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ࣗવՊֶݚڀͷಓ۩ͱͯ͠ͷػցֶश ୈ6ճ৘ใՊֶܥηϛφʔ
 2019೥12݄4೔ ୍઒ Ұֶ (͖͕ͨΘɾ͍͕ͪ͘) • ཧԽֶݚڀॴ ֵ৽஌ೳ౷߹ݚڀηϯλʔ (AIP)
 iPSࡉ๔࿈ܞҩֶతϦεΫճආνʔϜ@ژ౎ • ๺ւಓେֶ Խֶ൓Ԡ૑੒ݚڀڌ఺ (WPI-ICReDD) [email protected]

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ࣗݾ঺հɿ୍઒ Ұֶ(͖͕ͨΘɾ͍͕ͪ͘) 2 10೥ ๺େ (1995ʙ2004) 7೥ ژେ (2005ʙ2011) 7೥ ๺େ (2012ʙ2018) ౷ܭత৴߸ॲཧͱύλʔϯೝࣝ (޻ֶݚڀՊ)
 "ྼܾఆ৴߸ݯ෼཭ͷL1ϊϧϜ࠷খղͷཧ࿦෼ੳ" όΠΦΠϯϑΥϚςΟΫε (Խֶݚڀॴ)
 έϞΠϯϑΥϚςΟΫε (ༀֶݚڀՊ) σʔλۦಈՊֶɾ ཭ࢄߏ଄Λ൐͏ػցֶश
 (৘ใՊֶݚڀՊ)
 + JST͖͕͚͞: ࡐྉΠϯϑΥϚςΟΫε ʁ೥ ཧݚ(ژ౎) (2019ʙ) AIPηϯλʔ iPSࡉ๔࿈ܞҩֶతϦεΫճආνʔϜ
 (๺େ Խֶ൓Ԡ૑੒ݚڀڌ఺ͱΫϩΞϙ) ઐ໳ɿػցֶशɾσʔλϚΠχϯάͱͦͷՊֶͰͷར׆༻
 ɹɹʮσʔλ͔ΒͷֶशʯΛͲ͏໰୊ղܾʹ׆༻Ͱ͖Δͷ͔ʁ

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࠷ۙͷݚڀର৅ 3 v h(1) v =  xv 0 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 h(t 1) v a(t) v h(t) v (36 "⒏OF // // yv zv  h(T ) v xv *OJUJBMJ[BUJPO /PEF8JTF3FDVSSFOU6QEBUF (SBQI(BUIFSJOH tanh X v2V (yv) tanh(zv) ! hG = 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 "EBQUJWF8FJHIUJOH
 4PGU"UUFOUJPO .FTTBHF 6QEBUF 3FBEPVU ཭ࢄߏ଄ ػցֶश σʔλ
 ۦಈՊֶ ʵߏ଄ͱ஌ࣝʵ ʵֶशͱ஌ೳʵ ʵ৘ใͱՊֶʵ ཭ࢄߏ଄ͷදݱͱߏ੒๏ ཭ࢄߏ଄Λೖྗɾ੍໿ͱ͢Δػցֶश ໦ߏ଄Ξϯαϯϒϧ ਂ૚ֶश/ܭࢉάϥϑ ֬཰తϓϩάϥϛϯά LightGBM (Microsoft) See5/C5.0 & Cubist (RuleQuest) CART® MARS® TreeNet®
 Random Forests® (Salford Systems) CatBoost (Yandex) TFBoost (Google) TenscentBoost (Tenscent) Sherwood decision forests TM AAAB+nicbVC7SgNBFL0bXzG+opY2g0GwCrsiaBm0sREi5gXJEmYns8mQeSwzs0JY8wm22tuJrT9j65c4SbbQ6IELh3Pu5VxOlHBmrO9/eoWV1bX1jeJmaWt7Z3evvH/QMirVhDaJ4kp3ImwoZ5I2LbOcdhJNsYg4bUfj65nffqDaMCUbdpLQUOChZDEj2DrpvtG/7ZcrftWfA/0lQU4qkKPeL3/1BoqkgkpLODamG/iJDTOsLSOcTku91NAEkzEe0q6jEgtqwmz+6hSdOGWAYqXdSIvm6s+LDAtjJiJymwLbkVn2ZuK/XiSWkm18GWZMJqmlkiyC45Qjq9CsBzRgmhLLJ45gopn7HZER1phY11bJlRIsV/CXtM6qgV8N7s4rtau8niIcwTGcQgAXUIMbqEMTCAzhCZ7hxXv0Xr03732xWvDym0P4Be/jG0AQlD4= 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ʮ཭ࢄߏ଄ʯΛ൐͏ػցֶश 4 ܾఆ໦ɾܾఆDAG χϡʔϥϧωοτ ֬཰తϓϩάϥϛϯά • ର৅͕ʮ཭ࢄߏ଄ʯΛ࣋ͭ • Ϟσϧ͕ʮ཭ࢄߏ଄ʯΛ࣋ͭ • ର৅ͷؔ܎͕ʮ཭ࢄߏ଄ʯΛ࣋ͭ H H H H H H H H O N O O H H H O O H H N O O Cl Cl Cl ू߹ɺܥྻɺ૊߹ͤɺஔ׵ɺ෼ذ(໦)ɺωοτϫʔΫ(άϥϑ)ɺ…

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5 ౷ܭ਺ཧݚڀॴ DATߨ࠲ L-B2

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౷ܭ਺ཧݚڀॴ DATߨ࠲ L-S 6

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స৬ɿ2019೥4݄1೔ʙ 7 ๺ւಓେֶ৘ใՊֶݚڀՊͷݚڀࣨΛclose͠Լه̎૊৫ͷ
 ʮΫϩεΞϙΠϯτϝϯτʯ΁ • ཧԽֶݚڀॴ 
 ֵ৽஌ೳ౷߹ݚڀηϯλʔ (AIP)
 iPSࡉ๔࿈ܞҩֶతϦεΫճආνʔϜ ݚڀһ • ๺ւಓେֶ 
 Խֶ൓Ԡ૑੒ݚڀڌ఺ (WPI-ICReDD) ಛ೚।ڭत 70% 30%

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8 ๺ւಓେֶ Խֶ൓Ԡ૑੒ݚڀڌ఺ (WPI-ICReDD) ਆాϥϘ ૑੒ݚڀػߏ౩ 05-116 ૑੒ݚڀػߏ౩ 02-106

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9 ཧԽֶݚڀॴ ֵ৽஌ೳ౷߹ݚڀηϯλʔ 東京駅 理研AIP 神田ラボ 皇居

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10 ཧԽֶݚڀॴ ֵ৽஌ೳ౷߹ݚڀηϯλʔ COREDO೔ຊڮ15F

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11 ۈ຿஍ɿژࡕಸ஍۠(ژ౎෎૬ָ܊ਫ਼՚ொ) https://www.kobe.riken.jp/about/map/keihanna/

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12 ۈ຿஍ɿژࡕಸ஍۠(ژ౎෎૬ָ܊ਫ਼՚ொ) 礵螟٥銮加峸㖑⼒խ䒉暟ꂁ縧㔳

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13 ۈ຿஍ɿژࡕಸ஍۠(ژ౎෎૬ָ܊ਫ਼՚ொ) ࠃཱࠃձਤॻؗؔ੢ؗ

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14 ࠃࡍిؾ௨৴جૅٕज़ݚڀॴʢATRʣ ੴࠇಛݚͷ ΞϯυϩΠυ ʮΤϦΧʯ༷͕௟࠲
 (࣮෺͸ࡱӨېࢭ) • ೴৘ใ௨৴૯߹ݚڀॴ • ஌ೳϩϘςΟΫεݚڀॴ • దԠίϛϡχέʔγϣϯݚڀॴ • ೾ಈ޻ֶݚڀॴ • ੴࠇߒಛผݚڀॴ (ੴࠇERATO) • ࠤ౻ঊಙಛผݚڀॴ (ࠤ౻ERATO)

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15 ࠃࡍిؾ௨৴جૅٕज़ݚڀॴʢATRʣ • ೴৘ใݚڀॴ (CNS) • ೝ஌ػߏݚڀॴ (CMC) • ೴৘ใղੳݚڀॴ (NIA) • ܭࢉ೴Πϝʔδϯάݚڀࣨ (CBI)
 ≒ ཧݚAIP ܭࢉ೴μΠφϛΫενʔϜ (ࢁԼT) • ಈత೴Πϝʔδϯάݚڀࣨ (DBI)
 ≒ ཧݚAIP ೴৘ใ౷߹ղੳνʔϜ (઒ುT) • ೴৘ใ௨৴૯߹ݚڀॴ

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16 ATR಺ ཧݚAIP • ๷ࡂՊֶνʔϜ (্ా मޭ) • ೴৘ใ౷߹ղੳνʔϜ (઒ು Ұߊ) • ܭࢉ೴μΠφϛΫενʔϜ (ࢁԼ ஦ਓ) • iPSࡉ๔࿈ܞҩֶతϦεΫճආνʔϜ (্ా मޭ) ࢲͱ্ాTLҎ֎͸ژେCiRAͷํʑ...

