted to
Speaker 2: that fact. It's no longer as big of a problem
Speaker 2: because it's whose fault is that if you step in
Speaker 2: front of a train. It's like, oh, well, the train
Speaker 2: was supposed to stop. It was you know, supposed to know.
Speaker 2: And some of them do have safety protections in place,
Speaker 2: but realistically, you don't go on the tracks when the
Speaker 2: train is coming unless you have some reason you really
Speaker 2: want to be there. And I think realistically the same
Speaker 2: thing will happen if we're in the automated car space
Speaker 2: where the AI self autonomous cars are driving around and realistically,
Speaker 2: you know, don't step into the street. I mean, why
Speaker 2: would you go there? And I think with the the
Speaker 2: AI cars, we will need want to really redesign egress
Speaker 2: and flow for traffic and we will be able to
Speaker 2: do that effectively once everything has been automated.
Speaker 4: I think that's there's a really a really interesting change
Speaker 4: coming in terms of I guess similar to what we
Speaker 4: we would have seen with are okay and kind of
Speaker 4: precesses being automated is anyway they are going to have
Speaker 4: a bey are going to step change improot in efficiency.
Speaker 4: There is there going to be a resistance from within
Speaker 4: thenesses to work on these generative because they think it's
Speaker 4: going to put themselves.
Speaker 2: Out of the job.
Speaker 5: I think it's it's a it's.
Speaker 4: A really challenging space to try and try and tread
Speaker 4: the line of improving efficiency, making redundancies. I think going
Speaker 4: back many, many years and you think about things like
Speaker 4: the Industrial Revolution, and inevitably change is mean that some
Speaker 4: jobs are no longer needed. But those people that they
Speaker 4: find different roles. And if this kind of continue at
Speaker 4: the pace that they're going and disrupts industry at the
Speaker 4: pace it's predicted to, then people need to kind of
Speaker 4: change with it because yeah, very quickly you are you're
Speaker 4: already seeing not adverse to have experience in AI as essential.
Speaker 4: It's no longer a bonus and it's a you must
Speaker 4: be able to work with copy tools and effectively know
Speaker 4: how to say AI and do evopts or migrations or
Speaker 4: anything like that, because come these expecting then then expected.
Speaker 1: It's a really interesting change to see how the job
Speaker 1: landscape has changed a sort of the last twelve months
Speaker 1: of the Internet action of AI. You know, it felt
Speaker 1: like twelve months ago. Using Copilot and tools like that
Speaker 1: was seen as cheating, but now it's just seen as
Speaker 1: like a part of the job, and I'm interested to
Speaker 1: hear how it's being like, how are how are people
Speaker 1: testing or qualifying your your skills for that in the
Speaker 1: employment space.
Speaker 5: I think I can see you to use of tools.
Speaker 4: I want to use them. They're not fine and with
Speaker 4: it comes with the changes.
Speaker 5: If something to do and.
Speaker 4: That's musting field founding, then I don't think AI come
Speaker 4: into because if you're using Jude but don't understand exted,
Speaker 4: you the terms that buildings square or booking solutions that
Speaker 4: are going work, but there's also a pretty good chance
Speaker 4: that they're not, or you've left a security hold in it,
Speaker 4: or it's going to cost ten times as much because
Speaker 4: there's there's one best practice that you've you've not realized
Speaker 4: because these models were trained on open code. So from
Speaker 4: from my perspective, I mean I I've tried a few
Speaker 4: different co pilot tools to get how co pilot came
Speaker 4: came kind of fairly early doors given Amazon q Ago
Speaker 4: as well. At the moment, I'm trying out the windsurf
Speaker 4: editor and Cursor as a sort of more integrated i
Speaker 4: D experience, and both of them are fairly good. There's
Speaker 4: some really cool stuff being able to like if you
Speaker 4: want to just hack about on a project. I do
Speaker 4: quite a lot with the stream lit Python package, which
Speaker 4: is a great way to build or data and AI
Speaker 4: apps with an acceptable user interface without having to know
Speaker 4: how to write good front end code and being able
Speaker 4: to just say, like creating a project using stream Lit.
Speaker 4: It needs to be able to interact with Amazon Bedrock
Speaker 4: like stub out the methods for me and get something
Speaker 4: very quickly. It is good for that. I think the
Speaker 4: only way that these tools will excel will really be
Speaker 4: with true understanding and context of on what would be
Speaker 4: in a human brain. It's the getting into the flow
Speaker 4: state of I understand this whole repository, I see how
Speaker 4: different pieces kind of connect together. But also if you've
Speaker 4: got micro service architectures and actually the context you need
Speaker 4: is in a different repository, then it also kind of
Speaker 4: needs that as well. So of course there are there
Speaker 4: are limitations.
