Intensive Teaching · September 2026

The most important things

Before: Join the Iliad Discord and check you can see #intensive-september. That is where you post any material you need to share with participants. If you can't see it, tell Leonard (@leonardbereska on Discord).
During: At 17:40, make sure everyone fills out the feedback form.
After: Fill out the end-of-day log: rate the day and tell us anything that went wrong.
Fill out Who stood out: name anyone who made a positive impression.

Questions about pay, travel, logistics or materials: see the FAQ.

57 project proposals are open for review. Stars, a sentence, or both. Read them.

Your days

[D.3]Solomonoff Induction + AIXI Tue 15 Sep, leading 36 responses · energy 3.6/5

Overall -0.09 as expected n = 35

Content -0.23 as expected n = 35

Clarity -0.23 as expected n = 35

Motivation -0.43 below expected n = 35

Exercises +0.09 as expected n = 35

Readings -0.13 as expected n = 16

Other activities +0.14 as expected n = 14

Pace +0.40 too fast n = 35

Difficulty +0.26 too hard n = 35

Novelty +1.40 mostly new n = 35

Read what they wrote54

Worked well18

I think both the lecturing and the notes were very good.
exercises with a partner really made a difference!
It was easy to ask questions and the answers were helpful, but I don't think that gathering almost all the interesting information in personal communication in such group sessions it good
The lecturer was clear
- David was a really energetic speaker, made a heavy topic easy to stay engaged with - Good worksheets, and the balance of short lectures with lots of time on exercises
I like that it used policy and Bellman equation -- the material from yesterday. Same goes for Thursday-Friday transition with HMMs, and Friday-Monday flown to Tuesday seamlessly. Very nice. Also the nice and long list of exercises to work on by yourself.
I liked that the teacher was very eager to answer questions, I liked the explanation on KL divergence, and him recommending me a concrete section in the book that exactly answered my question
David is clearly excited about and deeply interested in the material; he did a very good job with questions both during and after presentations.
The website content is FANTASTIC, well done.
Personalized explanations.
Long exercise sessions dedicated to learning by doing. We went over all proofs ourselves rather than watching someone zoom through a derivation. Was very nice.
The clear layout of notes and encouragement to work in a way that suited. I also enjoyed listening to the questions asked by other colleagues.
working through the exercises which seemed very well written out
The abundant time for exercises and discussions.
The written lecture and the exercises were great, well written, and built up to interesting result. However, I question the usefulness of relying entirely on exercises to carry the core information. I am not necessarily interested in solving mathematical puzzles, but see how the experts in the field do things (because one day I might collaborate with them). For example, several exercises were either straightforward or tedious according to notational rigour, and I would have preferred seeing the lecturer carry out at least some solutions or proof by hand to see what is the style of the subject.
The website to the lecture materials and the problems was very accessible and the sections were well written.
The exercises were fun but still for someone with a background in bayesian stuff it wasn't very new, but perhaps for others it was useful.
The focus on the exercises + the incentive to work in pairs

