At their core, generative models are a type of AI system that take in vast Generative audio models typically focus their applications in music amounts of training data to be able to produce a novel item that is and speech generation, with recent models having human-like quality similar to and statistically likely to exist in the data it was trained in their audio output. This paper conducts a systematic literature on. Though generative models have been around for decades with review of 884 papers in the area of generative audio models in order origins in the 1980s [9], the outputs of these models saw unprecedented to both quantify the degree to which researchers in the field are considering advances with the introduction of the transformer in 2017 potential negative impacts and identify the types of ethical which revolutionized the field by introducing a mechanism called implications researchers in this area need to consider. Though 65% "attention" that allowed for much more accurate and complex outputs of generative audio research papers note positive potential impacts of generative models [61]. Generative models may continue to of their work, less than 10% discuss any negative impacts. This improve as (a) their training data becomes larger (for text, imagine jarringly small percentage of papers considering negative impact the entire internet) and (b) researchers continue to make advances is particularly worrying because the issues brought to light by the in the architecture of the models. This paper focuses specifically few papers doing so are raising serious ethical implications and on the current landscape of generative audio models.

In this paper, we present a computational analysis of the Persian language Twitter discourse with the aim to estimate the shift in stance toward gender equality following the death of Mahsa Amini in police custody. We present an ensemble active learning pipeline to train a stance classifier. Our novelty lies in the involvement of Iranian women in an active role as annotators in building this AI system. Our annotators not only provide labels, but they also suggest valuable keywords for more meaningful corpus creation as well as provide short example documents for a guided sampling step. Our analyses indicate that Mahsa Amini's death triggered polarized Persian language discourse where both fractions of negative and positive tweets toward gender equality increased. The increase in positive tweets was slightly greater than the increase in negative tweets. We also observe that with respect to account creation time, between the state-aligned Twitter accounts and pro-protest Twitter accounts, pro-protest accounts are more similar to baseline Persian Twitter activity.

Social media platforms such as Instagram and Twitter have emerged as critical channels for drug marketing and illegal sale. Detecting and labeling online illicit drug trafficking activities becomes important in addressing this issue. However, the effectiveness of conventional supervised learning methods in detecting drug trafficking heavily relies on having access to substantial amounts of labeled data, while data annotation is time-consuming and resource-intensive. Furthermore, these models often face challenges in accurately identifying trafficking activities when drug dealers use deceptive language and euphemisms to avoid detection. To overcome this limitation, we conduct the first systematic study on leveraging large language models (LLMs), such as ChatGPT, to detect illicit drug trafficking activities on social media. We propose an analytical framework to compose \emph{knowledge-informed prompts}, which serve as the interface that humans can interact with and use LLMs to perform the detection task. Additionally, we design a Monte Carlo dropout based prompt optimization method to further to improve performance and interpretability. Our experimental findings demonstrate that the proposed framework outperforms other baseline language models in terms of drug trafficking detection accuracy, showing a remarkable improvement of nearly 12\%. By integrating prior knowledge and the proposed prompts, ChatGPT can effectively identify and label drug trafficking activities on social networks, even in the presence of deceptive language and euphemisms used by drug dealers to evade detection. The implications of our research extend to social networks, emphasizing the importance of incorporating prior knowledge and scenario-based prompts into analytical tools to improve online security and public safety.

Innovation, typically spurred by reusing, recombining, and synthesizing existing concepts, is expected to result in an exponential growth of the concept space over time. However, our statistical analysis of TechNet, which is a comprehensive technology semantic network encompassing over four million concepts derived from patent texts, reveals a linear rather than exponential expansion of the overall technological concept space. Moreover, there is a notable decline in the originality of newly created concepts. These trends can be attributed to the constraints of human cognitive abilities to innovate beyond an ever-growing space of prior art, among other factors. Integrating creative artificial intelligence into the innovation process holds the potential to overcome these limitations and alter the observed trends in the future.

The creators of AI bots that masquerade as people should face harsh criminal sentences comparable to those who trade in counterfeit currency, the Israeli historian and author Yuval Noah Harari has said. He also called for sanctions, including prison sentences, to apply to tech company executives who fail to guard against fake profiles on their social media platforms. Addressing the UN's AI for Good global summit in Geneva, the author of Sapiens and Home Deus said the proliferation of fake humans could lead to a collapse in public trust and democracy. "Now it is possible, for the first time in history, to create fake people – billions of fake people," he said. "If this is allowed to happen it will do to society what fake money threatened to do to the financial system. If you can't know who is a real human, trust will collapse. "Maybe relationships will be able to manage somehow, but not democracy," Harari added. The advent of ChatGPT and other large language models means AI bots can not only amplify human content, but also artificially generate their own content at scale. "What happens if you have a social media platform where … millions of bots can create content that is in many ways superior to what humans can create – more convincing, more appealing," he said. "If we allow this to happen, then humans have completely lost control of the public conversation.

OpenAI Chief Executive Officer Sam Altman surprised everyone last month when he warned Congress of the dangers posed by artificial intelligence. Suddenly, it looked like tech companies had learned from the problems of social media and wanted to roll out AI differently. Even more remarkably: They wanted politicians' help. But a week later, Altman told a different story to reporters in London. The head of ChatGPT's creator said that he would try to comply with European Union rules but if that proved too difficult, his company would "cease operating" within the bloc.

You probably think the Supreme Court just ended racial discrimination in university admissions, euphemistically called affirmative action, and a new day of equal treatment without regard to race or skin color has dawned. Yes, SCOTUS invalidated the race-conscious practices of Harvard and UNC, holding that under the 14th Amendment a "student must be treated based on his or her experiences as an individual – not on the basis of race." That is a very important statement of our guiding constitutional principles. Yet already schools like Harvard are suggesting they will skirt the ruling by considering applicants' experience with race as opposed to the applicants' race itself. These games are not surprising and have been in the works for months.

Companies developing AI in China need to comply with specific laws on intellectual property rights, personal information protection, recommendation algorithms and synthetic content, also called deep fakes. In April, regulators also released a draft set of rules on generative AI, the technology behind image generator Stable Diffusion and chatbots such as OpenAI's ChatGPT and Google's Bard.

Fox News correspondent Matt Finn reports the defense team is asking the state to share the evidence given to the grand jury that indicted Bryan Kohberger. Lawyers for Idaho murder suspect Bryan Kohberger won a small victory this week when a judge granted his request to access training records of three police officers involved in the investigation of the murders of four University of Idaho students. The defense team argued that they wanted to understand the methods the officers utilized, citing their critical role in the probe against their client, News Idaho 6 reported. Bryan Kohberger enters the courtroom for his arraignment hearing in Latah County District Court on May 22. His lawyers have been granted access to officer training records for those involved in his murder case. Kohberger, 28, is accused of fatally stabbing the college students four University of Idaho students in a 4 a.m.

In this study, we analyze data-scarce classification scenarios, where available labeled legal data is small and imbalanced, potentially hurting the quality of the results. We focused on two finetuning objectives; SetFit (Sentence Transformer Finetuning), a contrastive learning setup, and a vanilla finetuning setup on a legal provision classification task. Additionally, we compare the features that are extracted with LIME (Local Interpretable Model-agnostic Explanations) to see which particular features contributed to the model's classification decisions. The results show that a contrastive setup with SetFit performed better than vanilla finetuning while using a fraction of the training samples. LIME results show that the contrastive learning approach helps boost both positive and negative features which are legally informative and contribute to the classification results. Thus a model finetuned with a contrastive objective seems to base its decisions more confidently on legally informative features.