'IN DISBELIEF': Scarlett Johansson is "angered and in disbelief" by tech company OpenAI over its ChatGPT app's voice, Sky, noting it uses a voice very similar to hers. NVIDIA'S RISE: Wall Street is eagerly awaiting the latest earnings report Wednesday from Nvidia, which has experienced rapid growth amid the boom in artificial intelligence technology. ELECTRIC RUNNING ROBOT: Standing as tall as an average human and powered by a symphony of sensors and processors, Tiangong has the ability to jog at a steady pace, navigate complex terrain and perform tasks with precision. Tiangong represents a future where robots could possibly become our companions, helpers and perhaps even our friends. GOOGLE'S BIG REVEALS: Google's flagship developer conference called I/O just wrapped up with interesting leaps in how the Big Tech giant is planning to change the world.

AI projects like OpenAI's ChatGPT get part of their savvy from some of the lowest-paid workers in the tech industry--contractors often in poor countries paid small sums to correct chatbots and label images. On Wednesday, 97 African workers who do AI training work or online content moderation for companies like Meta and OpenAI published an open letter to President Biden, demanding that US tech companies stop "systemically abusing and exploiting African workers." Most of the letter's signatories are from Kenya, a hub for tech outsourcing, whose president, William Ruto, is visiting the US this week. The workers allege that the practices of companies like Meta, OpenAI, and data provider Scale AI "amount to modern day slavery." The companies did not immediately respond to a request for comment.

Artificial intelligence has brought a big boost in productivity--to the criminal underworld. Generative AI provides a new, powerful tool kit that allows malicious actors to work far more efficiently and internationally than ever before. Over the past year, cybercriminals have mostly stopped developing their own AI models. Instead, they are opting for tricks with existing tools that work reliably. That's because criminals want an easy life and quick gains.

In a product demo last week, OpenAI showcased a synthetic but expressive voice for ChatGPT called "Sky" that reminded many viewers of the flirty AI girlfriend Samantha played by Scarlett Johansson in the 2013 film Her. One of those viewers was Johansson herself, who promptly hired legal counsel and sent letters to OpenAI demanding an explanation, according to a statement released later. In response, the company on Sunday halted use of Sky and published a blog post insisting that it "is not an imitation of Scarlett Johansson but belongs to a different professional actress using her own natural speaking voice." Johansson's statement, released Monday, said she was "shocked, angered, and in disbelief" by OpenAI's demo using a voice she called "so eerily similar to mine that my closest friends and news outlets could not tell the difference." Johansson revealed that she had turned down a request last year from the company's CEO, Sam Altman, to voice ChatGPT and that he had reached out again two days before last week's demo in an attempt to change her mind.

Add to that another thing OpenAI fumbled with GPT-4o: the data it used to train its tokenizer--a tool that helps the model parse and process text more efficiently--is polluted by Chinese spam websites. As a result, the model's Chinese token library is full of phrases related to pornography and gambling. This could worsen some problems that are common with AI models: hallucinations, poor performance, and misuse. I wrote about it on Friday after several researchers and AI industry insiders flagged the problem. They took a look at GPT-4o's public token library, which has been significantly updated with the new model to improve support of non-English languages, and saw that more than 90 of the 100 longest Chinese tokens in the model are from spam websites.

Sixteen companies at the forefront of developing artificial intelligence (AI) pledged on Tuesday at a global meeting to develop the technology safely at a time when regulators are scrambling to keep up with rapid innovation and emerging risks. The companies included U.S. leaders Google, Meta, Microsoft and OpenAI, as well as firms from China, South Korea and the United Arab Emirates. They were backed by a broader declaration from the Group of Seven (G7) major economies, the EU, Singapore, Australia and South Korea at a virtual meeting hosted by British Prime Minister Rishi Sunak and South Korean President Yoon Suk-yeol.

