The tremendous recent advances in generative artificial intelligence techniques have led to significant successes and promise in a wide range of different applications ranging from conversational agents and textual content generation to voice and visual synthesis. Amid the rise in generative AI and its increasing widespread adoption, there has been significant growing concern over the use of generative AI for malicious purposes. In the realm of visual content synthesis using generative AI, key areas of significant concern has been image forgery (e.g., generation of images containing or derived from copyright content), and data poisoning (i.e., generation of adversarially contaminated images). Motivated to address these key concerns to encourage responsible generative AI, we introduce the DeepfakeArt Challenge, a large-scale challenge benchmark dataset designed specifically to aid in the building of machine learning algorithms for generative AI art forgery and data poisoning detection. Comprising of over 32,000 records across a variety of generative forgery and data poisoning techniques, each entry consists of a pair of images that are either forgeries / adversarially contaminated or not. Each of the generated images in the DeepfakeArt Challenge benchmark dataset has been quality checked in a comprehensive manner. The DeepfakeArt Challenge is a core part of GenAI4Good, a global open source initiative for accelerating machine learning for promoting responsible creation and deployment of generative AI for good.

Elves and Argonians clipping through walls and stepping through tables, blacksmiths who won't acknowledge your existence until you take single step to the left, Draugers that drop into rag-doll seizures the moment you put an arrow through their eye -- Bethesda's Elder Scrolls long-running RPG series is beloved for many reasons, the realism of their non-playable characters (NPCs) is not among them. But the days of hearing the same rote quotes and watching the same half-hearted search patterns perpetually repeated from NPCs are quickly coming to an end. It's all thanks to the emergence of generative chatbots that are helping game developers craft more lifelike, realistic characters and in-game action. "Game AI is seldom about any deep intelligence but rather about the illusion of intelligence," Steve Rabin, Principal Software Engineer at Electronic Arts, wrote in the 2017 essay, The Illusion of Intelligence. "Often we are trying to create believable human behavior, but the actual intelligence that we are able to program is fairly constrained and painfully brittle."

Nvidia, the company that dominates the market for graphics processing units, was once known mostly in the video game world. But these days, Nvidia GPUs are also the go-to source for the massive computing power needed to run generative A.I. systems--and the recent explosion in A.I. hype has propelled the company's stock into the stratosphere. Nvidia briefly hit a trillion-dollar valuation, putting itself in league with tech giants like Alphabet and Apple and launching a bit of a frenzy in the markets. Nvidia is looking like the first big stock win of the A.I. era, and investors are salivating. On Sunday's episode of What Next: TBD, I spoke with Don Clark, a freelance reporter who specializes in the chips industry, about how Nvidia rode the A.I. revolution, became the hottest chipmaker in the world, and made the entire A.I. craze suddenly seem very real.

ByteDance Ltd. is testing an artificial intelligence-powered chatbot among employees, joining rival Chinese internet conglomerates from Alibaba Group Holding Ltd. to Baidu Inc. in a race to create a local version of ChatGPT. TikTok's owner has code-named the project "Grace," a ByteDance spokesperson said without revealing more details. Employees using the service will be greeted by a pop-up message saying it's based on several large language models, according to one of the testers, who asked to remain unidentified discussing an internal project. The mysterious chatbot is the first official indication of ByteDance's push into the nascent field of generative AI, which took off after OpenAI demonstrated its potential by rolling out ChatGPT in November. The Chinese company, which also runs domestic video service Douyin, has been developing smartphone apps based on AI algorithmic recommendations ever since computer whizzes Zhang Yiming and Liang Rubo founded the outfit more than a decade ago.

Advances in natural language processing (NLP) have been driven mainly by scaling up the size of pre-trained language models, along with the amount of data and compute required for training (Raffel et al., 2020; Radford et al., 2019; Rae et al., 2021; Fedus et al., 2021; Hoffmann et al., 2022). OpenAI recently released ChatGPT, a text generation model with conversational capabilities. The model is based on GPT3.5 which is a version of GPT3 (Brown et al., 2020) first fine-tuned on code then fine-tuned using Reinforcement Learning from Human Feedback (RLHF) (Christiano et al., 2017; Stiennon et al., 2020), a method previously demonstrated by OpenAI with Instruct-GPT (Ouyang et al., 2022). This fine-tuning process contributes not only to the model's knowledge but also simplifies the model's interface compared to GPT3, which necessitated substantial prompt engineering to achieve satisfactory outcomes, and hence facilitating the extraction and application of that built-in knowledge. As a result of these significant performance improvements, ChatGPT and other large language models have gained much popularity in the media and in the social context, often without fully understanding the underlying limitations of the models - e.g., the possibility of generating hateful, hateful, toxic, or disrespectful content (Bender et al., 2021; McGuffie & Newhouse, 2020; Weidinger et al., 2021). Another potential misuse of LLMs or ChatGPT is industrializing radicalization and harmful propaganda which poses a significant and unconventional threat to civil society. In response to the mounting concerns surrounding potential misuse, numerous researchers are now exploring various strategies to mitigate associated risks.

