Taipei, Taiwan – Typing "I have anxiety" into ChatGPT, OpenAI's ground-breaking artificial intelligence-powered chatbot gets to work almost immediately. "I'm sorry to hear that you're experiencing anxiety," scrawls across the screen. "It can be a challenging experience, but there are strategies you can try to help manage your symptoms." Then comes a numbered list of recommendations: working on relaxation, focusing on sleep, cutting caffeine and alcohol, challenging negative thoughts, and seeking the support of friends and family. While not the most original advice, it resembles what might be heard in a therapist's office or read online in a WebMD article about anxiety – not least because ChatGPT scrapes its answers from the wide expanse of the internet.

Deep Learning has recently emerged as a perfect prognosis downscaling technique to compute high-resolution fields from large-scale coarse atmospheric data. Despite their promising results to reproduce the observed local variability, they are based on the estimation of independent distributions at each location, which leads to deficient spatial structures, especially when downscaling precipitation. This study proposes the use of generative models to improve the spatial consistency of the high-resolution fields, very demanded by some sectoral applications (e.g., hydrology) to tackle climate change.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' Artificial intelligence leader OpenAI has introduced the ability to turn off chat history in its popular chatbot ChatGPT. In a Tuesday blog post, the company said conversations that are started when chat history is disabled will not be used to train and improve its models and will not appear in the history sidebar. The controls are found in the ChatGPT settings and can be changed at any time. The mode rolled out ot all users.

Welcome to the Education Issue, our latest print magazine. It's becoming increasingly clear that we're in an entirely new place when it comes to the use of AI in education, and it is far from clear what that is going to mean. The world has changed, and there's no going back. Technologies like ChatGPT, OpenAI's massively mind-blowing generative AI software, will have all sorts of genuinely useful and transformative applications in the classroom. Yes, they will almost certainly also be used for cheating.

And then I did what an increasing number of us are doing: I turned to ChatGPT, OpenAI's massively mind-blowing generative AI software, to help me out. After training it on some of my previous work, I asked about the use of AI in education. AI is already doing big things in education. By crunching massive amounts of data on student performance, AI algorithms can tailor instruction to fit the needs of individual learners, which can mean big improvements in student outcomes. Chatbots and virtual assistants can provide students with on-the-spot assistance and feedback.

TikTok may soon let you create AI stylized avatars not unlike what you can with deep learning apps like Midjourney or Lensa, according to a Twitter thread from social media guru Matt Navarra seen by The Verge. Called AI Avatars, the tool lets you upload three to 10 photos of yourself and choose from five art styles. It will then generate up to 30 separate avatars in a couple of minutes. You can then download one, several or all of the images to use as a profile picture or in stories. Though the styles are more limited than what you can get on Lensa, the results look pretty good -- so the feature is bound to be popular.

Recent advances in immunomics have shown that T-cell receptor (TCR) signatures can accurately predict active or recent infection by leveraging the high specificity of TCR binding to disease antigens. However, the extreme diversity of the adaptive immune repertoire presents challenges in reliably identifying disease-specific TCRs. Population genetics and sequencing depth can also have strong systematic effects on repertoires, which requires careful consideration when developing diagnostic models. We present an Adaptive Immune Repertoire-Invariant Variational Autoencoder (AIRIVA), a generative model that learns a low-dimensional, interpretable, and compositional representation of TCR repertoires to disentangle such systematic effects in repertoires. We apply AIRIVA to two infectious disease case-studies: COVID-19 (natural infection and vaccination) and the Herpes Simplex Virus (HSV-1 and HSV-2), and empirically show that we can disentangle the individual disease signals. We further demonstrate AIRIVA's capability to: learn from unlabelled samples; generate in-silico TCR repertoires by intervening on the latent factors; and identify disease-associated TCRs validated using TCR annotations from external assay data.

Generative multimodal models based on diffusion models have seen tremendous growth and advances in recent years. Models such as DALL-E and Stable Diffusion have become increasingly popular and successful at creating images from texts, often combining abstract ideas. However, like other deep learning models, they also reflect social biases they inherit from their training data, which is often crawled from the internet. Manually auditing models for biases can be very time and resource consuming and is further complicated by the unbounded and unconstrained nature of inputs these models can take. Research into bias measurement and quantification has generally focused on small single-stage models working on a single modality. Thus the emergence of multistage multimodal models requires a different approach. In this paper, we propose Multimodal Composite Association Score (MCAS) as a new method of measuring gender bias in multimodal generative models. Evaluating both DALL-E 2 and Stable Diffusion using this approach uncovered the presence of gendered associations of concepts embedded within the models. We propose MCAS as an accessible and scalable method of quantifying potential bias for models with different modalities and a range of potential biases.

We investigated the potential and limitations of generative artificial intelligence (AI) in reflecting the authors' cognitive processes through creative expression. The focus is on the AI-generated artwork's ability to understand human intent (alignment) and visually represent emotions based on criteria such as creativity, aesthetic, novelty, amusement, and depth. Results show a preference for images based on the descriptions of the authors' emotions over the main events. We also found that images that overrepresent specific elements or stereotypes negatively impact AI alignment. Our findings suggest that AI could facilitate creativity and the self-expression of emotions. Our research framework with generative AIs can help design AI-based interventions in related fields (e.g., mental health education, therapy, and counseling).

The company announced today that the AI chatbot's users can now turn off their chat histories, preventing their input from being used for training data. The controls, which roll out "starting today," can be found under ChatGPT user settings under a new section labeled Data Controls. After toggling the switch off for "Chat History & Training," you'll no longer see your recent chats in the sidebar. Even with the history and training turned off, OpenAI says it will still store your chats for 30 days. It does this to prevent abuse, with the company saying it will only review them if it needs to monitor them.