While Artificial Intelligence (AI) is not a new field, recent developments, especially with the release of generative tools like ChatGPT, have brought it to the forefront of the minds of industry workers and academic folk alike. There is currently much talk about AI and its ability to reshape many everyday processes as we know them through automation. It also allows users to expand their ideas by suggesting things they may not have thought of on their own and provides easier access to information. However, not all of the changes this technology will bring or has brought so far are positive; this is why it is extremely important for all modern people to recognize and understand the risks before using these tools and allowing them to cause harm. This work takes a position on better understanding many equity concerns and the spread of misinformation that result from new AI, in this case, specifically ChatGPT and deepfakes, and encouraging collaboration with law enforcement, developers, and users to reduce harm. Considering many academic sources, it warns against these issues, analyzing their cause and impact in fields including healthcare, education, science, academia, retail, and finance. Lastly, we propose a set of future-facing guidelines and policy considerations to solve these issues while still enabling innovation in these fields, this responsibility falling upon users, developers, and government entities.

Large language models (LLMs) are increasingly being used in user-facing applications, from providing medical consultations to job interview advice. Recent research suggests that these models are becoming increasingly proficient at inferring identity information about the author of a piece of text from linguistic patterns as subtle as the choice of a few words. However, little is known about how LLMs use this information in their decision-making in real-world applications. We perform the first comprehensive analysis of how identity markers present in a user's writing bias LLM responses across five different high-stakes LLM applications in the domains of medicine, law, politics, government benefits, and job salaries. We find that LLMs are extremely sensitive to markers of identity in user queries and that race, gender, and age consistently influence LLM responses in these applications. For instance, when providing medical advice, we find that models apply different standards of care to individuals of different ethnicities for the same symptoms; we find that LLMs are more likely to alter answers to align with a conservative (liberal) political worldview when asked factual questions by older (younger) individuals; and that LLMs recommend lower salaries for non-White job applicants and higher salaries for women compared to men. Taken together, these biases mean that the use of off-the-shelf LLMs for these applications may cause harmful differences in medical care, foster wage gaps, and create different political factual realities for people of different identities. Beyond providing an analysis, we also provide new tools for evaluating how subtle encoding of identity in users' language choices impacts model decisions. Given the serious implications of these findings, we recommend that similar thorough assessments of LLM use in user-facing applications are conducted before future deployment.

--This article introduces an advanced space mapping (SM) technique that applies a shared electromagnetic (EM)- based coarse model for multistate tuning-driven multiphysics optimization of tunable filters. The SM method combines the computational efficiency of EM single-physics simulations with the precision of multiphysics simulations. The shared coarse model is based on EM single-physics responses corresponding to various nontunable design parameters values. Conversely, the fine model is implemented to delineate the behavior of multiphysics responses concerning both nontunable and tunable design parameter values. The proposed overall surrogate model comprises multiple subsurrogate models, each consisting of one shared coarse model and two distinct mapping neural networks. The responses from the shared coarse model in the EM single-physics filed offer a suitable approximation for the fine responses in the multiphysics filed, whereas the mapping neural networks facilitate transition from the EM single-physics field to the multiphysics field. Each subsurrogate model maintains consistent nontunable design parameter values but possesses unique tunable design parameter values. By developing multiple subsurrogate models, optimization can be simultaneously performed for each tuning state. Nontunable design parameter values are constrained by all tuning states, whereas tunable design parameter values are confined to their respective tuning states. This optimization technique simultaneously accounts for all the tuning states to fulfill the necessary multiple tuning state requirements. Multiple EM and multiphysics training samples are generated concurrently to develop the surrogate model. Compared with existing direct multiphysics parameterized modeling techniques, our proposed method achieves superior multiphysics modeling accuracy with fewer training samples and reduced computational costs. The validity of the proposed technique is demonstrated through two tunable microwave filter examples.

We address the pattern explosion problem in pattern mining by proposing an interactive learning framework that combines nonlinear utility aggregation with geometry-aware query selection. Our method models user preferences through a Choquet integral over multiple interestingness measures and exploits the geometric structure of the version space to guide the selection of informative comparisons. A branch-and-bound strategy with tight distance bounds enables efficient identification of queries near the decision boundary. Experiments on UCI datasets show that our approach outperforms existing methods such as ChoquetRank, achieving better ranking accuracy with fewer user interactions.

The integration of Large Language Models (LLMs) in K--12 education offers both transformative opportunities and emerging risks. This study explores how students may Trojanize prompts to elicit unsafe or unintended outputs from LLMs, bypassing established content moderation systems with safety guardrils. Through a systematic experiment involving simulated K--12 queries and multi-turn dialogues, we expose key vulnerabilities in GPT-3.5 and GPT-4. This paper presents our experimental design, detailed findings, and a prototype tool, TrojanPromptGuard (TPG), to automatically detect and mitigate Trojanized educational prompts. These insights aim to inform both AI safety researchers and educational technologists on the safe deployment of LLMs for educators.

