You will soon find a kid-friendly section inside OpenAI's newly opened store for custom GPTs. The company has joined forces with Common Sense Media, a nonprofit organization that rates media and technology based on their suitability for children, to minimize the risks of AI use by teenagers. Together, they intend to create AI guidelines and educational materials for young people, their parents and their educators. The two organizations will also curate a collection of family-friendly GPTs in OpenAI's GPT store based on Common Sense's ratings, making it easy to see which ones are suitable for younger users. "Together, Common Sense and OpenAI will work to make sure that AI has a positive impact on all teens and families," James P. Steyer, founder and CEO of Common Sense Media, said in a statement.

This paper offers an insightful examination of how currently top-trending AI technologies, i.e., generative artificial intelligence (Generative AI) and large language models (LLMs), are reshaping the field of video technology, including video generation, understanding, and streaming. It highlights the innovative use of these technologies in producing highly realistic videos, a significant leap in bridging the gap between real-world dynamics and digital creation. The study also delves into the advanced capabilities of LLMs in video understanding, demonstrating their effectiveness in extracting meaningful information from visual content, thereby enhancing our interaction with videos. In the realm of video streaming, the paper discusses how LLMs contribute to more efficient and user-centric streaming experiences, adapting content delivery to individual viewer preferences. This comprehensive review navigates through the current achievements, ongoing challenges, and future possibilities of applying Generative AI and LLMs to video-related tasks, underscoring the immense potential these technologies hold for advancing the field of video technology related to multimedia, networking, and AI communities.

Creativity is core to being human. Generative artificial intelligence (GenAI) -- including ever more powerful large language models (LLMs) -- holds promise for humans to be more creative by offering new ideas, or less creative by anchoring on GenAI ideas. We study the causal impact of GenAI ideas on the production of a short story in an online experimental study where some writers could obtain story ideas from a GenAI platform. We find that access to GenAI ideas causes stories to be evaluated as more creative, better written, and more enjoyable, especially among less creative writers. However, GenAI-enabled stories are more similar to each other than stories by humans alone. These results point to an increase in individual creativity at the risk of losing collective novelty. This dynamic resembles a social dilemma: with GenAI, individual writers are better off, but collectively a narrower scope of novel content may be produced. Our results have implications for researchers, policy-makers and practitioners interested in bolstering creativity.

The rise of Generative Artificial Intelligence systems ("AI systems") has created unprecedented social engagement. AI code generation systems provide responses (output) to questions or requests by accessing the vast library of open-source code created by developers over the past few decades. However, they do so by allegedly stealing the open-source code stored in virtual libraries, known as repositories. This Article focuses on how this happens and whether there is a solution that protects innovation and avoids years of litigation. We also touch upon the array of issues raised by the relationship between AI and copyright. Looking ahead, we propose the following: (a) immediate changes to the licenses for open-source code created by developers that will limit access and/or use of any open-source code to humans only; (b) we suggest revisions to the Massachusetts Institute of Technology ("MIT") license so that AI systems are required to procure appropriate licenses from open-source code developers, which we believe will harmonize standards and build social consensus for the benefit of all of humanity, rather than promote profit-driven centers of innovation; (c) we call for urgent legislative action to protect the future of AI systems while also promoting innovation; and (d) we propose a shift in the burden of proof to AI systems in obfuscation cases.

Of all types of harm related to generative AI, nonconsensual deepfakes affect the largest number of people, with women making up the vast majority of those targeted, says Henry Ajder, an AI expert who specializes in generative AI and synthetic media. Thankfully, there is some hope. New tools and laws could make it harder for attackers to weaponize people's photos, and they could help us hold perpetrators accountable. Here are three ways we can combat nonconsensual deepfake porn. Social media platforms sift through the posts that are uploaded onto their sites and take down content that goes against their policies.