Speaker 5: There are.
Speaker 4: There's only so far these things will go. But from
Speaker 4: I think, from my stance, if somebody kind of turned
Speaker 4: up at an interview and they wanted to use an
Speaker 4: AI tool as part of a tech exercise, for example,
Speaker 4: then it wouldn't necessarily be a it would be hypocritical
Speaker 4: of me right to say this is a thing. But
Speaker 4: I think I'd be more I'd certainly be more aware
Speaker 4: and more yeah, be a bit more careful about asking
Speaker 4: the right questions about the code that has been generated, because.
Speaker 2: I think that's sort of like one of the really
Speaker 2: important parts here is that most companies don't spend enough
Speaker 2: time evaluating their interview process for what the right questions are,
Speaker 2: and now they're starting to realize that AI is getting
Speaker 2: in the way of them quote unquote effectively evaluating the candidates.
Speaker 2: And I think that really goes to the fact that
Speaker 2: the question didn't make sense and their evaluation strategy didn't
Speaker 2: make sense, and that there are tools that can easily
Speaker 2: solve that, and if they can solve it during an
Speaker 2: interview or during a take on interview test, then they
Speaker 2: could potentially or likely using that tool during their job.
Speaker 2: And I think we're already starting to see some companies
Speaker 2: intentionally telling candidates to use AI LM specifically to solve
Speaker 2: the problem because it is something that other engineers on
Speaker 2: their team are utilizing and that they would expect someone
Speaker 2: who comes into the team to also understand and how
Speaker 2: to utilize those tools, because things like configuration or linting, etc.
Speaker 2: Will be changed fundamentally by AI. And through that way,
Speaker 2: if someone who comes into your team that you're hiring
Speaker 2: into it doesn't have experience utilizing LMS to help them
Speaker 2: sufficiently or handling their weaknesses whatever they are, then they're
Speaker 2: going to not be as an effective team member when
Speaker 2: the rest of the team is expecting someone who is
Speaker 2: able to do that.
Speaker 4: I mean absolutely, and I think with with how much
Speaker 4: of the industry and how much of businesses genuinely there
Speaker 4: has the potential and promise to reach there's almost prerequisites
Speaker 4: that a lot of people are aren't really thinking about
Speaker 4: at the moment because they get they're going to get
Speaker 4: blindsided by the AI is great. AI is going to
Speaker 4: solve all the problems, whether in reality there are there
Speaker 4: are certain things that are foundational who successful use of AI,
Speaker 4: like okay, AI is software. How do you monitor your software?
Speaker 4: How do you make sure the responses from your AI
Speaker 4: powered applications are what you expect them to. If you
Speaker 4: were deploying an API, you'd spend time in thinking about okay,
Speaker 4: what are the useful metrics. Is CPU a useful metric?
Speaker 4: Or is latency a useful metrics? What are the things
Speaker 4: that actually have an impact on the end use of
Speaker 4: this and AI should be no different. You should have
Speaker 4: that kind of production wrapper, you should have monitoring, you
Speaker 4: should have security concerns that you're proactively protecting against. But
Speaker 4: then also it's that it's that foundation of data. It's
Speaker 4: the if you're an organization that has lots of data
Speaker 4: and lots of different places and you want to use
Speaker 4: it in AI, you need to have a good data platform.
Speaker 4: And you have conversations with people around productionizing some of
Speaker 4: the AI kind of proof of concepts or very early
Speaker 4: stage experiments they've done, and you will say, okay, well
Speaker 4: you've you've managed to get these little samples of data
Speaker 4: from various data stores to prove of this as a
Speaker 4: as a concept could work and is worth putting into
Speaker 4: production at a wider scale.
Speaker 2: So one of the problems is that with the pricing
Speaker 2: with a lot of the models today, if you're not
Speaker 2: running it yourself, you're pretty much paying for input and
Speaker 2: output tokens the amount of context you're adding, which you
Speaker 2: need a very high context to get an adequate answer. Event,
Speaker 2: and then for whatever reason, these companies are charging you
Speaker 2: for garbage nonsense coming out of the models. Their decoding
Speaker 2: process to get back at readable answer has a lot
Speaker 2: of nonsense in it, and then you're paying for that.
Speaker 2: So I think the industry is being driven towards trying
Speaker 2: to optimize for these two things, which have nothing to
Speaker 2: do with the quality of the answer in the first place.
Speaker 2: And I know we'll ask the question about, you know,
Speaker 2: bringing AI into the interview process, and I feel like,
Speaker 2: you know, will You're now in a great position for
Speaker 2: me to ask you, do you feel like your interview
Speaker 2: process has been changing to respond to the increased usage
Speaker 2: both in the workplace as well as candidates using potentially
Speaker 2: using AI during the interview process itself.
