With the rapid development of AI-generated content (AIGC) technology, the production of realistic fake facial images and videos that deceive human visual perception has become possible. Consequently, various face forgery detection techniques have been proposed to identify such fake facial content. However, evaluating the effectiveness and generalizability of these detection techniques remains a significant challenge. To address this, we have constructed a large-scale evaluation benchmark called DeepFaceGen, aimed at quantitatively assessing the effectiveness of face forgery detection and facilitating the iterative development of forgery detection technology. DeepFaceGen consists of 776, 990 real face image/video samples and 773, 812 face forgery image/video samples, generated using 34 mainstream face generation techniques. During the construction process, we carefully consider important factors such as content diversity, fairness across ethnicities, and availability of comprehensive labels, in order to ensure the versatility and convenience of DeepFaceGen. Subsequently, DeepFaceGen is employed in this study to evaluate and analyze the performance of 13 mainstream face forgery detection techniques from various perspectives. Through extensive experimental analysis, we derive significant findings and propose potential directions for future research.

Foundation models are increasingly ubiquitous in our daily lives, used in everyday tasks such as text-image searches, interactions with chatbots, and content generation. As use increases, so does concern over the disparities in performance and fairness of these models for different people in different parts of the world. To assess these growing regional disparities, we present World Wide Dishes, a mixed text and image dataset consisting of 765 dishes, with dish names collected in 131 local languages. World Wide Dishes has been collected purely through human contribution and decentralised means, by creating a website widely distributed through social networks. Using the dataset, we demonstrate a novel means of operationalising capability and representational biases in foundation models such as language models and text-to-image generative models. We enrich these studies with a pilot community review to understand, from a first-person perspective, how these models generate images for people in five African countries and the United States. We find that these models generally do not produce quality text and image outputs of dishes specific to different regions. This is true even for the US, which is typically considered to be more well-resourced in training data - though the generation of US dishes does outperform that of the investigated African countries. The models demonstrate a propensity to produce outputs that are inaccurate as well as culturally misrepresentative, flattening, and insensitive. These failures in capability and representational bias have the potential to further reinforce stereotypes and disproportionately contribute to erasure based on region. The dataset and code are available at https://github.com/oxai/world-wide-dishes/.

The recent integration of visual capabilities into Large Language Models (LLMs) has the potential to play a pivotal role in science and technology education, where visual elements such as diagrams, charts, and tables are commonly used to improve the learning experience. This study investigates the performance of ChatGPT-4 Vision, OpenAI's most advanced visual model at the time the study was conducted, on the Bachelor in Computer Science section of Brazil's 2021 National Undergraduate Exam (ENADE). By presenting the model with the exam's open and multiple-choice questions in their original image format and allowing for reassessment in response to differing answer keys, we were able to evaluate the model's reasoning and self-reflecting capabilities in a large-scale academic assessment involving textual and visual content. ChatGPT-4 Vision significantly outperformed the average exam participant, positioning itself within the top 10 best score percentile. While it excelled in questions that incorporated visual elements, it also encountered challenges with question interpretation, logical reasoning, and visual acuity. The involvement of an independent expert panel to review cases of disagreement between the model and the answer key revealed some poorly constructed questions containing vague or ambiguous statements, calling attention to the critical need for improved question design in future exams. Our findings suggest that while ChatGPT-4 Vision shows promise in multimodal academic evaluations, human oversight remains crucial for verifying the model's accuracy and ensuring the fairness of high-stakes educational exams. The paper's research materials are publicly available at https://github.com/nabormendonca/gpt-4v-enade-cs-2021.

