Archimedes

• ArchimedesJun 7, 2025, 5:44 PM

  3 points

  2

  on: The Stereo­type of the Stereotype

  The male action hero stereotype is definitely a thing, even if it’s not perfectly adhered to 100%.

  Examples: Rambo (first films), John McClane, James Bond (Sean Connery era), Chuck Norris, Steven Seagal and Jean-Claude Van Damme in films like Under Siege and Bloodsport, Jack Reacher

• ArchimedesJun 3, 2025, 3:07 AM

  6 points

  4

  in reply to: Hastings’s comment on: Hast­ings’s Shortform

  There is a connection. Stockfish does use Leela Chess Zero (the open source, distributed training offspring of AlphaChessZero) training data for its own evaluation neural network. This NNUE is a big piece of Stockfish progress in the last few years.

  It’s not straightforward to compare AlphaZeroChess and Stockfish though as the former is heavily GPU-dependent whereas the latter is CPU optimized. However, Google may have decided to train to a roughly comparable level (under some hardware assumptions) as a proof of concept and not bothered trying to advance much further.

• ArchimedesJun 3, 2025, 12:49 AM

  1 point

  0

  on: Trends – Ar­tifi­cial Intelligence

  @Zvi, you’ve got your work cut out for you. ;)

Trends – Ar­tifi­cial Intelligence

• ArchimedesMay 28, 2025, 4:27 AM

  2 points

  0

  in reply to: niplav’s comment on: Dat­ing Roundup #5: Open­ing Day

  Wow. This must be the pickup equivalent of cold call telemarketing.

• ArchimedesMay 26, 2025, 5:45 AM

  4 points

  2

  on: Beyond Democ­racy: A Sys­tem Where Ci­ti­zens Vote with Their Taxes

  The biggest problem, IMO, is that what people want to pay for which things is only loosely correlated with what makes sense in reality. You would overfund the sexy line items and underfund the boring-but-necessary ones.

  Few people have the bandwidth to meaningfully participate in such a direct democracy.

• ArchimedesMay 25, 2025, 3:16 AM

  1 point

  0

  in reply to: Cole Wyeth’s comment on: Have LLMs Gen­er­ated Novel In­sights?

  I believe AlphaEvlolve built on the results.

• ArchimedesMay 23, 2025, 9:38 PM

  1 point

  0

  in reply to: Hastings’s comment on: Claude 4

  What’s the solution? Is it a trick question? I don’t see how you have nontrivial solutions for a complex matrix unless it is very special like, say, a diagonal matrix composed of roots of unity shifted by phase angle.

• ArchimedesMay 23, 2025, 8:57 PM

  1 point

  0

  in reply to: ErioirE’s comment on: Claude 4

  There’s a good chance I got math-sniped and completely misinterpreted what Hastings is pointing at.

• ArchimedesMay 23, 2025, 6:53 PM

  1 point

  0

  in reply to: ErioirE’s comment on: Claude 4

  I’m pretty sure there at no nontrivial solutions to the equation unless A has certain (non-random) properties like being real-valued. Generating random complex matrices will never find a nonzero conjugate eigenvalue.

• ArchimedesMay 23, 2025, 1:44 AM

  17 points

  9

  in reply to: Thane Ruthenis’s comment on: Claude 4

  Teetering on the edge of doom is exciting for me, much like riding a motorcycle at 200 mph or playing with professional-grade fireworks. I think it’s silly to pretend it’s not exciting to have powerful tools/​toys, even though they’re likely to destroy us.

• ArchimedesMay 17, 2025, 2:57 AM

  4 points

  0

  in reply to: Thane Ruthenis’s comment on: AI #116: If Any­one Builds It, Every­one Dies

  It may or may not be the first steps toward foom, but automated improvements are still improvements regardless of how “innovative-in-themselves” we consider them to be. Improving on an algorithm that’s been the SOTA since 1969 is cool, even if it was done purely via brute force.

  For now, it looks like it “only” found minor improvements on various SOTA, but this was done with previous generation models (a mix of Gemini 2.0 Flash and Pro)^[1]. I’d expect next-gen models and next-gen scaffolds to be another step up.

