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

Your days

[C.1]Intro to ML Engineering and Transformers Mon 5 Oct, leading 42 responses · energy 3.7/5
Overall -0.05 as expected
Content +0.26 better than expected
Clarity +0.12 as expected
Motivation +0.38 better than expected
Exercises -0.02 as expected
Readings +0.04 as expected n = 23
Other activities +0.32 better than expected n = 34
Pace -0.31 too slow
Difficulty -0.52 too easy
Novelty -0.60 mostly review
Read what they wrote75
Worked well21
  • The live code examples were nice however they were hard to follow. I wish the code was accessible before the lecture.
  • The discussion was stripped down to the simple essentials, AND it involved live implementation. Both these contributed to efficient learning.
  • Long form disucssions.
  • Exercises and group work.
  • I thought the ability to ask questions mid talk was very useful for clarification.
  • the worked code examples
  • very happy to hear about the various opportunities (but i felt it was out of place at the beginning of "Intro to ML" class day)
  • The discussion during breaks.
  • I knew most of the material covered. Really enjoyed having the opportunity to discuss it with others who felt it was new. I also enjoyed watching the teacher code on the fly in front of others. That is super hard and I was impressed. (Helped me see what kind of skills an AI safety researcher should have).
  • The schedule structure is good.
  • The lecturer was very knowledgeable and answered all the questions well.
  • A very soft landing toward the basic but useful ML concepts was very helpful. The lecture was well organized and well explained. His answers to participants' questions from his own practical experience were very helpful to understand the general idea of the concepts. All the questions from the group were a good addition to the lecture.
  • mixing slides and theoretical explainations with exercises and implementations
  • its a good idea to give an introduction
  • Examples with TransformerLens and GPT-2
  • I loved the attention section, and I liked how easy it was to discuss questions during the lesson.
  • The lecturer is good and his answers were clear
  • I think the engagement with questions was nice.
  • Basic understanding of the transformer model structure
  • People were kind and receptive to chatting/working/eating together.
  • The exercise sessions were useful.
Would improve31
  • I think more breaks would be good. The second session was a marathon.
  • slightly more mathematical / formal clarity
  • The ordering of some of the concepts could be shuffled around to flow better for a math / physics audience.
  • More figures, and perhaps a better feel for the theory narrative: like constantly returning to central intuition for LLMs as a sequence predictor.
  • Some of the attention material was simply presented but not well motivated (in terms of where these blocks came from, why we made certain design decisions).
  • The explanation of the transformer architecture was a bit difficult to follow for me - structures sometimes seemed haphazard/unmotivated, and I got lost in understanding the various different parameters (especially when starting the coding exercises). But, it was not that the material was difficult per se.
  • Bit of a slow start
  • Some more diagrams and structure would be nice. Maybe a general overview of the transformer architechture to start. It may also be worth it to spend more time on the complexities of the transformers etc than on the very basic concepts at the beginning. Although there were lots of questions which makes it hard.
  • Maybe the presentation preparation could be better. (I found 3b1b explanation of attention much more intuitive -- but maybe he oversimplifies.)
  • it was not clear at all we would be using Jupyter notebooks. I specifically did not set my environment up bc the prereqs for this day were just "foundations" the material was very (too?) basic, bit the presentation was confused and unclear. the material should be presented more systematically by, for example, giving an overview of the architecture before explaining each part, defining the variables and where they live, given the intended academic audience.
  • Sorry for late feedback -- felt like some concepts could have been introduced more rigorously, given almost everyone is postgraduate level in maths, physics or CS. Useful review overall.
  • More breaks probably? I think also sectioning the talk into different parts would help better. So there's more granularity on what we're talking about.
  • There were many "why that choice" questions (ie, "please motivate that choice"), which I feel could have been better prepared in advance, knowing the sensibilities of a math-y group of participants. The lecture felt redundant with the 3B1B video series, or other tutorials. It may have been a good use of the day for the participants to be expected to be familiar with it in advance, and work in groups for discussions about the content, to solve various exercises, with some Colab guidance.
  • I think to get the point of residual stream across it may be helpful to show a counter example. Basically residual streams are added to by the (computations done by the) MLPs, attention heads etc. The counter example (what a residual is not) would be to show that the readout of one is the input of the other. I think this might help motivate the idea of the residual stream--why it is a "stream" where stuff gets taken out and added back in.
  • Not much. Since it was the first day, it wasn't technically very new, but I think that is expected.
