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Donald Knuth | AI Podcast by Lex Fridman

a guest Jan 6th, 2020 1,928 Never
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  1. [00:00]
  2. the following is a conversation with
  3.  
  4. [00:01]
  5. donald knuth one of the greatest and
  6.  
  7. [00:04]
  8. most impactful computer scientists and
  9.  
  10. [00:06]
  11. mathematicians ever he's the recipient
  12.  
  13. [00:10]
  14. of the 1974 Turing award considered the
  15.  
  16. [00:14]
  17. Nobel Prize of computing he's the author
  18.  
  19. [00:16]
  20. of the multi-volume work the magnum opus
  21.  
  22. [00:19]
  23. the art of computer programming he made
  24.  
  25. [00:23]
  26. several key contributions to the
  27.  
  28. [00:25]
  29. rigorous analysis of computational
  30.  
  31. [00:27]
  32. complexity of algorithms including the
  33.  
  34. [00:30]
  35. popularization of asymptotic notation
  36.  
  37. [00:33]
  38. that we all affectionately know as the
  39.  
  40. [00:35]
  41. Big O notation he also created the tech
  42.  
  43. [00:39]
  44. typesetting system which most computer
  45.  
  46. [00:41]
  47. scientists physicists mathematicians and
  48.  
  49. [00:44]
  50. scientists and engineers in general used
  51.  
  52. [00:47]
  53. to write technical papers and make them
  54.  
  55. [00:49]
  56. look beautiful I can imagine no better
  57.  
  58. [00:53]
  59. guest to in 2019 with than Don one of
  60.  
  61. [00:57]
  62. the kindest most brilliant people in our
  63.  
  64. [00:59]
  65. field this podcast was recorded many
  66.  
  67. [01:02]
  68. months ago it's one I avoided because
  69.  
  70. [01:04]
  71. perhaps counter-intuitively the
  72.  
  73. [01:06]
  74. conversation meant so much to me if you
  75.  
  76. [01:09]
  77. can believe it I knew even less about
  78.  
  79. [01:11]
  80. recording back then so the camera angle
  81.  
  82. [01:13]
  83. is a bit off I hope that's okay with you
  84.  
  85. [01:15]
  86. the office space was a bit cramped for
  87.  
  88. [01:18]
  89. filming but it was a magical space Ordon
  90.  
  91. [01:22]
  92. does most of his work it meant a lot to
  93.  
  94. [01:24]
  95. me that he would welcome me into his
  96.  
  97. [01:26]
  98. home it was quite a journey to get there
  99.  
  100. [01:28]
  101. as many people know he doesn't check
  102.  
  103. [01:30]
  104. email so I had to get creative the
  105.  
  106. [01:33]
  107. effort was worth it I've been doing this
  108.  
  109. [01:36]
  110. podcast on the side for just over a year
  111.  
  112. [01:38]
  113. sometimes I had to sacrifice a bit of
  114.  
  115. [01:40]
  116. sleep but always happy to do it and to
  117.  
  118. [01:42]
  119. be part of an amazing community of
  120.  
  121. [01:44]
  122. curious minds thank you for your kind
  123.  
  124. [01:47]
  125. words support for the interesting
  126.  
  127. [01:49]
  128. discussions and I look forward to many
  129.  
  130. [01:51]
  131. more of those in 2020 this is the
  132.  
  133. [01:55]
  134. artificial intelligence podcast if you
  135.  
  136. [01:57]
  137. enjoy it subscribe on YouTube give it
  138.  
  139. [02:00]
  140. five stars an Apple podcast follow on
  141.  
  142. [02:02]
  143. Spotify support on patreon or simply
  144.  
  145. [02:04]
  146. connect with me on Twitter at lex
  147.  
  148. [02:06]
  149. friedman spelled fri d-m am
  150.  
  151. [02:10]
  152. I recently started doing ads at the end
  153.  
  154. [02:12]
  155. of the introduction I'll do one or two
  156.  
  157. [02:14]
  158. minutes after introducing the episode
  159.  
  160. [02:16]
  161. and never any ads in the middle that
  162.  
  163. [02:18]
  164. break the flow of the conversation I
  165.  
  166. [02:19]
  167. hope that works for you and doesn't hurt
  168.  
  169. [02:21]
  170. the listening experience I provide time
  171.  
  172. [02:24]
  173. stamps for the start of the conversation
  174.  
  175. [02:25]
  176. that you can skip to but it helps if you
  177.  
  178. [02:28]
  179. listen to the ad and support this
  180.  
  181. [02:30]
  182. podcast by trying out the product the
  183.  
  184. [02:32]
  185. service being advertised this show is
  186.  
  187. [02:34]
  188. presented by cash app the number one
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  191. finance app in the App Store
  192.  
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  194. I personally use cash app to send money
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  196. [02:40]
  197. to friends but you can also use it to
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  200. buy sell and deposit Bitcoin in just
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  203. seconds cash app also has a new
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  253. [03:26]
  254. and use code Lex podcast you'll get ten
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  256. [03:29]
  257. dollars in cash up will also donate ten
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  259. [03:31]
  260. dollars the first which again is an
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  262. [03:34]
  263. organization that I've personally seen
  264.  
  265. [03:35]
  266. inspire girls and boys to dream of
  267.  
  268. [03:38]
  269. engineering a better world and now
  270.  
  271. [03:40]
  272. here's my conversation with Donald Knuth
  273.  
  274. [03:43]
  275. in 1957 atcase tech you were once
  276.  
  277. [03:50]
  278. allowed to spend several evenings with a
  279.  
  280. [03:53]
  281. IBM 650 computer as you've talked about
  282.  
  283. [03:56]
  284. in the past then you fell in love with
  285.  
  286. [03:57]
  287. computing then
  288.  
  289. [03:59]
  290. can you take me back to that moment with
  291.  
  292. [04:02]
  293. the IBM 650 what was it that grabs you
  294.  
  295. [04:07]
  296. about that computer so the IBM 650 was
  297.  
  298. [04:11]
  299. this this machine that
  300.  
  301. [04:14]
  302. well it didn't fill a room but it it was
  303.  
  304. [04:17]
  305. it was big and noisy but when I first
  306.  
  307. [04:21]
  308. saw it it was through a window and there
  309.  
  310. [04:23]
  311. were just a lot of lights flashing on it
  312.  
  313. [04:25]
  314. and I was a freshman I had a job with
  315.  
  316. [04:32]
  317. the statistics group and I was supposed
  318.  
  319. [04:35]
  320. to punch cards and pour data and then
  321.  
  322. [04:38]
  323. sort them on another machine but then
  324.  
  325. [04:40]
  326. they got this new computer came in and I
  327.  
  328. [04:44]
  329. and it had interesting like you know
  330.  
  331. [04:47]
  332. lights okay so well but I had it kind of
  333.  
  334. [04:50]
  335. key to the building so I can you know
  336.  
  337. [04:52]
  338. like I could get in and look at it and
  339.  
  340. [04:53]
  341. got a manual for it and and my first
  342.  
  343. [04:57]
  344. experience was based on the fact that I
  345.  
  346. [04:58]
  347. could punch cards basically would you a
  348.  
  349. [05:00]
  350. big thing for though deal with thick but
  351.  
  352. [05:02]
  353. the is
  354.  [6:50 ]was you know big in size but
  355.  
  356. [05:06]
  357. but incredibly small in power in memory
  358.  
  359. [05:12]
  360. it had it had 2,000 words of memory and
  361.  
  362. [05:16]
  363. in a word of memory was 10 decimal
  364.  
  365. [05:18]
  366. digits plus a sign and it it would do to
  367.  
  368. [05:22]
  369. add two numbers together you could
  370.  
  371. [05:24]
  372. probably expect that would take oh say
  373.  
  374. [05:28]
  375. three milliseconds so that's pretty fast
  376.  
  377. [05:31]
  378. it's the memories that constraint the
  379.  
  380. [05:33]
  381. memories the problem that was why it was
  382.  
  383. [05:35]
  384. three millisecond because it took five
  385.  
  386. [05:37]
  387. milliseconds for the drum to go around
  388.  
  389. [05:39]
  390. and you had to wait I don't know five
  391.  
  392. [05:43]
  393. cycle times if you have an instruction
  394.  
  395. [05:46]
  396. one position on the drum then it would
  397.  
  398. [05:49]
  399. be ready to read the data for the
  400.  
  401. [05:50]
  402. instruction and three notches the drum
  403.  
  404. [05:55]
  405. is 50 cycles around and you go three
  406.  
  407. [05:58]
  408. cycles and you can get the data and then
  409.  
  410. [06:00]
  411. you can go another three cycles and get
  412.  
  413. [06:02]
  414. and get to next instruction if the
  415.  
  416. [06:04]
  417. instruction is there otherwise otherwise
  418.  
  419. [06:06]
  420. you spin until you get to there
  421.  
  422. [06:08]
  423. play and and we had no random-access
  424.  
  425. [06:12]
  426. memory whatsoever until my senior year
  427.  
  428. [06:13]
  429. you see here we got fifty words of
  430.  
  431. [06:16]
  432. random access memory which were which
  433.  
  434. [06:17]
  435. were priceless and we would and we would
  436.  
  437. [06:19]
  438. move stuff up to the up to the random
  439.  
  440. [06:23]
  441. access memory in 60 word chunks and then
  442.  
  443. [06:26]
  444. we would start again so it's separating
  445.  
  446. [06:29]
  447. when to go up there and could you have
  448.  
  449. [06:31]
  450. predicted the future 60 years later of
  451.  
  452. [06:35]
  453. computing from then you know in fact the
  454.  
  455. [06:38]
  456. hardest question I was ever asked was
  457.  
  458. [06:42]
  459. what could I have predicted in other
  460.  
  461. [06:44]
  462. words the interviewer asked me she said
  463.  
  464. [06:47]
  465. you know what about computing has
  466.  
  467. [06:50]
  468. surprised you you know and immediately I
  469.  
  470. [06:51]
  471. ran I rattled off a couple dozen things
  472.  
  473. [06:54]
  474. and inches okay so what didn't surprise
  475.  
  476. [06:56]
  477. and I was I tried for five minutes to
  478.  
  479. [07:00]
  480. think of something that I thought I
  481.  
  482. [07:01]
  483. would have predicted and I and I and I
  484.  
  485. [07:03]
  486. couldn't but I let me say that this
  487.  
  488. [07:07]
  489. machine I didn't know well it there
  490.  
  491. [07:10]
  492. wasn't there wasn't much else in the
  493.  
  494. [07:12]
  495. world at that time the 650 was the first
  496.  
  497. [07:14]
  498. machine that was that there were more
  499.  
  500. [07:17]
  501. than a thousand of ever before that
  502.  
  503. [07:19]
  504. there were you know there was each
  505.  
  506. [07:22]
  507. machine there might be a half a dozen
  508.  
  509. [07:23]
  510. examples maybe my first mass-market
  511.  
  512. [07:26]
  513. mass-produced the first one yeah done in
  514.  
  515. [07:30]
  516. quantity and and IBM I didn't sell them
  517.  
  518. [07:35]
  519. they they rented them but but they they
  520.  
  521. [07:38]
  522. rented them to universities that at
  523.  
  524. [07:40]
  525. great you know I had a great deal and
  526.  
  527. [07:44]
  528. and so that's why a lot of students
  529.  
  530. [07:48]
  531. learned about computers at that time so
  532.  
  533. [07:51]
  534. you refer to people including yourself
  535.  
  536. [07:54]
  537. who gravitate toward a kind of
  538.  
  539. [07:57]
  540. computational thinking as geeks for at
  541.  
  542. [08:00]
  543. least I've heard you used that
  544.  
  545. [08:02]
  546. terminology it true that I think there's
  547.  
  548. [08:05]
  549. something that happened to me as I was
  550.  
  551. [08:07]
  552. growing up that made my brain structure
  553.  
  554. [08:10]
  555. in a certain way that resonates with
  556.  
  557. [08:12]
  558. with computers so there's the space of
  559.  
  560. [08:15]
  561. people it's 2% of the population you
  562.  
  563. [08:17]
  564. empirically estimate that's a prick
  565.  
  566. [08:21]
  567. it's been proven fairly constant over
  568.  
  569. [08:23]
  570. most of my career however it might be
  571.  
  572. [08:27]
  573. different now because kids have
  574.  
  575. [08:28]
  576. different experiences when they're young
  577.  
  578. [08:30]
  579. so what does the world look like to a
  580.  
  581. [08:34]
  582. geek what is what is this aspect of
  583.  
  584. [08:38]
  585. thinking that is unique to their makes
  586.  
  587. [08:42]
  588. it yeah that makes a geek this is cuter
  589.  
  590. [08:47]
  591. the important question in in the 50s
  592.  
  593. [08:51]
  594. IBM noticed that that there were geeks
  595.  
  596. [08:57]
  597. and non geeks and so they tried to hire
  598.  
  599. [08:59]
  600. geeks and they put out as worth papers
  601.  
  602. [09:01]
  603. saying you know if you play chess come
  604.  
  605. [09:03]
  606. to Madison Avenue and for an interview
  607.  
  608. [09:05]
  609. or something like this they were they
  610.  
  611. [09:06]
  612. were trying for some things so what it
  613.  
  614. [09:08]
  615. what what is it that I find easy and
  616.  
  617. [09:11]
  618. other people tend to find harder and and
  619.  
  620. [09:14]
  621. I think there's two main things one is
  622.  
  623. [09:17]
  624. this with is ability to jump jump levels
  625.  
  626. [09:23]
  627. of abstraction so you see something in
  628.  
  629. [09:27]
  630. the large and you see something in the
  631.  
  632. [09:30]
  633. small and and can you pass between those
  634.  
  635. [09:34]
  636. unconsciously so you know that in order
  637.  
  638. [09:37]
  639. to solve some big problem what you need
  640.  
  641. [09:41]
  642. to do is add one to a into a certain
  643.  
  644. [09:44]
  645. register or anything that gets you to
  646.  
  647. [09:46]
  648. another step and you can and we and
  649.  
  650. [09:48]
  651. below the yeah I mean I don't go down to
  652.  
  653. [09:50]
  654. the electron level but I knew what those
  655.  
  656. [09:53]
  657. milliseconds were what the drum was like
  658.  
  659. [09:55]
  660. on the 650 I knew how I was gonna factor
  661.  
  662. [09:59]
  663. her number or or find a root of an
  664.  
  665. [10:01]
  666. equation or something be alavés because
  667.  
  668. [10:03]
  669. of what was doing and and as I'm
  670.  
  671. [10:05]
  672. debugging I'm going through you know did
  673.  
  674. [10:08]
  675. I make a key punch err did I did I write
  676.  
  677. [10:12]
  678. the wrong instruction do I have the
  679.  
  680. [10:13]
  681. wrong wrong thing in a register and each
  682.  
  683. [10:16]
  684. level at each level it is different and
  685.  
  686. [10:20]
  687. so this idea of being able to see
  688.  
  689. [10:23]
  690. something at all at lots of levels and
  691.  
  692. [10:27]
  693. fluently go between them it seems to me
  694.  
  695. [10:30]
  696. to be more pronounced much more
  697.  
  698. [10:32]
  699. pronounced in in the people that
  700.  
  701. [10:34]
  702. with computers like I got so in my books
  703.  
  704. [10:38]
  705. I also don't stick after the high level
  706.  
  707. [10:41]
  708. but but i but i mix low level stuff with
  709.  
  710. [10:47]
  711. high level and this means that some
  712.  
  713. [10:50]
  714. people think you know that I that I
  715.  
  716. [10:54]
  717. should write better books and it's
  718.  
  719. [10:57]
  720. probably true but but other people say
  721.  
  722. [11:00]
  723. well but that's if you think like like
  724.  
  725. [11:03]
  726. that then that's the way to train
  727.  
  728. [11:04]
  729. yourself like to keep mixing the levels
  730.  
  731. [11:06]
  732. and and learn more and more how to jump
  733.  
  734. [11:10]
  735. between so that that's the one thing the
  736.  
  737. [11:11]
  738. other the other thing is that it's more
  739.  
  740. [11:14]
  741. of a talent it to be able to deal with
  742.  
  743. [11:19]
  744. non-uniformity where there's case one
  745.  
  746. [11:22]
  747. case two case three instead of instead
  748.  
  749. [11:26]
  750. of having one or two rules that govern
  751.  
  752. [11:28]
  753. everything so if so it doesn't bother me
  754.  
  755. [11:32]
  756. if I need like an algorithm has ten
  757.  
  758. [11:37]
  759. steps to it you know each step is does
  760.  
  761. [11:39]
  762. something else that doesn't bother me
  763.  
  764. [11:40]
  765. but a lot of a lot of pure mathematics
  766.  
  767. [11:43]
  768. is based on one or two rules which which
  769.  
  770. [11:46]
  771. are universal and and and so this means
  772.  
  773. [11:49]
  774. that people like me sometimes work with
  775.  
  776. [11:52]
  777. systems that are more complicated than
  778.  
  779. [11:54]
  780. necessary because it doesn't bother us
  781.  
  782. [11:55]
  783. that we don't that we didn't figure out
  784.  
  785. [11:58]
  786. the simple rule and you mentioned that
  787.  
  788. [12:01]
  789. while Jacobi boule Abel and all the
  790.  
  791. [12:05]
  792. mathematicians in 19th century may have
  793.  
  794. [12:08]
  795. had symptoms of geek the first hundred
  796.  
  797. [12:12]
  798. percent legit geek was touring Alan
  799.  
  800. [12:15]
  801. Torrie I I think he had yeah a lot more
  802.  
  803. [12:17]
  804. of this quality than anyone could from
  805.  
  806. [12:23]
  807. reading the kind of stuff he didn't so
  808.  
  809. [12:27]
  810. hot as touring what influence has
  811.  
  812. [12:31]
  813. touring had on you well well your way
  814.  
  815. [12:34]
  816. and so I didn't know that aspect of him
  817.  
  818. [12:38]
  819. until after I graduated some years I it
  820.  
  821. [12:40]
  822. has undergraduate we had a class that
  823.  
  824. [12:43]
  825. talked about computability theory and
  826.  
  827. [12:45]
  828. Turing machines and and that was all it
  829.  
  830. [12:49]
  831. sounded like a very specific kind of
  832.  
  833. [12:52]
  834. purely theoretical approach to stuff
  835.  
  836. [12:55]
  837. so when how old was I when I when I
  838.  
  839. [12:58]
  840. learned that he thought he had you know
  841.  
  842. [13:02]
  843. designed when she and that he wrote the
  844.  
  845. [13:06]
  846. you know you wrote a wonderful manual
  847.  
  848. [13:09]
  849. for for Manchester machines and and he
  850.  
  851. [13:13]
  852. invented all the subroutines and and and
  853.  
  854. [13:19]
  855. he was a real hacker that that he had
  856.  
  857. [13:22]
  858. his hands dirty
  859.  
  860. [13:23]
  861. I thought for many years that he had
  862.  
  863. [13:27]
  864. only done purely formal work as I
  865.  
  866. [13:31]
  867. started reading his own publications I
  868.  
  869. [13:32]
  870. could yeah you know I could feel this
  871.  
  872. [13:34]
  873. kinship and and of course he had a lot
  874.  
  875. [13:39]
  876. of peculiarities like he wrote numbers
  877.  
  878. [13:42]
  879. backwards because I mean left to right
  880.  
  881. [13:46]
  882. to the right to left because that's the
  883.  
  884. [13:48]
  885. that's it was easier for computers to
  886.  
  887. [13:50]
  888. process him that way what do you mean
  889.  
  890. [13:53]
  891. left to right he would write PI as you
  892.  
  893. [13:57]
  894. know nine five one four point three I
  895.  
  896. [14:01]
  897. mean okay right forget it for one point
  898.  
  899. [14:08]
  900. three on the blackboard I mean when he
  901.  
  902. [14:12]
  903. he we had trained himself to to do that
  904.  
  905. [14:16]
  906. because the computers he was working
  907.  
  908. [14:18]
  909. with I worked that way inside trained
  910.  
  911. [14:21]
  912. himself to think like a computer well
  913.  
  914. [14:22]
  915. there you go that's nuts geek thinking
  916.  
