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.
From the release: “At their core, many academic and commercial applications of natural language processing and machine learning can benefit from a controlled lexicon of expert-selected terms (i.e., a dictionary). This is especially true of highly technical language, such as legal text. However, after a search of the existing landscape, we were unable to find a high-quality open source or freely-available legal dictionary. Instead, the best existing versions, when available, exist under some form of restrictive licensing conditions.”
“Thus, in furtherance of both the legal profession as well as a range of legal technology providers and solutions, we are announcing another step in our broader open source plan that we outlined earlier this month. Namely, we are making available on Github the 1910 Version of Black’s Law (i.e., Black’s Law 2nd Edition) as a structured data object. This early version of arguably the premier legal dictionary is made available under the open source GPL license 3.0 which should allow both researchers and commercial providers to operate with limited restrictions.”
From the Announcement – “Starting on August 1st, this code base and our public development roadmap will be hosted on Github under a permissive open-source licensing model that will allow most organizations to quickly and freely implement and customize their own contract and document analytics. Like Redhat does for Linux, we will provide support, customization, and data services to “cover the last mile” for those organizations who need it.
We believe that a very important future for law lies in its central role in facilitating and regulating the modern information economy. But unless we start treating law itself like the production of information, we’ll never get there. Before we can solve big problems with smart contracts, we need to start by structuring existing legacy contracts. We hope our actions today will help lawyers, companies, and other LegalTech providers accelerate the pace of improvement and innovation through more open collaboration.” (click here for full announcement or access via Slideshare)
This is a very interesting paper!
This is an interesting development – click here to access story!
See the press release here.
From Venture Beat – “AI startup Bonsai has raised $7.6 million to grow its platform that simplifies open-source machine learning library TensorFlow to help businesses construct their own artificial intelligence models and incorporate AI into their business.”
From Science News – “In the new study, Weng and his colleagues compared use of the ACC/AHA guidelines with four machine-learning algorithms: random forest, logistic regression, gradient boosting, and neural networks.”
The underlying paper was published in Plos One (one of my favorite journals) and the location where we recently published our US Supreme Court Prediction paper. In that paper, we use a time evolving random forest (with the novel twist of a tree burning protocol).
Not sure this is actually a “set back for AI in Medicine.” Rather, long story short — it ain’t 2014 anymore … as we discuss in our talk – Machine Learning as a Service : #MLaaS, Open Source and the Future of Legal Analytics – what started with Watson has turned into significant competition among major technology industry players. Throw in a some open source and you have some really strong economic forces which are upending even business models which were sound just three years ago …
From the story — “The partnership between IBM and one of the world’s top cancer research institutions is falling apart. The project is on hold, MD Anderson confirms, and has been since late last year. MD Anderson is actively requesting bids from other contractors who might replace IBM in future efforts. And a scathing report from auditors at the University of Texas says the project cost MD Anderson more than $62 million and yet did not meet its goals. The report, however, states: ‘Results stated herein should not be interpreted as an opinion on the scientific basis or functional capabilities of the system in its current state’….”