Ever since Scarlett Johansson voiced an AI assistant in the sci-fi blockbuster'Her', many tech fans have dreamed of making that technology a reality. But it now seems that OpenAI may have pursued that dream too literally as they face accusations of deliberately copying Johansson's voice for ChatGPT's latest update. According to Ms Johansson's statement, the likeness is'so eerily similar to mine that close friends and news outlets could not tell the difference'. Following the allegations, OpenAI's'flirty' voice assistant has now been paused, yet tech fans have been weighing in on whether there really is a resemblance. So, do you think ChatGPT's AI voice sounds like Scarlett Johansson?

'The CyberGuy' Kurt Knutsson joins'Fox & Friends Weekend' to discuss Elon Musk's lawsuit against OpenAI and its CEO over a contractual breach, saying hes right on this one. "Avengers" and "Her" actress Scarlett Johansson revealed that legal action was likely behind OpenAI removing a voice that sounded eerily like hers. A statement released by NPR on Monday explained that OpenAI CEO Sam Altman reached out to Johansson in September about possibly hiring her to voice the ChatGPT 4.0 system. She claimed he suggested her "comforting" voice "could bridge the gap between tech companies and creatives" and help with the "seismic shift concerning humans and Al." Though she rejected the offer after "much consideration and for personal reasons," Johansson was furious to hear the public discuss how the "Sky" voice system resembled hers. Scarlett Johansson said in a statement that she took legal action against OpenAI CEO Sam Altman and the company.

Between the world leaders gathering in Seoul for the second AI Safety Summit this week and Google and OpenAI's launches of their supercharged new models, Astra and GPT-4o, the timing could not be better. AI feels hotter than ever. This year's EmTech will be all about how we can harness the power of generative AI while mitigating its risks,and how the technology will affect the workforce, competitiveness, and democracy. We will also get a sneak peek into the AI labs of Google, OpenAI, Adobe, AWS, and others. This year's top speakers include Nick Clegg, the president of global affairs at Meta, who will talk about what the platform intends to do to curb misinformation.

Scarlett Johansson said Monday that she was "shocked, angered and in disbelief" when she heard that OpenAI used a voice "eerily similar" to hers for its new ChatGPT 4.0 chatbot, even after she had declined to provide her voice. Earlier on Monday, OpenAI announced on X that it would pause the AI voice, known as "Sky," while it addresses "questions about how we chose the voices in ChatGPT." The company said in a blog post that the "Sky" voice was "not an imitation" of Johansson's voice, but that it was recorded by a different professional actor, whose identity the company would not reveal to protect her privacy. But Johansson said in a statement to NPR on Monday that OpenAI's Chief Executive Officer Sam Altman had asked her in September to voice the ChatGPT 4.0 system because he thought her "voice would be comforting to people." She declined, but nine months later, her friends, family and the public noticed how the "Sky" voice resembled hers.

A major challenge facing the world is the provision of equitable and universal access to quality education. Recent advances in generative AI (gen AI) have created excitement about the potential of new technologies to offer a personal tutor for every learner and a teaching assistant for every teacher. The full extent of this dream, however, has not yet materialised. We argue that this is primarily due to the difficulties with verbalising pedagogical intuitions into gen AI prompts and the lack of good evaluation practices, reinforced by the challenges in defining excellent pedagogy. Here we present our work collaborating with learners and educators to translate high level principles from learning science into a pragmatic set of seven diverse educational benchmarks, spanning quantitative, qualitative, automatic and human evaluations; and to develop a new set of fine-tuning datasets to improve the pedagogical capabilities of Gemini, introducing LearnLM-Tutor. Our evaluations show that LearnLM-Tutor is consistently preferred over a prompt tuned Gemini by educators and learners on a number of pedagogical dimensions. We hope that this work can serve as a first step towards developing a comprehensive educational evaluation framework, and that this can enable rapid progress within the AI and EdTech communities towards maximising the positive impact of gen AI in education.

The rise of Generative AI (GenAI) brings about transformative potential across sectors, but its dual-use nature also amplifies risks. Governments globally are grappling with the challenge of regulating GenAI, balancing innovation against safety. China, the United States (US), and the European Union (EU) are at the forefront with initiatives like the Management of Algorithmic Recommendations, the Executive Order, and the AI Act, respectively. However, the rapid evolution of GenAI capabilities often outpaces the development of comprehensive safety measures, creating a gap between regulatory needs and technical advancements. A workshop co-organized by Google, University of Wisconsin, Madison (UW-Madison), and Stanford University aimed to bridge this gap between GenAI policy and technology. The diverse stakeholders of the GenAI space -- from the public and governments to academia and industry -- make any safety measures under consideration more complex, as both technical feasibility and regulatory guidance must be realized. This paper summarizes the discussions during the workshop which addressed questions, such as: How regulation can be designed without hindering technological progress? How technology can evolve to meet regulatory standards? The interplay between legislation and technology is a very vast topic, and we don't claim that this paper is a comprehensive treatment on this topic. This paper is meant to capture findings based on the workshop, and hopefully, can guide discussion on this topic.