  917. [14:26]
  918. you've practiced some of the most
  919.  
  920. [14:28]
  921. elegant formalism in computer science
  922.  
  923. [14:30]
  924. and yet you're the creator of a concept
  925.  
  926. [14:34]
  927. like literate programming which seems to
  928.  
  929. [14:37]
  930. move closer to natural language type of
  931.  
  932. [14:41]
  933. description of programming yep yeah
  934.  
  935. [14:44]
  936. absolutely so how do you see those two
  937.  
  938. [14:45]
  939. as conflicting as the formalism of
  940.  
  941. [14:48]
  942. theory and the idea of literate
  943.  
  944. [14:50]
  945. programming so there we are in a non
  946.  
  947. [14:53]
  948. uniform system well I don't think one
  949.  
  950. [14:56]
  951. one-size-fits-all and I don't and I
  952.  
  953. [14:58]
  954. don't think all truth lies in one in one
  955.  
  956. [15:02]
  957. kind of expertise and so somehow in a
  958.  
  959. [15:05]
  960. way you'd say my what my life is a
  961.  
  962. [15:07]
  963. convex combination of English and
  964.  
  965. [15:11]
  966. mathematics and you're okay with that
  967.  
  968. [15:14]
  969. and not only that I think thriving I
  970.  
  971. [15:16]
  972. wish you know I want my kids to be that
  973.  
  974. [15:18]
  975. way I want cetera not used left-brain
  976.  
  977. [15:21]
  978. right-brain at the same time you got a
  979.  
  980. [15:24]
  981. lot more done that's that was part of
  982.  
  983. [15:25]
  984. the and I've heard that you didn't
  985.  
  986. [15:31]
  987. really read for pleasure until into your
  988.  
  989. [15:33]
  990. 30s literature true you know more about
  991.  
  992. [15:38]
  993. me than I do but I'll try to be
  994.  
  995. [15:40]
  996. consistent with what you're really ya
  997.  
  998. [15:41]
  999. know just believe me
  1000.  
  1001. [15:42]
  1002. yeah just go with whatever story I tell
  1003.  
  1004. [15:45]
  1005. you it'll be easier that way the
  1006.  
  1007. [15:46]
  1008. conversation I've heard mentioned a
  1009.  
  1010. [15:50]
  1011. Philip Roth's American pastoral which I
  1012.  
  1013. [15:53]
  1014. love as a book I don't know if it was it
  1015.  
  1016. [15:58]
  1017. was mentioned as something I think that
  1018.  
  1019. [15:59]
  1020. was meaningful to you as well in either
  1021.  
  1022. [16:03]
  1023. case what literary books had a lasting
  1024.  
  1025. [16:06]
  1026. impact on you what okay good so I so I
  1027.  
  1028. [16:09]
  1029. met Russ already well we both got
  1030.  
  1031. [16:14]
  1032. doctors from Harvard on the same day so
  1033.  
  1034. [16:16]
  1035. I so we were yeah we had lunch together
  1036.  
  1037. [16:20]
  1038. and stuff like that and but he knew that
  1039.  
  1040. [16:22]
  1041. you know computer books would never sell
  1042.  
  1043. [16:24]
  1044. well well all right so you say you you
  1045.  
  1046. [16:28]
  1047. you you're a teenager when you left
  1048.  
  1049. [16:32]
  1050. Russia so I I have to say that Tolstoy
  1051.  
  1052. [16:36]
  1053. was one of the big influences on me
  1054.  
  1055. [16:38]
  1056. I especially like Anna Karenina not
  1057.  
  1058. [16:42]
  1059. because of a particular area of the plot
  1060.  
  1061. [16:46]
  1062. of the story where but because there's
  1063.  
  1064. [16:51]
  1065. this character who you know did the
  1066.  
  1067. [16:54]
  1068. philosophical discussions it's all it's
  1069.  
  1070. [16:58]
  1071. a whole way of life is worked out there
  1072.  
  1073. [17:02]
  1074. it's among the characters until in and
  1075.  
  1076. [17:04]
  1077. so it that I thought was was especially
  1078.  
  1079. [17:07]
  1080. beautiful on the other hand does they
  1081.  
  1082. [17:09]
  1083. have ski I I didn't like at all because
  1084.  
  1085. [17:13]
  1086. I I felt that he his genius was mostly
  1087.  
  1088. [17:16]
  1089. because he kept forgetting what he what
  1090.  
  1091. [17:17]
  1092. he had started out to do and he was just
  1093.  
  1094. [17:19]
  1095. sloppy I didn't think that that it then
  1096.  
  1097. [17:23]
  1098. that he polished his stuff at all and
  1099.  
  1100. [17:26]
  1101. and I tend to admire somebody who who
  1102.  
  1103. [17:30]
  1104. Todd's the i's and cross the t's so that
  1105.  
  1106. [17:32]
  1107. the music of the prose this way you
  1108.  
  1109. [17:34]
  1110. admire more and that I certainly do
  1111.  
  1112. [17:37]
  1113. admire the music of the language which I
  1114.  
  1115. [17:39]
  1116. couldn't appreciate in the Russian
  1117.  
  1118. [17:41]
  1119. original but but I can and Victor Hugo
  1120.  
  1121. [17:44]
  1122. Glenn's close friendships much his
  1123.  
  1124. [17:46]
  1125. closer but but Tolstoy I like the same
  1126.  
  1127. [17:51]
  1128. reason I like Herman Wouk as a as a
  1129.  
  1130. [17:53]
  1131. novelist I that I think I like his book
  1132.  
  1133. [17:58]
  1134. Marjorie Morningstar has a similar
  1135.  
  1136. [18:00]
  1137. character in who who who developed his
  1138.  
  1139. [18:02]
  1140. own personal philosophy and export and
  1141.  
  1142. [18:05]
  1143. it called goes in in was consistent yeah
  1144.  
  1145. [18:10]
  1146. right and it's worth worth pondering uh
  1147.  
  1148. [18:14]
  1149. so zo like Nietzsche and like what you
  1150.  
  1151. [18:18]
  1152. don't like Friedrich Nietzsche or age
  1153.  
  1154. [18:20]
  1155. yeah no no you like this has like I keep
  1156.  
  1157. [18:24]
  1158. seeing quotations for Nietzsche and and
  1159.  
  1160. [18:26]
  1161. you never tempt me to read any further
  1162.  
  1163. [18:29]
  1164. please full of contradictions we will
  1165.  
  1166. [18:32]
  1167. certainly not appreciate him but
  1168.  
  1169. [18:34]
  1170. Schiller you know I'm trying to get the
  1171.  
  1172. [18:37]
  1173. cross what I appreciate in literature
  1174.  
  1175. [18:39]
  1176. and part of it is the is is as you say
  1177.  
  1178. [18:44]
  1179. the music of the language of the way it
  1180.  
  1181. [18:46]
  1182. flows and take Raymond Chandler versus
  1183.  
  1184. [18:51]
  1185. Dashiell Hammett Dashiell Hammett
  1186.  
  1187. [18:53]
  1188. sentences are awful and Raymond
  1189.  
  1190. [18:56]
  1191. Chandler's are beautiful they just flow
  1192.  
  1193. [18:59]
  1194. so I I don't I don't read literature
  1195.  
  1196. [19:04]
  1197. because it's supposed to be good for me
  1198.  
  1199. [19:07]
  1200. or because somebody said it's great but
  1201.  
  1202. [19:09]
  1203. but it I could find things that I like I
  1204.  
  1205. [19:14]
  1206. mean you mentioned you address like
  1207.  
  1208. [19:17]
  1209. James Bond so like I love Ian Fleming I
  1210.  
  1211. [19:20]
  1212. think he's got a he had a really great
  1213.  
  1214. [19:22]
  1215. gift for if he has a golf game or game
  1216.  
  1217. [19:26]
  1218. of bridge or something and this comes
  1219.  
  1220. [19:28]
  1221. into a story it'll it'll be the most
  1222.  
  1223. [19:30]
  1224. exciting golf game or or you know the
  1225.  
  1226. [19:33]
  1227. absolute best possible hands a bridge
  1228.  
  1229. [19:36]
  1230. that that exists and and any he exploits
  1231.  
  1232. [19:41]
  1233. it and tells it beautifully as well so
  1234.  
  1235. [19:45]
  1236. in connecting some things here looking
  1237.  
  1238. [19:49]
  1239. at literate programming and being able
  1240.  
  1241. [19:51]
  1242. to it convey encode algorithms to a
  1243.  
  1244. [19:59]
  1245. computer in a way that mimics how humans
  1246.  
  1247. [20:03]
  1248. speak how what do you think about
  1249.  
  1250. [20:06]
  1251. natural language in general and the
  1252.  
  1253. [20:08]
  1254. messiness of our human world about
  1255.  
  1256. [20:11]
  1257. trying to express yeah difficult things
  1258.  
  1259. [20:14]
  1260. so the idea of literate programming is
  1261.  
  1262. [20:17]
  1263. to is really to try to understand
  1264.  
  1265. [20:24]
  1266. something better by seeing it from these
  1267.  
  1268. [20:26]
  1269. two perspectives the formal and the
  1270.  
  1271. [20:28]
  1272. informal if we try to understand a
  1273.  
  1274. [20:31]
  1275. complicated thing if we can look at it
  1276.  
  1277. [20:33]
  1278. in different ways and so this is in fact
  1279.  
  1280. [20:36]
  1281. the key to technical writing a good
  1282.  
  1283. [20:39]
  1284. technical writer
  1285.  
  1286. [20:40]
  1287. try not to be obvious about it but says
  1288.  
  1289. [20:42]
  1290. everything twice formally and informally
  1291.  
  1292. [20:45]
  1293. or maybe three times but you try to give
  1294.  
  1295. [20:48]
  1296. the reader a way to put the concept into
  1297.  
  1298. [20:55]
  1299. his own brain or her own brain is that
  1300.  
  1301. [20:57]
  1302. better for the writer or the reader or
  1303.  
  1304. [21:00]
  1305. both well the writer just tries to
  1306.  
  1307. [21:05]
  1308. understand the reader that's the goal of
  1309.  
  1310. [21:07]
  1311. a writer is to have a good mental image
  1312.  
  1313. [21:10]
  1314. of the reader and to say what the reader
  1315.  
  1316. [21:13]
  1317. expects next and to to impress the
  1318.  
  1319. [21:18]
  1320. reader with what has impressed the
  1321.  
  1322. [21:19]
  1323. writer why something is interesting so
  1324.  
  1325. [21:24]
  1326. when you have a computer program we try
  1327.  
  1328. [21:26]
  1329. to instead of looking at it as something
  1330.  
  1331. [21:29]
  1332. that we're just trying to give an
  1333.  
  1334. [21:30]
  1335. instruction to the computer what we
  1336.  
  1337. [21:32]
  1338. really want to be is giving giving
  1339.  
  1340. [21:35]
  1341. insight to the person who's who's gonna
  1342.  
  1343. [21:39]
  1344. be maintaining this program or to the
  1345.  
  1346. [21:41]
  1347. programmer himself when he's debugging
  1348.  
  1349. [21:44]
  1350. it as to why this stuff is being done
  1351.  
  1352. [21:46]
  1353. and so all the techniques of exposition
  1354.  
  1355. [21:50]
  1356. that a teacher uses or book writers make
  1357.  
  1358. [21:54]
  1359. you better program or if your if your
  1360.  
  1361. [21:56]
  1362. program is going to be not just a
  1363.  
  1364. [21:59]
  1365. one-shot deal so how difficult is that
  1366.  
  1367. [22:03]
  1368. do you see hope for the combination of
  1369.  
  1370. [22:07]
  1371. informal and formal for the programming
  1372.  
  1373. [22:11]
  1374. task yeah I I'm the wrong person to ask
  1375.  
  1376. [22:14]
  1377. I guess because I'm a geek but but I
  1378.  
  1379. [22:17]
  1380. think for a geek it's easy I don't know
  1381.  
  1382. [22:19]
  1383. I don't know see not some people have
  1384.  
  1385. [22:23]
  1386. difficulty writing and that might be
  1387.  
  1388. [22:26]
  1389. because there's something in their brain
  1390.  
  1391. [22:29]
  1392. structure that makes it hard for them to
  1393.  
  1394. [22:32]
  1395. write or or it might be something just
  1396.  
  1397. [22:34]
  1398. that they haven't had enough practice
  1399.  
  1400. [22:35]
  1401. I'm not the right one to to uh to judge
  1402.  
  1403. [22:39]
  1404. but I don't think you teach any person
  1405.  
  1406. [22:42]
  1407. any particular skill like I do think
  1408.  
  1409. [22:45]
  1410. that that writing is is half of my life
  1411.  
  1412. [22:49]
  1413. and so I put it together and let
  1414.  
  1415. [22:51]
  1416. program he won't even when I'm writing a
  1417.  
  1418. [22:53]
  1419. one-shot program I I write it in
  1420.  
  1421. [22:58]
  1422. literate way because I get it right
  1423.  
  1424. [23:02]
  1425. faster though now does it get compiled
  1426.  
  1427. [23:05]
  1428. automatically or so I guess on the
  1429.  
  1430. [23:09]
  1431. technical side my question was how
  1432.  
  1433. [23:12]
  1434. difficult is a design a system where
  1435.  
  1436. [23:15]
  1437. much of the programming is done
  1438.  
  1439. [23:17]
  1440. informally informally yeah informally I
  1441.  
  1442. [23:21]
  1443. think whatever works to make it
  1444.  
  1445. [23:25]
  1446. understandable is good but then you have
  1447.  
  1448. [23:28]
  1449. to also understand how informal is you
  1450.  
  1451. [23:33]
  1452. have to know the limitations you have to
  1453.  
  1454. [23:35]
  1455. connect so so by putting the formula and
  1456.  
  1457. [23:38]
  1458. informal together this this is where
  1459.  
  1460. [23:41]
  1461. this is where it gets locked into your
  1462.  
  1463. [23:43]
  1464. into your brain now you can you can say
  1465.  
  1466. [23:48]
  1467. informally well I'm working on a problem
  1468.  
  1469. [23:51]
  1470. right now so let's go there I get that
  1471.  
  1472. [23:54]
  1473. can you give me an example of of
  1474.  
  1475. [23:57]
  1476. connecting the informal in the formal
  1477.  
  1478. [23:59]
  1479. well it's a little too complicated an
  1480.  
  1481. [24:02]
  1482. example there's a puzzle that that's
  1483.  
  1484. [24:05]
  1485. self referential it's called a Japanese
  1486.  
  1487. [24:07]
  1488. arrow puzzle and and and you're given a
  1489.  
  1490. [24:11]
  1491. a bunch of boxes each one points north
  1492.  
  1493. [24:14]
  1494. east south or west and at the end you're
  1495.  
  1496. [24:18]
  1497. supposed to fill in each box with the
  1498.  
  1499. [24:20]
  1500. number of distinct numbers that it
  1501.  
  1502. [24:23]
  1503. points to so if I put a three in a box
  1504.  
  1505. [24:26]
  1506. that means that and it's pointing to
  1507.  
  1508. [24:29]
  1509. five other boxes that means that there's
  1510.  
  1511. [24:30]
  1512. going to be three different numbers in
  1513.  
  1514. [24:32]
  1515. those five bucks and and those boxes are
  1516.  
  1517. [24:36]
  1518. pointing what I might be pointing to me
  1519.  
  1520. [24:38]
  1521. one of my might be pointing the other
  1522.  
  1523. [24:39]
  1524. way but anyway I kind of defined a set
  1525.  
  1526. [24:44]
  1527. of numbers that obeys this complicated
  1528.  
  1529. [24:47]
  1530. condition that each number counts how
  1531.  
  1532. [24:50]
  1533. many distinct numbers if it points do
  1534.  
  1535. [24:52]
  1536. well and still a guy sent me his
  1537.  
  1538. [24:57]
  1539. solution to this problem where he where
  1540.  
  1541. [25:00]
  1542. he presents
  1543.  
  1544. [25:03]
  1545. formal statements that that say either
  1546.  
  1547. [25:06]
  1548. this is true or this is true this is
  1549.  
  1550. [25:07]
  1551. true and and and so I try to render that
  1552.  
  1553. [25:10]
  1554. formal statement informally and I try
  1555.  
  1556. [25:14]
  1557. say I contain a three and and the guys
  1558.  
  1559. [25:20]
  1560. I'm pointing to contain the numbers one
  1561.  
  1562. [25:23]
  1563. two and six so by putting it in formally
  1564.  
  1565. [25:26]
  1566. and also I converted into a into a
  1567.  
  1568. [25:29]
  1569. dialogue statement that helps me
  1570.  
  1571. [25:32]
  1572. understand the logical statement that
  1573.  
  1574. [25:35]
  1575. he's written down as a string of numbers
  1576.  
  1577. [25:37]
  1578. in terms of some abstract variables
  1579.  
  1580. [25:40]
  1581. Eddie yeah
  1582.  
  1583. [25:40]
  1584. that's really interesting so maybe an
  1585.  
  1586. [25:43]
  1587. extension of that there has been a
  1588.  
  1589. [25:46]
  1590. resurgence in computer science and
  1591.  
  1592. [25:48]
  1593. machine learning and neural networks so
  1594.  
  1595. [25:52]
  1596. using data to construct algorithms so
  1597.  
  1598. [25:56]
  1599. it's another way to construct algorithms
  1600.  
  1601. [25:58]
  1602. really yes you can think of it that way
  1603.  
  1604. [26:03]
  1605. so as opposed to natural language to
  1606.  
  1607. [26:05]
  1608. construct algorithms use data to
  1609.  
  1610. [26:06]
  1611. construct other so what what's the view
  1612.  
  1613. [26:10]
  1614. of this branch of computer science where
  1615.  
  1616. [26:13]
  1617. data is almost more important than the
  1618.  
  1619. [26:16]
  1620. mechanism of the algorithm it seems to
  1621.  
  1622. [26:19]
  1623. be suited to a certain kind of non geek
  1624.  
  1625. [26:23]
  1626. and would you know which is probably why
  1627.  
  1628. [26:25]
  1629. it's it's like it's taken off that it
  1630.  
  1631. [26:29]
  1632. has its own community that I thought
  1633.  
  1634. [26:31]
  1635. really that really resonates with that
  1636.  
  1637. [26:33]
  1638. but it's hard to you know to trust
  1639.  
  1640. [26:37]
  1641. something like that because nobody even
  1642.  
  1643. [26:40]
  1644. the people who who work with it that
  1645.  
  1646. [26:43]
  1647. they have no idea what is what has been
  1648.  
  1649. [26:45]
  1650. learned that's a really interesting
  1651.  
  1652. [26:48]
  1653. thought that it's it makes algorithms
  1654.  
  1655. [26:53]
  1656. more accessible to a different community
  1657.  
  1658. [26:56]
  1659. a different type of brain yep and that's
  1660.  
  1661. [26:59]
  1662. really interesting because just like
  1663.  
  1664. [27:03]
  1665. literate programming perhaps could make
  1666.  
  1667. [27:06]
  1668. programming more accessible to a certain
  1669.  
  1670. [27:09]
  1671. kind of brain there are people who think
  1672.  
  1673. [27:11]
  1674. it's just a matter of Education and
  1675.  
  1676. [27:13]
  1677. anybody can learn to be a great program
  1678.  
  1679. [27:16]
  1680. or anybody can
  1681.  
  1682. [27:17]
  1683. to be a great skier uh yeah you know I I
  1684.  
  1685. [27:23]
  1686. wish that were true but but I know that
  1687.  
  1688. [27:25]
  1689. there's a lot of things that I've tried
  1690.  
  1691. [27:27]
  1692. to do and I and like I was well motivate
  1693.  
  1694. [27:30]
  1695. an icon and I kept trying to build
  1696.  
  1697. [27:33]
  1698. myself up and I never got past a certain
  1699.  
  1700. [27:35]
  1701. level I can't use for example I can't
  1702.  
  1703. [27:38]
  1704. view three-dimensional objects in my in
  1705.  
  1706. [27:43]
  1707. my head I have to I have to make a model
  1708.  
  1709. [27:45]
  1710. and look at it and study it from all
  1711.  
  1712. [27:47]
  1713. points of view and then I start to get
  1714.  
