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15 results for “ai contracts”
...those kinds of contracts to be able to spot where there's any sort of, misdrafting, ambiguity, or conflicts in the drafting. And those are the kinds of issues that can either make the drafting lopsided, not in your favor, for example, or even worse,
23:15 / 24:22
from transcript23:15 – 24:22
...kinds of contracts to be able to spot where there's any sort of, misdrafting,
22:31 / 23:32
from transcript22:31 – 23:32
And and they all it's also very templated. Right? So you're constantly trying to find templates. Hey. Other associate in this law firm, can you send me, you know, a previous document now? I can they have, you know, 80 different objections that you're
19:29 / 20:34
from transcript19:29 – 20:34
I realized I should have asked this earlier, but when we say AI, what does that actually entail in the legal field? Because as you point out, I mean, templates, standard forms, those sort of things have been around forever, and a lot of law firms alr
20:10 / 21:16
from transcript20:10 – 21:16
pertaining to their AI models. Now we talked on the show quite a bit about the push to ban state level AI regulation here in The US. That ended up not becoming law, so that's not currently really in place. And so we're seeing people begin to politic
17:02 / 18:15
from transcript17:02 – 18:15
So we invest a ton of upfront manual labor, time, and effort in doing the implementation and activation right. So I do think that's necessary for enterprises where you're changing the way they work. If you just think about a process, right, like, let
9:44 / 10:48
from transcript9:44 – 10:48
your consent really hasn't been provided for how to use the data. There's another idea where you can train language models only on data that has been licensed explicitly. So that kind of governing contract is provided. And I'm not sure if Aperdis is
1:29:13 / 1:30:15
from transcript1:29:13 – 1:30:15
law firm's client is saying, please use this, that probably reduces the time to sale by a factor of 10, if not more, just for you guys. Yeah. Exactly. Alright. Now I wanna talk about the use of AI inside the legal profession because we all know that
6:36 / 7:46
from transcript6:36 – 7:46
thought of a scientist. It's like, oh, I figured out to scale RL. And there's a group that did that, but I think most of the contributions is like On the data set. I'm gonna make the data better or I'm gonna make the infrastructure better so that eve
1:28:25 / 1:29:32
from transcript1:28:25 – 1:29:32
...wanted to review contracts, and they wanted to draft things. But people would come in with this expectation of,
17:14 / 18:50
from transcript17:14 – 18:50
And we started the the kind of moment was we went on, r slash legal advice, which is basically, you know, a subreddit where people ask a bunch of legal questions. And almost every single answer is, so who do I sue, almost every single time. And we to
1:26 / 2:36
from transcript1:26 – 2:36
how you deal with or if AI is helpful with in litigation, what would be called precedent. But I'm assuming you're building up a big database of all these different deal documents. Is it useful is AI useful to go back and look at previous documents in
27:43 / 28:49
from transcript27:43 – 28:49
And then they can connect it to here's a discovery corpus with all the emails, and then use all of this to start drafting, like, a motion or something like that. And it's so it's these really complex workflows across all these different data sources
8:13 / 9:24
from transcript8:13 – 9:24
about AI training data as the focus and this year being a focus much more on RAG, it's really us just saying that a lot of time has been building better AI models, and now people are using them. And so, of course, we're seeing a shift in the overall
36:42 / 37:48
from transcript36:42 – 37:48
an infinite amount of time, which is impossible. So, like, some of these problems may just in their definition not be solvable. I I do literally have a mathematician on speed dial, but I'm not gonna call up during the show, but I'll ask I'll ask Kirs
16:26 / 17:53
from transcript16:26 – 17:53
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