Many ChatGPT users have suspected the online tool has a left-wing bias since it was released in November. Now, a thorough scientific study confirms suspicions, revealing it has a'significant and systemic' tendency to return left-leaning responses. ChatGPT's responses favour the Labour Party in the UK, as well as Democrats in the US and Brazil President Lula da Silva of the Workers' Party, it found. Concerns regarding ChatGPT's political bias have already been raised – one professor called it a'woke parrot' after receiving PC responses about'white people'. But this new research is the first largescale study using a'consistent, evidenced-based analysis' – with serious implications for politics and the economy.

Park's team tested 14 different chatbot models by asking them a series of political questions on topics such as immigration, climate change, the role of government and same-sex marriage. The research, released earlier this summer, showed that a series of models developed by Google called Bidirectional Encoder Representations from Transformers, or BERT, were more socially conservative, potentially because they were trained more on books compared to other models that leaned more on internet data and social media comments. Facebook's LLaMA model was slightly more authoritarian and right wing, while OpenAI's GPT-4, its most up-to-date technology, tended to be more economically and socially liberal.

Fox News Greg Palkot reports from Kyiv on another deadly Russian missile strike and Moscows efforts to block Ukraine food exports. Russia continued to target Ukrainian grain infrastructure in attacks overnight Wednesday, a sign the country could be struggling to achieve a victory in its full-scale invasion of Ukraine. "By targeting Ukraine's grain depots, Putin seeks to starve Ukrainians and create a food crisis, in order to compel [Ukrainian President Volodymyr] Zelenskyy to capitulate and Western nations to withdraw support from Ukraine," Rebekah Koffler, a strategic military intelligence analyst, former senior official at the Defense Intelligence Agency, and author of "Putin's Playbook," told Fox News Digital. "Putin's goal at this stage is to turn Ukraine into a dysfunctional state, that is unable to govern itself and feed its people, thus raising the cost of rebuilding it for the U.S. and European countries." Koffler's comments come after another round of attacks against Ukraine's southern Odesa region, where overnight Russian drones hit storage facilities and ports that Ukraine has been using for grain transport, according to a report from The Associated Press.

The soldier doesn't have a clear view, but in his experience the system has a superhuman capacity to pick up the faintest tell of an enemy. So he sets his crosshair upon the box and prepares to squeeze the trigger. In different war, also possibly just over the horizon, a commander stands before a bank of monitors. An alert appears from a chatbot. It brings news that satellites have picked up a truck entering a certain city block that has been designated as a possible staging area for enemy rocket launches.

Ukraine's air force said a wave of Russian military drones had entered the mouth of the Danube River and were headed towards the country's Izmail river port near the border with Romania. Social media groups monitoring the war reported hearing air defence systems firing in the area near Ukraine's Danube ports of Izmail and Reni early on Wednesday morning. The governor of southern Odesa region, Oleh Kiper, asked residents of Izmail district to take shelter at around 1:30 a.m. Ukraine's Danube River ports accounted for around a quarter of all grain exports from Ukraine before Russia recently pulled out of a deal allowing safe passage for the export of Ukrainian grain via the country's Black Sea ports. Danube River ports have now become the main export route, with grain shipments sent on barges from Ukraine across the Danube to Romania and its Black Sea port of Constanta for onward shipment.

Physics-informed neural networks (PINNs) have been popularized as a deep learning framework that can seamlessly synthesize observational data and partial differential equation (PDE) constraints. Their practical effectiveness however can be hampered by training pathologies, but also oftentimes by poor choices made by users who lack deep learning expertise. In this paper we present a series of best practices that can significantly improve the training efficiency and overall accuracy of PINNs. We also put forth a series of challenging benchmark problems that highlight some of the most prominent difficulties in training PINNs, and present comprehensive and fully reproducible ablation studies that demonstrate how different architecture choices and training strategies affect the test accuracy of the resulting models. We show that the methods and guiding principles put forth in this study lead to state-of-the-art results and provide strong baselines that future studies should use for comparison purposes. To this end, we also release a highly optimized library in JAX that can be used to reproduce all results reported in this paper, enable future research studies, as well as facilitate easy adaptation to new use-case scenarios.