  1715. [27:49]
  1716. some idea but other people are good at
  1717.  
  1718. [27:52]
  1719. four dimensions I mean physicists yeah
  1720.  
  1721. [27:57]
  1722. so let's go to the art of computer
  1723.  
  1724. [28:03]
  1725. programming in 1962 you set the table of
  1726.  
  1727. [28:07]
  1728. contents for this magnum opus right yeah
  1729.  
  1730. [28:13]
  1731. it was supposed to be a single book for
  1732.  
  1733. [28:15]
  1734. 12 chapters now today what is it
  1735.  
  1736. [28:19]
  1737. 57 years later you're in the middle of
  1738.  
  1739. [28:23]
  1740. volume 4 of 7 and in the middle of going
  1741.  
  1742. [28:27]
  1743. for B is 4 B precisely can ask you for
  1744.  
  1745. [28:31]
  1746. an impossible task which is try to
  1747.  
  1748. [28:34]
  1749. summarize the book so far maybe by
  1750.  
  1751. [28:39]
  1752. giving a little examples so from the
  1753.  
  1754. [28:42]
  1755. sorting and the search in the
  1756.  
  1757. [28:43]
  1758. combinatorial algorithms if you were to
  1759.  
  1760. [28:46]
  1761. give a summary a quick elevator summary
  1762.  
  1763. [28:51]
  1764. yeah right what depending how many
  1765.  
  1766. [28:53]
  1767. floors that are in the building yes
  1768.  
  1769. [28:55]
  1770. the first volume called fundamental
  1771.  
  1772. [28:57]
  1773. algorithms talks about something that
  1774.  
  1775. [29:01]
  1776. you can't the stuff you can't do without
  1777.  
  1778. [29:03]
  1779. I guess that you have to know the basic
  1780.  
  1781. [29:07]
  1782. concepts of what is a program now what
  1783.  
  1784. [29:10]
  1785. is it what is it algorithm and and and
  1786.  
  1787. [29:13]
  1788. it also talks about a low-level machine
  1789.  
  1790. [29:15]
  1791. so you can have some some kind of an
  1792.  
  1793. [29:17]
  1794. idea what's going on and it has basic
  1795.  
  1796. [29:22]
  1797. concepts of input/output and subroutines
  1798.  
  1799. [29:26]
  1800. induction induction writes mathematical
  1801.  
  1802. [29:30]
  1803. so so the thing that makes my book
  1804.  
  1805. [29:33]
  1806. different from a lot of others is that
  1807.  
  1808. [29:37]
  1809. all that I try to not only present the
  1810.  
  1811. [29:40]
  1812. algún but I try to analyze them and
  1813.  
  1814. [29:42]
  1815. which means to quantitatively I say not
  1816.  
  1817. [29:44]
  1818. only does it work but it works this fast
  1819.  
  1820. [29:46]
  1821. okay and so I need math for them and
  1822.  
  1823. [29:49]
  1824. then there's the standard way to
  1825.  
  1826. [29:51]
  1827. structure data inside and represent
  1828.  
  1829. [29:53]
  1830. information in the computer so that's
  1831.  
  1832. [29:56]
  1833. all volume 1 volume 2 talks
  1834.  
  1835. [29:59]
  1836. it's called semi numerical algorithms
  1837.  
  1838. [30:01]
  1839. and here we're here we're writing
  1840.  
  1841. [30:03]
  1842. programs but we're also dealing with
  1843.  
  1844. [30:06]
  1845. numbers algorithms deal with with with
  1846.  
  1847. [30:09]
  1848. any kinds of objects but but specific
  1849.  
  1850. [30:11]
  1851. when there's objects or numbers well
  1852.  
  1853. [30:13]
  1854. then then we have certain special
  1855.  
  1856. [30:17]
  1857. paradigms that apply to things that have
  1858.  
  1859. [30:19]
  1860. 12 numbers and so there's there's what
  1861.  
  1862. [30:21]
  1863. there's like there's arithmetic on
  1864.  
  1865. [30:24]
  1866. numbers and and there's matrices full of
  1867.  
  1868. [30:26]
  1869. numbers there's random numbers and
  1870.  
  1871. [30:29]
  1872. there's power series full of numbers
  1873.  
  1874. [30:31]
  1875. there's different algebraic concepts
  1876.  
  1877. [30:34]
  1878. that have numbers in structured ways and
  1879.  
  1880. [30:37]
  1881. the arithmetic in the way a computer
  1882.  
  1883. [30:38]
  1884. would think about arithmetic is a
  1885.  
  1886. [30:40]
  1887. floating point floating point arithmetic
  1888.  
  1889. [30:42]
  1890. a high precision arithmetic not only
  1891.  
  1892. [30:45]
  1893. addition subtraction multiplication but
  1894.  
  1895. [30:47]
  1896. also comparison up number
  1897.  
  1898. [30:50]
  1899. so then check then volume three talks
  1900.  
  1901. [30:53]
  1902. about I like that one sort insert
  1903.  
  1904. [30:55]
  1905. sorting a circle of sorting right so so
  1906.  
  1907. [30:58]
  1908. here you know we're not getting
  1909.  
  1910. [30:59]
  1911. necessarily with numbers because you
  1912.  
  1913. [31:01]
  1914. slipped you saw it letters and other
  1915.  
  1916. [31:03]
  1917. objects and searching we're doing all
  1918.  
  1919. [31:04]
  1920. the time we googled nowadays but I mean
  1921.  
  1922. [31:06]
  1923. we have to find stuff
  1924.  
  1925. [31:08]
  1926. so again algorithms that that underlie
  1927.  
  1928. [31:13]
  1929. all kinds of applications like you know
  1930.  
  1931. [31:16]
  1932. none of these volumes it's about a
  1933.  
  1934. [31:17]
  1935. particular application but the
  1936.  
  1937. [31:19]
  1938. applications are examples of of why
  1939.  
  1940. [31:22]
  1941. people want to know about sorting why
  1942.  
  1943. [31:23]
  1944. people want to know about random numbers
  1945.  
  1946. [31:25]
  1947. so then volume 4 goes into combinatorial
  1948.  
  1949. [31:29]
  1950. I'll again this is where we have
  1951.  
  1952. [31:32]
  1953. zillions of things to deal with and we
  1954.  
  1955. [31:35]
  1956. and here we keep finding cases where one
  1957.  
  1958. [31:41]
  1959. good idea can can make something go more
  1960.  
  1961. [31:43]
  1962. than a million times faster and and and
  1963.  
  1964. [31:48]
  1965. we're dealing with problems that are
  1966.  
  1967. [31:50]
  1968. probably never going to be solved
  1969.  
  1970. [31:52]
  1971. efficiently but that doesn't mean we
  1972.  
  1973. [31:55]
  1974. give up on them and and and we have this
  1975.  
  1976. [31:58]
  1977. chance to have good ideas and and go
  1978.  
  1979. [32:00]
  1980. much much faster on them so so that's
  1981.  
  1982. [32:03]
  1983. comets are all algorithms and those are
  1984.  
  1985. [32:05]
  1986. the ones that are yeah I'm using
  1987.  
  1988. [32:07]
  1989. charting is most fun for you well how
  1990.  
  1991. [32:11]
  1992. many toriel algorithms are the ones that
  1993.  
  1994. [32:14]
  1995. I always that I always enjoyed the most
  1996.  
  1997. [32:17]
  1998. because that's when my skillet
  1999.  
  2000. [32:20]
  2001. programming had most payoff you know the
  2002.  
  2003. [32:23]
  2004. different the difference between an
  2005.  
  2006. [32:24]
  2007. obvious algorithm that you think up
  2008.  
  2009. [32:26]
  2010. first thing and you know and a good you
  2011.  
  2012. [32:29]
  2013. know an interesting subtle out algorithm
  2014.  
  2015. [32:32]
  2016. that not so obvious but but run circles
  2017.  
  2018. [32:36]
  2019. around the other one that's that's where
  2020.  
  2021. [32:39]
  2022. computer science 3d comes comes in and
  2023.  
  2024. [32:42]
  2025. and a lot of these comets are methods
  2026.  
  2027. [32:45]
  2028. were found first in applications to
  2029.  
  2030. [32:49]
  2031. artificial intelligence or cryptography
  2032.  
  2033. [32:53]
  2034. and in my case I I just liked him and it
  2035.  
  2036. [32:58]
  2037. was associated more with puzzles that
  2038.  
  2039. [33:00]
  2040. you like the most in the domain of
  2041.  
  2042. [33:02]
  2043. graphs and graph theory graphs are great
  2044.  
  2045. [33:05]
  2046. because they're terrific models of so
  2047.  
  2048. [33:08]
  2049. many things in the real world and and
  2050.  
  2051. [33:10]
  2052. and and you you throw numbers on a graph
  2053.  
  2054. [33:13]
  2055. you got a network and so there you're
  2056.  
  2057. [33:15]
  2058. right there you have but many more
  2059.  
  2060. [33:18]
  2061. things so but comma toriel in general is
  2062.  
  2063. [33:22]
  2064. in any arrangement of objects that that
  2065.  
  2066. [33:26]
  2067. has some kind of a higher structure non
  2068.  
  2069. [33:30]
  2070. non random structure and it's okay
  2071.  
  2072. [33:34]
  2073. it is possible to put something together
  2074.  
  2075. [33:37]
  2076. satisfying all these conditions like I
  2077.  
  2078. [33:39]
  2079. mentioned arrows a minute ago you know
  2080.  
  2081. [33:41]
  2082. is there a way to to put these numbers
  2083.  
  2084. [33:44]
  2085. on a bunch of boxes that that are
  2086.  
  2087. [33:46]
  2088. pointing to each other is that going to
  2089.  
  2090. [33:47]
  2091. be possible at all that's volume four
  2092.  
  2093. [33:49]
  2094. that's volume four what is a sage of
  2095.  
  2096. [33:52]
  2097. Hawaiian for a was part one and and what
  2098.  
  2099. [33:56]
  2100. happened was in 1962 when I started
  2101.  
  2102. [33:59]
  2103. writing down a table of contents it
  2104.  
  2105. [34:03]
  2106. wasn't going to be a book about computer
  2107.  
  2108. [34:06]
  2109. programming in general it was going to
  2110.  
  2111. [34:07]
  2112. be a book about how to write compilers
  2113.  
  2114. [34:09]
  2115. and I was asked to write a book
  2116.  
  2117. [34:13]
  2118. explaining how to how to write a
  2119.  
  2120. [34:15]
  2121. compiler and at that time there were
  2122.  
  2123. [34:20]
  2124. only a few dozen people in the world who
  2125.  
  2126. [34:22]
  2127. had written compilers and I happen to be
  2128.  
  2129. [34:24]
  2130. one of them so and I also had some
  2131.  
  2132. [34:29]
  2133. experience for writing for like the
  2134.  
  2135. [34:33]
  2136. campus newspaper and things like that so
  2137.  
  2138. [34:35]
  2139. so I said okay great I'm the only person
  2140.  
  2141. [34:39]
  2142. I know who who's written a compiler but
  2143.  
  2144. [34:42]
  2145. hasn't invented any new techniques for
  2146.  
  2147. [34:43]
  2148. writing compilers and and all the other
  2149.  
  2150. [34:45]
  2151. people I knew had super ideas but I
  2152.  
  2153. [34:50]
  2154. couldn't see that they would be able to
  2155.  
  2156. [34:51]
  2157. write a book that wouldn't that would
  2158.  
  2159. [34:53]
  2160. describe anybody else's ideas with their
  2161.  
  2162. [34:55]
  2163. own so I could be the I could be the
  2164.  
  2165. [34:57]
  2166. journalist and I could explained what
  2167.  
  2168. [34:59]
  2169. all these cool ideas about compiler
  2170.  
  2171. [35:02]
  2172. writing that were and and then I I
  2173.  
  2174. [35:06]
  2175. started pretty
  2176.  
  2177. [35:07]
  2178. well yeah let me you need and have a
  2179.  
  2180. [35:09]
  2181. chapter about data structures you need
  2182.  
  2183. [35:11]
  2184. to have some introductory material I
  2185.  
  2186. [35:13]
  2187. want to talk about searching because a
  2188.  
  2189. [35:15]
  2190. compiler writer has to it has to look up
  2191.  
  2192. [35:19]
  2193. the variables in a symbol table and find
  2194.  
  2195. [35:22]
  2196. out you know which which when you when
  2197.  
  2198. [35:27]
  2199. you write the name of a variable in one
  2200.  
  2201. [35:29]
  2202. place it's supposed to be the same as
  2203.  
  2204. [35:31]
  2205. the one you put somewhere else so you
  2206.  
  2207. [35:33]
  2208. need all these basic techniques and I
  2209.  
  2210. [35:35]
  2211. and I you know kind of know some
  2212.  
  2213. [35:38]
  2214. arithmetic to stuff so I throw I threw
  2215.  
  2216. [35:40]
  2217. in these chapters and I threw in a
  2218.  
  2219. [35:42]
  2220. chapter on comma talks because that was
  2221.  
  2222. [35:46]
  2223. what I really enjoyed programming the
  2224.  
  2225. [35:48]
  2226. most but there weren't many algorithms
  2227.  
  2228. [35:49]
  2229. and known about combinatorial methods in
  2230.  
  2231. [35:51]
  2232. 1962 so that was a kind of a short
  2233.  
  2234. [35:54]
  2235. chapter but it was sort of thrown in
  2236.  
  2237. [35:56]
  2238. just for fun and Chapter twelve was
  2239.  
  2240. [35:59]
  2241. going to be actual compilers applying
  2242.  
  2243. [36:01]
  2244. all the stuff in chapters 1 to 11 to
  2245.  
  2246. [36:05]
  2247. make compilers well ok so that was my
  2248.  
  2249. [36:07]
  2250. table of contents from 1962 and during
  2251.  
  2252. [36:11]
  2253. the 70s the whole field of combinatoric
  2254.  
  2255. [36:14]
  2256. s-- went through a huge explosion people
  2257.  
  2258. [36:18]
  2259. talk about it comet oil explosion and
  2260.  
  2261. [36:20]
  2262. they usually mean by that that the
  2263.  
  2264. [36:22]
  2265. number of cases goes up you know you
  2266.  
  2267. [36:25]
  2268. change n to n plus 1 and all of a sudden
  2269.  
  2270. [36:27]
  2271. you your problem has gotten more than
  2272.  
  2273. [36:29]
  2274. ten times harder but there was an
  2275.  
  2276. [36:33]
  2277. explosion of ideas about combinatoric
  2278.  
  2279. [36:36]
  2280. s-- in the 70s and to the point that but
  2281.  
  2282. [36:39]
  2283. Mike's take 1975 I bet you more than
  2284.  
  2285. [36:44]
  2286. half of all the journals of computer
  2287.  
  2288. [36:45]
  2289. science we're about combinatorial method
  2290.  
  2291. [36:48]
  2292. and what kind of problems were occupying
  2293.  
  2294. [36:50]
  2295. people's minds what kind of problems in
  2296.  
  2297. [36:53]
  2298. combinatorics was it's it's that gravity
  2299.  
  2300. [36:56]
  2301. graph theory yeah gravity was was quite
  2302.  
  2303. [36:59]
  2304. dominant I mean no but all of the
  2305.  
  2306. [37:03]
  2307. np-hard problems that you have like
  2308.  
  2309. [37:07]
  2310. Hamiltonian path or foul sail going
  2311.  
  2312. [37:10]
  2313. beyond yeah yeah going beyond graphs you
  2314.  
  2315. [37:12]
  2316. had a operation research whenever it was
  2317.  
  2318. [37:16]
  2319. a small class of problems that had
  2320.  
  2321. [37:18]
  2322. efficient solutions and they were
  2323.  
  2324. [37:19]
  2325. associated with Maitre D' a special
  2326.  
  2327. [37:22]
  2328. mathematical construction but once we
  2329.  
  2330. [37:25]
  2331. went to things that involve three things
  2332.  
  2333. [37:28]
  2334. at a time instead of instead of two all
  2335.  
  2336. [37:30]
  2337. of a sudden the things got harder so we
  2338.  
  2339. [37:32]
  2340. had satisfiability problems or if you
  2341.  
  2342. [37:35]
  2343. have if you have clauses every Clause
  2344.  
  2345. [37:38]
  2346. has two logical elements in it then we
  2347.  
  2348. [37:40]
  2349. can satisfy it linear time we can test
  2350.  
  2351. [37:43]
  2352. for satisfy building linear time but if
  2353.  
  2354. [37:45]
  2355. you allow yourself three variables in
  2356.  
  2357. [37:48]
  2358. the clause then nobody knows how to do
  2359.  
  2360. [37:52]
  2361. it so these articles were about trying
  2362.  
  2363. [37:54]
  2364. to find better or better ways to to
  2365.  
  2366. [37:58]
  2367. solve cryptography problems and graph
  2368.  
  2369. [38:00]
  2370. three problems where the we have lots of
  2371.  
  2372. [38:03]
  2373. data but we didn't know how to find the
  2374.  
  2375. [38:05]
  2376. best subset so the data like with
  2377.  
  2378. [38:08]
  2379. sorting we could get the answer didn't
  2380.  
  2381. [38:12]
  2382. take long so how did they continue to
  2383.  
  2384. [38:14]
  2385. change from the 70s to today yeah so now
  2386.  
  2387. [38:17]
  2388. there may be half a dozen conferences
  2389.  
  2390. [38:20]
  2391. whose topic is cognate arcs different
  2392.  
  2393. [38:24]
  2394. kind but fortunately I don't have to
  2395.  
  2396. [38:26]
  2397. rewrite my book every month you know
  2398.  
  2399. [38:28]
  2400. like I had to in in the 70 but still
  2401.  
  2402. [38:31]
  2403. there's huge amount of work being done
  2404.  
  2405. [38:33]
  2406. and people getting better ideas on these
  2407.  
  2408. [38:37]
  2409. problems that don't seem to have really
  2410.  
  2411. [38:40]
  2412. efficient solutions but we can still get
  2413.  
  2414. [38:42]
  2415. into a lot more with him and so this
  2416.  
  2417. [38:45]
  2418. book that I'm finishing now is I've got
  2419.  
  2420. [38:48]
  2421. a whole bunch of brand new methods that
  2422.  
  2423. [38:51]
  2424. the fires I know there's no other
  2425.  
  2426. [38:53]
  2427. there's no other book that covers that
  2428.  
  2429. [38:57]
  2430. covers this particular approach and and
  2431.  
  2432. [39:00]
  2433. so I'm trying to do my best of exploring
  2434.  
  2435. [39:04]
  2436. the tip of the iceberg and and and I try
  2437.  
  2438. [39:08]
  2439. out lots of things and and keep keep
  2440.  
  2441. [39:11]
  2442. rewriting finding as I find better
  2443.  
  2444. [39:14]
  2445. better method so what's your writing
  2446.  
  2447. [39:17]
  2448. process like what's your thinking and
  2449.  
  2450. [39:19]
  2451. writing process like every day so what's
  2452.  
  2453. [39:24]
  2454. your routine even
  2455.  
  2456. [39:25]
  2457. yeah I guess it's actually the best
  2458.  
  2459. [39:29]
  2460. question because I spent seven days a
  2461.  
  2462. [39:31]
  2463. week
  2464.  
  2465. [39:32]
  2466. you're doing it the most prepares to
  2467.  
  2468. [39:35]
  2469. answer it yeah yeah but okay so the
  2470.  
  2471. [39:41]
  2472. chair I'm sitting in is where I do
  2473.  
  2474. [39:44]
  2475. that's where the magic happens well
  2476.  
  2477. [39:47]
  2478. reading and writing that many chairs
  2479.  
  2480. [39:49]
  2481. usually sitting over there where I have
  2482.  
  2483. [39:50]
  2484. other books some reference book but but
  2485.  
  2486. [39:53]
  2487. I I found his chair which was designed
  2488.  
  2489. [39:58]
  2490. by a Swedish guy anyway it turns out
  2491.  
  2492. [40:01]
  2493. this was the only chair I can really sit
  2494.  
  2495. [40:02]
  2496. in for hours and hours and not know that
  2497.  
  2498. [40:04]
  2499. I'm in a chair but then I have the
  2500.  