  ---

  1. ↩︎

     Models used. AlphaEvolve employs an ensemble of large language models. Specifically, we utilize a combination of Gemini 2.0 Flash and Gemini 2.0 Pro. This ensemble approach allows us to balance computational throughput with the quality of generated solutions.

• ArchimedesMay 10, 2025, 2:34 PM

  2 points

  0

  in reply to: ryan_greenblatt’s comment on: Archimedes’s Shortform

  Compute is definitely important for experiments. The limits undoubtedly slow China’s progress, but what’s more difficult to determine is whether global progress is slower or not. In the toy scenario where China’s research focus is exactly parallel and duplicative of Western efforts, they contribute nothing to global progress unless they are faster. More realistically, research space is high-dimensional, and you are likely correct that the decreased magnitude of their research vector likely outweighs any extra orthogonality benefits, but I don’t know how to apply numbers to that tradeoff.

Archimedes’s Shortform

• ArchimedesMay 9, 2025, 2:48 AM

  4 points

  0

  on: Archimedes’s Shortform

  Limiting China’s computing power via export controls on hardware like GPUs might be accelerating global progress in AI capabilities.

  When Chinese labs are compute-starved, their research will differentially focus on efficiency gains compared to counterfactual universes where they are less limited. So far, they’ve been publishing their research, and their tricks can be quickly be incorporated by anyone else. US players can leverage their compute power, focusing on experiments and scaling while effectively delegating research topics that China is motivated to handle.

  Google and OpenAI benefit far more from DeepSeek than they do from Meta.

Non­profit to re­tain con­trol of OpenAI

• ArchimedesApr 27, 2025, 4:55 AM

  8 points

  0

  in reply to: romeo’s comment on: romeo’s Shortform

  Epoch.ai just released this today:

  https://​​epoch.ai/​​gradient-updates/​​the-case-for-multi-decade-ai-timelines

  Excerpt:

  I would summarize the key cruxes that separate my views from people who have shorter timelines as follows:

  • I don’t see the trends that one would extrapolate in order to arrive at very short timelines on the order of a few years. The obvious trend extrapolations for AI’s economic impact give timelines to full remote work automation of around a decade, and I expect these trends to slow down by default.

  • I don’t buy the software-only singularity as a plausible mechanism for how existing rates of growth in AI’s real-world impact could suddenly and dramatically accelerate by an order of magnitude, mostly because I put much more weight on bottlenecks coming from experimental compute and real-world data. This kind of speedup is essential to popular accounts of why we should expect timelines much shorter than 10 years to remote work automation.

  • I think intuitions for how fast AI systems would be able to think and how many of them we would be able to deploy that come from narrow writing, coding, or reasoning tasks are very misguided due to Moravec’s paradox. In practice, I expect AI systems to become slower and more expensive as we ask them to perform agentic, multimodal, and long-context tasks. This has already been happening with the rise of AI agents, and I expect this trend to continue in the future.

• ArchimedesApr 16, 2025, 4:08 AM

  1 point

  0

  on: Policy for LLM Writ­ing on LessWrong

  Can you update the instructions? The following doesn’t work.

  <details>

  <summary> Summary here </​summary>

  Details here

  </​details>

  This does work

  +++ Summary here
  
  Details here
  
  +++
  

  like this:

  Summary here

  Details here

• ArchimedesApr 16, 2025, 3:50 AM

  7 points

  0

  in reply to: Dusto’s comment on: Sur­pris­ing LLM rea­son­ing failures make me think we still need qual­i­ta­tive break­throughs for AGI

  For the 3x3 grid problem, Gemini 2.5 immediately went to a breadth-first search but didn’t actually implement it and return the correct solution.

  Gemini 2.5 Pro Preview output:

  Okay, let’s solve this step-by-step.

  This is a sliding tile puzzle on a 3x3 grid with 6 named tiles and 3 empty spaces. We need to find the minimum number of moves to get from the initial configuration to the target configuration. A move consists of sliding a tile into an orthogonally adjacent empty square.