Public data ecosystems (PDEs) represent complex socio-technical systems crucial for optimizing data use in the public sector and outside it. Recognizing their multifaceted nature, previous research pro-posed a six-generation Evolutionary Model of Public Data Ecosystems (EMPDE). Designed as a result of a systematic literature review on the topic spanning three decade, this model, while theoretically robust, necessitates empirical validation to enhance its practical applicability. This study addresses this gap by validating the theoretical model through a real-life examination in five European countries - Latvia, Serbia, Czech Republic, Spain, and Poland. This empirical validation provides insights into PDEs dynamics and variations of implementations across contexts, particularly focusing on the 6th generation of forward-looking PDE generation named "Intelligent Public Data Generation" that represents a paradigm shift driven by emerging technologies such as cloud computing, Artificial Intelligence, Natural Language Processing tools, Generative AI, and Large Language Models (LLM) with potential to contribute to both automation and augmentation of business processes within these ecosystems. By transcending their traditional status as a mere component, evolving into both an actor and a stakeholder simultaneously, these technologies catalyze innovation and progress, enhancing PDE management strategies to align with societal, regulatory, and technical imperatives in the digital era.

Next-generation (xG) wireless networks, with their complex and dynamic nature, present significant challenges to using traditional optimization techniques. Generative AI (GAI) emerges as a powerful tool due to its unique strengths. Unlike traditional optimization techniques and other machine learning methods, GAI excels at learning from real-world network data, capturing its intricacies. This enables safe, offline exploration of various configurations and generation of diverse, unseen scenarios, empowering proactive, data-driven exploration and optimization for xG networks. Additionally, GAI's scalability makes it ideal for large-scale xG networks. This paper surveys how GAI-based models unlock optimization opportunities in xG wireless networks. We begin by providing a review of GAI models and some of the major communication paradigms of xG (e.g., 6G) wireless networks. We then delve into exploring how GAI can be used to improve resource allocation and enhance overall network performance. Additionally, we briefly review the networking requirements for supporting GAI applications in xG wireless networks. The paper further discusses the key challenges and future research directions in leveraging GAI for network optimization. Finally, a case study demonstrates the application of a diffusion-based GAI model for load balancing, carrier aggregation, and backhauling optimization in non-terrestrial networks, a core technology of xG networks. This case study serves as a practical example of how the combination of reinforcement learning and GAI can be implemented to address real-world network optimization problems.

The effective integration of generative artificial intelligence in education is a fundamental aspect to prepare future generations. This study proposes an accelerated learning methodology in artificial intelligence, focused on its generative capacity, as a way to achieve this goal. It recognizes the challenge of getting teachers to engage with new technologies and adapt their methods in all subjects, not just those related to AI. This methodology not only promotes interest in science, technology, engineering and mathematics, but also facilitates student understanding of the ethical uses and risks associated with AI. Students' perceptions of generative AI are examined, addressing their emotions towards its evolution, evaluation of its ethical implications, and everyday use of AI tools. In addition, AI applications commonly used by students and their integration into other disciplines are investigated. The study aims to provide educators with a deeper understanding of students' perceptions of AI and its relevance in society and in their future career paths.

Generative artificial intelligence (GenAI) has made significant progress in understanding world knowledge and generating content from human languages across various modalities, like text-to-text large language models, text-to-image stable diffusion, and text-to-video Sora. While in this paper, we investigate the capability of GenAI for text-to-model generation, to see whether GenAI can comprehend hyper-level knowledge embedded within AI itself parameters. Specifically, we study a practical scenario termed train-once-for-all personalization, aiming to generate personalized models for diverse end-users and tasks using text prompts. Inspired by the recent emergence of neural network diffusion, we present Tina, a text-conditioned neural network diffusion for train-once-for-all personalization. Tina leverages a diffusion transformer model conditioned on task descriptions embedded using a CLIP model. Despite the astronomical number of potential personalized tasks (e.g., $1.73\times10^{13}$), by our design, Tina demonstrates remarkable in-distribution and out-of-distribution generalization even trained on small datasets ($\sim 1000$). We further verify whether and how \Tina understands world knowledge by analyzing its capabilities under zero-shot/few-shot image prompts, different numbers of personalized classes, prompts of natural language descriptions, and predicting unseen entities.