  2501. [40:06]
  2502. stand-up desk right next next to us and
  2503.  
  2504. [40:08]
  2505. and so after I write something with
  2506.  
  2507. [40:11]
  2508. pencil and eraser I get up and I type it
  2509.  
  2510. [40:15]
  2511. and revise and rewrite the kernel the
  2512.  
  2513. [40:21]
  2514. idea is first put on paper yep
  2515.  
  2516. [40:24]
  2517. that's worth right and I call right
  2518.  
  2519. [40:27]
  2520. maybe five programs a week of course
  2521.  
  2522. [40:31]
  2523. literate programming and these are
  2524.  
  2525. [40:34]
  2526. before I describe something in my book I
  2527.  
  2528. [40:36]
  2529. always program it to see how it's
  2530.  
  2531. [40:38]
  2532. working and I and I tried a lot so for
  2533.  
  2534. [40:42]
  2535. example I learned at the end of January
  2536.  
  2537. [40:44]
  2538. I learned of a breakthrough by for
  2539.  
  2540. [40:48]
  2541. Japanese people who had extended one of
  2542.  
  2543. [40:51]
  2544. the one of my methods in in a new
  2545.  
  2546. [40:53]
  2547. direction and so I I spent the next five
  2548.  
  2549. [40:56]
  2550. days writing a program to implement what
  2551.  
  2552. [40:59]
  2553. they did and then I you know but they
  2554.  
  2555. [41:01]
  2556. had only generalized part of what I had
  2557.  
  2558. [41:04]
  2559. done so that I had to see if I could
  2560.  
  2561. [41:06]
  2562. generalize more parts of it and then I
  2563.  
  2564. [41:08]
  2565. had to take their approach and I had to
  2566.  
  2567. [41:11]
  2568. I had to try it out on a couple of dozen
  2569.  
  2570. [41:13]
  2571. of the other problems I had already
  2572.  
  2573. [41:15]
  2574. worked out with that with my old methods
  2575.  
  2576. [41:17]
  2577. and so that took another couple of weeks
  2578.  
  2579. [41:19]
  2580. and then I would you know then I then I
  2581.  
  2582. [41:22]
  2583. started to see the light nicely and and
  2584.  
  2585. [41:26]
  2586. I started writing the final draft and
  2587.  
  2588. [41:29]
  2589. and then I would you know type it up
  2590.  
  2591. [41:32]
  2592. involves some new mathematical questions
  2593.  
  2594. [41:34]
  2595. and so I wrote to my friends and might
  2596.  
  2597. [41:37]
  2598. be good at solving those problems and
  2599.  
  2600. [41:39]
  2601. and they solve some of them so I put
  2602.  
  2603. [41:43]
  2604. that in his exercises and and so a month
  2605.  
  2606. [41:46]
  2607. later I had absorbed one new idea that I
  2608.  
  2609. [41:50]
  2610. that I learned and you know I'm glad I
  2611.  
  2612. [41:53]
  2613. heard about it in time otherwise my I
  2614.  
  2615. [41:55]
  2616. wouldn't put my book out before I heard
  2617.  
  2618. [41:57]
  2619. about the idea on the other hand this
  2620.  
  2621. [41:59]
  2622. book was supposed to come in at 300
  2623.  
  2624. [42:01]
  2625. pages and I'm up to 350 now that added
  2626.  
  2627. [42:04]
  2628. 10 pages to the book but if I learn
  2629.  
  2630. [42:07]
  2631. about another one I probably first gonna
  2632.  
  2633. [42:10]
  2634. shoot me well so in the process in that
  2635.  
  2636. [42:15]
  2637. one month process are some days harder
  2638.  
  2639. [42:18]
  2640. than others are some days harder than
  2641.  
  2642. [42:20]
  2643. others well yeah my work is fun but I
  2644.  
  2645. [42:23]
  2646. also work hard and every big job has
  2647.  
  2648. [42:26]
  2649. parts that are a lot more fun than
  2650.  
  2651. [42:28]
  2652. others and so many days I'll say why do
  2653.  
  2654. [42:32]
  2655. I have to have such high standards like
  2656.  
  2657. [42:35]
  2658. why couldn't I just be sloppy and not
  2659.  
  2660. [42:36]
  2661. try this out and you know just just
  2662.  
  2663. [42:38]
  2664. report the answer but I but I know that
  2665.  
  2666. [42:42]
  2667. people are conning me to do this and so
  2668.  
  2669. [42:45]
  2670. okay so okay Donald grit my teeth and do
  2671.  
  2672. [42:49]
  2673. it and and and then the joy comes out
  2674.  
  2675. [42:52]
  2676. when I see that actually you know I'm
  2677.  
  2678. [42:54]
  2679. getting good results and and and I get
  2680.  
  2681. [42:56]
  2682. and I even more when I see that somebody
  2683.  
  2684. [43:00]
  2685. has actually read and understood what I
  2686.  
  2687. [43:02]
  2688. wrote and told me how to make it even
  2689.  
  2690. [43:04]
  2691. better I did want to mention something
  2692.  
  2693. [43:08]
  2694. about the about the method so I got this
  2695.  
  2696. [43:12]
  2697. tablet here where I do the first you
  2698.  
  2699. [43:19]
  2700. know the first writing of concepts okay
  2701.  
  2702. [43:23]
  2703. so so and what language I didn't write
  2704.  
  2705. [43:28]
  2706. so hey take a look at but you know here
  2707.  
  2708. [43:30]
  2709. random say explain how to draw such
  2710.  
  2711. [43:33]
  2712. skewed pixel diagrams okay so I got this
  2713.  
  2714. [43:36]
  2715. paper about 40 years ago when I was
  2716.  
  2717. [43:40]
  2718. visiting my sister in Canada and they
  2719.  
  2720. [43:42]
  2721. make tablets of paper with this nice
  2722.  
  2723. [43:45]
  2724. large size and just the right very small
  2725.  
  2726. [43:48]
  2727. space between like oh yeah yeah
  2728.  
  2729. [43:50]
  2730. particularly also just yeah
  2731.  
  2732. [43:58]
  2733. you know I've got these manuscripts
  2734.  
  2735. [44:00]
  2736. going back to the 60s and and and those
  2737.  
  2738. [44:06]
  2739. are when I get my ideas on paper okay
  2740.  
  2741. [44:09]
  2742. but I'm a good typist in fact I went to
  2743.  
  2744. [44:11]
  2745. type in school when I was when I was in
  2746.  
  2747. [44:14]
  2748. high school and so I can type faster
  2749.  
  2750. [44:15]
  2751. than I think so then when I do the
  2752.  
  2753. [44:18]
  2754. editing you know stand up and type then
  2755.  
  2756. [44:21]
  2757. I then I revise this and it comes out a
  2758.  
  2759. [44:24]
  2760. lot different than what you look for
  2761.  
  2762. [44:27]
  2763. style and rhythm and things like that
  2764.  
  2765. [44:28]
  2766. come out at the at the typing state and
  2767.  
  2768. [44:30]
  2769. you type in tack and I type in tack and
  2770.  
  2771. [44:34]
  2772. can you can you think in tech No so to a
  2773.  
  2774. [44:38]
  2775. certain extent I have I have only a
  2776.  
  2777. [44:40]
  2778. small number of idioms that I use like
  2779.  
  2780. [44:44]
  2781. you know a beginning or theorem I do
  2782.  
  2783. [44:45]
  2784. something for displayed equation I do
  2785.  
  2786. [44:47]
  2787. something and and so on but I but I I
  2788.  
  2789. [44:50]
  2790. have to see it and in the way that it's
  2791.  
  2792. [44:54]
  2793. on here yeah right for example touring
  2794.  
  2795. [44:56]
  2796. wrote what the other direction
  2797.  
  2798. [44:59]
  2799. you don't write macros you don't think
  2800.  
  2801. [45:03]
  2802. in macros particularly but when I need a
  2803.  
  2804. [45:05]
  2805. macro I'll go ahead and and these and do
  2806.  
  2807. [45:09]
  2808. it but but the thing is they I also
  2809.  
  2810. [45:11]
  2811. write to fit I mean I'll I'll change
  2812.  
  2813. [45:15]
  2814. something if I can if I can save a line
  2815.  
  2816. [45:17]
  2817. I've got you know it's like haiku I'll
  2818.  
  2819. [45:19]
  2820. figure out a way to rewrite the sentence
  2821.  
  2822. [45:21]
  2823. so that it'll look better on the page
  2824.  
  2825. [45:24]
  2826. and I shouldn't be wasting my time on
  2827.  
  2828. [45:26]
  2829. that but but I can't resist because I
  2830.  
  2831. [45:29]
  2832. know it's only another three percent of
  2833.  
  2834. [45:32]
  2835. the time or something like that and it
  2836.  
  2837. [45:34]
  2838. could also be argued that that is what
  2839.  
  2840. [45:36]
  2841. life is about
  2842.  
  2843. [45:38]
  2844. ah yes in fact that's true like like I
  2845.  
  2846. [45:43]
  2847. worked in the garden one day a week and
  2848.  
  2849. [45:45]
  2850. that's that's kind of a description of
  2851.  
  2852. [45:47]
  2853. my life is getting rid of weeds you know
  2854.  
  2855. [45:50]
  2856. removing bugs for programs in so you
  2857.  
  2858. [45:53]
  2859. know a lot of writers talk about you
  2860.  
  2861. [45:55]
  2862. know basically suffering the writing
  2863.  
  2864. [45:57]
  2865. processes yeah having you know it's
  2866.  
  2867. [46:00]
  2868. extremely difficult and I think of
  2869.  
  2870. [46:02]
  2871. programming especially the or technical
  2872.  
  2873. [46:05]
  2874. writing that you're doing can be like
  2875.  
  2876. [46:08]
  2877. that do you find yourself
  2878.  
  2879. [46:11]
  2880. methodologically how do you every day
  2881.  
  2882. [46:14]
  2883. sit down to do the work is it a
  2884.  
  2885. [46:17]
  2886. challenge you kind of say it's you know
  2887.  
  2888. [46:20]
  2889. oh yeah it's fun
  2890.  
  2891. [46:24]
  2892. but it'd be interesting to hear if there
  2893.  
  2894. [46:27]
  2895. are non fun parts that you really
  2896.  
  2897. [46:29]
  2898. struggle with yes the fun comes with
  2899.  
  2900. [46:32]
  2901. when I'm able to put together ideas of
  2902.  
  2903. [46:36]
  2904. to two people who didn't know about each
  2905.  
  2906. [46:38]
  2907. other and and and so I might be the
  2908.  
  2909. [46:41]
  2910. first person that saw both of their
  2911.  
  2912. [46:42]
  2913. ideas and so then you know then I get to
  2914.  
  2915. [46:46]
  2916. make the synthesis and that gives me a
  2917.  
  2918. [46:49]
  2919. chance to be creative but the dredge
  2920.  
  2921. [46:52]
  2922. work is where I act I've got a chase
  2923.  
  2924. [46:55]
  2925. everything down to its root this leads
  2926.  
  2927. [46:58]
  2928. me into really interesting stuff i mean
  2929.  
  2930. [47:00]
  2931. like i learned about sanskrit nice yeah
  2932.  
  2933. [47:02]
  2934. and again you know I try to give credit
  2935.  
  2936. [47:05]
  2937. to all the authors and so I write like
  2938.  
  2939. [47:07]
  2940. so I write to people who know that the
  2941.  
  2942. [47:11]
  2943. people thought as if they're dead I
  2944.  
  2945. [47:13]
  2946. communicate this way I and I gotta get
  2947.  
  2948. [47:17]
  2949. the math right and I got a tack all my
  2950.  
  2951. [47:19]
  2952. programs try to find holes in them and I
  2953.  
  2954. [47:23]
  2955. rewrite the programs over after I get a
  2956.  
  2957. [47:25]
  2958. better idea
  2959.  
  2960. [47:26]
  2961. is there ever dead-ends data and so yeah
  2962.  
  2963. [47:29]
  2964. I throw stuff out yeah look one of the
  2965.  
  2966. [47:32]
  2967. things that I spent a lot of time
  2968.  
  2969. [47:35]
  2970. preparing a major example based on the
  2971.  
  2972. [47:38]
  2973. game of baseball and I know a lot of
  2974.  
  2975. [47:41]
  2976. people who for whom baseball is the most
  2977.  
  2978. [47:44]
  2979. important thing in the world you know
  2980.  
  2981. [47:46]
  2982. yes but it's but I also know a lot of
  2983.  
  2984. [47:47]
  2985. people from cricket is the most
  2986.  
  2987. [47:49]
  2988. important in the world or suck or
  2989.  
  2990. [47:52]
  2991. something you know and and I realized
  2992.  
  2993. [47:55]
  2994. that if if I had a big sample I mean it
  2995.  
  2996. [47:58]
  2997. was gonna have a fold-out illustration
  2998.  
  2999. [47:59]
  3000. and everything I was saying well what
  3001.  
  3002. [48:01]
  3003. what am I really teaching about
  3004.  
  3005. [48:02]
  3006. algorithms here where I had this this is
  3007.  
  3008. [48:05]
  3009. this baseball example and if I was a
  3010.  
  3011. [48:07]
  3012. person who who knew only cricket
  3013.  
  3014. [48:10]
  3015. wouldn't think what would they think
  3016.  
  3017. [48:12]
  3018. about this and and so I ripped the whole
  3019.  
  3020. [48:14]
  3021. thing out but I you know I had I had a
  3022.  
  3023. [48:17]
  3024. something that would really appeal to
  3025.  
  3026. [48:19]
  3027. people who grew up with baseball as as
  3028.  
  3029. [48:21]
  3030. has a major theme in their life which is
  3031.  
  3032. [48:24]
  3033. a lot of people but yeah so I said on
  3034.  
  3035. [48:27]
  3036. minority the small minority I took out
  3037.  
  3038. [48:30]
  3039. bowling to
  3040.  
  3041. [48:32]
  3042. even a smaller my noise what's the art
  3043.  
  3044. [48:37]
  3045. in the art of programming why why is
  3046.  
  3047. [48:42]
  3048. there of the few words in the title why
  3049.  
  3050. [48:45]
  3051. is art one of them yeah well that's
  3052.  
  3053. [48:47]
  3054. that's what I wrote my Turing lecture
  3055.  
  3056. [48:49]
  3057. about and and so when people talk about
  3058.  
  3059. [48:53]
  3060. art it really I mean what the word means
  3061.  
  3062. [48:57]
  3063. is something that's not a nature so when
  3064.  
  3065. [49:02]
  3066. you have artificial intelligence that
  3067.  
  3068. [49:05]
  3069. that art come from the same root saying
  3070.  
  3071. [49:09]
  3072. that this is something that was created
  3073.  
  3074. [49:11]
  3075. by by human beings and then it's gotten
  3076.  
  3077. [49:16]
  3078. a further meaning often a fine art which
  3079.  
  3080. [49:19]
  3081. has this beauty to the to the mix and
  3082.  
  3083. [49:21]
  3084. says you know we have things that are
  3085.  
  3086. [49:23]
  3087. artistically done and and this means not
  3088.  
  3089. [49:26]
  3090. only done by humans but also done in a
  3091.  
  3092. [49:29]
  3093. way that's elegant and brings joy and
  3094.  
  3095. [49:33]
  3096. and has has I guess what
  3097.  
  3098. [49:39]
  3099. Tolstoy burrs dusky but anyway it it's
  3100.  
  3101. [49:46]
  3102. that part that that says that it's done
  3103.  
  3104. [49:49]
  3105. well as well as not only a different
  3106.  
  3107. [49:53]
  3108. from nature in general then alright is
  3109.  
  3110. [49:58]
  3111. what human beings are specifically good
  3112.  
  3113. [50:01]
  3114. at and when they say hey like artificial
  3115.  
  3116. [50:03]
  3117. intelligence well they're trying to
  3118.  
  3119. [50:05]
  3120. mimic human beings but there's an
  3121.  
  3122. [50:07]
  3123. element of fine art and beauty you are
  3124.  
  3125. [50:11]
  3126. well that's what I that's what I try to
  3127.  
  3128. [50:13]
  3129. also say that you can write a program
  3130.  
  3131. [50:16]
  3132. and make a work of art so now in terms
  3133.  
  3134. [50:22]
  3135. of surprising you know what ideas in
  3136.  
  3137. [50:28]
  3138. writing from sort and search to the
  3139.  
  3140. [50:32]
  3141. combinatorial algorithms what ideas have
  3142.  
  3143. [50:35]
  3144. you come across that were particularly
  3145.  
  3146. [50:40]
  3147. surprising to you that that change the
  3148.  
  3149. [50:44]
  3150. way you see a space of
  3151.  
  3152. [50:47]
  3153. I get a surprise every time I have a bug
  3154.  
  3155. [50:49]
  3156. in my program but but that isn't really
  3157.  
  3158. [50:52]
  3159. what your transformational surprises for
  3160.  
  3161. [50:57]
  3162. example in volume for a I was especially
  3163.  
  3164. [51:00]
  3165. surprised when I learned about data
  3166.  
  3167. [51:03]
  3168. structure called B BDD boolean decision
  3169.  
  3170. [51:06]
  3171. diagram because I sort of had the
  3172.  
  3173. [51:10]
  3174. feeling that as an old-timer and you
  3175.  
  3176. [51:15]
  3177. know I've been programming since this
  3178.  
  3179. [51:16]
  3180. since the 50s and bTW these weren't
  3181.  
  3182. [51:21]
  3183. invented until 1986 and here comes a
  3184.  
  3185. [51:24]
  3186. brand new idea that revolutionized the
  3187.  
  3188. [51:27]
  3189. way to represent a boolean function and
  3190.  
  3191. [51:29]
  3192. boolean functions are so basic to all
  3193.  
  3194. [51:32]
  3195. kinds of things in it I mean logically
  3196.  
  3197. [51:37]
  3198. underlies it everything we can describe
  3199.  
  3200. [51:40]
  3201. all of what we know in terms of logic
  3202.  
  3203. [51:43]
  3204. somehow and and here and and
  3205.  
  3206. [51:46]
  3207. propositional logic I thought that was
  3208.  
  3209. [51:51]
  3210. cutting Dryden everything was known but
  3211.  
  3212. [51:54]
  3213. but but he but here comes a Randy Bryant
  3214.  
  3215. [51:59]
  3216. and oh and discovers that BDDs are
  3217.  
  3218. [52:03]
  3219. incredibly powerful then then that's all
  3220.  
  3221. [52:07]
  3222. so I that mean means I have a whole new
  3223.  
  3224. [52:11]
  3225. section to the book that I never would
  3226.  
  3227. [52:13]
  3228. have thought of until 1986
  3229.  
  3230. [52:15]
  3231. not until 1990s when I went when people
  3232.  
  3233. [52:18]
  3234. started to got to use it for you know
  3235.  
  3236. [52:23]
  3237. billion dollar of applications and it
  3238.  
  3239. [52:26]
  3240. was it was the standard way to design
  3241.  
  3242. [52:28]
  3243. computers for a long time until until
  3244.  
  3245. [52:31]
  3246. sad solvers came along when in the year
  3247.  
  3248. [52:33]
  3249. 2000 so that's another great big
  3250.  
  3251. [52:35]
  3252. surprise so uh a lot of these things
  3253.  
  3254. [52:38]
  3255. have have totally changed the structure
  3256.  
  3257. [52:40]
  3258. of my book and the middle third of
  3259.  
  3260. [52:44]
  3261. volume four B's is about that solvers
  3262.  
  3263. [52:46]
  3264. and that's
  3265.  
  3266. [52:49]
  3267. 300 plus pages which is which is all
  3268.  
  3269. [52:53]
  3270. about material mostly about material
  3271.  
  3272. [52:56]
  3273. that was discovered in this century and
  3274.  
  3275. [52:59]
  3276. I had to start from scratch and meet all
  3277.  
  3278. [53:03]
  3279. the people in the field and right
  3280.  
  3281. [53:05]
  3282. I have 15 different sets Alvers that i
  3283.  
  3284. [53:07]
  3285. wrote while preparing that seven of them
  3286.  
  3287. [53:10]
  3288. are described in the book others were
  3289.  
  3290. [53:13]
  3291. for my own experience so newly invented
  3292.  
