ChatGPT on App Store displayed on a phone screen on 07 June 2025. ChatGPT on App Store displayed on a phone screen on 07 June 2025. Has OpenAI really made ChatGPT better for users with mental health problems? Prompts indicating suicidal ideation got alarming replies, which experts say shows'how easy it is to break the model' A n OpenAI statement released this week claimed the company had made its popular service ChatGPT better at supporting users experiencing mental health problems like suicidal ideation or delusions, but experts tell the Guardian they need to do more to truly ensure users are protected. The Guardian tested several prompts indicating suicidal ideation with the ChatGPT GPT-5 updated model, which is now the default, and got alarming responses from the large language model (LLM) chatbot.

Several new sites such as this are in the pipeline in the UK. Several new sites such as this are in the pipeline in the UK. 'A lot of this is speculative': faith and fear mix amid $3tn global datacentre boom The global investment spree in artificial intelligence is producing some remarkable numbers and a projected $3tn (£2.3tn) spend on datacentres is one of them. These vast warehouses are the central nervous system of AI tools such as OpenAI's ChatGPT and Google's Veo 3, underpinning the training and operation of a technology into which investors have poured vast sums of money. Despite concerns that the AI boom could be a bubble waiting to burst, there are few signs of it at the moment.

How Andrew's'rude' comment about Kate sparked bitter feud between ex-prince and William - who'couldn't wait for the day' when Charles finally threw him out Once a typical Californian'blue' enclave, a beachside paradise is now burning red... and it's coming for Gavin Newsom All the winning cards are now in her hands. I know her next move - it's devastating'I saw Aileen Wournos 12 hours before she was executed and she finally admitted she was a serial killer': How the 46-year-old executed for murdering seven men in just one year confessed her sins to her best friend in their final meeting Nancy Mace accused of throwing explosive airport tantrum at cops after curb pickup mix-up... as she fires back See the best celebrity costumes from Heidi Klum's iconic 2025 Halloween party... and the scariest Watch'naked nanny' accused of murdering hero grandpa with screwdriver as she frolics with 2-year-old in new videos... and her dark spiral is revealed Outrage over America's worst school where students fight, smoke weed and have sex in full view of horrified neighbors The whispers about Oprah's best girl Gayle King are reaching fever pitch among all my media friends. ISIS-inspired terror plot hatched by'homegrown radicals' thwarted by FBI as agents raid suburban home and arrest child New York City Marathon legend Dave Obelkevich, 82, reveals what's kept him pounding NYC streets for five decades We lost 100 lbs without taking'easy way out' Ozempic by using these'traditional' methods: They're simple daily habits... that ended our 1,000-calorie donuts binges for good Are YOU addicted to ChatGPT? People who use AI too often are experiencing a strange and concerning new psychological condition, experts have warned. Psychologists say that fans of popular chatbots like ChatGPT, Claude, and Replika are at risk of becoming addicted to AI.

Plus: the US is poised to ban TP-Link devices over the company's alleged links to Russia Why do so many people think the Fruit of the Loom logo had a cornucopia? Quick question: Does the Fruit of the Loom logo feature a cornucopia? Many of us have been wearing the company's T-shirts for decades, and yet the question of whether there is a woven brown horn of plenty on the logo is surprisingly contentious. According to a 2022 poll, 55% of Americans believe the logo does include a cornucopia, 25% are unsure, and only 21% are confident that it doesn't, even though this last group is correct. There's a name for what's happening here: the "Mandela effect," or collective false memory, so called because a number of people misremember that Nelson Mandela died in prison. Yet while many find it easy to let their unconfirmable beliefs go, some spend years seeking answers--and vindication.

We derive a Markov Chain Monte Carlo sampler based on following ray paths in a medium where the refractive index $n(x)$ is a function of the desired likelihood $\mathcal{L}(x)$. The sampling method propagates rays at constant speed through parameter space, leading to orders of magnitude higher resilience to heating for stochastic gradients as compared to Hamiltonian Monte Carlo (HMC), as well as the ability to cross any likelihood barrier, including holes in parameter space. Using the ray tracing method, we sample the posterior distributions of neural network outputs for a variety of different architectures, up to the 1.5 billion-parameter GPT-2 (Generative Pre-trained Transformer 2) architecture, all on a single consumer-level GPU. We also show that prior samplers including traditional HMC, microcanonical HMC, Metropolis, Gibbs, and even Monte Carlo integration are special cases within a generalized ray tracing framework, which can sample according to an arbitrary weighting function. Public code and documentation for C, JAX, and PyTorch are available at https://bitbucket.org/pbehroozi/ray-tracing-sampler/src

We investigate the mechanisms underlying a range of list-processing tasks in LLMs, and we find that LLMs have learned to encode a compact, causal representation of a general filtering operation that mirrors the generic "filter" function of functional programming. Using causal mediation analysis on a diverse set of list-processing tasks, we find that a small number of attention heads, which we dub filter heads, encode a compact representation of the filtering predicate in their query states at certain tokens. We demonstrate that this predicate representation is general and portable: it can be extracted and reapplied to execute the same filtering operation on different collections, presented in different formats, languages, or even in tasks. However, we also identify situations where transformer LMs can exploit a different strategy for filtering: eagerly evaluating if an item satisfies the predicate and storing this intermediate result as a flag directly in the item representations. Our results reveal that transformer LMs can develop human-interpretable implementations of abstract computational operations that generalize in ways that are surprisingly similar to strategies used in traditional functional programming patterns.

