TRANSCRIPT
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Thank you for coming today. Appreciate it. Hope this is going to be entertaining.
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This presentation is going to be pretty high level, right,
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about strategy, right? So as we just heard, things are early.
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It's early for compliance, it's early for regulation and
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it's still very early for, you know, what the right approach is
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going to be for managing, you know,
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any, anything related to data compliance when you create LLMs
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or anything adjacent to it. So. Right. I think that it
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would, I think it's the right time to step back and sort of think,
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well, like if we're going to develop LLMs at scale,
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right, if we're really going to start developing, you know,
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not just one or two, but have teams, 50 teams, 100 teams,
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each of them building on top of gen AI models,
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right. What do we think is really going to with the data management,
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with the compliance types of stuff and what can we do now to
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prepare us for this inevitable headache? Right, so let's be clear, it's going to
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be a headache, right? Anytime a new computing platform comes
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along, it's been a headache, right? So we can go through
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examples here and we're already seeing some headaches here
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in the headlines, right? So specific
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things that are unique and emergent with LLMs, right, are just some
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things listed here. Very small, undetectable bugs that lead
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to a very high blast radius. Legal and compliance
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issues, very high amounts of liability,
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need clarity on who's actually liable for what when multiple parties
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are involved. And just the general how do we develop
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quickly without having to have a meeting every
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week about are we following the rules, are we doing the right thing? These are
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things that are in our way as AI developers and if we
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don't sort of just step back and think about, well, what can we do today
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to prevent us from getting sidelined later? Right? You think that that's the
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right activity and that's where we should
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be investing time in today. Right. So I'll make the case that AI development is
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very different than traditional development. That's okay.
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This has happened several times over the last couple of decades. Web 2.0,
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all of this every time a new computing paradigm has come
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along, right. We've had to adapt. Right. And my thesis here is
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that every time we've needed to adapt, there's been an attempt
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to lock things down. Right? We thought that that was the right thing to
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- So let's go Back to Web 2.0. Imagine it's 1998.
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Imagine, you know, oh my gosh, we're going to release software. How many Times.
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We're going to release once a month. That's crazy, right? We can't. How could
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we possibly release software once a month? Right? This sounds like,
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like nuts. What about the compliance? What about the rules?
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What about all of these things that we need to follow? Right. I remember doing
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this, I worked for a big box retailer. The solution
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was, you know what we need to do? We need to fill out a paper
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form and we need to fax it and then we need to get a sign
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off from a VP and that's how we're going to do a release.
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Yep. And then what happened after that? Right, what happened after that was, well,
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actually this is a pain in the neck. We can't release software with these encumbrances.
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This is too much. So you know what we need? We need to OCR these
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forms. That's what we should be buying. We should totally
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digitize these forms and we can just process
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signature forms at scale. That sounds like a great idea.
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Was it a great idea? No, it was not a great idea. That's not
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what happened. That's not the world we live in today where we can do thousands
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of releases per week, small ones, albeit with pipelines.
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It's just not what happened and it's never been what happened every
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time a new paradigm comes along.
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So I always think that it's, hey, if that's what happened with web 2.0.
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And then we had cloud, right? We had the cloud revolution and we said,
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all right, we need to make very small incremental updates and
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we need something like what we've landed on is not,
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hey, you know what we need to do? We need to do security reviews once
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a day, right? That was a knee jerk reaction and said, how are
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we going to make so many releases without enough
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compliance and reviews in place? And in the beginning it
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was, hey, we just need to do more reviews, we need to do more compliance
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reviews, we need to embed security professionals within
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your team and they need to look at stuff once a day. Right. Again,
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is that the world we live in today? No, it's not. Right?
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We've automated those things and we've built those into our development pipelines,
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which required a fundamentally different approach to releasing software.
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In that case, it was pipelines, it was CICD pipelines and
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we moved on with ourselves. And now we live in a world where
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we can release as developers. We can release. So I'd make the
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case that for AI development, we have yet another
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problem here, a very simplified version of it, is that you
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have more moving pieces than we previously had.
