AI image generators can be made more culturally sensitive and accurate by feeding them just a small number of photographs provided by people living in countries around the world. The images used to train these artificial intelligence systems "are mostly about the Western world", says Jean Oh at Carnegie Mellon University in Pennsylvania. As a result of this kind of limited training, generative AI image creators, such as Stable Diffusion, often misrepresent or stereotype non-Western cultures. How this moment for AI will change society forever (and how it won't)

Colleges across the US have added AI courses to their curriculum as companies scramble to find skilled employees and students look for higher paying fields. While much of the world sees the tech as the way of the future, some people have cautioned students to not gamble tens of thousands of dollars on technology that seems to evolve each day. Tiffany Hsieh, who works in the development of AI told, DailyMail.com: 'While there is plenty of data telling us about the number of skills that will be affected by Generative AI, much of that data doesn't tell us about the nature of the impact on those skills. But a graduate student majoring in the tech at New York's Yeshiva University said he believes AI is here to stay and the degree would be necessary for him to become a machine language engineer, which pays at least 160,000 a year.

Yes, San Francisco is a nexus of artificial intelligence innovation, but it's also one of the queerest cities in America. The Mission District, where ChatGPT maker OpenAI is headquartered, butts up against the Castro, where sidewalk crossings are coated with rainbows, and older nude men are often seen milling about. And queer people are joining the AI revolution. "So many people in this field are gay men, which is something I think few people talk about," says Spencer Kaplan, an anthropologist and PhD student at Yale who moved to San Francisco to study the developers building generative tools. Sam Altman, the CEO of OpenAI, is gay; he married his husband last year in a private, beachfront ceremony.

The event, attended by AI bosses including OpenAI's Sam Altman, Google DeepMind's Demis Hassabis and tech billionaire Elon Musk, saw both the UK and US create AI Safety Institutes which aim to evaluate open and closed-source AI systems.

OpenAI, Google, Anthropic and other companies developing generative AI are continuing to improve their technologies and releasing better and better large language models. In order to create a common approach for independent evaluation on the safety of those models as they come out, the UK and the US governments have signed a Memorandum of Understanding. Together, the UK's AI Safety Institute and its counterpart in the US, which was announced by Vice President Kamala Harris but has yet to begin operations, will develop suites of tests to assess the risks and ensure the safety of "the most advanced AI models." They're planning to share technical knowledge, information and even personnel as part of the partnership, and one of their initial goals seems to be performing a joint testing exercise on a publicly accessible model. UK's science minister Michelle Donelan, who signed the agreement, told The Financial Times that they've "really got to act quickly" because they're expecting a new generation of AI models to come out over the next year.

Over the past two decades, the Internet-of-Things (IoT) has been a transformative concept, and as we approach 2030, a new paradigm known as the Internet of Senses (IoS) is emerging. Unlike conventional Virtual Reality (VR), IoS seeks to provide multi-sensory experiences, acknowledging that in our physical reality, our perception extends far beyond just sight and sound; it encompasses a range of senses. This article explores existing technologies driving immersive multi-sensory media, delving into their capabilities and potential applications. This exploration includes a comparative analysis between conventional immersive media streaming and a proposed use case that leverages semantic communication empowered by generative Artificial Intelligence (AI). The focal point of this analysis is the substantial reduction in bandwidth consumption by 99.93% in the proposed scheme. Through this comparison, we aim to underscore the practical applications of generative AI for immersive media while addressing the challenges and outlining future trajectories.

On March 13, 2024, the European Parliament approved the final version of the European Artificial Intelligence Act (AI Act), and its publication in the Official Journal of the European Union is awaited. The AI Act is a long text comprising 180 recitals, XIII chapters with 113 articles, and XIII annexes. It is an essential legal framework for AI and the first comprehensive legislation on AI.

With the ever-growing power of generative artificial intelligence, deepfake and artificially generated (synthetic) media have continued to spread online, which creates various ethical and moral concerns regarding their usage. To tackle this, we thus present a novel exploration of the trends and patterns observed in real, deepfake and synthetic facial images. The proposed analysis is done in two parts: firstly, we incorporate eight deep learning models and analyze their performances in distinguishing between the three classes of images. Next, we look to further delve into the similarities and differences between these three sets of images by investigating their image properties both in the context of the entire image as well as in the context of specific regions within the image. ANOVA test was also performed and provided further clarity amongst the patterns associated between the images of the three classes. From our findings, we observe that the investigated deeplearning models found it easier to detect synthetic facial images, with the ViT Patch-16 model performing best on this task with a class-averaged sensitivity, specificity, precision, and accuracy of 97.37%, 98.69%, 97.48%, and 98.25%, respectively. This observation was supported by further analysis of various image properties. We saw noticeable differences across the three category of images. This analysis can help us build better algorithms for facial image generation, and also shows that synthetic, deepfake and real face images are indeed three different classes.

A text generation model is a machine learning model that uses neural networks, especially transformers architecture to generate contextually relevant text based on linguistic patterns learned from extensive corpora. The models are trained on a huge amount of textual data so that they can model and learn complex concepts of any language like its grammar, vocabulary, phrases, and styles. Text generation models can increase the productivity of humans in their current business processes. These models are already automating the process of content creation across industries for the generation of reports, summaries, and emails among others. These models are also allowing for a greater level of personalization in communications between businesses and their customers.

It now is possible to use technology to raise the dead. We haven't cracked the code on how to live forever, or discovered how to bring someone back to biological life. Instead, it has become much easier and more common to "resurrect" the dead by creating lifelike artificial intelligence (AI) avatars of them. Thanks to advancements in generative AI, artificial intelligence that can generate language, imagery, and audio (among other media), users are now able to speak with "ghostbots" that mimic people who have passed away. Think of it as ChatGPT for the dearly departed.