Generative artificial intelligence (AI) and large language models (LLMs) have gained rapid popularity through publicly available tools such as ChatGPT. The adoption of LLMs for personal and professional use is fueled by the natural interactions between human users and computer applications such as ChatGPT, along with powerful summarization and text generation capabilities. Given the widespread use of such generative AI tools, in this work we investigate how these tools can be deployed in a non-safety critical, strategic traffic flow management setting. Specifically, we train an LLM, CHATATC, based on a large historical data set of Ground Delay Program (GDP) issuances, spanning 2000-2023 and consisting of over 80,000 GDP implementations, revisions, and cancellations. We test the query and response capabilities of CHATATC, documenting successes (e.g., providing correct GDP rates, durations, and reason) and shortcomings (e.g,. superlative questions). We also detail the design of a graphical user interface for future users to interact and collaborate with the CHATATC conversational agent.

Generative AI's expanding footprint across numerous industries has led to both excitement and increased scrutiny. This paper delves into the unique security challenges posed by Generative AI, and outlines potential research directions for managing these risks. Generative AI (GenAI) systems enable users to quickly generate high-quality content. GenAI models are designed to understand and generate content with a degree of autonomy that surpasses traditional machine learning systems, providing novel capabilities to generate text and code, interact with humans and Internet services, generate realistic images, and understand visual scenes. This capability enables a broader range of applications, and in this way introduces new security challenges unique to these novel GenAI-integrated applications. In this paper we discuss the challenges and opportunities for the field, starting in this section with the security risks, including how GenAI models might become a target of attack, a "fool" that unintentionally harms security, or a tool for bad actors to attack others. While GenAI models have groundbreaking capabilities, they are also susceptible to adversarial attack and manipulation. Jailbreaking and prompt injection are two prominent threats to GenAI models and applications built using them. Jailbreaking is an emergent technique where adversaries use specially crafted prompts to manipulate AI models into generating harmful or misleading outputs (Chao et al., 2023; Wei et al., 2023; Liu et al., 2023d). This exploitation can lead to the AI system bypassing its own safety protocols or ethical guidelines.

In this paper, we delve into the rapidly evolving challenge of misinformation detection, with a specific focus on the nuanced manipulation of narrative frames - an under-explored area within the AI community. The potential for Generative AI models to generate misleading narratives underscores the urgency of this problem. Drawing from communication and framing theories, we posit that the presentation or 'framing' of accurate information can dramatically alter its interpretation, potentially leading to misinformation. We highlight this issue through real-world examples, demonstrating how shifts in narrative frames can transmute fact-based information into misinformation. To tackle this challenge, we propose an innovative approach leveraging the power of pre-trained Large Language Models and deep neural networks to detect misinformation originating from accurate facts portrayed under different frames. These advanced AI techniques offer unprecedented capabilities in identifying complex patterns within unstructured data critical for examining the subtleties of narrative frames. The objective of this paper is to bridge a significant research gap in the AI domain, providing valuable insights and methodologies for tackling framing-induced misinformation, thus contributing to the advancement of responsible and trustworthy AI technologies. Several experiments are intensively conducted and experimental results explicitly demonstrate the various impact of elements of framing theory proving the rationale of applying framing theory to increase the performance in misinformation detection.

This paper introduces a media service model that exploits artificial intelligence (AI) video generators at the receive end. This proposal deviates from the traditional multimedia ecosystem, completely relying on in-house production, by shifting part of the content creation onto the receiver. We bring a semantic process into the framework, allowing the distribution network to provide service elements that prompt the content generator, rather than distributing encoded data of fully finished programs. The service elements include fine-tailored text descriptions, lightweight image data of some objects, or application programming interfaces, comprehensively referred to as semantic sources, and the user terminal translates the received semantic data into video frames. Empowered by the random nature of generative AI, the users could then experience super-personalized services accordingly. The proposed idea incorporates the situations in which the user receives different service providers' element packages; a sequence of packages over time, or multiple packages at the same time. Given promised in-context coherence and content integrity, the combinatory dynamics will amplify the service diversity, allowing the users to always chance upon new experiences. This work particularly aims at short-form videos and advertisements, which the users would easily feel fatigued by seeing the same frame sequence every time. In those use cases, the content provider's role will be recast as scripting semantic sources, transformed from a thorough producer. Overall, this work explores a new form of media ecosystem facilitated by receiver-embedded generative models, featuring both random content dynamics and enhanced delivery efficiency simultaneously.

