AI expert Marva Bailer tells Fox News Digital how the open availability of artificial intelligence can have negative impacts and talks potential federal legislation to control it. We all have photos that capture our precious moments in life. Whether it's a family vacation, a graduation ceremony or a birthday party, we want to preserve these memories and share them with others. But sometimes our photos are not perfect. They might be blurry, overexposed or have unwanted objects in the background.

Social media platforms such as Twitter (now known as X) have revolutionized how the public engage with important societal and political topics. Recently, climate change discussions on social media became a catalyst for political polarization and the spreading of misinformation. In this work, we aim to understand how real world events influence the opinions of individuals towards climate change related topics on social media. To this end, we extracted and analyzed a dataset of 13.6 millions tweets sent by 3.6 million users from 2006 to 2019. Then, we construct a temporal graph from the user-user mentions network and utilize the Louvain community detection algorithm to analyze the changes in community structure around Conference of the Parties on Climate Change~(COP) events. Next, we also apply tools from the Natural Language Processing literature to perform sentiment analysis and topic modeling on the tweets. Our work acts as a first step towards understanding the evolution of pro-climate change communities around COP events. Answering these questions helps us understand how to raise people's awareness towards climate change thus hopefully calling on more individuals to join the collaborative effort in slowing down climate change.

Fake news on social media has large, negative implications for society. However, little is known about what linguistic cues make people fall for fake news and, hence, how to design effective countermeasures for social media. In this study, we seek to understand which linguistic cues make people fall for fake news. Linguistic cues (e.g., adverbs, personal pronouns, positive emotion words, negative emotion words) are important characteristics of any text and also affect how people process real vs. fake news. Specifically, we compare the role of linguistic cues across both cognitive processing (related to careful thinking) and affective processing (related to unconscious automatic evaluations). To this end, we performed a within-subject experiment where we collected neurophysiological measurements of 42 subjects while these read a sample of 40 real and fake news articles. During our experiment, we measured cognitive processing through eye fixations, and affective processing in situ through heart rate variability. We find that users engage more in cognitive processing for longer fake news articles, while affective processing is more pronounced for fake news written in analytic words. To the best of our knowledge, this is the first work studying the role of linguistic cues in fake news processing. Altogether, our findings have important implications for designing online platforms that encourage users to engage in careful thinking and thus prevent them from falling for fake news.

The remarkable abilities of large language models (LLMs) like GPT-4 partially stem from post-training processes like Reinforcement Learning from Human Feedback (RLHF) involving human preferences encoded in a reward model. However, these reward models (RMs) often lack direct knowledge of why, or under what principles, the preferences annotations were made. In this study, we identify principles that guide RMs to better align with human preferences, and then develop an axiomatic framework to generate a rich variety of preference signals to uphold them. We use these axiomatic signals to train a model for scoring answers to longform questions. Our approach yields a Preference Model with only about 220M parameters that agrees with gold human-annotated preference labels more often than GPT-4. The contributions of this work include: training a standalone preference model that can score human- and LLM-generated answers on the same scale; developing an axiomatic framework for generating training data pairs tailored to certain principles; and showing that a small amount of axiomatic signals can help small models outperform GPT-4 in preference scoring. We release our model on huggingface: https://huggingface.co/corbyrosset/axiomatic_preference_model

Lexicon-based sentiment analysis (SA) in finance leverages specialized, manually annotated lexicons created by human experts to extract sentiment from financial texts. Although lexicon-based methods are simple to implement and fast to operate on textual data, they require considerable manual annotation efforts to create, maintain, and update the lexicons. These methods are also considered inferior to the deep learning-based approaches, such as transformer models, which have become dominant in various NLP tasks due to their remarkable performance. However, transformers require extensive data and computational resources for both training and testing. Additionally, they involve significant prediction times, making them unsuitable for real-time production environments or systems with limited processing capabilities. In this paper, we introduce a novel methodology named eXplainable Lexicons (XLex) that combines the advantages of both lexicon-based methods and transformer models. We propose an approach that utilizes transformers and SHapley Additive exPlanations (SHAP) for explainability to learn financial lexicons. Our study presents four main contributions. Firstly, we demonstrate that transformer-aided explainable lexicons can enhance the vocabulary coverage of the benchmark Loughran-McDonald (LM) lexicon, reducing the human involvement in annotating, maintaining, and updating the lexicons. Secondly, we show that the resulting lexicon outperforms the standard LM lexicon in SA of financial datasets. Thirdly, we illustrate that the lexicon-based approach is significantly more efficient in terms of model speed and size compared to transformers. Lastly, the XLex approach is inherently more interpretable than transformer models as lexicon models rely on predefined rules, allowing for better insights into the results of SA and making the XLex approach a viable tool for financial decision-making.

Google's new generative AI experiment lets you create music "inspired by" over 100 instruments worldwide. Instrument Playground starts by asking for a simple prompt containing a musical instrument's name, optionally preceded by an adjective like "upbeat," "strange" or "gloomy." It will then spit out a 20-second audio clip as a starting point to compose (often extremely offbeat or abstract) music that may or may not include the sound of the specific instrument you entered. Simon Doury, an Artist in Residence at Google Arts & Culture Lab, designed the experiment. It taps into Google's MusicLM, a text-to-AI tool it made available to the public in May.

Kurt "The Cyberguy" Knutsson explains how facial recognition technology can help you find your perfect match. When it comes to dating, there are going to be barriers you and your partner will have to overcome together. For those in long-distance relationships, the physical space that separates the couple can be a big hurdle to overcome. Going on dates is not as easy when you are hundreds or even thousands of miles apart, but that does not mean it is impossible. Long-distance dates require a little extra creativity but can still be manageable and loads of fun.

At first it looked unbelievable, but Henry Kissinger had died. At 100 years old, news outlets--and the world--had been preparing for the passing of President Nixon's secretary of state for a while. Still, when people were finding out via emoji-filled chain texts, it seemed unreal. Deepfakes, the metaverse, Elon Musk telling advertisers to fuck themselves at a time when X could probably use the money. Perhaps this is why there is a premium on genuineness these days.

Microsoft is significantly expanding the functions of Paint in Windows 11. The app is also getting a new version. The outdated program is to become a modern image editor that also contains AI functions. In the future, you will be able to use the OpenAI-LLM Dall-E directly in Windows 11 and in Paint. The new functions are also available after installing the Microsoft Paint app from the App Store.

Fox News Flash top sports headlines are here. Check out what's clicking on Foxnews.com. As artificial intelligence continues to completely change the way millions of fans interact with live sporting events, a platform is introducing an innovative approach to monetization. Technology company Play Anywhere has developed a proven track record of increasing fan engagement and creating new revenue streams for its partners. The technology can be seemingly integrated into mobile devices, connected televisions or various streaming devices.