Apple is reportedly releasing its'biggest' iOS update in the company's history, which is predicted to include new AI-powered features for the iPhone. The company is expected to announce the iOS 18 update at the Worldwide Developers Conference in June, which will incorporate large language models (LLMS) and generative AI in iWork apps. Some of the predicted software updates are a revised version of Siri and the Messages app, which are said to be powered by advanced AI. Apple is likely to release the beta version of the iOS 18 update in July of this year, with a general release slated for September 2024. Apple's iPhone 15 experienced lackluster sales, causing Apple shares to drop by four percent Apple has yet to officially confirm what will be included in the new iOS 18 system, but Bloomberg's Mark Gurman reported the upcoming operating system is'ambitious and compelling.'

Social media platform X has blocked searches for one of the world's most popular personalties, Taylor Swift, after explicit artificial intelligence images of the singer-songwriter went viral. The deepfakes flooded several social media sites from Reddit to Facebook. This has renewed calls to strengthen legislation around AI, particularly when it is misused for sexual harassment. Here's what you need to know about the Swift episode and legality around deepfakes. On Wednesday, AI-generated, sexually explicit images began circulating on social media sites, particularly gaining traction on X.

Misinformation has become a major challenge in the era of increasing digital information, requiring the development of effective detection methods. We have investigated a novel approach to Out-Of-Context detection (OOCD) that uses synthetic data generation. We created a dataset specifically designed for OOCD and developed an efficient detector for accurate classification. Our experimental findings validate the use of synthetic data generation and demonstrate its efficacy in addressing the data limitations associated with OOCD. The dataset and detector should serve as valuable resources for future research and the development of robust misinformation detection systems.

Learning performance data (e.g., quiz scores and attempts) is significant for understanding learner engagement and knowledge mastery level. However, the learning performance data collected from Intelligent Tutoring Systems (ITSs) often suffers from sparsity, impacting the accuracy of learner modeling and knowledge assessments. To address this, we introduce the 3DG framework (3-Dimensional tensor for Densification and Generation), a novel approach combining tensor factorization with advanced generative models, including Generative Adversarial Network (GAN) and Generative Pre-trained Transformer (GPT), for enhanced data imputation and augmentation. The framework operates by first representing the data as a three-dimensional tensor, capturing dimensions of learners, questions, and attempts. It then densifies the data through tensor factorization and augments it using Generative AI models, tailored to individual learning patterns identified via clustering. Applied to data from an AutoTutor lesson by the Center for the Study of Adult Literacy (CSAL), the 3DG framework effectively generated scalable, personalized simulations of learning performance. Comparative analysis revealed GAN's superior reliability over GPT-4 in this context, underscoring its potential in addressing data sparsity challenges in ITSs and contributing to the advancement of personalized educational technology.

Generative AI and LLMs in particular are heavily used nowadays for various document processing tasks such as question answering and summarization. However, different LLMs come with different capabilities for different tasks as well as with different costs, tokenization, and latency. In fact, enterprises are already incurring huge costs of operating or using LLMs for their respective use cases. In this work, we propose optimizing the usage costs of LLMs by estimating their output quality (without actually invoking the LLMs), and then solving an optimization routine for the LLM selection to either keep costs under a budget, or minimize the costs, in a quality and latency aware manner. We propose a model to predict the output quality of LLMs on document processing tasks like summarization, followed by an LP rounding algorithm to optimize the selection of LLMs. We study optimization problems trading off the quality and costs, both theoretically and empirically. We further propose a sentence simplification model for reducing the number of tokens in a controlled manner. Additionally, we propose several deterministic heuristics for reducing tokens in a quality aware manner, and study the related optimization problem of applying the heuristics optimizing the quality and cost trade-off. We perform extensive empirical validation of our methods on not only enterprise datasets but also on open-source datasets, annotated by us, and show that we perform much better compared to closest baselines. Our methods reduce costs by 40%- 90% while improving quality by 4%-7%. We will release the annotated open source datasets to the community for further research and exploration.