Speaker 1: For me, no, because my interview process is probably a
Speaker 1: lot more old school than most people. If I'm interviewing
Speaker 1: a candidate, we're going to have a straight up bullshit
Speaker 1: session because I work largely around infrastructure and just through
Speaker 1: a casual conversation, I feel like I can get a
Speaker 1: lot better feel of whether you know what you're talking about,
Speaker 1: or whether you have heard the terms but don't really
Speaker 1: understand what they mean. So very a very small amount
Speaker 1: of my interview process has anything to do with like
Speaker 1: hands on the keyboard, technical coding.
Speaker 2: Do you have some part of it that's like any
Speaker 2: sort of technical validation or technical systems design or anything
Speaker 2: like that which could be impacted by AI at all?
Speaker 1: Potentially, Yeah, because we'll do like an exercise of you know,
Speaker 1: throw together a couple of micro services and explain to
Speaker 1: me the interaction between them. But then I'll spend a
Speaker 1: lot of time just talking about that, like, well, how
Speaker 1: does this part work, how does that part work? Tell
Speaker 1: me what happens if this does that? And I dig
Speaker 1: into a lot more of the operational stuff. I think,
Speaker 1: and if a candidate could pregame all of that and
Speaker 1: use AI, good for them. I just think it's unlikely
Speaker 1: given the dynamic nature of my interview process, So.
Speaker 2: I don't want to spoil it. But there is a
Speaker 2: product out there where in a remote interview, the candidate
Speaker 2: will run it and it will listen to the audio
Speaker 2: that's coming across and watch the chat and then dynamically
Speaker 2: generate text response for the candidate to answer on the call. Now,
Speaker 2: I do think that there is a challenge here of
Speaker 2: being able to adequately understand what words are important in
Speaker 2: a sentence. If you have a thought and you're sharing
Speaker 2: that thought, you know what the point of that thought
Speaker 2: is and which nouns are more important than others. But
Speaker 2: if you're reading a response from something else, you might
Speaker 2: as well all say it all monotone because there isn't
Speaker 2: any part of that that it makes sense upfront to you.
Speaker 2: You almost need to read it first and then answer back.
Speaker 2: But that exists. So unless you're bringing people into the office,
Speaker 2: and obviously we want to optimize for more remote working environments.
Speaker 2: You know, our our company, UH is one hundred percent remote.
Speaker 2: I know a part of yours is will I don't
Speaker 2: want to sweare to that, but you do have different.
Speaker 1: Yeah, we're one hundred percent remote as well.
Speaker 2: Yeah, So I mean there's only so much you can
Speaker 2: do there. I mean, you're not gonna meet in person
Speaker 2: every single candidate at a coffee shop or something and
Speaker 2: go through uh sort of validation that they're not doing that.
Speaker 2: I mean, you have to use your other skills to
Speaker 2: sort of figure out whether or not there are you know,
Speaker 2: they believe what they're saying.
Speaker 1: Yeah, and I think I would in that in that scenario,
Speaker 1: I just rely on like our our sixty day window,
Speaker 1: like every candidate would bring on has like a sixty
Speaker 1: day trial period and if if the expectations didn't line up,
Speaker 1: you know, we have sixty days to resolve that. And
Speaker 1: if not, cut ties.
Speaker 2: Are you at least for us, We're really transparent about that. Like,
Speaker 2: if you're cheating during the interview process, that hurts you
Speaker 2: because we're just going to fire you a couple of
Speaker 2: cup date months later. Is that what you want? I mean,
Speaker 2: you're risking it by coming and joining us, just like
Speaker 2: we're risking it on you, and we have people have
Speaker 2: the capability of ending that relationship. So you know, if
Speaker 2: you managed to get through our interview process faking every
Speaker 2: step of the way, and then you also managed to
Speaker 2: fake the next couple of years successfully, I mean I
Speaker 2: actually think that was a pretty good hire.
Speaker 1: Yeah, agreed. Like, if if you're faking it, because this
Speaker 1: is really where you want to be and I pick
Speaker 1: up on that, I'll do everything I can to help
Speaker 1: you get there because that's how I got here. I
Speaker 1: lied through my teeth on job interviews.
Speaker 3: Oh I got here too. I would just like, hey,
Speaker 3: I had a baby, that baby needed some food and
Speaker 3: I was like, all right, I don't know what we're
Speaker 3: talking about, but I have Google. Let's go figure this out.
Speaker 1: Yeah, I mean I read all five hundred pages of
Speaker 1: the Microsoft SEQL Server six book because that was the
Speaker 1: job I wanted.
