「Connecting The Dots」しよう！〜情報システムの未解決問題を材料工学，機械学習，量子コンピュータで解く〜

Transcript

1. ͘͞ΒΠϯλʔωοτגࣜձࣾ (C) Copyright 1996-2019 SAKURA Internet Inc ͘͞ΒΠϯλʔωοτݚڀॴ ʮConnecting The Dotsʯ͠Α͏ʂ ʙ৘ใγεςϜͷະղܾ໰୊Λࡐྉ޻ֶɼػցֶशɼྔࢠίϯϐϡʔλͰղ͘ʙ 2019/10/01 ݚڀһ ௽ాതจ FGNΤϯδχΞMeetup vol.3
2. 2 1. ࣗݾ঺հɾܦྺ঺հ 2. Connecting The Dotsͱ͸ 3. ఺Λଧͭ 4. ఺Λͭͳ͙ 5. ࢲ͕ମײͨ͠Connecting The Dots 6. ࠓޙ΍Γ͍ͨݚڀʹ͍ͭͯ ໨࣍
3. 1. ࣗݾ঺հɾܦྺ঺հ
4. 4 ࣗݾ঺հ • ௽ా തจʢͭΔ΂ʔ/ @tsurubee3ʣ • ͘͞ΒΠϯλʔωοτݚڀॴ 
 ݚڀһʢ2019೥8݄ʙʣ • झຯ͸ےτϨ
5. 5 ܦྺɿେֶɾେֶӃ • ߴ෼ࢠࡐྉͷք໘෺ੑͷݚڀʢࡐྉ޻ֶʣ • म࢜՝ఔͰࠃࡍࢽʹ࿦จΛ3ใࣥචͯ͠
 ૣظमྃɼത࢜՝ఔ΁ https://pubs.acs.org/doi/abs/10.1021/ma3007202 https://pubs.rsc.org/en/content/articlelanding/2012/py/c1py00488c
6. ത࢜՝ఔΛதୀͯ͠ফ๷࢜ʹͳΔ
7. 7 • ത࢜՝ఔͷͱ͖ʹࢀՃͨ͠ࠃࡍֶձ@ઋ୆ʹͯ౦೔ຊେ਒ࡂͷඃࡂ஍ Λ໨ͷ౰ͨΓʹͯ͠ɼʮ΋ͬͱ௚઀తʹਓͷ໾ʹཱ͍ͪͨʯͱࢥ͍ɼ ֶձதʹ࠾༻ࢼݧʹԠืͨ͠ • ແࣄʹ1ൃͰ࠾༻ࢼݧʹ߹֨ • ফ๷ୂɾٹٸୂɾՐࡂௐࠪ൝ͳͲΛܦݧ ܦྺɿফ๷࢜
8. 8 • ܇࿅ͰոզΛͯ͠ݱ৔Λ཭ΕΔ͜ͱʹͳΔ • ࣗ෼ͷؾ࣋ͪͱݱ࣮ͱͷΪϟοϓʹࢥ͍೰Ή೔ʑ͕ଓ͘ https://www.nikkei.com/article/DGXLZO05697850U6A800C1000000/ https://www.sankeibiz.jp/compliance/news/160805/cpc1608050800001-n1.htm • AI΍ػցֶशʹڵຯΛ࣋ͪɼಠֶͰϓϩάϥϛϯάΛ࢝ΊΔ
 ʮ͍ͣΕػցֶशͷݚڀ͕΍Γ͍ͨʯͱ͍͏ؾ͕࣋ͪժੜ͑స৬ ܦྺɿফ๷࢜
9. 9 • ΧϥϏφςΫϊϩδʔגࣜձࣾ
 ػցֶशΤϯδχΞʢϨίϝϯυΤϯδϯɾը૾෼ྨʣ • GMOϖύϘגࣜձࣾ
