The March of Machine Learning as a Service #MLaaS rolls on !
Regarding the quote above — we agree. However, it should be noted that the ‘simple substitution story’ works at the aggregate level over a period of time with the simple assumption that the tasks which comprise current jobs can be decomposed and recombined into new jobs. Certainly, institutions (both firms and public sector) will take some period of time to be able to repackage certain existing jobs. Thus, lags are to be expected. < Click Here to Access the Article >
Fish & Richardson is one of the largest IP firms in the US so it is cool to see them exploring these ideas. If you look at this intro using Microsoft Azure – this is very on point with lots of we have been saying about the mix of semistructured data and #MLaaS (machine learning as a service) … and why we teach both an introduction to quant methods and a machine learning for lawyers course.
ABSTRACT: In this paper, we investigate the application of text classication methods to support law professionals. We present several experiments applying machine learning techniques to predict with high accuracy the ruling of the French Supreme Court and the law area to which a case belongs to. We also investigate the inuence of the time period in which a ruling was made on the form of the case description and the extent to which we need to mask information in a full case ruling to automatically obtain training and test data that resembles case descriptions. We developed a mean probability ensemble system combining the output of multiple SVM classiers. We report results of 98% average F1 score in predicting a case ruling, 96% F1 score for predicting the law area of a case, and 87.07% F1 score on estimating the date of a ruling
See coverage of our conference here
This is my Presentation for the NALP Conference on Emerging Legal Careers.
Honored to deliver the keynote at yesterday’s NALP Summit on Emerging Careers for Law Grads
On August 1, we released Contrax Suite (an open source document analytics platform). It is important to note that we have decided upon dual licensing – (1) open source (AGPL) which is pretty hard core copyleft and (2) a more permissive license in specific circumstances. The key for us is to maintain the opensource ecosystem which requires balancing competing interests. We cannot grant the more permissive license to everyone under all conditions or it undermines the entire effort.
That said, we have a real problems in the A.I. + Law community. Some of the claims are outlandish and the business model (at its core) does not really make sense. We think that opensource helps solve for some (perhaps not all) of the adoption issues.
This is an interesting development – click here to access story!