ARTICLE HISTORY Compiled October 24, 2018 ABSTRACT Systems of deontic logic suffer either from being too expressive and therefore hard to mechanize, or from being too simple to capture relevant aspects of normative reasoning. In this article we look for a suitable way in between: the automation of a simple logic of normative ideality and sub-ideality that is not affected by many deontic paradoxes and that is expressive enough to capture contrary-to-duty reasoning. We show that this logic is very useful to reason on normative scenarios from which one can extract a certain kind of argumentative structure, called a Normative Detachment Structure with Ideal Conditions. The theoretical analysis of the logic is accompanied by examples of automated reasoning on a concrete legal text. Keywords: Deontic Logic - Legal Reasoning - Normative Ideality 1. Introduction In the last decades, computer systems have played an important role in assisting people in a wide range of tasks, from searching over data to decision-making, and their use is required in an increasing number of fields. One of these fields is legal reasoning. New court cases and legislations are accumulated every day. In addition, international organizations like the European Union are constantly aiming at combining and integrating separate legal systems (Burley and Walter 1993).

The impact that AI will have on different sectors of the economy is a widely debated topic. It comes as no surprise since leading technological innovations have always been met with fear and uncertainty. According to a study reported by Forbes, in 2016, something around US$8 billion to US$12 billion was invested in the development of AI worldwide. It's now difficult to imagine a job in near future that ultimately smart computers won't be able to do. So, with the advancement of AI, it's important to know where we stand and how it can alter the future.

Let's face it, Boston Dynamics is the SpaceX of Robotics, and if you're a fan of robotics like I am, you can't wait to see what they come up with next. While we wait, let's check out some robots developed in Japan capable of doing amazing things. First is the HRP-5P Developed by Japan's National Institute of Advanced Industrial Science or AIST and Technology. There is legitimate concern over robots taking away jobs in the future, Japan, however, wants this to happen. This is because the country will have a workforce shortage in the future due to declining birth rates and strict immigration laws.

Many economic applications including optimal pricing and inventory management requires prediction of demand based on sales data and estimation of sales reaction to a price change. There is a wide range of econometric approaches which are used to correct a bias in estimates of demand parameters on censored sales data. These approaches can also be applied to various classes of machine learning models to reduce the prediction error of sales volume. In this study we construct two ensemble models for demand prediction with and without accounting for demand censorship. Accounting for sales censorship is based on the idea of censored quantile regression method where the model estimation is splitted on two separate parts: a) prediction of zero sales by classification model; and b) prediction of non-zero sales by regression model. Models with and without accounting for censorship are based on the predictions aggregations of Least squares, Ridge and Lasso regressions and Random Forest model. Having estimated the predictive properties of both models, we empirically test the best predictive power of the model that takes into account the censored nature of demand. We also show that machine learning method with censorship accounting provide bias corrected estimates of demand sensitivity for price change similar to econometric models.

Abstract--Violence is an epidemic in Brazil and a problem on the rise worldwide. Mobile devices provide communication technologies which can be used to monitor and alert about violent situations. However, current solutions, like panic buttons or safe words, might increase the loss of life in violent situations. We propose an embedded artificial intelligence solution, using natural language and speech processing technology, to silently alert someone who can help in this situation. The corpus used contains 400 positive phrases and 800 negative phrases, totaling 1,200 sentences which are classified using two well-known extraction methods for natural language processing tasks: bag-of-words and word embeddings and classified with a support vector machine. We describe the proof-of-concept product in development with promising results, indicating a path towards a commercial product. More importantly we show that model improvements via word embeddings and data augmentation techniques provide an intrinsically robust model. The final embedded solution also has a small footprint of less than 10 MB.

Another industry that's starting to hone in on specific business use cases, instead of taking a technology-first approach, is financial services. Though the last few years have mainly seen companies looking at AI in the forms of automating content operations, enhancing trading tools and improving customer service, they're now demonstrating how AI can tackle much larger societal issues such as financial crime. Currently, only 1% of financial crime that happens through the banking system is stopped. AI has a real opportunity to bring together industry, government and regulators to consider a new approach. Various businesses – both start-ups and larger corporations alike – are making strides in fraud identification, sanctions screening, money-laundering, anti-bribery and corruption.

TEHRAN – Yunes Adiani, an official at the research center of the parliament, has said that Iran will use artificial intelligence in legislation. In an interview with IRNA published on Sunday, he said that Iran is the second country that has taken step in applying artificial intelligence in legislation. "It has been six months that we have started this project. In this project we follow the issues of human intelligence and legislation, artificial intelligence and legislation and artificial intelligence and legislation in the world to see what other countries have used by applying artificial intelligence," Adiani stated. Adiani added, "We are considering the kind of intelligence which receives data and helps us solve problems."

Over the past six years, the New York City police department has compiled a massive database containing the names and personal details of at least 17,500 individuals it believes to be involved in criminal gangs. The effort has already been criticized by civil rights activists who say it is inaccurate and racially discriminatory. "Now imagine marrying facial recognition technology to the development of a database that theoretically presumes you're in a gang," Sherrilyn Ifill, president and director-counsel of the NAACP Legal Defense fund, said at the AI Now Symposium in New York last Tuesday. Lawyers, activists, and researchers emphasize the need for ethics and accountability in the design and implementation of AI systems. But this often ignores a a couple of tricky questions: who gets to define those ethics, and who should enforce them?

Deciding which projects to invest in right away and what projects should wait a little longer is one of the big tasks corporate boards and CIOs are focused on right now during IT budget season. To help decision makers with the big task at hand, Gartner Distinguished VP and Analyst Daryl Plummer announced to a packed house Gartner's Top Strategic Predictions for 2019 and Beyond during Gartner Symposium/ITExpo yestereday in Orlando. "When we look at predicting the future, we typically have an 80 to 85% accuracy rate across all our predictions, and one of the things that I always say is that that's not good," Plummer said. "I'd be happier if our accuracy rate was 60% because I say if you aren't wrong you're not trying hard enough. I just found out one of our reports dropped to a 30% accuracy rate. I wasn't as happy about that as I thought I might be."

Text-based analytics, also known as text data mining, turns unstructured text into structured data that can be used in a multitude of ways by any business. Indian research firm MarketsandMarkets projects that the worldwide text-based analytics market will grow to 8.79 billion dollars by 2023, driven by major vendors like IBM and SAP. MarketsandMarkets continues that text analytics solutions empower users to perform quick data extraction and categorization with real-time insights from stored data and that the growing importance of insights generated from social media content to build effective marketing campaigns and enhance customer experience drives the market's growth. But while many companies are including text-based analytics to their roadmaps, the technology remains in the early stages of adoption. One reason for this is because companies are still struggling to master social media.