The use of Natural Language Processing (NLP) in high-stakes AI-based applications has increased significantly in recent years, especially since the emergence of Large Language Models (LLMs). However, despite their strong performance, LLMs introduce important legal/ethical concerns, particularly regarding privacy, data protection, and transparency. Due to these concerns, this work explores the use of Named-Entity Recognition (NER) to facilitate the privacy-preserving training (or adaptation) of LLMs. We propose a framework that uses NER technologies to anonymize sensitive information in text data, such as personal identities or geographic locations. An evaluation of the proposed privacy-preserving learning framework was conducted to measure its impact on user privacy and system performance in a particular high-stakes and sensitive setup: AI-based resume scoring for recruitment processes. The study involved two language models (BERT and RoBERTa) and six anonymization algorithms (based on Presidio, FLAIR, BERT, and different versions of GPT) applied to a database of 24,000 candidate profiles. The findings indicate that the proposed privacy preservation techniques effectively maintain system performance while playing a critical role in safeguarding candidate confidentiality, thus promoting trust in the experimented scenario. On top of the proposed privacy-preserving approach, we also experiment applying an existing approach that reduces the gender bias in LLMs, thus finally obtaining our proposed Privacy-and Bias-aware LLMs (PBa-LLMs). Note that the proposed PBa-LLMs have been evaluated in a particular setup (resume scoring), but are generally applicable to any other LLM-based AI application.

Clinical decision-making is inherently complex, often influenced by cognitive biases, incomplete information, and case ambiguity. Large Language Models (LLMs) have shown promise as tools for supporting clinical decision-making, yet their typical one-shot or limited-interaction usage may overlook the complexities of real-world medical practice. In this work, we propose a hybrid human-AI framework, MedSyn, where physicians and LLMs engage in multi-step, interactive dialogues to refine diagnoses and treatment decisions. Unlike static decision-support tools, MedSyn enables dynamic exchanges, allowing physicians to challenge LLM suggestions while the LLM highlights alternative perspectives. Through simulated physician-LLM interactions, we assess the potential of open-source LLMs as physician assistants. Results show open-source LLMs are promising as physician assistants in the real world. Future work will involve real physician interactions to further validate MedSyn's usefulness in diagnostic accuracy and patient outcomes.

Humanoid robots, with their human-like form, are uniquely suited for interacting in environments built for people. However, enabling humanoids to reason, plan, and act in complex open-world settings remains a challenge. World models, models that predict the future outcome of a given action, can support these capabilities by serving as a dynamics model in long-horizon planning and generating synthetic data for policy learning. We introduce Humanoid World Models (HWM), a family of lightweight, open-source models that forecast future egocentric video conditioned on humanoid control tokens. We train two types of generative models, Masked Transformers and Flow-Matching, on 100 hours of humanoid demonstrations. Additionally, we explore architectural variants with different attention mechanisms and parameter-sharing strategies. Our parameter-sharing techniques reduce model size by 33-53% with minimal impact on performance or visual fidelity. HWMs are designed to be trained and deployed in practical academic and small-lab settings, such as 1-2 GPUs.

AI tools have become deeply embedded in how many students learn and complete schoolwork--and that usage is only poised to increase. On Tuesday, the American Federation of Teachers announced an AI training hub for educators, backed by 23 million from Microsoft, OpenAI, and Anthropic. The AFT is the second-largest teachers' union, representing 1.8 million teachers and educational staffers across the country. Their training hub will open in New York City this fall, featuring workshops that will educate teachers on how to use AI tools for tasks like generating lesson plans and quizzes, or writing emails to parents. Microsoft is providing 12.5 million for AI teacher training over the next five years.

Microsoft's advanced AI feature, Copilot, is currently limited to a few selected laptops. But that could soon change. According to ZDNet Korea, via The Verge, Intel plans to update its Arrow Lake desktop processors later this year with a more powerful NPU (Neural Processing Unit) called "NPU 4." This upgrade will meet the 40 TOPS (trillion operations per second) threshold required for Copilot features like Windows Recall. This means full support to desktops, not just laptops or mini PCs. The new NPU design matches what's found in Lunar Lake laptop chips that currently support Copilot . While the updated Arrow Lake processors will include this enhanced NPU, they don't appear to have more CPU or GPU cores compared to the current Core Ultra 200 models.