  3293. [53:16]
  3294. data structures or ways to represent a
  3295.  
  3296. [53:20]
  3297. whole new class of algorithm calling you
  3298.  
  3299. [53:22]
  3300. classified yeah and the interesting
  3301.  
  3302. [53:24]
  3303. thing about the BD DS was that the
  3304.  
  3305. [53:28]
  3306. theoretician started looking at it and
  3307.  
  3308. [53:31]
  3309. started to describe all the things you
  3310.  
  3311. [53:33]
  3312. couldn't do with BD DS and so they were
  3313.  
  3314. [53:37]
  3315. getting a bad they were getting a bad
  3316.  
  3317. [53:39]
  3318. name because you know okay they were
  3319.  
  3320. [53:43]
  3321. they were useful but they didn't solve
  3322.  
  3323. [53:46]
  3324. everything I'm sure that the
  3325.  
  3326. [53:48]
  3327. theoreticians are in the next 10 years
  3328.  
  3329. [53:51]
  3330. are gonna show why machine learning
  3331.  
  3332. [53:54]
  3333. doesn't solve everything but I not only
  3334.  
  3335. [53:58]
  3336. worried about the worst case I get a
  3337.  
  3338. [54:00]
  3339. huge delight when I can actually solve a
  3340.  
  3341. [54:02]
  3342. problem that I couldn't solve before
  3343.  
  3344. [54:04]
  3345. yeah even though I can't solve the
  3346.  
  3347. [54:07]
  3348. problem that's that it suggests as a
  3349.  
  3350. [54:09]
  3351. further problem like I know that I'm Way
  3352.  
  3353. [54:12]
  3354. better than I was before and so I found
  3355.  
  3356. [54:14]
  3357. out that BD DS could do all kinds of
  3358.  
  3359. [54:17]
  3360. miraculous things and so I had been
  3361.  
  3362. [54:24]
  3363. quite a few years learning about the
  3364.  
  3365. [54:28]
  3366. that territory so in general what brings
  3367.  
  3368. [54:32]
  3369. you more pleasure in proving or showing
  3370.  
  3371. [54:37]
  3372. a worst case analysis of an algorithm or
  3373.  
  3374. [54:40]
  3375. showing a good average case or just
  3376.  
  3377. [54:43]
  3378. showing a good case that you know
  3379.  
  3380. [54:46]
  3381. something good pragmatically can be done
  3382.  
  3383. [54:47]
  3384. with this algorithm yeah I like a good
  3385.  
  3386. [54:50]
  3387. case that that is maybe only a million
  3388.  
  3389. [54:53]
  3390. times faster than I was able to do
  3391.  
  3392. [54:54]
  3393. before but and not worried about the
  3394.  
  3395. [54:57]
  3396. fact that
  3397.  
  3398. [54:58]
  3399. and that is still that is still gonna
  3400.  
  3401. [55:01]
  3402. take too long if I double the size of
  3403.  
  3404. [55:03]
  3405. the problem so that said you popularize
  3406.  
  3407. [55:08]
  3408. the asymptotic notation for describing
  3409.  
  3410. [55:10]
  3411. running time obviously in the analysis
  3412.  
  3413. [55:14]
  3414. of algorithms worst cases such as such
  3415.  
  3416. [55:17]
  3417. an important part do you see any aspects
  3418.  
  3419. [55:20]
  3420. of that kind of analysis is lacking so
  3421.  
  3422. [55:24]
  3423. and notation - well the main purpose you
  3424.  
  3425. [55:28]
  3426. have notations that that help us for the
  3427.  
  3428. [55:32]
  3429. problems we want to solve and so that
  3430.  
  3431. [55:33]
  3432. they match our they match our intuitions
  3433.  
  3434. [55:36]
  3435. and people who worked in number theory
  3436.  
  3437. [55:38]
  3438. had used asymptotic notation in what
  3439.  
  3440. [55:41]
  3441. Ennis in a certain way but it was only
  3442.  
  3443. [55:44]
  3444. known to a small group of people and and
  3445.  
  3446. [55:46]
  3447. I realized that in fact it was very
  3448.  
  3449. [55:50]
  3450. useful to be able to have a notation for
  3451.  
  3452. [55:52]
  3453. something that we don't know exactly
  3454.  
  3455. [55:54]
  3456. what it is but we only know partial
  3457.  
  3458. [55:56]
  3459. about it and so on stick so for example
  3460.  
  3461. [56:00]
  3462. instead of Big O notation let's just
  3463.  
  3464. [56:02]
  3465. let's just take us a much simpler
  3466.  
  3467. [56:04]
  3468. notation where I say 0 or 1 or 0 1 or 2
  3469.  
  3470. [56:09]
  3471. and suppose that suppose that when I had
  3472.  
  3473. [56:13]
  3474. been in high school we would be allowed
  3475.  
  3476. [56:15]
  3477. to put in the middle of our formula x +
  3478.  
  3479. [56:18]
  3480. 0 1 or 2 equals y okay and then then we
  3481.  
  3482. [56:24]
  3483. would learn how to multiply two such
  3484.  
  3485. [56:27]
  3486. expressions together and and you know
  3487.  
  3488. [56:30]
  3489. deal with them
  3490.  
  3491. [56:32]
  3492. well the same thing Big O notation says
  3493.  
  3494. [56:34]
  3495. here's something that's I'm not sure
  3496.  
  3497. [56:38]
  3498. what it is but I know it's not too big I
  3499.  
  3500. [56:40]
  3501. know it's not bigger than some constant
  3502.  
  3503. [56:43]
  3504. times N squared or something like that
  3505.  
  3506. [56:44]
  3507. fine so I write Big O of N squared and
  3508.  
  3509. [56:47]
  3510. now I learned how to add Big O of N
  3511.  
  3512. [56:49]
  3513. squared to Big O of N cubed and I know
  3514.  
  3515. [56:51]
  3516. how to add Big O of N squared 2 plus
  3517.  
  3518. 1[56:54]
  3519. and square that and how to take
  3520.  
  3521. [56:56]
  3522. logarithms and Exponential's to have big
  3523.  
  3524. [56:58]
  3525. O's in the middle of them and that
  3526.  
  3527. [57:01]
  3528. turned out to be hugely valuable in all
  3529.  
  3530. [57:04]
  3531. of the work that I was trying to do is
  3532.  
  3533. [57:06]
  3534. I'm trying to figure out how good
  3535.  
  3536. [57:08]
  3537. so I have there been algorithms in your
  3538.  
  3539. [57:12]
  3540. journey that perform very differently in
  3541.  
  3542. [57:15]
  3543. practice than they do in theory well the
  3544.  
  3545. [57:19]
  3546. worst case of a comet our logarithm is
  3547.  
  3548. [57:21]
  3549. almost always horrible but but we have
  3550.  
  3551. [57:26]
  3552. sad solvers that are solving where one
  3553.  
  3554. [57:28]
  3555. of the one of the last exercises in that
  3556.  
  3557. [57:31]
  3558. part of my book was to figure out a
  3559.  
  3560. [57:34]
  3561. problem that has a hundred variables
  3562.  
  3563. [57:37]
  3564. that's that's difficult for us at solver
  3565.  
  3566. [57:40]
  3567. but uh but you would think that a
  3568.  
  3569. [57:42]
  3570. problem with the hundred boolean
  3571.  
  3572. [57:44]
  3573. variables has required to do 2 to the
  3574.  
  3575. [57:47]
  3576. 100th operations because that's the
  3577.  
  3578. [57:51]
  3579. number of possibilities when you have
  3580.  
  3581. [57:53]
  3582. 200 boolean variables in 2 to the 100th
  3583.  
  3584. [57:56]
  3585. to the 100th is way bigger than then we
  3586.  
  3587. [57:59]
  3588. can handle 10 to the 17th is a lot
  3589.  
  3590. [58:02]
  3591. you've mentioned over the past few years
  3592.  
  3593. [58:04]
  3594. that you believe P may be equal to NP
  3595.  
  3596. [58:07]
  3597. but that it's not really you know
  3598.  
  3599. [58:11]
  3600. somebody does prove that P equals NP it
  3601.  
  3602. [58:14]
  3603. will not directly lead to an actual
  3604.  
  3605. [58:16]
  3606. algorithm to solve difficult problems
  3607.  
  3608. [58:19]
  3609. can you explain your intuition here has
  3610.  
  3611. [58:21]
  3612. it been changed and in general on the
  3613.  
  3614. [58:24]
  3615. difference between easy and difficult
  3616.  
  3617. [58:26]
  3618. problems of P and NP and so on yes so
  3619.  
  3620. [58:29]
  3621. the popular idea is if an algorithm
  3622.  
  3623. [58:33]
  3624. exists then somebody will find it and
  3625.  
  3626. [58:38]
  3627. it's just a matter of writing it down
  3628.  
  3629. [58:42]
  3630. one point well but many more algorithms
  3631.  
  3632. [58:47]
  3633. exist than anybody can end understand or
  3634.  
  3635. [58:51]
  3636. ever make you discover yeah because
  3637.  
  3638. [58:53]
  3639. they're just way beyond human
  3640.  
  3641. [58:55]
  3642. comprehension of the total number of
  3643.  
  3644. [58:57]
  3645. algorithms is more than mind-boggling
  3646.  
  3647. [59:03]
  3648. so so we have situations now where we
  3649.  
  3650. [59:06]
  3651. know that algorithm exists but we don't
  3652.  
  3653. [59:09]
  3654. know we don't the foggiest idea what the
  3655.  
  3656. [59:11]
  3657. algorithms are there's there are simple
  3658.  
  3659. [59:14]
  3660. examples based on on game playing where
  3661.  
  3662. [59:18]
  3663. you have
  3664.  
  3665. [59:20]
  3666. where you say well there must be an
  3667.  
  3668. [59:23]
  3669. algorithm that exists to win in the game
  3670.  
  3671. [59:25]
  3672. of hex because for the first player to
  3673.  
  3674. [59:28]
  3675. win in the game of hex because hex is
  3676.  
  3677. [59:31]
  3678. always either an a win for the first
  3679.  
  3680. [59:33]
  3681. player of the second player well what's
  3682.  
  3683. [59:35]
  3684. the game of hack there's a game of hex
  3685.  
  3686. [59:37]
  3687. which is which based on putting pebbles
  3688.  
  3689. [59:39]
  3690. onto a hexagonal board and and the white
  3691.  
  3692. [59:42]
  3693. player tries to get a light path from
  3694.  
  3695. [59:45]
  3696. left to right and the black player tries
  3697.  
  3698. [59:47]
  3699. to get a black path from bottom to top
  3700.  
  3701. [59:48]
  3702. and how does capture occur just so and
  3703.  
  3704. [59:51]
  3705. and and there's no capture you just put
  3706.  
  3707. [59:53]
  3708. levels down what one at a time but
  3709.  
  3710. [59:56]
  3711. there's no drawers because they after
  3712.  
  3713. [59:58]
  3714. all the white and black are played
  3715.  
  3716. [59:59]
  3717. there's either going to be a white path
  3718.  
  3719. [60:01]
  3720. across from each to west or a black path
  3721.  
  3722. [60:03]
  3723. from from bottom to top so there's
  3724.  
  3725. [60:06]
  3726. always you know it's the perfect
  3727.  
  3728. [60:08]
  3729. information game and people people play
  3730.  
  3731. [60:10]
  3732. take turns like like tic-tac-toe and hex
  3733.  
  3734. [60:16]
  3735. or it can be different sizes but we
  3736.  
  3737. [60:19]
  3738. there's no possibility of a draw and
  3739.  
  3740. [60:21]
  3741. player to move one at a time and so it's
  3742.  
  3743. [60:24]
  3744. got to be either a first player win or a
  3745.  
  3746. [60:26]
  3747. second player win
  3748.  
  3749. [60:27]
  3750. mathematically you follow out all the
  3751.  
  3752. [60:30]
  3753. trees and and either either there's
  3754.  
  3755. [60:33]
  3756. always the win for the percolator
  3757.  
  3758. [60:34]
  3759. second player okay and it's finite the
  3760.  
  3761. [60:37]
  3762. game is finite so there's an algorithm
  3763.  
  3764. [60:39]
  3765. that will decide you can show it has to
  3766.  
  3767. [60:42]
  3768. be one of the other because the second
  3769.  
  3770. [60:44]
  3771. player could mimic the first player with
  3772.  
  3773. [60:47]
  3774. kind of a pairing strategy and so you
  3775.  
  3776. [60:50]
  3777. can show that it has to be what it has
  3778.  
  3779. [60:55]
  3780. to be one or that but we don't know any
  3781.  
  3782. [60:57]
  3783. algorithm no way there there a case
  3784.  
  3785. [61:01]
  3786. where you can prove the existence of the
  3787.  
  3788. [61:05]
  3789. solution but we but nobody knows anyway
  3790.  
  3791. [61:07]
  3792. how to find it but more like the
  3793.  
  3794. [61:09]
  3795. algorithm question there's a very
  3796.  
  3797. [61:13]
  3798. powerful theorem and graph theory by
  3799.  
  3800. [61:15]
  3801. Robinson to see more that says that
  3802.  
  3803. [61:18]
  3804. every class of graphs that is closed
  3805.  
  3806. [61:23]
  3807. under taking minors
  3808.  
  3809. [61:26]
  3810. has a polynomial time algorithm to
  3811.  
  3812. [61:29]
  3813. determine whether it's in this class or
  3814.  
  3815. [61:30]
  3816. not now a class of graphs for example
  3817.  
  3818. [61:32]
  3819. planar graphs these are graphs that you
  3820.  
  3821. [61:34]
  3822. can draw in a plane without crossing
  3823.  
  3824. [61:36]
  3825. lines and and a planar graph is close
  3826.  
  3827. [61:39]
  3828. taking minors means that you can shrink
  3829.  
  3830. [61:42]
  3831. an edging into a point or you can delete
  3832.  
  3833. [61:46]
  3834. an edge and so you start with a planar
  3835.  
  3836. [61:50]
  3837. graph and drink any edge to a point is
  3838.  
  3839. [61:52]
  3840. still planar deleting edges to a planner
  3841.  
  3842. [61:55]
  3843. okay now but there are millions of
  3844.  
  3845. [62:02]
  3846. different ways to describe family of
  3847.  
  3848. [62:08]
  3849. graph that still is remains the same
  3850.  
  3851. [62:12]
  3852. undertaking minor and Robertson Nassim
  3853.  
  3854. [62:15]
  3855. are proved that any such family of
  3856.  
  3857. [62:17]
  3858. graphs there is a finite number of
  3859.  
  3860. [62:20]
  3861. minimum graphs that are obstructions so
  3862.  
  3863. [62:26]
  3864. that if it's not in the family then then
  3865.  
  3866. [62:31]
  3867. it has to contain then there has to be a
  3868.  
  3869. [62:34]
  3870. way to shrink it down and until you get
  3871.  
  3872. [62:37]
  3873. one of these bad minimum graphs that's
  3874.  
  3875. [62:39]
  3876. not in the family for in plate case for
  3877.  
  3878. [62:42]
  3879. planar graph the minimum graph is a is a
  3880.  
  3881. [62:45]
  3882. five-pointed star where there everything
  3883.  
  3884. [62:47]
  3885. pointed to another and the minimum graph
  3886.  
  3887. [62:49]
  3888. consisting of trying to connect three
  3889.  
  3890. [62:51]
  3891. utilities to three houses without
  3892.  
  3893. [62:53]
  3894. crossing lines and so there are two
  3895.  
  3896. [62:55]
  3897. there are two bad graphs that are not
  3898.  
  3899. [62:57]
  3900. planar and every every non planar graph
  3901.  
  3902. [63:00]
  3903. contains one of these two bad graphs by
  3904.  
  3905. [63:03]
  3906. by shrinking and he said again so he
  3907.  
  3908. [63:09]
  3909. proved that there's a finite number of
  3910.  
  3911. [63:11]
  3912. these bad guys always a finite know
  3913.  
  3914. [63:13]
  3915. somebody says here's a family it's hard
  3916.  
  3917. [63:15]
  3918. to believe and they present its sequence
  3919.  
  3920. [63:20]
  3921. of 20 papers I mean in there it's deep
  3922.  
  3923. [63:22]
  3924. work but it you know it's because that's
  3925.  
  3926. [63:25]
  3927. for any arbitrary class so it's for any
  3928.  
  3929. [63:28]
  3930. arbitrary class that's closed under
  3931.  
  3932. [63:29]
  3933. taking minors that's closed under maybe
  3934.  
  3935. [63:32]
  3936. I'm not understanding because it seems
  3937.  
  3938. [63:34]
  3939. like a lot of them are closed
  3940.  
  3941. [63:35]
  3942. taking minors almost all the important
  3943.  
  3944. [63:37]
  3945. classes of graphs are
  3946.  
  3947. [63:39]
  3948. there are tons of of such graphs but
  3949.  
  3950. [63:42]
  3951. also hundreds of them that arise in
  3952.  
  3953. [63:46]
  3954. applications like I have a book over
  3955.  
  3956. [63:48]
  3957. here called classes of graphs and then
  3958.  
  3959. [63:51]
  3960. and it it's amazing how many different
  3961.  
  3962. [63:56]
  3963. classes people have looked at so why do
  3964.  
  3965. [63:59]
  3966. you bring up this theorem lower this
  3967.  
  3968. [64:01]
  3969. proof so you know there are lots of
  3970.  
  3971. [64:04]
  3972. algorithms that that are known for
  3973.  
  3974. [64:06]
  3975. special class of graphs for example if I
  3976.  
  3977. [64:08]
  3978. have a certain if I have a chordal graph
  3979.  
  3980. [64:10]
  3981. then I can color it efficiently if I
  3982.  
  3983. [64:13]
  3984. have some kinds of graphs it'll make a
  3985.  
  3986. [64:16]
  3987. great Network very soon like you'd like
  3988.  
  3989. [64:19]
  3990. to test you somebody gives you a graph
  3991.  
  3992. [64:22]
  3993. that's always it in this family of grass
  3994.  
  3995. [64:24]
  3996. if so then I hope then I can I can go to
  3997.  
  3998. [64:27]
  3999. the library and find an algorithm that's
  4000.  
  4001. [64:29]
  4002. gonna solve my problem on that graph
  4003.  
  4004. [64:32]
  4005. okay so we we have we want to have a
  4006.  
  4007. [64:35]
  4008. graph that says number than that says
  4009.  
  4010. [64:41]
  4011. give me a graph I'll tell you whether
  4012.  
  4013. [64:43]
  4014. it's and whether it's in this family or
  4015.  
  4016. [64:46]
  4017. not okay and so all I have to do is test
  4018.  
  4019. [64:51]
  4020. whether or not that does this given
  4021.  
  4022. [64:54]
  4023. graph have a minor that's one of the bad
  4024.  
  4025. [64:56]
  4026. ones a minor is is everything you can
  4027.  
  4028. [64:58]
  4029. get by shrinking and removing edges and
  4030.  
  4031. [65:01]
  4032. given any minor there's a polynomial
  4033.  
  4034. [65:03]
  4035. time algorithm saying I can tell whether
  4036.  
  4037. [65:06]
  4038. this is a minor of you and there's a
  4039.  
  4040. [65:09]
  4041. finite number of bad cases so I just
  4042.  
  4043. [65:12]
  4044. tried you know does it have this bad
  4045.  
  4046. [65:13]
  4047. case by polynomial time I got the answer
  4048.  
  4049. [65:16]
  4050. does he have this bad case probably time
  4051.  
  4052. [65:18]
  4053. I got the answer a total polynomial time
  4054.  
  4055. [65:22]
  4056. and so I've solved the problem however
  4057.  
  4058. [65:25]
  4059. all we know is that the number of minors
  4060.  
  4061. [65:27]
  4062. is finite we don't know what we might
  4063.  
  4064. [65:31]
  4065. only know one or two of those minors but
  4066.  
  4067. [65:32]
  4068. we don't know that if we got it if we
  4069.  
  4070. [65:34]
  4071. got 20 of them we don't know there might
  4072.  
  4073. [65:36]
  4074. be 20 125 the Halloween all we know is
  4075.  
  4076. [65:40]
  4077. that is that it's finite so here we have
  4078.  