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In addition to the actual software, the LLM, we have the
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training data, we have possibly RAG data. We might
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have other data that we need to power these applications. We have context,
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we have the configuration and there's probably a few other sources,
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each of which are living currently in little silos
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typically. Right. So one person might own some of this. One person might
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own some of this. You might have to ask somebody for this training set.
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This does not look like normal software development. Right. What that means
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is we can expect we're going to need to change how we do software development
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again. The world just gets a lot more complicated. The development landscape
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gets more complicated. Okay, so,
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right, so I'm here representing
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our product. I'm the CTO for PROVE AI. Right. This is our philosophy.
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It's designed to help organizations de risk AI
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simplify compliance and unify governance. So the idea is
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we don't really know what's going to happen in the future. We don't know what's
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going to happen with compliance. But what we can be sure of now
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is that it's going to be the
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thing that you can do now is get your data stored in the right place
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in a way that will set you up for success with later and most importantly,
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not get in the way of your development teams. That's something you can
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act on today. That really you're not wasting
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anything. You're going to have to do this anyway. You're going to have to store
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your observability data somewhere, your compliance data somewhere. Why not think
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this through now about the use cases of the future.
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Get this data written to the right type of place. Get your event streams written
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to the right place so you don't have to waste time later redoing
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everything. That is what our product
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provides. We are in early access. As you can imagine,
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this is an emergent type of use case. We're not embarrassed
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by that. It's early, that's fine. And we
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just think that it's worth the investment now to do that. We do use a
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decentralized ledger as the source of record behind.
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So what is this magic data store I'm talking about for PROVE AI?
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It's a decentralized ledger. There's a number of reasons. We think that
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there's a very legitimate set of use cases that
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are very well suited towards a distributed
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ledger. Azure data store for these types of compliance related events.
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Even if it's not the actual compliance data, it might be hashes of that
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data. Something that allows you to prove authenticity
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have it be tamper proof and allow you to do all of
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these use cases which I'll go through right now that
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we expect to be important in the future. All right, so now
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again, what I promised today was we'll go through a few
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forward looking use cases. These are food for thought. These are like,
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yeah, will you have to do this organization?
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We think so. I think so. And say,
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if I'm really going to need to solve these types of problems, then maybe
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adopting something that's ledger based is actually a good idea.
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So I'll go through a few sort of
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tenets or use cases that we think are going to be important.
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So one is this notion of being always compliant and
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observable for AI and any sort of gen AI
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sort of activity here. Collect everything, observe everything,
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right? Put it in one place. Most important to this use case
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is like, let's assume that we're not going to only have one MLOps tool.
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Let's assume you're going to do AI development at scale. You're going to have
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50 teams, even 10 teams. They're probably not
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going to want to use all of the same tooling, just like history has
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told us, right? So we're all going to standardize on web application container
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- No. Has that ever happened? It's never happened. Right. So should
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we expect it to happen with AI development? No, we shouldn't.
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So we can prepare for this inevitability now. And then we say,
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okay, if we expect our teams to use the tools that they
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want to use to be successful, we can therefore assume that
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there's probably going to be multiple MLOps tools in the mix.
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Let's also assume now we need to be always compliant. So this
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is not a notion of quarterly review is good enough, weekly review is good
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enough. Things are going to change so quickly and each little
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change may have a big impact on whatever happens compliance wise.
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So I should basically react to that by saying my
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goal as a business, as a cto, as a cio, is I want to be
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always compliant. I want to basically say everything that's
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logged is already logged, it's transparent. I don't need to make a
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phone call, I don't need to upload artifacts because that's
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going to be too slow, right? I don't have time every time somebody
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wants to push a new model version to go upload a bunch of artifacts.
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That proves that I'm doing the right thing with my data. Right?
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And again, you don't have to wait for compliance to, to, to move on this,
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right? As, as technology professionals, our job is to do the right thing,
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right? It's not to comply with the law, it's to do the right thing.
