Who we become when we talk to machines
Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.
More from the blog
GPT-Lab
· gpt-lab.eu
Sep 23, 2026
Requirements Don’t Live in Isolation: What We’re Exploring with Req-Space
Requirements in large systems rarely exist in isolation. Their meaning depends on the wider project context - other requirements, policies, decisions, tests, and implementation details. That becomes especially important when AI is used for review, because spotting a possible conflict or gap is only the beginning. ReqSpace explores how AI, visualisation, and connected project context can help reviewers understand those findings, trace the relationships behind them, and focus on the questions that…
GPT-Lab
· gpt-lab.eu
Sep 17, 2026
Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
Measure by measure, studying society accurately
Naoki Egami has become a standout in political methodology, helping refine tools that give scholars durable results.
ToolGrad: Efficient tool-use dataset generation with textual "gradients"
Machine Intelligence
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Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
Grammar-Aligned Decoding
This paper proposes adaptive sampling with approximate expected futures (ASAp), a decoding algorithm that guarantees the output to be grammatical while provably producing outputs that match the conditional probability of the LLM's distribution conditioned on the given grammar constraint.
Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
AgentRM: Enhancing Agent Generalization with Reward Modeling
This work finds that finetuning a reward model to guide the policy model is more robust than directly finetuning the policy model, and proposes AgentRM, a generalizable reward model, to guide the policy model for effective test-time search.