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
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
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
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
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
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
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?
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
Whatever you think of my Stuttgart symposium trip report, at least I made the effort to attend it and report back. In this post I sink even lower, reporting from a conference I did not attend – the ICRA workshop on verification of autonomous systems. I did, however, watch the full 4.5 hour video (warning: … More The ICRA 2016 verification workshop
Summary: This post will dig into the verification challenges of autonomous systems, and especially into the challenge of verifying those which use machine learning. This post will discuss the verification of Intelligent Autonomous Systems (IAS) which have to interact with people and the outside world. Examples are AVs, UAVs, and autonomous robots of various kinds. … More Verification challenges of autonomous systems