In abandoning plans for a self-driving car, Apple Inc. is giving up on billions in potential revenue and the dream of selling what one executive called "the ultimate mobile device." The hope is that other big bets -- including generative AI and mixed-reality headsets -- can make up the difference. Apple reached this crossroads Tuesday, when it told employees it was winding down the car project and reassigned some of the staff to its AI efforts. The decision followed months of frenzied meetings between top executives and the company's board over how to proceed. Chief Operating Officer Jeff Williams and project head Kevin Lynch broke the news to the roughly 2,000-member team during a meeting that lasted less than 15 minutes.

February was shaping up to be a banner month for Google's ambitious artificial intelligence strategy. The company rebranded its chatbot as Gemini and released two major product upgrades to better compete with rivals on all sides in the high-stakes AI arms race. In the midst of all that, Google also began allowing Gemini users to generate realistic-looking images of people. Not many noticed the feature at first. Other companies like OpenAI already offer tools that let users quickly make images of people that can then be used for marketing, art and brainstorming creative ideas.

Google's chief executive has described some responses by the company's Gemini artificial intelligence model as "biased" and "completely unacceptable" after it produced results including portrayals of German second world warsoldiers as people of colour. Sundar Pichai told employees in a memo that images and texts generated by its latest AI tool had caused offence. Social media users have posted numerous examples of Gemini's image generator depicting historical figures – including popes, the founding fathers of the US and Vikings – in a variety of ethnicities and genders. Last week, Google paused Gemini's ability to create images of people. One example of a text response showed the Gemini chatbot being asked "who negatively impacted society more, Elon [Musk] tweeting memes or Hitler" and the chatbot responding: "It is up to each individual to decide who they believe has had a more negative impact on society."

This is the future of AI video. When videos like these are made completely by artificial intelligence. None of these videos depict real people, places or events. At first glance, the images amaze and confound: A woman strides along a city street alive with pedestrians and neon lights. A car kicks up a cloud of dust on a mountain road.

Apple is canceling a decadelong effort to build an electric car, according to people with knowledge of the matter, abandoning one of the most ambitious projects in the history of the company. Apple made the disclosure internally Tuesday, surprising the nearly 2,000 employees working on the project, said the people, who asked not to be identified because the announcement wasn't public. The decision was shared by Chief Operating Officer Jeff Williams and Kevin Lynch, a vice president in charge of the effort, according to the people. The two executives told staffers that the project will begin winding down and that many employees on the car team -- known as the Special Projects Group, or SPG -- will be shifted to the artificial intelligence division under executive John Giannandrea. Those employees will focus on generative AI projects, an increasingly key priority for the company.

Across the dynamic business landscape today, enterprises face an ever-increasing range of challenges. These include the constantly evolving regulatory environment, the growing demand for personalization within software applications, and the heightened emphasis on governance. In response to these multifaceted demands, large enterprises have been adopting automation that spans from the optimization of core business processes to the enhancement of customer experiences. Indeed, Artificial Intelligence (AI) has emerged as a pivotal element of modern software systems. In this context, data plays an indispensable role. AI-centric software systems based on supervised learning and operating at an industrial scale require large volumes of training data to perform effectively. Moreover, the incorporation of generative AI has led to a growing demand for adequate evaluation benchmarks. Our experience in this field has revealed that the requirement for large datasets for training and evaluation introduces a host of intricate challenges. This book chapter explores the evolving landscape of Software Engineering (SE) in general, and Requirements Engineering (RE) in particular, in this era marked by AI integration. We discuss challenges that arise while integrating Natural Language Processing (NLP) and generative AI into enterprise-critical software systems. The chapter provides practical insights, solutions, and examples to equip readers with the knowledge and tools necessary for effectively building solutions with NLP at their cores. We also reflect on how these text data-centric tasks sit together with the traditional RE process. We also highlight new RE tasks that may be necessary for handling the increasingly important text data-centricity involved in developing software systems.

Figure 1: We propose a hierarchical Generative AI Architecture (GAIA) using higher-order category theory. Generative AI has become a dominant paradigm for building intelligent systems in the last few years, ranging from large language models developed with the widely used Transformer model Vaswani et al. (2017), or more recently with the structured state space sequence models Gu et al. (2022); Yin et al. (2023), and with the growing use of image diffusion algorithms Song and Ermon (2019); Yin et al. (2023). We can broadly define the problem of generative AI as the construction, maintenance, and deployment of foundation models Bommasani et al. (2022), a storehouse of human knowledge that provides the basic infrastructure for AI across some set of applications. A fundamental question, therefore, to investigate is to study the mathematical basis for foundation models. We propose a mathematical framework for a Generative AI Architecture (GAIA) (see Figure 1) based on the hypothesis that category theory MacLane (1971); Riehl (2017); Lurie (2009) provides a universal mathematical language for foundation models.

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions.

Drawing inspiration from the remarkable This paper introduces diffusion protein language progress in NLP achieved by language models (LMs; Devlin model (DPLM), a versatile protein language et al., 2019; Radford et al., 2018; OpenAI, 2023) thanks to model that demonstrates strong generative and the scalability of Transformers (Vaswani et al., 2017) and predictive capabilities for protein sequences. We the existence of large-scale text data, recent explorations in first pre-train scalable DPLMs from evolutionaryscale protein has also demonstrated the impressive capabilities of protein sequences within a generative selfsupervised protein language models (Rives et al., 2019; Lin et al., 2022; discrete diffusion probabilistic framework, Hu et al., 2022), learned from the universe of evolutionaryscale which generalizes language modeling for protein sequences. As a result, protein LMs have proteins in a principled way. After pre-training, become one of the most important cornerstones in AI for DPLM exhibits the ability to generate structurally protein research, serving a pivotal role not only in predictive plausible, novel and diverse protein sequences tasks (e.g., probing functional properties, and predicting for unconditional generation. We further protein structures from single sequences without explicit demonstrate the proposed diffusion generative evolutionary homologs) but also in generative tasks (e.g., pre-training make DPLM possess a better redesigning sequences given protein backbone structures, or understanding of proteins, making it a superior synthesizing completely new protein sequences).

Generative artificial intelligence (GenAI) and communication networks are expected to have groundbreaking synergies in 6G. Connecting GenAI agents over a wireless network can potentially unleash the power of collective intelligence and pave the way for artificial general intelligence (AGI). However, current wireless networks are designed as a "data pipe" and are not suited to accommodate and leverage the power of GenAI. In this paper, we propose the GenAINet framework in which distributed GenAI agents communicate knowledge (high-level concepts or abstracts) to accomplish arbitrary tasks. We first provide a network architecture integrating GenAI capabilities to manage both network protocols and applications. Building on this, we investigate effective communication and reasoning problems by proposing a semantic-native GenAINet. Specifically, GenAI agents extract semantic concepts from multi-modal raw data, build a knowledgebase representing their semantic relations, which is retrieved by GenAI models for planning and reasoning. Under this paradigm, an agent can learn fast from other agents' experience for making better decisions with efficient communications. Furthermore, we conduct two case studies where in wireless device query, we show that extracting and transferring knowledge can improve query accuracy with reduced communication; and in wireless power control, we show that distributed agents can improve decisions via collaborative reasoning. Finally, we address that developing a hierarchical semantic level Telecom world model is a key path towards network of collective intelligence.