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ࣗݾ঺հɿ୍઒ Ұֶ(͖͕ͨΘɾ͍͕ͪ͘) 17 10೥ ๺େ (1995ʙ2004) 7೥ ژେ (2005ʙ2011) 7೥ ๺େ (2012ʙ2018) ౷ܭత৴߸ॲཧͱύλʔϯೝࣝ (޻ֶݚڀՊ)
 "ྼܾఆ৴߸ݯ෼཭ͷL1ϊϧϜ࠷খղͷཧ࿦෼ੳ" όΠΦΠϯϑΥϚςΟΫε (Խֶݚڀॴ)
 έϞΠϯϑΥϚςΟΫε (ༀֶݚڀՊ) σʔλۦಈՊֶɾ ཭ࢄߏ଄Λ൐͏ػցֶश
 (৘ใՊֶݚڀՊ)
 + JST͖͕͚͞: ࡐྉΠϯϑΥϚςΟΫε ʁ೥ ཧݚ(ژ౎) (2019ʙ) AIPηϯλʔ iPSࡉ๔࿈ܞҩֶతϦεΫճආνʔϜ
 (๺େ Խֶ൓Ԡ૑੒ݚڀڌ఺ͱΫϩΞϙ) ઐ໳ɿػցֶशɾσʔλϚΠχϯάͱͦͷՊֶͰͷར׆༻
 ɹɹʮσʔλ͔ΒͷֶशʯΛͲ͏໰୊ղܾʹ׆༻Ͱ͖Δͷ͔ʁ

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REVIEW Inverse molecular design using machine learning: Generative models for matter engineering Benjamin Sanchez-Lengeling1 and Alán Aspuru-Guzik2,3,4* The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials. Many of the challenges of the 21st century (1), from personalized health care to energy production and storage, share a common theme: materials are part of the solution (2). In some cases, the solu- tions to these challenges are fundamentally limited by the physics and chemistry of a ma- terial, such as the relationship of a materials bandgap to the thermodynamic limits for the generation of solar energy (3). Several important materials discoveries arose by chance or through a process of trial and error. For example, vulcanized rubber was prepared in the 19th century from random mixtures of com- pounds, based on the observation that heating with additives such as sulfur improved the rubber’s durability. At the molecular level, in- dividual polymer chains cross-linked, forming bridges that enhanced the macroscopic mechan- ical properties (4). Other notable examples in this vein include Teflon, anesthesia, Vaseline, Perkin’s mauve, and penicillin. Furthermore, these materials come from common chemical compounds found in nature. Potential drugs either were prepared by synthesis in a chem- ical laboratory or were isolated from plants, soil bacteria, or fungus. For example, up until 2014, 49% of small-molecule cancer drugs were natural products or their derivatives (5). In the future, disruptive advances in the dis- covery of matter could instead come from unex- plored regions of the set of all possible molecular and solid-state compounds, known as chemical space (6, 7). One of the largest collections of molecules, the chemical space project (8), has mapped 166.4 billion molecules that contain at most 17 heavy atoms. For pharmacologically rele- vant small molecules, the number of structures is estimated to be on the order of 1060 (9). Adding consideration of the hierarchy of scale from sub- nanometer to microscopic and mesoscopic fur- ther complicates exploration of chemical space in its entirety (10). Therefore, any global strategy for covering this space might seem impossible. Simulation offers one way of probing this space without experimentation. The physics and chemistry of these molecules are governed by quantum mechanics, which can be solved via the Schrödinger equation to arrive at their ex- act properties. In practice, approximations are used to lower computational time at the cost of accuracy. Although theory enjoys enormous progress, now routinely modeling molecules, clusters, and perfect as well as defect-laden periodic solids, the size of chemical space is still overwhelming, and smart navigation is required. For this purpose, machine learning (ML), deep learning (DL), and artificial intelligence (AI) have a potential role to play because their computational strategies automatically improve through experience (11). In the context of materials, ML techniques are often used for property prediction, seeking to learn a function that maps a molecular material to the property of choice. Deep generative models are a special class of DL methods that seek to model the underlying probability distribution of both structure and property and relate them in a nonlinear way. By exploiting patterns in massive datasets, these models can distill average and salient features that characterize molecules (12, 13). Inverse design is a component of a more complex materials discovery process. The time scale for deployment of new technologies, from discovery in a laboratory to a commercial pro- duct, historically, is 15 to 20 years (14). The pro- cess (Fig. 1) conventionally involves the following steps: (i) generate a new or improved material concept and simulate its potential suitability; (ii) synthesize the material; (iii) incorporate the ma- terial into a device or system; and (iv) characterize and measure the desired properties. This cycle generates feedback to repeat, improve, and re- fine future cycles of discovery. Each step can take up to several years. In the era of matter engineering, scientists seek to accelerate these cycles, reducing the FRONTIERS IN COMPUTATION 1Department of Chemistry and Chemical Biology, Harvard LOSKI on July 26, 2018 http://science.sciencemag.org/ Downloaded from REVIEW https://doi.org/10.1038/s41586-018-0337-2 Machine learning for molecular and materials science Keith T. Butler1, Daniel W . Davies2, Hugh Cartwright3, Olexandr Isayev4* & Aron Walsh5,6* Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence. The Schrödinger equation provides a powerful structure– property relationship for molecules and materials. For a given spatial arrangement of chemical elements, the distribution of electrons and a wide range of physical responses can be described. The development of quantum mechanics provided a rigorous theoretical foundation for the chemical bond. In 1929, Paul Dirac famously proclaimed that the underlying physical laws for the whole of chemistry are “completely known”1. John Pople, realizing the importance of rapidly developing computer technologies, created a program—Gaussian 70—that could perform ab initio calculations: predicting the behaviour, for molecules of modest size, purely from the fundamental laws of physics2. In the 1960s, the Quantum Chemistry Program Exchange brought quantum chemistry to the masses in the form of useful practical tools3. Suddenly, experi- mentalists with little or no theoretical training could perform quantum calculations too. Using modern algorithms and supercomputers, systems containing thousands of interacting ions and electrons can now be described using approximations to the physical laws that govern the world on the atomic scale4–6. The field of computational chemistry has become increasingly pre- dictive in the twenty-first century, with activity in applications as wide ranging as catalyst development for greenhouse gas conversion, materials discovery for energy harvesting and storage, and computer-assisted drug design7. The modern chemical-simulation toolkit allows the properties of a compound to be anticipated (with reasonable accuracy) before it has been made in the laboratory. High-throughput computational screening has become routine, giving scientists the ability to calculate the properties of thousands of compounds as part of a single study. In particular, den- sity functional theory (DFT)8,9, now a mature technique for calculating the structure and behaviour of solids10, has enabled the development of extensive databases that cover the calculated properties of known and hypothetical systems, including organic and inorganic crystals, single molecules and metal alloys11–13. The emergence of contemporary artificial-intelligence methods has the potential to substantially alter and enhance the role of computers in science and engineering. The combination of big data and artificial intel- ligence has been referred to as both the “fourth paradigm of science”14 and the “fourth industrial revolution”15, and the number of applications in the chemical domain is growing at an astounding rate. A subfield of artificial intelligence that has evolved rapidly in recent years is machine generating, testing and refining scientific models. Such techniques are suitable for addressing complex problems that involve massive combi- natorial spaces or nonlinear processes, which conventional procedures either cannot solve or can tackle only at great computational cost. As the machinery for artificial intelligence and machine learning matures, important advances are being made not only by those in main- stream artificial-intelligence research, but also by experts in other fields (domain experts) who adopt these approaches for their own purposes. As we detail in Box 1, the resources and tools that facilitate the application of machine-learning techniques mean that the barrier to entry is lower than ever. In the rest of this Review, we discuss progress in the application of machine learning to address challenges in molecular and materials research. We review the basics of machine-learning approaches, iden- tify areas in which existing methods have the potential to accelerate research and consider the developments that are required to enable more wide-ranging impacts. Nuts and bolts of machine learning With machine learning, given enough data and a rule-discovery algo- rithm, a computer has the ability to determine all known physical laws (and potentially those that are currently unknown) without human input. In traditional computational approaches, the computer is little more than a calculator, employing a hard-coded algorithm provided by a human expert. By contrast, machine-learning approaches learn the rules that underlie a dataset by assessing a portion of that data and building a model to make predictions. We consider the basic steps involved in the construction of a model, as illustrated in Fig. 1; this constitutes a blueprint of the generic workflow that is required for the successful application of machine learning in a materials-discovery process. Data collection Machine learning comprises models that learn from existing (train- ing) data. Data may require initial preprocessing, during which miss- ing or spurious elements are identified and handled. For example, the Inorganic Crystal Structure Database (ICSD) currently contains more than 190,000 entries, which have been checked for technical mistakes but are still subject to human and measurement errors. Identifying DNA to be sequences into distinct pieces, parcel out the detailed work of sequencing, and then reassemble these independent ef- forts at the end. It is not quite so simple in the world of genome semantics. Despite the differences between genome se- quencing and genetic network discovery, there are clear parallels that are illustrated in Table 1. In genome sequencing, a physical map is useful to provide scaffolding for assembling the fin- ished sequence. In the case of a genetic regula- tory network, a graphical model can play the same role. A graphical model can represent a high-level view of interconnectivity and help isolate modules that can be studied indepen- dently. Like contigs in a genomic sequencing project, low-level functional models can ex- plore the detailed behavior of a module of genes in a manner that is consistent with the higher level graphical model of the system. With stan- dardized nomenclature and compatible model- ing techniques, independent functional models can be assembled into a complete model of the cell under study. To enable this process, there will need to be standardized forms for model representa- tion. At present, there are many different modeling technologies in use, and although models can be easily placed into a database, they are not useful out of the context of their specific modeling package. The need for a standardized way of communicating compu- tational descriptions of biological systems ex- tends to the literature. Entire conferences have been established to explore ways of mining the biology literature to extract se- mantic information in computational form. Going forward, as a community we need to come to consensus on how to represent what we know about biology in computa- tional form as well as in words. The key to postgenomic biology will be the computa- tional assembly of our collective knowl- edge into a cohesive picture of cellular and organism function. With such a comprehen- sive model, we will be able to explore new types of conservation between organisms and make great strides toward new thera- peutics that function on well-characterized pathways. References 1. S. K. Kim et al., Science 293, 2087 (2001). 2. A. Hartemink et al., paper presented at the Pacific Symposium on Biocomputing 2000, Oahu, Hawaii, 4 to 9 January 2000. 3. D. Pe’er et al., paper presented at the 9th Conference on Intelligent Systems in Molecular Biology (ISMB), Copenhagen, Denmark, 21 to 25 July 2001. 4. H. McAdams, A. Arkin, Proc. Natl. Acad. Sci. U.S.A. 94, 814 ( 1997 ). 5. A. J. Hartemink, thesis, Massachusetts Institute of Technology, Cambridge (2001). V I E W P O I N T Machine Learning for Science: State of the Art and Future Prospects Eric Mjolsness* and Dennis DeCoste Recent advances in machine learning methods, along with successful applications across a wide variety of fields such as planetary science and bioinformatics, promise powerful new tools for practicing scientists. This viewpoint highlights some useful characteristics of modern machine learn- ing methods and their relevance to scientific applications. We conclude with some speculations on near-term progress and promising directions. Machine learning (ML) (1) is the study of computer algorithms capable of learning to im- prove their performance of a task on the basis of their own previous experience. The field is closely related to pattern recognition and statis- tical inference. As an engineering field, ML has become steadily more mathematical and more successful in applications over the past 20 years. Learning approaches such as data clus- tering, neural network classifiers, and nonlinear regression have found surprisingly wide appli- cation in the practice of engineering, business, and science. A generalized version of the stan- dard Hidden Markov Models of ML practice have been used for ab initio prediction of gene structures in genomic DNA (2). The predictions correlate surprisingly well with subsequent gene expression analysis (3). Postgenomic bi- ology prominently features large-scale gene ex- pression data analyzed by clustering methods (4), a standard topic in unsupervised learning. Many other examples can be given of learning and pattern recognition applications in science. Where will this trend lead? We believe it will lead to appropriate, partial automation of every element of scientific method, from hypothesis generation to model construction to decisive experimentation. Thus, ML has the potential to amplify every aspect of a working scientist’s progress to understanding. It will also, for better or worse, endow intelligent computer systems with some of the general analytic power of scientific thinking. Machine Learning at Every Stage of the Scientific Process Each scientific field has its own version of the scientific process. But the cycle of observing, creating hypotheses, testing by decisive exper- iment or observation, and iteratively building up comprehensive testable models or theories is shared across disciplines. For each stage of this abstracted scientific process, there are relevant developments in ML, statistical inference, and pattern recognition that will lead to semiauto- matic support tools of unknown but potentially broad applicability. Increasingly, the early elements of scientific method—observation and hypothesis genera- tion—face high data volumes, high data acqui- sition rates, or requirements for objective anal- ysis that cannot be handled by human percep- tion alone. This has been the situation in exper- imental particle physics for decades. There automatic pattern recognition for significant events is well developed, including Hough transforms, which are foundational in pattern recognition. A recent example is event analysis for Cherenkov detectors (8) used in neutrino oscillation experiments. Microscope imagery in cell biology, pathology, petrology, and other fields has led to image-processing specialties. So has remote sensing from Earth-observing satellites, such as the newly operational Terra spacecraft with its ASTER (a multispectral thermal radiometer), MISR (multiangle imag- ing spectral radiometer), MODIS (imaging Machine Learning Systems Group, Jet Propulsion Lab- oratory/California Institute of Technology, Pasadena, CA, 91109, USA. *To whom correspondence should be addressed. E- mail: [email protected] Table 1. Parallels between genome sequencing and genetic network discovery. Genome sequencing Genome semantics Physical maps Graphical model Contigs Low-level functional models Contig reassembly Module assembly Finished genome sequence Comprehensive model www.sciencemag.org SCIENCE VOL 293 14 SEPTEMBER 2001 2051 C O M P U T E R S A N D S C I E N C E on August 29, 2018 http://science.sciencemag.org/ Downloaded from Nature, 559
 pp. 547–555 (2018) Science, 293 pp. 2051-2055 (2001) Science, 361 pp. 360-365 (2018) Science is changing, the tools of science are changing. And that requires different approaches. ── Erich Bloch, 1925-2016 ʮσʔλར׆༻ٕज़ʯ͸Պֶݚڀͷಓ۩ͷҰͭʹ 18 ڭ܇ "low input, high throughput, no output science." (Sydney Brenner) → ࡶͳઃఆɾܥͰ໢ཏతͳϋΠεϧʔϓοτ࣮ݧΛ͍͘Βͯ͠΋Կ΋ಘΒΕͳ͍

