It certainly didn't take long for AI's other shoe to drop, what with the emergent technology already being perverted to commit confidence scams and generate spam content. We can now add censorship to that list as the Globe Gazette reports the school board of Mason City, Iowa has begun leveraging AI technology to cultivate lists of potentially bannable books from the district's libraries ahead of the 2023/24 school year. In May, the Republican-controlled state legislature passed, and Governor Kim Reynolds subsequently signed, Senate File 496 (SF 496), which enacted sweeping changes to the state's education curriculum. Specifically it limits what books can be made available in school libraries and classrooms, requiring titles to be "age appropriate" and without "descriptions or visual depictions of a sex act," per Iowa Code 702.17. But ensuring that every book in the district's archives adhere to these new rules is quickly turning into a mammoth undertaking.

Brian Montgomery, who lost his son to suicide after he was extorted, discussed the loss of his son and how teen boys have been blackmailed over explicit pictures on'America's Newsroom.' If you or someone you know is having thoughts of suicide, please contact the Suicide & Crisis Lifeline at 988 or 1-800-273-TALK (8255). Two Nigerian men accused of running an international sextortion ring that led to the suicide of a Michigan teenager were extradited to the U.S., and a third suspect is expected to follow. Samuel Ogoshi, 22, and Samson Ogoshi, 20, of Lagos, Nigeria, as well as Ezekial Ejehem Robert, 19, allegedly bought hacked social media accounts, posed as young women to lure teenagers and young adult men into sexual chats that included explicit images and videos, and threatened to release them unless they paid a ransom. Jordan DeMay, 17, was one of at least 100 American victims.

TikTok recently announced that its users in the European Union will soon be able to switch off its infamously engaging content-selection algorithm. The EU's Digital Services Act (DSA) is driving this change as part of the region's broader effort to regulate AI and digital services in accordance with human rights and values. TikTok's algorithm learns from users' interactions--how long they watch, what they like, when they share a video--to create a highly tailored and immersive experience that can shape their mental states, preferences, and behaviors without their full awareness or consent. An opt-out feature is a great step toward protecting cognitive liberty, the fundamental right to self-determination over our brains and mental experiences. Rather than being confined to algorithmically curated For You pages and live feeds, users will be able to see trending videos in their region and language, or a "Following and Friends" feed that lists the creators they follow in chronological order.

In addition to proposing the theory of evolution, Darwin studied the expressions and emotions of people and animals. He debated in his writing just how scientific, universal, and predictable emotions actually are, and he sketched characters with exaggerated expressions, which the library had on display. The subject rang a bell for me. Lately, as everyone has been up in arms about ChatGPT, AI general intelligence, and the prospect of robots taking people's jobs, I've noticed that regulators have been ramping up warnings against AI and emotion recognition. Emotion recognition, in this far-from-Darwin context, is the attempt to identify a person's feelings or state of mind using AI analysis of video, facial images, or audio recordings. The idea isn't super complicated: the AI model may see an open mouth, squinted eyes, and contracted cheeks with a thrown-back head, for instance, and register it as a laugh, concluding that the subject is happy.

Fox News correspondent Gillian Turner has the latest on the president's focus amid calls for an impeachment inquiry on "Special Report." Six police officers swarmed Porcha Woodruff's Detroit home before 8 a.m. one morning in February while she was getting her 12- and 6-year-old kids ready for school, the federal lawsuit says. "I have a warrant for your arrest, step outside," one of the officers told Woodruff, who initially thought it was a joke, according to the lawsuit. Officers told her she was being arrested for robbery and carjacking. Do you see that I am eight months pregnant?"

Various template-based and template-free approaches have been proposed for single-step retrosynthesis prediction in recent years. While these approaches demonstrate strong performance from a data-driven metrics standpoint, many model architectures do not incorporate underlying chemistry principles. Here, we propose a novel chemistry-aware retrosynthesis prediction framework that combines powerful data-driven models with prior domain knowledge. We present a tree-to-sequence transformer architecture that utilizes hierarchical SMILES grammar-based trees, incorporating crucial chemistry information that is often overlooked by SMILES text-based representations, such as local structures and functional groups. The proposed framework, grammar-based molecular attention tree transformer (G-MATT), achieves significant performance improvements compared to baseline retrosynthesis models. G-MATT achieves a promising top-1 accuracy of 51% (top-10 accuracy of 79.1%), invalid rate of 1.5%, and bioactive similarity rate of 74.8% on the USPTO-50K dataset. Additional analyses of G-MATT attention maps demonstrate the ability to retain chemistry knowledge without relying on excessively complex model architectures. Introduction Reaction prediction plays a pivotal role in computational chemistry, enabling efficient and precise synthetic route planning for complex organic molecules. Accurately modeling chemical processes has widespread implications, accelerating the discovery of novel compounds used in drug development, materials design, catalysis, polymer design, and more. Corresponding author Email address: venkat@columbia.edu Traditional reaction planning relied heavily on the expertise of chemists, which is both time-consuming and resource-intensive. In contrast, data-driven methods offer automated strategies for predicting accurate pathways. It has been argued that the development of hybrid approaches that combine data-driven techniques with chemistry knowledge is required for more robust and practical reaction prediction models [1, 2].

Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects. Machine learning (ML) methods have significantly enhanced our capacity to predict NOx concentrations at ground-level with high spatiotemporal resolution but may suffer from high estimation bias since they lack physical and chemical knowledge about air pollution dynamics. Chemical transport models (CTMs) leverage this knowledge; however, accurate predictions of ground-level concentrations typically necessitate extensive post-calibration. Here, we present a physics-informed deep learning framework that encodes advection-diffusion mechanisms and fluid dynamics constraints to jointly predict NO2 and NOx and reduce ML model bias by 21-42%. Our approach captures fine-scale transport of NO2 and NOx, generates robust spatial extrapolation, and provides explicit uncertainty estimation. The framework fuses knowledge-driven physicochemical principles of CTMs with the predictive power of ML for air quality exposure, health, and policy applications. Our approach offers significant improvements over purely data-driven ML methods and has unprecedented bias reduction in joint NO2 and NOx prediction.

Machine learning models may inadvertently memorize sensitive, unauthorized, or malicious data, posing risks of privacy violations, security breaches, and performance deterioration. To address these issues, machine unlearning has emerged as a critical technique to selectively remove specific training data points' influence on trained models. This paper provides a comprehensive taxonomy and analysis of machine unlearning research. We categorize existing research into exact unlearning that algorithmically removes data influence entirely and approximate unlearning that efficiently minimizes influence through limited parameter updates. By reviewing the state-of-the-art solutions, we critically discuss their advantages and limitations. Furthermore, we propose future directions to advance machine unlearning and establish it as an essential capability for trustworthy and adaptive machine learning. This paper provides researchers with a roadmap of open problems, encouraging impactful contributions to address real-world needs for selective data removal.

All public companies are required by federal securities law to disclose their business and financial activities in their annual 10-K reports. Each report typically spans hundreds of pages, making it difficult for human readers to identify and extract the material information efficiently. To solve the problem, I have fine-tuned BERT models and RNN models with LSTM layers to identify stakeholder-material information, defined as statements that carry information about a company's influence on its stakeholders, including customers, employees, investors, and the community and natural environment. The existing practice uses keyword search to identify such information, which is my baseline model. Using business expert-labeled training data of nearly 6,000 sentences from 62 10-K reports published in 2022, the best model has achieved an accuracy of 0.904 and an F1 score of 0.899 in test data, significantly above the baseline model's 0.781 and 0.749 respectively. Furthermore, the same work was replicated on more granular taxonomies, based on which four distinct groups of stakeholders (i.e., customers, investors, employees, and the community and natural environment) are tested separately. Similarly, fined-tuned BERT models outperformed LSTM and the baseline. The implications for industry application and ideas for future extensions are discussed.

Prompt-based pre-trained language models (PLMs) paradigm have succeeded substantially in few-shot natural language processing (NLP) tasks. However, prior discrete prompt optimization methods require expert knowledge to design the base prompt set and identify high-quality prompts, which is costly, inefficient, and subjective. Meanwhile, existing continuous prompt optimization methods improve the performance by learning the ideal prompts through the gradient information of PLMs, whose high computational cost, and low readability and generalizability are often concerning. To address the research gap, we propose a Dialogue-comprised Policy-gradient-based Discrete Prompt Optimization ($DP_2O$) method. We first design a multi-round dialogue alignment strategy for readability prompt set generation based on GPT-4. Furthermore, we propose an efficient prompt screening metric to identify high-quality prompts with linear complexity. Finally, we construct a reinforcement learning (RL) framework based on policy gradients to match the prompts to inputs optimally. By training a policy network with only 0.67% of the PLM parameter size on the tasks in the few-shot setting, $DP_2O$ outperforms the state-of-the-art (SOTA) method by 1.52% in accuracy on average on four open-source datasets. Moreover, subsequent experiments also demonstrate that $DP_2O$ has good universality, robustness, and generalization ability.