Message me here or at seth dot herd at gmail dot com.

I was a researcher in cognitive psychology and cognitive neuroscience for two decades and change. I studied complex human thought using neural network models of brain function. I'm applying that knowledge to figuring out how we can align AI as developers make it to "think for itself" in all the ways that make humans capable and dangerous.

New to alignment? See the Research Overview section
Field veteran? See More on My Approach at the end.

I work on technical alignment, but doing that has forced me to branch into alignment targets, alignment difficulty, and societal and field sociological issues, because choosing the best technical research approach depends on all of those.

Principal articles:

On technical alignment of LLM-based AGI agents:
- LLM AGI may reason about its goals and discover misalignments by default
  - An LLM-centric lens on why aligning Real AGI is hard
- System 2 Alignment Likely approaches for LLM AGI on the current trajectory
- Seven sources of goals in LLM agents brief problem statement
- Internal independent review for language model agent alignment
  - Updated in System 2 alignment
On LLM-based agents as a route to takeover-capable AGI
- LLM AGI will have memory, and memory changes alignment
- Brief argument for short timelines being quite possible
- Capabilities and alignment of LLM cognitive architectures
  - Cognitive psychology perspective on routes to LLM-based AGI with no breakthroughs needed
AGI risk interactions with societal power structures and incentives:
- Whether governments will control AGI is important and neglected
- If we solve alignment, do we die anyway?
  - Risks of proliferating human-controlled AGI
- Fear of centralized power vs. fear of misaligned AGI: Vitalik Buterin on 80,000 Hours
On the psychology of alignment as a field:
- Cruxes of disagreement on alignment difficulty
- Motivated reasoning/confirmation bias as the most important cognitive bias
On AGI alignment targets assuming technical alignment
- Problems with instruction-following as an alignment target
- Instruction-following AGI is easier and more likely than value aligned AGI
- Goals selected from learned knowledge: an alternative to RL alignment
On communicating AGI risks:

Research Overview:

Alignment is the study of how to design and train AI to have goals or values aligned with ours, so we're not in competition with our own creations.

Recent breakthroughs in AI like ChatGPT make it possible we'll have smarter-than-human AIs soon. If we don't understand how to make sure it has only goals we like, it will probably outcompete us, and we'll be either sorry or gone. See this excellent intro video.

There are good and deep reasons to think that aligning AI will be very hard. Section 1 of LLM AGI may reason about its goals is my attempt to describe those briefly and intuitively. But we also have promising solutions that might address those difficulties. They could also be relatively easy to use for the types of AGI we're most likely to develop first.

That doesn't mean I think building AGI is safe. Humans often screw up complex projects, particularly on the first try, and we won't get many tries. If it were up to me I'd Shut It All Down, but I don't see how we could get all of humanity to stop building AGI. So I focus on finding alignment solutions for the types of AGI people are building.

In brief, I think we can probably build and align language model agents (or language model cognitive architectures) up to the point that they're about as autonomous and competent as a human, but then it gets really dicey. We'd use a stacking suite of alignment methods that can mostly or entirely avoid using RL for alignment, and achieve corrigibility (human-in-the-loop error correction) by having a central goal of following instructions. This scenario leaves multiple humans in charge of ASIs, creating some dangerous dynamics, but those problems might be navigated, too.

Bio

I did computational cognitive neuroscience research from getting my PhD in 2006 until the end of 2022. I've worked on computational theories of vision, executive function, episodic memory, and decision-making, using neural network models of brain function to integrate data across levels of analysis from psychological down to molecular mechanisms of learning in neurons, and everything in between. I've focused on the interactions between different brain neural networks that are needed to explain complex thought. Here's a list of my publications.

I was increasingly concerned with AGI applications of the research, and reluctant to publish my full theories lest they be used to accelerate AI progress. I'm incredibly excited to now be working full-time on alignment, currently as a research fellow at the Astera Institute.

More on My Approach

The field of AGI alignment is "pre-paradigmatic." So I spend a lot of my time thinking about what problems need to be solved, and how we should go about solving them. Solving the wrong problems seems like a waste of time we can't afford.

When LLMs suddenly started looking intelligent and useful, I noted that applying cognitive neuroscience ideas to them might well enable them to reach AGI and soon ASI levels. Current LLMs are like humans with no episodic memory for their experiences, and very little executive function for planning and goal-directed self-control. Adding those cognitive systems to LLMs can make them into cognitive architectures with all of humans' cognitive capacities - a "real" artificial general intelligence that will soon be able to outsmart humans.

My work since then has convinced me that we might be able to align such an AGI so that it stays aligned as it grows smarter than we are. LLM AGI may reason about its goals and discover misalignments by default is my latest thinking; it's a definite maybe!

My plan: predict and debug System 2, instruction-following alignment approaches for our first AGIs

I'm trying to fill a particular gap in alignment work. My approach is to focus on thinking through plans for alignment on short timelines and realistic societal assumptions (competition, polarization, and conflicting incentives creating motivated reasoning that distorts beliefs). Many serious thinkers give up on this territory, assuming that either aligning LLM-based AGI turns out to be very easy, or we fail and perish because we don't have much time for new research.

I think it's fairly likely that alignment isn't impossibly hard but also not easy enough that developers get it right on their own despite all of their biases and incentives. so a little work in advance, from outside researchers like me could tip the scales. I think this is a neglected approach (although to be fair, most approaches are neglected at this point, since alignment is so under-funded compared to capabilities research).

One key to my approach is the focus on intent alignment instead of the more common focus on value alignment. Instead of trying to give it a definition of ethics it can't misunderstand or re-interpret (value alignment mis-specification), we'll probably continue with the alignment target developers currently focus on: Instruction-following.

It's counter-intuitive to imagine an intelligent entity that wants nothing more than to follow instructions, but there's no logical reason this can't be done. An instruction-following proto-AGI can be instructed to act as a helpful collaborator in keeping it aligned as it grows smarter.

There are significant Problems with instruction-following as an alignment target. It does not solve the problem with corrigibility once an AGI has left our control, merely gives another route to solving alignment (ordering it to collaborate) while it's still in our control, if we've gotten close enough to the initial target. It allows selfish humans to seize control. Nonetheless, it seems easier and more likely than value aligned AGI, so I continue to work on technical alignment under the assumption that's the target we'll pursue.

I increasingly suspect we should be actively working to build parahuman (human-like) LLM agents. It seems like our best hope of survival, since I don't see how we can convince the whole world to pause AGI efforts, and other routes to AGI seem much harder to align since they won't "think" in English chains of thought, or be easy to scaffold and train for System 2 Alignment backstops. Thus far, I haven't been able to engage enough careful critique of my ideas to know if this is wishful thinking, so I haven't embarked on actually helping develop language model cognitive architectures.

Even though these approaches are pretty straightforward, they'd have to be implemented carefully. Humans often get things wrong on their first try at a complex project. So my p(doom) estimate of our long-term survival as a species is in the 50% range, too complex to call. That's despite having a pretty good mix of relevant knowledge and having spent a lot of time working through various scenarios. So I think anyone with a very high or very low estimate is overestimating their certainty.