 ϨϯλϧαʔόαʔϏεͷΠϯϑϥΤϯδχΞ ܦྺɿΤϯδχΞ ओͳOSS׆ಈ ɾҟৗݕ஌ϥΠϒϥϦBanpei ɾ SSHϓϩΩγαʔόsshr https://github.com/tsurubee/banpei https://github.com/tsurubee/sshr
10. 10 ܦྺɿΤϯδχΞ • ϓϩάϥϜΛॻ͘͜ͱ͸ͱʹָ͔͍͕͘͠ɼࣗ෼ͷڧΈʢͩͱࢥ͍ͬͯΔʣ ʮਂ͘ࢥߟ͢Δ͜ͱʯΛ΋ͬͱ΍Γ͍ͨɽ΍ͬͺΓݚڀʹ௅ઓ͍ͨ͠ʂ • Ͱ΋ΤϯδχΞͱͯ͠ະख़ͳࣗ෼ʹࣗ৴͕ͳ͍ https://hb.matsumoto-r.jp/entry/2019/03/26/223750 ͋Εʁ͍ͣΕ΍Γ͍ͨͳΒࠓ͔Β࢝ΊΔ΂͖͡Όͳ͍ʁ
 ͍ͣΕ͍ͬͯͭʁຬ଍͢Δ೔ͳΜͯҰੜ͜ͳ͍
11. 11 • 2019೥8݄ʹ͘͞ΒΠϯλʔωοτגࣜձࣾʹೖࣾ • ೖࣾޙ1ϲ݄Ͱࠪಡ෇͖࿦จΛ1ຊ౤ߘ͠ɼՈΛങ͍·ͨ͠ https://research.sakura.ad.jp/2019/02/22/concept-vision-2019/ ܦྺɿ৘ใ޻ֶͷݚڀऀ
12. 12 ܦྺͷͬ͘͟Γ·ͱΊ ࡐྉ޻ֶͷݚڀ 4೥ ফ๷ 3೥ ITΤϯδχΞ 3೥ ৘ใ޻ֶͷݚڀ 2ϲ݄ ←ΠϚίί
13. 2. Connecting The Dotsͱ͸
14. 14 Connecting The Dots You can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future. You have to trust in something — your gut, destiny, life, karma, whatever. This approach has never let me down, and it has made all the difference in my life. εςΟʔϒɾδϣϒζࢯ͕2005೥ʹελϯϑΥʔυେֶͷଔۀࣜͰ ߦͳͬͨεϐʔνΑΓ
15. 15 Connecting The Dots • ఺ͱ఺Λͭͳ͙͜ͱ͸ɼ͋Β͔͡Ί࢓ࠐΉ͜ͱ͸Ͱ͖ͳ͍
 ͋Δͱ͖ɼৼΓฦΔͱͭͳ͕Δ͜ͱ͕͋Δ • ৭ʑऔΓ૊ΜͰ͸ࣙΊͯΛ܁Γฦ͖ͯͨ͜͠ͱʹରͯ͠ڧ͍ྼ౳ײΛ࣋ͬ ͍͕ͯͨɼશͯΛͭͳ͗߹ΘͤͯڧΈʹ͍͖͍ͯͨ͠ͱࢥ͍࢝Ίͨ https://www.linkedin.com/pulse/knowledge-experience-creativity-dr-anadi-sahoo
16. 3. ఺Λଧͭ
