Imagine you are driving toward an intersection. A truck parked on the corner blocks your view of a car speeding toward the intersection from the right. You can't see it, but the car next to you can. Your car now knows something its own sensors could never observe. But there's an important twist: not every neighboring vehicle is worth listening to, and not every message deserves the same level of trust.

Timnit Gebru Believes There Is No'Existential Threat' From AI One of AI's fiercest critics believes the doom talk is about founders making money, not saving humanity. As the industry booms, an entirely new vernacular has emerged in the debates over how consequential artificial intelligence really is. Within that new parlance, two phrases have become focal points: stochastic parrots and existential risk . And at the center of is one prominent AI researcher who has never stood down from a fight, Timnit Gebru . Gebru came into the public eye several years ago after sparring with Google over a research paper she coauthored that called out biases in the company's AI, saying that LLMs basically parroted their training data and risked perpetuating biased viewpoints. The contested paper resulted in Gebru's departure from the company, and led her to found an institute that investigates harms perpetuated by technology and supports the creation of unbiased tech tools. She has also authored a new book,, expected to ship early next year. More recently, Gebru has spoken out against a faction of the industry that believes AI is so powerful it could destroy humanity. In turn, some members of that community, including an Anthropic cofounder, have alleged that Gebru's earlier research about stochastic parrots is no longer relevant, and that AI does have the ability to "think" or reason. The AI debate is no longer just about the technology itself, but about ideological groups and strategic narratives, and Gebru believes these narratives are a "harmful distraction" from the real issues with AI. I recently spoke with Gebru about what she believes these arguments are distracting from, and also got her response to critiques that her earlier research underestimates the AI of today.

When Astra does arrive, there's another factor to keep an eye on: your usage allowance. According to OpenAI, Astra can use up your allowance more quickly than GPT-5.6 Sol, depending on the complexity of the task, the amount of context, the reasoning required and other usage settings. So a big coding assignment that requires more reasoning can use more of your allowance than a quick question. OpenAI estimates that Plus users can send approximately five to 45 local Astra messages per five-hour period.

Healthcare AI's next test is integration While advanced AI excels at processing clinical data, healthcare's true test lies in overcoming deeply fragmented administrative workflows. The entrance of major AI companies into healthcare is a meaningful and welcome development, accelerating the technical foundation available to the industry. Their models are increasingly capable of processing long clinical records, interpreting complex terminology, comparing documentation against evidence and generating coherent summaries from large volumes of information. For clinicians, operators, and administrative teams who spend significant time searching through fragmented data, these advances are helping reduce cognitive burden and make high-value information easier to access. But healthcare leaders should not confuse model capability with operational capability. Healthcare's administrative challenges are caused by fragmented information, fragmented workflows, and fragmented accountability, not a lack of information.

That's a major factor in how well models do on the most famous puzzle-based benchmark, ARC-AGI. These problems require you to infer abstract, general rules from a set of examples. Models do better on ARC puzzles when they receive each grid not as an image but as a string of numbers that encodes the color of each cell. Research suggests that even when models answer ARC-AGI questions correctly, they often do so using byzantine and non-generalizable rules, whereas humans draw on simple visual concepts. Despite these disadvantages, models have gotten quite good at ARC-AGI over the past year, but some puzzles--such as the one printed here--still stump them.

The 35th International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) will be held from 15-21 August, in Bremen, Germany. The conference will feature workshops and tutorials, keynote and invited talks, technical presentations, posters, diversity and inclusion event, outreach, and more. Experts from research, practice, public institutions and civil society discuss how AI is changing key areas of everyday life in moderated panel discussions. The AI Lounges will be held in German. The tutorials will also take place from 15-17 August.

'Patients will need doctors who can stand apart from the machine long enough to know when it is wrong, incomplete, or right for the wrong reason.' 'Patients will need doctors who can stand apart from the machine long enough to know when it is wrong, incomplete, or right for the wrong reason.' What happens when medical students rely on AI - and never develop their own judgment? AI's danger isn't just in experts losing the ability to reason. It's that trainees may never learn how to do so in the first place I n healthcare, there's growing concern over doctors becoming less clinically adept as they increasingly rely on AI tools.

CogTwin is a hybrid cognitive architecture framework designed to bring autonomous reasoning and real-time adaptation to digital twin systems. Presented at IJCAI 2025, this work aims to advance the state of digital twin technology by addressing key gaps in autonomy, cognition, and real-time decision-making. Digital twin technology has transformed how complex systems are managed, from smart cities to industrial processes. However, most current digital twins remain fundamentally reactive: they rely on pre-programmed rules and static data-driven models, and therefore struggle when confronted with unforeseen events or evolving conditions. Real-time learning, reasoning, and adaptation - hallmarks of human cognition - are largely absent.

We deploy a simple into our everyday lives as search engines (Jin et al., 2025; statistical framework based on risk control (Angelopoulos Xiong et al., 2024), coding assistants (Zhao et al., 2023), et al., 2022) that converts any safety signal into a binary and companions (Zhang et al., 2025a). As their applicability grows, so does the potential harm caused by malicious decision rule, and offers statistical guarantees on the false LLM outputs. Despite remarkable performance across a alarm or missed detection rate. The framework is universally applicable to different monitoring purposes and can leverage wide range of tasks, LLMs remain prone to generating halarbitrary proxy signals. Through experiments on mathematlucinated, factually incorrect (Ravichander et al., 2025), or ical problem solving and red teaming conversations, we harmful output (Yu et al., 2025) when deployed.

To answer this, we introduce Tropical Attention, an attention mechanism grounded in tropical geometry that lifts the attention kernel into tropical projective space, where reasoning is piecewise-linear and 1-Lipschitz, thus preserving the polyhedral decision structure inherent to combinatorial reasoning. We prove that multi-head Tropical Attention (MHTA) stacks universally approximate tropical circuits and realize tropical transitive closure through composition, achieving polynomial resource bounds without invoking recurrent mechanisms. These guarantees explain why the induced polyhedral decision boundaries remain sharp and scale-invariant, rather than smoothed by Softmax. Empirically, we show that Tropical Attention delivers stronger out-of-distribution generalization in both length and value, with high robustness against perturbative noise, and substantially faster inference with fewer parameters compared to Softmax-based and recurrent attention baselines, respectively.