Speaker 4: This is where we can almost flip it on his
Speaker 4: head a little bit, because hey, I can post interview
Speaker 4: then play advantageous because if your interview process is cultural
Speaker 4: and it is assessing primarily person fit to a business
Speaker 4: and their ability with how they learn, how they interact
Speaker 4: other people, then if you can make a great hire
Speaker 4: that has the ability to very quickly learn land on
Speaker 4: their feet, is a great team player, then AI is
Speaker 4: a great tool to be able to assist in their
Speaker 4: upskilling or things they don't know technically once they have
Speaker 4: got in the door. So it's there's two sides to
Speaker 4: I suppose.
Speaker 1: Ah, that's a really cool idea. I hadn't thought about
Speaker 1: that before. So instead of yeah, so you just kind
Speaker 1: of flip it around and say, hey, AI, here's this dude,
Speaker 1: where should I be helping them?
Speaker 2: I think the part of the trouble with that is
Speaker 2: you would need to have only verbal the most part
Speaker 2: interaction with the candidate during the interview and have to
Speaker 2: go through the process of like, Okay, you know, we
Speaker 2: want to record this session so that you know, later
Speaker 2: we can feed it back through and make it available.
Speaker 2: And I know that you know, there's no reason why
Speaker 2: this should be a problem, but you know, every single
Speaker 2: additional obstacle you add, there is another risk for potentially
Speaker 2: losing out on the candidate. I feel like, you know, hey,
Speaker 2: can we record this session and have this as recordings
Speaker 2: so we can share with the rest of the team.
Speaker 2: It's just another one of those things. So I mean,
Speaker 2: if you're getting the value out, you know, great. I
Speaker 2: think that's where there's a I would love to see
Speaker 2: the tool that actually helps there.
Speaker 5: I'm thinking of it from a from an individual.
Speaker 4: If you are hired for a role and you're hiring
Speaker 4: someone that is like ninety percent of the way that
Speaker 4: but they are one hundred and twenty percent in terms
Speaker 4: of their ability to learn, you know that they will
Speaker 4: know the stuff given the opportunity to learn it, because
Speaker 4: they've demonstrated they've they've picked up on these technologies in
Speaker 4: every job they've changed, They've come in relatively fresh. That's
Speaker 4: where they as an individual are able to potentially use
Speaker 4: AI to augment their learning, whether it is through co
Speaker 4: pilot type tools, whether it is chat, GPT, those kind
Speaker 4: of things, where previously you would reach for stuck overflow, which,
Speaker 4: again looking back on it, you think, well, yeah, stuck
Speaker 4: overflows great because you would get loads of answers to things,
Speaker 4: but you would still have to understand the answer because
Speaker 4: you don't know who that person is that's written that answer,
Speaker 4: much like you have no guarantee of what the model
Speaker 4: is is outputting from its response, you can rely upon, well,
Speaker 4: you can take indication from the amount of kind of
Speaker 4: crowdsourced endorsement I suppose of a particular answer, And that's
Speaker 4: I guess where responsibility maybe goes more onto model providers
Speaker 4: to actually say are the answers you are providing accurate?
Speaker 4: Maybe large language models are just too too general for something.
Speaker 4: Maybe this is where the the more niche models that
Speaker 4: are specifically focused on Python coding, for example, are better
Speaker 4: fit because they have been trained on vetted best practice
Speaker 4: Python code. Yeah, so many angles that you can explore
Speaker 4: with this. I feel like we could talk for hours.
Speaker 1: One of the things I'm doing, we're doing our annual
Speaker 1: performance reviews, and it consists of each employee gets a
Speaker 1: pure review, they do a self review, and then I
Speaker 1: write their review one of the things I've been doing.
Speaker 1: It's taking a huge amount of time, but I feel
Speaker 1: like it's still worth it is I'm giving AI the
Speaker 1: pure reviews and their self review and my review, along
Speaker 1: with the responsibilities for their current level and the next level,
Speaker 1: and then asking AI, how can I help this person
Speaker 1: better meet the expectations of their current role and meet
Speaker 1: and and start growing towards their next level within the company.
Speaker 1: And it's it's been insightful because it's picking up on
Speaker 1: things that I'd overlooked when reading through the reviews myself.
Speaker 4: I think that's a great, great use case. But the
Speaker 4: key bit in that is it's grinded by human effort
Speaker 4: that somebody has put into start with. It's grounded by
Speaker 4: truth and actual knowledge. If you took I mean, if
Speaker 4: you took the the part where you write your review
Speaker 4: of that person away and said, Okay, well, they've written
Speaker 4: a self review, they've had a peer review from somebody else.
Speaker 4: Now let's use AI to write the employer review. Make
Speaker 4: it this tone of voice, here's the metrics about this person.
Speaker 4: How many lines of code they've written, this year, those
Speaker 4: kinds of things where you could quite easily use AI
Speaker 4: for that. But that's where it then turns into the
Speaker 4: this is a little bit.
