We have also been awarded generous grants by the Berkeley Existential Risk Initiative ($300,000) and the Long-Term Future Fund ($100,000). Our thanks to everybody involved!

Other updates

• Buck Shlegeris of MIRI and Rohin Shah of CHAI discuss Rohin's 2018–2019 overview of technical AI alignment research on the AI Alignment Podcast.
• From MIRI's Abram Demski: Thinking About Filtered Evidence Is (Very!) Hard and Bayesian Evolving-to-Extinction. And from Evan Hubinger: Synthesizing Amplification and Debate.
• From OpenAI's Beth Barnes, Paul Christiano, Long Ouyang, and Geoffrey Irving: Progress on AI Safety via Debate.
• Zoom In: An Introduction to Circuits: OpenAI's Olah, Cammarata, Schubert, Goh, Petrov, and Carter argue, “Features are the fundamental unit of neural networks. They correspond to directions [in the space of neuron activations]. […] Features are connected by weights, forming circuits. […] Analogous features and circuits form across models and tasks.”
• DeepMind's Agent57 appears to meet one of the AI benchmarks in AI Impacts' 2016 survey, “outperform professional game testers on all Atari games using no game specific knowledge”, earlier than NeurIPS/ICML authors predicted.
• From DeepMind Safety Research: Specification gaming: the flip side of AI ingenuity.

As the COVID-19 pandemic progresses, the LessWrong team has put together a database of resources for learning about the disease and staying updated, and 80,000 Hours has a new write-up on ways to help in the fight against COVID-19. In my non-MIRI time, I've been keeping my own quick and informal notes on various sources' COVID-19 recommendations in this Google Doc. Stay safe out there!

Updates

• My personal cruxes for working on AI safety: a talk transcript from MIRI researcher Buck Shlegeris.
• Daniel Kokotajlo of AI Impacts discusses Cortés, Pizarro, and Afonso as Precedents for Takeover.
• O'Keefe, Cihon, Garfinkel, Flynn, Leung, and Dafoe's “The Windfall Clause” proposes “an ex ante commitment by AI firms to donate a significant amount of any eventual extremely large profits” that result from “fundamental, economically transformative breakthroughs” like AGI.
• Microsoft announces the 17-billion-parameter language model Turing-NLG.
• Oren Etzioni thinks AGI is too far off to deserve much thought, and cites Andrew Ng's “overpopulation on Mars” metaphor approvingly — but he's also moving the debate in a very positive direction by listing specific observations that would make him change his mind.

Updates

• Colm reviews our 2019 fundraiser: taking into account matching, we received a total of $601,120 from 250+ donors. Our thanks again for all the support!
• Evan Hubinger's Exploring Safe Exploration clarifies points he raised in Safe Exploration and Corrigibility. The issues raised here are somewhat subtler than may be immediately apparent, since we tend to discuss things in ways that collapse the distinctions Evan is making.
• Logician Arthur Milchior reviews the AIRCS workshop and MIRI's application process based on his first-hand experience with both. See also follow-up discussion with MIRI and CFAR staff.
• Rohin Shah posts an in-depth 2018–19 review of the field of AI alignment.
• From Shevlane and Dafoe's new “The Offense-Defense Balance of Scientific Knowledge: Does Publishing AI Research Reduce Misuse?”:

   

  [T]he existing conversation around AI has heavily borrowed concepts and conclusions from one particular field: vulnerability disclosure in computer security. We caution against AI researchers treating these lessons as immediately applicable. There are important differences between vulnerabilities in software and the types of vulnerabilities exploited by AI. […]

  Patches to software are often easy to create, and can often be made in a matter of weeks. These patches fully resolve the vulnerability. The patch can be easily propagated: for downloaded software, the software is often automatically updated over the internet; for websites, the fix can take effect immediately.

  [… F]or certain technologies, there is no low-cost, straightforward, effective defence. [… C]onsider biological research that provides insight into the manufacture of pathogens, such as a novel virus. A subset of viruses are very difficult to vaccinate for (there is still no vaccination for HIV) or otherwise prepare against. This lowers the defensive benefit of publication, by blocking a main causal pathway by which publication leads to greater protection. This contrasts with the case where an effective treatment can be developed within a reasonable time period[.]

• Yann LeCun and Eliezer Yudkowsky discuss the concept “AGI”.
• CFAR's Anna Salamon contrasts “reality-revealing” and “reality-masking” puzzles.
• Scott Alexander reviews Stuart Russell's Human Compatible.

Links from the research team

MIRI researchers anonymously summarize and comment on recent posts and papers:

• Re ACDT: a hack-y acausal decision theory — “Stuart Armstrong calls this decision theory a hack. I think it might be more elegant than he's letting on (i.e., a different formulation could look less hack-y), and is getting at something.”
• Re Predictors exist: CDT going bonkers… forever — “I don't think Stuart Armstrong's example really adds much over some variants of Death in Damascus, but there's some good discussion of CDT vs. EDT stuff in the comments.”
• Re Is the term mesa optimizer too narrow? — “Matthew Barnett poses the important question, '[I]f even humans are not mesa optimizers, why should we expect mesa optimizers to be the primary real world examples of [malign generalization]?'”
• Re Malign generalization without internal search — “I think Matthew Barnett's question here is an important one. I lean toward the 'yes, this is a problem' camp—I don't think we can entirely eliminate malign generalization by eliminating internal search. But it is possible that this falls into other categories of misalignment (which we don't want to term 'inner alignment').”
• Re (A -> B) -> A in Causal DAGs and Formulating Reductive Agency in Causal Models — “I've wanted something like this for a while. Bayesian influence diagrams model agents non-reductively, by boldly asserting that some nodes are agentic. Can we make models which represent agents, without declaring a basic 'agent' type like that? John Wentworth offers an approach, representing agents via 'strange loops' across a use-mention boundary; and discusses how this might break down even further, with fully reductive agency. I'm not yet convinced that Wentworth has gotten it right, but it's exciting to see an attempt.”

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.”