  4079. [65:43]
  4080. a polynomial time algorithm that we
  4081.  
  4082. [65:44]
  4083. don't know
  4084.  
  4085. [65:45]
  4086. mm-hm that's a really great example of
  4087.  
  4088. [65:47]
  4089. what you worry about or why you think P
  4090.  
  4091. [65:50]
  4092. equals NP won't be useful
  4093.  
  4094. [65:52]
  4095. but still why do you hold the intuition
  4096.  
  4097. [65:56]
  4098. that P equals NP because you have to
  4099.  
  4100. [66:02]
  4101. rule out so many possible algorithms
  4102.  
  4103. [66:05]
  4104. have been not working you know you can
  4105.  
  4106. [66:11]
  4107. you can take the graph and you can
  4108.  
  4109. [66:13]
  4110. represent it as in terms of certain
  4111.  
  4112. [66:17]
  4113. prime numbers and then you can multiply
  4114.  
  4115. [66:19]
  4116. those together and then you can then you
  4117.  
  4118. [66:21]
  4119. can take the bitwise and and and you
  4120.  
  4121. [66:25]
  4122. know and construct some certain constant
  4123.  
  4124. [66:29]
  4125. in polynomial time and then that's you
  4126.  
  4127. [66:31]
  4128. know perfectly valid algorithm and that
  4129.  
  4130. [66:33]
  4131. there's so many algorithms of that kind
  4132.  
  4133. [66:36]
  4134. a lot of times we see random you take
  4135.  
  4136. [66:42]
  4137. data and and and we get coincidences
  4138.  
  4139. [66:46]
  4140. that that that some fairly random
  4141.  
  4142. [66:49]
  4143. looking number actually is useful
  4144.  
  4145. [66:51]
  4146. because because it god it happens to it
  4147.  
  4148. [66:57]
  4149. happens to self it happens to solve a
  4150.  
  4151. [66:59]
  4152. problem just because you know there's
  4153.  
  4154. [67:02]
  4155. there's so many hairs on your head
  4156.  
  4157. [67:05]
  4158. but it seems like unlikely that two
  4159.  
  4160. [67:10]
  4161. people are going to have the same number
  4162.  
  4163. [67:11]
  4164. of hairs on their head but but they're
  4165.  
  4166. [67:16]
  4167. obvious but you can count how many
  4168.  
  4169. [67:17]
  4170. people there are and how many hairs on
  4171.  
  4172. [67:19]
  4173. there so there must be people walking
  4174.  
  4175. [67:21]
  4176. around in the country to have the same
  4177.  
  4178. [67:23]
  4179. number of hairs on their head well
  4180.  
  4181. [67:24]
  4182. that's the kind of a coincidence that
  4183.  
  4184. [67:26]
  4185. you might say also you know this this
  4186.  
  4187. [67:29]
  4188. particular combination of operations
  4189.  
  4190. [67:31]
  4191. just happens to prove that a graph is
  4192.  
  4193. [67:34]
  4194. has a Hamiltonian path and I see lots of
  4195.  
  4196. [67:37]
  4197. cases where unexpected things happen
  4198.  
  4199. [67:41]
  4200. when you have enough enough
  4201.  
  4202. [67:42]
  4203. possibilities but because the space of
  4204.  
  4205. [67:45]
  4206. possibility is so huge I have to rule
  4207.  
  4208. [67:48]
  4209. them all out and so that's the reason
  4210.  
  4211. [67:50]
  4212. for my intuition is good by no means
  4213.  
  4214. [67:52]
  4215. approve I mean some people say you know
  4216.  
  4217. [67:56]
  4218. well P can't equal NP because you've had
  4219.  
  4220. [67:59]
  4221. all these smart people you know the
  4222.  
  4223. [68:03]
  4224. smartest designers of algorithms that
  4225.  
  4226. [68:05]
  4227. have been
  4228.  
  4229. [68:06]
  4230. wrecking their brains for years and
  4231.  
  4232. [68:07]
  4233. years and and there's million-dollar
  4234.  
  4235. [68:10]
  4236. prizes out there and you know none of
  4237.  
  4238. [68:11]
  4239. them nobody has thought of the algorithm
  4240.  
  4241. [68:16]
  4242. so it must must be no such job on the
  4243.  
  4244. [68:19]
  4245. other hand I can use exactly the same
  4246.  
  4247. [68:22]
  4248. logic and I can say well P must be equal
  4249.  
  4250. [68:25]
  4251. to NP because there's so many smart
  4252.  
  4253. [68:27]
  4254. people out here been trying to prove it
  4255.  
  4256. [68:28]
  4257. unequal to NP and they've all failed you
  4258.  
  4259. [68:32]
  4260. know this kind of reminds me of the
  4261.  
  4262. [68:36]
  4263. discussion about the search for aliens
  4264.  
  4265. [68:38]
  4266. they've been trying to look for them and
  4267.  
  4268. [68:40]
  4269. we haven't found them yet therefore they
  4270.  
  4271. [68:42]
  4272. don't exist
  4273.  
  4274. [68:42]
  4275. yeah but you can show that there's so
  4276.  
  4277. [68:45]
  4278. many planets out there that they very
  4279.  
  4280. [68:46]
  4281. possibly could exist yeah and right and
  4282.  
  4283. [68:50]
  4284. then there's also the possibility that
  4285.  
  4286. [68:52]
  4287. that they exist but they they all
  4288.  
  4289. [68:55]
  4290. discovered machine learning or something
  4291.  
  4292. [68:57]
  4293. and and and then blew each other up well
  4294.  
  4295. [69:01]
  4296. on that small quick danger
  4297.  
  4298. [69:03]
  4299. let me ask do you think there's
  4300.  
  4301. [69:04]
  4302. intelligent life out there in the
  4303.  
  4304. [69:05]
  4305. universe I have no idea do you hope so
  4306.  
  4307. [69:09]
  4308. do you think about it it I I don't I
  4309.  
  4310. [69:12]
  4311. don't spend my time thinking about
  4312.  
  4313. [69:14]
  4314. things that I could never know really
  4315.  
  4316. [69:16]
  4317. and yet you do enjoy the fact that there
  4318.  
  4319. [69:18]
  4320. are many things you don't know you do
  4321.  
  4322. [69:20]
  4323. enjoy the mystery of things I enjoy the
  4324.  
  4325. [69:24]
  4326. fact that there that I have limits yeah
  4327.  
  4328. [69:26]
  4329. but I don't but but I don't take time to
  4330.  
  4331. [69:31]
  4332. answer unsolvable questions I got it
  4333.  
  4334. [69:35]
  4335. well because you've taken on some tough
  4336.  
  4337. [69:38]
  4338. questions that may seem unsolvable you
  4339.  
  4340. [69:40]
  4341. have taken on some tough questions and
  4342.  
  4343. [69:42]
  4344. you seem unsolvable if there is because
  4345.  
  4346. [69:44]
  4347. we are thrilled when I can get further
  4348.  
  4349. [69:46]
  4350. than I ever thought I could right yeah
  4351.  
  4352. [69:48]
  4353. but but I don't what much like was
  4354.  
  4355. [69:51]
  4356. religion these I'm glad the dirt that
  4357.  
  4358. [69:54]
  4359. that there are no proof that God exists
  4360.  
  4361. [69:57]
  4362. or not I mean I think it would spoil the
  4363.  
  4364. [69:59]
  4365. mystery it it would be too dull yeah so
  4366.  
  4367. [70:05]
  4368. to quickly talk about the other art of
  4369.  
  4370. [70:08]
  4371. artificial intelligence
  4372.  
  4373. [70:10]
  4374. what is if you what's your view
  4375.  
  4376. [70:13]
  4377. you know artificial intelligence
  4378.  
  4379. [70:15]
  4380. community has developed as part of
  4381.  
  4382. [70:17]
  4383. computer science and in parallel with
  4384.  
  4385. [70:18]
  4386. computer science
  4387.  
  4388. [70:19]
  4389. since the 60s what's your view of the AI
  4390.  
  4391. [70:22]
  4392. community from the 60s to now so all the
  4393.  
  4394. [70:27]
  4395. way through it was the people who were
  4396.  
  4397. [70:29]
  4398. inspired by trying to mimic intelligence
  4399.  
  4400. [70:35]
  4401. or to do things that that were somehow
  4402.  
  4403. [70:37]
  4404. the greatest achievements of
  4405.  
  4406. [70:39]
  4407. intelligence that had been inspiration
  4408.  
  4409. [70:41]
  4410. to people who have pushed the envelope
  4411.  
  4412. [70:43]
  4413. of computer science maybe more than any
  4414.  
  4415. [70:48]
  4416. other group of people so it's all the
  4417.  
  4418. [70:51]
  4419. way through it's been a great source of
  4420.  
  4421. [70:53]
  4422. of good problems to to sink teeth into
  4423.  
  4424. [70:59]
  4425. and and getting getting partial answers
  4426.  
  4427. [71:06]
  4428. and then more and more successful
  4429.  
  4430. [71:08]
  4431. answers over the year so this has this
  4432.  
  4433. [71:12]
  4434. has been the inspiration for lots of the
  4435.  
  4436. [71:14]
  4437. great discoveries of computer science
  4438.  
  4439. [71:16]
  4440. are you yourself captivated by the
  4441.  
  4442. [71:18]
  4443. possibility of creating of algorithms
  4444.  
  4445. [71:20]
  4446. having echoes of intelligence in them
  4447.  
  4448. [71:26]
  4449. not as much as most of the people in the
  4450.  
  4451. [71:29]
  4452. field I guess I would say but but that's
  4453.  
  4454. [71:32]
  4455. not to say that they're wrong or that
  4456.  
  4457. [71:34]
  4458. it's just you asked about my own
  4459.  
  4460. [71:36]
  4461. personal preferences and yeah but but
  4462.  
  4463. [71:39]
  4464. the thing that I that I worry about is
  4465.  
  4466. [71:47]
  4467. when people start believing that they've
  4468.  
  4469. [71:49]
  4470. actually succeeded and because the seems
  4471.  
  4472. [71:56]
  4473. to me this huge gap between really
  4474.  
  4475. [71:59]
  4476. understanding something and being able
  4477.  
  4478. [72:02]
  4479. to pretend to understand something and
  4480.  
  4481. [72:05]
  4482. give these give the illusion of
  4483.  
  4484. [72:06]
  4485. understanding something do you think
  4486.  
  4487. [72:08]
  4488. it's possible to create without
  4489.  
  4490. [72:10]
  4491. understanding yeah
  4492.  
  4493. [72:12]
  4494. so to uh I do that all the time to run I
  4495.  
  4496. [72:15]
  4497. mean that's why I use random members I
  4498.  
  4499. [72:18]
  4500. like yeah but I but but there's there's
  4501.  
  4502. [72:23]
  4503. still what this great gap I don't know
  4504.  
  4505. [72:25]
  4506. certain it's impossible but I'm like but
  4507.  
  4508. [72:27]
  4509. I don't see a anything coming any closer
  4510.  
  4511. [72:31]
  4512. to really
  4513.  
  4514. [72:33]
  4515. the the kind of stuff that I would
  4516.  
  4517. [72:36]
  4518. consider intelligence say you've
  4519.  
  4520. [72:39]
  4521. mentioned something that on that line of
  4522.  
  4523. [72:41]
  4524. thinking which I very much agree with so
  4525.  
  4526. [72:45]
  4527. the art of computer programming as the
  4528.  
  4529. [72:48]
  4530. book is focused on single processor
  4531.  
  4532. [72:51]
  4533. algorithms and for the most part and you
  4534.  
  4535. [72:56]
  4536. mentioned that's only because I set the
  4537.  
  4538. [72:59]
  4539. table of contents in 1962 you have to
  4540.  
  4541. [73:01]
  4542. remember for sure there's no I'm glad I
  4543.  
  4544. [73:05]
  4545. didn't wait until 1965 or one book maybe
  4546.  
  4547. [73:11]
  4548. will touch in the Bible but one book
  4549.  
  4550. [73:13]
  4551. can't always cover the entirety of
  4552.  
  4553. [73:15]
  4554. everything so I'm glad yeah I'm glad the
  4555.  
  4556. [73:20]
  4557. the table of contents for the art of
  4558.  
  4559. [73:24]
  4560. computer programming is what it is but
  4561.  
  4562. [73:26]
  4563. you did mention that that you thought
  4564.  
  4565. [73:29]
  4566. that an understanding of the way ant
  4567.  
  4568. [73:30]
  4569. colonies are able to perform incredibly
  4570.  
  4571. [73:33]
  4572. organized tasks might well be the key to
  4573.  
  4574. [73:36]
  4575. understanding human cognition
  4576.  
  4577. [73:38]
  4578. so these fundamentally distributed
  4579.  
  4580. [73:40]
  4581. systems so what do you think is the
  4582.  
  4583. [73:43]
  4584. difference between the way Don Knuth
  4585.  
  4586. [73:46]
  4587. would sort a list and an ant colony
  4588.  
  4589. [73:48]
  4590. would sort a list or performing
  4591.  
  4592. [73:51]
  4593. algorithm sorting a list isn't same as
  4594.  
  4595. [73:54]
  4596. cognition though but but I know what
  4597.  
  4598. [73:57]
  4599. you're getting at is well the advantage
  4600.  
  4601. [74:00]
  4602. of ant colony at least we can see what
  4603.  
  4604. [74:04]
  4605. they're doing we we know which ant has
  4606.  
  4607. [74:06]
  4608. talked to which other ant and and and
  4609.  
  4610. [74:08]
  4611. and it's much harder with the quick
  4612.  
  4613. [74:11]
  4614. brains to just to know how to what
  4615.  
  4616. [74:14]
  4617. extent of neurons are passing signal so
  4618.  
  4619. [74:18]
  4620. I understand that aunt Connie might be a
  4621.  
  4622. [74:21]
  4623. if they have the secret of cognition
  4624.  
  4625. [74:24]
  4626. think of an ant colony as a cognitive
  4627.  
  4628. [74:27]
  4629. single being rather than as a colony of
  4630.  
  4631. [74:31]
  4632. lots of different ants I mean just like
  4633.  
  4634. [74:32]
  4635. the cells of our brain are and and the
  4636.  
  4637. [74:37]
  4638. microbiome and all that is interacting
  4639.  
  4640. [74:41]
  4641. entities but but somehow I consider
  4642.  
  4643. [74:46]
  4644. myself to be
  4645.  
  4646. [74:46]
  4647. single person well you know aunt Connie
  4648.  
  4649. [74:50]
  4650. you can say might be cognitive is
  4651.  
  4652. [74:54]
  4653. somehow and it's yeah I mean you know I
  4654.  
  4655. [74:57]
  4656. okay I like I smash a certain aunt and
  4657.  
  4658. [75:03]
  4659. mmm that's stung what was that right you
  4660.  
  4661. [75:06]
  4662. know but if we're going to crack the the
  4663.  
  4664. [75:08]
  4665. the secret of cognition it might be that
  4666.  
  4667. [75:11]
  4668. we could do so by but my psyche note how
  4669.  
  4670. [75:16]
  4671. ants do it because we have a better
  4672.  
  4673. [75:18]
  4674. chance to measure and they're
  4675.  
  4676. [75:19]
  4677. communicating by pheromones and by
  4678.  
  4679. [75:21]
  4680. touching each other and sight but but
  4681.  
  4682. [75:24]
  4683. not by much more subtle phenomenon Mike
  4684.  
  4685. [75:27]
  4686. electric currents going through but even
  4687.  
  4688. [75:30]
  4689. a simpler version of that what are your
  4690.  
  4691. [75:32]
  4692. thoughts of maybe Conway's Game of Life
  4693.  
  4694. [75:34]
  4695. okay so Conway's Game of Life is is able
  4696.  
  4697. [75:39]
  4698. to simulate any any computable process
  4699.  
  4700. [75:43]
  4701. and any deterministic process is like
  4702.  
  4703. [75:47]
  4704. how you went there I mean that's not its
  4705.  
  4706. [75:49]
  4707. most powerful thing I would say I mean
  4708.  
  4709. [75:54]
  4710. you can simulate it but the magic is
  4711.  
  4712. [75:58]
  4713. that the individual units are
  4714.  
  4715. [76:00]
  4716. distributed yes and extremely simple yes
  4717.  
  4718. [76:03]
  4719. we can we understand exactly what the
  4720.  
  4721. [76:06]
  4722. primitives are the permit is the just
  4723.  
  4724. [76:07]
  4725. like with the anthology even simple but
  4726.  
  4727. [76:09]
  4728. if we but still it doesn't say that I
  4729.  
  4730. [76:12]
  4731. understand I understand life I mean I
  4732.  
  4733. [76:16]
  4734. understand it it gives me an it gives me
  4735.  
  4736. [76:23]
  4737. a better insight into what does it mean
  4738.  
  4739. [76:24]
  4740. to to have a deterministic universe what
  4741.  
  4742. [76:30]
  4743. does it mean to to have free choice for
  4744.  
  4745. [76:34]
  4746. example do you think God plays dice yes
  4747.  
  4748. [76:38]
  4749. I don't see any reason why God should be
  4750.  
  4751. [76:40]
  4752. forbidden from using the most efficient
  4753.  
  4754. [76:42]
  4755. ways to to to I mean we we know that
  4756.  
  4757. [76:51]
  4758. dice are extremely important and
  4759.  
  4760. [76:53]
  4761. inefficient algorithms there are things
  4762.  
  4763. [76:55]
  4764. like that couldn't be done well without
  4765.  
  4766. [76:57]
  4767. randomness and so I don't see any reason
  4768.  
  4769. [76:59]
  4770. why
  4771.  
  4772. [77:00]
  4773. my god should be prohibited but when the
  4774.  
  4775. [77:03]
  4776. when the algorithm requires it
  4777.  
  4778. [77:05]
  4779. you don't see why the know the physics
  4780.  
  4781. [77:09]
  4782. should constrain it yeah
  4783.  
  4784. [77:11]
  4785. so in 2001 you gave a series of lectures
  4786.  
  4787. [77:13]
  4788. at MIT about religion and science
  4789.  
  4790. [77:17]
  4791. well that would 1999 but you published
  4792.  
  4793. [77:19]
  4794. the book came out in Cooper so in 1999
  4795.  
  4796. [77:23]
  4797. you spent a little bit of time in Boston
  4798.  
  4799. [77:26]
  4800. enough to give those lectures yeah and I
  4801.  
  4802. [77:31]
  4803. read in the 2001 version that most of it
  4804.  
  4805. [77:36]
  4806. it's quite fascinating read I recommend
  4807.  
  4808. [77:37]
  4809. people its transcription of your
  4810.  
  4811. [77:39]
  4812. lectures so what did you learn about how
  4813.  
  4814. [77:43]
  4815. ideas get started and grow from studying
  4816.  
  4817. [77:45]
  4818. the history of the Bible sieve
  4819.  
  4820. [77:47]
  4821. rigorously studied a very particular
  4822.  
  4823. [77:49]
  4824. part of the Bible what did you learn
  4825.  
  4826. [77:52]
  4827. from this process about the way us human
  4828.  
  4829. [77:54]
  4830. beings as a society develop and grow
  4831.  
  4832. [77:57]
  4833. ideas share ideas and I'm by those idea
  4834.  
  4835. [78:01]
  4836. I I tried to summarize that I wouldn't
  4837.  
  4838. [78:05]
  4839. say that I that I learned a great deal
  4840.  
  4841. [78:08]
  4842. of really definite things like right
  4843.  
  4844. [78:10]
  4845. where I could make conclusions but I
  4846.  
  4847. [78:12]
  4848. learned more about what I don't know you
  4849.  
  4850. [78:15]
  4851. have a complex subject which is really
  4852.  
  4853. [78:17]
  4854. beyond human understanding so so we give
  4855.  
  4856. [78:22]
  4857. up on saying I'm never going to get to
  4858.  
  4859. [78:24]
  4860. the end of the road and I'm never going
  4861.  
  4862. [78:25]
  4863. to understand it but you say but but
  4864.  
  4865. [78:27]
  4866. maybe it might be good for me to to get
  4867.  
  4868. [78:31]
  4869. closer and closer and learn more about
  4870.  
  4871. [78:33]
  4872. more and more about something and so you
  4873.  
