AI and the Future of Work: Artificial Intelligence in the Workplace, Business, Ethics, HR, and IT for AI Enthusiasts, Leaders and Academics
00:11:00 · George Sivulka
[00:11:00] S0: Yeah. I think almost all of our customers wants AI. They see it as this incredibly important and impactful thing, But almost all of them are are still just experimenting with it. They have, like, a few different tech forward users that are going out, and they're they're using AI in, a a small way. They're spinning the wheels on them. When people get heavier, right, they expect that it'll be something like a chatbot or something like all the other AI application companies in the world that are very good at doing a single task. Hey. Rewrite this email. Or, hey. Go and find something in this document or summarize this document. But that's actually just one step. It's like the calculator. It can do only a single step on the way to actually augment... Augmenting an entire workflow. They'll get heavier, and really quickly, they'll realize that the entire platform is built around answering questions or doing tasks that require many steps. And that can affect any workflow. So, you know, if if you wanna go into, you know, investing as a canonical example, one of the questions that people always wanna answer, they just get a new opportunity. They just get a new company. They're reviewing a new pitch deck, and they wanna answer, hey. Is this company a good investment? A ChatGPT or any other kind of search products on the market will go in, they'll try to find areas in the document where it says whether or not it's a good investment. But, obviously, that's biased. Obviously, the CEO is gonna say all kinds of things and make it look like a great investment. Actually, we'd do if you were a good investor would be you'd take all the marketing materials. You take everything on the Internet. You take everything off the web. And you'd say, hey. Give me something about the strength of their management team that might not be mentioned. Or go look into their competitors and see what's strong about their competitive positioning. Or tell me something like their customer concentration. And each of these individual steps, you can think of as a decomposition of that question. Hebby will take a question like, is this company a good investment? Review documents like a data room, a sim, or anything else that's brought if you're an investor.
00:13:00 · George Sivulka, Dan Turchin
And it can then go and answer customer concentration, strength of the management team, and everything else that you'd like to know down to your specific investing process. [00:13:09] S1: We talk frequently on this show about what it means to practice responsible AI. And with a product like Matrix is the name of the the product from Hebia, gosh, it is in control of making a lot of important decisions. And what I'd be curious to know is from the perspective of, you know, the CEO and your culture, what do you think about in terms of taking ownership or accountability for unintended consequences? Not because your customer asked bad questions, but because Matrix may do something. These are statistical... You know, these models are looking at statistical probability. The decisions can have high impact, ramifications. How do think about the responsibility of you as the developer?
00:13:52 · George Sivulka
[00:13:52] S0: It's it's incredibly important to our company and really to the collective dialogue of our industry about the ethical implications and then really how to responsibly roll out this technology. You can think of of new technologies as as really a tool or an opportunity to go out and and and do anything. But when they have some slight bias or they have some slight kind of tendency to to skew information, that becomes an incredibly dangerous. With Matrix, we actually engineer the entire product to be very human centric, human first, and transparent. And that's very different than anything else in the market. Whereas other things might just reply or show you a few search results and a few citations, Hevia actually shows you its work, and it builds a piece of collateral. That's actually the matrix that will show you every step the AI takes, actually where documents add up and where they don't add up. And if data is mixed or data is slightly skewed one way or the other way, it won't tell you, okay, 52% of the time people say this, and 48% of the time folks disagree. It'll actually, you know, it won't it won't it won't really skew just to the 52% of time. It will actually tell you that breakdown. It'll it'll it'll show you every side of a problem. And that's that's inherent not only in how we kind of how we think about our responsibility, but how we build our product. In investing, that's even more important because you care about not only the majority opinion, but maybe the secrets, or the information that people might not be talking.
00:15:22 · Dan Turchin, George Sivulka
[00:15:22] S1: This is unanswerable, but you're pretty good. And so I'm gonna ask ask it anyway. Maybe it's unfair. [00:15:27] S0: Hit me. [00:15:27] S1: What do you think about the... Is the cost of a false positive, so you make a poor decision that someone acts upon, is that higher or lower than the cost of a false negative where you have a right matrix, has a right answer, and because it's not confident enough, it chooses to not provide it?
00:15:44 · George Sivulka
[00:15:44] S0: I think that, you know, it's it's interesting. It's a problem, maybe even a paradox that we've tried to engineer the product for. And and so it... Maybe it won't really skew false negative or or false positive, but rather it'll it'll show you... It... It'll really say, I'm not sure. And this is... These are the possibilities. And so and so it it kind of... To the point of it being human centric and human first, it puts the onus back on the user and is more around surfacing the insight rather than making the insight. And so the majority of the time, we're not actually trying to make the insight. I don't believe in a world where AI is running everything. You could think of, you know, if every investment firm had the exact same instance of Trachi BT Enterprises, the exact same instance of whatever application they'd like, they'd all get the exact same answer. Maybe it has their own data. But, ultimately, what Hebiot tries to convey is it... It'll also understand your own custom process. It'll treat your firm and your investing criteria different than it will treat another firm and its investing criteria. And that's not only for investors or for finance. If you're a lawyer, a lot of the time, what makes a great law firm is not the data that they have, but actually the way that they engage with their clients, the way that they understand things, the way that they deal with problems. And so that process is actually just as important as the data. And whereas other companies are are kind of working on the models, training the models, giving them just your data, Hebi is saying that's not enough. We're also gonna give you your own custom process.
00:17:12 · Dan Turchin, George Sivulka
[00:17:12] S1: But a big part of that custom process involves simulating insights from external sources, obviously. So the the the rag search would restrict it to some, you know, known sanctioned content. Presumably to do this research, you want to extend it to content that hasn't been sanctioned. Right? [00:17:29] S0: A 100%. If you if you actually think about how RAG works, it's ultimately, you know, taking a a a group of of sanctioned content or a group of whatever contents you'd like and giving you just the most similar, the most relevant results and feeding that to a large language model to answer a question. What really matters and what matrix does is it'll take in entire worlds of information, that sanctioned content, but also other things as well. And it'll say, okay. Hey. It's it's... We're gonna give you, not just the relevant results, but just everything that's even remotely related. Package it into something that's a bit more kind of unbiased and and level headed in in its analysis of of the, of whatever it's been given and then present that to the user.