  • Maybe It would be nice (I don't know if it will be done int he following days) to have more mathematical definitions of the objects we are working it (as example, in the transformers part, as generating kernel), in order to figure out what possible research directions would be
  • Better clarity for the slides and explanations. There are some typos and confusing explanations during the lectures.
  • I think the presentation could be improved. In particular, just practicing giving the lecture before and doing it less on the fly would allow us to spend more time on learning. I think visualizations would do great for learning how attention works, how the numbers flow through the vector, instead of solely relying on the mathematical equations. Perhaps even some interactive thing generated by claude code or something.
  • I could follow the discussion and it was well done. For some other participants maybe they could watch 3Blue1Brown videos.
  • I wish the speaker had rehearsed the presentation, or otherwise prepared it better. Though the speaker clearly knew what they were talking about in the big picture, he was often lost on details he "hadn't reviewed recently". Separately, I wish the presentation was presented with an eye to a mathematical perspective: the audience is mathematicians and theoretical physicists.
  • I would try make the room less stuffy. Maybe AC would help.
  • it was a little slow at the beginning and a little fast in the end. meaning I would have spent more time on the more difficult concepts and less on the easier.
  • the beginning on the "basics" of ML was a bit loose/not as clear the connection to more modern methods and some small mistakes, the attention part was pretty great but the programming problems are mostly bottlenecked by not rembering the syntax rather than not understanding the topic well enough, especially since the formulas are written on the sheet
  • I would prefer the coding challenges to be more difficult. Today they involved writing down math formulas in pytorch syntax, which is okay but does not teach me much.
  • More breaks. I found it hard to focus with such a long session.
  • The introduction could have been more energetic and engaging.
  • Some references for mathematical and computational precision.
  • Some typos, nothing serious.
  • Make the lectures slightly faster
  • I think more examples would be good.
  • Didn’t have much expectations for an intro/refresher day but always happy to recall stuff!
Changed my mind19
  • No changes.
  • not really, but we were only covering an intro to ML
  • Just how much LLM building is trial and error
  • considering applying to this funding! meta: is this the level of preparedness / knowledge of successful researchers / funders?
  • I had a time to properly think about transformer architecture, which was useful.
  • Seeing Dan work in Colab was instructive!
  • I had a really interesting conversation at the beginning of the ice-breaker sessions. I learned some ideas about decision theory that I had questions about. It also opened up new questions that I plan to follow up on.
  • Didn't change my mind on anything today, just a good review on transformers.
  • Before the program started, I was worried about the level of mathematical concepts. This first day provided a good overview of the practical and theoretical understanding of AI/ML models. I got much more comfortable participating into the program.
  • I was surprised at just how simple the recipe for a transformer is.
  • On prospects of procuring required resources from notoriously obscure sources.
  • today was mostly notions, nothing really worth mentioning. I definitely learned a lot!
  • not much this was more introduction
  • About possibly applying for a founder's programme.
  • No
  • n/a
  • No change.
  • We could access the modalities(parameters) of any model
  • I feel more confident in my ability to pivot to AI safety given how much more at place I feel here than expected.
Other thoughts4
  • if the material were less familiar, I would expect to have gotten less out of it, as some of the presentation was not (sufficiently) self-contained
  • Thanks for bringing us together.
  • Nope
  • no
[C.2]Mechanistic Interpretability Wed 7 Oct, leading 38 responses · energy 3.9/5
Overall +0.45 better than expected
Content +0.66 better than expected
Clarity -0.08 as expected
Motivation +0.61 better than expected
Exercises +0.03 as expected n = 35
Readings +0.15 as expected n = 20
Other activities +0.40 better than expected n = 25
Pace +0.74 too fast
Difficulty +0.50 too hard
Novelty +0.79 mostly new
Read what they wrote64
Worked well20
  • The second instructor was very well versed in the material and engaged with everyones questions.
  • very interactive. Participants actively engaged with lecturer by asking good questions and we discussed freely.
  • The overview was broad and looked comprehensive.
  • I liked the coding exercises to reinforce the concepts we learned
  • Exercises
  • The brief presentation given by one of the students. Involvement is nice and sharpens the attention.
  • I think the afternoon lesson was quite nice -- due to examples.
  • The first half was well taught and quite clear.
  • Helped me get an initial but a very useful broad overview of different mech interpret techniques. Starting with CNN helped understand the history of such methods. Later, although there were lots of different ideas presented I understood and am taking home some useful stuffs.
  • I think the exercise where we had to deduce the type of circuit from the attention pattern worked quite well to help me internalize the method.
