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.”
I think this is something better discussed in private. Could you DM me? Thanks!
This is a genuinely difficult and interesting question that I want to provide a good answer for, but that might take me some time to write up, I’ll get back to you at a later date.
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:
Having total control over the models we use can give us more insights into the phenomena we study, such as training models at a range of sizes to study scaling properties of alignment techniques.
Some properties we want to study may only appear in close-to-SOTA models—most of which are private.
We are trying to make products, and close-to-SOTA models help us do that better. Though as we note in our post, we plan to avoid work that pushes the capabilities frontier.
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.
Probably. It is likely that we will publish a lot of our interpretability work and tools, but we can’t commit to that because, unlike some others, we think it’s almost guaranteed that some interpretability work will lead to very infohazardous outcomes. For example, obvious ways in which architectures could be trained more efficiently, and as such we need to consider each result on a case by case basis. However, if we deem them safe, we would definitely like to share as many of our tools and insights as possible.
We would love to collaborate with anyone (from academia or elsewhere) wherever it makes sense to do so, but we honestly just do not care very much about formal academic publication or citation metrics or whatever. If we see opportunities to collaborate with academia that we think will lead to interesting alignment work getting done, excellent!
Our current plan is to work on foundational infrastructure and models for Conjecture’s first few months, after which we will spin up prototypes of various products that can work with a SaaS model. After this, we plan to try them out and productify the most popular/useful ones.
More than profitability, our investors are looking for progress. Because of the current pace of progress, it would not be smart from their point of view to settle on a main product right now. That’s why we are mostly interested in creating a pipeline that lets us build and test out products flexibly.
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.
The founders have a supermajority of voting shares and full board control and intend to hold on to both for as long as possible (preferably indefinitely). We have been very upfront with our investors that we do not want to ever give up control of the company (even if it were hypothetically to go public, which is not something we are currently planning to do), and will act accordingly.
For the second part, see the answer here.
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.
Answered here and here.
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.
See the reply to Michaël for answers as to what kind of products we will develop (TLDR we don’t know yet).
As for the conceptual research side, we do not do conceptual research with product in mind, but we expect useful corollaries to fall out by themselves for sufficiently good research. We think the best way of doing fundamental research like this is to just follow the most interesting, useful looking directions guided by the “research taste” of good researchers (with regular feedback from the rest of the team, of course). I for one at least genuinely expect product to be “easy”, in the sense that AI is advancing absurdly fast and the economic opportunities are falling from the sky like candy, so I don’t expect us to need to frantically dedicate our research to finding worthwhile fruit to pick.
The incubator has absolutely nothing to do with our for profit work, and is truly meant to be a useful space for independent researchers to develop their own directions that will hopefully be maximally beneficial to the alignment community. We will not put any requirements or restrictions on what the independent researchers work on, as long as it is useful and interesting to the alignment community.
We currently have a (temporary) office in the Southwark area, and are open to visitors. We’ll be moving to a larger office soon, and we hope to become a hub for AGI Safety in Europe.
And yes! Most of our staff will be attending EAG London. See you there?
See a longer answer here.
TL;DR: For the record, EleutherAI never actually had a policy of always releasing everything to begin with and has always tried to consider each publication’s pros vs cons. But this is still a bit of change from EleutherAI, mostly because we think it’s good to be more intentional about what should or should not be published, even if one does end up publishing many things. EleutherAI is unaffected and will continue working open source. Conjecture will not be publishing ML models by default, but may do so on a case by case basis.
Our decision to open-source and release the weights of large language models was not a haphazard one, but was something we thought very carefully about. You can read my short post here on our reasoning behind releasing some of our models. The short version is that we think that the danger of large language models comes from the knowledge that they’re possible, and that scaling laws are true. We think that by giving researchers access to the weights of LLMs, we will aid interpretability and alignment research more than we will negatively impact timelines. At Conjecture, we aren’t against publishing, but by making non-disclosure the default, we force ourselves to consider the long-term impact of each piece of research and have a better ability to decide not to publicize something rather than having to do retroactive damage control.