In this paper, we present a hybrid model that combines a neural conversational model and a rule-based graph dialogue system that assists users in scheduling reminders through a chat conversation. The graph based system has high precision and provides a grammatically accurate response but has a low recall. The neural conversation model can cater to a variety of requests, as it generates the responses word by word as opposed to using canned responses. The hybrid system shows significant improvements over the existing baseline system of rule based approach and caters to complex queries with a domain-restricted neural model. Restricting the conversation topic and combination of graph based retrieval system with a neural generative model makes the final system robust enough for a real world application.

In this work we study a rational extension $SROEL^R T$ of the low complexity description logic SROEL, which underlies the OWL EL ontology language. The extension involves a typicality operator T, whose semantics is based on Lehmann and Magidor's ranked models and allows for the definition of defeasible inclusions. We consider both rational entailment and minimal entailment. We show that deciding instance checking under minimal entailment is in general $\Pi^P_2$-hard, while, under rational entailment, instance checking can be computed in polynomial time. We develop a Datalog calculus for instance checking under rational entailment and exploit it, with stratified negation, for computing the rational closure of simple KBs in polynomial time.

Note: This is an edited version of an article I wrote for RT Insights. I'm convinced we are entering the Golden Age of artificial intelligence (AI); with so much promise and potential in front of us, I am feeling a little like Neo in The Matrix as he swallows the red pill. However, rather than science fiction, my recent work at FICO to make AI better has drawn upon my background in theoretical physics to create what we call defensive AI. This is needed, because AI-based attacks are not science fiction -- they are happening today. Businesses have relied on AI to fight fraud and financial crime for more than 25 years.

The current era of computer science has seen a significant increase in the application of machine learning (ML) and knowledge representation (KR). The problem with the current situation regarding ethics and AI is the weaknesses of ML and KR when used separately. ML will “learn” ethical behaviour as it is observed and may therefore disagree with human morals. On the other hand, KR is too rigid and can only process scenarios that have been predefined. This paper proposes a solution to the question posed by Rossi (2016) “How to combine bottom-up learning approaches with top-down rule-based approaches in defining ethical principles for AI systems?” This system focuses on potential unethical behaviors that are caused by human nature instead of ethical dilemmas caused by technology insufficiency in the wartime scenarios. Our solution is an architecture that combines a classifier to identify targets in wartime scenarios and a rules-based system in the form of ontologies to guide an AI agent’s behaviour in the given circumstance.

"We will always do what is necessary to defend ourselves, our allies and our values against an attack of this sort, which is an attack not only on the United Kingdom, but upon the international rules-based system on which all countries, including Russia, depend for their safety and security," Bristow told reporters.

In an industry known for its love of buzzwords and hype, artificial intelligence (AI) has become marketing's new'big data'. But where big data ultimately led to new layers of complexity, AI promises the opposite. Big data forced marketers to become data scientists (or hire them, if they could be found), but AI holds out the hope that marketers may get to go back to doing what they signed up for the in the first place. Recent months have seen technology providers such as Salesforce, Oracle and Microsoft bring new AI-based technologies to market, promising to derive insights and improve conversions by mimicking the processes of the human brain in software. Salesforce, for example, is rolling out its Einstein AI technology to provide functions such as product recommendations within the Commerce Cloud, email content recommendations within its Marketing Cloud, and predictive forecasting tools for sales managers with its Sales Cloud.

It's important to understand the distinction of machine learning versus other forms of automation analytics. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered. You have exceeded the maximum character limit.

The Trump administration issued a ruling that expands the entities which can exempt themselves from the contraception mandate in the Affordable Care Act. A federal judge in Boston Monday threw out a lawsuit by Massachusetts' attorney general that attempted to block the Trump administration's rules expanding exemptions from ObamaCare's birth control mandate. U.S. District Judge Nathaniel Gorton said Massachusetts lacked standing to sue and noted that "the record is uniquely obscure" regarding whether employers in the state would take advantage of the exemptions. In a statement, Massachusetts Attorney General Maura Healey said that she was disappointed in the decision but remained committed to ensuring "affordable and reliable reproductive health care for women." ObamaCare originally required most companies to cover birth control at no additional cost, though it included exemptions for religious organizations.

A federal judge has tossed the Massachusetts attorney general's lawsuit against President Donald Trump's administration over rules allowing more employers to opt out of providing no-cost birth control to women.

BOSTON (Reuters) - A federal judge on Monday rejected a lawsuit by Massachusetts' attorney general challenging new rules by President Donald Trump's administration that make it easier for employers to avoid providing insurance that covers women's birth control.