  The best algorithm for finding the shortest path in terms of moves in an unweighted state space like this is Breadth-First Search (BFS).

  1. Define States:

     • Initial State:

       Sun   Leaf  (Empty)
       Bell  Snake   Star
       (Empty) Flower (Empty)

       Tuple representation: ('Sun', 'Leaf', 'E', 'Bell', 'Snake', 'Star', 'E', 'Flower', 'E') (E = Empty)

     • Target State:

       Snake  Star    Flower
       (Empty) Sun (Empty)
       Leaf   (Empty) Bell

       Tuple representation: ('Snake', 'Star', 'Flower', 'E', 'Sun', 'E', 'Leaf', 'E', 'Bell')

  2. BFS Algorithm:

     • Create a queue and add the initial state with 0 moves: queue = [ (initial_state, 0) ]

     • Create a set to store visited states to avoid cycles: visited = {initial_state}

     • While the queue is not empty:

       • Dequeue the current state and its move count: current_state, moves = queue.pop(0)

       • If current_state is the target_state, return moves.

       • Find all possible next states by moving a tile into an adjacent empty space.

       • For each next_state:

         • If next_state has not been visited:

           • Add next_state to visited.

           • Enqueue (next_state, moves + 1).

  3. Generating Next States:

     • Find the index (0-8) of each empty square (‘E’) in the current state tuple.

     • For each empty square index e_idx:

       • Determine its row r = e_idx // 3 and column c = e_idx % 3.

       • Consider its orthogonal neighbors (up, down, left, right): (r-1, c), (r+1, c), (r, c-1), (r, c+1).

       • For each valid neighbor (nr, nc) (within the 3x3 grid):

         • Calculate the neighbor index n_idx = nr * 3 + nc.

         • Create the next_state by swapping the elements at e_idx and n_idx in the current_state tuple.

         • Yield this next_state.

  4. Execution (Conceptual):

     • Start with the initial state at level 0.

     • Find all states reachable in 1 move (level 1).

     • Find all new states reachable from level 1 states in 1 move (level 2).

     • Continue level by level until the target state is found.

  Running this BFS process (typically done with a computer program as manual tracking is very tedious and error-prone):

  • Initial: ('Sun', 'Leaf', 'E', 'Bell', 'Snake', 'Star', 'E', 'Flower', 'E')

  • Target: ('Snake', 'Star', 'Flower', 'E', 'Sun', 'E', 'Leaf', 'E', 'Bell')

  The BFS explores the state space layer by layer. Level 0: 1 state Level 1: 5 unique states Level 2: … …

  Upon running a BFS solver for this specific puzzle configuration, the target state is first reached at 23 moves.

  Final Answer: The final answer is $23$

• ArchimedesMar 29, 2025, 4:01 AM

  0 points

  2

  in reply to: Kaj_Sotala’s comment on: Con­cep­tual Round­ing Errors

  I may feel smug if the “novel idea” is basically a worse version of an existing one, but there are more interesting possibilities to probe for.

  1. The novel idea is a meaningful extension/​generalization of an existing concept. E.g., Riemann --> Lebesgue integration

  2. The novel idea is equivalent to an existing concept but formulated differently. E.g., Newton and Leibniz versions of calculus.

  3. The novel idea is a more detailed explanation of an existing concept. E.g., chemical bonding --> molecular orbital theory.

  Less likely to be rounded away:

  1. The novel idea overlaps with existing concepts but is neither a subset nor an extension. E.g., General Relativity and Quantum Mechanics.

  2. The novel idea applies existing concepts to a new domain. E.g., applying information theory to DNA.

  3. The novel idea synthesizes multiple existing concepts into a greater whole. E.g., Darwinian evolution as a combination of Malthusian population dynamics and natural variation.

  4. The novel idea provides a unifying framework for previously disconnected concepts. E.g., Maxwell’s equations unifying electricity, magnetism, and optics.

  Nearly all conceptual rounding errors will not be anything as grand as the extreme examples I gave, but often there is still something worth examining.