Access to the ChatGPT chatbot has been restored in Italy after its maker OpenAI "addressed or clarified" issues raised by Italy's data protection authority, Italian authorities and OpenAI have said. Microsoft Corp-backed OpenAI took ChatGPT offline in Italy last month after the country's Data Protection Authority, also known as Garante, temporarily banned the chatbot and launched a probe into the artificial intelligence application's suspected breach of privacy rules. The Italian Data Protection Authority described its action as provisional "until ChatGPT respects privacy". The watchdog said ChatGPT developer OpenAI had no legal basis to justify "the mass collection and storage of personal data for the purpose of'training' the algorithms underlying the operation of the platform". It further referenced a data breach on March 20 when user conversations and payment information were compromised, a problem the United States firm blamed on a bug.

In particular, the spokesperson said, around "implementing an age verification system and planning and conducting an information campaign to inform Italians of what happened as well as of their right to opt-out from the processing of their personal data for training algorithms."

ChatGPT appears to have a better'bedside manner' than some doctors – at least when their written advice is rated for quality and empathy, a study has shown. The findings highlight the potential for AI assistants to play a role in medicine, according to the authors of the work, who suggest such agents could help draft doctors' communications with patients. "The opportunities for improving healthcare with AI are massive," said Dr John Ayers, of the University of California San Diego. However, others noted that the findings do not mean ChatGPT is actually a better doctor and cautioned against delegating clinical responsibility given that the chatbot has a tendency to produce "facts" that are untrue. The study, published in the journal JAMA Internal Medicine, used data from Reddit's AskDocs forum, in which members can post medical questions that are answered by verified healthcare professionals. The team randomly sampled 195 exchanges from AskDocs where a verified doctor responded to a public question.

If you're sick of being turned down by people who are'out of your league', a new online tool may finally get you the right match. Hot Chat 3000 is a bizarre online chatroom that uses AI to score your looks and connect you with someone of a similar'hotness' ranking. The chatroom is the creation of MSCHF, a US art collective based in New York that counts Wordle creator Josh Wardle among its staff members. According to MSCHF, attractiveness ratings are predicted by a large machine learning model that was trained by OpenAI, the company responsible for ChatGPT. It follows the new online game that makes you guess whether you're speaking to an AI bot or a fellow human.

ChatGPT has revealed some scathing stereotypes of UK residents in a merciless study of what clichés exist in every county. The cutting-edge bot labeled Yorkshiremen as'rude' while Londoners were slammed for their arrogance in the nationwide analysis. The truly insulting results came after MailOnline asked ChatGPT to expose what'negative stereotypes' exist of people from our nation. While the bot insisted that it did not condone stereotypes, it offered a list of those associated with each place when prompted. On the whole, residents of the UK were deemed to have bad teeth while being overly polite and obsessed with the Royal Family.

A geography professor shared his method to detect AI-generated plagiarism with Fox News. He developed it after noticing that ChatGPT produced fake citations. A college professor said he found an easy way to catch AI-generated plagiarism after finding phony citations in some of ChatGPT's content. "It's very easy to identify the fake references," said Terence Day, a physical geography professor at Okanagan College in British Columbia. "All you need to do, really, is to check them up on the internet."

Twitter CEO Elon Musk provides insight on the consequences of developing artificial intelligence and the potential impact on elections on'Tucker Carlson Tonight.' ChatGPT is being touted as the superpowered AI of science fiction lore, with the potential to inflame academic dishonesty, render jobs obsolete, and perpetuate political bias. Unsurprisingly, governments are now taking heavy-handed, drastic measures to combat this perceived AI problem. Italy's recent ChatGPT ban has prompted several countries – including France, Ireland, Germany and Canada – to consider similar policies blocking OpenAI's popular artificial intelligence program. According to the Italian Data Protection Authority, ChatGPT does not have "any legal basis that justifies the massive collection and storage of personal data." The agency gave the company 20 days to respond with changes or face a hefty multimillion-dollar fine.

Artificial intelligence is changing the game on education as many students and even professors embrace platforms such as ChatGPT for classwork. Other students, however, are shunning the tech for school, citing the risks of getting caught using the platforms outweigh the benefits. OpenAI released ChatGPT, a chatbot that can mimic human conversations based on prompts it is given, in November, and it quickly became the fastest-growing user base with 100 million monthly active users in January. With the release of the tech came concerns from some educators, ranging from grade schools to the college level, that students would use the platform to plagiarize or cheat. Some college students report they are policing themselves away from using artificial intelligence to complete coursework.

Large Language Models (LLMs) have made significant progress in recent years, achieving remarkable results in question-answering tasks (QA). However, they still face two major challenges: hallucination and outdated information after the training phase. These challenges take center stage in critical domains like climate change, where obtaining accurate and up-to-date information from reliable sources in a limited time is essential and difficult. To overcome these barriers, one potential solution is to provide LLMs with access to external, scientifically accurate, and robust sources (long-term memory) to continuously update their knowledge and prevent the propagation of inaccurate, incorrect, or outdated information. In this study, we enhanced GPT-4 by integrating the information from the Sixth Assessment Report of the Intergovernmental (IPCC AR6), the most comprehensive, up-to-date, and reliable source in this domain. We present our conversational AI prototype, available at www.chatclimate.ai and demonstrate its ability to answer challenging questions accurately in three different QA scenarios: asking from 1) GPT-4, 2) chatClimate, and 3) hybrid chatClimate. The answers and their sources were evaluated by our team of IPCC authors, who used their expert knowledge to score the accuracy of the answers from 1 (very-low) to 5 (very-high). The evaluation showed that the hybrid chatClimate provided more accurate answers, highlighting the effectiveness of our solution. This approach can be easily scaled for chatbots in specific domains, enabling the delivery of reliable and accurate information.

Segment Anything Model (SAM) is a foundation model for semantic segmentation and shows excellent generalization capability with the prompts. In this empirical study, we investigate the robustness and zero-shot generalizability of the SAM in the domain of robotic surgery in various settings of (i) prompted vs. unprompted; (ii) bounding box vs. points-based prompt; (iii) generalization under corruptions and perturbations with five severity levels; and (iv) state-of-the-art supervised model vs. SAM. We conduct all the observations with two well-known robotic instrument segmentation datasets of MICCAI EndoVis 2017 and 2018 challenges. Our extensive evaluation results reveal that although SAM shows remarkable zero-shot generalization ability with bounding box prompts, it struggles to segment the whole instrument with point-based prompts and unprompted settings. Furthermore, our qualitative figures demonstrate that the model either failed to predict the parts of the instrument mask (e.g., jaws, wrist) or predicted parts of the instrument as different classes in the scenario of overlapping instruments within the same bounding box or with the point-based prompt. In fact, it is unable to identify instruments in some complex surgical scenarios of blood, reflection, blur, and shade. Additionally, SAM is insufficiently robust to maintain high performance when subjected to various forms of data corruption. Therefore, we can argue that SAM is not ready for downstream surgical tasks without further domain-specific fine-tuning.