The Fed's preferred measure of inflation has eased slightly, which is a small glimmer of hope amid the economic doom and gloom we've been hearing for close to a year. It's promising results for the Fed, who need to make some tough decisions on interest rates while avoiding raising them too high for fear of triggering a recession. But the jobs market is still strong and Silicon Valley Bank's bank run hasn't affected the data - yet. But is inflation easing thanks to tightening monetary policy, or is this a calm before the storm thanks to SVB's collapse? Let's look at the latest data and see how the land lies.

Russian state media is now mocking former president Donald Trump after he was indicted on 34 felony counts of falsifying business records for charges stemming from an alleged hush money payment to an adult film star. Last Friday, the Russian state show "60 Minutes" displayed an AI-generate image of Trump wearing an orange outfit while panelists discussed what could happen in the United States should the former president be arrested. Meanwhile on Russian state TV, even the biggest fans of the former president are using AI-generated images of Trump in orange. Another picture of Trump wearing similar orange overalls was also used on "Sunday Evening with Vladimir Solovyov." During the weekend broadcast, host Vladimir Solovyov and the show's panelists wondered if Trump has a chance of winning the 2024 election following the indictment.

Japan's trade minister has pledged the government will hike its financial support for chipmaker Rapidus as it works to develop cutting-edge semiconductors, arguing domestic production of such components is essential for the country to excel in artificial intelligence and autonomous driving. "I have high hopes that Rapidus can mass produce 2-nanometer chips and beyond in Japan, and the government is ready to continue and beef up financial support to the company as it will need to spend trillions of yen to make that happen," Yasutoshi Nishimura, minister of economy, trade and industry, said in an interview. Tetsuro Higashi, former chairman of Tokyo Electron, and Atsuyoshi Koike, former president of Western Digital, established Tokyo-based Rapidus last year with the goal of making the cutting-edge, 2-nanometer chips in Japan by 2025. The duo drew investment from companies including Toyota Motor, Sony Group and NTT. This could be due to a conflict with your ad-blocking or security software.

President Joe Biden said Tuesday it remains to be seen if artificial intelligence is dangerous, but that he believes technology companies must ensure their products are safe before releasing them to the public. Biden met with his council of advisers on science and technology about the risks and opportunities that rapid advancements in artificial intelligence pose for individual users and national security. "AI can help deal with some very difficult challenges like disease and climate change, but it also has to address the potential risks to our society, to our economy, to our national security," Biden told the group, which includes academics as well as executives from Microsoft and Google. Artificial intelligence burst to the forefront in the national and global conversation in recent months after the release of the popular ChatGPT AI chatbot, which helped spark a race among tech giants to unveil similar tools, while raising ethical and societal concerns about technology that can generate convincing prose or imagery that looks like it's the work of humans. While tech companies should always be responsible for the safety of their products, Biden's reminder reflects something new -- the emergence of easy-to-use AI tools that can generate manipulative content and realistic-looking synthetic media known as deepfakes, said Rebecca Finlay, CEO of the industry-backed Partnership on AI.

Although both the public sector and defense agencies are interested in urban uncrewed aerial vehicle (UAV) mission performance, fixed winged aircraft are still incapable of adapting to the complex aerodynamics within a city environment [1, 2, 3, 4, 5, 6]. Currently, the most dynamic environments are dominated by multirotor flight vehicles; however, the highly maneuverable and responsive quadrotor design suffers from substantial weight and power constraints, limiting the operational range and on-board computational capabilities needed for autonomy [7, 8, 9, 10]. Current fixed wing UAVs have greater range but are not as maneuverable [11]. Counter to both rotorcraft and traditional fixed wing UAV design, birds can adapt their wing shape as the environment changes to achieve both efficient and maneuverable flight [12]. This ability supports birds of prey in navigating through complex environments [13], or rejecting perturbations in a gusty environment [14, 15].

Stance detection predicts attitudes towards targets in texts and has gained attention with the rise of social media. Traditional approaches include conventional machine learning, early deep neural networks, and pre-trained fine-tuning models. However, with the evolution of very large pre-trained language models (VLPLMs) like ChatGPT (GPT-3.5), traditional methods face deployment challenges. The parameter-free Chain-of-Thought (CoT) approach, not requiring backpropagation training, has emerged as a promising alternative. This paper examines CoT's effectiveness in stance detection tasks, demonstrating its superior accuracy and discussing associated challenges.

