The co-pilot accused of trying to hijack a flydubai plane planned to crash the aircraft into Tel Aviv's Ben Gurion airport or a tower block in the Israeli city, US and Israeli reports suggest. The Omani national was a lone wolf extremist, according to the reports. The United Arab Emirates' attorney-general has said the co-pilot attempted to carry out a terrorist act on flight FZ1073 from Dubai to Tel Aviv last Wednesday. He attacked and wounded the captain with the emergency crash axe kept in the cockpit before being overpowered by passengers. Another crew travelling on the plane was able to land it in Saudi Arabia.

Australian authorities are investigating the links between the country and a Flydubai co-pilot who attempted to take control of a plane travelling to Israel. The co-pilot is alleged to have attacked the captain with a crash axe during a flight from Dubai to Tel Aviv before passengers managed to overpower him and successfully take control of the plane. The Australian authorities have not identified the co-pilot, who has been named by multiple media outlets including Reuters, CNN and The New York Times as Hamam al-Hammami. The prosecutor in the UAE has said it is investigating the incident and any possible motives. In a statement to the BBC, the Australian Federal Police (AFP) confirmed its investigation into the man's links to Australia. The investigation involves state police and the country's security agency, it said, adding that an update would be provided at the appropriate time.

Goal-conditioned reinforcement learning (RL) usually suffers from sparse reward and inefficient exploration in long-horizon tasks. Planning can find the shortest path to a distant goal that provides dense reward/guidance but is inaccurate without a precise environment model. We show that RL and planning can collaboratively learn from each other to overcome their own drawbacks. In ''CO-PILOT'', a learnable path-planner and an RL agent produce dense feedback to train each other on a curriculum of tree-structured sub-tasks. Firstly, the planner recursively decomposes a long-horizon task to a tree of sub-tasks in a top-down manner, whose layers construct coarse-to-fine sub-task sequences as plans to complete the original task.

Way back in 2023, Andrej Karpathy, an eminent AI guru, made waves with a striking claim that "the hottest new programming language is English". This was because the advent of large language models (LLMs) meant that from now on humans would not have to learn arcane programming languages in order to tell computers what to do. Henceforth, they could speak to machines like the Duke of Devonshire spoke to his gardener, and the machines would do their bidding. Ever since LLMs emerged, programmers have been early adopters, using them as unpaid assistants (or "co-pilots") and finding them useful up to a point – but always with the proviso that, like interns, they make mistakes, and you need to have real programming expertise to spot those. Recently, though, Karpathy stirred the pot by doubling down on his original vision.

After more than a year of hearing people talk about artificial intelligence (AI) and co-pilots, I finally tried one on a small project. I even paid for the privilege of doing so, figuring that the paid version would be superior to the free one. But what I have found confuses me, and I am wondering if you too have tried any of these tools. From your previous columns, it seems you might not be focused on the latest tools in our industry. So, maybe you have just continued to use vim and Makefiles.

This paper presents SYMBIOSIS, an AI-powered framework and platform designed to make Systems Thinking accessible for addressing societal challenges and unlock paths for leveraging systems thinking frameworks to improve AI systems. The platform establishes a centralized, open-source repository of systems thinking/system dynamics models categorized by Sustainable Development Goals (SDGs) and societal topics using topic modeling and classification techniques. Systems Thinking resources, though critical for articulating causal theories in complex problem spaces, are often locked behind specialized tools and intricate notations, creating high barriers to entry. To address this, we developed a generative co-pilot that translates complex systems representations - such as causal loop and stock-flow diagrams - into natural language (and vice-versa), allowing users to explore and build models without extensive technical training. Rooted in community-based system dynamics (CBSD) and informed by community-driven insights on societal context, we aim to bridge the problem understanding chasm. This gap, driven by epistemic uncertainty, often limits ML developers who lack the community-specific knowledge essential for problem understanding and formulation, often leading to ill informed causal assumptions, reduced intervention effectiveness and harmful biases. Recent research identifies causal and abductive reasoning as crucial frontiers for AI, and Systems Thinking provides a naturally compatible framework for both. By making Systems Thinking frameworks more accessible and user-friendly, SYMBIOSIS aims to serve as a foundational step to unlock future research into responsible and society-centered AI that better integrates societal context by leveraging systems thinking frameworks and causal modeling methods. Our work underscores the need for ongoing research into AI's capacity to understand essential characteristics of complex adaptive systems - such as feedback processes and time delays - paving the way for more socially attuned, effective AI systems.

In collaborative environments, a deep understanding of multi-human teaming dynamics is essential for optimizing performance. However, the relationship between individuals' behavioral and physiological markers and their combined influence on overall team performance remains poorly understood. To explore this, we designed a triadic human collaborative sensorimotor task in virtual reality (VR) and introduced a novel predictability metric to examine team dynamics and performance. Our findings reveal a strong connection between team performance and the predictability of a team member's future actions based on other team members' behavioral and physiological data. Contrary to conventional wisdom that high-performing teams are highly synchronized, our results suggest that physiological and behavioral synchronizations among team members have a limited correlation with team performance. These insights provide a new quantitative framework for understanding multi-human teaming, paving the way for deeper insights into team dynamics and performance.

Machine learning (ML) has the potential to revolutionize various domains, but its adoption is often hindered by the disconnect between the needs of domain experts and translating these needs into robust and valid ML tools. Despite recent advances in LLM-based co-pilots to democratize ML for non-technical domain experts, these systems remain predominantly focused on model-centric aspects while overlooking critical data-centric challenges. This limitation is problematic in complex real-world settings where raw data often contains complex issues, such as missing values, label noise, and domain-specific nuances requiring tailored handling. To address this we introduce CliMB-DC, a human-guided, data-centric framework for LLM co-pilots that combines advanced data-centric tools with LLM-driven reasoning to enable robust, context-aware data processing. At its core, CliMB-DC introduces a novel, multi-agent reasoning system that combines a strategic coordinator for dynamic planning and adaptation with a specialized worker agent for precise execution. Domain expertise is then systematically incorporated to guide the reasoning process using a human-in-the-loop approach. To guide development, we formalize a taxonomy of key data-centric challenges that co-pilots must address. Thereafter, to address the dimensions of the taxonomy, we integrate state-of-the-art data-centric tools into an extensible, open-source architecture, facilitating the addition of new tools from the research community. Empirically, using real-world healthcare datasets we demonstrate CliMB-DC's ability to transform uncurated datasets into ML-ready formats, significantly outperforming existing co-pilot baselines for handling data-centric challenges. CliMB-DC promises to empower domain experts from diverse domains -- healthcare, finance, social sciences and more -- to actively participate in driving real-world impact using ML.