  4874. [78:35]
  4875. know oh how can I do that efficiently
  4876.  
  4877. [78:38]
  4878. and the answer is well use randomness
  4879.  
  4880. [78:43]
  4881. and so to try a random subset of the
  4882.  
  4883. [78:49]
  4884. that that is within my grasp and and and
  4885.  
  4886. [78:53]
  4887. and study that in detail instead of just
  4888.  
  4889. [78:57]
  4890. studying parts that somebody tells me to
  4891.  
  4892. [79:00]
  4893. study or instead of studying nothing
  4894.  
  4895. [79:03]
  4896. because it's too hard so I I i decided
  4897.  
  4898. [79:11]
  4899. for my own amusement that one ones that
  4900.  
  4901. [79:14]
  4902. I would I would take a subset of the of
  4903.  
  4904. [79:18]
  4905. the verses of the Bible
  4906.  
  4907. [79:21]
  4908. and I would try to find out what the
  4909.  
  4910. [79:25]
  4911. best thinkers have said about that small
  4912.  
  4913. [79:28]
  4914. subset and I had had about let's say 660
  4915.  
  4916. [79:32]
  4917. verses out of out of 3,000 I think it's
  4918.  
  4919. [79:35]
  4920. one out of 500 or something like this
  4921.  
  4922. [79:37]
  4923. and so then I went to the libraries
  4924.  
  4925. [79:39]
  4926. which which are well indexed uh you can
  4927.  
  4928. [79:42]
  4929. you you know I spent for example at at
  4930.  
  4931. [79:46]
  4932. Boston Public Library I I would go once
  4933.  
  4934. [79:49]
  4935. a week for a year and I went to I went I
  4936.  
  4937. [79:54]
  4938. have done time stuff and over Harvard
  4939.  
  4940. [79:57]
  4941. library to look at this yes that weren't
  4942.  
  4943. [80:01]
  4944. in the Boston Public where they where
  4945.  
  4946. [80:04]
  4947. scholars had looked at and you can call
  4948.  
  4949. [80:06]
  4950. in the eight and you can go down the
  4951.  
  4952. [80:08]
  4953. shelves and and you can pretty you can
  4954.  
  4955. [80:11]
  4956. look at the index and say oh there it is
  4957.  
  4958. [80:12]
  4959. this verse I mentioned anywhere in this
  4960.  
  4961. [80:15]
  4962. book if so look at page 105 so I was
  4963.  
  4964. [80:18]
  4965. like I could learn not only about the
  4966.  
  4967. [80:20]
  4968. Bible but about the secondary literature
  4969.  
  4970. [80:22]
  4971. about the Bible the things that scholars
  4972.  
  4973. [80:24]
  4974. have written about it and so that that
  4975.  
  4976. [80:26]
  4977. gave me a way to uh to zoom in on parts
  4978.  
  4979. [80:32]
  4980. of the things so that I could get more
  4981.  
  4982. [80:34]
  4983. more insight and and so I look at it as
  4984.  
  4985. [80:37]
  4986. a way of giving me some firm pegs which
  4987.  
  4988. [80:43]
  4989. icon which I could hang pieces of
  4990.  
  4991. [80:44]
  4992. information but not as as things where I
  4993.  
  4994. [80:47]
  4995. would say and therefore this is true in
  4996.  
  4997. [80:50]
  4998. this random approach of sampling the
  4999.  
  5000. [80:54]
  5001. Bible what did you learn about the the
  5002.  
  5003. [80:58]
  5004. most you know central oh one of the
  5005.  
  5006. [81:03]
  5007. biggest accumulation of ideas you know
  5008.  
  5009. [81:05]
  5010. to me that the that the main thrust was
  5011.  
  5012. [81:08]
  5013. not the one that most people think of as
  5014.  
  5015. [81:11]
  5016. saying you know you know don't have sex
  5017.  
  5018. [81:13]
  5019. or something like this but that the main
  5020.  
  5021. [81:16]
  5022. thrust was to try to to try to figure
  5023.  
  5024. [81:22]
  5025. out how to live in harmony
  5026.  
  5027. [81:24]
  5028. with God's wishes I'm assuming that God
  5029.  
  5030. [81:27]
  5031. exists and I say I'm glad that I that
  5032.  
  5033. [81:31]
  5034. there's no way to prove this because
  5035.  
  5036. [81:32]
  5037. that would that would I would run
  5038.  
  5039. [81:36]
  5040. through the proof once and then I'd
  5041.  
  5042. [81:37]
  5043. forget it and and it would and and I
  5044.  
  5045. [81:40]
  5046. would never just speculate about
  5047.  
  5048. [81:44]
  5049. spiritual things and mysteries otherwise
  5050.  
  5051. [81:48]
  5052. and I think my life would be very
  5053.  
  5054. [81:50]
  5055. incomplete so I so I'm assuming that God
  5056.  
  5057. [81:55]
  5058. exists but it if but a lot of things the
  5059.  
  5060. [81:59]
  5061. people say God doesn't exist but that's
  5062.  
  5063. [82:02]
  5064. still important to them and so in a way
  5065.  
  5066. [82:04]
  5067. in a way that might still be other God
  5068.  
  5069. [82:07]
  5070. is there or not in some sense so it it
  5071.  
  5072. [82:11]
  5073. guys important to them it's one of the
  5074.  
  5075. [82:14]
  5076. one of the verses I studied act is you
  5077.  
  5078. [82:17]
  5079. can interpret as saying you know it's
  5080.  
  5081. [82:19]
  5082. much better to be an atheist that not to
  5083.  
  5084. [82:22]
  5085. care at all so I would say it's yeah
  5086.  
  5087. [82:26]
  5088. it's similar to the P equals NP
  5089.  
  5090. [82:27]
  5091. discussion yeah you you mentioned a
  5092.  
  5093. [82:30]
  5094. mental exercise that I'd love it if you
  5095.  
  5096. [82:33]
  5097. could partake in yourself a mental
  5098.  
  5099. [82:37]
  5100. exercise of being God and so how would
  5101.  
  5102. [82:39]
  5103. you if you were God dot Knuth how would
  5104.  
  5105. [82:42]
  5106. you present yourself to the people of
  5107.  
  5108. [82:43]
  5109. Earth you mentioned your love of
  5110.  
  5111. [82:47]
  5112. literature and there was it there's this
  5113.  
  5114. [82:48]
  5115. book that would that really uh I can
  5116.  
  5117. [82:50]
  5118. recommend to you if I can't think yeah
  5119.  
  5120. [82:53]
  5121. the title I think is blasphemy it talks
  5122.  
  5123. [82:56]
  5124. about God revealing himself through a
  5125.  
  5126. [82:58]
  5127. computer in in in Los Alamos and and it
  5128.  
  5129. [83:06]
  5130. it's the only book that I've ever read
  5131.  
  5132. [83:09]
  5133. where the punchline was really the very
  5134.  
  5135. [83:13]
  5136. last word of the book and it explained
  5137.  
  5138. [83:16]
  5139. the whole idea of the book and so I
  5140.  
  5141. [83:18]
  5142. don't want to give that away but it but
  5143.  
  5144. [83:21]
  5145. it's really very much about this
  5146.  
  5147. [83:22]
  5148. question that that she raised
  5149.  
  5150. [83:26]
  5151. but but suppose God said okay that my
  5152.  
  5153. [83:31]
  5154. previous on means of communication with
  5155.  
  5156. [83:35]
  5157. the world are and not the best for the
  5158.  
  5159. [83:37]
  5160. 21st century so what should I do now and
  5161.  
  5162. [83:40]
  5163. and and it's conceivable that that it
  5164.  
  5165. [83:45]
  5166. would that that God would choose the way
  5167.  
  5168. [83:48]
  5169. that's described in this book and
  5170.  
  5171. [83:50]
  5172. another way to look at this exercise is
  5173.  
  5174. [83:52]
  5175. looking at the human mind looking at the
  5176.  
  5177. [83:55]
  5178. human spirit the human life in a
  5179.  
  5180. [83:57]
  5181. systematic way I think it mostly you
  5182.  
  5183. [84:00]
  5184. want to learn humility you want to
  5185.  
  5186. [84:02]
  5187. realize that once we solve one problem
  5188.  
  5189. [84:03]
  5190. that doesn't mean it worked at all so no
  5191.  
  5192. [84:06]
  5193. other problems are going to drop out and
  5194.  
  5195. [84:08]
  5196. and and and we have to realize that that
  5197.  
  5198. [84:15]
  5199. that there are there are things beyond
  5200.  
  5201. [84:17]
  5202. our beyond our ability I see hubris all
  5203.  
  5204. [84:24]
  5205. around yeah well said if you were to run
  5206.  
  5207. [84:29]
  5208. program analysis on your own life how
  5209.  
  5210. [84:33]
  5211. did you do in terms of correctness
  5212.  
  5213. [84:35]
  5214. running time resource use asymptotically
  5215.  
  5216. [84:39]
  5217. speaking of course okay yeah well I
  5218.  
  5219. [84:42]
  5220. would say that question has not been
  5221.  
  5222. [84:46]
  5223. asked me before and i i i started out
  5224.  
  5225. [84:57]
  5226. with library subroutines and and
  5227.  
  5228. [85:04]
  5229. learning how to be a automaton that was
  5230.  
  5231. [85:07]
  5232. obedient and i had the great advantage
  5233.  
  5234. [85:10]
  5235. that i didn't have anybody to blame for
  5236.  
  5237. [85:14]
  5238. my failures if I started getting not
  5239.  
  5240. [85:20]
  5241. understanding something I I knew that I
  5242.  
  5243. [85:23]
  5244. should stop playing ping pong and that
  5245.  
  5246. [85:24]
  5247. was that into it was my fault that I was
  5248.  
  5249. [85:26]
  5250. that I wasn't studying hard enough or
  5251.  
  5252. [85:28]
  5253. something rather than that somebody was
  5254.  
  5255. [85:30]
  5256. discriminating against me in some way
  5257.  
  5258. [85:32]
  5259. and
  5260.  
  5261. [85:33]
  5262. I don't know how to avoid this the
  5263.  
  5264. [85:37]
  5265. existence of biases in the world but i
  5266.  
  5267. [85:38]
  5268. but i but i know that that's an extra
  5269.  
  5270. [85:41]
  5271. burden that i didn't have to suffer from
  5272.  
  5273. [85:45]
  5274. and and and then i I found the from from
  5275.  
  5276. [85:54]
  5277. parents I learned the idea of of
  5278.  
  5279. [85:58]
  5280. altruist to other people as being more
  5281.  
  5282. [86:03]
  5283. important than then when I get out of
  5284.  
  5285. [86:08]
  5286. stuff myself I you know that I need to I
  5287.  
  5288. [86:11]
  5289. need to be happy enough enough in order
  5290.  
  5291. [86:16]
  5292. to be able to speed up service but I
  5293.  
  5294. [86:18]
  5295. thought but I you know but I I came to a
  5296.  
  5297. [86:21]
  5298. philosophy for finally that that I
  5299.  
  5300. [86:23]
  5301. phrased as point eight is enough there
  5302.  
  5303. [86:29]
  5304. was a TV show once called hate is enough
  5305.  
  5306. [86:31]
  5307. which was about a you know somebody had
  5308.  
  5309. [86:33]
  5310. eight kids but but I I say point a is
  5311.  
  5312. [86:38]
  5313. enough which means if I can have a way
  5314.  
  5315. [86:41]
  5316. of rating happiness I think it's good
  5317.  
  5318. [86:45]
  5319. design that to have to have an organism
  5320.  
  5321. [86:51]
  5322. that's happy about eighty percent of the
  5323.  
  5324. [86:53]
  5325. time and if it was a hundred percent of
  5326.  
  5327. [86:58]
  5328. the time it would be like every like
  5329.  
  5330. [87:00]
  5331. everybody's on drugs and and never and
  5332.  
  5333. [87:02]
  5334. and and and everything collapses nothing
  5335.  
  5336. [87:06]
  5337. works because everybody's just too happy
  5338.  
  5339. [87:08]
  5340. do you think you've achieved that point
  5341.  
  5342. [87:10]
  5343. eight optimal work there are times when
  5344.  
  5345. [87:13]
  5346. I when I'm down and I you know and I
  5347.  
  5348. [87:17]
  5349. think I mean I know that I'm chemically
  5350.  
  5351. [87:19]
  5352. right I know that I've actually been
  5353.  
  5354. [87:21]
  5355. programmed to be I to be depressed a
  5356.  
  5357. [87:26]
  5358. certain amount of time and and and if
  5359.  
  5360. [87:28]
  5361. that gets out of kilter and I'm more
  5362.  
  5363. [87:30]
  5364. depressed and you know sometimes like
  5365.  
  5366. [87:32]
  5367. like I find myself trying to say now who
  5368.  
  5369. [87:34]
  5370. should I be mad at today there must be a
  5371.  
  5372. [87:36]
  5373. reason why
  5374.  
  5375. [87:38]
  5376. but I but then I realize you know it's
  5377.  
  5378. [87:41]
  5379. just my it's just my chemistry telling
  5380.  
  5381. [87:43]
  5382. me that I'm supposed to be mad at
  5383.  
  5384. [87:45]
  5385. somebody and so and so I triggered up
  5386.  
  5387. [87:47]
  5388. say okay go to sleep and get better but
  5389.  
  5390. [87:50]
  5391. but if I'm but if I'm not a hundred
  5392.  
  5393. [87:53]
  5394. percent happy that doesn't mean that I
  5395.  
  5396. [87:56]
  5397. should find somebody that that's
  5398.  
  5399. [87:57]
  5400. screaming and and try to size them up
  5401.  
  5402. [88:00]
  5403. but I'd be like I'm saying you know okay
  5404.  
  5405. [88:04]
  5406. I'm not 100% happy but but I'm happy
  5407.  
  5408. [88:08]
  5409. enough to death to be a you know part of
  5410.  
  5411. [88:11]
  5412. a sustainable situation so so that's
  5413.  
  5414. [88:15]
  5415. kind of the numerical analysis I do you
  5416.  
  5417. [88:21]
  5418. invert stores the human life is a point
  5419.  
  5420. [88:24]
  5421. eight yeah I hope it's okay to talk
  5422.  
  5423. [88:27]
  5424. about as you talked about previously in
  5425.  
  5426. [88:29]
  5427. two thousand six six you were diagnosed
  5428.  
  5429. [88:32]
  5430. with prostate cancer has that encounter
  5431.  
  5432. [88:35]
  5433. with mortality changed you in some way
  5434.  
  5435. [88:38]
  5436. or the way you see the world the first
  5437.  
  5438. [88:42]
  5439. encounter with mortality with Mike when
  5440.  
  5441. [88:44]
  5442. my dad died and I I went through a month
  5443.  
  5444. [88:47]
  5445. when I sort of came to kink you know be
  5446.  
  5447. [88:56]
  5448. comfortable with the fact that I was
  5449.  
  5450. [88:57]
  5451. going to die someday and during that
  5452.  
  5453. [88:59]
  5454. month I don't know I I felt okay but I
  5455.  
  5456. [89:06]
  5457. couldn't sing and you know I and I and I
  5458.  
  5459. [89:11]
  5460. couldn't do original research either
  5461.  
  5462. [89:14]
  5463. like tighten right I sort of remember
  5464.  
  5465. [89:16]
  5466. after three or four weeks the first time
  5467.  
  5468. [89:19]
  5469. I started having a technical thought
  5470.  
  5471. [89:21]
  5472. that made sense and was maybe slightly
  5473.  
  5474. [89:23]
  5475. creative I could sort of feel they know
  5476.  
  5477. [89:25]
  5478. that and that something was starting to
  5479.  
  5480. [89:28]
  5481. move again but that was you know so I
  5482.  
  5483. [89:31]
  5484. felt very empty for until I came to
  5485.  
  5486. [89:35]
  5487. grips with the I yes I learned that this
  5488.  
  5489. [89:39]
  5490. is a sort of a standard grief process
  5491.  
  5492. [89:40]
  5493. that people go through ok so then now
  5494.  
  5495. [89:43]
  5496. I'm at a point in my life even more so
  5497.  
  5498. [89:47]
  5499. than in 2006 where where all of my go
  5500.  
  5501. [89:50]
  5502. have been fulfilled except for finishing
  5503.  
  5504. [89:52]
  5505. narrative computer programming
  5506.  
  5507. [89:54]
  5508. i I I had one made unfulfilled goal that
  5509.  
  5510. [90:01]
  5511. I'd wanted all my life to write a piece
  5512.  
  5513. [90:04]
  5514. of a piece piece of music that and I had
  5515.  
  5516. [90:08]
  5517. an idea for for a certain kind of music
  5518.  
  5519. [90:12]
  5520. that I thought ought to be written at
  5521.  
  5522. [90:13]
  5523. least somebody ought to try to do it and
  5524.  
  5525. [90:15]
  5526. I and I felt that it was a that it
  5527.  
  5528. [90:19]
  5529. wasn't going to be easy but I wanted to
  5530.  
  5531. [90:20]
  5532. I wanted it proof of concept I wanted to
  5533.  
  5534. [90:24]
  5535. know if it was going to work or not and
  5536.  
  5537. [90:25]
  5538. so I spent a lot of time and finally I
  5539.  
  5540. [90:29]
  5541. finished that piece and we had the we
  5542.  
  5543. [90:31]
  5544. had the world premiere last year on my
  5545.  
  5546. [90:34]
  5547. 80th birthday and we had another
  5548.  
  5549. [90:36]
  5550. premiere in Canada and there's talk of
  5551.  
  5552. [90:39]
  5553. concerts in Europe and various things so
  5554.  
  5555. [90:41]
  5556. that but that's done it's part of the
  5557.  
  5558. [90:44]
  5559. world's music now and it's either good
  5560.  
  5561. [90:45]
  5562. or bad but I did what I was hoping to do
  5563.  
  5564. [90:49]
  5565. so the only thing that I know that that
  5566.  
  5567. [90:53]
  5568. I have on my agenda is to is to try to
  5569.  
  5570. [90:58]
  5571. do as well as I can with the art of
  5572.  
  5573. [91:00]
  5574. computer programming until I go see now
  5575.  
  5576. [91:02]
  5577. do you think there's an element of point
  5578.  
  5579. [91:05]
  5580. eight that might point eight yeah well I
  5581.  
  5582. [91:08]
  5583. look at it more that I got actually took
  5584.  
  5585. [91:13]
  5586. 21.0 with when that concert was over
  5587.  
  5588. [91:18]
  5589. with I mean I you know I so in 2006 I
  5590.  
  5591. [91:25]
  5592. was at point eight um so when I was
  5593.  
  5594. [91:28]
  5595. diagnosed with prostate cancer then I
  5596.  
  5597. [91:30]
  5598. said okay well maybe this is yet you
  5599.  
  5600. [91:33]
  5601. know I've I've had all kinds of good
  5602.  
  5603. [91:37]
  5604. luck all my life and there's no I'm
  5605.  
  5606. [91:39]
  5607. nothing to complain about so I might die
  5608.  
  5609. [91:41]
  5610. now and we'll see what happened and so
  5611.  
  5612. [91:45]
  5613. so it's quite seriously I went and I
  5614.  
  5615. [91:49]
  5616. didn't I had no expectation that I
  5617.  
  5618. [91:54]
  5619. deserved better
  5620.  
  5621. [91:56]
  5622. I didn't make any plans for the future I
  5623.  
  5624. [91:59]
  5625. had my surgery I came out of the surgery
  5626.  
  5627. [92:03]
  5628. and and spend some time learning how to
  5629.  
  5630. [92:09]
  5631. walk again and so on is painful for a
  5632.  
  5633. [92:13]
  5634. while but I got home and I realized I
  5635.  
  5636. [92:17]
  5637. hadn't really thought about what what to
  5638.  
  5639. [92:20]
  5640. do next I hadn't I hadn't any
  5641.  
  5642. [92:22]
  5643. expectation and I'm still alive okay now
  5644.  
  5645. [92:27]
  5646. I can write some more books but it but I
  5647.  
  5648. [92:29]
  5649. didn't come with the attitude that you
  5650.  
  5651. [92:30]
  5652. know I you know this was this was
  5653.  
