Building on related concepts, like, decentralized identifiers (DIDs), proof of personhood, anonymous credentials, personhood credentials (PHCs) emerged as an alternative approach, enabling individuals to verify to digital service providers that they are a person without disclosing additional information. However, new technologies might introduce some friction due to users misunderstandings and mismatched expectations. Despite their growing importance, limited research has been done on users perceptions and preferences regarding PHCs. To address this gap, we conducted competitive analysis, and semi-structured online user interviews with 23 participants from US and EU to provide concrete design recommendations for PHCs that incorporate user needs, adoption rules, and preferences. Our study -- (a)surfaces how people reason about unknown privacy and security guarantees of PHCs compared to current verification methods -- (b) presents the impact of several factors on how people would like to onboard and manage PHCs, including, trusted issuers (e.g. gov), ground truth data to issue PHC (e.g biometrics, physical id), and issuance system (e.g. centralized vs decentralized). In a think-aloud conceptual design session, participants recommended -- conceptualized design, such as periodic biometrics verification, time-bound credentials, visually interactive human-check, and supervision of government for issuance system. We propose actionable designs reflecting users preferences.

Cross-domain recommendation (CDR) is a task that aims to improve the recommendation performance in a target domain by leveraging the information from source domains. Contrastive learning methods have been widely adopted among intra-domain (intra-CL) and inter-domain (inter-CL) users/items for their representation learning and knowledge transfer during the matching stage of CDR. However, we observe that directly employing contrastive learning on mixed-up intra-CL and inter-CL tasks ignores the difficulty of learning from inter-domain over learning from intra-domain, and thus could cause severe training instability. Therefore, this instability deteriorates the representation learning process and hurts the quality of generated embeddings. To this end, we propose a novel framework named SCCDR built up on a separated intra-CL and inter-CL paradigm and a stop-gradient operation to handle the drawback. Specifically, SCCDR comprises two specialized curriculum stages: intra-inter separation and inter-domain curriculum scheduling. The former stage explicitly uses two distinct contrastive views for the intra-CL task in the source and target domains, respectively. Meanwhile, the latter stage deliberately tackles the inter-CL tasks with a curriculum scheduling strategy that derives effective curricula by accounting for the difficulty of negative samples anchored by overlapping users. Empirical experiments on various open-source datasets and an offline proprietary industrial dataset extracted from a real-world recommender system, and an online A/B test verify that SCCDR achieves state-of-the-art performance over multiple baselines.

Due to the remarkable reasoning ability, Large language models (LLMs) have demonstrated impressive performance in knowledge graph question answering (KGQA) tasks, which find answers to natural language questions over knowledge graphs (KGs). To alleviate the hallucinations and lack of knowledge issues of LLMs, existing methods often retrieve the question-related information from KGs to enrich the input context. However, most methods focus on retrieving the relevant information while ignoring the importance of different types of knowledge in reasoning, which degrades their performance. To this end, this paper reformulates the KGQA problem as a graphical model and proposes a three-stage framework named the Evidence Path Enhanced Reasoning Model (EPERM) for KGQA. In the first stage, EPERM uses the fine-tuned LLM to retrieve a subgraph related to the question from the original knowledge graph. In the second stage, EPERM filters out the evidence paths that faithfully support the reasoning of the questions, and score their importance in reasoning. Finally, EPERM uses the weighted evidence paths to reason the final answer. Since considering the importance of different structural information in KGs for reasoning, EPERM can improve the reasoning ability of LLMs in KGQA tasks. Extensive experiments on benchmark datasets demonstrate that EPERM achieves superior performances in KGQA tasks.

February 18, 2025 A BSTRACT We show that Large Language Model from Power Law Decoder Representations (PLDR-LLM) is a foundational model whose deductive outputs are invariant tensors up to a small perturbation. PLDR-LLM learns a singularity condition for the deductive outputs that enable the once-inferred energy-curvature tensor G LMto replace the deep neural network of power law graph attention (PLGA) generating the deductive outputs at inference. We demonstrate that a cache for G LM(G-cache) and KV -cache can be implemented in a straightforward manner to improve the inference time. The invariance and generalizable nature of deductive outputs is at a very high fidelity where deductive outputs have same RMSE and determinant values up to 15 decimal places after caching, and zero-shot benchmark scores remain unchanged. Ablation studies show that learned deductive outputs have distinct loss and accuracy characteristics from models pretrained with transferred, randomly initialized or identity tensors as a constant tensor operator and an LLM with scaled-dot product attention (SDP A) is a special case of PLDR-LLM where G LMis predefined as identity. The observed invariance characteristic introduces a novel asymmetry between training and inference phases with caching. We outline observed common characteristics of the deductive outputs for the learned singularity condition. We provide an implementation of a training and inference framework for PLDR-LLM with KV -cache and G-cache. 1 Introduction Large Language Model from Power Law Decoder Representations (PLDR-LLM) is a novel language model architecture with well-defined deductive and inductive outputs [Gokden, 2024]. It is composed of deep layers of decoders with multi-headed Power Law Graph Attention (PLGA) [Gokden, 2021, 2019]. The deductive outputs are intended to observe and regularize the model, while the inductive output is the next-token prediction of a language model. PLGA is a series of non-linear and linear transformations that attend to an input sentence that can be considered as a weighted graph G = ( V, E) where nodes are the tokens densely represented by an N-dimensional embedding space. The PLGA learns a metric tensor A LMof the embedding space after applying a custom fully connected layer and iSwiGLU, a positive semi-definite activation function, to the output A of a deep residual network of gated linear units (GLUs) whose input is a density matrix operator derived from the query.