Speaker 1: Too far, Yeah, for sure, because at that point I personally,
Speaker 1: I would feel like, well, I'm not really adding any
Speaker 1: value here. Their review came from AI. At that point,
Speaker 1: I feel like I've still got to put some skin
Speaker 1: in the game and do my job to help them.
Speaker 3: Well, I think with that said, like a lot of
Speaker 3: industries are going to be creating verification processes that are
Speaker 3: specific to the problem. So this whole idea that the
Speaker 3: AI is going to be running amuck, it's like, well, no,
Speaker 3: we don't really do that. That's not like how things
Speaker 3: in the real world works. So, for example, in biotech,
Speaker 3: I think there's going to be a ton of AI
Speaker 3: generated drugs, but they still have to go through the
Speaker 3: same verification process as all the other drugs, which take years.
Speaker 3: You have to be able to create it, Like just
Speaker 3: just being able to create the thing, just because the
Speaker 3: computer says that it's a valid like it's a valid
Speaker 3: drug and all that doesn't doesn't mean that it is.
Speaker 3: It still has to go through clinical trials and still
Speaker 3: has to go through peer review, and I feel like
Speaker 3: every industry is going to have some it has something similar, right,
Speaker 3: Like I don't know, so I don't I don't worry
Speaker 3: about that one quite as much, except I worry a
Speaker 3: lot about greed and the loop, Like that's what that's
Speaker 3: the one. That's the one that I worry about. Like, oh,
Speaker 3: look now, now we can make all these biosimilars to
Speaker 3: you know, this drug but this patent expired for and
Speaker 3: we can just be pumping these out and then, like
Speaker 3: you know, if there's not enough like regulation and things,
Speaker 3: or if somebody can to get pushed through any of
Speaker 3: these any of these kind of verification steps, then I
Speaker 3: could see that going very, very sideways. So I'm just
Speaker 3: gonna hope that doesn't happen, and if it does, I'm
Speaker 3: going to move off to the woods and there's going
Speaker 3: to be no more computers in my life.
Speaker 2: And that's going to be I think you hit on
Speaker 2: something that's quite ingenious here, actually, Jillian. If you just
Speaker 2: go through previous patents for drugs and then you ask
Speaker 2: an LLM to generate a new drug that has the
Speaker 2: same bonding you know, activation sites, the same you know,
Speaker 2: interactions with other molecules, but is fundamentally different enough that
Speaker 2: it could be classified as a new drug that could
Speaker 2: be patented. Then these companies will start losing a lot
Speaker 2: of money because they won't their patents won't mean a
Speaker 2: lot anymore, and will have a lot cheaper medication in
Speaker 2: the world.
Speaker 3: That's the hope. That's not actually how things have been going.
Speaker 3: I mean, like, I really, I really appreciate your optimism there,
Speaker 3: but I'm not I don't know, I'm not sure. Like
Speaker 3: if you look at like biologics, right, like biologics are
Speaker 3: probably I think are like one of the biggest medical
Speaker 3: innovations you know, in like decades, and they're so expensive.
Speaker 3: They cost like I don't know, I think Humerica costs
Speaker 3: like a couple grand a month without insurance or something
Speaker 3: like that. So then yeah, hopefully we do get this
Speaker 3: next wave where we're creating all the biosimilar drugs and
Speaker 3: so on and so forth. But I mean, when when
Speaker 3: the biologics first came out, they had their patents and
Speaker 3: they had an absolute lock on the market. Legally speaking,
Speaker 3: you could not create a drug that was you know,
Speaker 3: slightly similar. They're called biosimilar as you couldn't do that
Speaker 3: because there's legal red tape and stuff. But yeah, I hope,
Speaker 3: So that would be great if you know, if like
Speaker 3: just producing these drugs got cheap enough that the patents
Speaker 3: were no longer even worth it, then that would be
Speaker 3: like a pretty huge disruptor to the medical industry.
Speaker 2: I mean, they could make it much worse because the
Speaker 2: company that produced the drug initially, if they had used
Speaker 2: LLLMS anywhere in their process, technically they can't patent it
Speaker 2: in the first place. So I think we're very close
Speaker 2: to the point where, uh, there will not be any
Speaker 2: patentable artifacts that exist in the fundamental laws are fundamentally changed.
Speaker 3: I think we're already there though, like with the and
Speaker 3: patents are still around. So I think it's less about
Speaker 3: the the computery stuff, you know. So like I work
Speaker 3: with a lot of companies and they're like, oh, can
Speaker 3: I patent this process, like the software process, And it's like, well, no,
Speaker 3: you shouldn't even really bother with that. Go patent the
Speaker 3: process that you use in the lab to actually create
Speaker 3: the drugs, And so that's where everybody's at. The actual
Speaker 3: like data generation is is like a throwaway kind of yeah,
Speaker 3: you know, like yeah, it's just it's just throw away
Speaker 3: because a lot of that has to be open anyways,
Speaker 3: like the data generation that you used to actually get
Speaker 3: to your drug, because it has to it has to
Speaker 3: be like peer reviewed and all that kind of thing.
