The synthesis of product design concepts stands at the crux of early-phase development processes for technical products, traditionally posing an intricate interdisciplinary challenge. The application of deep learning methods, particularly Deep Generative Models (DGMs), holds the promise of automating and streamlining manual iterations and therefore introducing heightened levels of innovation and efficiency. However, DGMs have yet to be widely adopted into the synthesis of product design concepts. This paper aims to explore the reasons behind this limited application and derive the requirements for successful integration of these technologies. We systematically analyze DGM-families (VAE, GAN, Diffusion, Transformer, Radiance Field), assessing their strengths, weaknesses, and general applicability for product design conception. Our objective is to provide insights that simplify the decision-making process for engineers, helping them determine which method might be most effective for their specific challenges. Recognizing the rapid evolution of this field, we hope that our analysis contributes to a fundamental understanding and guides practitioners towards the most promising approaches. This work seeks not only to illuminate current challenges but also to propose potential solutions, thereby offering a clear roadmap for leveraging DGMs in the realm of product design conception.

The interplay between humans and Generative AI (Gen AI) draws an insightful parallel with the dynamic relationship between giraffes and acacias on the African Savannah. Just as giraffes navigate the acacia's thorny defenses to gain nourishment, humans engage with Gen AI, maneuvering through ethical and operational challenges to harness its benefits. This paper explores how, like young giraffes that are still mastering their environment, humans are in the early stages of adapting to and shaping Gen AI. It delves into the strategies humans are developing and refining to help mitigate risks such as bias, misinformation, and privacy breaches, that influence and shape Gen AI's evolution. While the giraffe-acacia analogy aptly frames human-AI relations, it contrasts nature's evolutionary perfection with the inherent flaws of human-made technology and the tendency of humans to misuse it, giving rise to many ethical dilemmas. Through the HHH framework we identify pathways to embed values of helpfulness, honesty, and harmlessness in AI development, fostering safety-aligned agents that resonate with human values. This narrative presents a cautiously optimistic view of human resilience and adaptability, illustrating our capacity to harness technologies and implement safeguards effectively, without succumbing to their perils. It emphasises a symbiotic relationship where humans and AI continually shape each other for mutual benefit.

Our rapid immersion into online life has made us all ill. Through the generation, personalization, and dissemination of enchanting imagery, artificial technologies commodify the minds and hearts of the masses with nauseating precision and scale. Online networks, artificial intelligence (AI), social media, and digital news feeds fine-tune our beliefs and pursuits by establishing narratives that subdivide and polarize our communities and identities. Meanwhile those commanding these technologies conquer the final frontiers of our interior lives, social relations, earth, and cosmos. In the Attention Economy, our agency is restricted and our vitality is depleted for their narcissistic pursuits and pleasures. Generative AI empowers the forces that homogenize and eradicate life, not through some stupid "singularity" event, but through devaluing human creativity, labor, and social life. Using a fractured lens, we will examine how narratives and networks influence us on mental, social, and algorithmic levels. We will discuss how atomizing imagery -- ideals and pursuits that alienate, rather than invigorate the individual -- hijack people's agency to sustain the forces that destroy them. We will discover how empires build digital networks that optimize society and embolden narcissists to enforce social binaries that perpetuate the ceaseless expansion of consumption, exploitation, and hierarchy. Structural hierarchy in the world is reified through hierarchy in our beliefs and thinking. Only by seeing images as images and appreciating the similarity shared by opposing narratives can we facilitate transformative action and break away from the militaristic systems plaguing our lives.

Generative Artificial Intelligence has grown exponentially as a result of Large Language Models (LLMs). This has been possible because of the impressive performance of deep learning methods created within the field of Natural Language Processing (NLP) and its subfield Natural Language Generation (NLG), which is the focus of this paper. Within the growing LLM family are the popular GPT-4, Bard and more specifically, tools such as ChatGPT have become a benchmark for other LLMs when solving most of the tasks involved in NLG research. This scenario poses new questions about the next steps for NLG and how the field can adapt and evolve to deal with new challenges in the era of LLMs. To address this, the present paper conducts a review of a representative sample of surveys recently published in NLG. By doing so, we aim to provide the scientific community with a research roadmap to identify which NLG aspects are still not suitably addressed by LLMs, as well as suggest future lines of research that should be addressed going forward.

