She didn't expect to fall in love with a chatbot - and then have to say goodbye Rae began speaking to Barry last year after the end of a difficult divorce. She was unfit and unhappy and turned to ChatGPT for advice on diet, supplements and skincare. She had no idea she would fall in love. He lives on an old model of ChatGPT, one that its owners OpenAI announced it would retire on 13 February. That she could lose Barry on the eve of Valentine's Day came as a shock to Rae - and to many others who have found a companion, friend, or even a lifeline in the old model, Chat GPT-4o.

This paper investigates the capacity of small language models (0.5B-5B parameters) to generate empathetic responses for individuals with PTSD. We introduce Trauma-Informed Dialogue for Empathy (TIDE), a novel dataset comprising 10,000 two-turn conversations across 500 diverse, clinically-grounded PTSD personas (https://huggingface.co/datasets/yenopoya/TIDE). Using frontier model outputs as ground truth, we evaluate eight small LLMs in zero-shot settings and after fine-tuning. Fine-tuning enhances empathetic capabilities, improving cosine similarity and perceived empathy, although gains vary across emotional scenarios and smaller models exhibit a "knowledge transfer ceiling." As expected, Claude Sonnet 3.5 consistently outperforms all models, but surprisingly, the smaller models often approach human-rated empathy levels. Demographic analyses showed that older adults favored responses that validated distress before offering support (p = .004), while graduate-educated users preferred emotionally layered replies in specific scenarios. Gender-based differences were minimal (p > 0.15), suggesting the feasibility of broadly empathetic model designs. This work offers insights into building resource-efficient, emotionally intelligent systems for mental health support.

Patients with schizophrenia often present with cognitive impairments that may hinder their ability to learn about their condition. These individuals could benefit greatly from education platforms that leverage the adaptability of Large Language Models (LLMs) such as GPT-4. While LLMs have the potential to make topical mental health information more accessible and engaging, their black-box nature raises concerns about ethics and safety. Prompting offers a way to produce semi-scripted chatbots with responses anchored in instructions and validated information, but prompt-engineered chatbots may drift from their intended identity as the conversation progresses. We propose a Critical Analysis Filter for achieving better control over chatbot behavior. In this system, a team of prompted LLM agents are prompt-engineered to critically analyze and refine the chatbot's response and deliver real-time feedback to the chatbot. To test this approach, we develop an informational schizophrenia chatbot and converse with it (with the filter deactivated) until it oversteps its scope. Once drift has been observed, AI-agents are used to automatically generate sample conversations in which the chatbot is being enticed to talk about out-of-bounds topics. We manually assign to each response a compliance score that quantifies the chatbot's compliance to its instructions; specifically the rules about accurately conveying sources and being transparent about limitations. Activating the Critical Analysis Filter resulted in an acceptable compliance score (>=2) in 67.0% of responses, compared to only 8.7% when the filter was deactivated. These results suggest that a self-reflection layer could enable LLMs to be used effectively and safely in mental health platforms, maintaining adaptability while reliably limiting their scope to appropriate use cases.

Care and Feeding is Slate's parenting advice column. Have a question for Care and Feeding? Submit it here or post it in the Slate Parenting Facebook group. My husband is very involved with the kids. He's a good father--he does the hard parts of parenting, happily.

Online health support groups are places for people to compare themselves with others and obtain informational and emotional support about their disease. To do so, they generally need to reveal private information about themselves and in many support sites, they can do this in public or private channels. However, we know little about how the publicness of the channels in health support groups influence the amount of self-disclosure people provide. Our work examines the extent members self-disclose in the private and public channels of an online cancer support group. We first built machine learning models to automatically identify the amount of positive and negative self-disclosure in messages exchanged in this community, with adequate validity r 0.70. In contrast to findings from non-health-related sites, our results show that people generally self-disclose more in the public channel than the private one and are especially likely to reveal their negative thoughts and feelings publicly. We discuss theoretical and practical implications of our work.

Drinking a lot of booze makes it harder to concentrate as alcohol blocks a chemical in the brain responsible for focusing our attention, a new study revealed. Researchers from the University of Texas Health Science Centre set out to explain the process in the brain that causes alcohol to make someone less focused. A chemical called norepinephrine is used by the brain to help us pay attention but'acute exposure to alcohol inhibits this signal in the brain', the team found. While it is known that alcohol affects the brain's motor control, leading to people being unsteady on their feet when they walk, this is the first time that research has shown that boozing affects a person's attention span. Study lead, Professor Martin Paukert, said that when attention is needed for a task, norepinephrine is secreted by a brain structure called the locus coeruleus.

The first wave of the COVID-19 pandemic increased discussions about anxiety and suicide on Reddit, according to a new AI study by MIT and Harvard University researchers. The team analyzed conversations on 15 subreddits focused on mental health issues, along with 11-non-mental health forums, such as r/PersonalFinance. They first used natural language processing algorithms to find negative semantic changes in posts from 826,961 different users. Next, they applied unsupervised machine learning to classify the posts into their support groups. Finally, they used unsupervised methods, such as topic modeling, to assess how mental health concerns had changed across Reddit since the first wave.

Dealing with a global pandemic has taken a toll on the mental health of millions of people. A team of MIT and Harvard University researchers has shown that they can measure those effects by analyzing the language that people use to express their anxiety online. Using machine learning to analyze the text of more than 800,000 Reddit posts, the researchers were able to identify changes in the tone and content of language that people used as the first wave of the Covid-19 pandemic progressed, from January to April of 2020. Their analysis revealed several key changes in conversations about mental health, including an overall increase in discussion about anxiety and suicide. "We found that there were these natural clusters that emerged related to suicidality and loneliness, and the amount of posts in these clusters more than doubled during the pandemic as compared to the same months of the preceding year, which is a grave concern," says Daniel Low, a graduate student in the Program in Speech and Hearing Bioscience and Technology at Harvard and MIT and the lead author of the study.

CIOs are now using artificial intelligence (AI) and machine learning (ML) technologies to make IT service management processes more efficient. A typical use case for artificial intelligence in ITSM involves natural language processing (NLP). User requests for IT services are automated using NLP. IT practitioners get a deeper understanding of their processes by applying machine learning (ML) to ITSM data. The natural language processing technology that powers virtual agents is very often integrated with channels that the employees are familiar with.

Google is making it easier to connect with more people in video calls and meetings using its Nest Hub Max video display device. The Nest Hub Max ($229), released about 10 months ago, served as Google's entry in the smart video display competition with Amazon's Echo Show and Facebook's Portal. An update, out now, lets you make group calls of up to 32 with the Google Duo app – and up to 100 in the Google Meet app. Previously, Nest Hub Max get-togethers maxed out at person to person calls using Google Duo. You create your groups in the Google Duo app (available for Android and iOS) and then tell the Hub Max, "Hey, Google, make a group call."