Tag: ML-system verification

Coverage-driven alignment – What ‘Teaching Claude Why’ can borrow from AV verification

June 8, 2026

Summary: This post suggests that alignment training could benefit from coverage-driven verification. Anthropic recently reported that teaching Claude alignment rules (via pretraining-style next-token learning on alignment-related stories) is more effective than relying primarily on RL-style behavioral shaping. Some AV developers reached a related conclusion, but in addition tend to use a systematic, coverage-driven methodology for … More Coverage-driven alignment – What ‘Teaching Claude Why’ can borrow from AV verification

GPT-3 and verification

July 20, 2020

Summary: This post talks about GPT-3, a new Machine Learning (ML) system currently making waves in the ML community. It explains why GPT-3 is a big deal, and then considers the verification implications of such systems. One way to look at GPT-3 (and the even-bigger GPT-4, GPT-5 etc. which are sure to follow) is as … More GPT-3 and verification

Misc. stuff: ASAM, DeepMind, Tesla and more

May 4, 2019

Summary: This is another one of those “misc. stuff” posts, with no unifying theme other than “Interesting inputs regarding Autonomous Vehicles verification”. It will discuss: What I learned regarding the ASAM OSC standardization effort, DeepMind’s “Rigorous Agent Evaluation” paper, Tesla’s “400,0000-car regression farm” idea, some good papers by Philip Koopman, and the upcoming Stuttgart symposium. … More Misc. stuff: ASAM, DeepMind, Tesla and more

Where Machine Learning meets rule-based verification

July 6, 2017

Summary: This post addresses some high-level questions like: Longer term, how much of the verification of Intelligent Autonomous Systems can be done with just Machine Learning (ML)? Should most requirements remain rule-based, and if so – how does that connect to the ML part? And how will the uneasy interface between ML and rules influence … More Where Machine Learning meets rule-based verification

Dynamic verification in one picture

July 1, 2017

Summary: This post tries to summarize what dynamic verification is, using a single picture. It then puts various verification tools, and diverse verification projects, in the context of that picture. It also explains Coverage Driven Verification (CDV). The Foretellix blog is about verifying complex systems. However, as I discussed here, there is no agreed-upon verification … More Dynamic verification in one picture

DeepXplore and new ideas for verifying ML systems

June 6, 2017

Summary: This post talks about the DeepXplore paper, and uses it to revisit the topic of verification of ML-based systems The paper DeepXplore: Automated Whitebox Testing of Deep Learning Systems (by folks from Columbia U and Lehigh U) describes a new and (in my view) pretty important way to verify ML-based systems. And it somehow … More DeepXplore and new ideas for verifying ML systems

Misc stuff: The verification gap, ML training and more

October 16, 2016

This post covers recent updates in machine learning, autonomous systems and verification. It has four sections: Automation / ML keep accelerating, but verification of automation / ML seems to lag behind HVC is coming, and I plan to attend (and even present) The idea of training an ML-based system using synthetic inputs (which I like) … More Misc stuff: The verification gap, ML training and more

Using Machine Learning to verify Machine Learning?

September 14, 2016

Summary: Can one use ML to verify ML-based systems? This post claims the answer is mostly “no”: You mainly have to use other system verification methodologies. However, some ML-based techniques may still be quite useful. How does one verify ML-based systems? A previous post in this series claimed that the “right” way is CDV: Essentially, … More Using Machine Learning to verify Machine Learning?

Machine Learning verification and Explainable AI

August 31, 2016

Summary: This post – part of a series about Machine Learning (ML) and verification – discusses the hard problem of verifying ML-based systems, and how “Explainable AI” might help. I can think of three interesting topics whose title contains both “ML” and “verification”: Verifying ML-based systems Using a verification infrastructure to train ML-based systems Using … More Machine Learning verification and Explainable AI