Google Bard, the search giant's ChatGPT rival, is already available in 180 countries and territories. But even though it's been widely available for months and was the centerpiece of Google's recent I/O event, it's missing one big region. The 450 million people living in the European Union are still unable to access Bard, or any of the company's other generative AI technologies. It's a move that has surprised lawmakers, and even Google won't say why it's holding back. Brando Benifei, the MEP leading the negotiations on Europe's new artificial intelligence rules, is not sure why the bloc had been excluded, describing the omission of the EU from Bard's rollout as a "big issue."

Exploiting the recent advancements in artificial intelligence, showcased by ChatGPT and DALL-E, in real-world applications necessitates vast, domain-specific, and publicly accessible datasets. Unfortunately, the scarcity of such datasets poses a significant challenge for researchers aiming to apply these breakthroughs in engineering design. Synthetic datasets emerge as a viable alternative. However, practitioners are often uncertain about generating high-quality datasets that accurately represent real-world data and are suitable for the intended downstream applications. This study aims to fill this knowledge gap by proposing comprehensive guidelines for generating, annotating, and validating synthetic datasets. The trade-offs and methods associated with each of these aspects are elaborated upon. Further, the practical implications of these guidelines are illustrated through the creation of a turbo-compressors dataset. The study underscores the importance of thoughtful sampling methods to ensure the appropriate size, diversity, utility, and realism of a dataset. It also highlights that design diversity does not equate to performance diversity or realism. By employing test sets that represent uniform, real, or task-specific samples, the influence of sample size and sampling strategy is scrutinized. Overall, this paper offers valuable insights for researchers intending to create and publish synthetic datasets for engineering design, thereby paving the way for more effective applications of AI advancements in the field. The code and data for the dataset and methods are made publicly accessible at https://github.com/cyrilpic/radcomp .

Nine language-vision AI models trained on web scrapes with the Contrastive Language-Image Pretraining (CLIP) objective are evaluated for evidence of a bias studied by psychologists: the sexual objectification of girls and women, which occurs when a person's human characteristics, such as emotions, are disregarded and the person is treated as a body. We replicate three experiments in psychology quantifying sexual objectification and show that the phenomena persist in AI. A first experiment uses standardized images of women from the Sexual OBjectification and EMotion Database, and finds that human characteristics are disassociated from images of objectified women: the model's recognition of emotional state is mediated by whether the subject is fully or partially clothed. Embedding association tests (EATs) return significant effect sizes for both anger (d >0.80) and sadness (d >0.50), associating images of fully clothed subjects with emotions. GRAD-CAM saliency maps highlight that CLIP gets distracted from emotional expressions in objectified images. A second experiment measures the effect in a representative application: an automatic image captioner (Antarctic Captions) includes words denoting emotion less than 50% as often for images of partially clothed women than for images of fully clothed women. A third experiment finds that images of female professionals (scientists, doctors, executives) are likely to be associated with sexual descriptions relative to images of male professionals. A fourth experiment shows that a prompt of "a [age] year old girl" generates sexualized images (as determined by an NSFW classifier) up to 73% of the time for VQGAN-CLIP and Stable Diffusion; the corresponding rate for boys never surpasses 9%. The evidence indicates that language-vision AI models trained on web scrapes learn biases of sexual objectification, which propagate to downstream applications.

Many deep generative models are defined as a push-forward of a Gaussian measure by a continuous generator, such as Generative Adversarial Networks (GANs) or Variational Auto-Encoders (VAEs). This work explores the latent space of such deep generative models. A key issue with these models is their tendency to output samples outside of the support of the target distribution when learning disconnected distributions. We investigate the relationship between the performance of these models and the geometry of their latent space. Building on recent developments in geometric measure theory, we prove a sufficient condition for optimality in the case where the dimension of the latent space is larger than the number of modes. Through experiments on GANs, we demonstrate the validity of our theoretical results and gain new insights into the latent space geometry of these models. Additionally, we propose a truncation method that enforces a simplicial cluster structure in the latent space and improves the performance of GANs.