  5654. [92:34]
  5655. terribly unfair and and I just said okay
  5656.  
  5657. [92:39]
  5658. I was accepting whatever it turned out
  5659.  
  5660. [92:43]
  5661. you know I look like I gotten I got more
  5662.  
  5663. [92:48]
  5664. than my shirt already so why should I
  5665.  
  5666. [92:54]
  5667. and I didn't and I really when I got
  5668.  
  5669. [92:58]
  5670. home I read I realized that I had really
  5671.  
  5672. [93:00]
  5673. not thought about the next step what I
  5674.  
  5675. [93:01]
  5676. would do after I would doubt after I
  5677.  
  5678. [93:03]
  5679. would be able to work and I had sort of
  5680.  
  5681. [93:05]
  5682. thought of it as if as this might you
  5683.  
  5684. [93:08]
  5685. know I was comfortable with with the
  5686.  
  5687. [93:11]
  5688. fact that it was at the end but but I
  5689.  
  5690. [93:14]
  5691. was hoping that I would still you know
  5692.  
  5693. [93:18]
  5694. be able to learn about satisfiability
  5695.  
  5696. [93:23]
  5697. and and also someday even write music I
  5698.  
  5699. [93:29]
  5700. didn't start I didn't started seriously
  5701.  
  5702. [93:31]
  5703. on the music project until 2012 so I'm
  5704.  
  5705. [93:35]
  5706. gonna be in huge trouble if I don't talk
  5707.  
  5708. [93:37]
  5709. to you about this in in the 70s you've
  5710.  
  5711. [93:41]
  5712. created the tech typesetting system
  5713.  
  5714. [93:43]
  5715. together with meta font language for
  5716.  
  5717. [93:45]
  5718. font description and computer modern
  5719.  
  5720. [93:47]
  5721. family of typefaces that has basically
  5722.  
  5723. [93:50]
  5724. defined the methodology in the aesthetic
  5725.  
  5726. [93:53]
  5727. of the countless research fields right
  5728.  
  5729. [93:57]
  5730. math physics well beyond design and so
  5731.  
  5732. [94:02]
  5733. on okay well first of all thank you I
  5734.  
  5735. [94:04]
  5736. think I speak for a lot of people in
  5737.  
  5738. [94:07]
  5739. saying that but question in terms of
  5740.  
  5741. [94:10]
  5742. beauty there's a beauty to typography
  5743.  
  5744. [94:12]
  5745. that you've created and yet beauty is
  5746.  
  5747. [94:16]
  5748. hard to
  5749.  
  5750. [94:16]
  5751. five right how does one create beautiful
  5752.  
  5753. [94:22]
  5754. letters and beautiful equations like
  5755.  
  5756. [94:25]
  5757. what what
  5758.  
  5759. [94:26]
  5760. so I mean perhaps there's no words to be
  5761.  
  5762. [94:30]
  5763. describing you know be described in the
  5764.  
  5765. [94:33]
  5766. process but so the great Harvard
  5767.  
  5768. [94:38]
  5769. mathematician Georg deeper cut wrote a
  5770.  
  5771. [94:43]
  5772. book in the 30s called the aesthetic
  5773.  
  5774. [94:44]
  5775. measure rate where he would have
  5776.  
  5777. [94:47]
  5778. pictures of vases and underneath would
  5779.  
  5780. [94:49]
  5781. be a number and this was how beautiful
  5782.  
  5783. [94:51]
  5784. the vase was and he had a formula for
  5785.  
  5786. [94:53]
  5787. this and and he actually also right over
  5788.  
  5789. [94:56]
  5790. brought about music and so he could he
  5791.  
  5792. [94:59]
  5793. could you know so I thought maybe I
  5794.  
  5795. [95:01]
  5796. would part of my musical composition I
  5797.  
  5798. [95:04]
  5799. would try to program his algorithms and
  5800.  
  5801. [95:08]
  5802. and you know so that I would I would
  5803.  
  5804. [95:11]
  5805. write something that had the highest
  5806.  
  5807. [95:13]
  5808. number by his score well it wasn't quite
  5809.  
  5810. [95:15]
  5811. rigorous enough work for a computer to
  5812.  
  5813. [95:18]
  5814. to do but anyway people have tried to
  5815.  
  5816. [95:21]
  5817. put numerical value on beauty but and
  5818.  
  5819. [95:24]
  5820. and he did probably the most serious
  5821.  
  5822. [95:28]
  5823. attempt and and George Gershwin's
  5824.  
  5825. [95:32]
  5826. teacher also wrote two volumes where he
  5827.  
  5828. [95:34]
  5829. talked about his method of of composing
  5830.  
  5831. [95:38]
  5832. music but but you're talking about
  5833.  
  5834. [95:40]
  5835. another kind of beauty and beauty and
  5836.  
  5837. [95:42]
  5838. letters and letter fell against and
  5839.  
  5840. [95:44]
  5841. whatever that overture is right so so
  5842.  
  5843. [95:47]
  5844. and so that's the beholder as they say
  5845.  
  5846. [95:52]
  5847. but kinder striving for excellence in
  5848.  
  5849. [95:55]
  5850. whatever definition you want to give to
  5851.  
  5852. [95:57]
  5853. beauty then you try to get as close to
  5854.  
  5855. [95:59]
  5856. that as you can somehow with it I guess
  5857.  
  5858. [96:02]
  5859. I guess I'm trying to ask and there may
  5860.  
  5861. [96:04]
  5862. not be a good answer
  5863.  
  5864. [96:06]
  5865. what loose definitions were you're
  5866.  
  5867. [96:09]
  5868. operating under with the community of
  5869.  
  5870. [96:11]
  5871. people that you're working on oh the
  5872.  
  5873. [96:13]
  5874. loose definition I wanted I wanted it to
  5875.  
  5876. [96:18]
  5877. appeal to me to me I knew you personally
  5878.  
  5879. [96:21]
  5880. yeah that's a good start
  5881.  
  5882. [96:23]
  5883. yeah no and it failed that test went
  5884.  
  5885. [96:25]
  5886. when I got volume two came out with this
  5887.  
  5888. [96:28]
  5889. with the new printing and
  5890.  
  5891. [96:30]
  5892. I was expecting to be the happiest day
  5893.  
  5894. [96:31]
  5895. of my life and I felt like burning like
  5896.  
  5897. [96:37]
  5898. how angry I was that I opened the book
  5899.  
  5900. [96:40]
  5901. and it it was in the same beige covers
  5902.  
  5903. [96:44]
  5904. and and but but it didn't look right on
  5905.  
  5906. [96:47]
  5907. the page the number two was particularly
  5908.  
  5909. [96:52]
  5910. ugly I couldn't stand any page that had
  5911.  
  5912. [96:54]
  5913. a to in his page number and I was
  5914.  
  5915. [96:57]
  5916. expecting that it was you know I spent
  5917.  
  5918. [96:59]
  5919. all this time making measurements and I
  5920.  
  5921. [97:01]
  5922. and I had Kent had looked at dolphins in
  5923.  
  5924. [97:05]
  5925. different different ways and I hate I
  5926.  
  5927. [97:08]
  5928. had great technology but but it did you
  5929.  
  5930. [97:12]
  5931. know but I but I wasn't done I had I had
  5932.  
  5933. [97:16]
  5934. to retune the whole thing after 196
  5935.  
  5936. 1[97:19]
  5937. has it ever made you happy finally oh oh
  5938.  
  5939. [97:22]
  5940. yes
  5941.  
  5942. [97:24]
  5943. or is it appointing oh no no and so many
  5944.  
  5945. [97:27]
  5946. books have come out that would never
  5947.  
  5948. [97:29]
  5949. have been written without this I just
  5950.  
  5951. [97:31]
  5952. didn't just draw it's just it's a joy
  5953.  
  5954. [97:34]
  5955. but I could but now I I mean all these
  5956.  
  5957. [97:37]
  5958. pages that are sitting up there I don't
  5959.  
  5960. [97:40]
  5961. have a it if I didn't like him I would
  5962.  
  5963. [97:43]
  5964. change him like that's my nobody else
  5965.  
  5966. [97:47]
  5967. has this ability they have to stick with
  5968.  
  5969. [97:49]
  5970. what I gave them
  5971.  
  5972. [97:50]
  5973. yes so in terms of the other side of it
  5974.  
  5975. [97:53]
  5976. there's the typography so the look of
  5977.  
  5978. [97:55]
  5979. the top of the type and the curves and
  5980.  
  5981. [97:57]
  5982. the lines what about the spacing but
  5983.  
  5984. [98:01]
  5985. what about the spacing because you know
  5986.  
  5987. [98:04]
  5988. the white space you know it seems like
  5989.  
  5990. [98:07]
  5991. you could be a little bit more
  5992.  
  5993. [98:08]
  5994. systematic about the layout or oh yeah
  5995.  
  5996. [98:12]
  5997. you can always go further
  5998.  
  5999. [98:13]
  6000. III I didn't I didn't stop at point
  6001.  
  6002. [98:17]
  6003. eight I stopped I stopped about point
  6004.  
  6005. [98:19]
  6006. nine eight seems like you're not
  6007.  
  6008. [98:22]
  6009. following your own rule for happiness or
  6010.  
  6011. [98:25]
  6012. is no no no I
  6013.  
  6014. [98:29]
  6015. there's okay the course there's just
  6016.  
  6017. [98:32]
  6018. what is the Japanese word wabi-sabi or
  6019.  
  6020. [98:35]
  6021. something they wear the
  6022.  
  6023. [98:38]
  6024. the most beautiful works of art are
  6025.  
  6026. [98:40]
  6027. those that have flaws because then the
  6028.  
  6029. [98:42]
  6030. person who who perceives them as their
  6031.  
  6032. [98:46]
  6033. own
  6034.  
  6035. [98:47]
  6036. appreciation and that gives the viewer
  6037.  
  6038. [98:50]
  6039. more satisfaction or a so on but but I
  6040.  
  6041. [98:54]
  6042. but no no with typography I wanted it to
  6043.  
  6044. [98:57]
  6045. look as good as I could in in the vast
  6046.  
  6047. [99:00]
  6048. majority of cases and then when it
  6049.  
  6050. [99:03]
  6051. doesn't then I I say okay that's 2% more
  6052.  
  6053. [99:08]
  6054. work for the wrote for the author but
  6055.  
  6056. [99:11]
  6057. but I didn't want to I didn't want to
  6058.  
  6059. [99:14]
  6060. say that my job was to get 200% with and
  6061.  
  6062. [99:18]
  6063. take all the work away from the author
  6064.  
  6065. [99:20]
  6066. that's what I meant by that so if you
  6067.  
  6068. [99:23]
  6069. were to venture a guess how much of the
  6070.  
  6071. [99:27]
  6072. nature of reality do you think we humans
  6073.  
  6074. [99:29]
  6075. understand so you mentioned you
  6076.  
  6077. [99:32]
  6078. appreciate mystery how much of the world
  6079.  
  6080. [99:35]
  6081. about us is shrouded in mystery are we
  6082.  
  6083. [99:38]
  6084. are we if you were to put a number on it
  6085.  
  6086. [99:41]
  6087. what what percent of it all do we
  6088.  
  6089. [99:44]
  6090. understand oh we totally how many
  6091.  
  6092. [99:46]
  6093. leading zeroes any point zero point zero
  6094.  
  6095. [99:48]
  6096. zero there I don't know now I think it's
  6097.  
  6098. [99:50]
  6099. infinitesimal how do we think about that
  6100.  
  6101. [99:53]
  6102. what do we do about that do we continue
  6103.  
  6104. [99:55]
  6105. one step at a time yeah we muddle
  6106.  
  6107. [99:58]
  6108. through I mean we do our best we
  6109.  
  6110. [100:01]
  6111. realized that one that nobody's perfect
  6112.  
  6113. [100:03]
  6114. then we and we try to keep advancing but
  6115.  
  6116. [100:07]
  6117. we don't spend time saying we're not
  6118.  
  6119. [100:12]
  6120. there we're not all the way to the end
  6121.  
  6122. [100:14]
  6123. some some mathematicians that that would
  6124.  
  6125. [100:18]
  6126. be in the office next to me when I was
  6127.  
  6128. [100:19]
  6129. in the math department they would never
  6130.  
  6131. [100:21]
  6132. think about anything smaller than
  6133.  
  6134. [100:23]
  6135. countable infinity and I never you know
  6136.  
  6137. [100:26]
  6138. we intersect that countable infinity
  6139.  
  6140. [100:28]
  6141. because I really got up to countable
  6142.  
  6143. [100:31]
  6144. infinity I was always talking about
  6145.  
  6146. [100:32]
  6147. finite stuff but but even even limiting
  6148.  
  6149. [100:36]
  6150. to finite stuff which was which is which
  6151.  
  6152. [100:40]
  6153. the universe might be there's no way to
  6154.  
  6155. [100:43]
  6156. really know what whether the universe is
  6157.  
  6158. [100:47]
  6159. in
  6160.  
  6161. [100:48]
  6162. isn't just made out of capital in
  6163.  
  6164. [100:56]
  6165. whenever you want to call them quarks or
  6166.  
  6167. [100:59]
  6168. whatever where capital n is some fun a
  6169.  
  6170. [101:01]
  6171. number all of the numbers that are
  6172.  
  6173. [101:03]
  6174. comprehensible are still way smaller
  6175.  
  6176. [101:05]
  6177. than most almost all finite numbers III
  6178.  
  6179. [101:08]
  6180. I got this one paper called supernatural
  6181.  
  6182. [101:13]
  6183. numbers where I what I guess you've
  6184.  
  6185. [101:16]
  6186. probably ran into something called Knuth
  6187.  
  6188. [101:18]
  6189. arrow notation did you ever run into
  6190.  
  6191. [101:19]
  6192. that where anyway so you take the number
  6193.  
  6194. [101:22]
  6195. I think it's like I and I called it
  6196.  
  6197. [101:25]
  6198. super K but I named it after myself but
  6199.  
  6200. [101:29]
  6201. it's it's but in arrow notation is
  6202.  
  6203. [101:31]
  6204. something like ten and then four arrows
  6205.  
  6206. [101:34]
  6207. and a three or something might not okay
  6208.  
  6209. [101:36]
  6210. no the arrow notation if you have if you
  6211.  
  6212. [101:40]
  6213. have no arrows that means multiplication
  6214.  
  6215. [101:41]
  6216. XY means x times X times X times X Y
  6217.  
  6218. [101:46]
  6219. times if you have one arrow that means
  6220.  
  6221. [101:49]
  6222. exponentiation so x one arrow Y means X
  6223.  
  6224. [101:51]
  6225. to the X to the X to the X to the X Y
  6226.  
  6227. [101:55]
  6228. times so I find out by the way that this
  6229.  
  6230. [101:58]
  6231. is notation was invented by a guy in
  6232.  
  6233. [102:02]
  6234. 1830 and and he was like he was a a a
  6235.  
  6236. [102:08]
  6237. [Music]
  6238.  
  6239. [102:10]
  6240. one of the English nobility who who
  6241.  
  6242. [102:13]
  6243. spent his time thinking about stuff like
  6244.  
  6245. [102:14]
  6246. this and it was exactly the same concept
  6247.  
  6248. [102:18]
  6249. that I that I'm that I used arrows and
  6250.  
  6251. [102:21]
  6252. he used a slightly different notation
  6253.  
  6254. [102:23]
  6255. but anyway this and then this Ackerman's
  6256.  
  6257. [102:25]
  6258. function is is based on the same kind of
  6259.  
  6260. [102:28]
  6261. ideas but Ackerman was 1920s but anyway
  6262.  
  6263. [102:31]
  6264. you got this number 10
  6265.  
  6266. [102:34]
  6267. quadruple arrow 3 so that's that says
  6268.  
  6269. [102:37]
  6270. well we take you know we take 10 to the
  6271.  
  6272. [102:41]
  6273. 10 to the 10 to the 10 to the 10 to the
  6274.  
  6275. [102:44]
  6276. 10th anyway how many times do we do that
  6277.  
  6278. [102:46]
  6279. oh Ken double arrow two times or
  6280.  
  6281. [102:49]
  6282. something I mean how tall is that stack
  6283.  
  6284. [102:51]
  6285. but but but then we do that again
  6286.  
  6287. [102:54]
  6288. because that was the only 10 triple
  6289.  
  6290. [102:56]
  6291. quadruple arrow to we take quadruple
  6292.  
  6293. [102:59]
  6294. three large number it
  6295.  
  6296. [103:01]
  6297. gets way beyond comprehension okay yeah
  6298.  
  6299. [103:06]
  6300. and and and so but it's so small
  6301.  
  6302. [103:11]
  6303. compared to what finite numbers really
  6304.  
  6305. [103:13]
  6306. are because I want to using four arrows
  6307.  
  6308. [103:15]
  6309. and you know in ten and a three I mean
  6310.  
  6311. [103:18]
  6312. let's have that let's have that many
  6313.  
  6314. [103:21]
  6315. number arrows I mean the boundary
  6316.  
  6317. [103:23]
  6318. between infinite and finite is
  6319.  
  6320. [103:26]
  6321. incomprehensible for us humans anyway
  6322.  
  6323. [103:29]
  6324. infinity is a good is a useful way for
  6325.  
  6326. [103:32]
  6327. us to think about extremely large
  6328.  
  6329. [103:36]
  6330. extremely large things and and and and
  6331.  
  6332. [103:42]
  6333. we we can manipulate it but but we can
  6334.  
  6335. [103:46]
  6336. never know that the universe is actually
  6337.  
  6338. [103:49]
  6339. and we're near that so it just so I
  6340.  
  6341. [103:57]
  6342. realize how little we know but but but
  6343.  
  6344. [104:03]
  6345. what we we found an awful lot of things
  6346.  
  6347. [104:09]
  6348. that are too hard for any one person to
  6349.  
  6350. [104:11]
  6351. know even with even in our small
  6352.  
  6353. [104:13]
  6354. universe yeah and we did pretty good so
  6355.  
  6356. [104:17]
  6357. when you go up to heaven and meet God
  6358.  
  6359. [104:19]
  6360. and get to ask one question that would
  6361.  
  6362. [104:23]
  6363. get answered what question would you ask
  6364.  
  6365. [104:29]
  6366. what kind of browser do you have up here
  6367.  
  6368. [104:32]
  6369. [Laughter]
  6370.  
  6371. [104:41]
  6372. [Music]
  6373.  
  6374. [104:48]
  6375. okay and then oh that's beautiful
  6376.  
  6377. [104:51]
  6378. actually Don thank you so much it was a
  6379.  
  6380. [104:54]
  6381. huge honor to talk to you I really well
  6382.  
  6383. [104:56]
  6384. thanks for the gamut of questions yeah
  6385.  
  6386. [104:59]
  6387. it was fun
  6388.  
  6389. [105:00]
  6390. thanks for listening to this
  6391.  
  6392. [105:01]
  6393. conversation with donald knuth thank you
  6394.  
  6395. [105:04]
  6396. to our presenting sponsor cash app
  6397.  
  6398. [105:07]
  6399. downloaded use cold Luck's podcast
  6400.  
  6401. [105:09]
  6402. you'll get ten dollars and ten dollars
  6403.  
  6404. [105:11]
  6405. will go to first a stem education
  6406.  
  6407. [105:13]
  6408. nonprofit that inspires hundreds of
  6409.  
  6410. [105:15]
  6411. thousands of young minds to learn and to
  6412.  
  6413. [105:18]
  6414. dream of engineering our future if you
  6415.  
  6416. [105:21]
  6417. enjoy this podcast subscribe on YouTube
  6418.  
  6419. [105:23]
  6420. give it five stars an apple podcast
  6421.  
  6422. [105:25]
  6423. supported on patreon or connect with me
  6424.  
  6425. [105:27]
  6426. on Twitter and now let me leave you with
  6427.  
  6428. [105:30]
  6429. some words of wisdom from donald knuth
  6430.  
  6431. [105:33]
  6432. we should continually be striving to
  6433.  
  6434. [105:35]
  6435. transform every art into a science and
  6436.  
  6437. [105:38]
  6438. in the process we advance the art thank
  6439.  
  6440. [105:43]
  6441. you for listening and hope to see you
  6442.  
  6443. [105:45]
  6444. next time
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