Between a cascade of indictments against former US president Donald Trump, a tumultuous 2024 election season (in which Trump is a main character), and the rapid rise of generative artificial intelligence, 2024 is shaping up to be a complete nightmare. At the center of it will be a rise in personalized disinformation. Not only will there be more BS to sift through thanks to tools like ChatGPT and Google's Bard, but the disinformation will likely be more effective, and even tailored to target specific groups with frightening consequences. Of course, some of this could be fixed with new regulations. But the US Congress still hasn't figured out how to tackle privacy, and regulating AI will only be more difficult.

In recent years, diffusion models have achieved tremendous success in the field of image generation, becoming the stateof-the-art technology for AI-based image processing applications. Despite the numerous benefits brought by recent advances in diffusion models, there are also concerns about their potential misuse, specifically in terms of privacy breaches and intellectual property infringement. In particular, some of their unique characteristics open up new attack surfaces when considering the real-world deployment of such models. With a thorough investigation of the attack vectors, we develop a systematic analysis of membership inference attacks on diffusion models and propose novel attack methods tailored to each attack scenario specifically relevant to diffusion models. Our approach exploits easily obtainable quantities and is highly effective, achieving near-perfect attack performance (>0.9 AUCROC) in realistic scenarios. Our extensive experiments demonstrate the effectiveness of our method, highlighting the importance of considering privacy and intellectual property risks when using diffusion models in image generation tasks.

The trial will begin in the midst of a boom in generative AI -- a wave of new technology that has been pushed by Google's competitors and has thrown the company onto its back foot. Google executives have already begun arguing that the rise of AI companies like OpenAI shows that the tech world is still competitive and that the company doesn't have an unfair grip on who wins and who loses, as some antitrust experts and the company's competitors have argued.

The recent burst of generative artificial intelligence is forcing the US Senate into a debate lawmakers have put off for years: privacy reform. While Americans' personal data is a commodity sold, traded, mined, and even "recycled," passing from second party to third party to digital banana stand, some senators believe your personal data is siloed off from the earth-altering AI work those companies, like OpenAI and Google, are testing, tweaking, and deploying daily. "They want to predict the future for purposes of marketing and selling products, and that's already there," says Florida Republican Marco Rubio, the vice-chair of the Senate Intelligence Committee, dismissing the need for an overhaul of federal privacy laws. Rubio is far from an outlier. Ted Cruz of Texas, the top Republican on the Senate Commerce Committee, agrees.

Just like pretty much every other major tech company, Amazon is placing a heavy focus on generative artificial intelligence. CEO Andy Jassy noted on Amazon's latest earnings call that every division has multiple generative AI projects in the works. "Inside Amazon, every one of our teams is working on building generative AI applications that reinvent and enhance their customers' experience," Jassy said. "But while we will build a number of these applications ourselves, most will be built by other companies, and we're optimistic that the largest number of these will be built on [Amazon Web Services]. Remember, the core of AI is data. People want to bring generative AI models to the data, not the other way around."

In 2020, the U.S. Department of Defense officially disclosed a set of ethical principles to guide the use of Artificial Intelligence (AI) technologies on future battlefields. Despite stark differences, there are core similarities between the military and medical service. Warriors on battlefields often face life-altering circumstances that require quick decision-making. Medical providers experience similar challenges in a rapidly changing healthcare environment, such as in the emergency department or during surgery treating a life-threatening condition. Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise. As computing power becomes more accessible and the abundance of health data, such as electronic health records, electrocardiograms, and medical images, increases, it is inevitable that healthcare will be revolutionized by this technology. Recently, generative AI has captivated the research community, leading to debates about its application in healthcare, mainly due to concerns about transparency and related issues. Meanwhile, concerns about the potential exacerbation of health disparities due to modeling biases have raised notable ethical concerns regarding the use of this technology in healthcare. However, the ethical principles for generative AI in healthcare have been understudied, and decision-makers often fail to consider the significance of generative AI. In this paper, we propose GREAT PLEA ethical principles, encompassing governance, reliability, equity, accountability, traceability, privacy, lawfulness, empathy, and autonomy, for generative AI in healthcare. We aim to proactively address the ethical dilemmas and challenges posed by the integration of generative AI in healthcare.

This study investigates the consistency of feedback ratings generated by OpenAI's GPT-4, a state-of-the-art artificial intelligence language model, across multiple iterations, time spans and stylistic variations. The model rated responses to tasks within the Higher Education (HE) subject domain of macroeconomics in terms of their content and style. Statistical analysis was conducted in order to learn more about the interrater reliability, consistency of the ratings across iterations and the correlation between ratings in terms of content and style. The results revealed a high interrater reliability with ICC scores ranging between 0.94 and 0.99 for different timespans, suggesting that GPT-4 is capable of generating consistent ratings across repetitions with a clear prompt. Style and content ratings show a high correlation of 0.87. When applying a non-adequate style the average content ratings remained constant, while style ratings decreased, which indicates that the large language model (LLM) effectively distinguishes between these two criteria during evaluation. The prompt used in this study is furthermore presented and explained. Further research is necessary to assess the robustness and reliability of AI models in various use cases.

Humans are able to detect artificially generated speech only 73% of the time, a study has found, with the same levels of accuracy found in English and Mandarin speakers. Researchers at University College London used a text-to-speech algorithm trained on two publicly available datasets, one in English and the other in Mandarin, to generate 50 deepfake speech samples in each language. Deepfakes, a form of generative artificial intelligence, are synthetic media that is created to resemble a real person's voice or the likeness of their appearance. The sound samples were played for 529 participants to see whether they could detect the real sample from fake speech. The participants were able to identify fake speech only 73% of the time.

Meta is making it easier for artists and sound designers to produce audio using only AI. The Facebook owner has released an open source AudioCraft kit that bundles three existing generative AI models for creating sounds from text descriptions. AudioGen and MusicGen respectively produce sound effects and music, while EnCodec compresses sounds to produce higher-quality results. A musician or sound designer might have everything they need to compose pieces. The release includes pre-trained AudioGen models for those who want to start quickly, and tinkerers will have access to the entire AudioCraft code and model weighting.

The images pop up in Mophat Okinyi's mind when he's alone, or when he's about to sleep. Okinyi, a former content moderator for Open AI's ChatGPT in Nairobi, Kenya, is one of four people in that role who have filed a petition to the Kenyan government calling for an investigation into what they describe as exploitative conditions for contractors reviewing the content that powers artificial intelligence programs. "It has really damaged my mental health," said Okinyi. The 27-year-old said he would would view up to 700 text passages a day, many depicting graphic sexual violence. He recalls he started avoiding people after having read texts about rapists and found himself projecting paranoid narratives on to people around him.