As AI enters the workforce and seeps into all facets of our lives at unprecedented speed, we're told by leaders across industries that if you're not using it, you're falling behind. Yet when AI's use in art enters the conversation, some retreat in discomfort, shunning it as an affront to the very essence of art. This ongoing debate continues to create disruptions among artists. AI is fundamentally changing the creative process, and its purpose, significance, and influence are subjective to one's own values--making its trajectory hard to predict, and even harder to confront. Miami-based Panamanian photographer Dahlia Dreszer stands out as an optimist and believer in AI's powers.

If you were asked to visualise a stereotypical British man, what would you think of? According to AI, the answer is an overweight man wearing a football shirt. Instagram account @reimagineuk asked AI to create videos of the most stereotypical men around the world - with hilarious results. While the British man looks casual in his football shirt, men from other countries are depicted with fancier outfits. The stereotypical man from Portugal sports a white shirt and a waistcoat, while the man from Nigeria can be seen wearing a bright orange suit.

The Heaviside activation function is for instance used in Hopfield networks [ 1 ] that have recently seen a resurge due to their connections t o attention layers [ 2, 3 ] and the 2024 Nobel Prize in Physics that was partially award ed for their development. Moreover, the Heaviside activation function is closely related to quantized neural networks [ 4, 5 ], playing a key role in enabling energy efficient deployment o f large language models (LLMs) [ 6, 7 ]. We refer to neural networks with several hidden layers and th e Heaviside activation function as deep Heaviside (neural) networks (DHNs). These networks are also known as (linear) threshold networks. The Heaviside activation function can be traced back to the fi rst attempts to build an artificial counterpart of a biological neuron. In the brain, the inputs of a neuron contribute to its membrane potential and the neuron discharges/fires if th e membrane potential exceeds a certain threshold.

This paper proposed an approach to automatically discovering subject dimension, action dimension, object dimension and adverbial dimension from texts to efficiently operate texts and support query in natural language. The high quality of trees guarantees that all subjects, actions, objects and adverbials and their subclass relations within texts can be represented. The independency of trees ensures that there is no redundant representation between trees. The expressiveness of trees ensures that the majority of sentences can be accessed from each tree and the rest of sentences can be accessed from at least one tree so that the tree-based search mechanism can support querying in natural language. Experiments show that the average precision, recall and F1-score of the abstraction trees constructed by the subclass relations of subject, action, object and adverbial are all greater than 80%. The application of the proposed approach to supporting query in natural language demonstrates that different types of question patterns for querying subject or object have high coverage of texts, and searching multiple trees on subject, action, object and adverbial according to the question pattern can quickly reduce search space to locate target sentences, which can support precise operation on texts.

Qualitative research often involves labor-intensive processes that are difficult to scale while preserving analytical depth. This paper introduces The AI Co-Ethnographer (AICoE), a novel end-to-end pipeline developed for qualitative research and designed to move beyond the limitations of simply automating code assignments, offering a more integrated approach. AICoE organizes the entire process, encompassing open coding, code consolidation, code application, and even pattern discovery, leading to a comprehensive analysis of qualitative data.

Objectives: While Large Language Models (LLMs) have been widely used to assist clinicians and support patients, no existing work has explored dialogue systems for standard diagnostic interviews and assessments. This study aims to bridge the gap in mental healthcare accessibility by developing an LLM-powered dialogue system that replicates clinician behavior. Materials and Methods: We introduce TRUST, a framework of cooperative LLM modules capable of conducting formal diagnostic interviews and assessments for Post-Traumatic Stress Disorder (PTSD). To guide the generation of appropriate clinical responses, we propose a Dialogue Acts schema specifically designed for clinical interviews. Additionally, we develop a patient simulation approach based on real-life interview transcripts to replace time-consuming and costly manual testing by clinicians. Results: A comprehensive set of evaluation metrics is designed to assess the dialogue system from both the agent and patient simulation perspectives. Expert evaluations by conversation and clinical specialists show that TRUST performs comparably to real-life clinical interviews. Discussion: Our system performs at the level of average clinicians, with room for future enhancements in communication styles and response appropriateness. Conclusions: Our TRUST framework shows its potential to facilitate mental healthcare availability.

