Generative AI such as ChatGPT have been carrying on interactive online conversations meant to ... [ ] manipulate humans, raising serious concerns, We've all dealt with those manipulative personalities that try to convince us that up is down and aim to gaslight us into the most unsettling of conditions. Their rhetoric can be overtly powerful and overwhelming. You can't decide what to do. Should you merely cave in and hope that the verbal tirade will end? But if you are played into doing something untoward, acquiescing might be quite endangering. Trying to verbally fight back is bound to be ugly and can devolve into even worse circumstances. It can be a no-win situation, that's for sure. The manipulator wants and demands that things go their way. For them, the only win possible is that you completely capitulate to their professed bidding. They will incessantly verbally pound away with their claims of pure logic and try to make it appear as though they are occupying the high ground. You are made to seem inconsequential and incapable. Any number of verbal tactics will be launched at you, over and over again. Repetition and steamrolling are the insidious tools of those maddening manipulators. Turns out that we not only need to be on the watch for humans that are manipulators, but we now also need to be wary of Artificial Intelligence (AI) that does likewise. AI can be a masterful manipulator of humans. When it comes to AI, there is the hoped-for AI For Good, while in the same breath, we are faced with AI For Bad. I've previously covered in my columns that AI is considered to have a dual-use capacity, see my analysis at the link here. Seems that if we can make AI that can generate amazingly fluent and upbeat essays, the same capacity can be readily switched over to produce tremendously wrongful bouts of fluently overbearing manipulations. This is especially impactful when experienced in an interactive conversational dialogue with the AI. All of this happens via a type of AI known as Generative AI.

Kyiv, Ukraine – "UFOs" have rained on Russia in recent days – some dangerously close to the capital Moscow and President Vladimir Putin's hometown. Russian officials and media, using that term – "unidentified foreign objects" – seem unnerved and are accusing Ukraine of drone attacks. Ukraine on Wednesday denied targeting Russia, suggesting attempts at domestic assaults – which Moscow did not accept. With a dash of black humour, presidential adviser Mykhailo Podolyak tweeted that a sense of "panic and collapse" was growing in Russia, "manifested by increasing domestic attacks of unidentified flying objects on infrastructure sites". Throughout the war, Ukrainian leaders and top brass have routinely refused any responsibility for attacks on Russian soil – and often resort to ridiculing disorganised Russian servicemen.

The transfer learning paradigm of model pre-training and subsequent fine-tuning produces high-accuracy models. While most studies recommend scaling the pre-training size to benefit most from transfer learning, a question remains: what data and method should be used for pre-training? We investigate the impact of pre-training data distribution on the few-shot and full fine-tuning performance using 3 pre-training methods (supervised, contrastive language-image and image-image), 7 pre-training datasets, and 9 downstream datasets. Through extensive controlled experiments, we find that the choice of the pre-training data source is essential for the few-shot transfer, but its role decreases as more data is made available for fine-tuning. Additionally, we explore the role of data curation and examine the trade-offs between label noise and the size of the pre-training dataset. We find that using 2000X more pre-training data from LAION can match the performance of supervised ImageNet pre-training. Furthermore, we investigate the effect of pre-training methods, comparing language-image contrastive vs. image-image contrastive, and find that the latter leads to better downstream accuracy

Taipei, Taiwan – At opposite ends of Southeast Asia, researchers Pornampai Narenpitak and Heri Kuswanto are both working on the same problem: Is it possible to mimic the cooling effects of volcanic eruptions to halt global warming? Using computer modelling and analysis, Narenpitak and Kuswanto are separately studying whether shooting large quantities of sulphur dioxide into the earth's stratosphere could have a similar effect on global temperatures as the eruption of Indonesia's Mount Tambora in 1815. The eruption, the most powerful in recorded history, spewed an estimated 150 cubic kilometres (150,000 gigalitres) of exploded rock and ash into the air, causing global temperatures to fall as much as 3 degrees Celsius (5.4 degrees Fahrenheit) in what became known as the "year without a summer". Stratospheric aerosol injection is among a number of nascent – and controversial – technologies in the field of solar geoengineering (SRM) that have been touted as potential solutions to mitigating the effects of climate change. Other proposed strategies include brightening marine clouds to reflect the sun or breaking up cirrus clouds that capture heat.