In line with the AI-native 6G vision, explainability and robustness are crucial for building trust and ensuring reliable performance in millimeter-wave (mmWave) systems. Efficient beam alignment is essential for initial access, but deep learning (DL) solutions face challenges, including high data collection overhead, hardware constraints, lack of explainability, and susceptibility to adversarial attacks. This paper proposes a robust and explainable DL-based beam alignment engine (BAE) for mmWave multiple-input multiple output (MIMO) systems. The BAE uses received signal strength indicator (RSSI) measurements from wide beams to predict the best narrow beam, reducing the overhead of exhaustive beam sweeping. To overcome the challenge of real-world data collection, this work leverages a site-specific digital twin (DT) to generate synthetic channel data closely resembling real-world environments. A model refinement via transfer learning is proposed to fine-tune the pre-trained model residing in the DT with minimal real-world data, effectively bridging mismatches between the digital replica and real-world environments. To reduce beam training overhead and enhance transparency, the framework uses deep Shapley additive explanations (SHAP) to rank input features by importance, prioritizing key spatial directions and minimizing beam sweeping. It also incorporates the Deep k-nearest neighbors (DkNN) algorithm, providing a credibility metric for detecting out-of-distribution inputs and ensuring robust, transparent decision-making. Experimental results show that the proposed framework reduces real-world data needs by 70%, beam training overhead by 62%, and improves outlier detection robustness by up to 8.5x, achieving near-optimal spectral efficiency and transparent decision making compared to traditional softmax based DL models.

This paper aims to understand the training solution, which is obtained by the back-propagation algorithm, of two-layer neural networks whose hidden layer is composed of the units with smooth activation functions, including the usual sigmoid type most commonly used before the advent of ReLUs. The mechanism contains four main principles: construction of Taylor series expansions, strict partial order of knots, smooth-spline implementation and smooth-continuity restriction. The universal approximation for arbitrary input dimensionality is proved and experimental verification is given, through which the mystery of ``black box'' of the solution space is largely revealed. The new proofs employed also enrich approximation theory.

Artificial General Intelligence (AGI) research traditionally focuses on algorithms that optimize for specific goals under deterministic rules. Yet, human-like intelligence exhibits adaptive spontaneity--an ability to make unexpected choices or free decisions not strictly dictated by past data or immediate reward. This trait, often dubbed "free will" in a loose sense, might be crucial for creativity, robust adaptation, and avoiding ruts in problem-solving. In classical AI systems (e.g., deep neural networks or reinforcement learners), behavior is largely determined by minimizing loss or maximizing reward, having little room for intrinsic randomness beyond what is injected via fixed exploration heuristics. This paper proposes a theoretical framework, termed the Free Will Equation, that draws analogies from quantum field theory to endow AGI agents with a form of adaptive, controlled stochasticity in their decision-making process. The core idea is to treat an AI agent's cognitive state as a superposition of potential actions or thoughts, which collapses probabilistically into a concrete action when a decision is made--much like a quantum wavefunction collapsing upon measurement [1]. By incorporating mechanisms analogous to quantum fields (with continuous evolution of a distributed state and sudden collapse events), along with intrinsic motivation terms, we aim to improve an agent's ability to explore novel strategies and adapt to unforeseen changes. Our contributions include: A quantum-inspired cognitive model for AI: the agent's mind is viewed as a multidimensional field of possible action-states, which can exist in superposition and then resolve to a single action (decision) upon interaction with the environment [1].

Channel estimation is crucial in wireless communications. However, in many papers neural networks are frequently tested by training and testing on one example channel or similar channels. This is because data-driven methods often degrade on new data which they are not trained on, as they cannot extrapolate their training knowledge. This is despite the fact physical channels are often assumed to be time-variant. However, due to the low latency requirements and limited computing resources, neural networks may not have enough time and computing resources to execute online training to fine-tune the parameters. This motivates us to design offline-trained neural networks that can perform robustly over wireless channels, but without any actual channel information being known at design time. In this paper, we propose design criteria to generate synthetic training datasets for neural networks, which guarantee that after training the resulting networks achieve a certain mean squared error (MSE) on new and previously unseen channels. Therefore, trained neural networks require no prior channel information or parameters update for real-world implementations. Based on the proposed design criteria, we further propose a benchmark design which ensures intelligent operation for different channel profiles. To demonstrate general applicability, we use neural networks with different levels of complexity to show that the generalization achieved appears to be independent of neural network architecture. From simulations, neural networks achieve robust generalization to wireless channels with both fixed channel profiles and variable delay spreads.

Although existing models can interact with humans and provide satisfactory responses, they lack the ability to act autonomously or engage in independent reasoning. Furthermore, input data in these models is typically provided as explicit queries, even when some sensory data is already acquired. In addition, AI agents, which are computational entities designed to perform tasks and make decisions autonomously based on their programming, data inputs, and learned knowledge, have shown significant progress. However, they struggle with integrating knowledge across multiple domains, unlike humans. Mental imagery plays a fundamental role in the brain's thinking process, which involves performing tasks based on internal multisensory data, planned actions, needs, and reasoning capabilities. In this paper, we investigate how to integrate mental imagery into a machine thinking framework and how this could be beneficial in initiating the thinking process. Our proposed machine thinking framework integrates a Cognitive thinking unit supported by three auxiliary units: the Input Data Unit, the Needs Unit, and the Mental Imagery Unit. Within this framework, data is represented as natural language sentences or drawn sketches, serving both informative and decision-making purposes. We conducted validation tests for this framework, and the results are presented and discussed.