Would improve24

Maybe a more balanced distribution of lectures and exercises - while I found the exercises to be interesting and reasonable in difficulty, it was a bit "intellectually exhausting" to work through them for multiple hours. I also would have liked to see more material on the current research paradigm in AIXI and what it hopes to achieve.
Needs a bit more context and applications. It all seems a bit far from what is happening now in the world.
It's surely teaching style, but extremely short lectures, not engaging constructively with questions, and exercise sessions that are mostly symbol juggling together don't show how this all is relevant to safety and alignment.
I think adding more lecture material and not deferring everything to questions and exercises could be much more helpful. If felt like we were given some abstract nonsense introductions and definitions, and had to clarify everything ourselves. The connection to alignment and reasonable agents we observe and want to make predictions about it also kinda vague. If there are known results about AIXI application to known environments (e.g. Markovian), adding them to presentations and allowing to play with them locally could be much more convincing.
I really enjoyed the lectures. I think today would have benefited from more lecture time. The exercises, although nice, were a bit difficult to parse (there was a lot of notation) and probably something that would be better to look at in our own time.
- A bit more intuition before the proofs i.e what the Bayesian mixture is doing on a small example
This day's structure (i.e. very short talk in the morning and afternoon and then exercises) did not work the best for me. The exercises were mostly around proving the results from the talk but most of the times I didn't know if I was proving something that was just a middle step for the big proof a few exercises ahead OR an important result that I should be able to interpret in the big picture of Solomonoff Induction/AIXI. I think a few changes that would help me would be (a bit) longer more comprehensive talk AND/OR more discussion in the notes in-between the exercises. (Please note that I'm quite in favour of the philosophy behind this structure, I just didn't feel it was accomplished in the best of manners)
I feel the model is too far from any computational process that could be implemented in the real world to be any useful
Today was a big jump in terms of usage of technical terms. Complexity, computability, etc. had not been discussed previously and this is a hurdle for those with a weaker background in computer science, including the quiz at the end of the day, likewise (but to a lesser extent) during the lecture. Regarding the lecture, I strongly believe that taking more time and perhaps showing one of the proofs (possibly partially) would help. This could also break up the long exercise session during the afternoon. But generally slowing down the pace of the lecture would be good. I heard from multiple people that they had to go through the lecture material again, because it was too fast in the morning. In my opinion, it is important to answer questions and confusions during the lecture even if that means slightly adjusting the schedule.
sadly for me the lectures were much too fast, felt quite very rushed, there was little space to think about what's on the slides and I left both lectures not knowing what actually just happened :(( I would have benefited from a less rushed presentation, more motivation of what we are doing, more coming back to what all the notation means, and importantly again how this fits into ai safety research. we asked how the content today is relevant to ai safety and David provided a reasonable argument but I really think for such highly theoretical days it's super relevant to embed things into an alignment perspective
The presentation was generally quite good. However, it introduced several ideas at once; I wonder if it would've been clearer to separate them. We could've started by discussing optimal prediction and optimal behavior under an arbitrary prior/hypothesis class. Most of the results we discussed can be two-thirds proven in that setting anyways. The Kolmogorov prior is cool, but I think it makes more sense to introduce it afterwards, since it comes with a bunch of philosophical and technical questions. I felt like these got somewhat mixed up with the relatively straightforward general-optimal-agent story. Also, more discussion of how this relates to AI safety would be good. I'm not sure if this is a complaint I have about today's presentation or about the actual state of the AIXI field; quite possibly the latter. The exercises were reasonable, but not a great notational complexity to conceptual complexity ratio. I ended up spending a lot of the allocated time working on unrelated stuff anyways, so maybe the later exercises were better.
This was a really interesting day. There is a lot of material so I would go back to two days (delete the world models day!) and this would allow you to cover too recent papers. ie. https://djsutherland.ml/papers/nfl-ait.pdf seems quite interesting and show a bit what AIXI labs is doing. From the lecturer, we got the impression that AIXI is "old stuff" that might not be useful, but seeing what these people are working on might give another viewpoint. Overall the format was good, I enjoyed the zap lecture and then doing a lot of self-study. Maybe going together through a paper (e.g. the one I suggested) elicits a deeper understanding of the material + fruitful discussion between people.
Too much independence, and the lectures were so fast. I would have liked more time for teaching and less independence.
After a week of mainly mathsy work I'm really yearning for a link to practical alignment. How could these frameworks apply to analysing transformers? How are these methodologies applied for interpretability? How would the interplay of theory and empirical work look?
a bit rushed at times and sometimes I wanted to hear the question and answer as I would not get to hear it later.
more discussion about the answers as a group :)
A better way for slides to be presented. The TV screen is simply way too small and not conducive at all for a presentation to 40 people.
I would like longer sessions and less time for exercises. Overall I find this approach a bit too formal, but this is personal taste
The room layout and lecture setting was more conducive to coworking than teaching. This was clearly by design, but I found the format offputting for a completely new subject.
I would show more concrete empirical research examples instead of just going through the theory.
In my opinion, today was too much exercise-based. I would prefer to discuss the theory in detail in class, so I can do exercises in my own time later. The first few exercises were just math manipulation which I personally find a bit tedious and not very insightful. I would much rather learn the theory in the context of AI safety, rather than proving theorems.
There were many exercises that were not giving the insight I would have expected and the notation was a bit too heavy. There were to some extend a bit of overlap with yesterday's session, but the theory was still interesting.
Talk was slightly too fast for me but I understand the philosophy of the lecturer. But maybe a tick slower, but then again I was slightly late so its on me.
Maybe adding one or two recent paper to showcase current research in that field

Changed my mind10

I think AIXI seems fairly promising, but I didn't feel like I got much context on what exactly it aims to prove.
not sure
n/a
That even so called weak assumptions might not be that weak. Also, infinite compute, really? I do share the sentiment that we have to be studying idealized agents, but significant amount of time should be devoted to realistic scenario too. And that's where the math has to be employed the most, as all these theorems might well be inapplicable (like no one in the market cares about the existence of Nash equilibrium because no one bothers computing it and you can't really arrive at it in reasonable time).
How useful aixi is
I used to think SI smuggled a very strong assumption into the choice of the Kolmogorov prior. Now I think it smuggles a very strong assumption into the choice of UTM instead. (This is a real improvement in my eyes.)
This feels particularly useful to derive mathematical bounds on agent behaviour- but not a lot of people work on this although it seems quite interesting. I feel like this can be more promising than things like computational mechanics (which feels like fancy mech interp). I think the roadmap on how to use AIXI is somehow fuzzier but more promising.
I discovered AI safety initiatives in Barcelona, my city.
This work is too theoretical so having an impact.
I understand now why AIXIs might be useful to AI alignment, albeit not immediately.

Other thoughts2

n/a
Today I spent most of the day reading Simplex papers because I found that much more interesting than the Solomonoff stuff.

The day

10:00 Lecture starts

12:30 Lunch at LISA

13:30 Afternoon session

17:40 Feedback form

18:00 Day ends. Dinner at LISA

When	Where
Mon 7 Sep	LISA, 25 Holywell Row, London EC2A 4XE
Tue 8 – Mon 14 Sep	23 Curtain Road, London EC2A 3LT
From Tue 15 Sep	69 Wilson Street, Moorgate, London EC2A 2BB

Resources

Elsewhere