CEO at Conjecture.
I don't know how to save the world, but dammit I'm gonna try.
I initially liked this post a lot, then saw a lot of pushback in the comments, mostly of the (very valid!) form of "we actually build reliable things out of unreliable things, particularly with computers, all the time". I think this is a fair criticism of the post (and choice of examples/metaphors therein), but I think it may be missing (one of) the core message(s) trying to be delivered.
I wanna give an interpretation/steelman of what I think John is trying to convey here (which I don't know whether he would endorse or not):
"There are important assumptions that need to be made for the usual kind of systems security design to work (e.g. uncorrelation of failures). Some of these assumptions will (likely) not apply with AGI. Therefor, extrapolating this kind of thinking to this domain is Bad™️." ("Epistemological vigilance is critical")
So maybe rather than saying "trying to build robust things out of brittle things is a bad idea", it's more like "we can build robust things out of certain brittle things, e.g. computers, but Godzilla is not a computer, and so you should only extrapolate from computers to Godzilla if you're really, really sure you know what you're doing."
Yes, we do expect this to be the case. Unfortunately, I think explaining in detail why we think this may be infohazardous. Or at least, I am sufficiently unsure about how infohazardous it is that I would first like to think about it for longer and run it through our internal infohazard review before sharing more. Sorry!
Redwood is doing great research, and we are fairly aligned with their approach. In particular, we agree that hands-on experience building alignment approaches could have high impact, even if AGI ends up having an architecture unlike modern neural networks (which we don’t believe will be the case). While Conjecture and Redwood both have a strong focus on prosaic alignment with modern ML models, our research agenda has higher variance, in that we additionally focus on conceptual and meta-level research. We’re also training our own (large) models, but (we believe) Redwood are just using pretrained, publicly available models. We do this for three reasons:
We’re also for-profit, while Redwood is a nonprofit, and we’re located in London! Not everyone lives out in the Bay :)
For the record, having any person or organization in this position would be a tremendous win. Interpretable aligned AGI?! We are talking about a top .1% scenario here! Like, the difference between egoistical Connor vs altruistic Connor with an aligned AGI in his hands is much much smaller than Connor with an aligned AGI and anyone, any organization or any scenario, with a misaligned AGI.
But let’s assume this.
Unfortunately, there is no actual functioning reliable mechanism by which humans can guarantee their alignment to each other. If there was something I could do that would irreversibly bind me to my commitment to the best interests of mankind in a publicly verifiable way, I would do it in a heartbeat. But there isn’t and most attempts at such are security theater.
What I can do is point to my history of acting in ways that, I hope, show my consistent commitment to doing what is best for the longterm future (even if of course some people with different models of what is “best for the longterm future” will have legitimate disagreements with my choices of past actions), and pledge to remain in control of Conjecture and shape its goals and actions appropriately.
On a meta-level, I think the best guarantee I can give is simply that not acting in humanity’s best interest is, in my model, Stupid. And my personal guiding philosophy in life is “Don’t Be Stupid”. Human values are complex and fragile, and while many humans disagree about many details of how they think the world should be, there are many core values that we all share, and not fighting with everything we’ve got to protect these values (or dying with dignity in the process) is Stupid.
Ideally, we would like Conjecture to scale quickly. Alignment wise, in 5 years time, we want to have the ability to take a billion dollars and turn it into many efficient, capable, aligned teams of 3-10 people working on parallel alignment research bets, and be able to do this reliably and repeatedly. We expect to be far more constrained by talent than anything else on that front, and are working hard on developing and scaling pipelines to hopefully alleviate such bottlenecks.
For the second question, we don't expect it to be a competing force (as in, we have people who could be working on alignment working on product instead). See point two in this comment.
This is why we will focus on SaaS products on top of our internal APIs that can be built by teams that are largely independent from the ML engineering. As such, this will not compete much with our alignment-relevant ML work. This is basically our thesis as a startup: We expect it to be EV+, as this earns much more money than we would have had otherwise.
To point 1: While we greatly appreciate what OpenPhil, LTFF and others do (and hope to work with them in the future!), we found that the hurdles required and strings attached were far greater than the laissez-faire silicon valley VC we encountered, and seemed less scalable in the long run. Also, FTX FF did not exist back when we were starting out.