In the domain of text-to-image generative models, the inadvertent propagation of biases inherent in training datasets poses significant ethical challenges, particularly in the generation of socially sensitive content. This paper introduces EquiPrompt, a novel method employing Chain of Thought (CoT) reasoning to reduce biases in text-to-image generative models. EquiPrompt uses iterative bootstrapping and bias-aware exemplar selection to balance creativity and ethical responsibility. It integrates iterative reasoning refinement with controlled evaluation techniques, addressing zero-shot CoT issues in sensitive contexts. Experiments on several generation tasks show EquiPrompt effectively lowers bias while maintaining generative quality, advancing ethical AI and socially responsible creative processes.Code will be publically available.

As soon as Apple announced its plans to inject generative AI into the iPhone, it was as good as official: The technology is now all but unavoidable. AI has already colonized web search, appearing in Google and Bing. OpenAI, the 80 billion start-up that has partnered with Apple and Microsoft, feels ubiquitous; the auto-generated products of its ChatGPTs and DALL-Es are everywhere. Rarely has a technology risen--or been forced--into prominence amid such controversy and consumer anxiety. Certainly, some Americans are excited about AI, though a majority said in a recent survey, for instance, that they are concerned AI will increase unemployment; in another, three out of four said they believe it will be abused to interfere with the upcoming presidential election.

We don't know if OpenAI, the creator of ChatGPT, is actually making any money so far. But thanks to a Wednesday report in The Information, what we do know is that the company doubled its annualized revenue -- a measure of the previous month's revenue multiplied by 12, as the publication helpfully explained -- in the last six months. OpenAI's annualized revenue was 3.4 billion, CEO Sam Altman reportedly told staff. Most of this revenue came from a subscription version of ChatGPT, which offers higher messaging limits to people who pay at least 20 a month, as well as from developers who pay the company to use the company's large language models in their own apps and services. About 200 million on an annualized basis comes from Microsoft, which gives OpenAI a cut of sales of OpenAI's large language models to customers using Azure, Microsoft's cloud computing platform aimed at businesses.

While AI could change the world in many unforeseen ways, it's already having one massive impact: a voracious consumption of energy. Generative AI does not simply float upon ephemeral intuition. Rather, it gathers strength via thousands of computers in data centers across the world, which operate constantly on full blast. In January, the International Energy Agency (IEA) forecast that global data center electricity demand will more than double from 2022 to 2026, with AI playing a major role in that increase. AI industry insiders say the world has plenty of energy capacity to absorb this increased demand, and that technological efficiency improvements could offset these increases.

Elon Musk has withdrawn his lawsuit against OpenAI, a day before a judge was set to hear a request for dismissal. Musk sued OpenAI, saying its founders had violated its nonprofit status, to become a de-facto part of Microsoft. OpenAI said there was no such violation, and the lawsuit was likely a way for Musk to gain access to its secrets. Despite ending the suit, Musk might be nursing this grudge, tweeting if Apple integrates OpenAI's tools into its software, he'll ban iPhones from his companies. You can't mirror your iPhone while mirroring your Mac on Apple Vision Pro Netflix drops a proper trailer for Arcane's second (and last) season Apple Intelligence: What devices and features will actually be supported?

AI safety is a growing concern as more companies integrate generative AI into their offerings and use large language models in consumer products. Last month, Google faced sharp criticism after its experimental "AI Overviews" tool, which purports to answer users' questions, suggested dangerous activities such as eating one small rock per day or adding glue to pizza. In February, Air Canada came under fire when its AI-enabled chatbot promised a fake discount to a traveler.

Elon Musk has dropped his lawsuit accusing OpenAI and its co-founders Sam Altman and Greg Brockman of reneging on the startup's pledge to develop artificial intelligence for the benefit of humanity. Lawyers in the United States representing Musk, on Tuesday asked a California judge to dismiss the suit, court filings showed. No reason was provided for the application to dismiss the suit. Musk in February filed a suit claiming that ChatGPT had set "aflame" its founding agreement to put the good of humanity ahead of profit-seeking when it signed an investment deal with Microsoft. "To this day, OpenAI Inc's website continues to profess that its charter is to ensure that AGI'benefits all of humanity'," Musk claimed in the suit.