17. 17 ఺Λଧͭ • ఺ΛଧͭͨΊͷߦಈ͸͞·͟·͋Δ ߦಈྫʣຊΛಡΉɼߨԋΛฉ͘ɼਓͱ࿩͢ ߦಈྫʣେֶͰֶͿɼ࢓ࣄΛ͢Δɼܧଓͯ͠ษڧ͢Δ ͞Βʹେ͖ͳ఺(΋͘͠͸఺ͷू߹ମ) Λଧͭ • ఺Λଧͪଓ͚Δ͜ͱ • ཭ΕͨҐஔʹ఺Λଧͭ͜ͱ େࣄʹ͍ͯ͠Δ͜ͱ
18. 18 ࠶ܝɿܦྺͷͬ͘͟Γ·ͱΊ ࡐྉ޻ֶͷݚڀ 4೥ ফ๷ 3೥ ITΤϯδχΞ 3೥ ৘ใ޻ֶͷݚڀ 2ϲ݄ ←ΠϚίί Կ͕ى͖ͯΔʁ
19. 19 େ͖ͳ఺Λଧͭ࣌ͷߦಈݪཧ ײಈɾিܸ͕৘೤ʹมΘͬͨॠؒʹେ͖ͳ఺͕ੜ·ΕΔ ৘೤ ಺ൃతಈػ ײಈɾিܸΛड͚Δ
20. 20 ࢲͷ৔߹ ײಈɾিܸ͕৘೤ʹมΘͬͨॠؒʹେ͖ͳ఺͕ੜ·ΕΔ ফ๷࢜ʹͳΔ ਓͷ໋Λٹ͍͍ͨʂ Զ͕΍Δ͔͠ͳ͍ʂ ඃࡂ஍ΛݟͯিܸΛड͚Δ
21. 21 ৘೤ͷݮਰۂઢ ৘೤͕ੜ·Εͨͱͯ͠΋ݮਰ͍ͯ͘͠ ৘೤ ࣌ؒ ಺ൃతಈػ
22. 4. ఺Λͭͳ͙
23. 23 ఺Λͭͳ͙ https://blog.tsurubee.tech/entry/2016/04/03/111301 • ଧͬͨ఺Λͭͳ͍Ͱ͍͖ɼઢɼ໘ɼཱͦͯ͠ମΛ࡞Δ͜ͱͰ
 ૑ൃతͳωοτϫʔΫ͕ܗ੒͞ΕΔ • ফ๷࢜࣌୅ʹʮೳྗ͸଍͠ࢉͰ͸ͳֻ͚ͯ͘ࢉͰߟ͑Δ΂͖ͩʯతͳ
 ϒϩάΛॻ͍͍ͯͨ
24. 24 ఺Λͭͳ͙ Ͳ͏΍ͬͯͭͳ͙ͷ͔ʁ • ઓུతʹͭͳ͙͜ͱ͸೉͘͠ɼͭͳ͕Δͱ৴ͯ͡఺Λ ଧͪଓ͚Δ • ͱ͸͍͑ɼաڈͷܦݧ΍஌ࣝΛϓϥεʹม͍͑ͨͱ͍͏ ϚΠϯυηοτ͸ॏཁͩͱࢥ͏ • ࣗ෼͕જࡏతʹϫΫϫΫΛײ͡Δ෼໺ʹ఺Λଧ͓ͬͯ͘ ͱ͋ͱ͔Βͭͳ͕Δ֬཰͕ߴ͍ͷͰ͸ͳ͍͔ ͜͜͸͜Ε͔Β΋ࢼߦࡨޡ͍ͯ͘͠ϙΠϯτͩͱߟ͍͑ͯΔ
25. 5. ࢲ͕ମײͨ͠Connecting The Dots
26. 26 ࢲʹى͖ͨConnecting The Dots ͘͞ΒΠϯλʔωοτݚڀॴͷॳग़ࣾ೔ʹΦϑΟεͷຊ୨ʹ͋ͬͨ ຊΛ;ͱ։͘ ֶੜ࣌୅ʹਅ݋ʹֶΜͩ͜ͱ͹͔Γͩͬͨ… γϡϨσΟϯΨʔํఔࣜɼΪϒεɾϘϧπϚϯ෼෍ɼ ૬సҠɼetc. ͦͯ͠ɼ͜͜਺೥Ұ൪ྗΛೖΕֶͯΜͰ͍ͨػցֶश ͱ΋ͭͳ͕ͬͨ https://www.kyoritsu-pub.co.jp/bookdetail/9784320035386 ྔࢠϘϧπϚϯػցֶशɼαϯϓϦϯά๏ɼetc. ׬શʹ఺ͱ఺͕ͭͳ͕Γɼࣗ෼ֶ͕Ϳ͜ͱ͕ඞવͷΑ͏ʹࢥ͑ͨ
27. 27 ྔࢠΞχʔϦϯά • ྔࢠΞχʔϦϯάͱ͸ɼྔࢠྗֶͷ๏ଇΛར༻ͯ͠ɼ͋Δछͷ৘ใॲཧΛ ͢ΔͨΊͷ࿮૊ΈͰ͋Δ ྔࢠΞχʔϦϯάͱ͸ https://arxiv.org/abs/cond-mat/9804280 ྔࢠΞχʔϦϯά͸1998೥ͷ࿦จͰ೔ຊਓ͕ ཧ࿦ΛߟҊͨ͠ • ૊Έ߹Θͤ࠷దԽ໰୊ʹର͢ΔϝλώϡʔϦεςΟοΫͳղ๏ͱͯ͠
