Human brains image complicated scenes when reading a novel. Replicating this imagination is one of the ultimate goals of AI-Generated Content (AIGC). However, current AIGC methods, such as score-based diffusion, are still deficient in terms of rapidity and efficiency. This deficiency is rooted in the difference between the brain and digital computers. Digital computers have physically separated storage and processing units, resulting in frequent data transfers during iterative calculations, incurring large time and energy overheads. This issue is further intensified by the conversion of inherently continuous and analog generation dynamics, which can be formulated by neural differential equations, into discrete and digital operations. Inspired by the brain, we propose a time-continuous and analog in-memory neural differential equation solver for score-based diffusion, employing emerging resistive memory. The integration of storage and computation within resistive memory synapses surmount the von Neumann bottleneck, benefiting the generative speed and energy efficiency. The closed-loop feedback integrator is time-continuous, analog, and compact, physically implementing an infinite-depth neural network. Moreover, the software-hardware co-design is intrinsically robust to analog noise. We experimentally validate our solution with 180 nm resistive memory in-memory computing macros. Demonstrating equivalent generative quality to the software baseline, our system achieved remarkable enhancements in generative speed for both unconditional and conditional generation tasks, by factors of 64.8 and 156.5, respectively. Moreover, it accomplished reductions in energy consumption by factors of 5.2 and 4.1. Our approach heralds a new horizon for hardware solutions in edge computing for generative AI applications.

Forthcoming in Collaborative Intelligence: How Humans and AI are Transforming our World, Arathi Sethumadhavan and Mira Lane (eds.), Seth Lazar, Australian National University Man, a child in understanding of himself, has placed in his hands physical tools of incalculable power. He plays with them like a child, and whether they work harm or good is largely a matter of accident. The instrumentality becomes a master and works fatally as if possessed of a will of its own-- not because it has a will but because man has not. Introduction As rapid advances in Artificial Intelligence and the rise of some of history's most potent corporations meet the diminished neoliberal state, people are increasingly subject to power exercised by means of automated systems. Machine learning, big data, and related computational technologies now underpin vital government services from criminal justice to tax auditing, public health to social services, immigration to defence (Citron, 2008; Calo and Citron, 2020; Engstrom et al., 2020). Google and Amazon connect consumers and producers in new algorithmic markets (Nadler and Cicilline, 2020). Google's search algorithm--and possibly in the near future OpenAI's GPT-4 or another large language model--determines, for many, how they find out about everything from how to vote to where to get vaccinated. Meta, Twitter, TikTok, Google and others algorithmically decide whose speech is amplified, reduced, or restricted (Vaidhyanathan, 2011; Pasquale, 2015; Gillespie, 2018; Suzor, 2019). And a new wave of products based on rapid advances in Large Language Models (LLMs) have the potential to further transform our economic and political lives. Automatic Authorities are automated computational systems used to exercise power over us by substantially determining what we may know, what we may have, and what our options will be. This chapter is based on, and substantially revises, my'Power and AI: Nature and Justification', in the Oxford Handbook of AI Governance (Justin Bullock et al., eds). My thanks to the publisher for their permission to use this material. But what normative lessons should we draw from these analyses? Power is everywhere, and is not necessarily bad.

In recent years, generative AI (GenAI), like large language models and text-to-image models, has received significant attention across various domains. However, ensuring the responsible generation of content by these models is crucial for their real-world applicability. This raises an interesting question: \textit{What should responsible GenAI generate, and what should it not?} To answer the question, this paper investigates the practical responsible requirements of both textual and visual generative models, outlining five key considerations: generating truthful content, avoiding toxic content, refusing harmful instruction, leaking no training data-related content, and ensuring generated content identifiable. Specifically, we review recent advancements and challenges in addressing these requirements. Besides, we discuss and emphasize the importance of responsible GenAI across healthcare, education, finance, and artificial general intelligence domains. Through a unified perspective on both textual and visual generative models, this paper aims to provide insights into practical safety-related issues and further benefit the community in building responsible GenAI.

Background: The integration of artificial intelligence (AI) into daily life, particularly through chatbots utilizing natural language processing (NLP), presents both revolutionary potential and unique challenges. This intended to investigate how different input forms impact ChatGPT, a leading language model by OpenAI, performance in understanding and executing complex, multi-intention tasks. Design: Employing a case study methodology supplemented by discourse analysis, the research analyzes ChatGPT's responses to inputs varying from natural language to pseudo-code engineering. The study specifically examines the model's proficiency across four categories: understanding of intentions, interpretability, completeness, and creativity. Setting and Participants: As a theoretical exploration of AI interaction, this study focuses on the analysis of structured and unstructured inputs processed by ChatGPT, without direct human participants. Data collection and analysis: The research utilizes synthetic case scenarios, including the organization of a "weekly meal plan" and a "shopping list," to assess ChatGPT's response to prompts in both natural language and pseudo-code engineering. The analysis is grounded in the identification of patterns, contradictions, and unique response elements across different input formats. Results: Findings reveal that pseudo-code engineering inputs significantly enhance the clarity and determinism of ChatGPT's responses, reducing ambiguity inherent in natural language. Enhanced natural language, structured through prompt engineering techniques, similarly improves the model's interpretability and creativity. Conclusions: The study underscores the potential of pseudo-code engineering in refining human-AI interaction and achieving more deterministic, concise, and direct outcomes, advocating for its broader application across disciplines requiring precise AI responses.