Threat actors, some likely based in China and Iran, are formulating new ways to hijack and utilize American artificial intelligence (AI) models for malicious intent, including covert influence operations, according to a new report from OpenAI. The February report includes two disruptions involving threat actors that appear to have originated from China. According to the report, these actors have used, or at least attempted to use, models built by OpenAI and Meta. In one example, OpenAI banned a ChatGPT account that generated comments critical of Chinese dissident Cai Xia. The comments were posted on social media by accounts that claimed to be people based in India and the U.S.

Generative artificial intelligence (AI) has taken off at lightning speed in the past couple of years, creating disruption in many industries. A new report published this week finds that news audiences and journalists alike are concerned about how news organisations are – and could be – using generative AI such as chatbots, image, audio and video generators, and similar tools. The report draws on three years of interviews and focus group research into generative AI and journalism in Australia and six other countries (United States, United Kingdom, Norway, Switzerland, Germany and France). Only 25% of our news audience participants were confident they had encountered generative AI in journalism. About 50% were unsure or suspected they had.

Harrison Ford isn't impressed by or afraid of artificial intelligence. In a recent interview with The Wall Street Journal, the "Captain America: Brave New World" star was asked if he was planning on securing control of his likeness from studios, and he brushed off the concern. "You don't need artificial intelligence to steal my soul. You can already do it for nickels and dimes with good ideas and talent," he told the outlet. Ford was referring to the 2024 video game "Indiana Jones and the Great Circle," with actor Troy Baker, who provided the voice and motion-capture performance for the character.

Artificial Intelligence (AI) writers have emerged as a signi ficant force in the realm of content creation. These advanced tools leverage natural language processing techniques to g enerate coherent and logical texts, applicable across vari ous domains such as journalism, advertising, and educational m aterials. This document delves into the capabilities, applications, and implications of AI writers, examining thei r technological underpinnings, market influence, strength s, limitations, future trajectories, and ethical considerat ions. In the rapidly evolving landscape of artificial intelligenc e technologies today, AI models are increasingly being appl ied across various domains, with literary creation being no exc eption.

This paper goes into depth on the effect that US News Sentiment from national newspapers has on US interstate migration trends. Through harnessing data from the New York Times between 2010 and 2020, an average sentiment score was calculated, allowing for data to be entered into a neural network. Then a logistic regression model was used to predict interstate migration. The results indicate the model was highly accurate as the mean margin of error was +/- 900 citizens. The predictions from the model were compared with the US Census data from 2010 to 2020 that was used to train the model. Since the input for the model was not exposed to any migration data, the model clearly demonstrated that its results were drawn from sentiment data alone. These findings are significant as they indicate that the role of the press could be used as a predictor for domestic migration which can help the government and businesses understand better what is influencing people to move to certain places.

--Drawing inspiration from neurosciences, artificial neural networks (ANNs) have evolved from shallow architectures to highly complex, deep structures, yielding exceptional performance in auditory recognition tasks. However, traditional ANNs often struggle to align with brain regions due to their excessive depth and lack of biologically realistic features, like recurrent connection. T o address this, a brain-like auditory network (BAN) is introduced, which incorporates four neuroanatomically mapped areas and recurrent connection, guided by a novel metric called the brain-like auditory score (BAS). BAS serves as a benchmark for evaluating the similarity between BAN and human auditory recognition pathway. We further propose that specific areas in the cerebral cortex, mainly the middle and medial superior temporal (T2/T3) areas, correspond to the designed network structure, drawing parallels with the brain's auditory perception pathway. Our findings suggest that the neuroanatomical similarity in the cortex and auditory classification abilities of the ANN are well-aligned. In addition to delivering excellent performance on a music genre classification task, the BAN demonstrates a high BAS score. In conclusion, this study presents BAN as a recurrent, brain-inspired ANN, representing the first model that mirrors the cortical pathway of auditory recognition. EARING plays a vital role in human sound recognition and is especially important for the comprehension and creation of music. One key task in this domain is genre classification, which involves predicting the genre of a piece of music based on its audio signal. Music genres, such as jazz, rock, and classical, serve as descriptive labels that provide high-level information about a musical piece. As noted by previous work [2], genres are classes introduced by humans to categorize musical works.