Speaker 3: But everything that goes on in the lab can still
Speaker 3: be a lot more I don't know, I can still
Speaker 3: have a patent, see. And this is why greed in
Speaker 3: the loop is such a problem because then there's always
Speaker 3: like there's always ways around the things, and then people
Speaker 3: want to be making money, which I get.
Speaker 2: I feel like that's that's its own episode in itself.
Speaker 2: I think greed and software and tech companies and yeah.
Speaker 3: I could a little bit depressing though it's not like
Speaker 3: a fun topic.
Speaker 5: Okay.
Speaker 2: So Jillian's like, I have tons of optimistic topics that
Speaker 2: we should talk about. Let's pick one of those, especially
Speaker 2: regarding any sort of AI or mL Okay, I'm all
Speaker 2: for it.
Speaker 3: Yeah, let's just talk about the cool stuff and not
Speaker 3: talk about, you know, potentially people flooding the market with
Speaker 3: crazy patents and then nobody getting their drugs is how
Speaker 3: that works.
Speaker 1: So, speaking of cool topics, Alex, you work with a
Speaker 1: lot of companies implementing AI into their business processes. A
Speaker 1: lot of our listeners are in the devlops field or
Speaker 1: deal with software engineering and infrastructure. What are the key
Speaker 1: pieces of AD you would have for them to continue
Speaker 1: their career and be ready for the next evolution.
Speaker 5: That's a great question.
Speaker 4: I think it's about embracing it, but also being super
Speaker 4: critical of the solutions and the tools are available. So
Speaker 4: it's very, very easy to feel overwhelmed. I think by
Speaker 4: the number of AI solutions that are.
Speaker 5: Available in any space.
Speaker 4: I think in DevOps, particularly if we're including kind of
Speaker 4: the developer side of that tools in there, we're just
Speaker 4: going to see more and more come out.
Speaker 5: I mean, there's.
Speaker 4: A company I came across who their niche is co
Speaker 4: pilot tools, but they only offer models trained on your
Speaker 4: company's data, so they haven't got like a public offering.
Speaker 4: They're aiming at an enterprise. So the idea is they
Speaker 4: they trade a model based on your organization's code bases,
Speaker 4: and that is your private copilot. So I think there's
Speaker 4: there's lots and lots to come in this area operationally.
Speaker 4: I think the whole I guess principle of DevOps is
Speaker 4: trying to break down that a sort of metaphorical wall
Speaker 4: between the two sides and empowered developers to the operational tasks.
Speaker 4: I think we're seeing quite a lot come out around
Speaker 4: explaining operational events using generity they are and that kind
Speaker 4: of almost like trace analysis of well, this happened. We've
Speaker 4: got ten different data points here, and how do we
Speaker 4: correlate them, How do we say this happened because this happened,
Speaker 4: and this happened, this happened, and the chain reaction was this.
Speaker 5: Being able to.
Speaker 4: Even as simple as put those things together in a
Speaker 4: some sort of structured data and.
Speaker 5: Then using a large language model.
Speaker 4: To summarize it into a black message to say this
Speaker 4: has failed. This is why I think the one the
Speaker 4: one piece of advice I'd give people if they're looking
Speaker 4: to to start experimenting with AI, is like, so solve
Speaker 4: your own problems, find things in your processes, in your
Speaker 4: workflows that take the most time, and use AI almost
Speaker 4: as like like your shadow. I suppose it would be
Speaker 4: a good way to describe it. So if you haven't
Speaker 4: got confidence in it straightway, tell it to the same
Speaker 4: things that you would do, but build it so that it
Speaker 4: does it as a dry run. Make sure it is
Speaker 4: going to execute the same steps right on.
Speaker 1: Yeah, right, very cool, And that feels like a good
Speaker 1: segue into some picks.
Speaker 5: Do you think some picks so.
Speaker 4: I would say, I would say, do your picks have
Speaker 4: to be physical or can there be be software?
Speaker 1: Anything?
Speaker 4: Anything goes, anything goes okay. So I'm going to go
Speaker 4: for some that are some that are related, some of
Speaker 4: that are are not. So my my first one is
Speaker 4: AWS have a free to public, generally said I experimentation
Speaker 4: website called party Rock. This was born from a it's
Speaker 4: actually an AWS engineer that builds it internally as a
Speaker 4: way to experiment with large language models, and then it
Speaker 4: got adopted by AWS as an organization. So if you
Speaker 4: go to I think you are at a party rock
Speaker 4: dot AWS. There's no credit cards to anything required.
