misc edits

blog/graphcore.md

@@ -4,6 +4,8 @@ created: 23/02/2026
 description: Trying exotic parallel processing hardware which is dead for fairly good reasons.
 slug: graphcore
 tags: ["hardware", "ai"]
+series: graphcore
+series_index: 1
 ---
 ::: epigraph attribution=@softminus link=https://x.com/softminus/status/1933747679315833356
 The farther you get away from the flops in the physical register file; the longer the wires get, the less bandwidth there is; and with those the microarchitecture's magic fades, its power and abstractions decay, and you are left with the mundane, real, and slow.

blog/superintelligence-lower-bounds.md

@@ -48,7 +48,7 @@ Abstraction amortises intellect, taking good solutions to simpler and more gener
 
 We see the abstractions still even when they have gaps, and this is usually a security threat. A hacker doesn't care that you think your code "parses XML" or "checks authentication" - they care about [what you actually wrote down](https://gwern.net/unseeing), and what the computer will do with it[^3], which is quite possibly [not what you intended](https://blog.siguza.net/psychicpaper/). Your nice "secure" cryptographic code is [running on hardware](http://wiki.newae.com/Correlation_Power_Analysis) which reveals correlates of what it's doing. Your "air-gapped" computer is able to emit [sounds](https://arxiv.org/abs/2409.04930v1) and [radio signals](https://arxiv.org/abs/2207.07413) and [is connected to power cables](https://pushstack.wordpress.com/2017/07/24/data-exfiltration-from-air-gapped-systems-using-power-line-communication/). A "blank wall" [leaks information](https://www.cs.princeton.edu/~fheide/steadystatenlos) through diffuse reflections. Commodity "communication" hardware can [sense people](https://www.usenix.org/system/files/nsdi24-yi.pdf), because the signals travel through the same physical medium as everything else. Strange side channels are everywhere and systematically underestimated. These are the examples we *have* found, but new security vulnerabilities are detected continually and I am confident that essentially all complex software is hopelessly broken in at least one way.
 
-Because of the ability to avoid unhelpful human assumptions, from-scratch <span class="hoverdefn" title="reinforcement learning">RL</span>, gradient descent and evolutionary approaches have been successful in designing [silicon photonics](https://x.company/blog/posts/ai-siph-design/), [antennas](https://en.wikipedia.org/wiki/Evolved_antenna), [chip floorplans](https://deepmind.google/discover/blog/how-alphachip-transformed-computer-chip-design/) and [FPGA](/assets/misc/evolved-fpga-modern.pdf) [designs](/assets/misc/evolved-circuit.pdf) in impressive ways humans can't, or at least don't typically[^5]. Similarly, since [AlphaGo](https://en.wikipedia.org/wiki/AlphaGo)'s "move 37", self-play game solvers running without human preconceptions have been able to discover strange strategies and nowadays utterly dominate all human players in chess and go, to the point that human/engine teams are not better than engines. [A paper](https://arxiv.org/abs/2310.16410) evaluating concept transfer from AlphaZero in chess finds that grandmasters can learn some of AlphaZero's strategies, but they are considered unnatural and not always understood[^20]. It also optimizes for win probability directly rather than caring about proxies like margin of victory, leading to further alienation from human play and greater success.
+Because of the ability to avoid unhelpful human assumptions, from-scratch <span class="hoverdefn" title="reinforcement learning">RL</span>, gradient descent and evolutionary approaches have been successful in designing [silicon photonics](https://x.company/blog/posts/ai-siph-design/), [antennas](https://en.wikipedia.org/wiki/Evolved_antenna), [chip floorplans](https://deepmind.google/discover/blog/how-alphachip-transformed-computer-chip-design/) and [FPGA](/assets/misc/evolved-fpga-modern.pdf) [designs](/assets/misc/evolved-circuit.pdf) in impressive ways humans can't, or at least don't typically[^5]. Similarly, since [AlphaGo](https://en.wikipedia.org/wiki/AlphaGo)'s "move 37", self-play game solvers running without human preconceptions have been able to discover strange strategies and nowadays utterly dominate all human players in chess and go, to the point that human/engine teams are not better than engines. [A paper](https://arxiv.org/abs/2310.16410) evaluating concept transfer from AlphaZero in chess finds that grandmasters can learn some of AlphaZero's strategies, but they are considered unnatural and not always understood[^20]. It also optimizes for win probability directly rather than caring about proxies like margin of victory, leading to further alienation from human play and greater success. See also [absurd TAS speedruns](https://norvidstudies.substack.com/p/incomprehensible-speedrun-footage), which ignore ideas about how the game is "meant to" work - these are designed by humans, mostly, but this much optimization pressure isn't applied to most human actions.
 
 RL is also disliked by researchers for its [tendency to](https://docs.google.com/spreadsheets/d/e/2PACX-1vRPiprOaC3HsCf5Tuum8bRfzYUiKLRqJmbOoC-32JorNdfyTiRRsR7Ea5eWtvsWzuxo8bjOxCG84dAg/pubhtml) [reward-hack](https://arxiv.org/abs/1803.03453), i.e. find solutions to the specification of a problem which don't satisfy the "spirit" of it. This is inconvenient for anyone using it for design optimization, but highlights how human reliance on prior knowledge misses things.