Kurt "The Cyberguy" Knutsson explains how facial recognition technology can help you find your perfect match. From a poisonous date to finding love with a serial killer, these six chilling cases show how unsuspecting dating app users on the quest for romance led them into the clutches of danger. Dating apps – from Tinder to Grindr – are the modern way for people to connect with potential partners from the comfort of their own space. Brace yourself for stories that blur the line between love and terror. Here is Fox News Digital's list of some recent cases where love went wrong.

Community Data Coop presents its continuing series of Education Spotlight events! Our next event is a panel discussion on Artificial Intelligence and Ethics. The proliferation of AI in every aspect of our lives has resulted in many new and exciting advances, but the speed at which the field has developed has resulted in many concerns from privacy to labor replacement. Come hear from a panel of AI professionals as they discuss the true impact of Artificial Intelligence on our lives beyond the obvious pros and cons. Alissa Yeganeh, Senior Consultant, Coppei Partners - Alissa Yeganeh is a Data-Focused Customer & Operations leader, currently working as a Senior Consultant at Coppei Partners, a business and technology consulting firm based in Mercer Island, WA.

Welcome to My Unique House, a weekly video series that profiles the most unconventional homes around the country and their equally interesting owners. Check back next week for another wild story, from a mermaid-inspired bungalow, an old airplane transformed into the ultimate bachelor pad and an entire house dedicated to cats. This Washington, D.C. home is sleek in design, fully soundproof and crafted with the highest-end materials. Oh, and it was built in seven days. John Thompson, co-founder of Prefab Partners and the man behind Farmhouse Modern, explained to AOL Lifestyle how Aerospace Robotic Panelization is leading the way for architecture through the use of robots.

Declarative logic programs (LP) based on the well-founded semantics (WFS) are widely used for knowledge representation (KR).  Logical functions are desirable expressively in KR, but when present make LP inferencing become undecidable. In this paper, we present radial restraint : a novel approach to bounded rationality in LP. Radial restraint is parameterized by a norm that measures the syntactic complexity of a term, along with an abstraction function based on that norm.  When a term exceeds a bound for the norm, the term is assigned the WFS's third truth-value of undefined .  If the norm is finitary, radial restraint guarantees finiteness of models and decidability of inferencing, even when logical functions are present.  It further guarantees soundness, even when non-monotonicity is present.  We give a fixed-point semantics for radially restrained well-founded models which soundly approximate well-founded models.  We also show how to perform correct inferencing relative to such models, via SLG_ABS, an extension of tabled SLG resolution that uses norm-based abstraction functions.  Finally we discuss how SLG_ABS is implemented in the engine of XSB Prolog, and scales to knowledge bases with more than 10^8 rules and facts.