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ຊ೔ͷ࿩ͷલஔ͖ 19 ࠓ೔͸ɺͦͷޙͷ࿩Λ൓өͨ͠࠶ʑฤ൛Ͱ͢ɻ

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Deepͳ৘ใܥͷํʹ͸ 20 ͱ͜ΖͰɺࠓ݄ͷਓ޻஌ೳֶձࢽͷ୍઒͞ΜͷSIG-FPAIهࣄɺ ͨͩͷݚڀձͷ঺հهࣄͷϋζͷͳͷʹɺ͑Β͍ݟࣝʹͳ͍ͬͯͯɺ ଞͷݚڀձͷهࣄͱͷམࠩʹࠔ࿭͠·ͨ͠ɻ Έͳ͞·΋ੋඇɻ kashi_pong ڭत (ژ౎େֶ) ਓ޻஌ೳֶձࢽ Vol.34 No.5 (2019/9) ಛूɿʮݚڀձ঺հʯ ਓ޻஌ೳجຊ໰୊ݚڀձʢ'1"*ʣ ΦʔϓϯΞΫηε https://sig-fpai.org/

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ਓ޻஌ೳֶձ ਓ޻஌ೳجຊ໰୊ݚڀձ(SIG-FPAI) 21

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ষཱͯ 1. ͸͡Ίʹ 2. ͦͷ্ʹ෺ͷݐͨͳ͍΋ͷ͸جૅͱ͸͍Θͳ͍ 3. มΘΔ΋ͷɼมΘΒͳ͍΋ͷ 4. The Hard Thing about Hard Things 5. ػցֶशͱࣗಈϓϩάϥϛϯά: બ୒ͱֶशͷؒ 6. ૊߹ͤͷ൚Խ: ཭ࢄͱ࿈ଓͷؒ 7. ػցൃݟͱࣗಈԽͷເ: ֶशͱൃݟͷؒ 8. දݱͱհೖ: ܦݧ࿦ͱ߹ཧ࿦ͷؒ 9. աఔͱ࣮ࡏ: ༗ݶͱແݶͷؒ ݹ͍จݙαʔϕΠ
 ʹΑΔFAI/FPAIͷྺ࢙ ౰࣌ͷ࿩୊ͱݱ୅ͷ
 ࿩୊ͷࢲͳΓͷϦϯΫ झຯతࡶײͱల๬ (ࢀߟจݙ100݅) ਓ޻஌ೳֶձࢽ Vol.34 No.5 (2019/9) ಛूɿʮݚڀձ঺հʯ ਓ޻஌ೳجຊ໰୊ݚڀձʢ'1"*ʣ ΦʔϓϯΞΫηε Permalink : http://id.nii.ac.jp/1004/00010296/ https://sig-fpai.org/

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Take Home Message 24 Պֶ͕ٻΊΔ͜ͱ: ෼͔Βͳ͍͜ͱ͕෼͔Δ(Պֶతൃݟ) ൃݟ ཧղ ݪҼͱ݁Ռ(ҼՌؔ܎)Λݟग़͢ ࠓ·Ͱݟग़͞Ε͍ͯͳ͍ྑ͍ର৅Λݟग़͢ ࠓ೔఻͍͑ͨͨͬͨ3ͭͷ͜ͱ 1. ୯७ʹػցֶशΛ࢖͏͚ͩͰ͸͍ͣΕ΋ղ͚ͳ͍ 2. ػցֶशҎ֎ͷ΋ͷ(հೖ΍υϝΠϯ஌ࣝ)͕ݪཧ্ඞਢ 3. ࠷ۙ·͞ʹݚڀ͕ਐߦதͷະղܾྖҬ͕ͩݚڀ͸৭ʑ͋Δ ͷաఔΛཧղ͠(ਓ͕ؒ)ൃݟ͢Δ x!y AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 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AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= x!y AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5