Honeyfiles are security assets designed to attract and detect intruders on compromised systems. Honeyfiles are a type of honeypot that mimic real, sensitive documents, creating the illusion of the presence of valuable data. Interaction with a honeyfile reveals the presence of an intruder, and can provide insights into their goals and intentions. Their practical use, however, is limited by the time, cost and effort associated with manually creating realistic content. The introduction of large language models has made high-quality text generation accessible, but honeyfiles contain a variety of content including charts, tables and images. This content needs to be plausible and realistic, as well as semantically consistent both within honeyfiles and with the real documents they mimic, to successfully deceive an intruder. In this paper, we focus on an important component of the honeyfile content generation problem: document charts. Charts are ubiquitous in corporate documents and are commonly used to communicate quantitative and scientific data. Existing image generation models, such as DALL-E, are rather prone to generating charts with incomprehensible text and unconvincing data. We take a multi-modal approach to this problem by combining two purpose-built generative models: a multitask Transformer and a specialized multi-head autoencoder. The Transformer generates realistic captions and plot text, while the autoencoder generates the underlying tabular data for the plot. To advance the field of automated honeyplot generation, we also release a new document-chart dataset and propose a novel metric Keyword Semantic Matching (KSM). This metric measures the semantic consistency between keywords of a corpus and a smaller bag of words. Extensive experiments demonstrate excellent performance against multiple large language models, including ChatGPT and GPT4.

The report, which describes the lengths OpenAI, Google and Meta have gone to in order to maximize the amount of data they can feed to their AIs, cites numerous people with knowledge of the companies' practices. It comes just days after YouTube CEO Neal Mohan said in an interview with Bloomberg Originals that OpenAI's alleged use of YouTube videos to train its new text-to-video generator, Sora, would go against the platform's policies. According to the NYT, OpenAI used its Whisper speech recognition tool to transcribe more than one million hours of YouTube videos, which were then used to train GPT-4. The Information previously reported that OpenAI had used YouTube videos and podcasts to train the two AI systems. OpenAI president Greg Brockman was reportedly among the people on this team.

This paper reviews Trustworthy Artificial Intelligence (TAI) and its various definitions. Considering the principles respected in any society, TAI is often characterized by a few attributes, some of which have led to confusion in regulatory or engineering contexts. We argue against using terms such as Responsible or Ethical AI as substitutes for TAI. And to help clarify any confusion, we suggest leaving them behind. Given the subjectivity and complexity inherent in TAI, developing a universal framework is deemed infeasible. Instead, we advocate for approaches centered on addressing key attributes and properties such as fairness, bias, risk, security, explainability, and reliability. We examine the ongoing regulatory landscape, with a focus on initiatives in the EU, China, and the USA. We recognize that differences in AI regulations based on geopolitical and geographical reasons pose an additional challenge for multinational companies. We identify risk as a core factor in AI regulation and TAI. For example, as outlined in the EU-AI Act, organizations must gauge the risk level of their AI products to act accordingly (or risk hefty fines). We compare modalities of TAI implementation and how multiple cross-functional teams are engaged in the overall process. Thus, a brute force approach for enacting TAI renders its efficiency and agility, moot. To address this, we introduce our framework Set-Formalize-Measure-Act (SFMA). Our solution highlights the importance of transforming TAI-aware metrics, drivers of TAI, stakeholders, and business/legal requirements into actual benchmarks or tests. Finally, over-regulation driven by panic of powerful AI models can, in fact, harm TAI too. Based on GitHub user-activity data, in 2023, AI open-source projects rose to top projects by contributor account. Enabling innovation in TAI hinges on the independent contributions of the open-source community.

Deep learning [6, 29] is a technology that has revolutionized many areas of modern life. The term describes the gradient-based training of deep neural networks. Since its breakthrough in image classification in 2012 [28], deep learning is essentially the only viable technology for this application. Moreover, it is the basis of multiple recent breakthroughs in science [25] and even mathematical research [14]. Recently, deep learning has received wide public attention through the advent of generative AI in the form of large language models such as ChatGPT [39]. It is well-documented that deep learning in modern applications can have extreme requirements on computational resources and the hardware requirements scale in an unsustainable way [52]. In constrained settings, this can become a serious bottleneck preventing the employment of deep learning methods. In addition, these comprehensive computations come with an immense environmental cost.

Meta, owner of Facebook and Instagram, announced major changes to its policies on digitally created and altered media on Friday, before elections poised to test its ability to police deceptive content generated by artificial intelligence technologies. The social media giant will start applying "Made with AI" labels in May to AI-generated videos, images and audio posted on Facebook and Instagram, expanding a policy that previously addressed only a narrow slice of doctored videos, the vice-president of content policy, Monika Bickert, said in a blogpost. Bickert said Meta would also apply separate and more prominent labels to digitally altered media that poses a "particularly high risk of materially deceiving the public on a matter of importance", regardless of whether the content was created using AI or other tools. Meta will begin applying the more prominent "high-risk" labels immediately, a spokesperson said. The approach will shift the company's treatment of manipulated content, moving from a focus on removing a limited set of posts toward keeping the content up while providing viewers with information about how it was made.

Elon Musk says Tesla is raising compensation for its AI engineers, saying OpenAI is "aggressively recruiting" them. 'CRAZIEST TALENT WAR': Tesla CEO Elon Musk said the electric vehicle giant is giving its artificial intelligence engineers a raise as the automaker tries to fend off poaching efforts by ChatGPT creator OpenAI. COSTLY GAME: More and more sports bettors appear to be turning to artificial intelligence to help counter the notoriously unpredictable tournament, which is often referred to as March Madness. LEISURE TIME: Billionaire investor and New York Mets owner Steve Cohen said in a Wednesday appearance on CNBC's "Squawk Box," that he believes that the majority of workers will eventually have a four-day work week and three-day weekend, which will expand opportunities for individuals to engage in leisurely pursuits. FIGHT AGAINST AI: Comedian George Carlin's estate has agreed to a settlement with the media company it sued earlier this year over the use of artificial intelligence.