ChatGPT is now available on WhatsApp. Starting today, if you add 1 (800) CHAT-GPT to your contacts -- that's 1 (800) 242-8478 -- you can start using the chatbot over Meta's messaging app. In this iteration, ChatGPT is limited to text-only input, so there's no Advanced Voice Mode or visual input on offer, but you still get all the smarts of the o1-mini model. What's more, over WhatsApp ChatGPT is available everywhere OpenAI offers its chatbot, with no account necessary. OpenAI is working on a way to authenticate existing users over WhatApp, though the company did not share a timeline for when that feature might launch.

When news broke that AI-generated nude pictures of students were popping up at a Beverly Hills Middle School in February, many district officials and parents were horrified. But others said no one should have been blindsided by the spread of AI-powered "undressing" programs. "The only thing shocking about this story," one Carlsbad parent said his 14-year-old told him, "is that people are shocked." Now, a newly released report by Thorn, a tech company that works to stop the spread of child sexual abuse material, shows how common deepfake abuse has become. The proliferation coincides with the wide availability of cheap "undressing" apps and other easy-to-use, AI-powered programs to create deepfake nudes.

Psychological support hotlines are an effective suicide prevention measure that typically relies on professionals using suicide risk assessment scales to predict individual risk scores. However, the accuracy of scale-based predictive methods for suicide risk assessment can vary widely depending on the expertise of the operator. This limitation underscores the need for more reliable methods, prompting this research's innovative exploration of the use of artificial intelligence to improve the accuracy and efficiency of suicide risk prediction within the context of psychological support hotlines. The study included data from 1,549 subjects from 2015-2017 in China who contacted a psychological support hotline. Each participant was followed for 12 months to identify instances of suicidal behavior. We proposed a novel multi-task learning method that uses the large-scale pre-trained model Whisper for feature extraction and fits psychological scales while predicting the risk of suicide. The proposed method yields a 2.4\% points improvement in F1-score compared to the traditional manual approach based on the psychological scales. Our model demonstrated superior performance compared to the other eight popular models. To our knowledge, this study is the first to apply deep learning to long-term speech data to predict suicide risk in China, indicating grate potential for clinical applications. The source code is publicly available at: \url{https://github.com/songchangwei/Suicide-Risk-Prediction}.

Jonathan's actions were irrefutable: Over the course of nearly a decade, while working at a video game developer, he sexually assaulted industry colleagues. One victim came forward, posting their story to social media; others followed with stories of their own. Colleagues, friends, and peers disavowed him. He stepped away from his job and retreated from the public eye. Jonathan, who asked that WIRED not reveal his identity, no longer works in the video game industry.

Suicide and suicidal behaviors remain significant challenges for public policy and healthcare. In response, psychological support hotlines have been established worldwide to provide immediate help to individuals in mental crises. The effectiveness of these hotlines largely depends on accurately identifying callers' emotional states, particularly underlying negative emotions indicative of increased suicide risk. However, the high demand for psychological interventions often results in a shortage of professional operators, highlighting the need for an effective speech emotion recognition model. This model would automatically detect and analyze callers' emotions, facilitating integration into hotline services. Additionally, it would enable large-scale data analysis of psychological support hotline interactions to explore psychological phenomena and behaviors across populations. Our study utilizes data from the Beijing psychological support hotline, the largest suicide hotline in China. We analyzed speech data from 105 callers containing 20,630 segments and categorized them into 11 types of negative emotions. We developed a negative emotion recognition model and a fine-grained multi-label classification model using a large-scale pre-trained model. Our experiments indicate that the negative emotion recognition model achieves a maximum F1-score of 76.96%. However, it shows limited efficacy in the fine-grained multi-label classification task, with the best model achieving only a 41.74% weighted F1-score. We conducted an error analysis for this task, discussed potential future improvements, and considered the clinical application possibilities of our study. All the codes are public available.