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ࠓ೔ͷ಺༰ 25 1. Πϯτϩ
 ػցֶशͱՊֶ(͋Δ͍͸"΋ͷͮ͘Γ") 2. ػցֶशͰԿ͔ΛʮཧղʯͰ͖Δ͔ʁ
 Answer: ௚઀తʹ͸ݪཧ্ࠔ೉ 3. ػցֶशͰԿ͔ΛʮൃݟʯͰ͖Δ͔ʁ
 Answer: ௚઀తʹ͸ݪཧ্ࠔ೉ 4. ͡Ό͋Ͳ͏͢ΜͷʂʁԿ͕͍Δͷʂʁ
 Answer:ʮදݱʯͱʮհೖʯ
 2ͱ3Λલఏʹػցֶश෼໺ͷτϐοΫΛ؆୯ʹ঺հ

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ࠓ೔ͷ಺༰ 25 1. Πϯτϩ
 ػցֶशͱՊֶ(͋Δ͍͸"΋ͷͮ͘Γ") 2. ػցֶशͰԿ͔ΛʮཧղʯͰ͖Δ͔ʁ
 Answer: ௚઀తʹ͸ݪཧ্ࠔ೉ 3. ػցֶशͰԿ͔ΛʮൃݟʯͰ͖Δ͔ʁ
 Answer: ௚઀తʹ͸ݪཧ্ࠔ೉ 4. ͡Ό͋Ͳ͏͢ΜͷʂʁԿ͕͍Δͷʂʁ
 Answer:ʮදݱʯͱʮհೖʯ
 2ͱ3Λલఏʹػցֶश෼໺ͷτϐοΫΛ؆୯ʹ঺հ

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Empirical optimization or "Edisonian empiricism" 26 ط஌ͷ஌ݟɾ ؍ଌ(σʔλ) • ࣮ݧ • γϛϡϨʔγϣϯ Ծઆܗ੒ ݁Ռͷ֬ೝͱ ݕূ ࣍ͷ࣮ݧܭը΁feedback Thomas Edisonઌੜ • Genius is 1% inspiration and 99% perspiration. • There is no substitute for hard work. • I have not failed. I've just found 10,000 ways that won't work. : ໰୊ɿ࣌ؒͱίετ͸༗ݶʂʂ
 ཧ࿦తʹՄೳͳ͋ΒΏΔީิΛ
 ͜ͷํࣜͰݕূ͢Δ͜ͱ͸ෆՄೳ Α͘ߟ͑ΔͱϒϥοΫͳ͜ͱ͔͠ݴͬͯͳ͍ʂ Ծઆݕূ "؍࡯ͱؼೲ (empirical/inductive)" "࿦ཧͱԋ៷ (rational/deductive)"

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ՊֶతൃݟͱηϨϯσΟϐςΟ 27 ط஌ͷ஌ݟɾ ؍ଌ(σʔλ) • ࣮ݧ • γϛϡϨʔγϣϯ Ծઆܗ੒ ݁Ռͷ֬ೝͱ ݕূ ࣍ͷ࣮ݧܭը΁feedback • ͦΕΏ͑ʮݚڀऀͷηϯεɾ࿹ͷݟͤॴʯʴʮ޾ӡ(ηϨϯσΟϐςΟ)ʯʹґଘ͢Δ
 ےͷྑͦ͞͏ͳީิΛબͿɺࠓ·Ͱࢼ͞Εͯͳ͍શ͘৽͍͠΍ΓํΛࢥ͍ͭ͘ɺetc • ީิ͕͋·Γʹ๲େ(࣮࣭΄΅ແݶ)ͳͷͰ(਺ଟ͘ࢼ͢ͷ͸༗རͩͱ͸ݴ͑...)
 ඞͣ͠΋ʮྗٕͱ͓ۚͱਓւઓज़Ͱ਺ଟ͘ࢼͨ͠ऀ͕উͭʯͱ͸ݶΒͳ͍ ໰୊ɿ࣌ؒͱίετ͸༗ݶʂʂ
 ཧ࿦తʹՄೳͳ͋ΒΏΔީิΛ
 ͜ͷํࣜͰݕূ͢Δ͜ͱ͸ෆՄೳ Ծઆݕূ

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ػցֶश͸γϛϡϨʔγϣϯɾ࣮ݧͱ૬ิత 28 ط஌ͷ஌ݟɾ ؍ଌ(σʔλ) ݁Ռͷ֬ೝͱ ݕূ ߴ଎ɾߴਫ਼౓ͳ Data-Driven༧ଌ ࣍ͷ࣮ݧܭը΁feedback Ծઆܗ੒ (γϛϡϨʔγϣϯ+࣮ݧ) • ࠶ݱੑΛ୲อ͢Δߴਫ਼౓ɾߴ଎࣮ݧܥ • Ծ૝Խݕূ͕ՄೳͳҼࢠͷγϛϡϨʔ γϣϯ(ܭࢉՊֶ)ʹΑΔ୳ࡧ
 → ๬·͍͠ର৅ͷ͞ΒͳΔߜΓࠐΈ Ծઆݕূ (ػցֶशɾσʔλϚΠχϯά) • Ͳ͏͍͏࣮ݧɾγϛϡϨʔγϣϯΛ
 ࣍ʹߦ͏͔ͷܭըཱҊ • ࣌ؒͷ͔͔Δܭࢉͷߴਫ਼౓ߴ଎ۙࣅ • ᐆດͳҼࢠ΍࣮ݧ৚݅ͷ࠷దԽ • Multilevelͷ৘ใ౷߹

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ࠓ೔ͷ಺༰ 29 1. Πϯτϩ
 ػցֶशͱՊֶ(͋Δ͍͸"΋ͷͮ͘Γ") 2. ػցֶशͰԿ͔ΛʮཧղʯͰ͖Δ͔ʁ
 Answer: ௚઀తʹ͸ݪཧ্ࠔ೉ 3. ػցֶशͰԿ͔ΛʮൃݟʯͰ͖Δ͔ʁ
 Answer: ௚઀తʹ͸ݪཧ্ࠔ೉ 4. ͡Ό͋Ͳ͏͢ΜͷʂʁԿ͕͍Δͷʂʁ
 Answer:ʮදݱʯͱʮհೖʯ
 2ͱ3Λલఏʹػցֶश෼໺ͷτϐοΫΛ؆୯ʹ঺հ

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ʮػցֶशʯతγνϡΤʔγϣϯ 30 ࣄྫɿࣸਅΛA͞Μ͔B͞Μ͔ʹ෼ྨ͢Δίϯϐϡʔλ
 ϓϩάϥϜΛ࡞Γ͍ͨɻ(େྔͷࣸਅΛਓख෼ྨ͢Δͷݏ) ? ? ? ? ? ? • ਓؒ͸؆୯ʹͰ͖Δ • ͕ɺͲ͏΍ͬͯ΍͍ͬͯΔͷ͔ݪཧ͸ෆ໌֬ • ൅ܕɺ֯౓ɺর໌ɺഎܠɺද৘ɺԽহɺ೥ྸɺͳͲ
 Λߟ͑Δͱ໌ࣔతͳϓϩάϥϛϯά͸ͱͯ΋೉͍͠ .. ?

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ػցֶश: ৽͍͠ϓϩάϥϛϯάͷ͔ͨͪ 31 Ұൠ෺ମೝࣝ ήʔϜϓϨΠ “͋Γ͕ͱ͏” J’aime la musiqu e I love music ೖग़ྗͷؔ܎͕Α͘෼͔Βͳ͍ม׵աఔ(ؔ਺)Λେྔͷೖग़ྗͷ ݟຊྫ͔Β໌ࣔతʹϓϩάϥϛϯά͢Δ͜ͱͳ͘ߏ੒͢Δٕ๏ Ի੠ೝࣝ ػց຋༁ ௒ղ૾

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ػցֶशͰ͖ͨͱ͖ͷ̎छྨͷظ଴ 32 1. ಘΒΕͨม׵աఔ(ؔ਺)ʹΑΔ༧ଌΛ৭ʑͳ໨తʹ࢖͏ 2. ಘΒΕͨม׵աఔ(ؔ਺)Λ෼ੳͯ͠എܠաఔͷ࢓૊ΈΛ஌Δ Ұൠ෺ମೝࣝ ήʔϜϓϨΠ “͋Γ͕ͱ͏” J’aime la musiqu e I love music Ի੠ೝࣝ ػց຋༁ ௒ղ૾