  • The afternoon session was excellent: good pace, good explanation, good examples & slides.
  • Lots of discussion
  • Julian Schulz' presentation was _amazing_. clarity, enthusiasm, knowledge, humour. overall great. the exercise with the K- and Q-composition was brutal (especially because the first day's on transformers was not clear enough to learn the architecture–i'm glad i spent the evenings learning abt it) but enlightening
  • The circuits expansion was particularly good.
  • Discussion was great during the lectures, i wish we had had more time.
  • I really enjoyed the balance between Dan's transparency and pragmatism on the basics of mech interp and Julian's enthusiasm on the newer methods/approaches.
  • I particularly found the live coding demo useful for the activation diffing.
  • breadth of the material was good, but too much to follow, like i should know all of this but maybe in more depth but I understand its the intensive
  • Discussion was helpful for having people break down concepts and ask questions rapidly
  • I thought the questions and answers were v helpful
Would improve25
  • The pace was faster in the afternoon than in the morning. This might have been due to the teachers' individual preferences. However, I would suggest maintaining a more consistent pace throughout the day.
  • I would add more specific examples into these slides--I feel like I best understand what's going on when there's specific examples. (This is in part because of my background: I am a mathematician and feel like I understand the ML setup less well than the theoretical one. This might not be true for everyone.)
  • Talk slower
  • The pace was quite fast in both halves. Splitting this or pre-readings would have been much more helpful.
  • The speaker talked too fast and the explanations were confusing. I had to read the cited papers to understand what was going on.
  • More structure, fewer slides. (E.g. slides don't even define the J-space. It was defined verbally in 5 seconds, and, as I understood after asking an AI -- not even correctly.) I think if we do exercises at all, we could discuss the details of these python libraries. Also too many vague questions from the audience, very hard to keep the focus
  • A little bit more discussion and hands-on, and more intuitive examples (metaphors) to help understand the complex new concepts.
  • I would increase information density
  • The part on CLTs was difficult to follow for me.
  • Explanations could have been a bit more clear.
  • very quick explainations of many difficult concepts
  • Great! Honestly a lot of information, maybe a bit slower through the more recent mech interp stuff.
  • I really enjoyed today. We did take a lot of time to go through older methods and I would've liked to spend more time going through more recent stuff, especially J-space. That being said, I had pretty decent exposure to previous interp research before NLAs and J-space.
  • More time for the Mech Interp session, part 2. That, or references to expository materials.
  • Was excellent
  • Better explanation of what circuits are. May be give more concrete examples.
  • again, i found the Dan Wilhelm's presentation extremely underwhelming, way below the level or rigour or technical knowledge needed for this audience
  • The slides could be more precise.
  • I wish the style of the presentation was more mathematical, even if that would require a little more notational baggage. (e.g. https://transformer-circuits.pub/2021/framework/index.html )
  • We probably needed the full day to get through the material, so more time would be great. Questions and discussion were helpful.
  • Not much, really enjoyed today!
  • maybe a little more time for exercises, but there was a lot of material so I guess it is hard to fit everything in.
  • less material more in depth
  • Less time on introducing Mech Interp, more time on deep-dive and math/matrix multiplications behind concepts introduced
  • More equations. At least for me, I find the graphs by themselves hard to interpret without the relevant maths alongside.
Changed my mind16
  • Random trial ams error seems much more effective than I would have expected.
  • I did not mech interp was used for safety and alignment.
  • I thought mech interp was doing pretty poorly but it seems like Jacobian lenses are worth more of my time.
  • N/A\
  • J-Space is interesting!
  • Not in particular.
  • Circuits and NLAs are, perhaps, more impactful than I had thought.
  • Mech interp is cool
  • not really, i guess i was surprised to hear that the more "mechanistic" technique of mech-interp are
  • `The mathematics behind mechanistic interpretability is close to non-existence.
  • Yeah, I now find these methods more interesting.
  • Feel more comfortable discussing research and collaborating w/ people here in the near future Feel more motivated to start diving deeper into mech interp
  • not really, but developed my understanding of circuits and cross encoders
  • the NLAs were really cool, the fact that LLMs had hidden logics that NLAs can detect was pretty cool and new
  • I learned about J-space for the first time today and I will definitely look into it more. I also feel that my impression that SAEs were a dead end due to the Deepmind deprioritization was overblown.
  • I didnt realise how quickly mech interp was changing as a field
Other thoughts3
  • N/A
  • Bit more of exercise, like doing coding would have been better. While coding, we could understand better.
  • n/a

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