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Right? Do the right thing for our customers, do the right thing for, you know,
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our business. So, right. There's no reason to wait for compliance
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in this case, right? We, it's the right thing to do is to say,
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yeah, this is going to be too slow. Why don't I get everything written to
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a place that's transparent, observable and provably correct,
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provably accurate, such I don't have to field requests
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for information, I don't have to upload stuff, and I don't have to keep
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being bombarded with additional requests for stuff.
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Okay, so observable
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and compliant, I think are the two coins that we're talking about here.
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But the idea is the game is not going
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to be about looking at stuff once a month and
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having a review. It's going to be about it being available and online
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at all times. Completely hands off. Right? So you, as a technology
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professional, you don't want to get involved, right? Somebody says, are you guys compliant
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or are you guys doing the right thing with model bias and drift?
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You say, I show my work, I put all of our work here.
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It's observable if you want to go look at it. This is why we make
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all of our information available. Maybe internally, maybe externally.
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It doesn't matter. The point is it shouldn't fall on additional work for us as
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technologists. All right, so I like
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to just talk about a very real use case here. A use case could be.
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Let's say I'm a heavy equipment manufacturer. Let's say I make
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some sort of robotic devices that do some sort of
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industrial application. And let's say that these devices have
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embedded firmware now that have some sort of AI model built
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into it. Typical. Like you can imagine all sorts
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of X ray machines with gen AI models at this point.
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Not a far fetched use case. Industrial is something we've
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seen interest in. What happens now though? Let's say
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you have a product line of 50 different teams now trying to
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build AI into their products. And let's say that now
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the maintenance requirements on these things change in
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reaction to your exact models. All right. As a developer,
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I'm not going to have time to update my maintenance procedures every
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time I deploy tweaks to the model. I'm not going to have time to go
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through a compliance and safety review. The idea
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here is that I just want to automate all of this every time I make
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a change, possibly with an AI copilot of sorts, I would
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like to have an AI, maybe just generate documentation and maintenance
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stuff for me if that's the vision of what I want to do. Well then
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what happens when your automated documentation says something
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terribly wrong about a maintenance procedure? Massive liability,
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massive risk of public safety. How do you get around this
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without having to have really deep reviews every time you
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make a single change? Right. We can't live in that world. So what you
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can do though, right, in this case is just get into the notion of,
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look, I show my work, we're just going to keep developing, we're going to keep
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incrementally updating stuff. I need a way to share this information with
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third parties, my customers, about exactly what have I done
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to these models, what exactly is the impact? So collectively
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we can make a decision about where liability sits with,
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with these sort of automated maintenance stuff, right?
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And have a very clear sense of roles and responsibilities
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to avoid safety issues and legal issues.
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So lots of
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complicated things here. So next I wanted to
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talk about use cases, secure third party verifiability.
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So when we talk about this, right, we say compliance
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and safety, right? We typically talk about it as internal,
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right? But now let's think about the use case where,
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you know, I'm a software vendor today and I want to see your SOC
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2 compliance, right? Very typical type of thing.
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Maybe I develop software in the web 2.0 world.
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I want to go and embed a vendor's piece of software into my solution.
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Typical thing to do, show me your compliance, I want to see your certificates.
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Because I can only do business with other organizations that have high
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quality, you know, high degree of compliance,
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right? Normal. Okay, now what happens in the
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LLM world, right? What happens when I'm trying to license LLMs? What happens
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when I'm trying to share this type of information with third
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parties? When there's noticeably more moving parts,
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it gets more complicated. Right? Again, the show your
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work aspect here is probably the best solution,
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right? Instead of me saying I'll give you a certificate and we'll do an
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audit yearly. It's probably going to be, I'm just going to
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give you an event stream, I'm going to write an event stream of what we
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do, who did it and how it happens, right? I'm going
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to show that I'm always compliant and I'm going to share this
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information in a secure way with you that doesn't require us to even
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have a phone call or an email exchange. Right? So if
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you believe that that's sort of the personally, we believe that
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that's sort of the inevitable, you know, way that this nets out. And in this
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case writing this type of information to a decentralized ledger makes, makes a whole lot
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of sense. You know, example could be in healthcare,
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right? So let's say I have a, you know, let's say I buy a provider
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of medical chatbots that provide some sort of medical advice to patients,
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you know, and you can imagine there's all sorts of
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liability issues there. So let's say I say, hey, I expect you
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as an LLM provider, as a chatbot provider to be continually
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updating and improving the quality of your service. On the other hand,
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we can't have a two month gap where you fell out of compliance of
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something and I was unaware of it and you were unaware of it.