What is the future of the like button in the age of artificial intelligence? Max Levchin--the PayPal cofounder and Affirm CEO--sees a new and hugely valuable role for liking data to train AI to arrive at conclusions more in line with those a human decisionmaker would make. It's a well-known quandary in machine learning that a computer presented with a clear reward function will engage in relentless reinforcement learning to improve its performance and maximize that reward--but that this optimization path often leads AI systems to very different outcomes than would result from humans exercising human judgment. To introduce a corrective force, AI developers frequently use what is called reinforcement learning from human feedback (RLHF). Essentially they are putting a human thumb on the scale as the computer arrives at its model by training it on data reflecting real people's actual preferences.

--The increased demand for data privacy and security in machine learning (ML) applications has put impetus on effective edge training on Internet-of-Things (IoT) nodes. Edge training aims to leverage speed, energy efficiency and adaptability within the resource constraints of the nodes. This paper presents a Dynamic Tsetlin Machine (DTM) training accelerator as an alternative to DNN implementations. Underpinned on the V anilla and Coalesced Tsetlin Machine algorithms, the dynamic aspect of the accelerator design allows for a run-time reconfiguration targeting different datasets, model architectures, and model sizes without resynthesis. This makes the DTM suitable for targeting multivariate sensor-based edge tasks. Compared to DNNs, DTM trains with fewer multiply-accumulates, devoid of derivative computation. It is a data-centric ML algorithm that learns by aligning Tsetlin automata with input data to form logical propositions enabling efficient Lookup-T able (LUT) mapping and frugal Block RAM usage in FPGA training implementations. The proposed accelerator offers 2.54x more Giga operations per second per Watt (GOP/s per W) and uses 6x less power than the next-best comparable design. Index T erms --Edge Training, Coalesced Tsetlin Machines, Dynamic Tsetlin Machines, Embedded FPGA, Machine Learning Accelerator, On-Chip Learning, Logic-based-learning. ACHINE Learning (ML) offers a generalized approach to developing autonomous applications from "Internet-of-Things" (IoT) sensor data. Having ML execution units in close proximity to the sensor, at the so-called edge, enables faster task execution with high data security and privacy. However, sensor degradation and environmental factors may require recalibration [1] or user-personalized on-field training [2] to ensure continued functionality. Implementing solutions to these challenges is nontrivial. It requires finding the right balance between achieving the appropriate learning efficacy for the ML problem and the restrictive compute/ memory resources available on the platforms [3]. This work was supported by EPSRC EP/X036006/1 Scalability Oriented Novel Network of Event Triggered Systems (SONNETS) project and by EPSRC EP/X039943/1 UKRI-RCN: Exploiting the dynamics of self-timed machine learning hardware (ESTEEM) project. For ML inference tasks on edge nodes, these challenges have been widely explored, e.g., quantization [4], sparsity-based compression, and pruning for the most commonly used Deep Neural Network (DNN) models [3], [5], [6].

Recent advancements in LLMs enable chatbots to interact with individuals on a range of queries, including sensitive mental health contexts. Despite uncertainties about their effectiveness and reliability, the development of LLMs in these areas is growing, potentially leading to harms. To better identify and mitigate these harms, it is critical to understand how the values of people with lived experiences relate to the harms. In this study, we developed a technology probe, a GPT-4o based chatbot called Zenny, enabling participants to engage with depression self-management scenarios informed by previous research. We used Zenny to interview 17 individuals with lived experiences of depression. Our thematic analysis revealed key values: informational support, emotional support, personalization, privacy, and crisis management. This work explores the relationship between lived experience values, potential harms, and design recommendations for mental health AI chatbots, aiming to enhance self-management support while minimizing risks.

Automating structured clinical interviews could revolutionize mental healthcare accessibility, yet existing large language models (LLMs) approaches fail to align with psychiatric diagnostic protocols. We present MAGI, the first framework that transforms the gold-standard Mini International Neuropsychiatric Interview (MINI) into automatic computational workflows through coordinated multi-agent collaboration. MAGI dynamically navigates clinical logic via four specialized agents: 1) an interview tree guided navigation agent adhering to the MINI's branching structure, 2) an adaptive question agent blending diagnostic probing, explaining, and empathy, 3) a judgment agent validating whether the response from participants meet the node, and 4) a diagnosis Agent generating Psychometric Chain-of- Thought (PsyCoT) traces that explicitly map symptoms to clinical criteria. Experimental results on 1,002 real-world participants covering depression, generalized anxiety, social anxiety and suicide shows that MAGI advances LLM- assisted mental health assessment by combining clinical rigor, conversational adaptability, and explainable reasoning.