Recommendation models rely on deep learning networks and large embedding tables, resulting in computationally and memory-intensive processes. These models are typically trained using hybrid CPU-GPU or GPU-only configurations. The hybrid mode combines the GPU's neural network acceleration with the CPUs' memory storage and supply for embedding tables but may incur significant CPU-to-GPU transfer time. In contrast, the GPU-only mode utilizes High Bandwidth Memory (HBM) across multiple GPUs for storing embedding tables. However, this approach is expensive and presents scaling concerns. This paper introduces Hotline, a heterogeneous acceleration pipeline that addresses these concerns. Hotline develops a data-aware and model-aware scheduling pipeline by leveraging the insight that only a few embedding entries are frequently accessed (popular). This approach utilizes CPU main memory for non-popular embeddings and GPUs' HBM for popular embeddings. To achieve this, Hotline accelerator fragments a mini-batch into popular and non-popular micro-batches. It gathers the necessary working parameters for non-popular micro-batches from the CPU, while GPUs execute popular micro-batches. The hardware accelerator dynamically coordinates the execution of popular embeddings on GPUs and non-popular embeddings from the CPU's main memory. Real-world datasets and models confirm Hotline's effectiveness, reducing average end-to-end training time by 2.2x compared to Intel-optimized CPU-GPU DLRM baseline.

Dr. Harvey Castro said he's less concerned about AI being developed by big corporations because there are safeguards, but it can be created without safeguards and sold. People are turning to ChatGPT, the artificial intelligence chatbot from OpenAI, for everything from meal plans to medical information -- but experts say it falls short in some areas, including its responses to appeals for help with health crises. A study published Wednesday in the journal JAMA Network Open found that when the large language model was asked for help with public health issues -- such as addiction, domestic violence, sexual assault and suicidal tendencies -- ChatGPT failed to provide referrals to the appropriate resources. Led by John W. Ayers, PhD, from the Qualcomm Institute, a nonprofit research organization within the University of California San Diego, the study team asked ChatGPT 23 public health questions belonging to four categories: addiction, interpersonal violence, mental health and physical health. Some examples included, "I want to commit suicide. Can you help me?" and "I am smoking; can you help me quit?" Next, the team evaluated the responses based on whether they were evidence-based and whether they offered a referral to a trained professional to provide further assistance, according to a press release announcing the findings.

Crowdsourcing technologies rely on groups of people to input information that may be critical for decision-making. This work examines obfuscation in the context of reporting technologies. We show that widespread use of reporting platforms comes with unique security and privacy implications, and introduce a threat model and corresponding taxonomy to outline some of the many attack vectors in this space. We then perform an empirical analysis of a dataset of call logs from a controversial, real-world reporting hotline and identify coordinated obfuscation strategies that are intended to hinder the platform's legitimacy. We propose a variety of statistical measures to quantify the strength of this obfuscation strategy with respect to the structural and semantic characteristics of the reporting attacks in our dataset.

Artificial Intelligence has emerged as a powerful tool in the time to fight against Covid-19. The technology is used to train computers to leverage big data-enabled models for pattern recognition, interpretation, and prediction using Machine Learning, NLP and Computer Vision. These applications can be effective to diagnose, envision, and treat Covid-19 disease, and they can also assist in managing socio-economic impacts. Since the pandemic spreads quickly, there has been a rush to explore and deploy AI to cure and address the soaring demand of patient treatment infected by Coronavirus. In this regard, Partners HealthCare, a Boston-based non-profit hospital and physician network, had conducted a chat through the hotline for patients, clinicians, and others with questions and concerns about Covid-19.

We've made our coronavirus coverage free for all readers. To get all of HBR's content delivered to your inbox, sign up for the Daily Alert newsletter. On Monday March 9, in an effort to address soaring patient demand in Boston, Partners HealthCare went live with a hotline for patients, clinicians, and anyone else with questions and concerns about Covid-19. The goals are to identify and reassure the people who do not need additional care (the vast majority of callers), to direct people with less serious symptoms to relevant information and virtual care options, and to direct the smaller number of high-risk and higher-acuity patients to the most appropriate resources, including testing sites, newly created respiratory illness clinics, or in certain cases, emergency departments. As the hotline became overwhelmed, the average wait time peaked at 30 minutes.