Updates

• Our 2019 fundraiser ended Dec. 31. We'll have more to say in a few weeks in our fundraiser retrospective, but for now, a big thank you to the ~240 donors who together donated more than $526,000, including $67,484 in the first 20 seconds of Giving Tuesday (not counting matching dollars, which have yet to be announced). 
• Jan. 15 is the final day of CFAR's annual fundraiser. CFAR also recently ran an AMA and has posted their workshop participant handbook online.
• Understanding “Deep Double Descent”: MIRI researcher Evan Hubinger describes a fascinating phenomenon in ML, and an interesting case study in ML research aimed at deepening our understanding, and not just advancing capabilities. In a follow-up post, Evan also considers possible implications for alignment research.
• Safe Exploration and Corrigibility: Evan notes an important (and alignment-relevant) way that notions of exploration in deep RL have shifted.
• “Learning Human Objectives by Evaluating Hypothetical Behavior”: UC Berkeley and DeepMind researchers “present a method for training reinforcement learning agents from human feedback in the presence of unknown unsafe states”.

Links from the research team

This continues my experiment from last month: having MIRI researchers anonymously pick out AI Alignment Forum posts to highlight and comment on.

• Re (When) is Truth-telling Favored in AI debate? — “A paper by Vojtěch Kovařík and Ryan Carey; it's good to see some progress on the debate model!”
• Re Recent Progress in the Theory of Neural Networks — “Noah MacAulay provides another interesting example of research attempting to explain what's going on with NNs.”
• Re When Goodharting is optimal — “I like Stuart Armstrong's post for the systematic examination of why we might be afraid of Goodharting. The example at the beginning is an interesting one, because it seems (to me at least) like the robot really should go back and forth (staying a long time at each side to minimize lost utility). But Stuart is right that this answer is, at least, quite difficult to justify.”
• Re Seeking Power is Instrumentally Convergent in MDPs and Clarifying Power-Seeking and Instrumental Convergence — “It's nice to finally have a formal model of this, thanks to Alex Turner and Logan Smith. Instrumental convergence has been an informal part of the discussion for a long time.”
• Re Critiquing “What failure looks like” — “I thought that Grue Slinky's post was a good critical analysis of Paul Christiano's ‘going out with a whimper’ scenario, highlighting some of the problems it seems to have as a concrete AI risk scenario. In particular, I found the analogy given to the simplex algorithm persuasive in terms of showcasing how, despite the fact that many of our current most powerful tools already have massive differentials in how well they work on different problems, those values which are not served well by those tools don't seem to have lost out massively as a result. I still feel like there may be a real risk along the lines of ‘going out with a whimper’, but I think this post presents a real challenge to that scenario as it has been described so far.”
• Re Counterfactual Induction — “A proposal for logical counterfactuals by Alex Appel. This could use some more careful thought and critique; it's not yet clear exactly how much or little it accomplishes.”
• Re A dilemma for prosaic AI alignment — “Daniel Kokotajlo outlines key challenges for prosaic alignment: ‘[…] Now I think the problem is substantially harder than that: To be competitive prosaic AI safety schemes must deliberately create misaligned mesa-optimizers and then (hopefully) figure out how to align them so that they can be used in the scheme.’”

One of our biggest updates, I'm happy to announce, is that we've hired five new research staff, with a sixth to join us in February. For details, see Workshops and Scaling Up in the fundraiser post.

Also: Facebook's Giving Tuesday matching opportunity is tomorrow at 5:00am PT! See Colm's post for details on how to get your donation matched.

Other updates

• Our most recent hire, “Risks from Learned Optimization” co-author Evan Hubinger, describes what he'll be doing at MIRI. See also Nate Soares' comment on how MIRI does nondisclosure-by-default.
• Buck Shlegeris discusses EA residencies as an outreach opportunity.
• OpenAI releases Safety Gym, a set of tools and environments for incorporating safety constraints into RL tasks.
• CHAI is seeking interns; application deadline Dec. 15.

Thoughts from the research team

This month, I'm trying something new: quoting MIRI researchers' summaries and thoughts on recent AI safety write-ups.

I've left out names so that these can be read as a snapshot of people's impressions, rather than a definitive “Ah, researcher X believes Y!” Just keep in mind that these will be a small slice of thoughts from staff I've recently spoken to, not anything remotely like a consensus take.

• Re Will transparency help catch deception? — “A good discussion of an important topic. Matthew Barnett suggests that any weaknesses in a transparency tool may turn it into a detrimental middle-man, and directly training supervisors to catch deception may be preferable.”
• Re Chris Olah’s views on AGI safety — “I very much agree with Evan Hubinger's idea that collecting different perspectives — different ‘hats’ — is a useful thing to do. Chris Olah's take on transparency is good to see. The concept of microscope AI seems like a useful one, and Olah's vision of how the ML field could be usefully shifted is quite interesting.”
• Re Defining AI Wireheading — “Stuart Armstrong takes a shot at making a principled distinction between wireheading and the rest of Goodhart.”
• Re How common is it to have a 3+ year lead? — “This seems like a pretty interesting question for AI progress models. The expected lead time and questions of expected takeoff speed greatly influence the extent to which winner-take-all dynamics are plausible.”
• Re Thoughts on Implementing Corrigible Robust Alignment — “Steve Byrnes provides a decent overview of some issues around getting ‘pointer’ type values.”