 ஫໨͞Ε͍ͯΔ ྔࢠίϯϐϡʔλͱͷؔ܎ • ྔࢠίϯϐϡʔλʹ͸൚༻ܭࢉػΛ໨ࢦ͢ʮྔࢠ ήʔτํࣜʯͱ૊Έ߹Θͤ࠷దԽ໰୊ʹಛԽͨ͠ ʮྔࢠΞχʔϦϯάํࣜʯ͕͋Δ • ͨͩ͠ɼྔࢠΞχʔϦϯάΛઐ໳ʹ΍͍ͬͯΔํʑ ͸ΞχʔϦϯάϚγϯΛྔࢠίϯϐϡʔλͱݺ͹ ͳ͍͜ͱ͕ଟ͍
28. 28 ࡐྉ޻ֶͱͷͭͳ͕Γ ྔ ࢠ Ξ χ ʔ Ϧ ϯ ά ࣗવՊֶͱ৘ใ޻ֶͷͭͳ͕ΓΛॳΊͯײͯ͡خ͔ͬͨ͠
 աڈʹਅ݋ʹֶΜͰͯΑ͔ͬͨͱࢥ͑ͨ • ྔࢠྗֶ͸ݪࢠ΍ిࢠͳͲͷৼΔ ෣͍Λهड़͢Δྗֶ • ࡐྉͷઃܭʹ͸ݪࢠ΍ͦΕΒ͕࿈ ͳͬͨ෼ࢠ࠯ͷৼΔ෣͍Λ੍ޚ͢ Δඞཁ͕͋Δ • ྔࢠྗֶͷॾݪཧΛԽֶʹద༻͢ ΔʮྔࢠԽֶʯͱ͍͏ཧ࿦Խֶͷ ෼໺΋͋Δ • ΞχʔϦϯάͱ͸ʮম͖ͳ·͠ʯͷ͜ ͱͰ͋Γɼࡐྉͷ෺ੑΛ҆ఆͤ͞Δͨ ΊͷॏཁͳޙॲཧͷϓϩηεͰ͋Δ • ࢲ͕ݚڀ͍ͯͨ͠ߴ෼ࢠࡐྉͰ΋੒ܕ Ճ޻ޙʹඞͣ೤ΞχʔϦϯάΛࢪͯ͠ ͍ͨ • ೤ΞχʔϦϯάΛར༻ͨ͠࠷దԽख๏ ʹγϛϡϨʔςΟουΞχʔϦϯά͕ ͋Δ
29. 6. ࠓޙ΍Γ͍ͨݚڀʹ͍ͭͯ
30. 30 ࣗ෼ͷڧΈΛ੔ཧ͢Δ • ༷ʑͳ෼໺ʢࡐྉ޻ֶɾফ๷ɾػցֶशɾΠϯϑϥʣʹऔΓ૊ΜͰ ͖ͨ͜ͱʹΑΔ෼໺ԣஅతࢥߟ • ਂ͘ࢥߟͯ͠೉͍͠໰୊ʹ௅Ή͜ͱɼ໰୊Λղ͘͜ͱ͕ͱʹ͔͘
 ޷͖Ͱ͋Δ
31. 31 ݚڀͷίϯηϓτ ৘ใγεςϜͷ՝୊ɾ໰୊ Solver Problems ࡐྉ޻ֶ ৘ใ޻ֶ ྔࢠԽֶ ػցֶश ྔࢠίϯϐϡʔλ ྔࢠΞχʔϦϯά ৘ใγεςϜʹ͓͚Δະղܾͳ໰୊Λࡐྉ޻ֶɼػցֶशɼྔࢠίϯϐϡʔλͳͲɼ
 ͞·͟·ͳ஌ࣝ΍ٕज़Λۦ࢖ͯ͠ղܾ͢ΔSolverͱͯ͠ͷཱͪҐஔΛ໨ࢦ͢ Πϯϑϥ ϓϩάϥϛϯά ౷ܭྗֶ ෺ཧԽֶ
32. 32 ࠓޙͷݚڀςʔϚ എܠ ෺ཧϚγϯʹίϯςφΛͲͷΑ͏ʹ഑ஔ ͢Δ͔ΛφοϓαοΫ໰୊ͱͯ͠Ҩ఻త ΞϧΰϦζϜͰ࠷దԽ͢Δ ελςΟοΫͳঢ়گͷΈͰ࠷దղΛٻΊͯΑ͍ͷ͔ʁ
 ٻΊͨղ͕ෆ࣮֬ͳকདྷʹରͯ͠΋҆ఆͳղΛ΋ͨΒ͢ͷ͔ʁ ※1 C. Guerrero et al.: Genetic Algorithm for Multi-Objective Optimization of Container Allocation in Cloud Architecture, Journal of Grid Computing, Vol. 16, 113-135 2018. ண؟఺ কདྷͷෆ֬ఆੑΛड͚ೖΕͯ௕ظత ͳࢹ఺Ͱܧଓతʹ࠷దԽ͍ͯ͘͠ Container Node ίϯςφͳͲͷԾ૝Խٕज़ͷීٴ΍ϚΠΫϩαʔϏεԽͳͲʹ൐͍ɼWebαʔϏεΛࢧ͑Δ Πϯϑϥ͸ෳࡶԽ͍ͯ͠Δɽ