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ʮཧղʯฤͷϙΠϯτʂ 33 1͸OK͕ͩ 2͸ͱͯ΋ඍົʂ ͦͯͦ͠ͷཧ༝΋·ͨඍົͳͷͰ͕͢ ͋ͨͬͯ͞͠... 1. ಘΒΕͨม׵աఔ(ؔ਺)ʹΑΔ༧ଌΛ৭ʑͳ໨తʹ࢖͏ 2. ಘΒΕͨม׵աఔ(ؔ਺)Λ෼ੳͯ͠എܠաఔͷ࢓૊ΈΛ஌Δ

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ػցֶश༧ଌͱͦͷཧղͷʮਂ͍ߔʯ 34 Լه͸ͲΕ΋ػցֶशͰ͔ͳΓߴਫ਼౓ͳ༧ଌ͕Ͱ͖·͕͢ɺ
 Ռͨͯͦ͠ͷ࢓૊Έͷཧղ͕ಘΒΕͨͷͰ͠ΐ͏͔...? Ͳ͏΍ͬͯݟ͍͑ͯΔ΋ͷΛೝࣝʁ Ͳ͏΍ͬͨΒғޟউͯΔʁ “͋Γ͕ͱ͏” J’aime la musiqu e I love music Ͳ͏΍ͬͯԻ೾ͷҙຯΛཧղʁ Ͳ͏΍ͬͯଟݴޠΛཧղʁ ࣮৘ɿ༧ଌ͸౰ͨΔΜ͚ͩͲཧ༝͸Α͘Θ͔Βͳ͍ʂ

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༧ଌ͕͋ͨΕ͹ཧ༝͸Θ͔Βͳͯ͘΋OKʁ 35 Ͳ͏ߟ͑ͯ΋OKͳΘ͚ͳ͍΍Ζ...😫ͱࢥ͏͔΋͠Ε·ͤΜ ͕ • ͦΕ͸༻్ʹΑΔ (༧ଌ͕ߴ͍ਫ਼౓Ͱ౰ͨΔͱ͍͏લఏͰ) ݱࡏͷ঎ۀత੒ޭΛݗҾ͢Δଟ͘ͷ༻్Ͱ͸ཁΒͳ͍৔߹΋ɻ ݕࡧɺ޿ࠂɺਪનɺηϯαʔ/IoTɺը૾ɾԻ੠ೝࣝɺܳज़ͳͲ • ࠷ۙɺద༻ઌ͕޿͕Γʮཧ༝ʯΛٻΊΒΕΔΑ͏ʹ ࣾձతʹΫϦςΟΧϧͳ໨తʹ౤ೖ͢ΔͳΒʮཧ༝ʯඞཁɻ ҩྍɺࣗಈ੍ޚɺΠϯϑϥ੍ޚɺޏ༻ɺ੓ࡦܾఆɺ༥ࢿͳͲ આ໌੹೚ɾಁ໌ੑɾެฏੑɾ҆શੑɾྙཧΛ୲อՄೳʁ

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࿦จɿUse and Abuse of Regression (1966) 36 ࠓͷػցֶश 😉 ίϯϐϡʔλ͕ॳΊͯ൰ۙͳಓ۩ʹͳΓɺਓʑ͸͋ΒΏΔ໨తʹ
 Data-driven(ճؼ෼ੳ)Λଟ༻͢ΔΑ͏ʹͳͬͯ͠·ͬͨ… 😅

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࿦จɿUse and Abuse of Regression (1966) 37 "one of the great statistical minds of the 20th century" େ౷ܭֶऀ George E. P. Box (1919-2013) https://en.wikipedia.org/wiki/All_models_are_wrong "Essentially, all models are wrong, but some are useful" 1. આ໌ม਺Λ؍ଌͨ͠ͱ͖ͷ໨తม਺ͷ༧ଌ 2. આ໌ม਺ʹ֎తૢ࡞ΛՃ͑ͨͱ͖ͷ
 ໨తม਺΁ͷҼՌతޮՌͷൃݟ ճؼ෼ੳͷ໨త: (ڭࢣ෇ֶ͖शҰൠʹ౰ͯ͸·Δ) Use 😆 Abuse 😫 ...ۤݴʁ😅

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૬ؔؔ܎͸ඞͣ͠΋ҼՌؔ܎Λҙຯ͠ͳ͍ 38 ମॏ ਎௕ Ԡ༻౷ܭֶͷΠϩϋɿCorrelation does not imply causation ମॏΛ૿΍ͤ͹
 ਎௕΋৳ͼΔʂʁ🤔 ೔ຊϓϩ໺ٿ։ນҰ܉બखͷ਎௕ɾମॏσʔλ (2016೥ٿஂެࣜαΠτબखσʔλΑΓࣗ࡞) ͜Ε͕͓͔͍͠ͱ ͍͏͜ͱ͸͜ͷσʔλ ͚͔ͩΒ͸෼͔Βͳ͍ ͦͯ͠ػցֶश͸σʔλʹ಺ࡏ͢Δ૬ؔؔ܎ͷར׆༻ٕज़

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N Engl J Med 2012; 367:1562-1564 39 νϣίϨʔτফඅྔ IF(2018) 70.670😳ͷ࠷΋ྺ࢙ͱݖҖͷ͋Δҩֶࢽ ਓޱ1ઍສਓ͋ͨΓͷ ϊʔϕϧ৆ड৆ऀ਺

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؍࡯σʔλ͚͔ͩΒ͸ҼՌ͸෼͔Βͳ͍ 40 http://phenomena.nationalgeographic.com/2015/09/11/nick-cage-movies- vs-drownings-and-more-strange-but-spurious-correlations/ ΞϝϦΧͷՊֶ༧ࢉ vs ट௻ΓʹΑΔࣗࡴऀ਺ ϓʔϧͰͷṆࢮऀ਺ vs χίϥεέΠδͷөըग़ԋ਺

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͍ͭ૬ؔͱҼՌ͸ဃ཭͠͏Δͷ͔ʁ 41 ☺ؠ೾σʔλαΠΤϯε Vol. 3, ಛूɿҼՌਪ࿦――࣮ੈքͷσʔλ͔ΒҼՌΛಡΉ, 2016ΑΓ ૬ؔʹ ͍ͭͯ ͦΕҎ֎
 (Check೉) ҼՌؔ܎൑ఆͷHillͷΨΠυϥΠϯ (Hill, 1965)

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ަབྷҼࢠͱݟ͔͚ͤͷ૬ؔ 42 ྫɿҿञ͸ഏ͕ΜͷϦεΫཁҼͰ͋Δ(?) ٤Ԏ ަབྷҼࢠ(cofounders) ഏ͕Μ ҿञ ݟ͔͚ͤͷ૬ؔ(spurious correlation) Ҽࢠʮ٤Ԏʯ͕ަབྷ͍ͯ͠Δ ަབྷʹͲ͏ରॲ͢Δ͔ʁ ཧ૝ʮ࣮ݧ͢Δ(հೖ͢Δ)ʯɿ
 հೖ͢Δ͔൱͔Λແ࡞ҝʹׂΓ෇͚ΔϥϯμϜԽൺֱࢼݧ(RCT) ؍࡯ݚڀͰ͸Ͱ͖ͳ͍: ٤Ԏ͢Δ͔Ͳ͏͔ΛׂΓ෇͚Ͱ͖ͳ͍ ҼՌͷ্ྲྀʹڞ௨Ҽࢠ͕ଘࡏ

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؍࡯ݚڀʹΑΔҼՌਪ࿦ͷجຊ 43 ᶃ ૚ผ(Stratification) ᶄ ճؼ෼ੳͷར༻ ٤Ԏ=༗ͷ܈ͱɺ٤Ԏ=ແͷ܈ʹ෼͚ɺ֤ʑղੳͨ͋͠ͱ౷߹ ʮ٤ԎʯΛઆ໌ม਺ʹؚΊͯճؼ෼ੳͰ༗ҙੑݕఆ ඞཁͳલఏɿڵຯͷର৅ͷؔ܎ҼࢠͱަབྷҼࢠ͕͢΂ͯ
 ଌఆ͞Ε͍ͯΔ (͞ΒʹҼࢠͷؒͷҼՌߏ଄΋෼͔͍ͬͯΔ) ަབྷͦ͠͏ͳҼࢠ͸શͯઆ໌ม਺ʹೖΕ͓͚ͯ͹ྑ͍͕
 αϯϓϧ਺ʹΑͬͯ͸ճؼ෼ੳ͕ഁ୼ͯ͠͠·͏ • ʮ܏޲είΞʯʹΑͬͯଟ਺ͷڞมྔΛ̍࣍ݩʹม׵͢Δ • ʮόοΫυΞج४ʯʹΑͬͯऔΓೖΕΔ΂͖આ໌ม਺ΛબͿ Α͘෼͔Βͳ͍ର৅Ͱ͸ݱ࣮తʹຬͨ͞ΕͮΒ͍...

