Hi, all! The Machine Intelligence Research Institute (MIRI) is answering questions here tomorrow, October 12 at 10am PDT. You can post questions below in the interim.
MIRI is a Berkeley-based research nonprofit that does basic research on key technical questions related to smarter-than-human artificial intelligence systems. Our research is largely aimed at developing a deeper and more formal understanding of such systems and their safety requirements, so that the research community is better-positioned to design systems that can be aligned with our interests. See here for more background.
Through the end of October, we're running our 2016 fundraiser — our most ambitious funding drive to date. Part of the goal of this AMA is to address questions about our future plans and funding gap, but we're also hoping to get very general questions about AI risk, very specialized questions about our technical work, and everything in between. Some of the biggest news at MIRI since Nate's AMA here last year:
- We developed a new framework for thinking about deductively limited reasoning, logical induction.
- Half of our research team started work on a new machine learning research agenda, distinct from our agent foundations agenda.
- We received a review and a $500k grant from the Open Philanthropy Project.
Likely participants in the AMA include:
- Nate Soares, Executive Director and primary author of the AF research agenda
- Malo Bourgon, Chief Operating Officer
- Rob Bensinger, Research Communications Manager
- Jessica Taylor, Research Fellow and primary author of the ML research agenda
- Tsvi Benson-Tilsen, Research Associate
Nate, Jessica, and Tsvi are also three of the co-authors of the "Logical Induction" paper.
EDIT (10:04am PDT): We're here! Answers on the way!
EDIT (10:55pm PDT): Thanks for all the great questions! That's all for now, though we'll post a few more answers tomorrow to things we didn't get to. If you'd like to support our AI safety work, our fundraiser will be continuing through the end of October.
In short: there’s a big difference between building a system that follows the letter of the law (but not the spirit), and a system that follows the intent behind a large body of law. I agree that the legal system is a large corpus of data containing information about human values and how humans currently want their civilization organized. In order to use that corpus, we need to be able to design systems that reliably act as intended, and I’m not sure how the legal corpus helps with that technical problem (aside from providing lots of training data, which I agree is useful).
In colloquial terms, MIRI is more focused on questions like “if we had a big corpus of information about human values, how could we design a system to learn from that corpus how to act as intended”, and less focused on the lack of corpus.
The reason that we have to work on corrigibility ourselves is that we need advanced learning systems to be corrigible before they’ve finished learning how to behave correctly from a large training corpus. In other words, there are lots of different training corpuses and goal systems where, if the system is fully trained and working correctly, we get corrigibility for free; the difficult part is getting the system to behave corrigibly before it’s smart enough to be doing corrigibility for the “right reasons”.