The release of DeepSeek's R1 – China's powerful new open-source AI model – has sent shockwaves through the global tech industry. Offered for free and royalty-free, it has disrupted financial markets, challenged the United States' dominance in artificial intelligence, and prompted fears that Silicon Valley's tightly guarded business model may no longer hold. DeepSeek's open-source launch is widely seen as a key trigger behind a trillion-dollar tech sell-off in the US, signalling deep investor anxiety over the commodification of AI and China's growing competitiveness. Dubbed "China's answer" to OpenAI's GPT‑4, R1 has unsettled investors and shifted global AI geopolitics. While full development expenses remain undisclosed, this points to a markedly more cost-effective model than proprietary counterparts.

These are our subscriber-only events where you get to listen in to conversations between editors and reporters. Now, I'm delighted to say we've got an absolute cracker of an event today. I'm very happy to have our prodigal daughter, Karen Hao, a fabulous AI journalist, here with us to talk about her new book. Hello, Karen, how are you doing? Thank you so much for having me back, Niall.

Candice Lim is joined by Dazed senior writer Laura Pitcher to discuss her piece, "Meet the people using ChatGPT as their therapist." Since ChatGPT's public release in late 2022, there has been a growing reliance on the artificially intelligent chatbot in people's everyday lives. TikTok users are talking about the way they use ChatGPT as their therapist, their best friend, their life organizer, and more. But is reliant, daily use of an AI service worth possible consequences such as climate change, loneliness, and data privacy concerns? On today's episode, ICYMI dives into the unexpected ways people have been using ChatGPT and whether we can predict its role in the near future.

Kernel development in deep learning requires optimizing computational units across hardware while balancing memory management, parallelism, and hardware-specific optimizations through extensive empirical tuning. Although domain-specific languages like Triton simplify GPU programming by abstracting low-level details, developers must still manually tune critical parameters such as tile sizes and memory access patterns through iterative experimentation, creating substantial barriers to optimal performance and wider adoption. In this work, we introduce AutoTriton, the first model dedicated to Triton programming powered by reinforcement learning (RL). AutoTriton performs supervised fine-tuning (SFT) to be equipped with essential Triton programming expertise using a high-quality data gathering pipeline, and conducts RL with Group Relative Policy Optimization (GRPO) algorithm, combining a rule-based reward and an execution-based reward to further improve Triton programming ability, sequentially. Experiments across five evaluation channels of TritonBench and KernelBench illustrate that our 8B model AutoTriton achieves performance comparable to mainstream large models, including Claude-4-Sonnet and DeepSeek-R1-0528. Further experimental analysis demonstrates the crucial role of each module within AutoTriton, including the SFT stage, the RL stage, and the reward design strategy. These findings underscore the promise of RL for automatically generating high-performance kernels, and since high-performance kernels are core components of AI systems, this breakthrough establishes an important foundation for building more efficient AI systems. The model and code will be available at https://github.com/AI9Stars/AutoTriton.

Creating a cast of characters by attending to their relational dynamics is a critical aspect of most long-form storywriting. However, our formative study (N=14) reveals that writers struggle to envision new characters that could influence existing ones, to balance similarities and differences among characters, and to intricately flesh out their relationships. Based on these observations, we designed Constella, an LLM-based multi-agent tool that supports storywriters' interconnected character creation process. Constella suggests related characters (FRIENDS DISCOVERY feature), reveals the inner mindscapes of several characters simultaneously (JOURNALS feature), and manifests relationships through inter-character responses (COMMENTS feature). Our 7-8 day deployment study with storywriters (N=11) shows that Constella enabled the creation of expansive communities composed of related characters, facilitated the comparison of characters' thoughts and emotions, and deepened writers' understanding of character relationships. We conclude by discussing how multi-agent interactions can help distribute writers' attention and effort across the character cast.