Lmst

The Interpretable AI playbook: What Anthropic’s research means for your enterprise LLM strategy https://venturebeat.com/ai/the-interpretable-ai-playbook-what-anthropics-research-means-for-your-enterprise-llm-strategy/ #AI #interpretability

Text Shot: Sayash Kapoor, an AI safety researcher, suggests that while interpretability is valuable, it is just one of many tools for managing AI risk. In his view, “interpretability is neither necessary nor sufficient” to ensure models behave safely — it matters most when paired with filters, verifiers and human-centered design. This more expansive view sees interpretability as part of a larger ecosystem of control strategies, particularly in real-world AI deployments where models are components in broader decision-making systems

'Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretability', by Atticus Geiger et al.

http://jmlr.org/papers/v26/23-0058.html

#abstraction #interpretability #ai

Not understanding their models isn't news for AI companies. It's a fundamental part of the underlying technology's architecture. Pretending that we are just a step away from interpretability is simply disingenuous.
#AI #interpretability
https://www.axios.com/2025/06/09/ai-llm-hallucination-reason

AI interpretability is further along than I thought by Sean Goedecke https://www.seangoedecke.com/ai-interpretability/ #AI #interpretability

$Text Shot: AI models are commonly understood to be black boxes, but we can actually say a surprising amount about what’s going on inside them We can approximate a subset of the concepts the model is thinking in - called “features” - and how the model connects those concepts in a particular response - called “circuits” Internally, models represent features as a complex combination of many individual “neurons” (intermediate weight activations), so to effectively analyze them they must be expanded into a much larger model where one feature maps to one neuron I don’t want to overstate how much we know about AI models. The concepts and circuits we can identify are a fraction of the total processing that’s going on, and even how we label the concepts is a human guess - the model could be drawing much subtler distinctions than we realize. But as someone whose mental picture of all this was “we don’t know anything, neural networks are always black boxes”, it’s exciting to learn that we can at…$

AI interpretability is further along than I thought by Sean Goedecke https://www.seangoedecke.com/ai-interpretability/ #AI #interpretability

🧪 The Knowledge Graphs for Responsible AI Workshop is now underway at #ESWC2025!
📍 Room 7 – Nautilus Floor 0

The Knowledge Graphs for Responsible AI Workshop aims to explore how Knowledge Graphs (KGs) can promote the principles of Responsible AI—such as fairness, transparency, accountability, and inclusivity—by enhancing the interpretability, trustworthiness, and ethical grounding of AI systems. 📊🤖

#KnowledgeGraphs #ESWC2025 #ResponsibleAI #fairness #trustworthiness #Interpretability

Beyond the Black Box: Interpretability of LLMs in Finance

https://arxiv.org/abs/2505.24650

#HackerNews #Interpretability #LLMs #Finance #AI #Research #BlackBox

Circuit tracing for AI interpretability:
#ai #llm #interpretability #research #innovation
🤖

https://www.anthropic.com/research/open-source-circuit-tracing

Are LM more than their behavior? 🤔

Join our Conference on Language Modeling (COLM) workshop and explore the interplay between what LMs answer and what happens internally ✨

See you in Montréal 🍁

CfP: shorturl.at/sBomu
Page: shorturl.at/FT3fX
Reviewer Nomination: shorturl.at/Jg1BP

#nlproc #interpretability

Call for Papers, Interplay Workshop at COLM: June 23rd - submissions due. July 24th - acceptance notification. October 10th - workshop day.

Unlock the Secrets of AI Learning! ????Ever wondered how generative AI, the powerhouse behind stunning images and sophisticated text, truly learns? Park et al.'s groundbreaking study, ‘Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space,’ offers a revolutionary new perspective. Forget black boxes – this research unveils a "concept space" where AI learning becomes a visible journey!By casting ideas into geometric space, the authors bring to life how AI models learn step by step, stripping bare the order and timing of their knowledge. See the crucial role played by the "concept signal" in predicting what a model is first going to learn and note the fascinating "trajectory turns" revealing the sudden "aha!" moments of emergent abilities.This is not a theoretical abstraction – the framework has deep implications in the real world:Supercharge AI Training: Optimise training data to speed learning and improve efficiency.Demystify New Behaviours: Understand and even manage unforeseen strengths of state-of-the-art AI.Debug at Scale: Gain unprecedented insights into the knowledge state of a model to identify and fix faults.Future-Proof AI: This mode-agnostic feature primes the understanding of learning in other AI systems.This study is a must-read for all who care about the future of AI, from scientists and engineers to tech geeks and business executives. It's not only what AI can accomplish, but how it comes to do so.Interested in immersing yourself in the captivating universe of AI learning?Click here to read the complete article and discover the secrets of the concept space! #AI #MachineLearning #GenerativeAI #DeepLearning #Research #Innovation #ConceptSpace #EmergentCapabilities #AIDevelopment #Tech #ArtificialIntelligence #DataScience #FutureofAI #Interpretability

Anthropic's CEO admits the quiet part loud: we dont fully understand how AI works. They're building tools to decode it, like an MRI for AI, aiming for safety before it gets too powerful.
#AI #Interpretability #Anthropic