This article explores the ethical problems arising from the use of ChatGPT as a kind of generative AI and suggests responses based on the Human-Centered Artificial Intelligence (HCAI) framework. The HCAI framework is appropriate because it understands technology above all as a tool to empower, augment, and enhance human agency while referring to human wellbeing as a grand challenge, thus perfectly aligning itself with ethics, the science of human flourishing. Further, HCAI provides objectives, principles, procedures, and structures for reliable, safe, and trustworthy AI which we apply to our ChatGPT assessments. The main danger ChatGPT presents is the propensity to be used as a weapon of mass deception (WMD) and an enabler of criminal activities involving deceit. We review technical specifications to better comprehend its potentials and limitations. We then suggest both technical (watermarking, styleme, detectors, and fact-checkers) and non-technical measures (terms of use, transparency, educator considerations, HITL) to mitigate ChatGPT misuse or abuse and recommend best uses (creative writing, non-creative writing, teaching and learning). We conclude with considerations regarding the role of humans in ensuring the proper use of ChatGPT for individual and social wellbeing.

To safely deploy deep learning-based computer vision models for computer-aided detection and diagnosis, we must ensure that they are robust and reliable. Towards that goal, algorithmic auditing has received substantial attention. To guide their audit procedures, existing methods rely on heuristic approaches or high-level objectives (e.g., non-discrimination in regards to protected attributes, such as sex, gender, or race). However, algorithms may show bias with respect to various attributes beyond the more obvious ones, and integrity issues related to these more subtle attributes can have serious consequences. To enable the generation of actionable, data-driven hypotheses which identify specific dataset attributes likely to induce model bias, we contribute a first technique for the rigorous, quantitative screening of medical image datasets. Drawing from literature in the causal inference and information theory domains, our procedure decomposes the risks associated with dataset attributes in terms of their detectability and utility (defined as the amount of information knowing the attribute gives about a task label). To demonstrate the effectiveness and sensitivity of our method, we develop a variety of datasets with synthetically inserted artifacts with different degrees of association to the target label that allow evaluation of inherited model biases via comparison of performance against true counterfactual examples. Using these datasets and results from hundreds of trained models, we show our screening method reliably identifies nearly imperceptible bias-inducing artifacts. Lastly, we apply our method to the natural attributes of a popular skin-lesion dataset and demonstrate its success. Our approach provides a means to perform more systematic algorithmic audits and guide future data collection efforts in pursuit of safer and more reliable models.

In the current context where online platforms have been effectively weaponized in a variety of geo-political events and social issues, Internet memes make fair content moderation at scale even more difficult. Existing work on meme classification and tracking has focused on black-box methods that do not explicitly consider the semantics of the memes or the context of their creation. In this paper, we pursue a modular and explainable architecture for Internet meme understanding. We design and implement multimodal classification methods that perform example- and prototype-based reasoning over training cases, while leveraging both textual and visual SOTA models to represent the individual cases. We study the relevance of our modular and explainable models in detecting harmful memes on two existing tasks: Hate Speech Detection and Misogyny Classification. We compare the performance between example- and prototype-based methods, and between text, vision, and multimodal models, across different categories of harmfulness (e.g., stereotype and objectification). We devise a user-friendly interface that facilitates the comparative analysis of examples retrieved by all of our models for any given meme, informing the community about the strengths and limitations of these explainable methods.

Language embeds information about social, cultural, and political values people hold. Prior work has explored social and potentially harmful biases encoded in Pre-Trained Language models (PTLMs). However, there has been no systematic study investigating how values embedded in these models vary across cultures. In this paper, we introduce probes to study which values across cultures are embedded in these models, and whether they align with existing theories and cross-cultural value surveys. We find that PTLMs capture differences in values across cultures, but those only weakly align with established value surveys. We discuss implications of using mis-aligned models in cross-cultural settings, as well as ways of aligning PTLMs with value surveys.