In the year or so since large language models hit the big time, researchers have demonstrated numerous ways of tricking them into producing problematic outputs including hateful jokes, malicious code and phishing emails, or the personal information of users. It turns out that misbehavior can take place in the physical world, too: LLM-powered robots can easily be hacked so that they behave in potentially dangerous ways. Researchers from the University of Pennsylvania were able to persuade a simulated self-driving car to ignore stop signs and even drive off a bridge, get a wheeled robot to find the best place to detonate a bomb, and force a four-legged robot to spy on people and enter restricted areas. "We view our attack not just as an attack on robots," says George Pappas, head of a research lab at the University of Pennsylvania who helped unleash the rebellious robots. "Any time you connect LLMs and foundation models to the physical world, you actually can convert harmful text into harmful actions."

MAMMO MISHAP: A U.K. woman is thanking artificial intelligence for saving her life. The technology picked up cancer cells in the patient's screening that were undetectable by the human eye, according to SWNS. READY AND WILLING: Sam Altman, CEO of OpenAI, the creator of ChatGPT, on Sunday said he is looking forward to working with the incoming Trump administration, adding that he thinks President-elect Trump will succeed at helping to make America a world-leading force in artificial intelligence infrastructure. SEEING IS REPEATING: In a groundbreaking development, researchers at Johns Hopkins University and Stanford University have successfully trained a robotic surgical system to perform complex tasks with the skill of human doctors. "Like all technology, there's the potential for incredible innovation and a real threat and obviously needs to be highly regulated," she told Fox News Digital.

Today's weather forecasts rely on simulations that require a lot of computing power Google DeepMind claims its latest weather forecasting AI can make predictions faster and more accurately than existing physics-based simulations. GenCast is the latest in DeepMind's ongoing research project to use artificial intelligence to improve weather forecasting. The model was trained on four decades of historical data from the European Centre for Medium-Range Weather Forecasts's (ECMWF) ERA5 archive, which includes regular measurements of temperature, wind speed and pressure at various altitudes around the globe. Data up to 2018 was used to train the model and then data from 2019 was used to test its predictions against known weather. The company found that it beat ECMWF's industry-standard ENS forecast 97.4 per cent of the time in total, and 99.8 per cent of the time when looking ahead more than 36 hours.

MLCommons, a nonprofit that helps companies measure the performance of their artificial intelligence systems, is launching a new benchmark to gauge AI's bad side too. The new benchmark, called AILuminate, assesses the responses of large language models to more than 12,000 test prompts in 12 categories including inciting violent crime, child sexual exploitation, hate speech, promoting self-harm, and intellectual property infringement. Models are given a score of "poor," "fair," "good," "very good," or "excellent," depending on how they perform. The prompts used to test the models are kept secret to prevent them from ending up as training data that would allow a model to ace the test. Peter Mattson, founder and president of MLCommons and a senior staff engineer at Google, says that measuring the potential harms of AI models is technically difficult, leading to inconsistencies across the industry.

OpenAI announced today it has hired three senior computer vision and machine learning engineers from rival Google DeepMind, all of whom will work in a newly opened OpenAI office in Zurich, Switzerland. OpenAI executives told staff in an internal memo on Tuesday that Lucas Beyer, Alexander Kolesnikov, and Xiaohua Zhai will be joining the company to work on multimodal AI, artificial intelligence models capable of performing tasks in different mediums ranging from images to audio. OpenAI has long been at the forefront of multimodal AI and released the first version of its text-to-image platform Dall-E in 2021. Its flagship chatbot ChatGPT, however, was initially only capable of interacting with text inputs. The company later added voice and image features as multimodal functionality became an increasingly important part of its product line and AI research.

Former OpenAI executive Mira Murati says it could take decades, but AI systems eventually will perform a wide range of cognitive tasks as well as humans do--a prospective technological milestone widely known as artificial general intelligence, or AGI. "Right now, it feels quite achievable," Murati said at WIRED's The Big Interview event in San Francisco on Tuesday. In her first interview since resigning as OpenAI's chief technology officer in September, Murati told WIRED's Steven Levy that she's not overly concerned about recent chatter in the AI industry that developing more powerful generative AI models is proving challenging. "Current evidence shows that progress will likely continue," Murati said. Whether we need new ideas to get to AGI-level systems, that's uncertain.

