Message me here or at seth dot herd at gmail dot com.
I was a researcher in cognitive psychology and cognitive neuroscience for about two decades; I studied complex human thought. Now I’m applying what I’ve learned to the study of AI alignment.
Research overview:
Alignment is the study of how to give AIs 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. So we’d better get ready. If their goals don’t align well enough with ours, they’ll probably outsmart us and get their way, possibly much to our chagrin. See this excellent intro video for more.
There are good and deep reasons to think that aligning AI will be very hard. But I think we have promising solutions that bypass most of those difficulties, and could 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 actually accomplish that for all of humanity. So I focus on finding alignment solutions.
In brief I think we can probably build and align language model agents (or language model cognitive architectures) even when they’re more autonomous and competent than humans. 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. I’ve focused on the emergent interactions 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 directly on alignment, currently as a research fellow at the Astera Institute.
More on 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 could probably also align such an AGI so that it stays aligned even if it grows much smarter than we are. Instead of trying to give it a definition of ethics it can’t misunderstand or re-interpret (value alignment mis-specification), we’ll do the obvious thing: design it to follow instructions. 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.
I increasingly suspect we should be actively working to build such intelligences. It seems like our 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. 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.
One factor is different incentives for decision-makers. The incentives (and the mindset) for tech companies is to move fast and break things. The incentives (and mindset) for government workers is usually vastly more conservative.
So if it is the government making decisions about when to test and deploy new systems, I think we’re probably far better off WRT caution.
That must be weighed against the government typically being very bad at technical matters. So even an attempt to be cautious could be thwarted by lack of technical understanding of risks.
Of course, the Trump administration is attempting to instill a vastly different mindset, more like tech companies. So if it’s that administration we’re talking about, we’re probably worse off on net with a combination of lack of knowledge and YOLO attitudes. Which is unfortunate—because this is likely to happen anyway.
As Habryka and others have noted, it also depends on whether it reduces race dynamics by aggregating efforts across companies, or mostly just throws funding fuel on the race fire.