In other news, MIRI researcher Buck Shlegeris has written over 12,000 words on a variety of MIRI-relevant topics in an EA Forum AMA. (Example topics: advice for software engineers; what alignment plans tend to look like; and decision theory.)

Other updates

• Abram Demski's The Parable of Predict-O-Matic is a great read: the predictor/optimizer issues it covers are deep, but I expect a fairly wide range of readers to enjoy it and get something out of it.
• Evan Hubinger's Gradient Hacking describes an important failure mode that hadn't previously been articulated.
• Vanessa Kosoy's LessWrong shortform has recently discussed some especially interesting topics related to her learning-theoretic agenda.
• Stuart Armstrong's All I Know Is Goodhart constitutes nice conceptual progress on expected value maximizers that are aware of Goodhart's law and trying to avoid it.
• Reddy, Dragan, and Levine's paper on modeling human intent cites (of all things) Harry Potter and the Methods of Rationality as inspiration. 

News and links

• Artificial Intelligence Research Needs Responsible Publication Norms: Crootof provides a good review of the issue on Lawfare.
• Stuart Russell's new book is out: Human Compatible: Artificial Intelligence and the Problem of Control (excerpt). Rohin Shah's review does an excellent job of contextualizing Russell's views within the larger AI safety ecosystem, and Rohin highlights the quote:
  

  The task is, fortunately, not the following: given a machine that possesses a high degree of intelligence, work out how to control it. If that were the task, we would be toast. A machine viewed as a black box, a fait accompli, might as well have arrived from outer space. And our chances of controlling a superintelligent entity from outer space are roughly zero. Similar arguments apply to methods of creating AI systems that guarantee we won’t understand how they work; these methods include whole-brain emulation — creating souped-up electronic copies of human brains — as well as methods based on simulated evolution of programs. I won’t say more about these proposals because they are so obviously a bad idea.

• Jacob Steinhardt releases an AI Alignment Research Overview.
• Patrick LaVictoire's AlphaStar: Impressive for RL Progress, Not for AGI Progress raises some important questions about how capable today's state-of-the-art systems are.

Updates

• Ben Pace summarizes a second round of AI Alignment Writing Day posts.
• The Zettelkasten Method: MIRI researcher Abram Demski describes a note-taking system that's had a large positive effect on his research productivity.
• Will MacAskill writes a detailed critique of functional decision theory; Abram Demski (1, 2) and Matthew Graves respond in the comments.

News and links

• Recent AI alignment posts: Evan Hubinger asks “Are minimal circuits deceptive?”, Paul Christiano describes the strategy-stealing assumption, and Wei Dai lists his resolved confusions about Iterated Distillation and Amplification. See also Rohin Shah's comparison of recursive approaches to AI alignment.
• Also on LessWrong: A Debate on Instrumental Convergence Between LeCun, Russell, Bengio, Zador, and More.
• FHI's Ben Garfinkel and Allan Dafoe argue that conflicts between nations tend to exhibit “offensive-then-defensive scaling”.
• OpenAI releases a follow-up report on GPT-2, noting that several groups “have explicitly adopted similar staged release approaches” to OpenAI.
• NVIDIA Applied Deep Learning Research has trained a model that appears to essentially replicate GPT-2, with 5.6x as many parameters, slightly better WikiText perplexity, and slightly worse LAMBADA accuracy. The group has elected to share their training and evaluation code, but not the model weights.
• OpenAI fine-tunes GPT-2 for text continuation and summarization tasks that incorporate human feedback, noting, “Our motivation is to move safety techniques closer to the general task of ‘machines talking to humans,’ which we believe is key to extracting information about human values.”

Updates

• We ran a very successful MIRI Summer Fellows Program, which included a day where participants publicly wrote up their thoughts on various AI safety topics. See Ben Pace’s first post in a series of roundups.
• A few highlights from the writing day: Adele Lopez's Optimization Provenance; Daniel Kokotajlo's Soft Takeoff Can Still Lead to Decisive Strategic Advantage and The "Commitment Races" Problem; Evan Hubinger's Towards a Mechanistic Understanding of Corrigibility; and John Wentworth's Markets are Universal for Logical Induction and Embedded Agency via Abstraction.
• New posts from MIRI staff and interns: Abram Demski's Troll Bridge; Matthew Graves' View on Factored Cognition; Daniel Filan's Verification and Transparency; and Scott Garrabrant's Intentional Bucket Errors and Does Agent-like Behavior Imply Agent-like Architecture?
• See also a forum discussion on "proof-level guarantees" in AI safety.

News and links

• From Ben Cottier and Rohin Shah: Clarifying Some Key Hypotheses in AI Alignment
• Classifying Specification Problems as Variants of Goodhart's Law: Victoria Krakovna and Ramana Kumar relate DeepMind's SRA taxonomy to mesa-optimizers, selection and control, and Scott Garrabrant's Goodhart taxonomy. Also new from DeepMind: Ramana, Tom Everitt, and Marcus Hutter's Designing Agent Incentives to Avoid Reward Tampering.
• From OpenAI: Testing Robustness Against Unforeseen Adversaries. 80,000 Hours also recently interviewed OpenAI's Paul Christiano, with some additional material on decision theory.
• From AI Impacts: Evidence Against Current Methods Leading to Human-Level AI and Ernie Davis on the Landscape of AI Risks
• From Wei Dai: Problems in AI Alignment That Philosophers Could Potentially Contribute To
• Richard Möhn has put together a calendar of upcoming AI alignment events.
• The Berkeley Existential Risk Initiative is seeking an Operations Manager.