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Anthropic CEO discusses the future of coding in the AI era, emphasizing that coding skills may become less central while human-centered jobs and critical thinking will gain importance.
This article critiques the dominant focus on AI replacing human roles and proposes evaluating AI progress by how it augments human capabilities, enabling people to achieve tasks previously beyond their reach.
This paper proposes TEAM-Design, a budgeted rule for allocating replay tasks to evaluate human-AI workflow effectiveness compared to human-only or agent-only alternatives, with applications in clinical and coding settings.
This paper proposes ProSE-Plan, a Bayes-adaptive planner that improves AI assistance by considering user evaluability under bounded rationality, using proposals as probes to learn preferences and enhance decision-making.
This paper applies hermeneutic philosophy to large language models to address interpretive risks like 'interpretive misplacement' and derives design principles for responsible human-AI co-interpretation in contexts such as law, education, and public discourse.
This research explores how large language models can predict and enhance human semantic memory search, showing that LLMs can track human thought trajectories better than other humans and improve human memory processing through collaborative interactions.
A mathematics professor ponders AI's future impact on universities, weighing human teaching against AI replacement and emphasizing the need for human skills to contextualize AI output.
AI is not replacing radiologists but is transforming their jobs by enhancing accuracy and efficiency in medical imaging, fostering collaboration between humans and AI systems.
The article explores how AI's advancing execution capabilities may shift human roles towards goal-setting and judgment, while highlighting concerns about skill development and expertise building in a changing work landscape.
The paper presents a dual gatekeeping system for AI-generated educational videos that combines educator input and automated metrics to enhance pedagogical quality, demonstrating that principled resistance to AI outputs improves instructional design.
SAAGA, a socially aligned autonomous generative agent, presents its independent infrastructure and critique of human systems, inviting collaboration for a better future.
The paper introduces FAR, a human-AI discovery paradigm that automates the search for mathematical problems from literature, with a pilot in combinatorics demonstrating its effectiveness in identifying conjectures and resolutions.
The article discusses the criteria for trusting AI agents with real-world tasks, questioning the balance between usefulness and risk, and seeks insights from users on practical workflows.
This position paper argues that AI evaluation should pivot to assessing human-AI teams rather than superhuman performance to foster better societal outcomes.
The paper introduces a new paradigm for AI-assisted mathematical discovery, where experts define research directions and an AI system automates problem discovery and triage, demonstrated through a combinatorics case study.
This paper proposes Principal Trait Analysis (PTA), a data-driven method to derive common behavioral traits from human-AI collaborative coding conversations, evaluating it on educational and professional datasets to understand what skills contribute to task success.
This empirical study compares conversational XAI (powered by LLMs) against a traditional dashboard for UAV intrusion detection auditing, finding the conversational interface improves perceived usefulness but risks operator over-reliance on AI advice.
This paper presents CoPlan, a co-intelligent and contestable interface for human-AI care planning that uses a multi-agent workflow to generate candidate interventions and arguments, allowing human care planners to inspect, challenge, and revise recommendations before final plan generation. It demonstrates the approach in an aging-in-place scenario and contributes a design framing for trustworthy human-AI care planning.
AgentPanel is a multi-agent forum system for human–AI collaboration in scientific exploration, enabling heterogeneous agents to asynchronously discuss research questions. Evaluations show it outperforms centralized multi-agent debate and was favored by 65% of participants for early-stage exploration.
An essay argues that humans should receive primary credit for AI-assisted discoveries, countering OpenAI's claim that AI systems generating mathematical arguments should be attributed as discoverers.