In this paper, we explore the transformative impact of Artificial Intelligence (AI) in the manufacturing sector, highlighting its potential to revolutionize industry practices and enhance operational efficiency. We delve into various applications of AI in manufacturing, with a particular emphasis on human-machine interfaces (HMI) and AI-powered milling machines, showcasing how these technologies contribute to more intuitive operations and precision in production processes. Through rigorous market analysis, the paper presents insightful data on AI adoption rates among German manufacturers, comparing these figures with global trends and exploring the specific uses of AI in production, maintenance, customer service, and more. In addition, the paper examines the emerging field of Generative AI and the potential applications of large language models in manufacturing processes. The findings indicate a significant increase in AI adoption from 6% in 2020 to 13.3% in 2023 among German companies, with a projection of substantial economic impact by 2030. The study also addresses the challenges faced by companies, such as data quality and integration hurdles, providing a balanced view of the opportunities and obstacles in AI implementation.

This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware detection, and phishing detection. We present an overview of LLM evolution and its current state, focusing on advancements in models such as GPT-4, GPT-3.5, Mixtral-8x7B, BERT, Falcon2, and LLaMA. Our analysis extends to LLM vulnerabilities, such as prompt injection, insecure output handling, data poisoning, DDoS attacks, and adversarial instructions. We delve into mitigation strategies to protect these models, providing a comprehensive look at potential attack scenarios and prevention techniques. Furthermore, we evaluate the performance of 42 LLM models in cybersecurity knowledge and hardware security, highlighting their strengths and weaknesses. We thoroughly evaluate cybersecurity datasets for LLM training and testing, covering the lifecycle from data creation to usage and identifying gaps for future research. In addition, we review new strategies for leveraging LLMs, including techniques like Half-Quadratic Quantization (HQQ), Reinforcement Learning with Human Feedback (RLHF), Direct Preference Optimization (DPO), Quantized Low-Rank Adapters (QLoRA), and Retrieval-Augmented Generation (RAG). These insights aim to enhance real-time cybersecurity defenses and improve the sophistication of LLM applications in threat detection and response. Our paper provides a foundational understanding and strategic direction for integrating LLMs into future cybersecurity frameworks, emphasizing innovation and robust model deployment to safeguard against evolving cyber threats.

In this work we investigate how children ages 5-12 perceive, understand, and use generative AI models such as a text-based LLMs ChatGPT and a visual-based model DALL-E. Generative AI is newly being used widely since chatGPT. Children are also building mental models of generative AI. Those haven't been studied before and it is also the case that the children's models are dynamic as they use the tools, even with just very short usage. Upon surveying and experimentally observing over 40 children ages 5-12, we found that children generally have a very positive outlook towards AI and are excited about the ways AI may benefit and aid them in their everyday lives. In a forced choice, children robustly associated AI with positive adjectives versus negative ones. We also categorize what children are querying AI models for and find that children search for more imaginative things that don't exist when using a visual-based AI and not when using a text-based one. Our follow-up study monitored children's responses and feelings towards AI before and after interacting with GenAI models. We even find that children find AI to be less scary after interacting with it. We hope that these findings will shine a light on children's mental models of AI and provide insight for how to design the best possible tools for children who will inevitably be using AI in their lifetimes. The motivation of this work is to bridge the gap between Human-Computer Interaction (HCI) and Psychology in an effort to study the effects of AI on society. We aim to identify the gaps in humans' mental models of what AI is and how it works. Previous work has investigated how both adults and children perceive various kinds of robots, computers, and other technological concepts. However, there is very little work investigating these concepts for generative AI models and not simply embodied robots or physical technology.

This study evaluates the security of web application code generated by Large Language Models, analyzing 2,500 GPT-4 generated PHP websites. These were deployed in Docker containers and tested for vulnerabilities using a hybrid approach of Burp Suite active scanning, static analysis, and manual review. Our investigation focuses on identifying Insecure File Upload, SQL Injection, Stored XSS, and Reflected XSS in GPT-4 generated PHP code. This analysis highlights potential security risks and the implications of deploying such code in real-world scenarios. Overall, our analysis found 2,440 vulnerable parameters. According to Burp's Scan, 11.56% of the sites can be straight out compromised. Adding static scan results, 26% had at least one vulnerability that can be exploited through web interaction. Certain coding scenarios, like file upload functionality, are insecure 78% of the time, underscoring significant risks to software safety and security. To support further research, we have made the source codes and a detailed vulnerability record for each sample publicly available. This study emphasizes the crucial need for thorough testing and evaluation if generative AI technologies are used in software development.