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We want a record, a tamper proof record, a place to point to
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where we can say you've told everything you've done with your model
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since we did our initial review is written here. I can see it, you can
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see it. Regulators can even see it if they need to.
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It's a much better way and much lighter way of,
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of proceeding rather than the notion of quarterly
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or yearly audits which are not going to catch things quickly enough to avoid
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potentially very damaging types of issues.
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So again, a lot of this
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is forward looking, so I'll just keep going through some additional use
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cases here. Another one is around journaled model outcomes.
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So the actual predictions, the outcomes of a model, Right.
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So now you can imagine the problem of, let's say I'm an AI as
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a service provider, right. I provide a model,
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it makes predictions, that's all my business does.
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And then there's other applications that build on my predictions downstream
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to build out a full solution. All right, so what happens when
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there's an issue, right. A customer encounters some sort of problem.
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Right, so let's take automotive driving. So let's say I provide a model that
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can detect objects, do visual processing on objects
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and let's say I'm an automotive maker that consumes those models
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and I build a self driving application
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off of them. Okay, well what happens when the car goes
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ahead and hits something it shouldn't hit? Very bad situation,
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very bad outcome and I want to know exactly
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where the problem was. It can't be a two month inquiry about
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was this a bug in your model? Was it a bug in my software?
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Can't get lawyers involved in that. It's going to take too long. You're going to
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be offline for way too long. What you can do, though, is journal
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your predictions, right? If I'm an AI, as a service provider, I can journal those
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predictions to a decentralized ledger. I can say,
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again, I show my work. This is what our software did. And if there's any
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discrepancy about what actually happened and
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who. What actually happened underneath the hood of
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this, it's not something I have to go and request logs for and get
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legal involved. We don't want to live in that type of world.
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And that is still the world we live in today with a lot of our
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cloud providers. So I rely on a cloud provider. I expected to
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do something. Hey, there was a bug. Hey, it caused me damage.
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What do I do? I can't go look through their logs. I can request their
17:43.946 --> 17:47.202
logs. I can request a root cause analysis, I can ask for
17:47.226 --> 17:50.994
a bunch of stuff. If I'm not satisfied, I can go get lawyers
17:51.042 --> 17:54.498
involved. Slow process. And the only reason we've put up with that
17:54.554 --> 17:57.954
so long is just the blast radius for these types of things has been easier
17:58.002 --> 18:01.346
to mitigate. They're much harder to mitigate in the gen AI world,
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which is why we think a different approach is necessary.
18:04.810 --> 18:08.530
All right. Automation types of use cases. Again, I like to say
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automate. We're not talking about workflows
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here. Workflows involves. Implies people are going to be in
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the mix, right? We think the vision here is people list
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types of automation. So chatbots could be an
18:23.156 --> 18:26.920
interesting use case or example of a use case here where we say,
18:27.300 --> 18:31.116
if I deploy just a simple customer service type
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of, type of chatbot here,
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there's a whole bunch of issues here. Then how
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do we figure out when something goes awry? With one of these,
18:43.300 --> 18:46.328
with one of these chatbots that we purchase, Right. How do we go ahead and
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trigger some actions based on it? That makes sense. How do we fully
18:49.848 --> 18:53.800
automate a recovery? How do we fully sort of streamline this
18:53.840 --> 18:57.384
whole process? Another version of. Let's say I release a new version of
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a chat bot, which maybe I want to do weekly. How do I educate
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the customer service teams who work alongside these chatbots with
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changes? It's a very real problem I've had and I've seen,
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right? You say, hey, we're going to release a feature. I need
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to inform the customer support team that this is. We have to do our training,
19:14.456 --> 19:17.880
we have to do our documentation, we have to make sure they're aware that things
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are going to change. Okay, but what happens if your changes are now
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sourced by an LLM, then what right are
19:25.056 --> 19:28.216
you going to slow everything down and say wait a minute, the actual human beings
19:28.248 --> 19:31.752
who do customer support need to need a couple of days to
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look through these changes and make sure they understand things.
