Generative Pre-trained Transformer (GPT) models have exhibited exciting progress in their capabilities, capturing the interest of practitioners and the public alike. Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications such as healthcare and finance -- where mistakes can be costly. To this end, this work proposes a comprehensive trustworthiness evaluation for large language models with a focus on GPT-4 and GPT-3.5, considering diverse perspectives -- including toxicity, stereotype bias, adversarial robustness, out-of-distribution robustness, robustness on adversarial demonstrations, privacy, machine ethics, and fairness. Based on our evaluations, we discover previously unpublished vulnerabilities to trustworthiness threats. For instance, we find that GPT models can be easily misled to generate toxic and biased outputs and leak private information in both training data and conversation history. We also find that although GPT-4 is usually more trustworthy than GPT-3.5 on standard benchmarks, GPT-4 is more vulnerable given jailbreaking system or user prompts, potentially because GPT-4 follows (misleading) instructions more precisely. Our work illustrates a comprehensive trustworthiness evaluation of GPT models and sheds light on the trustworthiness gaps. Our benchmark is publicly available at https://decodingtrust.github.io/; our dataset can be previewed at https://huggingface.co/datasets/AI-Secure/DecodingTrust; a concise version of this work is at https://openreview.net/pdf?id=kaHpo8OZw2.

Alter3 is a humanoid robot first introduced in 2016. Imagine what it would be like to have a robot friend that can do things like take selfies, toss a ball, eat popcorn and play air guitar? Well, you might not have to wait too long. Researchers at the University of Tokyo have created a robot that can do all that and more, thanks to the power of GPT-4, the latest and most advanced large language model (LLM) in the world. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER A researcher gives Alter3, a humanoid robot, verbal instructions.

Many artificial intelligence researchers see the possible future development of superhuman AI as having a non-trivial chance of causing human extinction – but there is also widespread disagreement and uncertainty about such risks. Those findings come from a survey of 2700 AI researchers who have recently published work at six of the top AI conferences – the largest such survey to date. The survey asked participants to share their thoughts on possible timelines for future AI technological milestones, as well as the good or bad societal consequences of those achievements. Almost 58 per cent of researchers said they considered that there is a 5 per cent chance of human extinction or other extremely bad AI-related outcomes. "It's an important signal that most AI researchers don't find it strongly implausible that advanced AI destroys humanity," says Katja Grace at the Machine Intelligence Research Institute in California, an author of the paper.

Fox News' Eben Brown reports on how more companies are using AI technology to set retail prices based on data-driven supply and demand. A new way of using artificial intelligence to streamline traffic could soon be coming to cities across the country. Tech giant Google's new Project Green Light system is currently being used in Seattle as a way to combat the city's gridlocked streets, using the company's Maps database and AI to optimize traffic lights and suggest changes to city engineers, according to a report from CBS News. Such a system might be the ideal use for AI in its current form, according to Pioneer Development Group Chief Analytics Officer Christopher Alexander, who noted that managing traffic takes "sifting through massive amounts of data to find patterns," something that AI is perfectly capable of through the use of machine learning. This view shows the Seattle Space Needle and the downtown skyline with Mount Rainier in the background.

Elvis Presley's immersive concert experience is set to leave London all shook up, with an AI rendering of the king of rock'n'roll ready to enthral fans from November 2024. But this is not the first holographic performance – nor will it be the last. Here are some of the other artists whom technology has allowed to tour from beyond the grave, or as their younger selves. Abba's concert kicks off with a lithe and fresh-faced Benny Andersson reassuring the crowd: "This is really me. I just look very good for my age."

Mr Bean argues that AI fitness apps will only continue to improve. "From an industry perspective, we're very early on," he says. "But one of the brilliant features of AI is its ability to learn. It's not perfect now, but as it learns over time, AI fitness will get better and better."

Radio frequency (RF) signal mapping, which is the process of analyzing and predicting the RF signal strength and distribution across specific areas, is crucial for cellular network planning and deployment. Traditional approaches to RF signal mapping rely on statistical models constructed based on measurement data, which offer low complexity but often lack accuracy, or ray tracing tools, which provide enhanced precision for the target area but suffer from increased computational complexity. Recently, machine learning (ML) has emerged as a data-driven method for modeling RF signal propagation, which leverages models trained on synthetic datasets to perform RF signal mapping in "unseen" areas. In this paper, we present Geo2SigMap, an ML-based framework for efficient and high-fidelity RF signal mapping using geographic databases. First, we develop an automated framework that seamlessly integrates three open-source tools: OpenStreetMap (geographic databases), Blender (computer graphics), and Sionna (ray tracing), enabling the efficient generation of large-scale 3D building maps and ray tracing models. Second, we propose a cascaded U-Net model, which is pre-trained on synthetic datasets and employed to generate detailed RF signal maps, leveraging environmental information and sparse measurement data. Finally, we evaluate the performance of Geo2SigMap via a real-world measurement campaign, where three types of user equipment (UE) collect over 45,000 data points related to cellular information from six LTE cells operating in the citizens broadband radio service (CBRS) band. Our results show that Geo2SigMap achieves an average root-mean-square-error (RMSE) of 6.04 dB for predicting the reference signal received power (RSRP) at the UE, representing an average RMSE improvement of 3.59 dB compared to existing methods.

In the age of music streaming platforms, the task of automatically tagging music audio has garnered significant attention, driving researchers to devise methods aimed at enhancing performance metrics on standard datasets. Most recent approaches rely on deep neural networks, which, despite their impressive performance, possess opacity, making it challenging to elucidate their output for a given input. While the issue of interpretability has been emphasized in other fields like medicine, it has not received attention in music-related tasks. In this study, we explored the relevance of interpretability in the context of automatic music tagging. We constructed a workflow that incorporates three different information extraction techniques: a) leveraging symbolic knowledge, b) utilizing auxiliary deep neural networks, and c) employing signal processing to extract perceptual features from audio files. These features were subsequently used to train an interpretable machine-learning model for tag prediction. We conducted experiments on two datasets, namely the MTG-Jamendo dataset and the GTZAN dataset. Our method surpassed the performance of baseline models in both tasks and, in certain instances, demonstrated competitiveness with the current state-of-the-art. We conclude that there are use cases where the deterioration in performance is outweighed by the value of interpretability.

Understanding and identifying musical shape plays an important role in music education and performance assessment. To simplify the otherwise time- and cost-intensive musical shape evaluation, in this paper we explore how artificial intelligence (AI) driven models can be applied. Considering musical shape evaluation as a classification problem, a light-weight Siamese residual neural network (S-ResNN) is proposed to automatically identify musical shapes. To assess the proposed approach in the context of piano musical shape evaluation, we have generated a new dataset, containing 4116 music pieces derived by 147 piano preparatory exercises and performed in 28 categories of musical shapes. The experimental results show that the S-ResNN significantly outperforms a number of benchmark methods in terms of the precision, recall and F1 score.

Fake news detection models are critical to countering disinformation but can be manipulated through adversarial attacks. In this position paper, we analyze how an attacker can compromise the performance of an online learning detector on specific news content without being able to manipulate the original target news. In some contexts, such as social networks, where the attacker cannot exert complete control over all the information, this scenario can indeed be quite plausible. Therefore, we show how an attacker could potentially introduce poisoning data into the training data to manipulate the behavior of an online learning method. Our initial findings reveal varying susceptibility of logistic regression models based on complexity and attack type.