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౷ܭֶͱػցֶशͷʮߔʯ 44 • ౷ܭֶͱػցֶशͷҧ͍: σʔλ΍ม਺ʹର͢ΔԾఆ͕ҧ͏ ౷ܭֶ: ੍ޚ͞Ε࣮ͨݧܭը (ྟচࢼݧ, ࣾձௐࠪ, ೶ۀࢼݧ,...) ಛ௃ྔ vs આ໌ม਺: Ҽࢠతҙຯ͸ͳ͍৔߹΋(ը૾ͷϐΫηϧ) • ૬ؔؔ܎ͷར׆༻Ͱ"༧ଌ͕ΊͬͪΌ౰ͨΔΜͳΒ͑͑΍Μ..." ͠͹͠͹ʮ͑͑Θ͚ͳ͍΍Ζ😫ʯͱ͍͏᫁᫟ΛੜΜͰ͖ͨ... e.g. ݴޠֶͷڊਓ Chomsky vs Googleݚڀ෦໳௕ Norvig http://norvig.com/chomsky.html ☺Statistical Modeling: The Two Cultures (Breiman, Statist. Sci. 16(3), 199-231, 2001) ػցֶश: ੍ޚ͞Εͳ͍σʔλ (ը૾, Ի੠, ςΩετ, ৴߸, ...) ஫ҙɿػցֶश԰͸ҼՌΛ͋·Γؾʹ͠ͳ͍

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σʔλۦಈՊֶ: Պֶ΋ҼՌ(ཧ༝)͕ओͨΔؔ৺ 45 Պֶͷؔ৺͸ʮ࢓૊Έ΍ݪཧ͕Α͘෼͔Βͳ͍ݱ৅ʯ ? ؍ଌʗσʔλ • Theory-driven / Hypothesis-driven (ࣗવՊֶ) ExplicitͳԾઆɾཧ࿦ ؍ଌʗσʔλ • Data-driven (ػցֶशɺਓ޻஌ೳɺ౷ܭֶͳͲ) ৭ʑͳؔ਺ΛදݱͰ͖Δ൚༻Ϟσϧ ؍ଌʗσʔλ ԋ៷ ؼೲ σʔλʹ࠷΋ద߹͢ΔΑ͏ʹϑΟοςΟϯά γϛϡϨʔγϣϯ & ਖ਼͍͔͠Ͳ͏͔͸(հೖ)࣮ݧͰ֬ೝ

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Data-driven vs Theory-driven 46 David Hand Data-driven͸Theory-drivenͱߟ͑ํɾ໾ׂ͕ҟͳΔͷͰ஫ҙ All models are wrong, but some are useful (George Box) Theory-driven models can be wrong But data-driven models cannot be wrong http://videolectures.net/kdd2018_hand_data_science/

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Data-driven vs Theory-driven 46 David Hand Data-driven͸Theory-drivenͱߟ͑ํɾ໾ׂ͕ҟͳΔͷͰ஫ҙ All models are wrong, but some are useful (George Box) Theory-driven models can be wrong But data-driven models cannot be wrong or right http://videolectures.net/kdd2018_hand_data_science/

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Data-driven vs Theory-driven 46 David Hand Data-driven͸Theory-drivenͱߟ͑ํɾ໾ׂ͕ҟͳΔͷͰ஫ҙ All models are wrong, but some are useful (George Box) Theory-driven models can be wrong But data-driven models cannot be wrong or right Data-driven are not trying to describe an underlying reality. so they could be poor or useless, but not wrong But are merely intended to be useful http://videolectures.net/kdd2018_hand_data_science/

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With enough data, the numbers speak for themselves. Chris Anderson (2008) cf.

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ʮཧղʯฤ: ·ͱΊ 48 σʔλͷ૬ؔؔ܎ͷར׆༻ٕज़Ͱ͋Δػցֶश͚ͩͰ͸
 ର৅ݱ৅ͷഎޙʹ͋Δ࢓૊ΈΛཧղ͢Δͷ͸ݪཧ্ࠔ೉ • ૬ؔؔ܎͸ඞͣ͠΋ҼՌؔ܎Λҙຯ͠ͳ͍ • ҼՌͷݕূʹ͸؍࡯ݚڀͰ͸ͳ͘հೖݚڀ͕ඞཁ • ҩྍ΍೴ՊֶͳͲྙཧతʹհೖݚڀ͕೉͍͠৔߹΋ଟ͘ ҼՌਪ࿦ͷཧ࿦ɾख๏͸௕Β͘ݚڀ͞Ε͖͍ͯͯΔ • ҼՌਪ࿦Ͱ͸ؔ࿈Ҽࢠ΍ҼՌߏ଄͕͢΂ͯ෼͔͍ͬͯΔ ͳͲͷݱ࣮తʹ͸೉͍͠લఏ͕ຬͨ͞ΕΔඞཁ͕͋Δ • ૬ؔؔ܎͸ҼՌͷࣔࠦͰ͸͋ΔͷͰ஫ҙਂ͘ߟ͑Α͏ʂ

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ࠓ೔ͷ಺༰ 49 1. Πϯτϩ
 ػցֶशͱՊֶ(͋Δ͍͸"΋ͷͮ͘Γ") 2. ػցֶशͰԿ͔ΛʮཧղʯͰ͖Δ͔ʁ
 Answer: ௚઀తʹ͸ݪཧ্ࠔ೉ 3. ػցֶशͰԿ͔ΛʮൃݟʯͰ͖Δ͔ʁ
 Answer: ௚઀తʹ͸ݪཧ্ࠔ೉ 4. ͡Ό͋Ͳ͏͢ΜͷʂʁԿ͕͍Δͷʂʁ
 Answer:ʮදݱʯͱʮհೖʯ
 2ͱ3Λલఏʹػցֶश෼໺ͷτϐοΫΛ؆୯ʹ঺հ

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ʮൃݟʯ͸ֶशͷൃలܥʁ 50 ൃݟ = ͍··Ͱʹͳ͍΋ͷɾ͜ͱΛݟ͚ͭΔ • ࠓ·Ͱʹͳ͍ըظతͳ৽ༀ • ࠓ·ͰͷσʔλͷͲΕΑΓ΋௕࣋ͪ͢Δి஑ࡐྉ • ࠓ·Ͱ୭΋ࢼ͞ͳ͔ͬͨըظతͳձࣾܦӦઓུ • ࠓ·ͰະൃݟͩͬͨըظతͳՊֶ๏ଇ΍Պֶཧ࿦ • ࠓ·Ͱରઓͨ͠୭ΑΓ΋ڧ͍ϘʔυήʔϜউརઓུ ൃݟ͸;ͭ͏׬શʹߦ͖౰ͨΓ͹ͬͨΓͰ͸ͳ͍ɻ
 ʮצͱܦݧʯ͕ඇৗʹେ੾ ʮ޾ӡ͸४උ͞Εͨऀʹ߱ΓΔʯ ܦݧ(աڈͷσʔλ)͔Βֶशͨ͠צ(๏ଇੑ) = ػցֶश
 ͱߟ͑ΔͱͳΜ͔ͩΠέͦ͏ͳؾ͕͢Δʙʁ

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Ұํɺػցֶशͱ͸Կ͔ͩͬͨ 51 Ұൠ෺ମೝࣝ ήʔϜϓϨΠ “͋Γ͕ͱ͏” J’aime la musiqu e I love music ೖग़ྗͷؔ܎͕Α͘෼͔Βͳ͍ม׵աఔ(ؔ਺)Λେྔͷೖग़ྗͷ ݟຊྫ͔Β໌ࣔతʹϓϩάϥϛϯά͢Δ͜ͱͳ͘ߏ੒͢Δٕ๏ Ի੠ೝࣝ ػց຋༁ ௒ղ૾

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ػցֶशͷ࢓૊Έ = ߴ࣍ݩͰͷۂ໘͋ͯ͸Ί 52 ίϯϐϡʔλ ϓϩάϥϜ ೖྗ ग़ྗ ೖग़ྗͷݟຊ ڭࢣσʔλ ೖग़ྗͷݟຊ {(x1, y1), (x2, y2), . . . , (xn, yn)} 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 Ұൠʹ͸ߴ࣍ݩ

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ػցֶशͷ࢓૊Έ = ߴ࣍ݩͰͷۂ໘͋ͯ͸Ί 52 ίϯϐϡʔλ ϓϩάϥϜ ೖྗ ग़ྗ ೖग़ྗͷݟຊ ڭࢣσʔλ ೖग़ྗͷݟຊ {(x1, y1), (x2, y2), . . . , (xn, yn)} 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 Ұൠʹ͸ߴ࣍ݩ

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ػցֶशͷ࢓૊Έ = ߴ࣍ݩͰͷۂ໘͋ͯ͸Ί 52 ίϯϐϡʔλ ϓϩάϥϜ ೖྗ ग़ྗ ೖग़ྗͷݟຊ ڭࢣσʔλ ิؒ ೖग़ྗͷݟຊ {(x1, y1), (x2, y2), . . . , (xn, yn)} 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 Ұൠʹ͸ߴ࣍ݩ

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ػցֶशͷ࢓૊Έ = ߴ࣍ݩͰͷۂ໘͋ͯ͸Ί 53 Inputs Outputs ML model x 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 y 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 A function best fitted to a given set of example input-output pairs (the training data). (x1, y1), (x2, y2), . . . , (xn, yn) 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 f(x; ✓) 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 x 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 y 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 ✓ 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 x 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 y 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 x 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 y 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 x 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 y 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 interpolative prediction (High-dimensional) High Low Model Complexity Underfitting (High bias, Low variance) Overfitting (Low bias, High variance) "The bias-variance tradeoff" The training data f(x; ✓) 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 extrapolative prediction

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஫ҙɿػցֶश͸ʮൃݟʯʹ޲͍͍ͯͳ͍ 54 ػցֶश = ܇࿅σʔλͷฏۉత๏ଇੑΛͱΒ͑Δ ൃݟ = ݟຊσʔλͷதʹͳ͍΋ͷΛݟ͚͍ͭͨ ༧ଌϞσϧͱͷޡࠩͷʮظ଴஋ʯΛ࠷খԽ = ൚Խ ೖྗ (Ұൠʹ͸ߴ࣍ݩ) ग़ྗ x AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= ฏۉతڍಈ ͷϞσϧ ܇࿅σʔλͷ࠷େҎ্ͷ ܇࿅σʔλͷ࠷େ (ظ଴ޡࠩ࠷খԽ) ໨త෺͕֎Ε஋(֎ૠత)Ͱ
 ෼෍ͷ੄ʹདྷͯ͠·͏ ໨త͕
 ෆ੔߹ ʮ֎Ε஋ʯ ฏۉత(ຌ༱)ͳͷ͸ Α͘౰ͨΔ...