This paper aims to serve as a comprehensive guide for researchers and practitioners, offering insights into the current state, potential applications, and future research directions for generative artificial intelligence and foundation models within the context of intelligent vehicles. As the automotive industry progressively integrates AI, generative artificial intelligence technologies hold the potential to revolutionize user interactions, delivering more immersive, intuitive, and personalised in-car experiences. We provide an overview of current applications of generative artificial intelligence in the automotive domain, emphasizing speech, audio, vision, and multimodal interactions. We subsequently outline critical future research areas, including domain adaptability, alignment, multimodal integration and others, as well as, address the challenges and risks associated with ethics. By fostering collaboration and addressing these research areas, generative artificial intelligence can unlock its full potential, transforming the driving experience and shaping the future of intelligent vehicles.

In February, the company announced it was forming a new product group to "turbocharge" its use of generative AI. The group, led by former Apple executive Ahmed Al-Dahle, aims to bring together key teams from research and consumer-focused groups to create new products, according to the company. Zuckerberg has said he expects to build generative AI-powered chat experiences in WhatsApp and Messenger as well as innovations in business messaging and customer support.

The last year has been characterized by a rush of new artificial intelligence (AI) programs being released into the world since OpenAI, a lab backed by Microsoft, launched ChatGPT in November 2022. Both Microsoft and Google rolled out products in March that they say will use AI to transform work, and IBM's CEO Arvind Krishna said the company's AI tool will be able to reduce 30 to 50% of repetitive office work. Since taking the helm at Microsoft in 2014, at a time when its market dominance with traditional software offerings was waning, Satya Nadella has focused on ensuring the company remains relevant. . The company has invested heavily in Azure, its cloud computing platform, and in AI, pouring at least $13 billion in the leading lab OpenAI. Microsoft's share price has risen nearly tenfold since Nadella became CEO, outperforming the S&P 500, which has merely doubled its value over the same time.

Google executives hosted the company's I/O developer conference this week, an annual ritual that has in recent years centered on artificial intelligence. With OpenAI's ChatGPT and Microsoft's Bing chatbot seen as challenging Google's search domination, Google CEO Sundar Pichai seemed intent on projecting the message that his company is still the leader in AI--and is speeding up deployment of the technology. Google's own chatty large language model, Bard, was the headliner, and it is now publicly available in 180 countries. Following along behind came about a dozen generative AI product features and experiments that can do things like help programmers write code, draft emails, or generate speaker notes for Google Slides presentations. But hardly a word was said about Google Assistant, the clunkily named and voice-centric AI assistant that was the company's previous AI champion.

SafeGraph, the data broker famous for selling location data linked to abortion clinic visits, is now a US military contractor. Documents obtained by WIRED reveal that the company landed an initial contract with the US Air Force and is hoping the Pentagon will buy a tool that SafeGraph says will pinpoint locations not to bomb, like schools and hospitals. Your data is, of course, everywhere--likely including in the training data of generative AI tools like ChatGPT. Fortunately, at least some users can request that OpenAI, which created the tool, delete their data. It's also possible to delete your chat history with ChatGPT.

Over the last few months, AI chatbots have exploded in popularity off the surging success of OpenAI's revolutionary ChatGPT--which, amazingly, only burst onto the scene around December. But when Microsoft seized the opportunity to hitch its wagon to OpenAI's rising star for a steep $10 billion dollars, it chose to do so by introducing a GPT-4-powered chatbot under the guise of Bing, its swell-but-also-ran search engine, in a bid to upend Google's search dominance. Google quickly followed suit with its own homegrown Bard AI and unleashed plans to put AI answers before traditional search results, an utterly monumental alteration to one of the most significant places on the Internet. Both are touted as experiments. And these "AI chatbots" are truly wondrous advancements--I've spent many nights with my kids joyously creating fantastic stuff-of-your-dreams artwork with Bing Chat's Dall-E integration and prompting sick raps about wizards who think lizards are the source of all magic, and seeing them come to life in mere moments with these fantastic tools.