Ever posted or left a comment on Reddit? Your words will soon be used to train an artificial intelligence companies' models, according to Bloomberg. The website signed a deal that's "worth about 60 million on an annualized basis" earlier this year, it reportedly told potential investors ahead of its expected initial public offering (IPO). Bloomberg didn't name the "large AI company" that's paying Reddit millions for access to its content, but their agreement could apparently serve as a model for future contracts, which could mean more multi-million deals for the firm. Reddit first announced that it was going to start charging companies for API access in April last year.

Microsoft-backed OpenAI has completed a deal that values the artificial intelligence company at 80bn or more, the New York Times reported on Friday, citing multiple people with knowledge of the deal. The company would sell existing shares in a so-called tender offer led by venture firm Thrive Capital, the report said. Employees will be able to cash out their shares of the company rather than a traditional funding round, which would raise money for the business, the report added. OpenAI did not immediately respond to a request for comment. The artificial intelligence firm agreed to a similar deal early last year.

OpenAI has unveiled its latest artificial intelligence system, a program called Sora that can transform text descriptions into photorealistic videos. The video generation model is spurring excitement about advancing AI technology, along with growing concerns over how artificial deepfake videos worsen misinformation and disinformation during a pivotal election year worldwide. The Sora AI model can currently create videos up to 60 seconds long using either text instructions alone or text combined with an image. One demonstration video starts with a text prompt that describes how "a stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage". Other examples include a dog frolicking in the snow, vehicles driving along roads and more fantastical scenarios such as sharks swimming in midair between city skyscrapers.

The rapid advancement of artificial intelligence (AI) has highlighted ChatGPT as a pivotal technology in the field of information retrieval (IR). Distinguished from its predecessors, ChatGPT offers significant benefits that have attracted the attention of both the industry and academic communities. While some view ChatGPT as a groundbreaking innovation, others attribute its success to the effective integration of product development and market strategies. The emergence of ChatGPT, alongside GPT-4, marks a new phase in Generative AI, generating content that is distinct from training examples and exceeding the capabilities of the prior GPT-3 model by OpenAI. Unlike the traditional supervised learning approach in IR tasks, ChatGPT challenges existing paradigms, bringing forth new challenges and opportunities regarding text quality assurance, model bias, and efficiency. This paper seeks to examine the impact of ChatGPT on IR tasks and offer insights into its potential future developments.

For the past couple of weeks, I've been making a home video on my phone, using Apple's iMovie software. The idea is to weave together clips of my family that I've taken during the month of February; I plan to keep working on it until March. So far, the movie shows my five-month-old daughter cooing and waving her arms; my five-year-old son chasing me with a snowball; and a visit to the spooky, run-down amusement park in our town, among other things. I thought of my movie while absorbing the announcement, yesterday, of Sora, an astonishing new text-to-video system from OpenAI, the makers of ChatGPT. Sora can take prompts from users and produce detailed, inventive, and photorealistic one-minute-long videos.

Major technology companies signed a pact Friday to voluntarily adopt "reasonable precautions" to prevent artificial intelligence tools from being used to disrupt democratic elections around the world. Executives from Adobe, Amazon, Google, IBM, Meta, Microsoft, OpenAI and TikTok gathered at the Munich Security Conference to announce a new framework for how they respond to AI-generated deepfakes that deliberately trick voters. Twelve other companies – including Elon Musk's X – are also signing on to the accord. "Everybody recognizes that no one tech company, no one government, no one civil society organization is able to deal with the advent of this technology and its possible nefarious use on their own," said Nick Clegg, president of global affairs for Meta, the parent company of Facebook and Instagram, in an interview ahead of the summit. The accord is largely symbolic, but targets increasingly realistic AI-generated images, audio and video "that deceptively fake or alter the appearance, voice, or actions of political candidates, election officials, and other key stakeholders in a democratic election, or that provide false information to voters about when, where, and how they can lawfully vote".