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ػցֶश͸༩͑ͨ܇࿅σʔλΛ୅ද͢Δ͚ͩ 55 Highly Inaccurate Model Predictions from Extrapolation (Lohninger 1999) ༩͑ͨσʔλͷ܏޲Λ(ۂઢ͋ͯ͸ΊͰ) ද͚ͩ͢Ͱσʔλ͕ͳ͍֎ૠྖҬͰ͸ ແࠜڌͳ༧ଌΛฦ͢ ར༻ "exploitation" ୳ࡧ "exploration" ৽͍͠஌ࣝ/σʔλΛ֫ಘ ֫ಘͨ͠஌ࣝ/σʔλΛར༻ τϨʔυΦϑ

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հೖ࣮ݧͷܭըɿ࣮ݧܭը๏ͱԠ౴ۂ໘๏ 56 ౰વBox͸ͲͷΑ͏ʹճؼ෼ੳΛ࢖͑͹ྑ͍͔୳ٻࡁΈʂ☺ Empirical Model-Building and Response Surfaces (1987) Statistics for Experimenters: Design, Innovation, and Discovery (2005)

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Ԡ౴ۂ໘๏ (Box & Wilson, 1951) ม਺ͷ਺͕গͳ͘౷ܭֶతͳԾఆ͕͋Δఔ౓༗ޮͳΒ͜ΕͰOKɻ ୳ࡧۭؒ(ؔ৺ྖҬ)ͷ಺ૠʹͳΔΑ͏࣮ݧܭըͰࣄྫ఺ΛಘΔ 1. Ԡ౴ۂ໘(Response Surface)ΛϞσϧԽ
 (e.g. ೋ࣍ଟ߲ࣜճؼ) 2. ্هϞσϧΛ౰ͯ͸ΊΔͨΊͷ࣮ݧܭը(e.g. த৺ෳ߹ܭը)Ͱ ݕࠪ఺ΛಘΔ 3. Ԡ౴ۂ໘Λݕࠪ఺ʹ౰ͯ͸ΊͦΕ͕࠷େʹͳΔ఺ΛٻΊΔ ͔͠͠ɺݱ୅ͷ࣮໰୊ͷσʔλ͸... Box-WilsonͷԠ౴ۂ໘๏

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ೖྗ͕ଟ༷ + গ͠ͷมԽͰग़ྗ͕มΘΓಘΔ 58 J. Med. Chem. 2012, 55, 2932−2942 ෺ੑ΍׆ੑͷϥϯυεέʔϓ͸ඇฏ׈త
 (গ͠ͷߏ଄มԽ͕ٸफ़ͳӨڹΛ΋ͨΒ ͢) Activity cliffs Selectivity cliffs

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͞Βʹߴ࣍ݩͰ͸֎ૠ͔಺ૠ͔ͷ൑ఆ͢Β೉͍͠... 59 ߴ࣍ݩۭؒ͸ඇ௚ײతͳੑ࣭Λ࣋ͭ • ِ૬ؔɿม਺ͷ਺͕͋·Γʹଟ͍ͱ܇࿅σʔλ͢΂ͯΛ
 ͱ͓Δۂ໘͕ࣗ༝ʹ࡞Εͯ͠·ِ͍૬͕ؔੜ͡΍͘͢ͳΔ • ଌ౓ͷूதݱ৅ɿݟຊ఺ͷؒͷڑ཭͕શͯ΄΅ಉ͡ʹͳΔ ༩͑ΒΕͨ܇࿅σʔλ ಺ૠ or ֎ૠʁ

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੒ޭྫ΋಺ૠతͩͱ௚ײ͢Δͷ͸ඇৗʹࠔ೉ 60 QJYQJY $ZDMF("/ :0-0 FH%FFQ'BLF

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੒ޭྫ΋಺ૠతͩͱ௚ײ͢Δͷ͸ඇৗʹࠔ೉ 60 QJYQJY $ZDMF("/ :0-0 FH%FFQ'BLF

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͋ͯ͸ΊΔۂ໘ͷ΄͏΋ߴ࣍ݩͰ͸ඇ௚ײత 61 http://www.evolvingai.org/fooling https://arxiv.org/pdf/ 1610.06940.pdf https://towardsdatascience.com/know-your-adversary- understanding-adversarial-examples-part-1-2-63af4c2f5830 e.g. Adversarial examples (GANͷൃ૝)ɺ಺ૠ or ֎ૠͷ൑ఆͷ೉͠͞

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ʮൃݟʯฤ: ·ͱΊ 62 σʔλͷۂ໘͋ͯ͸ΊʹΑΔ಺ૠͰ͋Δػցֶश͚ͩͰ͸
 ܇࿅σʔλʹશ͘ͳ͍৽نൃݟΛ͢Δͷ͸ݪཧ্ࠔ೉ • ػցֶश͸ۂ໘͋ͯ͸ΊͰ܇࿅σʔλΛ୅ද͢Δ͚ͩ • ۂ໘͸ݟຊσʔλʹ͋͏Α͏ϑΟοτ͞ΕΔͷͰ
 ֎ૠతͳτϨϯυ͸༧ଌࠜڌ͕͖ΘΊͯബ͘ͳΔ • ൃݟ(஌ࣝ֫ಘ)ʹ͸஌ࣝͷར༻ͱ୳ࡧͷτϨʔυΦϑͷ ߟྀ͕ඞਢ • ࠷ۙͷσʔλ͸ෳࡶͰଟ༷Ͱ੍ޚ͞Ε͍ͯͳ͍ͷͰݹయ తͳ࣮ݧܭը΍ۂ໘Ԡ౴ํͰ͸ͳ͔ͳ͔े෼Ͱ͸ͳ͍

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ࠓ೔ͷ಺༰ 63 1. Πϯτϩ
 ػցֶशͱՊֶ(͋Δ͍͸"΋ͷͮ͘Γ") 2. ػցֶशͰԿ͔ΛʮཧղʯͰ͖Δ͔ʁ
 Answer: ௚઀తʹ͸ݪཧ্ࠔ೉ 3. ػցֶशͰԿ͔ΛʮൃݟʯͰ͖Δ͔ʁ
 Answer: ௚઀తʹ͸ݪཧ্ࠔ೉ 4. ͡Ό͋Ͳ͏͢ΜͷʂʁԿ͕͍Δͷʂʁ
 Answer:ʮදݱʯͱʮհೖʯ
 2ͱ3Λલఏʹػցֶश෼໺ͷτϐοΫΛ؆୯ʹ঺հ

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Պֶతʮཧղɾൃݟʯʹඞཁͳೋେཁૉ? 64 ʮදݱʯ ʮհೖʯ • ର৅ΛͲ͏දݱ͢Δ͔ʁԿΛଌΔ͔ʁ • ໰୊Λ಺ૠతʹ͢Δදݱͷֶश
 (͍·ͷͱ͜ΖઃܭʹཁυϝΠϯ஌ࣝ) • എܠաఔʹ͍ͭͯ෼͔͍ͬͯΔ
 ͜ͱͷ൓ө΍׆༻ (ؼೲόΠΞε) • ػցֶशʹʮ࣮ࡍʹ௥ՃσʔλΛ औΓʹߦ͘ʯ࢓૊ΈΛ༥߹ • ࣍ʹԿΛ࣮ݧ͢Δ͔ͷ࠷దܭը Պֶతʮཧղʯ΍ʮൃݟʯͱ͸Կ͔(ԿͰ͋Δ΂͖͔)͸
 Պֶ఩ֶͷ໰୊

