Protecting sensitive information is crucial in today's world of Large Language Models (LLMs) and data-driven services. One common method used to preserve privacy is by using data perturbation techniques to reduce overreaching utility of (sensitive) Personal Identifiable Information (PII) data while maintaining its statistical and semantic properties. Data perturbation methods often result in significant information loss, making them impractical for use. In this paper, we propose 'Life of PII', a novel Obfuscation Transformer framework for transforming PII into faux-PII while preserving the original information, intent, and context as much as possible. Our approach includes an API to interface with the given document, a configuration-based obfuscator, and a model based on the Transformer architecture, which has shown high context preservation and performance in natural language processing tasks and LLMs. Our Transformer-based approach learns mapping between the original PII and its transformed faux-PII representation, which we call "obfuscated" data. Our experiments demonstrate that our method, called Life of PII, outperforms traditional data perturbation techniques in terms of both utility preservation and privacy protection. We show that our approach can effectively reduce utility loss while preserving the original information, offering greater flexibility in the trade-off between privacy protection and data utility. Our work provides a solution for protecting PII in various real-world applications.

How can AI enhance creative coding experiences for families? This study explores the potential of large language models (LLMs) in helping families with creative coding using Scratch. Based on our previous user study involving a prototype AI assistant, we devised three evaluation scenarios to determine if LLMs could help families comprehend game code, debug programs, and generate new ideas for future projects. We utilized 22 Scratch projects for each scenario and generated responses from LLMs with and without practice tasks, resulting in 120 creative coding support scenario datasets. In addition, the authors independently evaluated their precision, pedagogical value, and age-appropriate language. Our findings show that LLMs achieved an overall success rate of more than 80\% on the different tasks and evaluation criteria. This research offers valuable information on using LLMs for creative family coding and presents design guidelines for future AI-supported coding applications. Our evaluation framework, together with our labeled evaluation data, is publicly available.

In a clinical setting it is essential that deployed image processing systems are robust to the full range of inputs they might encounter and, in particular, do not make confidently wrong predictions. The most popular approach to safe processing is to train networks that can provide a measure of their uncertainty, but these tend to fail for inputs that are far outside the training data distribution. Recently, generative modelling approaches have been proposed as an alternative; these can quantify the likelihood of a data sample explicitly, filtering out any out-of-distribution (OOD) samples before further processing is performed. In this work, we focus on image segmentation and evaluate several approaches to network uncertainty in the far-OOD and near-OOD cases for the task of segmenting haemorrhages in head CTs. We find all of these approaches are unsuitable for safe segmentation as they provide confidently wrong predictions when operating OOD. We propose performing full 3D OOD detection using a VQ-GAN to provide a compressed latent representation of the image and a transformer to estimate the data likelihood. Our approach successfully identifies images in both the far- and near-OOD cases. We find a strong relationship between image likelihood and the quality of a model's segmentation, making this approach viable for filtering images unsuitable for segmentation. To our knowledge, this is the first time transformers have been applied to perform OOD detection on 3D image data. Code is available at github.com/marksgraham/transformer-ood.

OpenAI CEO Sam Altman took questions from reporters following his congressional hearing and defined "scary AI." OpenAI CEO Sam Altman said people should not try to "anthropomorphize" artificial intelligence and should discuss the powerful tech systems in the context of it being a "tool" and not a "creature." "I think there's a huge amount of speculation on that question," Altman told reporters Tuesday on Capitol Hill when asked how quickly AI could become "self-aware" if Congress does not regulate the technology. The line of questioning had echoes of the "Terminator" film series, in which AI brings about the apocalypse on the day it becomes "self-aware." "I think it's very important that we keep talking about this as a tool, not a creature, because it's so tempting to anthropomorphize it," he added. "I totally understand where the anxiety comes from. I think it's the wrong frame … the wrong way to think about it."

OpenAI CEO Sam Altman made an appeal to members of Congress under oath: Regulate artificial intelligence. Altman, whose company is on the extreme forefront of generative A.I. technology with its ChatGPT tool, testified in front of the Senate Judiciary Committee for the first time in a Tuesday hearing. And while he said he is ultimately optimistic that innovation will benefit people on a grand scale, Altman echoed his previous assertion that lawmakers should create parameters for AI creators to avoid causing "significant harm to the world." "We think it can be a printing press moment," Altman said. "We have to work together to make it so."

OpenAI CEO Sam Altman, the artificial intelligence lab behind ChatGPT, took questions from reporters following his congressional hearing, including defining "scary AI." OpenAI CEO Sam Altman outlined examples of "scary AI" to Fox News Digital after he served as a witness for a Senate subcommittee hearing on potential regulations on artificial intelligence. "Sure," Altman said when asked by Fox News Digital to provide an example of "scary AI." "An AI that could design novel biological pathogens. An AI that could hack into computer systems. I think these are all scary." "These systems can become quite powerful, which is why I was happy to be here today and why I think this is so important." Altman appeared before the Senate Judiciary Subcommittee on Privacy, Technology, and the Law on Tuesday morning to speak with lawmakers about how to best regulate the technology.

Altman advocated for a number of regulations, including a new government agency charged with creating government standards for the field, to address mounting concerns that generative AI could distort reality and create unprecedented safety risks. The CEO tallied a litany of "risky" behaviors presented by technology like ChatGPT, including spreading "one-on-one interactive disinformation" and emotional manipulation. At one point he acknowledged AI could be used to target drone strikes.

Sam Altman, the CEO of artificial intelligence lab OpenAI, told a Senate panel he welcomes federal regulation on the technology "to mitigate" its risks. The CEO of the company that delivered ChatGPT to the world said Tuesday he was not interested in becoming the federal government's top regulator of artificial intelligence technology. CEO Sam Altman and other witnesses at a Senate Judiciary subcommittee were asked what they would do to ensure the government has a firm grip on how AI is developed and deployed, and Altman said his first step would be to create a new federal agency. "I would form a new agency that licenses any effort above a certain scale of capabilities and can take that license away and ensure compliance with safety standards," he said in response to a question from Sen. John Kennedy, R-La. Sam Altman, CEO and co-founder of OpenAI, speaks during a Senate Judiciary Subcommittee hearing in Washington, D.C., Tuesday, May 16, 2023.

Sam Altman, the CEO of artificial intelligence lab OpenAI, told a Senate panel he welcomes federal regulation on the technology "to mitigate" its risks. Sam Altman, the CEO of artificial intelligence lab OpenAI, told a Senate panel he welcomes government regulation on the technology "to mitigate" its risks. "As this technology advances, we understand that people are anxious about how it could change the way we live. But we believe that we can and must work together to identify and manage the potential downsides so that we can all enjoy the tremendous upsides. It is essential that powerful AI is developed with democratic values in mind. And this means that U.S. leadership is critical," Altman said Tuesday.

OpenAI CEO Sam Altman is speaking in front of Congress about the dangers of AI after his company's ChatGPT exploded in popularity in the past few months. Lawmakers are grilling the CEO, stressing that ChatGPT and other models could shape'human history' like the printing press or the atomic bomb. The printing press, according to officials, brought liberty to the American people, while the atomic bomb left behind haunting consequences. Altman told senators that generative AI could be a'printing press moment,' but he is not blind to its fault, noting policymakers and industry leaders need to work together to'make it so.' Tuesday's hearing is the first of a series intended to write rules for AI, which lawmakers said should have been done with the birth of social media.