This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Casetext’s acquisition by Thomson Reuters illustrates the present-day limitations of large language models trained primarily on caselaw. And, you know, are you seeing that, that there’s some that kind of have some definite concrete use cases? It’s been trained on caselaw.
This protects the researcher from the AI “creating” the answer from all the non-relevant information it has collected in its large language model of machinelearning. The MyJr product works as a browser extension and identifies Canadian and US caselaw citations on any web page. And they wanted to explore legal.
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. So those are the kind of use cases where we didn’t jump in. They continue to do that.
This guidance, which draws on the GDPR as well as national and EU caselaw, contains relevant advice for using AI in the healthcare space more broadly. For further discussion on the principle of “security by design”, see our previous blog post. The Italian Garante published guidance on the use of AI in the healthcare sector.
So as part of their marketing strategy, definitely the board, or, you know, and definitely the executives thought through this and said, This is a good marketing tool for us going into fundraise getting our valuation nice and frothy, so that we can go and raise a lot of money very quickly.
Casetext’s acquisition by Thomson Reuters illustrates the present-day limitations of large language models trained primarily on caselaw. And, you know, are you seeing that, that there’s some that kind of have some definite concrete use cases? It’s been trained on caselaw.
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. So those are the kind of use cases where we didn’t jump in. They continue to do that.
Next, we plan to expand the product’s scope to cover more aspects of the litigation process, to improve the machinelearning summarization model, and to develop visualizations of evidence based on the data present in the chronology. Finally, we plan to build integrations with e-discovery and practice management products.
Most legal tech startups make bold declarations about public interest, access to justice and democratizing the law when it suits them. Caselaw books waiting to be scanned. Harvard would contribute the law books and run the scanning process inside the law library. Ultimately, by mid-2015, the deal had taken shape.
Most legal tech startups make bold declarations about public interest, access to justice and democratizing the law when it suits them. Caselaw books waiting to be scanned. Harvard would contribute the law books and run the scanning process inside the law library. Ultimately, by mid-2015, the deal had taken shape.
Next, we plan to expand the product’s scope to cover more aspects of the litigation process, to improve the machinelearning summarization model, and to develop visualizations of evidence based on the data present in the chronology. Finally, we plan to build integrations with e-discovery and practice management products.
We organize all of the trending information in your field so you don't have to. Join 5,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content