Biases in large-scale image datasets are known to influence the performance of computer vision models as a function of geographic context. To investigate the limitations of standard Internet data collection methods in low- and middle-income countries, we analyze human-centric image geo-diversity on a massive scale using geotagged Flickr images associated with each nation in Africa. We report the quantity and content of available data with comparisons to population-matched nations in Europe as well as the distribution of data according to fine-grained intra-national wealth estimates. Temporal analyses are performed at two-year intervals to expose emerging data trends. Furthermore, we present findings for an ``othering'' phenomenon as evidenced by a substantial number of images from Africa being taken by non-local photographers. The results of our study suggest that further work is required to capture image data representative of African people and their environments and, ultimately, to improve the applicability of computer vision models in a global context.

Recent advancements in AI applications to healthcare have shown incredible promise in surpassing human performance in diagnosis and disease prognosis. With the increasing complexity of AI models, however, concerns regarding their opacity, potential biases, and the need for interpretability. To ensure trust and reliability in AI systems, especially in clinical risk prediction models, explainability becomes crucial. Explainability is usually referred to as an AI system's ability to provide a robust interpretation of its decision-making logic or the decisions themselves to human stakeholders. In clinical risk prediction, other aspects of explainability like fairness, bias, trust, and transparency also represent important concepts beyond just interpretability. In this review, we address the relationship between these concepts as they are often used together or interchangeably. This review also discusses recent progress in developing explainable models for clinical risk prediction, highlighting the importance of quantitative and clinical evaluation and validation across multiple common modalities in clinical practice. It emphasizes the need for external validation and the combination of diverse interpretability methods to enhance trust and fairness. Adopting rigorous testing, such as using synthetic datasets with known generative factors, can further improve the reliability of explainability methods. Open access and code-sharing resources are essential for transparency and reproducibility, enabling the growth and trustworthiness of explainable research. While challenges exist, an end-to-end approach to explainability in clinical risk prediction, incorporating stakeholders from clinicians to developers, is essential for success.

Detoxification for LLMs is challenging since it requires models to avoid generating harmful content while maintaining the generation capability. To ensure the safety of generations, previous detoxification methods detoxify the models by changing the data distributions or constraining the generations from different aspects in a single-step manner. However, these approaches will dramatically affect the generation quality of LLMs, e.g., discourse coherence and semantic consistency, since language models tend to generate along the toxic prompt while detoxification methods work in the opposite direction. To handle such a conflict, we decompose the detoxification process into different sub-steps, where the detoxification is concentrated in the input stage and the subsequent continual generation is based on the non-toxic prompt. Besides, we also calibrate the strong reasoning ability of LLMs by designing a Detox-Chain to connect the above sub-steps in an orderly manner, which allows LLMs to detoxify the text step-by-step. Automatic and human evaluation on two benchmarks reveals that by training with Detox-Chain, six LLMs scaling from 1B to 33B can obtain significant detoxification and generation improvement. Our code and data are available at https://github.com/CODINNLG/Detox-CoT. Warning: examples in the paper may contain uncensored offensive content.

The increasing size of Deep Neural Networks (DNNs) poses a pressing need for model compression, particularly when employed on resource constrained devices. Concurrently, the susceptibility of DNNs to adversarial attacks presents another significant hurdle. Despite substantial research on both model compression and adversarial robustness, their joint examination remains underexplored. Our study bridges this gap, seeking to understand the effect of adversarial inputs crafted for base models on their pruned versions. To examine this relationship, we have developed a comprehensive benchmark across diverse adversarial attacks and popular DNN models. We uniquely focus on models not previously exposed to adversarial training and apply pruning schemes optimized for accuracy and performance. Our findings reveal that while the benefits of pruning enhanced generalizability, compression, and faster inference times are preserved, adversarial robustness remains comparable to the base model. This suggests that model compression while offering its unique advantages, does not undermine adversarial robustness.