NEW DELHI: India and Japan will work together to introduce artificial intelligence and robotics in the defence sector, the next level of strategic cooperation between the two Asian partners. Kentaro Sonoura, Japan's state minister for foreign affairs and a close adviser to PM Shinzo Abe, told TOI in an exclusive chat, "You should expect to see increased bilateral cooperation between us to develop unmanned ground vehicles (UGV) and robotics." The strategic sphere is where the bulk of India-Japan convergence lies. After the nuclear agreement was ratified by the Japanese parliament late 2017, Sonoura said India and Japan would be setting up a joint task force for commercial agreements by the end of January. With the legislation behind them, the Japanese minister said Tokyo was keen to get this going.

Meticulous research, deep study of case law, and intricate argument-building--lawyers have used similar methods to ply their trade for hundreds of years. But they'd better watch out, because artificial intelligence is moving in on the field. As of 2016, there were over 1,300,000 licensed lawyers and 200,000 paralegals in the U.S. Consultancy group McKinsey estimates that 22 percent of a lawyer's job and 35 percent of a law clerk's job can be automated, which means that while humanity won't be completely overtaken, major businesses and career adjustments aren't far off (see "Is Technology About to Decimate White-Collar Work?"). In some cases, they're already here. "If I was the parent of a law student, I would be concerned a bit," says Todd Solomon, a partner at the law firm McDermott Will & Emery, based in Chicago.

This is the first in a series of blog posts BSR will publish in 2018 exploring the intersection of disruptive technologies and sustainability. Artificial intelligence (AI) is rapidly advancing, thanks to ever-more-powerful computing, massive growth in the availability of digital data and increasingly sophisticated algorithms. The world's largest technology firms are investing billions to develop their AI capabilities, and companies across industries, from travel to real estate to fashion, are racing to bring AI-enabled services to market. AI has the potential to bring significant social benefits, including health care (via improved diagnostics), transportation (through self-driving vehicles) and law enforcement (with improved fraud detection). AI also brings new social risks, including to non-discrimination (from algorithmic bias), privacy (through the misuse of personal information), child rights (through lack of informed consent) and labor rights (because of the mass displacement of workers by machines).

Note, there are many variations in the way we calculate the term-frequency(tf) and inverse document frequency (idf), in this post we have seen one variation. Below images show as the other recommended variations of tf and idf, taken from wiki. Mathematically, closeness between two vectors is calculated by calculating the cosine angle between two vectors. In similar lines, we can calculate cosine angle between each document vector and the query vector to find its closeness. To find relevant document to the query term, we may calculate the similarity score between each document vector and the query term vector by applying cosine similarity .

Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively. Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from "What is the right fairness criterion?" to "What do we want to assume about our model of the causal data generating process?" Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them.

In this paper, we consider the problem of fair statistical inference involving outcome variables. Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data. The issue of fairness arises in such problems where some covariates or treatments are "sensitive," in the sense of having potential of creating discrimination. In this paper, we argue that the presence of discrimination can be formalized in a sensible way as the presence of an effect of a sensitive covariate on the outcome along certain causal pathways, a view which generalizes (Pearl, 2009). A fair outcome model can then be learned by solving a constrained optimization problem. We discuss a number of complications that arise in classical statistical inference due to this view and provide workarounds based on recent work in causal and semi-parametric inference.

There are numerous usecases in Legal industry, especially to review briefings, document handling is major segment where data science (Text classification, document segmentation etc) will help. Other use cases are OCR and Document generation like narrative science etc. IBM Watson has specific features/ usecases for legal bots.

At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. To close out 2017, we recently asked some of the leading experts in Big Data, Data Science, Artificial Intelligence, and Machine Learning for their opinion on the most important developments of 2017 and key trends they 2018. This post considers what happened in Machine Learning & Artificial Intelligence this year, and what may be on the horizon for 2018. "What were the main machine learning & artificial intelligence related developments in 2017, and what key trends do you see in 2018?"

Intellectual property may be the legal term for creations, including literary or artistic, but there is something inherently human about it as well. It has long been taken that only human beings are capable of being intelligent in its fullest form, and the concept of intellectual property strives to protect the product of such human intelligence. This is reflected in a number of intellectual property laws. But what if a piece of art, music, literature, photography or other product were not created by a human mind at all, but by a machine embedded with artificial intelligence (AI)? But, ultimately, the judge rejected the claim.

We are the frequent subjects of predictive algorithms that determine music recommendations, product advertising, university admission, job placement, and bank loan qualification. In the criminal justice system, predictive algorithms have been used to predict where crimes will most likely occur, who is most likely to commit a violent crime, who is likely to fail to appear at their court hearing, and who is likely to reoffend at some point in the future (1). One widely used criminal risk assessment tool, Correctional Offender Management Profiling for Alternative Sanctions (COMPAS; Northpointe, which rebranded itself to "equivant" in January 2017), has been used to assess more than 1 million offenders since it was developed in 1998. The recidivism prediction component of COMPAS--the recidivism risk scale--has been in use since 2000. This software predicts a defendant's risk of committing a misdemeanor or felony within 2 years of assessment from 137 features about an individual and the individual's past criminal record.