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Cake day: June 12th, 2023

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  • It has access to a python interpreter and can use that to do math, but it shows you that this is happening, and it did not when i asked it.

    That’s not what I meant.

    You have access to a dictionary, that doesn’t prove you’re incapable of spelling simple words on your own, like goddamn people what’s with the hate boners for ai around here

    ??? You just don’t understand the difference between a LLM and a chat application using many different tools.



  • If you fine tune a LLM on math equations, odds are it won’t actually learn how to reliably solve novel problems. Just the same as it won’t become a subject matter expert on any topic, but it’s a lot harder to write simple math that “looks, but is not, correct” than it is to waffle vaguely about a topic. The idea of a LLM creating a robust model of the semantics of the text it’s trained on is, at face value, plausible; it just doesn’t seem to actually happen in practice.











  • “Measure” is meant in the specific sense of measure theory. The prototypical example is the Lebesgue measure, which generalizes the intuitive definition of length, area, volume, etc. to N-dimensional space.

    As a pseudo definition, we may assume:

    1. The measure of a rectangle is its length times its width.

    2. The measure of the disjoint union of two sets is the sum of their measures.

    In 2), we can relax the assumption that the two sets are disjoint slightly, as long as the overlap is small (e.g. two rectangles overlapping on an edge). This suggests a definition for the measure of any set: cover it with rectangles and sum their areas. For most sets, the cover will not be exact, i.e. some rectangles will lie partially outside the set, but these inexact covers can always be refined by subdividing the overhanging rectangles. The (Lebesgue) measure of a set is then defined as the greatest lower bound of all possible such approximations by rectangles.

    There are 2 edge cases that quickly arise with this definition. One is the case of zero measure: naturally, a finite set of points has measure zero, since you can cover each point with a rectangle of arbitrarily small area, hence the greatest lower bound is 0. One can cover any countably infinite set with rectangles of area epsilon/n^(2) so that the sum can be made arbitrarily small, too. Even less intuitively, an uncountably infinite and topologically dense set of points can have measure 0 too, e.g. the Cantor set.

    The other edge case is the unmeasurable set. Above, I mentioned a subdivision process and defined the measure as the limit of that process. I took for granted that the limit exists. Indeed, it is hard to imagine otherwise, and that is precisely because under reasonably intuitive axioms (ZF + dependent choice) it is consistent to assume the limit always exists. If you take the full axiom of choice, you may “construct” a counterexample, e.g. the Vitali set. The necessity of the axiom of choice in defining this set ensures that it is difficult to gain any geometric intuition about it. Suffice it to say that the set is both too “substantial” to have measure 0, yet too “fragmented” to have any positive measure, and is therefore not well behaved enough to have a measure at all.




  • By performing measure-preserving transformations to non-measurable sets and acting surprised when at the end of the day measure isn’t preserved. I don’t blame AC for that. AC only implies the existence of a non-measurable set, which is in itself not totally counter-intuitive.