Modern language models are internally -- and mathematically -- distributions over token strings rather than \emph{character} strings, posing numerous challenges for programmers building user applications on top of them. For example, if a prompt is specified as a character string, it must be tokenized before passing it to the token-level language model. Thus, the tokenizer and consequent analyses are very sensitive to the specification of the prompt (e.g., if the prompt ends with a space or not). This paper presents algorithms for converting token-level language models to character-level ones. We present both exact and approximate algorithms. In the empirical portion of the paper, we benchmark the practical runtime and approximation quality. We find that -- even with a small computation budget -- our method is able to accurately approximate the character-level distribution (less than 0.00021 excess bits / character) at reasonably fast speeds (46.3 characters / second) on the Llama 3.1 8B language model.

This study investigates the potential of using ChatGPT as a teachable agent to support students' learning by teaching process, specifically in programming education. While learning by teaching is an effective pedagogical strategy for promoting active learning, traditional teachable agents have limitations, particularly in facilitating natural language dialogue. Our research explored whether ChatGPT, with its ability to engage learners in natural conversations, can support this process. The findings reveal that interacting with ChatGPT improves students' knowledge gains and programming abilities, particularly in writing readable and logically sound code. However, it had limited impact on developing learners' error-correction skills, likely because ChatGPT tends to generate correct code, reducing opportunities for students to practice debugging. Additionally, students' self-regulated learning (SRL) abilities improved, suggesting that teaching ChatGPT fosters learners' higher self-efficacy and better implementation of SRL strategies. This study discussed the role of natural dialogue in fostering socialized learning by teaching, and explored ChatGPT's specific contributions in supporting students' SRL through the learning by teaching process. Overall, the study highlights ChatGPT's potential as a teachable agent, offering insights for future research on ChatGPT-supported education.

In the rapidly evolving financial sector, the accurate and timely interpretation of market news is essential for stakeholders needing to navigate unpredictable events. This paper introduces FANAL (Financial Activity News Alerting Language Modeling Framework), a specialized BERT-based framework engineered for real-time financial event detection and analysis, categorizing news into twelve distinct financial categories. FANAL leverages silver-labeled data processed through XGBoost and employs advanced fine-tuning techniques, alongside ORBERT (Odds Ratio BERT), a novel variant of BERT fine-tuned with ORPO (Odds Ratio Preference Optimization) for superior class-wise probability calibration and alignment with financial event relevance. We evaluate FANAL's performance against leading large language models, including GPT-4o, Llama-3.1 8B, and Phi-3, demonstrating its superior accuracy and cost efficiency. This framework sets a new standard for financial intelligence and responsiveness, significantly outstripping existing models in both performance and affordability.

Background: Feedback as one of the most influential factors for learning has been subject to a great body of research. It plays a key role in the development of educational technology systems and is traditionally rooted in deterministic feedback defined by experts and their experience. However, with the rise of generative AI and especially Large Language Models (LLMs), we expect feedback as part of learning systems to transform, especially for the context of programming. In the past, it was challenging to automate feedback for learners of programming. LLMs may create new possibilities to provide richer, and more individual feedback than ever before. Objectives: This paper aims to generate specific types of feedback for introductory programming tasks using LLMs. We revisit existing feedback taxonomies to capture the specifics of the generated feedback, such as randomness, uncertainty, and degrees of variation. Methods: We iteratively designed prompts for the generation of specific feedback types (as part of existing feedback taxonomies) in response to authentic student programs. We then evaluated the generated output and determined to what extent it reflected certain feedback types. Results and Conclusion: The present work provides a better understanding of different feedback dimensions and characteristics. The results have implications for future feedback research with regard to, for example, feedback effects and learners' informational needs. It further provides a basis for the development of new tools and learning systems for novice programmers including feedback generated by AI.