When you scan the headlines on your favourite news app each morning, do you ever stop to think who -- or what -- wrote the story? The assumption is there are human beings doing the work. But it's also possible an algorithm wrote it. Artificial intelligence is capable of producing text, images and audio with little to no human intervention. For instance, the neural network called Generative Pre-trained Transformer 3 (GPT-3) is capable of producing text -- a fictional story, a poem or even a programming code -- virtually indistinguishable from text written by a person.

Maybe LLMs aren't all that great at it yet, but why can't they be thinking? They're producing output that looks like it's the result of thinking. One thing is, that result you're talking about doesn't really correspond to what the LLM "thought" if it actually could be called that. Very simplified explanation from someone who is definitely not an expert. You feed it tokens and you get back a token like "the", right?

This paper introduces RiskCards, a framework for structured assessment and documentation of risks associated with an application of language models. As with all language, text generated by language models can be harmful, or used to bring about harm. Automating language generation adds both an element of scale and also more subtle or emergent undesirable tendencies to the generated text. Prior work establishes a wide variety of language model harms to many different actors: existing taxonomies identify categories of harms posed by language models; benchmarks establish automated tests of these harms; and documentation standards for models, tasks and datasets encourage transparent reporting. However, there is no risk-centric framework for documenting the complexity of a landscape in which some risks are shared across models and contexts, while others are specific, and where certain conditions may be required for risks to manifest as harms. RiskCards address this methodological gap by providing a generic framework for assessing the use of a given language model in a given scenario. Each RiskCard makes clear the routes for the risk to manifest harm, their placement in harm taxonomies, and example prompt-output pairs. While RiskCards are designed to be open-source, dynamic and participatory, we present a "starter set" of RiskCards taken from a broad literature survey, each of which details a concrete risk presentation. Language model RiskCards initiate a community knowledge base which permits the mapping of risks and harms to a specific model or its application scenario, ultimately contributing to a better, safer and shared understanding of the risk landscape.

Online Social Network (OSN) has become a hotbed of fake news due to the low cost of information dissemination. Although the existing methods have made many attempts in news content and propagation structure, the detection of fake news is still facing two challenges: one is how to mine the unique key features and evolution patterns, and the other is how to tackle the problem of small samples to build the high-performance model. Different from popular methods which take full advantage of the propagation topology structure, in this paper, we propose a novel framework for fake news detection from perspectives of semantic, emotion and data enhancement, which excavates the emotional evolution patterns of news participants during the propagation process, and a dual deep interaction channel network of semantic and emotion is designed to obtain a more comprehensive and fine-grained news representation with the consideration of comments. Meanwhile, the framework introduces a data enhancement module to obtain more labeled data with high quality based on confidence which further improves the performance of the classification model. Experiments show that the proposed approach outperforms the state-of-the-art methods.

Deep generative models have the capacity to render high fidelity images of content like human faces. Recently, there has been substantial progress in conditionally generating images with specific quantitative attributes, like the emotion conveyed by one's face. These methods typically require a user to explicitly quantify the desired intensity of a visual attribute. A limitation of this method is that many attributes, like how "angry" a human face looks, are difficult for a user to precisely quantify. However, a user would be able to reliably say which of two faces seems "angrier". Following this premise, we develop the $\textit{PrefGen}$ system, which allows users to control the relative attributes of generated images by presenting them with simple paired comparison queries of the form "do you prefer image $a$ or image $b$?" Using information from a sequence of query responses, we can estimate user preferences over a set of image attributes and perform preference-guided image editing and generation. Furthermore, to make preference localization feasible and efficient, we apply an active query selection strategy. We demonstrate the success of this approach using a StyleGAN2 generator on the task of human face editing. Additionally, we demonstrate how our approach can be combined with CLIP, allowing a user to edit the relative intensity of attributes specified by text prompts. Code at https://github.com/helblazer811/PrefGen.