  • This already exists. https://libraryofbabel.info/

    Your comment appears in page 241 of Volume 3, Shelf 4, Wall 4 of Hexagon: 0kdr8nz6w20ww0qkjaxezez7tei3yyg4453xjblflfxx0ygqgenvf1rqo7q3fskaw2trve3cihcrl6gja1bwwprudyp9hzip5jsljqlrc8b9ofmryole35cbirl79kzc9cv2bjpkd26kcdi9cxf1bbhmpmgyc0l1fxz81fsc0p878e6u2rc6dci6n0lv52ogqkvov5yokmhs3ahi89i1erq46nv7d0h3dp2ezbb1kxdz7b4k9rm9vl32glohfxmk2c4t1v5wblssk6abtzxdlhc6g00ytdyree9q4w43j8eh57j8j8d4ddrpoale93glnwoaqunj8j2uli4uqscjfwwh6xafh119s4mwkdxk5trcqhv7wlcphfmvkx97i5k54dntoyrogo51n5i23lsms7xmdkoznop6nbsphpbi0hpm6mq3tuzy1qb677yrk832anjas7jybzxvuhgox49bhi21xhvfu0ny27888wv76hbtpkfyv4s57ljmn9sinju3iuc6na2stn9qvm1vo5yb9ktz1lcbjp0q9102ugpft1f7ngdzmnzv6qomn7zfnopn02v9wwe2gr2m6mo0o9vjmrvmd7fp4kjivsy6iu9cfz9dyu6gv7542ujz0vtj7m2ifpnfeezrb2gbwbgkbdx2taq7vlgjedqze22ywsyt1cacfxxpftjumke4vbtvmn6skj3mi5qnprrv9w4pq5t23xlvrxufsmri2uljpw72228q6jvh82e6936400czpzs8w6i25dvgk7vgj9o9r3k4nombsl3iiv1cogggcw9e5non0jn9ni1aacbisa1oqlzgi9qyhmmd67hkxsfri5958cyj6ryou5vgz7uc7j9kkjix420ys1tkcrhgf0jm6la9h7e06z7sdijeiw31junshzgvmmpplqw6qbzzqzs39jictbygt8u6704h48hsc7hlffm513zagtdbfvpbz32r0vqmjz2sudta4gfsnx6ac4h76djsh7th2h4265qeeainsx2xgslfst5namazisk6swvsbpcv3osvr7wiabkh61f5vuxymqadzxilggym3kfqtbdl3xsmwqcr6wuf4gpoviog89h5xfyawlh7k79k9j5fn1wq47f1m73lah1qhfdxyt1pv2biean3jjb8qv0raxz1oxi9zs9tnmmwrhccd9fij39ddgn1g7t03norvzjqcbqbzl9ibq9qrksnutbfc47z2727u9d9tp68z7u2hyb805wy3d4d0ia9q50p4xvevryycogr6212tau2iv3ya0fy09rsh0wilpqj8vxqug9zj7h2ya1hbnapmqecwtbjnetmt2t91mhb7hky32tl3fa5dqtuba2hm5faawvkazugbmngeojzw88p6hl0duaiup166r20tubj16x78c5m73rwpecco5w6z8ti8b2pgof8k8vu99jvyqiaq6c6adybtjwi8i6u95efp1hxpdsvtbo6nm7j1lmv9jzdspp0sd3qk5jmfpfs1cy18euwpk0apiuqqdy7hakfjx83nx920p4ptxu6bsl98iywgdydrr54u6nvmyxwg1hd2vxnu2yq3utvbx5z886ezzblw0izmntai8jstisdju4n12eed5yr1avv9k7mrr9fzqs6zo7uc3ixkv6fz2figpb5tr1obrlf4c30ghf8exsdgwn1e0uo76r3klnqfys51extpnq5v5swql36lirgok8frxnntoywaqtmyzm3vclnnvfqohz56hh823k5d50049f3lye9cil24yk5031u27dpi7895319mkyi2pkpwxgay4fnqfj38emdc0990ezpheam8ab132v588je7ur4dv1wazezyokate2rccnii1y4gy4subra7y5of25xxu8s3mjumal1lypu1360mrzmqdqdfsm9lvmewzg1608hlxx4le00jhcowg7xcxsbhbx4swwze9pkyk0x8vpsr5j1yja5jl0wn8mjh3gh9wvszd2tazvj7fbuym2pee0r0ifsky61fulwloxc5jkon63tvarj5jsxg3kghl44e7o1w2deeboaodjpuvgzg82wrxszd5jk0hwhvaopdb8wcqqopob4mj2pn36yhnw6k6sz5y56xlijr5a4s6xmc68r0c20d7zq53mrjbq281nfkrrtqgnv2i2rag3f6ara9t616vovgqn8kjwrivene6yyskzxb0d4b0qy0aq743dptvxr85sfhqbcevkn17rvqv1l9hzx983cngckwhdu15kzdv10mqf8yibu0q8s3khd8d3fi5lbl9yespks1q1tnc4y1bgjtvyf1oppnxhvpou71olv0yapyq46w9ld0ntigpba6equ55fvs0j1tp7qw1hr8gbjz10gwxhsx3lu0hubgukht7mbkwfsu4x9980z1srfhj2ayw2e2xf2627vgctymfbooy4eythnm8nzr4mqnfycwovjvbyg95luo4h3smka9d3jlr7dn9e3xwwkpl4dg8i1mj7g63ludud8q7chfh4xajosfaps2n6ntye7j8o4lrcsbqas8ayiutyq8ckbn67ejioufkowogubs8o5670nz13bb9gq3obf0y9xq60j8n8d6i8ahzhxlj2rfc7ndsfmzhusihkiz9fdovslzad7in5kldzhqk8z0cua8n8l0vjfsy96qytgz4wgkq41h6rrsegy9yg1fqhnavpltd067gicomzhye6czk4voghysqscrwavw3li9qdj0ikrlwumyymf8n5luhz1orsrxfw1rek6ghsqyu486dfp2hkbilyccquihck0269nu8y7bsha503ax2ecpxjiug54viy229k4ienp6lcnyx03mnpadeslwa87mu6tcb7t3c3ug7g0yf5le9v2hp094n60ipetkyfu21vqxah8sjjmuhk1gzxnmz01o1s9ndefpfcat0vn1x1anypagcboxp515nmnj9f2yol1opdytfx2dmy5ypdpyamsp2p3zsegmd15e1jbo6xsznda92oqxo9kvsww9k1kzsuwjl73drq038uls6izgqzmhry879ctrhryaj750b2s3hus9f1ainad3vzphmataq0lkn7bi62pu0xf4uqb5k2o8656zn6vzilgl653t38y12723v193fe5c7vbv6p5lw5ernj7bl4aev1ccyakxmkwl11hot51pvrsvqd8vdptfq2ezq2jjaebwx