Wednesday, August 3, 2016

WayBlazer issues statement outlining operations of “Connie the Concierge”

Felix Laboy, CEO of WayBlazer, today provided Etopia News with the following responses to its recently-submitted questions regarding the operations of “Connie the Concierge,” a small, semi-humanoid robot that channels the power of IBM Watson into answering questions about travel and tourism destinations:

How is the project working?
The pilot is a joint collaboration between Hilton and IBM Watson together with WayBlazer.  Connie enables guests at the Hilton McLean in Virginia, to interact with the first Watson-enabled robot concierge, to learn more about in-hotel offerings and local attractions.

When guests ask questions in natural language about restaurants, attractions and things to do “off-property” (Local Area Knowledge) WayBlazer is able to make local recommendations in the form of visual tiles including images and insights about the POI.

How much is it being used?
WayBlazer focuses on the local area knowledge component of Connie’s recommendations.  The technology combines WayBlazer’s extensive travel domain knowledge with IBM Watson's cognitive computing technology platform to enable Connie to greet guests upon arrival and to answer questions about hotel amenities, services and hours of operation. Guests access local area knowledge data daily with a large focus of questions on restaurants in the local area, as well as places to grab a drink and attractions like movie theaters, parks, and museums.

How people are reacting to it?
The WayBlazer team is not on the ground in McLean but from the data we can see that guests are engaged with the local area knowledge piece that WayBlazer provides evidenced by the number of queries, clicks into recommendations cards and clicks for directions.

Plans for the future
WayBlazer is excited to see where Hilton and IBM take this project next and will be ready to support with our natural language search and local area knowledge as well as other technologies where there is a fit.

What has WayBlazer learned?
Our training process has allowed us to learn from guest queries and continually improve the product to be more accurate in understanding the questions and providing a relevant response. We are currently at an accuracy of 90%.

Monday, July 25, 2016

Blue J Legal CEO Benjamin Alarie outlines company’s expansion plans, commits to remaining “on the forefront” in regard to deep learning technologies

In a recent Etopia News article about Blue J Legal, a Toronto-based start-up dedicated to commercializing the insights found in the ground-breaking article “The Death of Rules and Standards,” by Anthony Casey and Anthony Niblett, Professor Niblett explained how machine learning can be used to resolve gray legal issues into black-or-white “micro-directives” predicting the right answer based on the application of algorithms to extensive datasets of relevant case law.

Today, Benjamin Alarie, CEO of Blue J Legal and Osler Chair in Business Law on the Faculty of Law at the University of Toronto, provided Etopia News with further details regarding the directions that this pioneering legaltech firm might soon be pursuing.

Asked what new fields of law the firm might consider as grist for its analytical and predictive mill, Professor Alarie wrote:

We expect that in the next decade that our approach to the law will become widely available throughout the world’s legal systems. Obvious applications [can be found] in private law (contracts, torts especially), criminal law, family law, labour and employment law, antitrust, tax, etc., etc.” 
Asked about Blue J Legal’s relationship with IBM, he replied:

“We are customers of IBM and use a number of Watson’s capabilities in delivering our solution. In addition to the NLP [Natural Language Processing] abilities of Watson, we also draw upon other, more structured, machine learning approaches in our solutions. We are committed to incorporating the leading deep learning techniques as they become commercially viable and will remain on the forefront in this regard.”

Friday, July 22, 2016

Legal innovator Anthony Niblett discusses Blue J Legal’s machine learning research tool

Anthony Niblett, Associate Professor and Canada Research Chair in Law, Economics, & Innovation at the University of Toronto Faculty of Law, and a co-founder of machine learning start-up Blue J Legal, spoke this afternoon to Etopia News, and provided additional details about a reference to the firm in a recent Economist article entitled If computers wrote laws:  Decisions handed down by data” in its special supplement “The World If.”

According to the Economist article, Blue J Legal is “a startup combining law and machine learning to provide answers about complex areas of tax, such as how to determine if a person is an employee or independent contractor.”  According to the Economist, this approach represents the possible replacement of “rules and standards” as the basis of legal reasoning by “micro-directives” derived by algorithms from existing case law. 
This idea is introduced and elaborated upon by Professor Niblett and his co-author, University of Chicago Law Professor Anthony Casey, in an article entitled "The Death of Rules and Standards," available online here.

According to Niblett, the company’s operations are now in beta test mode, designed to provide a proof-of-concept for the idea that gray areas in the law can be reduced to black-and-white answers by applying machine learning algorithms to masses of existing case law, in order to determine more definitively the answers to questions of interest, in this initial case to questions about tax law.

You can watch a video explanation of the origins of Blue J Legal from its CEO, Benjamin Alarie, on the company’s Facebook page, here.

The legal data being crunched by the algorithms is Canadian tax law, which is, conveniently enough, uniform throughout Canada, which avoids complications generated by multi-jurisdictional issues.  The system’s data set includes 600 instances of case law relevant to the question of whether a particular person is an employee or an independent contractor.  Instead of having to wait for a judge or the IRS to rule on any particular instance, a tax professional or lawyer can use the Blue J Legal system to input specific facts of the case to determine with 95% probability of accuracy the right answer to this question.

Not only can it provide a yes/no answer to the question, it can also provide the reasoning behind the answer and links to the relevant cases involved.

The system is now being used by accountants in the beta test to answer this and other questions.  According to Niblett, three of the Big 4 accounting firms are using the system in the beta trial.  You can see a client list here.
This process can also be applied to questions of legal residency in Canada, which, he said, revolve around the “center of vital interest test.”

The more consistent the existing case law is, the higher the confidence level that can be applied to the answer.  According to Niblett, Canadian tax case law is very consistent, yielding high confidence levels for the system’s answers.  Users of the product are, he says, “happy with it.”

This innovative start-up’s name derives from the blue that represents the University of Toronto and the “J” that follows the name of judges in official documents in Canada.

Other fields of law that is susceptible to this kind of machine learning are corporate and anti-trust law.  Even criminal law might be a possible area subject to this kind of analysis and prediction.  The company envisions an eventual expansion to include U.S. law within its purview.

The company co-founder emphasizes that their system is, essentially, a “legal research tool.”  According to Niblett, neither judges nor lawyers are worried about being disintermediated or replaced by the Blue J Legal system.   “Not yet,” he adds.