Dictionaries are one of the oldest and most used linguistic resources. Building them is a complex task that, to the best of our knowledge, has yet to be explored with generative Large Language Models (LLMs). We introduce the "Spanish Built Factual Freectianary" (Spanish-BFF) as the first Spanish AI-generated dictionary. This first-of-its-kind free dictionary uses GPT-3. We also define future steps we aim to follow to improve this initial commitment to the field, such as more additional languages.

Due to the success of the pre-trained language model (PLM), existing PLM-based summarization models show their powerful generative capability. However, these models are trained on general-purpose summarization datasets, leading to generated summaries failing to satisfy the needs of different readers. To generate summaries with topics, many efforts have been made on topic-focused summarization. However, these works generate a summary only guided by a prompt comprising topic words. Despite their success, these methods still ignore the disturbance of sentences with non-relevant topics and only conduct cross-interaction between tokens by attention module. To address this issue, we propose a topic-arc recognition objective and topic-selective graph network. First, the topic-arc recognition objective is used to model training, which endows the capability to discriminate topics for the model. Moreover, the topic-selective graph network can conduct topic-guided cross-interaction on sentences based on the results of topic-arc recognition. In the experiments, we conduct extensive evaluations on NEWTS and COVIDET datasets. Results show that our methods achieve state-of-the-art performance.

Machine learning models have been employed to perform either physics-free data-driven or hybrid dynamical downscaling of climate data. Most of these implementations operate over relatively small downscaling factors because of the challenge of recovering fine-scale information from coarse data. This limits their compatibility with many global climate model outputs, often available between $\sim$50--100 km resolution, to scales of interest such as cloud resolving or urban scales. This study systematically examines the capability of convolutional neural networks (CNNs) to downscale surface wind speed data over land surface from different coarse resolutions (25 km, 48 km, and 100 km resolution) to 3 km. For each downscaling factor, we consider three CNN configurations that generate super-resolved predictions of fine-scale wind speed, which take between 1 to 3 input fields: coarse wind speed, fine-scale topography, and diurnal cycle. In addition to fine-scale wind speeds, probability density function parameters are generated, through which sample wind speeds can be generated accounting for the intrinsic stochasticity of wind speed. For generalizability assessment, CNN models are tested on regions with different topography and climate that are unseen during training. The evaluation of super-resolved predictions focuses on subgrid-scale variability and the recovery of extremes. Models with coarse wind and fine topography as inputs exhibit the best performance compared with other model configurations, operating across the same downscaling factor. Our diurnal cycle encoding results in lower out-of-sample generalizability compared with other input configurations.

Anthropic, an AI startup co-founded by former employees of OpenAI, has quietly begun testing a new, ChatGPT-like AI assistant named Claude. The team at Anthropic was gracious enough to grant us access, and updates to Anthropic's social media policies mean we can now share some of our early, informal comparison findings between Claude and ChatGPT. To show how Claude is different, we'll begin by asking ChatGPT and Claude to introduce themselves with the same prompt. Short and to the point -- ChatGPT is an assistant made to answer questions and sound human. The interface to Claude is a Slack channel using a bot that edits messages to make text appear word-by-word. This causes "(edited)" to appear.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Iranian attacks in the waterways of the Middle East and elsewhere in the region "have the attention of everyone" as tensions rise over Tehran's advancing nuclear program, the head of the U.S. Navy's 5th Fleet said Tuesday. Vice Adm. Brad Cooper also told The Associated Press that he's seen a rise in what he described as Iran's "malign activities" in the region over his two years leading the Bahrain-based 5th Fleet. While Cooper pointed to recent seizures of weapons by American and allied forces in the region as a success, he acknowledged that Iran has been able to carry out drone attacks targeting shipping in the Mideast and other assaults in the region.

Empathy is an important characteristic to be considered when building a more intelligent and humanized dialogue agent. However, existing methods did not fully comprehend empathy as a complex process involving three aspects: cognition, affection and behavior. In this paper, we propose CAB, a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses. For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge. This is because keywords in a dialogue are the core of sentences. Building the logic relationship between keywords, which is overlooked by the majority of existing works, can improve the understanding of keywords and contextual logic, thus enhance the cognitive ability. For affection, we capture the emotional dependencies with dual latent variables that contain both interlocutors' emotions. The reason is that considering both interlocutors' emotions simultaneously helps to learn the emotional dependencies. For behavior, we use appropriate dialogue acts to guide the dialogue generation to enhance the empathy expression. Extensive experiments demonstrate that our multi-perspective model outperforms the state-of-the-art models in both automatic and manual evaluation.