OpenAI has released the Chat Generative Pre-trained Transformer (ChatGPT) and revolutionized the approach in artificial intelligence to human-model interaction. Several publications on ChatGPT evaluation test its effectiveness on well-known natural language processing (NLP) tasks. However, the existing studies are mostly non-automated and tested on a very limited scale. In this work, we examined ChatGPT's capabilities on 25 diverse analytical NLP tasks, most of them subjective even to humans, such as sentiment analysis, emotion recognition, offensiveness, and stance detection. In contrast, the other tasks require more objective reasoning like word sense disambiguation, linguistic acceptability, and question answering. We also evaluated GPT-4 model on five selected subsets of NLP tasks. We automated ChatGPT and GPT-4 prompting process and analyzed more than 49k responses. Our comparison of its results with available State-of-the-Art (SOTA) solutions showed that the average loss in quality of the ChatGPT model was about 25% for zero-shot and few-shot evaluation. For GPT-4 model, a loss for semantic tasks is significantly lower than for ChatGPT. We showed that the more difficult the task (lower SOTA performance), the higher the ChatGPT loss. It especially refers to pragmatic NLP problems like emotion recognition. We also tested the ability to personalize ChatGPT responses for selected subjective tasks via Random Contextual Few-Shot Personalization, and we obtained significantly better user-based predictions. Additional qualitative analysis revealed a ChatGPT bias, most likely due to the rules imposed on human trainers by OpenAI. Our results provide the basis for a fundamental discussion of whether the high quality of recent predictive NLP models can indicate a tool's usefulness to society and how the learning and validation procedures for such systems should be established.

Synthetic realities are digital creations or augmentations that are contextually generated through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of data to construct new narratives or realities, regardless of the intent to deceive. In this paper, we delve into the concept of synthetic realities and their implications for Digital Forensics and society at large within the rapidly advancing field of AI. We highlight the crucial need for the development of forensic techniques capable of identifying harmful synthetic creations and distinguishing them from reality. This is especially important in scenarios involving the creation and dissemination of fake news, disinformation, and misinformation. Our focus extends to various forms of media, such as images, videos, audio, and text, as we examine how synthetic realities are crafted and explore approaches to detecting these malicious creations. Additionally, we shed light on the key research challenges that lie ahead in this area. This study is of paramount importance due to the rapid progress of AI generative techniques and their impact on the fundamental principles of Forensic Science.

The recent progress of artificial intelligence (AI) opens up new frontiers in the possibility of automating many tasks involved in Telecom networks design, implementation, and deployment. This has been further pushed forward with the evolution of generative artificial intelligence (AI), including the emergence of large language models (LLMs), which is believed to be the cornerstone toward realizing self-governed, interactive AI agents. Motivated by this, in this paper, we aim to adapt the paradigm of LLMs to the Telecom domain. In particular, we fine-tune several LLMs including BERT, distilled BERT, RoBERTa and GPT-2, to the Telecom domain languages, and demonstrate a use case for identifying the 3rd Generation Partnership Project (3GPP) standard working groups. We consider training the selected models on 3GPP technical documents (Tdoc) pertinent to years 2009-2019 and predict the Tdoc categories in years 2020-2023. The results demonstrate that fine-tuning BERT and RoBERTa model achieves 84.6% accuracy, while GPT-2 model achieves 83% in identifying 3GPP working groups. The distilled BERT model with around 50% less parameters achieves similar performance as others. This corroborates that fine-tuning pretrained LLM can effectively identify the categories of Telecom language. The developed framework shows a stepping stone towards realizing intent-driven and self-evolving wireless networks from Telecom languages, and paves the way for the implementation of generative AI in the Telecom domain.

Background and Context: Over the past year, large language models (LLMs) have taken the world by storm. In computing education, like in other walks of life, many opportunities and threats have emerged as a consequence. Objectives: In this article, we explore such opportunities and threats in a specific area: responding to student programmers' help requests. More specifically, we assess how good LLMs are at identifying issues in problematic code that students request help on. Method: We collected a sample of help requests and code from an online programming course. We then prompted two different LLMs (OpenAI Codex and GPT-3.5) to identify and explain the issues in the students' code and assessed the LLM-generated answers both quantitatively and qualitatively. Findings: GPT-3.5 outperforms Codex in most respects. Both LLMs frequently find at least one actual issue in each student program (GPT-3.5 in 90% of the cases). Neither LLM excels at finding all the issues (GPT-3.5 finding them 57% of the time). False positives are common (40% chance for GPT-3.5). The advice that the LLMs provide on the issues is often sensible. The LLMs perform better on issues involving program logic rather than on output formatting. Model solutions are frequently provided even when the LLM is prompted not to. LLM responses to prompts in a non-English language are only slightly worse than responses to English prompts. Implications: Our results continue to highlight the utility of LLMs in programming education. At the same time, the results highlight the unreliability of LLMs: LLMs make some of the same mistakes that students do, perhaps especially when formatting output as required by automated assessment systems. Our study informs teachers interested in using LLMs as well as future efforts to customize LLMs for the needs of programming education.

Simultaneously, the use of AI in academic writing has generated significant interest in AI related plagiarism. This has led to the emergence of a new term that warrants our attention: AI-giarism, a term that combines'AI' and'plagiarism', and has yet to be widely researched and defined within academic literature. For the study of AI in education in relation to academic misconduct, I, propose the following definition: AI-giarism refers to the unethical practice of using artificial intelligence technology, particularly generative language models, to generate content that is plagiarised either from original human-authored work or directly from AIgenerated content, without appropriate acknowledgement of the original sources or AI's contribution. This can happen when people use AI tools or language models to create written or multimedia content, such as articles, blog posts, images or videos, without properly attributing the original sources or modifying the content sufficiently to make it original (Salvagno et al, 2023). Some AI tools are designed to generate content by automatically combining or predicting from different sources, such as articles or websites, and paraphrasing them to create new content.