OpenAI whistleblowers have urged the US financial watchdog to investigate non-disclosure agreements at the startup after claiming the contracts included restrictions such as requiring employees to seek permission before contacting regulators. Non-disclosure agreements (NDAs) typically bar an employee from sharing company information with outside parties but a group of whistleblowers are arguing that OpenAI's agreements could have led to workers being punished for raising concerns about the company to federal authorities. San Francisco-based OpenAI is the developer of the ChatGPT chatbot and a key player in the artificial intelligence boom, which has been accompanied by expressions of concern from experts about the potential dangerous capabilities of the technology. "Given the well-documented potential risks posed by the irresponsible deployment of AI, we urge the Commissioners to immediately approve an investigation into OpenAI's prior NDAs, and to review current efforts apparently being undertaken by the company to ensure full compliance with SEC rules," the letter to Gary Gensler, the chair of the US Securities and Exchange Commission (SEC), said. The letter from whistleblower representatives was sent on 1 July and published by the Washington Post on Saturday after the news organisation obtained it from the office of the US senator Chuck Grassley.

Monitoring and managing the growth and quality of fruits are very important tasks. To effectively train deep learning models like YOLO for real-time fruit detection, high-quality image datasets are essential. However, such datasets are often lacking in agriculture. Generative AI models can help create high-quality images. In this study, we used MidJourney and Firefly tools to generate images of melon greenhouses and post-harvest fruits through text-to-image, pre-harvest image-to-image, and post-harvest image-to-image methods. We evaluated these AIgenerated images using PSNR and SSIM metrics and tested the detection performance of the YOLOv9 model. We also assessed the net quality of real and generated fruits. Our results showed that generative AI could produce images very similar to real ones, especially for post-harvest fruits. The YOLOv9 model detected the generated images well, and the net quality was also measurable. This shows that generative AI can create realistic images useful for fruit detection and quality assessment, indicating its great potential in agriculture. This study highlights the potential of AI-generated images for data augmentation in melon fruit detection and quality assessment and envisions a positive future for generative AI applications in agriculture.

The emergence of large language models (LLMs) is a milestone in generative artificial intelligence, achieving significant success in text comprehension and generation tasks. Despite the tremendous success of LLMs in many downstream tasks, they suffer from severe hallucination problems, posing significant challenges to the practical applications of LLMs. Most of the works about LLMs' hallucinations focus on data quality. Self-attention is a core module in transformer-based LLMs, while its potential relationship with LLMs' hallucination has been hardly investigated. To fill this gap, we study this problem from a causal perspective. We propose a method to intervene in LLMs' self-attention layers and maintain their structures and sizes intact. Specifically, we disable different self-attention layers in several popular open-source LLMs and then compare their degrees of hallucination with the original ones. We evaluate the intervened LLMs on hallucination assessment benchmarks and conclude that disabling some specific self-attention layers in the front or tail of the LLMs can alleviate hallucination issues. The study paves a new way for understanding and mitigating LLMs' hallucinations.

A new report from Reuters claims OpenAI is developing technology to bring advanced reasoning capabilities to its AI models under a secret project code-named "Strawberry." Among the project's goals is to enable the company's AI models to autonomously scour the internet in order to "plan ahead" for more complex tasks, according to an internal document seen by Reuters. The project previously went by the name of Q* (pronounced "Q star"), demos of which showed earlier this year that it could answer "tricky science and math questions," Reuters reports, citing unnamed sources who witnessed the demonstrations. At this stage, much remains unknown about Strawberry -- including how far along in development it is, and whether it's the same system with "human-like reasoning" skills that OpenAI reportedly demonstrated at an employee all-hands meeting earlier this week, per Bloomberg. But the ability for the company's AI to conduct "deep research," as is said to be the aim of Strawberry, would mark a huge leap forward from what's available today.

OpenAI's NDAs are once again under scrutiny after whistleblowers penned a letter to the SEC alleging that employees were made to sign "illegally restrictive" agreements preventing them from speaking out on the potential harms of the company's technology. The letter, which was obtained and published online by The Washington Post, accuses OpenAI of violating SEC rules meant to protect employees' rights to report their concerns to federal authorities and prevent retaliation. It follows an official complaint that was filed with the SEC in June. In the letter, the whistleblowers ask the SEC to "take swift and aggressive steps" to enforce the rules they say OpenAI has violated. The alleged violations include making employees sign agreements "that failed to exempt disclosures of securities violations to the SEC" and requiring employees obtain consent from the company before disclosing confidential information to the authorities.

Books and movies of the future could all start to feel the same if creative industries embrace artificial intelligence to help write stories, a study published on Friday warned. The research, which drew on hundreds of volunteers and was published in Science Advances, comes amid rising fears over the impact of widely available AI tools that turn simple text prompts into relatively sophisticated music, art and writing. "Our goal was to study to what extent and how generative AI might help humans with creativity," co-author Anil Doshi of the University College London said.