Sen. Pete Ricketts of Nebraska told Fox News Digital he's concerned about China's use of Artificial Intelligence after a report claimed pro-Chinese groups were spreading CCP propaganda using AI-generated news anchors. Turkish President Recep Tayyip Erdogan's main political opponent accused Russia of using deepfakes and other artificial intelligence (AI)-generated material to meddle in the country's upcoming presidential election. "The Russians have a vested interest in backing an Erdogan presidency to ensure that he basically stays in power, mainly because the Russians benefit [from] driving a wedge between Turkey and NATO, and they've been very successful about that in the last decade or so," Sinan Ciddi, non-resident senior fellow on Turkey at the Foundation for Defense of Democracies, told Fox News Digital. "So, in the last several days, weeks, it has been credibly reported by Turkish sources that Russian bot accounts, Twitter accounts, all sorts of disinformation campaigns have started pressing the thumb down on backing the Erdogan presidency, and that comes as no surprise." The election, scheduled for May 14 alongside parliamentary elections, has proven difficult for Erdogan as his election rival Kemal Kilicdaroglu maintains a slight lead in opinion polls.

ChatGPT has political biases when answering questions, opening the door for competition whose models provide objectivity in their answers, an AI developer said. LOS ANGELES – An AI researcher developed a free speech alternative to ChatGPT and argued that the mainstream model has a liberal bias that prevents it from answering certain questions. "ChatGPT has political motivations, and it's seen through the product," said Arvin Bhangu, who founded the AI model Superintelligence. We've seen where you can ask it give me 10 things Joe Biden has done well and give me 10 things Donald Trump has done well and it refuses to give quality answers for Donald Trump." "Superintelligence is much more in line with the freedom to ask any type of question, so it's much more in line with the First Amendment than ChatGPT," Bhangu said. ChatGPT, an AI chatbot that can write essays, code and more, has been criticized for having politically biased responses.

Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. California's reparations task force is recommending as part of its set of proposals to make amends for slavery and anti-Black racism that state lawmakers address what it calls "racially biased" artificial intelligence used in health care. The task force, created by state legislation signed by Gov. Gavin Newsom in 2020, formally approved last weekend its final recommendations to the California Legislature, which will decide whether to enact the measures and send them to the governor's desk to be signed into law. The recommendations include several proposals related to health care, including some concerning medical artificial intelligence (AI), which the task force describes as "racially biased" and contributing to alleged systemic racism against Black Californians.

The rapid proliferation of information in the digital era underscores the importance of accurate historical representation and interpretation. While artificial intelligence has shown promise in various fields, its potential for historical fact-checking and gap-filling remains largely untapped. This study evaluates the performance of three large language models LLMs GPT 3.5, GPT 4, and GoogleBARD in the context of predicting and verifying historical events based on given data. A novel metric, Distance to Reality (DTR), is introduced to assess the models' outputs against established historical facts. The results reveal a substantial potential for AI in historical studies, with GPT 4 demonstrating superior performance. This paper underscores the need for further research into AI's role in enriching our understanding of the past and bridging historical knowledge gaps.

When a deep learning model is deployed in the wild, it can encounter test data drawn from distributions different from the training data distribution and suffer drop in performance. For safe deployment, it is essential to estimate the accuracy of the pre-trained model on the test data. However, the labels for the test inputs are usually not immediately available in practice, and obtaining them can be expensive. This observation leads to two challenging tasks: (1) unsupervised accuracy estimation, which aims to estimate the accuracy of a pre-trained classifier on a set of unlabeled test inputs; (2) error detection, which aims to identify mis-classified test inputs. In this paper, we propose a principled and practically effective framework that simultaneously addresses the two tasks. The proposed framework iteratively learns an ensemble of models to identify mis-classified data points and performs self-training to improve the ensemble with the identified points. Theoretical analysis demonstrates that our framework enjoys provable guarantees for both accuracy estimation and error detection under mild conditions readily satisfied by practical deep learning models. Along with the framework, we proposed and experimented with two instantiations and achieved state-of-the-art results on 59 tasks. For example, on iWildCam, one instantiation reduces the estimation error for unsupervised accuracy estimation by at least 70% and improves the F1 score for error detection by at least 4.7% compared to existing methods.