While EA funds as they currently exist are great at handing out small to medium sized grants, the ~8 digit investment we were looking for to get started asap was not something that these kinds of orgs were generally interested in giving out (which seems to be changing lately!), especially to slightly unusual research directions and unproven teams. If our timelines were longer and the VC money had more strings attached (as some of us had expected before seeing it for ourselves!), we may well have gone another route. But the truth of the current state of the market is that if you want to scale to a billion dollars as fast as possible with the most founder control, this is the path we think is most likely to succeed.
To point 2: This is why we will focus on SaaS products on top of our internal APIs that can be built by teams that are largely independent from the ML engineering. As such, this will not compete much with our alignment-relevant ML work. This is basically our thesis as a startup: We expect it to be EV+, as this earns much more money than we would have had otherwise.
Notice this is a contingent truth, not an absolute one. If tomorrow, OpenPhil and FTX contracted us with 200M/year to do alignment work, this would of course change our strategy.
To point 3: We don’t think this has to be true. (Un)fortunately, given the current pace of capability progress, we expect keeping up with the pace to be more than enough for building new products. Competition on AI capabilities is extremely steep and not in our interest. Instead, we believe that (even) the (current) capabilities are so crazy that there is an unlimited potential for products, and we plan to compete instead on building a reliable pipeline to build and test new product ideas.
Calling it competition is actually a misnomer from our point of view. We believe there is ample space for many more companies to follow this strategy, still not have to compete, and turn a massive profit. This is how crazy capabilities and their progress are.
To address the opening quote - the copy on our website is overzealous, and we will be changing it shortly. We are an AGI company in the sense that we take AGI seriously, but it is not our goal to accelerate progress towards it. Thanks for highlighting that.
We don’t have a concrete proposal for how to reliably signal that we’re committed to avoiding AGI race dynamics beyond the obvious right now. There is unfortunately no obvious or easy mechanism that we are aware of to accomplish this, but we are certainly open to discussion with any interested parties about how best to do so. Conversations like this are one approach, and we also hope that our alignment research speaks for itself in terms of our commitment to AI safety.
If anyone has any more trust-inducing methods than us simply making a public statement and reliably acting consistently with our stated values (where observable), we’d love to hear about them!
To respond to the last question - Conjecture has been “in the making” for close to a year now and has not been a secret, we have discussed it in various iterations with many alignment researchers, EAs and funding orgs. A lot of initial reactions were quite positive, in particular towards our mechanistic interpretability work, and just general excitement for more people working on alignment. There have of course been concerns around organizational alignment, for-profit status, our research directions and the founders’ history with EleutherAI, which we all have tried our best to address.
But ultimately, we think whether or not the community approves of a project is a useful signal for whether a project is a good idea, but not the whole story. We have our own idiosyncratic inside-views that make us think that our research directions are undervalued, so of course, from our perspective, other people will be less excited than they should be for what we intend to work on. We think more approaches and bets are necessary, so if we would only work on the most consensus-choice projects we wouldn’t be doing anything new or undervalued. That being said, we don’t think any of the directions or approaches we’re tackling have been considered particularly bad or dangerous by large or prominent parts of the community, which is a signal we would take seriously.
We (the founders) have a distinct enough research agenda to most existing groups such that simply joining them would mean incurring some compromises on that front. Also, joining existing research orgs is tough! Especially if we want to continue along our own lines of research, and have significant influence on their direction. We can’t just walk in and say “here are our new frames for GPT, can we have a team to work on this asap?”.
You’re right that SOTA models are hard to develop, but that being said, developing our own models is independently useful in many ways - it enables us to maintain controlled conditions for experiments, and study things like scaling properties of alignment techniques, or how models change throughout training, as well as being useful for any future products. We have a lot of experience in LLM development and training from EleutherAI, and expect it not to take up an inordinate amount of developer hours.
We are all in favor of high bandwidth communication between orgs. We would love to work in any way we can to set these channels up with the other organizations, and are already working on reaching out to many people and orgs in the field (meet us at EAG if you can!).
In general, all the safety orgs that we have spoken with are interested in this, and that’s why we expect/hope this kind of initiative to be possible soon.
We strongly encourage in person work - we find it beneficial to be able to talk over or debate research proposals in person at any time, it’s great for the technical team to be able to pair program or rubber duck if they’re hitting a wall, and all being located in the same city has a big impact on team building.
That being said, we don’t mandate it. Some current staff want to spend a few months a year with their families abroad, and others aren’t able to move to London at all. While we preferentially accept applicants who can work in person, we’re flexible, and if you’re interested but can’t make it to London, it’s definitely still worth reaching out.