Large language models (LLMs) have demonstrated impressive results on natural language tasks, and security researchers are beginning to employ them in both offensive and defensive systems. In cyber-security, there have been multiple research efforts that utilize LLMs focusing on the pre-breach stage of attacks like phishing and malware generation. However, so far there lacks a comprehensive study regarding whether LLM-based systems can be leveraged to simulate the post-breach stage of attacks that are typically human-operated, or "hands-on-keyboard" attacks, under various attack techniques and environments. As LLMs inevitably advance, they may be able to automate both the pre- and post-breach attack stages. This shift may transform organizational attacks from rare, expert-led events to frequent, automated operations requiring no expertise and executed at automation speed and scale. This risks fundamentally changing global computer security and correspondingly causing substantial economic impacts, and a goal of this work is to better understand these risks now so we can better prepare for these inevitable ever-more-capable LLMs on the horizon. On the immediate impact side, this research serves three purposes. First, an automated LLM-based, post-breach exploitation framework can help analysts quickly test and continually improve their organization's network security posture against previously unseen attacks. Second, an LLM-based penetration test system can extend the effectiveness of red teams with a limited number of human analysts. Finally, this research can help defensive systems and teams learn to detect novel attack behaviors preemptively before their use in the wild....

Artificial Intelligence (AI) techniques are widely used in video games. Recently, AI planning methods have been applied to maintain plot consistency in the face of player's agency over the narrative. Combined with an automatically populated player model, such AI experience managers can dynamically create a consistent narrative tailored to a specific player. These tools help game narrative designers achieve narrative goals while affording players a choice. On the other hand, they increase the number of feasible plot branches making it more difficult for the author to ensure that each branch carries the player along a desired emotion arc. In this paper we discuss the problem and call for an extension of experience managers with player emotion models. When successful, interactive narrative can be then automatically produced to satisfy authorial goals not only in terms of specific events but also in terms of emotions evoked in the player.

The typical goal of an experience manager in an interactive narrative is to create a sense of shared authorship that lends the player freedom to personalize the experience while still meeting the author's constraints on structure. This can be difficult when the player and author only communicate with one another through their actions. Each new action causes new questions to arise, assumptions to be made, and old questions to be answered. In this paper, I propose a technique called Mutual Implicit Question Answering, or MIQA, designed to allow an experience manager to both perceive and influence the momentum of an interactive story. It combines a generative model of narrative planning with analytical models of question answering and salience. I also present the results of a small, qualitative study of how people construct interactive narratives that lends insight for the eventual evaluation of a MIQA experience manager.

CMO vs CIO: Why it shouldn't be a battle Chief data officer and chief digital officer roles are on the rise with significant implications for IT leaders and their position in relation to the top table. Zeta Global uses its vast database of profiles and technologies like artificial intelligence to help companies improve their marketing return on investment. Customers include British Airways, American Airlines, Citizens Bank, Ralph Lauren and Sprint. ZDNet talked to the company's CIO, Jeffry Nimeroff, to find out more about the company's plans and priorities. ZDNet: Tell me a little bit about your company and the focus of the business.

Adobe and Nvidia have announced a partnership that will see both companies deliver new artificial intelligence (AI) and deep learning services for Adobe Creative. Making the announcement during the Adobe Summit keynote in Las Vegas on Wednesday, Adobe CEO and president Shantanu Narayen was joined by Nvidia founder and CEO Jensen Huang. Machine learning, task automation and robotics are already widely used in business. These and other AI technologies are about to multiply, and we look at how organizations can best take advantage of them. The CEOs said the partnership will see both companies work to optimise the Adobe Sensei AI and machine learning framework for Nvidia GPUs.

Adobe Systems Inc. is applying artificial intelligence to new areas, for example recently infusing machine learning into Photoshop to save time for designers. Today, it's bringing similar automation features to Experience Manager, its marketing content platform for enterprises, in an effort to help companies more easily create and target marketing content. "Everyone's becoming a creator," Elliot Sedegah, Adobe's group manager of strategy and product marketing for Experience Manager, said in an interview. "There are intelligent things you can do along the way without having to think about what's underneath." Headlining the seven update is a capability called Smart Layout.

The goal of an interactive narrative system is to immerse users in a virtual world such that they believe that they are an integral part of an unfolding story and that their actions can significantly alter the direction or outcome of the story. In this article we review the ways in which artificial intelligence can be brought to bear on the creation of interactive narrative systems. We lay out the landscape of about 20 years of interactive narrative research and explore the successes as well as open research questions pertaining to the novel use of computational narrative intelligence in the pursuit of entertainment, education, and training. The prevalence of storytelling in human culture may be explained by the use of narrative as a cognitive tool for situated understanding (Gerrig 1993). This narrative intelligence -- the ability to organize experience into narrative form -- is central to the cognitive processes employed across a range of experiences, from entertainment to active learning.

This paper describes entries to the third Playable Experiences track to be held at the AIIDE conference. We discuss the five entries accepted to the track for 2015, as well as the ongoing development of the track as part of AIIDE.

An open area of research for AI in games is how to provide unique gameplay experiences that present specialized game content to users based on their preferences, in-game actions, or the system's goals. The area of procedural content generation (PCG) focuses on creating or modifying game worlds, assets, and mechanics to generate tailored or personalized game experiences. Similarly, the area of interactive narrative (IN) focuses on creating or modifying story worlds, events, and domains to generate tailored or personalized story experiences. In this paper we describe a game engine that utilizes a PCG pipeline to generate and control a range of gameplay experiences from an underlying IN experience management construct.

Artificial Intelligence (AI) techniques have been widely used in video games to control non-playable characters. More recently, AI has been applied to automated story generation with the objective of managing the player's experience in an interactive narrative. Such AI experience managers can generate and adapt narrative dynamically, often in response to the player's in-game actions. We implement and evaluate a recently proposed AI experience manager, PACE, which predicts the player's emotional response to a narrative event and uses such predictions to shape the narrative to keep the player on an author-supplied target emotional curve.