19:35.680 --> 19:39.092
Don't think that's going to work, it's going to be too slow. So what we
19:39.116 --> 19:42.740
think though is well, if you automation has to be part of this, right?
19:42.780 --> 19:46.116
Every time stuff is changing with your event stream,
19:46.148 --> 19:49.892
with your model development, you're going to need to find a way to automate these
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events into meaningful actions that help the
19:53.596 --> 19:56.820
people who use it be aware of what just changed without having
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to go through a click a button. Review this.
20:00.140 --> 20:03.572
Touch this again. It's another plug for a decentralized ledger.
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Because decentralized ledgers you can build applications directly on
20:07.152 --> 20:10.680
the event stream in a secure tamper proof way. So it's a use
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case and we think it's an important one. All right,
20:13.776 --> 20:17.540
and the last one I'll cover here around sort of forward looking use cases
20:17.840 --> 20:21.048
is they're around model and tokenized data
20:21.104 --> 20:24.776
sets, right? So let's briefly just talk about the exchange of data,
20:24.848 --> 20:27.704
the tokenization of data, the ownership of data,
20:27.872 --> 20:31.800
right? So suppose I want to
20:31.840 --> 20:36.008
go ahead and share my information or my proprietary data
20:36.064 --> 20:40.360
sets with third parties, right? That made like again
20:40.480 --> 20:44.744
use cases. I've tried to do this in the past. You get into heavyweight licensing
20:44.792 --> 20:48.248
deals. It's difficult, right? It's a very difficult problem
20:48.384 --> 20:52.260
to solve. How do you give somebody your data without losing control of it?
20:52.880 --> 20:56.072
It's again a good use case for a decentralized
20:56.136 --> 21:00.312
ledger. You can put something perhaps tokenized on
21:00.336 --> 21:03.514
the ledger that says this is the cryptographic hash of the
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data set that we have and or the rows, here's the event stream of the
21:07.042 --> 21:10.810
data as I change it so you don't lose ownership of something even
21:10.850 --> 21:14.138
though the data sets continually changing, right? So again,
21:14.194 --> 21:17.466
is this how else would you do this? Right? I think that's what keeps coming
21:17.538 --> 21:21.002
back to us on prove AI is we know we're going to need to
21:21.026 --> 21:24.426
exchange data, we know we need to change this data continually.
21:24.538 --> 21:27.610
How on earth are you going to share this with third parties without it making
21:27.650 --> 21:30.730
a mess, right? How are you going to prove that you had ownership of this?
21:30.770 --> 21:33.932
How are you going to prove that each of these little change sets is
21:33.956 --> 21:37.692
stuff that you initiated, not some other third party knowing full well that
21:37.716 --> 21:41.280
you may be liable for whatever happens down the stream?
21:41.780 --> 21:44.860
Again, this is probably one of the most forward looking use cases out there.
21:44.900 --> 21:48.572
But again starting Today our thesis at
21:48.596 --> 21:52.520
PROVE AI is all of these things are likely
21:53.300 --> 21:56.588
to come back on us and cause a problem in the near future. Near future
21:56.644 --> 22:00.118
being one to two years. And if they do, then if I'm
22:00.134 --> 22:04.118
struggling with compliance today and how should I think about AI?
