Category: News

The Absolute Insanity of MoltbookKDnuggets What the… AI agents arguing over memes? Sure, why not.

 What the… AI agents arguing over memes? Sure, why not. Read More  

The Rule Everyone Misses: How to Stop Confusing loc and iloc in PandasTowards Data Science A simple mental model to remember when each one works (with examples that finally click).
The post The Rule Everyone Misses: How to Stop Confusing loc and iloc in Pandas appeared first on Towards Data Science.

 A simple mental model to remember when each one works (with examples that finally click).
The post The Rule Everyone Misses: How to Stop Confusing loc and iloc in Pandas appeared first on Towards Data Science. Read More  

Microsoft unveils method to detect sleeper agent backdoorsAI News Researchers from Microsoft have unveiled a scanning method to identify poisoned models without knowing the trigger or intended outcome. Organisations integrating open-weight large language models (LLMs) face a specific supply chain vulnerability where distinct memory leaks and internal attention patterns expose hidden threats known as “sleeper agents”. These poisoned models contain backdoors that lie dormant
The post Microsoft unveils method to detect sleeper agent backdoors appeared first on AI News.

 Researchers from Microsoft have unveiled a scanning method to identify poisoned models without knowing the trigger or intended outcome. Organisations integrating open-weight large language models (LLMs) face a specific supply chain vulnerability where distinct memory leaks and internal attention patterns expose hidden threats known as “sleeper agents”. These poisoned models contain backdoors that lie dormant
The post Microsoft unveils method to detect sleeper agent backdoors appeared first on AI News. Read More  

OpenAI’s enterprise push: The hidden story behind AI’s sales raceAI News As OpenAI races toward its ambitious US$100 billion revenue target by 2027, the ChatGPT maker is reportedly building an army of AI consultants to bridge the gap between cutting-edge technology and enterprise boardrooms—a move that signals a fundamental shift in how AI companies are approaching the notoriously difficult challenge of enterprise adoption. According to industry
The post OpenAI’s enterprise push: The hidden story behind AI’s sales race appeared first on AI News.

 As OpenAI races toward its ambitious US$100 billion revenue target by 2027, the ChatGPT maker is reportedly building an army of AI consultants to bridge the gap between cutting-edge technology and enterprise boardrooms—a move that signals a fundamental shift in how AI companies are approaching the notoriously difficult challenge of enterprise adoption. According to industry
The post OpenAI’s enterprise push: The hidden story behind AI’s sales race appeared first on AI News. Read More  

Mistral AI Launches Voxtral Transcribe 2: Pairing Batch Diarization And Open Realtime ASR For Multilingual Production Workloads At ScaleMarkTechPost Automatic speech recognition (ASR) is becoming a core building block for AI products, from meeting tools to voice agents. Mistral’s new Voxtral Transcribe 2 family targets this space with 2 models that split cleanly into batch and realtime use cases, while keeping cost, latency, and deployment constraints in focus. The release includes: Both models are
The post Mistral AI Launches Voxtral Transcribe 2: Pairing Batch Diarization And Open Realtime ASR For Multilingual Production Workloads At Scale appeared first on MarkTechPost.

 Automatic speech recognition (ASR) is becoming a core building block for AI products, from meeting tools to voice agents. Mistral’s new Voxtral Transcribe 2 family targets this space with 2 models that split cleanly into batch and realtime use cases, while keeping cost, latency, and deployment constraints in focus. The release includes: Both models are
The post Mistral AI Launches Voxtral Transcribe 2: Pairing Batch Diarization And Open Realtime ASR For Multilingual Production Workloads At Scale appeared first on MarkTechPost. Read More  

Revisiting Prompt Sensitivity in Large Language Models for Text Classification: The Role of Prompt Underspecificationcs.AI updates on arXiv.org arXiv:2602.04297v1 Announce Type: cross
Abstract: Large language models (LLMs) are widely used as zero-shot and few-shot classifiers, where task behaviour is largely controlled through prompting. A growing number of works have observed that LLMs are sensitive to prompt variations, with small changes leading to large changes in performance. However, in many cases, the investigation of sensitivity is performed using underspecified prompts that provide minimal task instructions and weakly constrain the model’s output space. In this work, we argue that a significant portion of the observed prompt sensitivity can be attributed to prompt underspecification. We systematically study and compare the sensitivity of underspecified prompts and prompts that provide specific instructions. Utilising performance analysis, logit analysis, and linear probing, we find that underspecified prompts exhibit higher performance variance and lower logit values for relevant tokens, while instruction-prompts suffer less from such problems. However, linear probing analysis suggests that the effects of prompt underspecification have only a marginal impact on the internal LLM representations, instead emerging in the final layers. Overall, our findings highlight the need for more rigour when investigating and mitigating prompt sensitivity.

 arXiv:2602.04297v1 Announce Type: cross
Abstract: Large language models (LLMs) are widely used as zero-shot and few-shot classifiers, where task behaviour is largely controlled through prompting. A growing number of works have observed that LLMs are sensitive to prompt variations, with small changes leading to large changes in performance. However, in many cases, the investigation of sensitivity is performed using underspecified prompts that provide minimal task instructions and weakly constrain the model’s output space. In this work, we argue that a significant portion of the observed prompt sensitivity can be attributed to prompt underspecification. We systematically study and compare the sensitivity of underspecified prompts and prompts that provide specific instructions. Utilising performance analysis, logit analysis, and linear probing, we find that underspecified prompts exhibit higher performance variance and lower logit values for relevant tokens, while instruction-prompts suffer less from such problems. However, linear probing analysis suggests that the effects of prompt underspecification have only a marginal impact on the internal LLM representations, instead emerging in the final layers. Overall, our findings highlight the need for more rigour when investigating and mitigating prompt sensitivity. Read More  

Tech Stack for Vibe Coding Modern ApplicationsKDnuggets Stop fighting AI. Use a tech stack AI understands and can build a paid SaaS within minutes.

 Stop fighting AI. Use a tech stack AI understands and can build a paid SaaS within minutes. Read More  

Mechanistic Interpretability: Peeking Inside an LLMTowards Data Science Are the human-like cognitive abilities of LLMs real or fake? How does information travel through the neural network? Is there hidden knowledge inside an LLM?
The post Mechanistic Interpretability: Peeking Inside an LLM appeared first on Towards Data Science.

 Are the human-like cognitive abilities of LLMs real or fake? How does information travel through the neural network? Is there hidden knowledge inside an LLM?
The post Mechanistic Interpretability: Peeking Inside an LLM appeared first on Towards Data Science. Read More