Whether or not this really amounts to an "iPhone moment" or a serious threat to Google search isn't obvious at present -- while it will likely push a change in user behaviors and expectations, the first shift will be organizations pushing to bring tools trained on large language models (LLMs) to learn from their own data and services. And this, ultimately, is the key -- the significance and value of generative AI today is not really a question of societal or industry-wide transformation. It's instead a question of how this technology can open up new ways of interacting with large and unwieldy amounts of data and information. OpenAI is clearly attuned to this fact and senses a commercial opportunity: although the list of organizations taking part in the ChatGPT plugin initiative is small, OpenAI has opened up a waiting list where companies can sign up to gain access to the plugins. In the months to come, we will no doubt see many new products and interfaces backed by OpenAI's generative AI systems.

LinkedIn influencers use ChatGPT as a brainstorming aid, should you? OpenAI's chatbot responds in a conversational tone to text prompts, and millions of users continue to experiment with it. The chatbot helps software developers with coding, scientists with research, and students with homework. With a little repetition and exploration, ChatGPT is worth trying out as part of your brainstorming process. Business leaders can use it to consider multiple approaches for crucial conversations or long-term decisions.

Artificial intelligence was once something the average person described in the abstract. They had no tactile relationship with it that they were aware of, even if their devices were often utilizing it. That's all changed over the past year as people have started to engage with AI programs like OpenAI's DALL-E and ChatGPT, and the technology is rapidly advancing. As AI is democratized, democracy itself is falling under new pressures. There will likely be many exciting ways it will be deployed, but it may also start to distort reality and could become a major threat to the 2024 presidential election if AI-generated audio, images, and videos of candidates proliferate.

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.