9 months ago, LessWrong Netherlands sat down to brainstorm on Actually Trying to make a difference in AI Safety.
Knowing AIS is talent-constrained, we felt that academia wasn’t fit to give the field the attention it deserves.
So we decided to take matters in our own hands, and make the road to AI safety excellence as easy as possible.
Fast forward 9 months, and project RAISE has finished it’s first pilot lesson.
We are of course grateful for all the volunteers that extended a helping hand. But to produce lesson material of the highest quality, we must professionalize.
That is why RAISE is seeking funds to establish itself as the next AIS charity.
As quoted from here:
Within the LW community there are plenty of talented people that bear a sense of urgency about AI. They are willing to switch careers to doing research, but they are unable to get there. This is understandable: the path up to research-level understanding is lonely, arduous, long, and uncertain. It is like a pilgrimage.
One has to study concepts from the papers in which they first appeared. This is not easy. Such papers are undistilled. Unless one is lucky, there is no one to provide guidance and answer questions. Then should one come out on top, there is no guarantee that the quality of their work will be sufficient for a paycheck or a useful contribution.
The field of AI safety is in an innovator phase. Innovators are highly risk-tolerant and have a large amount of agency, which allows them to survive an environment with little guidance or supporting infrastructure. Let community organisers not fall for the typical mind fallacy, expecting risk-averse people to move into AI safety all by themselves.
Unless one is particularly risk-tolerant or has a perfect safety net, they will not be able to fully take the plunge.
Plenty of measures can be made to make getting into AI safety more like an “It’s a small world”-ride:
- Let there be a tested path with signposts along the way to make progress clear and measurable.
- Let there be social reinforcement so that we are not hindered but helped by our instinct for conformity.
- Let there be high-quality explanations of the material to speed up and ease the learning process, so that it is cheap.
What we have done so far
The study group
The bulk of our work has been to take a subfield of AIS (corrigibility), gather all the papers we know of, and turn them into a set of scripts. We have devised an elaborate process for this involving summaries and mind maps. Another strategy would have been to simply copy the structure of existing papers, like in this early iteration, but we think of it as a feature that ideas are individually recompiled and explained. Crafting a good explanation is a creative process: it adds "shortcut" inferences. And so we did.
For the most part, it’s been a success. Since its inception the group has created 9 summaries, 4 mind maps, 12 lecture scripts and 4 paper presentation recordings. It's already a rich store of material to draw from. The scripts are now being converted to lectures.
A software platform
I have met a local EA who runs a platform for teaching statistics, and it’s a close match to our needs. We may use it for free, and the devs are responsive to our feature requests. It will include a white-label option, and the domain name will be changed to something more neutral.
We enlisted Robert Miles (who you might know from his Youtube channel) to shoot our lectures, and I visited him in London to help build a light board. The light board was a welcome solution to the problem of setup, in which we put considerable thought.
These developments culminated in our most tangible output: a prototype lesson. It shows a first glimpse of what the course will eventually look like.
What funding will change
While it has proved possible to run entirely on volunteers, it has also been a hurdle. Without the strong accountability mechanism of a contract, we have been suffering from high turnover. This has created some problems:
- The continuous organisational effort required to on-board new volunteers, distracting the team from other work
- An inability to plan ahead too far, not knowing what the study group attendance over a given period would be
- Quality control being somewhat intractable because it takes time to assess the general quality of someone's work
Of the capital we hope to receive, one of it's main allocations will be hiring a content developer. They will oversee the course content development process with a team of volunteers that have proven (or promise) high dedication. Given the increased time spent and reduced overhead, we expect this setup to gain a lot more traction. See the pamphlet here.
(Strongly) increase course quality
With higher net attentional resources coming from hiring someone, and reducing turnover by separating out loyal volunteers, we can do quality control. We will also benefit more from learning effects for the same reasons: with a core team that spends a lot of focused time on crafting good explanations, they might actually get uniquely good at it (that is, better than anyone who didn't do dedicated practice).
(Strongly) increase course creation speed
Right now, the amount of work that goes into creating content is about 4 hours per volunteer per week. As we learned, this is enough to compile a prototype lesson over the course of roughly 3 months. It is reasonable to assume that this time will go down with further iterations (not having to do much trailblazing) and the figure is somewhat misleading because, for about 6 or 7 more lessons, roughly 60% of the work has been done. Still the speed doesn't have the order of magnitude that we would prefer. At this rate, we will be done with corrigibility in about 6 months, and the whole of AI safety in 5+ years. This doesn't seem acceptable. The speed that we prefer, provided that it doesn't hurt quality, is about one unit (like corrigibility) per (at most) 3 months, and the whole course in (at most) 2 years.
Allow us to broaden our set of strategies
The ultimate vision of RAISE isn't a course, it's a campus. Our goal is to to facilitate the training of new AIS researchers by whatever means necessary.
But we can only do as much as our organisational bandwidth allows, and right now it's purely taken up by the creation of a course.
Examples of such strategies are: a central online hub for study groups, creating a licensing center/talent agency that specializes in measuring AIS research talent, and partnering with the EA hotel to provide a free living space for high-performing students.
Of course, all of this is subject to change
Our first target is $30.000 to cover expenses for the coming year. For that amount, we expect to:
- Hire a content developer (2/5 fte)
- Hire a software developer (1/5 fte)
- Compensate Robert for his lectures
- Compensate one of the management team
- Allocate $2400 for misc spending
Our second target is another $30.000, from which we expect to:
- Compensate the management team (adding up to 3/5 fte)
- Hire an animator/editor (1/5 fte)
- Extend the contract of our content developer (1/5 fte)
- Extend the contract of our software developer (1/5 fte)
- Allocate another $1200 for misc spending
We aren’t too sure about what amount of funding to expect. Should our estimates be too low: returns will not start diminishing until well beyond $200.000.
Call to action
If you believe in us:
- First of all, consider donating
- Stay up to date in our Facebook group or through our website
- Signal-boost this post iff you think it needs more attention
- Consider volunteering - we might need your skills
I would like to end with a pep talk:
What we are doing here isn’t hard. Courses at universities are often created on the fly by one person in a matter of weeks. They get away with it.
There is little risk. There is a lot of opportunity. If we do this well, we might just multiply the amount of AIS researchers by a significant fraction.
If that’s not impact, I don’t know what is.