Speaker 5: Go on.
Speaker 4: You get a free amount of usage each month, and
Speaker 4: you can build these these generative I apps. One caveat
Speaker 4: is it's free. It's public. Going go and upload like
Speaker 4: your company's financial records to it, all personal data. It's
Speaker 4: if you're going to use that kind of data, just
Speaker 4: anonymize it first. Then what else? I I'm going to
Speaker 4: go four.
Speaker 5: Something.
Speaker 4: A lot of developers spend probably some money on, but
Speaker 4: I don't think you can spend enough money on it.
Speaker 4: Which is keyboard and mouse. I put myself out with
Speaker 4: a comfortable mouse and a comfortable keyboard, the one that
Speaker 4: comes with your Mac or comes with your PC. It's functional, right,
Speaker 4: but after a while.
Speaker 5: It's going to hurt your wrists. So what do you got?
Speaker 4: My little Logitech MX Master three. It has far too
Speaker 4: many buttons on it to know what to do with,
Speaker 4: but it's comfortable. And then I have a key chron
Speaker 4: K two mechanical wireless keyboard, really slim from mechanical keyboard, uh,
Speaker 4: but super nice type on as well. And you said
Speaker 4: socks were cool for that was my That was my
Speaker 4: prep for this was socks. So I have some really
Speaker 4: cool socks, but they've all come from conferences. I can't
Speaker 4: give people links, so maybe like.
Speaker 2: Who which which vendor it gives out the best sucks?
Speaker 1: Right?
Speaker 4: There were some I got from uh Influx TB last
Speaker 4: or year before last, which were they were really cool.
Speaker 4: They were like every color under the sun, super stripey
Speaker 4: but really really comfortable socks. Or the like how degrail
Speaker 4: of conference swag, which is the red hat red hat
Speaker 4: which you might be able to see like up there
Speaker 4: on top of the bookcase.
Speaker 5: Yeah, like swag is a yeah, a bit.
Speaker 4: Of a a debatable topic, but you can normally go
Speaker 4: to a conference with significantly less close than you need.
Speaker 5: On day one for sure.
Speaker 1: All right, Gillian, what'd you bring for picks?
Speaker 3: I actually have a tech pic this week. I was
Speaker 3: looking for some type of UI to build out my
Speaker 3: terraform code, mostly because of this AI product that I
Speaker 3: was talking about where I deploy it like on the
Speaker 3: client site and it has to have you know, like
Speaker 3: that database and S three bucket, a couple of lamb
Speaker 3: of functions and then e C two instances, And I
Speaker 3: was like, wouldn't it be nice if there was just
Speaker 3: like a parameter I is quy then I could just
Speaker 3: go and type and click a couple buttons because I'm
Speaker 3: really in my I don't want to be typing era
Speaker 3: of my life.
Speaker 5: Uh.
Speaker 3: And I found resourcely and it is very very cool.
Speaker 3: I would like to point out there's no way that
Speaker 3: I can afford their like their plan that's actually very useful.
Speaker 3: So this is part pick and part me ebagging. You know,
Speaker 3: if the guys at resourcely, I could be the voice
Speaker 3: of your tool on the podcast and like, I'm sure
Speaker 3: that would just be amazing, So there you go. But
Speaker 3: it is. It's really neat, and I like that the
Speaker 3: back end is all just run by Terraform and cookie
Speaker 3: Cutter because those are like my two those are just
Speaker 3: my two favorite tools of all time. It's like half
Speaker 3: my life is run with Terraform, cookie Cutter and make files,
Speaker 3: and then when you're throwing the make files, it's like
Speaker 3: ninety percent of my life.
Speaker 2: So we definitely have a like a full sponsor section
Speaker 2: on Adventures and DevOps dot com where if if someone
Speaker 2: wants to be a sponsor this podcast, they can go
Speaker 2: there and read what we have and then decide if
Speaker 2: it's for them. Uh, you know, Jillian, what I found
Speaker 2: that works is ask your customer if you know how
Speaker 2: to incidentals work, and whether or not the usage of
Speaker 2: third party tools to help cut down the amount of
Speaker 2: time that you would have to charge them for would
Speaker 2: be included under their contract. A lot of times the
Speaker 2: contracts that when I was doing consulting, you would include
Speaker 2: those in there, and of course you would charge that
Speaker 2: to the customer at to optimize what they're actually getting
Speaker 2: out of the value that you're providing.
