Generative artificial intelligence (GenAI) models are a type of deep learning neural network model capable of learning from large datasets and generating new content from a given context. They represent a significant leap in the ability of the artificial intelligence (AI) field to not just interpret data but also to create something new, including text, images, videos, code, and sound [2]. Large language models (LLMs) are a type of GenAI model designed to understand and generate natural language. The market for LLMs is estimated to reach 40.8 billion USD by 2029, up from 10.5 billion USD in 2022 [10]. Organizations are currently competing to develop the most sophisticated LLM capable of mimicking human-like conversations and tasks. This has led to the creation of models such as OpenAI's ChatGPT,

We study universal deepfake detection. Our goal is to detect synthetic images from a range of generative AI approaches, particularly from emerging ones which are unseen during training of the deepfake detector. Universal deepfake detection requires outstanding generalization capability. Motivated by recently proposed masked image modeling which has demonstrated excellent generalization in self-supervised pre-training, we make the first attempt to explore masked image modeling for universal deepfake detection. We study spatial and frequency domain masking in training deepfake detectors. Based on empirical analysis, we propose a novel deepfake detector via frequency masking. Our focus on frequency domain is different from the majority, which primarily target spatial domain detection. Our comparative analyses reveal substantial performance gains over existing methods. Code and models are publicly available.

This survey delves into the application of diffusion models in time-series forecasting. Diffusion models are demonstrating state-of-the-art results in various fields of generative AI. The paper includes comprehensive background information on diffusion models, detailing their conditioning methods and reviewing their use in time-series forecasting. The analysis covers 11 specific time-series implementations, the intuition and theory behind them, the effectiveness on different datasets, and a comparison among each other. Key contributions of this work are the thorough exploration of diffusion models' applications in time-series forecasting and a chronologically ordered overview of these models. Additionally, the paper offers an insightful discussion on the current state-of-the-art in this domain and outlines potential future research directions. This serves as a valuable resource for researchers in AI and time-series analysis, offering a clear view of the latest advancements and future potential of diffusion models.

As more companies ramp up development of artificial intelligence systems, they are increasingly turning to graphics processing unit (GPU) chips for the computing power they need to run large language models (LLMs) and to crunch data quickly at massive scale. Between video game processing and AI, demand for GPUs has never been higher, and chipmakers are rushing to bolster supply. In new findings released today, though, researchers are highlighting a vulnerability in multiple brands and models of mainstream GPUs--including Apple, Qualcomm, and AMD chips--that could allow an attacker to steal large quantities of data from a GPU's memory. The silicon industry has spent years refining the security of central processing units, or CPUs, so they don't leak data in memory even when they are built to optimize for speed. However, since GPUs were designed for raw graphics processing power, they haven't been architected to the same degree with data privacy as a priority.

As the US gears up for the 2024 presidential election, OpenAI shares its plans on suppressing misinformation related to elections worldwide, with a focus set on boosting the transparency around the origin of information. One such highlight is the use of cryptography -- as standardized by the Coalition for Content Provenance and Authenticity -- to encode the provenance of images generated by DALL-E 3. This will allow the platform to better detect AI-generated images using a provenance classifier, in order to help voters assess the reliability of certain content. This approach is similar to, if not better than, DeepMind's SynthID for digitally watermark AI-generated images and audio, as part of Google's own election content strategy published last month. Meta's AI image generator also adds an invisible watermark to its content, though the company has yet to share its readiness on tackling election-related misinformation.

Global executives are increasingly worried about the long term viability of their businesses, a pre-Davos survey by PricewaterhouseCoopers showed, with pressures mounting from generative artificial intelligence and climate disruption. Some 45% of more than 4,700 global CEOs surveyed do not believe their businesses will survive, barring significant changes, in the next 10 years, the "Big Four" auditor said. "There's the 55% who think they don't have to change radically, and I would argue that's a little naive because the world is changing so fast around them," PwC Global Chairman Bob Moritz told the Reuters Global Markets Forum (GMF) ahead of the World Economic Forum's annual meeting in Davos.

OpenAI is rolling out a series of initiatives to prevent its products from being used for misinformation ahead of a major year for elections globally. On Monday, the artificial intelligence startup announced new tools that will attribute information about current events provided by its chatbot ChatGPT, and help users determine if an image was created by its AI software. The changes come as concerns rise over the risks of so-called deepfakes -- manipulated videos or other digital representations -- and other AI-produced content that could misguide voters during campaigns. "Protecting the integrity of elections requires collaboration from every corner of the democratic process, and we want to make sure our technology is not used in a way that could undermine this process," the company wrote in a blog post on Monday.

Online reviews in the form of user-generated content (UGC) significantly impact consumer decision-making. However, the pervasive issue of not only human fake content but also machine-generated content challenges UGC's reliability. Recent advances in Large Language Models (LLMs) may pave the way to fabricate indistinguishable fake generated content at a much lower cost. Leveraging OpenAI's GPT-4-Turbo and DALL-E-2 models, we craft AiGen-FoodReview, a multi-modal dataset of 20,144 restaurant review-image pairs divided into authentic and machine-generated. We explore unimodal and multimodal detection models, achieving 99.80% multimodal accuracy with FLAVA. We use attributes from readability and photographic theories to score reviews and images, respectively, demonstrating their utility as hand-crafted features in scalable and interpretable detection models, with comparable performance. The paper contributes by open-sourcing the dataset and releasing fake review detectors, recommending its use in unimodal and multimodal fake review detection tasks, and evaluating linguistic and visual features in synthetic versus authentic data.

The company, which makes the popular ChatGPT chatbot, DALL-E image generator and provides AI technology to many companies, including Microsoft, said in a Monday blog post that it wouldn't allow people to use its tech to build applications for political campaigns and lobbying, to discourage people from voting or spread misinformation about the voting process. OpenAI said it would also begin putting embedded watermarks -- a tool to detect AI-created photographs -- into images made with its DALL-E image-generator "early this year."

Funding AI chatbots is expensive, and Microsoft will ask those users who want the latest features of its Copilot AI chatbot to subscribe to a new plan: Copilot Pro, available for 20 per user per month. And the new Copilot Pro subscription doesn't appear to be placing any limits on or restricting users of the free tier, except in one small way. Instead, it's offering consumers access to some of the Copilot features that businesses have access to, plus a cohesive Copilot experience. Some users have noticed that Microsoft is quietly upgrading the general version of Copilot to a model known as GPT-4 Turbo, OpenAI's latest version. While OpenAI's link provides significant technical detail, it essentially allows input up to 300 pages of text, and requires between two to three times less computational power. Its knowledge is current up to April 2023.