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In today’s episode, we’ll be diving into the fascinating world of one of the most advanced machinelearning tools out there: ChatGPT. Professor Hoofnagle] 03:03 ChatGPT is the newest iteration of a machinelearning technology that can generate text. I’m your host, Eric Ahern.
Oh, I could I could hear them. When it comes to things like machinelearning natural language, processing delays, semantic indexing, those things where you don’t have to worry about the machine, whether it’s hallucinating or lying to you, you just need to worry about whether or not a model is learning away from what you taught it right.
And I spent a lot of my time in the early years of practice as your listeners are, for sure familiar, you know, in the legal research universe, trying to find the right cases, running endless Boolean search strings, right. And as we see the cost of computing power come down, as we see more and more legal information becoming digitized.
We’d love to hear your thoughts on what value you see in ChatGPT and GPT 3.5 in the legal industry. And so like when I when I describe it to people, it’s like, you know, if you’re comfortable with getting your your legaladvice from Chora or just from your first five results from Google? We’d love to hear from you.
So you’re probably familiar with that yourself when you’re using this machinelearning. We’d love to hear from you. So one of these in these in these and they would see that these will actually pop up in one of the final training cycles. And then you get the feeling that’s you can control things.
And obviously, now we’re looking to expand the team more and more, I think we’ve looked into hiring, you know, ml ops people, machinelearning engineers, software engineers, and it has produced already a tremendous amount of value for the firm. We’d love to hear from you. So on that generative seed, we just leave.
Oh, I could I could hear them. When it comes to things like machinelearning natural language, processing delays, semantic indexing, those things where you don’t have to worry about the machine, whether it’s hallucinating or lying to you, you just need to worry about whether or not a model is learning away from what you taught it right.
And I spent a lot of my time in the early years of practice as your listeners are, for sure familiar, you know, in the legal research universe, trying to find the right cases, running endless Boolean search strings, right. And as we see the cost of computing power come down, as we see more and more legal information becoming digitized.
And obviously, now we’re looking to expand the team more and more, I think we’ve looked into hiring, you know, ml ops people, machinelearning engineers, software engineers, and it has produced already a tremendous amount of value for the firm. We’d love to hear from you. So on that generative seed, we just leave.
If you search on the web, you’ll see over 50 library guides that highlight the project as a source for legal research or scholarly data and hundreds of thousands of links into the project’s website. If you talk to lots of legal tech startups, like I do, you’ll hear how much easier it is to start something new because of the project.
If you search on the web, you’ll see over 50 library guides that highlight the project as a source for legal research or scholarly data and hundreds of thousands of links into the project’s website. If you talk to lots of legal tech startups, like I do, you’ll hear how much easier it is to start something new because of the project.
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