Improvising Linguistic Style: Social and Affective Bases for Agent Personality

This paper introduces Linguistic Style Improvisation, a theory and set of algorithms for improvisation of spoken utterances by artificial agents, with applications to interactive story and dialogue systems. We argue that linguistic style is a key aspect of character, and show how speech act representations common in AI can provide abstract representations from which computer characters can improvise. We show that the mechanisms proposed introduce the possibility of socially oriented agents, meet the requirements that lifelike characters be believable, and satisfy particular criteria for improvisation proposed by Hayes-Roth.Comment: 10 pages, uses aaai.sty, lingmacros.sty, psfig.st

Friendly Superintelligent AI: All You Need is Love

There is a non-trivial chance that sometime in the (perhaps somewhat distant) future, someone will build an artificial general intelligence that will surpass human-level cognitive proficiency and go on to become "superintelligent", vastly outperforming humans. The advent of superintelligent AI has great potential, for good or ill. It is therefore imperative that we find a way to ensure-long before one arrives-that any superintelligence we build will consistently act in ways congenial to our interests. This is a very difficult challenge in part because most of the final goals we could give an AI admit of so-called "perverse instantiations". I propose a novel solution to this puzzle: instruct the AI to love humanity. The proposal is compared with Yudkowsky's Coherent Extrapolated Volition, and Bostrom's Moral Modeling proposals

Nicole Eagan: “Cybersecurity is very fast becoming an all-out arms race”

The CEO of Darktrace says organisations need to have AI act like the human immune syste

Game Real-Time Strategy Dengan Menggunakan Artificial Intelligence Quantified Judgement Model Dan Backpropagation Neural Network

Real-Time Strategy (RTS) Game is a quite popular video game genre. The uniqueness of RTS Games is that it is a Strategy Game where time will still continue for all the players. This creates situations where the player must determine their strategies in a matter of seconds. To get a good gameplay experience, then we would need enemies for the player. The way we could do that is to create an AI that could take into account the mechanics of the game. This thesis is aimed to broaden our knowledge on how to develop AI for RTS games.The game will be created in Unity Game Engine 5.1.2f1, where the AI that is going to be implemented is the Quantified Judgement Model and the Neural Network backpropagation. The Quantified Judgement Model will act as Abstract Controller, giving orders to his troops much like a general in a war. Neural Network Backpropagation will be used for the Virtual Character, where the AI will act for each of the troops to think on what they should do. Whether they have to fall back, or keep going forward according to the orders given to him

LIDA: A Working Model of Cognition

In this paper we present the LIDA architecture as a working model of cognition. We argue that such working models are broad in scope and address real world problems in comparison to experimentally based models which focus on specific pieces of cognition. While experimentally based models are useful, we need a working model of cognition that integrates what we know from neuroscience, cognitive science and AI. The LIDA architecture provides such a working model. A LIDA based cognitive robot or software agent will be capable of multiple learning mechanisms. With artificial feelings and emotions as primary motivators and learning facilitators, such systems will ‘live’ through a developmental period during which they will learn in multiple ways to act in an effective, human-like manner in complex, dynamic, and unpredictable environments. We discuss the integration of the learning mechanisms into the existing IDA architecture as a working model of cognition

The Affordable Care Act and implications for health care services for American Indian and Alaska Native individuals

American Indian and Alaska Native (AI/AN) populations report poor physical and mental health outcomes while tribal health providers and the Indian Health Service (IHS) operate in a climate of significant under funding. Understanding how the Patient Protection and Affordable Care Act (ACA) affects Native American tribes and the IHS is critical to addressing the improvement of the overall access, quality, and cost of health care within AI/AN communities. This paper summarizes the ACA provisions that directly and/or indirectly affect the service delivery of health care provided by tribes and the IHS

Synthesis of Triphenylethylene Bisphenols as Aromatase Inhibitors that Also Modulate Estrogen Receptors

A series of triphenylethylene bisphenol analogues of the selective estrogen receptor modulator (SERM) tamoxifen were synthesized and evaluated for their abilities to inhibit aromatase, bind to estrogen receptor α (ER-α) and estrogen receptor β (ER-β), and antagonize the activity of β-estradiol in MCF-7 human breast cancer cells. The long-range goal has been to create dual aromatase inhibitor (AI)/selective estrogen receptor modulators (SERMs). The hypothesis is that in normal tissue the estrogenic SERM activity of a dual AI/SERM could attenuate the undesired effects stemming from global estrogen depletion caused by the AI activity of a dual AI/SERM, while in breast cancer tissue the antiestrogenic SERM activity of a dual AI/SERM could act synergistically with AI activity to enhance the antiproliferative effect. The potent aromatase inhibitory activities and high ER-α and ER-β binding affinities of several of the resulting analogues, together with the facts that they antagonize β-estradiol in a functional assay in MCF-7 human breast cancer cells and they have no E/Z isomers, support their further development in order to obtain dual AI/SERM agents for breast cancer treatment