Alec Boyd Not you?

Iliad Intensive

October 2026

The most important things

Before
Join the Iliad Discord and check you can see #intensive-october. 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 17:10, 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.

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

Your days

[D.7]World Models and Transducers Tue 20 Oct, leading 34 responses · energy 3.7/5
Overall +0.33 better than expected n = 33
Content +0.27 better than expected n = 33
Clarity +0.55 better than expected n = 33
Motivation +0.21 as expected n = 33
Exercises +0.12 as expected n = 33
Readings 0.00 as expected n = 15
Other activities +0.25 better than expected n = 20
Pace -0.09 about right n = 33
Difficulty -0.06 about right n = 33
Novelty +0.45 mostly new n = 33
Read what they wrote39
Worked well14
  • frontal class with a lot of partecipation and questions from the audience plus nice exercises
  • Questions to the audience
  • The birds eye view in the end and applications to multi agent systems seemed like a good perspective on how such theories could be practically relevant for safety, even when we do not have a full theory
  • Possibly the only day so far where we were not rushing to go through content, and definitely the only day I was able to complete all the exercises in the alloted time.
  • the exercises went well for me, but I was not a fan of the material in general.
  • The exercises were very well-chosen to reinforce the theoretical foundations covered in the lectures.
  • Working together in subgroups on particular exercises. Enough breaks (and suggestion to go outside for a while). The presentation itself was just well-paced and I liked the simple examples to really get the hang of the processes ourselves.
  • great pedagogical approach, putting things into context, building the basics, and then eventually getting to complex applications
  • The lecturer was very good: kind, clear, engaging.
  • We discuss about a possible project together with the mentor. I am very happy about it and I look forward continuing working on this!
  • Some content reviewed Mondays lecture but I liked it, it gave time to properly digest the material, and also having 2 different people explain similar concepts gave nice perspective.
  • You might find this in many of my feedbacks, but I enjoy doing the exercises! During the time allocated to exercises, Alec was pacing the room and offering to help out (not that this is necessary, all mentors have made themselves very available) which made it easy to ask the simplest questions!
  • It was a good idea to introduce transcoders more formally. We had seen something like this the day before in Paul's lecture (where he constructed belief states using a Bayesian update rule), but this helped to make this concrete.
  • I liked the presentation style and motivation. The presenter was very clear and deliberate
Would improve16
  • nothing it was great
  • The lecture in general had a lot of overlap with the one from yesterday and hence at times was a bit boring/too slow, especially in the beginning However, the later exercises was a good reinforcement of the things we did yesterday
  • The concepts today felt more intuitive than that of Day 11 (Computational Mechanics), and would have helped to see it before Day 11.
  • This day had a remarkably large overlap with yesterday's topics. In some cases, it presented the details we wish we had the day before; in other cases, it felt like an unnecessary review. It is nice to spend two days on related topics, but they should be better coordinated together.
  • I was not a fan of the topic, so I quickly did not follow and got lost. Perhaps this topic is a little out of place and should be skipped?
  • It would have been nice to focus more on the implications when applying this analysis at scale for LLMs and factors/multi-agent systems
  • I only attended up to Section II - up to that point this was interesting but the upshot hit a bit less well as I feel like world model theory is already implicit in the computational mechanics lecture, so to a certain extent it *felt* redundant.
  • This felt too overlapped with the computational mechanics day. A lot of things (including the MSP exercises) were extremely similar to yesterday.
  • The lectures are nice but would like to see how the things we learned can be applied to understanding LeWM and other SOTA world models, not just toy examples
  • The lecture was well structured. Went from good overview to very new studies. One improvement would be to include more codes to practice. There was one exercise with coding. I would increase the proportion.
  • I would make the content of today more independent from that of yesterday.
  • this was great!
  • At times I felt we got a little too in the weeds and I was struggling to understand how this relates to the big picture of alignment and what we are trying to figure out to make AI go well. That being said, he was a great lecturer. The exercises were helpful, he asked probing questions, and he was very nice and easy to talk to.
  • Maybe some more coordination with the previous day to prevent overlap
  • This lecture was really useful, but it seemed like there was a lot of overlap with Paul's lecture from the day before. It was really helpful for me to see this again, and I think Alec made a really good choice by introducing the topic in the language of transcoders. I'm really interested to understand what this formalism has to say about multi-agent interactions, and I'm looking forward to hearing more later on.
  • I think this lesson was redundant from yesterday
Changed my mind8
  • I feel that studying multi-agent systems is a bit more feasible and am a bit more optimistic about practicality of breaking down the enormous complexity in AI systems into interpretable parts.
  • By this point I've already developed an interest in SLT/mech interp/computational mechanics, but am starting to get a bit overwhelmed by the material and want to focus on how I can realistically best make an impact in this field versus broadening my horizons even further then going back to my status quo. Given my more technical background vs other participants, I feel like my path to impact in AI safety would initially be in the research engineering route while keeping up to date with AI alignment research progress vs driving it theoretically. I *am* considering a MSc and research supervised by academics in SLT (already in discussion) but I feel like short-term I should aim to stay in SF and continue engaging with Mox/Iliad. As such, I've been applying to jobs (in data/evals/post-training) in SF and am currently in a couple interview processes, and hoping to informally re-attend future Iliad Intensive lectures and cover gaps in my knowledge in a longer time horizon if that's okay with the organizers.
  • Automata can actually be an interesting approach to understanding agents.
  • cool research! i was unaware of the material from yesterday and today and how interpretable this aspect is!
  • The multiple sub-agent picture seems like an interesting line of work for understanding AI.
  • Comp. mech seems very interesting, might be what I work on if I go for AI safety.
  • I was surprised to learn that the Geometry of belief states reflects geometry of prediction, and it had me thinking about Paul’s talk on belief state geometry the previous day. I haven’t had enough time to mull over this to create a project idea out of it yet, but I certainly have questions I’m thinking about!
  • I came away pretty confident that "transformers encode belief states in the final layer of their residual stream" is a solid starting point for understanding transformers.
Other thoughts1
  • just as a suggestion: there could be a day on autoformalisation -> ai-maths for ai-safety

The day

10:00 Lecture starts
12:30 Lunch at Mox
13:30 Afternoon session
17:10 Feedback form
17:30 Day ends. Dinner

Resources

Elsewhere