Speaker 3: So that's always really tricky for me, just because like
Speaker 3: the companies that I work for, they're not creating technology
Speaker 3: as their product. Like if they could get rid of me,
Speaker 3: they would, okay, like if they could just be like
Speaker 3: you just go away, we just want to work on
Speaker 3: our laptops with Excel, like they absolutely would, So that one,
Speaker 3: that one is always a little bit of a tough
Speaker 3: sell for me instead of just start emailing people and
Speaker 3: try to get stuff for free, which is probably questionably
Speaker 3: like in terms of ethics.
Speaker 2: But I get emailed all the time people asking stuff
Speaker 2: for free, So I mean, I don't think you're doing
Speaker 2: any thing especially wrong there.
Speaker 3: All right, well, thank you for thank you for the
Speaker 3: ethical vote anyways, think you appreciate that. Sometimes I am
Speaker 3: a little bit like, hmm, maybe I'm a little bit
Speaker 3: too much on the side of the ebagging, but I
Speaker 3: do like money, so here we are. But it is anyways,
Speaker 3: it is a really great tool. It actually does generate
Speaker 3: you like this really nice UI you can do. It
Speaker 3: has to sort of parameterize like in multi tenancy built
Speaker 3: in that I really like because I find a lot
Speaker 3: of tools they just I don't know, they just they
Speaker 3: just don't have that, and that tends to immediately not
Speaker 3: work for me because I'm so rarely working on my
Speaker 3: own AWS account right Like, my WS account is as
Speaker 3: bare bones as it can possibly be, just for you know,
Speaker 3: DEVA for whatever it is that I'm working on, and
Speaker 3: then everything else is deployed on client sites. So it did.
Speaker 3: It did genuinely look like a really nice tool that
Speaker 3: has like everything that I want. And I think that
Speaker 3: I can even make the free plan like mostly work
Speaker 3: for me. But we'll see.
Speaker 1: Right on, Barren, what'd you bring for a pick?
Speaker 2: Yeah, So I've got something really interesting. It's actually a
Speaker 2: old paper research paper from Yale in twenty ten, and
Speaker 2: the name of the paper is comparing genomes to computer
Speaker 2: operating systems in terms of the topology and evolution of
Speaker 2: the regulatory control networks or regulatory control networks. For short,
Speaker 2: it compares the Linux operating system to E. Coli bacterium.
Speaker 2: And I find this really interesting from an architecture standpoint
Speaker 2: of how what we build in technology is so wrong.
Speaker 2: If we look at the evolution of biology for millions
Speaker 2: of years, you look at the evolution of E. Coli
Speaker 2: and you see what's currently there, and it's only a
Speaker 2: six page paper. It's very short, and it really gives
Speaker 2: a lot of insight into the sorts of things we're
Speaker 2: building and whether or not we're building them effectively, and
Speaker 2: being in the infrastructure systems design space having new insights
Speaker 2: for how to build things or what actually is really important.
Speaker 2: I always find really interesting. Is that from Wade Schultz.
Speaker 2: I don't think so okay, but I could be totally wrong,
Speaker 2: and so I don't want to swear to it, and
Speaker 2: I will have to confirm for you after the episode
Speaker 2: is over.
Speaker 1: Right on, because Wade's a really good friend of mine,
Speaker 1: and he's the head of computational health over at Yale,
Speaker 1: and that sounds exactly like something he would offer.
Speaker 3: Right on.
Speaker 1: So my pick for the week, I'm picking a book
Speaker 1: this week, a sci fi fiction book called Jurish Juris
Speaker 1: ex Machina I think that's how it's pronounced. It's from
Speaker 1: John W. Maylee. It's a really cool book that has
Speaker 1: a lot of tie into the episode we've talked about
Speaker 1: here today. It's a future Earth where the legal system
Speaker 1: has been largely replaced by AI and the main hero
Speaker 1: of the story has been wrongly convicted goes to prison,
Speaker 1: but it's a really well written book. It's got a
Speaker 1: lot of super cool, nerdy tie ins into it, and
Speaker 1: the writing is well done. It's fast paced, so you
Speaker 1: get sucked into it immediately. And on top of that,
Speaker 1: in about a month we're going to have John on
Speaker 1: the show to talk about the book and AI. So
Speaker 1: I'm looking forward to that episode. And so that's my pick.
Speaker 5: For the week. Awesome.
Speaker 2: I'm gonna have to read this in preparation.
Speaker 1: Yeah, it's it's been a really cool book. Like I
Speaker 1: struggle to get into too fiction books, but this one
Speaker 1: just like slurped me on in right on. Alex, thank
Speaker 1: you so much for joining us on the episode. Was
Speaker 1: a pleasure talking with you.
Speaker 5: Thank you for having me. It's been good fun.
Speaker 1: Right on, and to all our listeners, thank you for listening.
Speaker 1: Appreciate your support. Gillian Warren, thank you for joining me
Speaker 1: co hosting with me, and we'll see everyone next week.
Speaker 5: S