Large Language Models (LLMs) can struggle to balance gullibility to misinformation and resistance to valid corrections in persuasive dialogues, a critical challenge for reliable deployment. We introduce DuET-PD (Dual Evaluation for Trust in Persuasive Dialogues), a framework evaluating multi-turn stance-change dynamics across dual dimensions: persuasion type (corrective/misleading) and domain (knowledge via MMLU-Pro, and safety via SALAD-Bench). We find that even a state-of-the-art model like GPT-4o achieves only 27.32% accuracy in MMLU-Pro under sustained misleading persuasions. Moreover, results reveal a concerning trend of increasing sycophancy in newer open-source models. To address this, we introduce Holistic DPO, a training approach balancing positive and negative persuasion examples. Unlike prompting or resist-only training, Holistic DPO enhances both robustness to misinformation and receptiveness to corrections, improving Llama-3.1-8B-Instruct's accuracy under misleading persuasion in safety contexts from 4.21% to 76.54%. These contributions offer a pathway to developing more reliable and adaptable LLMs for multi-turn dialogue. Code is available at https://github.com/Social-AI-Studio/DuET-PD.

To combat the shortcuts and risk-taking, Lorenzo is working on a tool for the San Francisco–based company DroneDeploy, which sells software that creates daily digital models of work progress from videos and images, known in the trade as "reality capture." The tool, called Safety AI, analyzes each day's reality capture imagery and flags conditions that violate Occupational Safety and Health Administration (OSHA) rules, with what he claims is 95% accuracy. That means that for any safety risk the software flags, there is 95% certainty that the flag is accurate and relates to a specific OSHA regulation. Launched in October 2024, it's now being deployed on hundreds of construction sites in the US, Lorenzo says, and versions specific to the building regulations in countries including Canada, the UK, South Korea, and Australia have also been deployed. Safety AI is one of multiple AI construction safety tools that have emerged in recent years, from Silicon Valley to Hong Kong to Jerusalem.

In Artificial Intelligence and Quantum Computing for Advanced Wireless Networks, the authors deliver a practical and timely review of AI-based learning algorithms, with several case studies in both Python and R. The book discusses the game-theory-based learning algorithms used in decision making, along with various specific applications in wireless networks, like channel, network state, and traffic prediction. Additional chapters include Fundamentals of ML, Artificial Neural Networks (NN), Explainable and Graph NN, Learning Equilibria and Games, AI Algorithms in Networks, Fundamentals of Quantum Communications, Quantum Channel, Information Theory and Error Correction, Quantum Optimization Theory, and Quantum Internet, to name a few.

In 2017, Lorenzo, who works in health care, took on a temporary document-reviewing gig he could do from home. He was between full-time office jobs, and liked that the temporary gig allowed him to take his son to school in the morning, walk his dog at lunch, and generally have more flexibility during the day. But Lorenzo soon found that his employer was monitoring him more closely than he had anticipated. The company used the workplace messaging service Microsoft Lync, which would display a green icon on employee profiles if they were active and a yellow icon if their computer had gone to sleep. Whenever Lorenzo would step away for a few minutes to refill his coffee or use the restroom, his Lync profile invariably reported him as inactive.

Error-bounded lossy compression is becoming an indispensable technique for the success of today's scientific projects with vast volumes of data produced during the simulations or instrument data acquisitions. Not only can it significantly reduce data size, but it also can control the compression errors based on user-specified error bounds. Autoencoder (AE) models have been widely used in image compression, but few AE-based compression approaches support error-bounding features, which are highly required by scientific applications. To address this issue, we explore using convolutional autoencoders to improve error-bounded lossy compression for scientific data, with the following three key contributions. (1) We provide an in-depth investigation of the characteristics of various autoencoder models and develop an error-bounded autoencoder-based framework in terms of the SZ model. (2) We optimize the compression quality for main stages in our designed AE-based error-bounded compression framework, fine-tuning the block sizes and latent sizes and also optimizing the compression efficiency of latent vectors. (3) We evaluate our proposed solution using five real-world scientific datasets and comparing them with six other related works. Experiments show that our solution exhibits a very competitive compression quality from among all the compressors in our tests. In absolute terms, it can obtain a much better compression quality (100% ~ 800% improvement in compression ratio with the same data distortion) compared with SZ2.1 and ZFP in cases with a high compression ratio.

If science were a dating app, quantum physics and machine learning probably wouldn't be a match. They're from completely different fields and often require completely different backgrounds and skills. But, throw in a little quantum computing and, suddenly, that science-matchmaking app becomes Tinder and the attraction between the two is palpable. Even though the extent of change that quantum computing will unleash on AI is up for debate, many experts now more than suspect that quantum computing will definitely alter AI at some level. "Quantum machine learning can be more efficient than classic machine learning, at least for certain models that are intrinsically hard to learn using conventional computers," says Samuel Fernández Lorenzo, a quantum algorithm researcher who collaborates with BBVA's New Digital Businesses area.

When my parents first joined Facebook to stalk me, I thought the social network was going to become uncool and fade away like Myspace, Friendster, and the other social networks that came and went before it. Since then, we've found out that Russian spies have used it to influence American elections, that a shady British marketing firm harvested the personal data of 50 million Americans to target voters with political ads, that Facebook researchers devised an experiment to see if they could make us depressed, and the UN has claimed it played a role in genocide. In fact, my colleague Daniel Oberhaus quit, and wrote a guide on how do it if you want to do the same. But we also understand if that many people want or have to stay on Facebook to do their job or stay in touch with their family. And, after all, quitting Facebook is the ultimate first world privilege.