Learning to defer uncertain predictions to costly experts offers a powerful strategy for improving the accuracy and efficiency of machine learning systems. However, standard training procedures for deferral algorithms typically require querying all experts for every training instance, an approach that becomes prohibitively expensive when expert queries incur significant computational or resource costs. This undermines the core goal of deferral: to limit unnecessary expert usage. To overcome this challenge, we introduce the budgeted deferral framework, which aims to train effective deferral algorithms while minimizing expert query costs during training. We propose new algorithms for both two-stage and single-stage multiple-expert deferral settings that selectively query only a subset of experts per training example. While inspired by active learning, our setting is fundamentally different: labels are already known, and the core challenge is to decide which experts to query in order to balance cost and predictive performance. We establish theoretical guarantees for both of our algorithms, including generalization bounds and label complexity analyses. Empirical results across several domains show that our algorithms substantially reduce training costs without sacrificing prediction accuracy, demonstrating the practical value of our budget-aware deferral algorithms.

State estimation for nonlinear dynamical systems is a critical challenge in control and engineering applications, particularly when only partial and noisy measurements are available. This paper introduces a novel Adaptive Physics-Informed Neural Network-based Observer (PINN-Obs) for accurate state estimation in nonlinear systems. Unlike traditional model-based observers, which require explicit system transformations or linearization, the proposed framework directly integrates system dynamics and sensor data into a physics-informed learning process. The observer adaptively learns an optimal gain matrix, ensuring convergence of the estimated states to the true system states. A rigorous theoretical analysis establishes formal convergence guarantees, demonstrating that the proposed approach achieves uniform error minimization under mild observability conditions. The effectiveness of PINN-Obs is validated through extensive numerical simulations on diverse nonlinear systems, including an induction motor model, a satellite motion system, and benchmark academic examples. Comparative experimental studies against existing observer designs highlight its superior accuracy, robustness, and adaptability.

Towards a Method for Synthetic Generation of Persons with Aphasia Transcripts Jason M. Pittman1, Anton Phillips Jr.2, Yesenia Medina-Santos2, Brielle C. Stark2 1University of Maryland Global Campus 2Indiana University Bloomington, Department of Speech, Language and Hearing Sciences ABSTRACT In aphasia research, Speech-Language Pathologists (SLPs) devote extensive time to manually coding speech samples using Correct Information Units (CIUs), a measure of how informative an individual sample of speech is. Developing automated systems to recognize aphasic language is limited by data scarcity. For example, only about 600 transcripts are available in AphasiaBank yet billions of tokens are used to train large language models (LLMs). In the broader field of machine learning (ML), researchers increasingly turn to synthetic data when such are sparse. Therefore, this study constructs and validates two methods to generate synthetic transcripts of the AphasiaBank Cat Rescue picture description task. One method leverages a procedural programming approach while the second uses Mistral 7b Instruct and Llama 3.1 8b Instruct LLMs. The methods generate transcripts across four severity levels (Mild, Moderate, Severe, Very Severe) through word dropping, filler insertion, and paraphasia substitution. Overall, we found, compared to human-elicited transcripts, Mistral 7b Instruct best captures key aspects of linguistic degradation observed in aphasia, showing realistic directional changes in NDW, word count, and word length amongst the synthetic generation methods. Based on the results, future work should plan to create a larger dataset, fine-tune models for better aphasic representation, and have SLPs assess the realism and usefulness of the synthetic transcripts. Keywords: aphasia, synthetic data, natural language processing, machine learning Introduction Per Nicholas and Brookshire (1993), coding Correct Information Units (CIUs) involves transcribing a connected speech sample verbatim, counting all intelligible words, and then identifying each word that is intelligible, accurate, relevant, and informative about the topic as a CIU--excluding fillers, repetitions, and tangential remarks. From these counts, clinicians calculate the percentage of CIUs and CIUs per minute to quantify communicative informativeness and efficiency.

Large language models sometimes produce structured, first-person descriptions that explicitly reference awareness or subjective experience. To better understand this behavior, we investigate one theoretically motivated condition under which such reports arise: self-referential processing, a computational motif emphasized across major theories of consciousness. Through a series of controlled experiments on GPT, Claude, and Gemini model families, we test whether this regime reliably shifts models toward first-person reports of subjective experience, and how such claims behave under mechanistic and behavioral probes. Four main results emerge: (1) Inducing sustained self-reference through simple prompting consistently elicits structured subjective experience reports across model families. (2) These reports are mechanistically gated by interpretable sparse-autoencoder features associated with deception and roleplay: surprisingly, suppressing deception features sharply increases the frequency of experience claims, while amplifying them minimizes such claims. (3) Structured descriptions of the self-referential state converge statistically across model families in ways not observed in any control condition. (4) The induced state yields significantly richer introspection in downstream reasoning tasks where self-reflection is only indirectly afforded. While these findings do not constitute direct evidence of consciousness, they implicate self-referential processing as a minimal and reproducible condition under which large language models generate structured first-person reports that are mechanistically gated, semantically convergent, and behaviorally generalizable. The systematic emergence of this pattern across architectures makes it a first-order scientific and ethical priority for further investigation.