In this study, we investigate the capacity of large language models (LLMs), specifically GPT-3.5, to operationalise natural language descriptions of cooperative, competitive, altruistic, and self-interested behavior in social dilemmas. Our focus is on the iterated Prisoner's Dilemma, a classic example of a non-zero-sum interaction, but our broader research program encompasses a range of experimental economics scenarios, including the ultimatum game, dictator game, and public goods game. Using a within-subject experimental design, we instantiated LLM-generated agents with various prompts that conveyed different cooperative and competitive stances. We then assessed the agents' level of cooperation in the iterated Prisoner's Dilemma, taking into account their responsiveness to the cooperative or defection actions of their partners. Our results provide evidence that LLMs can translate natural language descriptions of altruism and selfishness into appropriate behaviour to some extent, but exhibit limitations in adapting their behavior based on conditioned reciprocity. The observed pattern of increased cooperation with defectors and decreased cooperation with cooperators highlights potential constraints in the LLM's ability to generalize its knowledge about human behavior in social dilemmas. We call upon the research community to further explore the factors contributing to the emergent behavior of LLM-generated agents in a wider array of social dilemmas, examining the impact of model architecture, training parameters, and various partner strategies on agent behavior. As more advanced LLMs like GPT-4 become available, it is crucial to investigate whether they exhibit similar limitations or are capable of more nuanced cooperative behaviors, ultimately fostering the development of AI systems that better align with human values and social norms.

In this study, we propose a new method for combining in situ buoy measurements with Earth system models (ESMs) to improve the accuracy of temperature predictions in the ocean. The technique utilizes the dynamics and modes identified in ESMs to improve the accuracy of buoy measurements while still preserving features such as seasonality. Using this technique, errors in localized temperature predictions made by the MPAS-O model can be corrected. We demonstrate that our approach improves accuracy compared to other interpolation and data assimilation methods. We apply our method to assimilate the Model for Prediction Across Scales Ocean component (MPAS-O) with the Global Drifter Program's in-situ ocean buoy dataset.

The weather phenomenon of frost poses great threats to agriculture. As recent frost prediction methods are based on on-site historical data and sensors, extra development and deployment time are required for data collection in any new site. The aim of this article is to eliminate the dependency on on-site historical data and sensors for frost prediction methods. In this article, a frost prediction method based on spatial interpolation is proposed. The models use climate data from existing weather stations, digital elevation models surveys, and normalized difference vegetation index data to estimate a target site's next hour minimum temperature. The proposed method utilizes ensemble learning to increase the model accuracy. Climate datasets are obtained from 75 weather stations across New South Wales and Australian Capital Territory areas of Australia. The results show that the proposed method reached a detection rate up to 92.55%.

Temporal Knowledge graph completion (TKGC) is a crucial task that involves reasoning at known timestamps to complete the missing part of facts and has attracted more and more attention in recent years. Most existing methods focus on learning representations based on graph neural networks while inaccurately extracting information from timestamps and insufficiently utilizing the implied information in relations. To address these problems, we propose a novel TKGC model, namely Pre-trained Language Model with Prompts for TKGC (PPT). We convert a series of sampled quadruples into pre-trained language model inputs and convert intervals between timestamps into different prompts to make coherent sentences with implicit semantic information. We train our model with a masking strategy to convert TKGC task into a masked token prediction task, which can leverage the semantic information in pre-trained language models. Experiments on three benchmark datasets and extensive analysis demonstrate that our model has great competitiveness compared to other models with four metrics. Our model can effectively incorporate information from temporal knowledge graphs into the language models.

To understand the growing phenomena of new vocabulary on nationwide online social media, we analyzed monthly word count time series extracted from approximately 1 billion Japanese blog articles from 2007 to 2019. In particular, we first introduced the extended logistic equation by adding one parameter to the original equation and showed that the model can consistently reproduce various patterns of actual growth curves, such as the logistic function, linear growth, and finite-time divergence. Second, by analyzing the model parameters, we found that the typical growth pattern is not only a logistic function, which often appears in various complex systems, but also a nontrivial growth curve that starts with an exponential function and asymptotically approaches a power function without a steady state. Furthermore, we observed a connection between the functional form of growth and the peak-out. Finally, we showed that the proposed model and statistical properties are also valid for Google Trends data (English, French, Spanish, and Japanese), which is a time series of the nationwide popularity of search queries.