The rampant spread of fake news has adversely affected society, resulting in extensive research on curbing its spread. As a notable milestone in large language models (LLMs), ChatGPT has gained significant attention due to its exceptional natural language processing capabilities. In this study, we present a thorough exploration of ChatGPT's proficiency in generating, explaining, and detecting fake news as follows. Generation -- We employ four prompt methods to generate fake news samples and prove the high quality of these samples through both self-assessment and human evaluation. Explanation -- We obtain nine features to characterize fake news based on ChatGPT's explanations and analyze the distribution of these factors across multiple public datasets. Detection -- We examine ChatGPT's capacity to identify fake news. We explore its detection consistency and then propose a reason-aware prompt method to improve its performance. Although our experiments demonstrate that ChatGPT shows commendable performance in detecting fake news, there is still room for its improvement. Consequently, we further probe into the potential extra information that could bolster its effectiveness in detecting fake news.

There is limited information on how voice assistants may spread misinformation, yet some researchers argue they could be particularly effective vectors for falsehoods. Users have "higher trust" in the assistants due to their humanlike characteristics, according to a paper written by researchers at King's College London. Customers may also think the information they're getting is coming directly from the tech companies, rather than a third-party provider, making it seem more reliable, according to the paper.

The demo explains how AI is used in the app and its features. Cross-border data flow will play a vital role in shaping the international artificial intelligence landscape, but fear of balkanized technology shouldn't blind Western countries to China's long-standing ambitions and approach, experts argued. "No one wants a balkanized world, and China doesn't, either," Nate Picarsic, senior fellows focusing on China policy at the Foundation for Defense of Democracies (FDD), told Fox News Digital. "But we shouldn't be leaving them in the driver's seat and defining the terms of all of these new realms just in defense of the global system." "We have to be clear eyed about what they're trying to do, defend our interests, have teeth and guardrails to make sure that they're playing by the rules… otherwise, we end up in an AI and data environment that is defined by Chinese norms and standards, because that's what their ambition is," he added.

Both optical flow and stereo disparities are image matches and can therefore benefit from joint training. Depth and 3D motion provide geometric rather than photometric information and can further improve optical flow. Accordingly, we design a first network that estimates flow and disparity jointly and is trained without supervision. A second network, trained with optical flow from the first as pseudo-labels, takes disparities from the first network, estimates 3D rigid motion at every pixel, and reconstructs optical flow again. A final stage fuses the outputs from the two networks. In contrast with previous methods that only consider camera motion, our method also estimates the rigid motions of dynamic objects, which are of key interest in applications. This leads to better optical flow with visibly more detailed occlusions and object boundaries as a result. Our unsupervised pipeline achieves 7.36% optical flow error on the KITTI-2015 benchmark and outperforms the previous state-of-the-art 9.38% by a wide margin. It also achieves slightly better or comparable stereo depth results. Code will be made available.

This work aims at decreasing the end-to-end generation latency of large language models (LLMs). One of the major causes of the high generation latency is the sequential decoding approach adopted by almost all state-of-the-art LLMs. In this work, motivated by the thinking and writing process of humans, we propose Skeleton-of-Thought (SoT), which first guides LLMs to generate the skeleton of the answer, and then conducts parallel API calls or batched decoding to complete the contents of each skeleton point in parallel. Not only does SoT provide considerable speed-ups across 12 LLMs, but it can also potentially improve the answer quality on several question categories. SoT is an initial attempt at data-centric optimization for inference efficiency, and further underscores the potential of pushing LLMs to think more like a human for answer quality.

Reinforcement Learning with Human Feedback (RLHF) has been demonstrated to significantly enhance the performance of large language models (LLMs) by aligning their outputs with desired human values through instruction tuning. However, RLHF is constrained by the expertise and productivity limitations of human evaluators. A response to this downside is to fall back to supervised fine-tuning (SFT) with additional carefully selected expert demonstrations. However, while this method has been proven to be effective, it invariably also leads to increased human-in-the-loop overhead. In this study, we propose another alternative approach: Reinforcement Learning with Generative Adversarial Feedback (RLGAF) to RLHF and SFT, which uses a generative adversarial training style to enable the LLMs to learn useful human expert demonstrations without being directly exposed to the training examples, thus enabling good generalization capabilities while preserving sample efficiency. Our preliminary findings indicate that RLGAF can help align LLMs outputs with competitive performance against RLHF and SFT, while not suffering from their respective inherent restrictions, suggesting promising avenues for further research on automating AI alignment.

CyberGuy shows you how to save money with these apps. On Sept. 27-28, Meta rolled out the red carpet at Meta Connect 2023, an event focused on the future of the metaverse, a shared virtual space where people can interact with each other and digital content. The big reveal was "Meta AI," a new generative AI assistant powered by Meta's own recipe of a large language model, Llama 2. Meta AI lets you chat with different AIs, each with their own personality and interests. They are original characters created by Meta's AI team. You can ask them questions, get recommendations or just have fun conversations with them.

Problem Solvers Caucus co-chair Rep. Brian Fitzpatrick explains Republicans' frustration with Democratic colleagues for refusing to help the party'buy some time' to come to an agreement to secure McCarthy's speakership. The likely candidates for House speaker are crisscrossing their way across the GOP conference Friday to make their case for the top job. Majority Leader Steve Scalise, R-La., Judiciary Chair Jim Jordan, R-Ohio, and Republican Study Committee Chair Kevin Hern, R-Okla., are pitching themselves to lead the House of Representatives, which is run by a highly fractured House GOP majority. Scalise made his case to the pragmatic and business-minded Main Street Caucus late Friday morning, Fox News Digital was told. Majority Leader Steve Scalise, left, and Judiciary Chair Jim Jordan, center, are running for House Speaker.

What do androids really dream about? It's apparently not electric sheep, according to this surprising video of the'world's most advanced robot'. In the video, Ameca, a humanoid robot designed by Cornish startup Engineered Arts, is asked whether she dreams. Ameca's response might come as quite a shock, as she replies: 'Yeah!' Accompanied by strangely lifelike facial expressions, she continues: 'Last night I dreamed of dinosaurs fighting a space war on Mars against aliens.' However, Ameca quickly follows this up by saying: 'I'm kidding, I don't dream like humans do but I can simulate it by running through scenarios in my head which help me learn about the world.'

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The oldest direct evidence of human presence in the Americas are likely fossilized human footprints found in New Mexico, challenging once-conventional wisdom regarding humans migrating to the New World from Russia roughly 15,000 years ago, new research confirms. The new discovery suggests that the first people actually arrived in the Americas much earlier than previously believed. According to research published Thursday in the journal Science, footprints discovered at the edge of an ancient lake bed in White Sands National Park date back to between 21,000 and 23,000 years ago.