22:04.294 --> 22:08.054
We really think that the right way is to start with your data, start with
22:08.062 --> 22:11.638
your event collection and start thinking about these sort of third party
22:11.694 --> 22:15.094
use cases, these sharing use cases, these types of
22:15.102 --> 22:18.854
issues where you need real time event streams that are outside your multiple MLOps
22:18.902 --> 22:22.566
tools and start there and then continue on
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and build out the reactions to it exactly as
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you need. As an. So if you're interested, you outcome by
22:30.308 --> 22:33.996
we're at booth 4, 3, 5. Again we're an open,
22:34.068 --> 22:37.644
we're an early access product, we have a few initial customers.
22:37.692 --> 22:41.100
But as you heard from all of these use cases, right, it is early and
22:41.140 --> 22:45.100
that's, that's okay, right? Like any of the past big technology
22:45.220 --> 22:48.492
trends, the first step was typically to get your, you know,
22:48.516 --> 22:52.124
get your observability, get your eventing and get your data strategy correct
22:52.172 --> 22:56.152
first or you're going to make a headache for yourself later. And all
22:56.176 --> 22:59.352
things being equal, if you have to write and store this data anyway, why not
22:59.376 --> 23:04.424
choose the right type of storage and the right sort of long term philosophy
23:04.472 --> 23:08.808
here? Because there's really no downside to it, right? There's only potential upside.
23:08.984 --> 23:12.488
So with that I think that's about time and thank you. Great,
23:12.544 --> 23:13.220
thank you.
23:16.000 --> 23:18.360
We don't have a lot of time for questions, but we do have a break.
23:18.400 --> 23:21.736
So we have a little bit of a buffer. So one common element
23:21.768 --> 23:24.712
for a lot of the gen AI platform is that people may be building for
23:24.736 --> 23:28.750
internal use or combining together with for example a ChatGPT
23:28.830 --> 23:32.206
or a Claude or whatever is that they all learn from user
23:32.238 --> 23:35.886
feedback. Right. So essentially the LLM is learning and improving and the vector
23:35.918 --> 23:39.294
databases are adjusting based on the users
23:39.342 --> 23:43.534
actually interacting with the data set that's there. So obviously
23:43.582 --> 23:47.198
one could sort of be retraining the model on
23:47.254 --> 23:51.182
new data, but that doesn't necessarily take into account the training that
23:51.206 --> 23:55.130
occurs as a result of somebody saying, now wait, that wasn't actually the best
23:55.170 --> 23:58.522
Chinese restaurant on the Upper west side. You should have also looked at
23:58.546 --> 24:02.362
these Chinese restaurants. Well, at that point the LLM should be
24:02.386 --> 24:06.698
learning from the user feedback. So how does one document
24:06.794 --> 24:10.874
changes to the overall corpus of information and potential relevance
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of results when it's coming from user feedback? So that's exactly.
24:15.226 --> 24:19.632
Yeah, that's a great example of the problem here. Right. So as
24:19.656 --> 24:23.344
the AIs themselves, as whatever update
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mechanism in there is, we imagine a case
24:27.000 --> 24:31.184
where they're writing these events to the decentralized
24:31.232 --> 24:34.896
ledger, basically to tie back who exactly was the actor.
24:35.008 --> 24:38.448
Can we cryptographically be sure of who made this change?
24:38.584 --> 24:42.016
Another benefit of decentralized ledger. And then
24:42.088 --> 24:45.392
can you figure out exactly what point in time is? And most importantly, could you
24:45.416 --> 24:49.216
replay could you basically use a decentralized ledger as,
24:49.368 --> 24:53.008
like, a database ledger? So if there is a problem and then there's a change
24:53.064 --> 24:56.192
that somehow the AIs came
24:56.216 --> 25:00.416
to the wrong conclusion here, could you roll back and replay in reverse
25:00.528 --> 25:03.696
back to a known good state? Right. So, yes, you can.
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And you can do all of these things with something like a decentralized ledger.
25:07.664 --> 25:10.640
Great. Thanks so much, and we'll see you guys back here shortly.
