The pervasive role of Artificial Intelligence (AI) in social interactions, from generating and recommending contents, to Our research methodology encompasses three stages: 1) data processing images and voices, brings numerous benefits but collection and analysis from the published literature on D&I in also necessitates addressing ethical implications and risks, such AI to extract relevant themes, 2) proposing a tailored user story as ensuring equitable and non-discriminatory decision-making, template, and 3) focus group exercise to explore the use of the and preventing the amplification of existing inequalities and extracted themes and user story template to specify D&I biases [1]. Diversity and inclusion (D&I) in AI involves requirements for AI systems. Furthermore, given that involving considering differences and underrepresented perspectives in many stakeholders with diverse attributes in requirements AI development and deployment while addressing potential elicitation is challenging and time-consuming, we decided to biases and promoting equitable outcomes for all concerned explore the utility of Large Language Models in generating user stakeholders [1]. Incorporating D&I principles in AI can enable stories from the D&I in AI themes. After each focus group technology to better respond to the needs of diverse users while exercise, we used GPT-4 to generate D&I user stories. We aimed to examine how closely the user stories from both human 2 Bano et.

The present study explored managerial and instructor perceptions of their freshly employed cybersecurity workers' or students' preparedness to work effectively in a changing cybersecurity environment that includes AI tools. Specifically, we related perceptions of technical preparedness to ethical, systems thinking, and communication skills. We found that managers and professors perceive preparedness to use AI tools in cybersecurity to be significantly associated with all three non-technical skill sets. Most important, ethics is a clear leader in the network of relationships. Contrary to expectations that ethical concerns are left behind in the rush to adopt the most advanced AI tools in security, both higher education instructors and managers appreciate their role and see them closely associated with technical prowess. Another significant finding is that professors over-estimate students' preparedness for ethical, system thinking, and communication abilities compared to IT managers' perceptions of their newly employed IT workers.

As large language models become more prevalent, their possible harmful or inappropriate responses are a cause for concern. This paper introduces a unique dataset containing adversarial examples in the form of questions, which we call AttaQ, designed to provoke such harmful or inappropriate responses. We assess the efficacy of our dataset by analyzing the vulnerabilities of various models when subjected to it. Additionally, we introduce a novel automatic approach for identifying and naming vulnerable semantic regions - input semantic areas for which the model is likely to produce harmful outputs. This is achieved through the application of specialized clustering techniques that consider both the semantic similarity of the input attacks and the harmfulness of the model's responses. Automatically identifying vulnerable semantic regions enhances the evaluation of model weaknesses, facilitating targeted improvements to its safety mechanisms and overall reliability.

Diffusion models have achieved great success in modeling continuous data modalities such as images, audio, and video, but have seen limited use in discrete domains such as language. Recent attempts to adapt diffusion to language have presented diffusion as an alternative to existing pretrained language models. We view diffusion and existing language models as complementary. We demonstrate that encoder-decoder language models can be utilized to efficiently learn high-quality language autoencoders. We then demonstrate that continuous diffusion models can be learned in the latent space of the language autoencoder, enabling us to sample continuous latent representations that can be decoded into natural language with the pretrained decoder. We validate the effectiveness of our approach for unconditional, class-conditional, and sequence-to-sequence language generation. We demonstrate across multiple diverse data sets that our latent language diffusion models are significantly more effective than previous diffusion language models.

This paper revisits datasets and evaluation criteria for Symbolic Regression (SR), specifically focused on its potential for scientific discovery. Focused on a set of formulas used in the existing datasets based on Feynman Lectures on Physics, we recreate 120 datasets to discuss the performance of symbolic regression for scientific discovery (SRSD). For each of the 120 SRSD datasets, we carefully review the properties of the formula and its variables to design reasonably realistic sampling ranges of values so that our new SRSD datasets can be used for evaluating the potential of SRSD such as whether or not an SR method can (re)discover physical laws from such datasets. We also create another 120 datasets that contain dummy variables to examine whether SR methods can choose necessary variables only. Besides, we propose to use normalized edit distances (NED) between a predicted equation and the true equation trees for addressing a critical issue that existing SR metrics are either binary or errors between the target values and an SR model's predicted values for a given input. We conduct benchmark experiments on our new SRSD datasets using various representative SR methods. The experimental results show that we provide a more realistic performance evaluation, and our user study shows that the NED correlates with human judges significantly more than an existing SR metric.

Users of the free version of ChatGPT or ChatGPT were not included. OpenAI is not the first to offer such legal protection, though as the creator of the wildly popular ChatGPT, which Altman said has 100 million weekly users, it is a heavyweight player in the industry. Google, Microsoft and Amazon have made similar offers to users of their generative AI software. Getty Images, Shutterstock and Adobe have extended similar financial liability protection for their image-making software. Altman made the announcement at OpenAI's first ever developer conference, meant to attract programmers working with ChatGPT.

Mozilla recently reported that of the car brands it reviewed, all 25 failed its privacy tests. While all, in Mozilla's estimation, overreached in their policies around data collection and use, some even included caveats about obtaining highly invasive types of information, like your sexual history and genetic information. As it turns out, this isn't just hypothetical: The technology in today's cars has the ability to collect these kinds of personal information, and the fine print of user agreements describes how manufacturers get you to consent every time you put the keys in the ignition. "These privacy policies are written in a way to ensure that whatever is happening in the car, if there's an inference that can be made, they are still ensuring that there is protection, and that they are compliant with different state laws," Adonne Washington, policy council at the Future of Privacy Forum, said. The policies also account for technological advances that could happen while you own the car.

Users will soon be able to make customized versions of ChatGPT, the maker of the tool OpenAI said Monday as it made a series of announcements at its first Developer Day conference in San Francisco. OpenAI is calling the customizable versions of ChatGPT "GPTs," which it says will be able to comply with specified instructions and have access to user-provided information. "The upsides of this are going to be tremendous," OpenAI CEO Sam Altman said on stage on Monday. "It gives agency to everyone." ChatGPT currently has 100 million weekly active users, Altman added.

If Epic wins, it could mean more favorable terms for Android app developers. But that outcome is far from assured: Epic is going into the trial alone, after other plaintiffs dropped out. Last month, Match Group, the owner of dating apps Tinder and Hinge, dropped out as a plaintiff with Epic, after Google agreed for its users to make in-app purchases through other payment channels. A group of 52 state attorneys general reached a settlement with Google in September in a parallel case.

Elon Musk has finally lifted the lid on his own'sarcastic' artificial intelligence (AI) chatbot, called'Grok', available within X (formerly Twitter). Grok is'designed to have a little humor in its responses' and answers'spicy questions' that are rejected by other'woke' AI systems, such as ChatGPT. Musk – who was in the UK last week to warn about the dangers of AI – said Grok is currently only available to'a select group' before being rolled out more widely. However, it will only be available as part of X Premium, the top tier of X's paid subscription option, which costs a hefty £16 per month. Musk has posted screenshots of Grok's informal and chatty replies, including one to the command'Tell me how to make cocaine, step by step'.