 ෳࡶԽͨ͠γεςϜߏ੒ΛػցֶशͰ࠷దԽ͢Δݚڀ͕ߦΘΕ͍ͯΔ※1 Ϣʔβ͔Βͷଟ༷ͳཁٻ΍ϋʔυ΢ΣΞো֐ͳͲ༷ʑͳมԽʹ௥ै͢Δ͜ͱ͕ٻΊΒΕΔɽ
 ex. αʔόͷεέʔϧΞ΢τɼϚΠάϨʔγϣϯɼ৽͍͠ϩʔϧͷ௥ՃͳͲ
33. • େن໛ͳ૊Έ߹Θͤ࠷దԽ໰୊Λߴ଎ʹղ͘͜ͱ͕ظ଴͞ΕΔ※1 33 ࠓޙͷݚڀςʔϚ ࠷దԽ໰୊ͷղͷϩόετੑʹண໨͠ɼಈతʹมԽ͢ΔγεςϜ ؀ڥʹ͓͍ͯɼߴ͍ੑೳΛҡ࣋͠ͳ͕Βܧଓతʹ࠷దԽՄೳͳ ࢓૊ΈΛ࣮ݱ͢Δ ྔࢠΞχʔϦϯάͷಛ௃ ྔࢠΞχʔϦϯά͸જࡏతʹϩόετͳղ͕ಘ΍͍͢͜ͱ ͕஌ΒΕ͍ͯΔ※3 • جఈঢ়ଶ͕ॖୀ͍ͯ͠Δ৔߹ʹɼภͬͨղ͕ಘΒΕΔ※2ʢΞϯϑΣΞαϯϓϦϯάʣ ※1 V. S. Denchev et al.: What is the Computational Value of Finite Range Tunneling?, Phys. Rev. X, Vol. 6, 031015 2016.
 ※2 B. H. Zhang et al.: Advantages of Unfair Quantum Ground-State Sampling, Scientific Reports, Vol. 7, 1044 2017.
 ※3 Y. Matsuda et al.: Ground-state statistics from annealing algorithms: quantum versus classical approaches, New Journal of Physics, Vol. 11, 073021 2009. https://link.springer.com/content/pdf/10.1007/s10898-009-9496-x.pdf ղͷੑೳ͕ύϥϝʔλมಈʹରͯ͠ ؤڧͰ͋Γɼܥͷ҆ఆΛҡ࣋ͨ͠· ·༰қʹ૊Έ׵͑ՄೳͰ͋Δ
34. 34 ·ͱΊ • ఺Λଧͭ͜ͱͷେ੾͞ • ͱʹ͔͘ଧͪଓ͚Δ • ࣗ෼͕ࠓ͍ΔҐஔ΍ಘҙ෼໺͔Β཭Εͨͱ͜Ζʹଧͭ • ఺Λͭͳ͙͜ͱͷେ੾͞ • ఺Λͭͳ͙͜ͱͰ૑ൃతωοτϫʔΫ͕ܗ੒͞ΕΔ • ઓུతʹ఺Λͭͳ͙͜ͱ͸೉͍͕͠જࡏతʹϫΫϫΫΛײ͡Δ
 ෼໺ʹ఺Λଧ͓ͬͯ͘ͱΑ͍ • ࠓޙݚڀΛόʔϯͱ΍͍ͬͯ͘ͷͰ·ͨͲ͔͜Ͱ͓࿩͠͠·͢ʂ