In this paper, we propose a new low-rank matrix factorization model dubbed bounded simplex-structured matrix factorization (BSSMF). Given an input matrix $X$ and a factorization rank $r$, BSSMF looks for a matrix $W$ with $r$ columns and a matrix $H$ with $r$ rows such that $X \approx WH$ where the entries in each column of $W$ are bounded, that is, they belong to given intervals, and the columns of $H$ belong to the probability simplex, that is, $H$ is column stochastic. BSSMF generalizes nonnegative matrix factorization (NMF), and simplex-structured matrix factorization (SSMF). BSSMF is particularly well suited when the entries of the input matrix $X$ belong to a given interval; for example when the rows of $X$ represent images, or $X$ is a rating matrix such as in the Netflix and MovieLens datasets where the entries of $X$ belong to the interval $[1,5]$. The simplex-structured matrix $H$ not only leads to an easily understandable decomposition providing a soft clustering of the columns of $X$, but implies that the entries of each column of $WH$ belong to the same intervals as the columns of $W$. In this paper, we first propose a fast algorithm for BSSMF, even in the presence of missing data in $X$. Then we provide identifiability conditions for BSSMF, that is, we provide conditions under which BSSMF admits a unique decomposition, up to trivial ambiguities. Finally, we illustrate the effectiveness of BSSMF on two applications: extraction of features in a set of images, and the matrix completion problem for recommender systems.

In this episode of Your Message Matters, Lisa Manyon answers the question, "Should I use AI to Create Content?" AI is getting quite the buzz, and a member of the Write On Creative Community asked Lisa to share her thoughts. As promised, the blog post, found on the Ask Lisa section of the Write On Creative blog, can be found here, 'Should I use AI to Create Content?', and your comments are welcome. Let's keep the conversation going. And, when YOU have questions, you're invited to Ask Lisa and submit your questions on the Write On Creative website.. Lisa Manyon is the Business Marketing Architect and President of Write On Creative®. She pioneered the values-based “Challenge. Solution. Invitation.™” communication framework to create marketing messages with integrity, focusing on PASSION points. Her strategies create million-dollar results, and she is dedicated to reverse engineering your most powerful solutions into profitable revenue streams. Her marketing philosophies are featured in Inc. Magazine and multiple #1 Bestselling books, including Wonder Women: How Western Women Will Save the World. Recipient of the People’s Choice Award at the California Women’s Conference, Lisa has created training for Small Business Development Centers and is available for speaking and training engagements. She offers custom coaching, consulting, and copywriting training. She’s also the #1 international bestselling author of Spiritual Sugar: The Divine Ingredients to Heal Yourself With Love and an award-winning speaker available to teach, train, and transform your audience with interactive Business Breakthrough Boutiques. Visit www.WriteOnCreative.com to access business-building resources.

Are tech companies moving too fast in rolling out powerful artificial intelligence technology that could one day outsmart humans? That's the conclusion of a group of prominent computer scientists and other tech industry notables such as Elon Musk and Apple co-founder Steve Wozniak who are calling for a 6-month pause to consider the risks. Their petition published Wednesday is a response to San Francisco startup OpenAI's recent release of GPT-4, a more advanced successor to its widely-used AI chatbot ChatGPT that helped spark a race among tech giants Microsoft and Google to unveil similar applications. The letter warns that AI systems with "human-competitive intelligence can pose profound risks to society and humanity" -- from flooding the internet with disinformation and automating away jobs to more catastrophic future risks out of the realms of science fiction. It says "recent months have seen AI labs locked in an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control."

DataGrade CEO Joe Toscano says the danger with artificial intelligence programs is'how fast it's moving' as Elon Musk calls for a six month pause on new AI. The United Nations Educational, Scientific and Cultural Organization (UNESCO) called Thursday for countries to implement its global ethical framework immediately following pleas by more than a thousand tech workers for a pause in the training of the most powerful artificial intelligence (AI) systems. The agency said in a release that the "Recommendation on the Ethics of Artificial Intelligence" provides all necessary safeguards. "The world needs stronger ethical rules for artificial intelligence: this is the challenge of our time. UNESCO's Recommendation on the ethics of A.I. sets the appropriate normative framework. Our member states all endorsed this recommendation in November 2021. It is high time to implement the strategies and regulations at national level," Audrey Azoulay, UNESCO's director-general, said in a statement.

Amazon Personalize is excited to announce the new Trending-Now recipe to help you recommend items gaining popularity at the fastest pace among your users. Amazon Personalize is a fully managed machine learning (ML) service that makes it easy for developers to deliver personalized experiences to their users. It enables you to improve customer engagement by powering personalized product and content recommendations in websites, applications, and targeted marketing campaigns. You can get started without any prior ML experience, using APIs to easily build sophisticated personalization capabilities in a few clicks. All your data is encrypted to be private and secure, and is only used to create recommendations for your users.