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65 ػցֶशͰ͖ͨͱ͖ͷ̎छྨͷظ଴ 1. ಘΒΕͨม׵աఔ(ؔ਺)ʹΑΔ༧ଌΛ৭ʑͳ໨తʹ࢖͏ 2. ಘΒΕͨม׵աఔ(ؔ਺)Λ෼ੳͯ͠എܠաఔͷ࢓૊ΈΛ஌Δ 1.ʹ͍ͭͯ • ໰୊͕಺ૠతʹͳΔΑ͏޻෉ (දݱֶशɾӅΕߏ଄ಉఆ) • ಺ૠɾ֎ૠ൑ఆ (༧ଌͷ৴པ౓ܭࢉ) • Ϟσϧϕʔε࠷దԽͱ୳ࡧ (࠷ద࣮ݧܭը) 2.ʹ͍ͭͯ • ϙετϗοΫղੳͱղऍੑϞσϧ (ֶशࡁΈϞσϧ෼ੳ)

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65 ػցֶशͰ͖ͨͱ͖ͷ̎छྨͷظ଴ 1. ಘΒΕͨม׵աఔ(ؔ਺)ʹΑΔ༧ଌΛ৭ʑͳ໨తʹ࢖͏ 2. ಘΒΕͨม׵աఔ(ؔ਺)Λ෼ੳͯ͠എܠաఔͷ࢓૊ΈΛ஌Δ 1.ʹ͍ͭͯ • ໰୊͕಺ૠతʹͳΔΑ͏޻෉ (දݱֶशɾӅΕߏ଄ಉఆ) • ಺ૠɾ֎ૠ൑ఆ (༧ଌͷ৴པ౓ܭࢉ) • Ϟσϧϕʔε࠷దԽͱ୳ࡧ (࠷ద࣮ݧܭը) 2.ʹ͍ͭͯ • ϙετϗοΫղੳͱղऍੑϞσϧ (ֶशࡁΈϞσϧ෼ੳ)

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ਂ૚ֶशʹΑΔදݱֶश 66 ೖྗͷʮଟ࣍ݩͷ਺஋૊(ϕΫτϧ)ʯΛগͮͭ͠ ผͷʮଟ࣍ݩͷ਺஋૊(ϕΫτϧ)ʯ΁ม׵͢Δϓϩηε ม׵͞Εͨ࠷ऴྔ ʹ͍ͭͯ༧ଌ End
 (खʹೖΔ··) End (๬Ήग़ྗ) End to End (׬શͳσʔλۦಈ) Raw Pixel Values
 (RGB Color Image) … .01 Bill .96 Ichi .01 Jeff .02 Larry … ೖྗม਺ͷஈ֊Ͱ͸ ಺ૠత͡Όͳͯ͘΋... ༧ଌʹ࢖͏தؒදݱ(ӅΕ
 ߏ଄)Ͱ಺ૠతͰ͋Ε͹OK!

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ྫ) ਂ૚ֶश͸ೖྗͷଟஈͷม׵ϓϩηεΛֶश 67 IUUQTDPMBIHJUIVCJPQPTUT//.BOJGPMET5PQPMPHZ ઢܗ෼཭Ͱ͖ΔΑ͏ͳม׵Λֶश ࣦഊྫ(ৗʹ੒ޭ͢Δͱ͸ݶΒͳ͍)

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ྫ) ਂ૚ֶश͸ೖྗͷଟஈͷม׵ϓϩηεΛֶश 67 IUUQTDPMBIHJUIVCJPQPTUT//.BOJGPMET5PQPMPHZ ઢܗ෼཭Ͱ͖ΔΑ͏ͳม׵Λֶश ࣦഊྫ(ৗʹ੒ޭ͢Δͱ͸ݶΒͳ͍)

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ྫ) ਂ૚ֶश͸ೖྗͷଟஈͷม׵ϓϩηεΛֶश 67 IUUQTDPMBIHJUIVCJPQPTUT//.BOJGPMET5PQPMPHZ ઢܗ෼཭Ͱ͖ΔΑ͏ͳม׵Λֶश ࣦഊྫ(ৗʹ੒ޭ͢Δͱ͸ݶΒͳ͍)

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ྫ) ਂ૚ֶश͸ೖྗͷଟஈͷม׵ϓϩηεΛֶश 67 IUUQTDPMBIHJUIVCJPQPTUT//.BOJGPMET5PQPMPHZ ઢܗ෼཭Ͱ͖ΔΑ͏ͳม׵Λֶश ࣦഊྫ(ৗʹ੒ޭ͢Δͱ͸ݶΒͳ͍)

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Ϟσϧߏ଄΁ͷυϝΠϯ஌ࣝͷΤϯίʔυ 68 Model https://www.kaggle.com/c/recursion-cellular-image-classification/discussion/ 110543#latest-637352 ͱʹ͔͘ඍ෼Մೳͳԋࢉͷ(ਂ͍)߹੒ؔ਺ʹ͑͞ͳ͍ͬͯΕ͹
 Ϟσϧύϥϝλ͸ࣗಈඍ෼(aka backprop)ͰֶशͰ͖ΔͷͰ
 എܠաఔΛཧղͦ͠ΕʹͦͬͯॊೈʹϞσϧߏ଄Λઃܭ Head Backbone Neck ࣄલֶश͔ΒͷసҠ΋༗ޮ

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ࣄલֶश͕ޮ͔ͳ͍ͱ͞ΕͨݴޠλεΫͰ΋... 69 ͔ͭͯݴޠλεΫͰ͸RNN(LSTM/GRU)→CNNͷྲྀΕ͕ͩͬͨ... GoogleͷݴޠϞσϧBERT OpenAIͷݴޠϞσϧGPT-2 GLUEϕϯνϚʔΫͷશݴޠཧղλεΫͰͿͬͪ͗ΓͷSOTAʂ ࣭ٙԠ౴λεΫͷSQuADͰ΋SOTAʂ CMUͷXLNet MicrosoftͷݴޠϞσϧMT-DNN 2018/10/18 2019/01/31 ࡞จੑೳ͕ߴ͗ͯ͢Φʔϓϯιʔεͱͯ͠ެ։ͯ͠͠·͏ͱϑΣΠΫχϡʔε ͕࡞Γ์୊ʹͳͬͯ͠·͏ݒ೦͔Βݚڀऀ޲͚ʹن໛ॖখ൛ͷΈΛެ։ 2019/02/14 2019/06/19 Googleͷ՚ྷͳ࿦จΛܖػʹRNN΍ CNNΑΓTransformer͕ࢧ഑తʹ!? ௒ڊେͳࣄલֶशϞσϧͷ
 (Attentiveͳ)ʮసҠֶशʯ΁

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֎ૠతྖҬͰͷػցֶशͷ׆༻ 70 ݟຊ఺͕ͳ͍֎ૠతྖҬͰػցֶश(ۂ໘͋ͯ͸Ί)Λ׆༻͢Δɻ • ྨࣅͨ͠໰୊ɾσʔλͰͷؔ܎ੑΛԣஅతʹ׆༻ͯ͠ྨਪ 
 (సҠֶशɺ൒ڭࢣֶ͖ͭशɺϚϧνλεΫֶशɺ஫ࢹతֶश) • ۙ͞ͷଌΓํΛద੾ʹֶशͯ͠໰୊Λ಺ૠతʹ (ܭྔֶश) • എܠաఔͷୈҰݪཧϞσϧ(γϛϡϨʔγϣϯ)Λෆ࣮֬ͳҼࢠ
 ΛؚΊཱͯͯͦͷ࠷దͳਪఆ஋Λػցֶश͢Δ (σʔλಉԽ) • γϛϡϨʔγϣϯσʔλ΍ܦݧऀͷڭࣔΛ༻͍ͯσʔλΛ
 ૿΍͠Ͱ͖Δ͚ͩ໰୊Λ಺ૠతʹ (ఢରతੜ੒ɺ໛฿ֶश) • γϛϡϨʔγϣϯ݁Ռ͔Β࣮ݱ৅ͷΪϟοϓΛػցֶश͢Δ
 (సҠֶशɺϝλֶश)

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71 ػցֶशͰ͖ͨͱ͖ͷ̎छྨͷظ଴ 1. ಘΒΕͨม׵աఔ(ؔ਺)ʹΑΔ༧ଌΛ৭ʑͳ໨తʹ࢖͏ 2. ಘΒΕͨม׵աఔ(ؔ਺)Λ෼ੳͯ͠എܠաఔͷ࢓૊ΈΛ஌Δ 1.ʹ͍ͭͯ • ໰୊͕಺ૠతʹͳΔΑ͏޻෉ (දݱֶशɾӅΕߏ଄ಉఆ) • ಺ૠɾ֎ૠ൑ఆ (༧ଌͷ৴པ౓ܭࢉ) • Ϟσϧϕʔε࠷దԽͱ୳ࡧ (࠷ద࣮ݧܭը) 2.ʹ͍ͭͯ • ϙετϗοΫղੳͱղऍੑϞσϧ (ֶशࡁΈϞσϧ෼ੳ)

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֎ૠ൑ఆ΋͘͠͸৴པྖҬਪఆ 72 • ύλʔϯೝࣝʹ͓͚Δغ٫Φϓγϣϯ • ৴པ۠ؒ(Confidence Interval) • ৴པྖҬ(Trust Region) • ϕΠζʹ͓͚Δ༧ଌ෼෍ • Cheminfoʹ͓͚ΔApplicability Domain(AD) ࣗൢػ χ η ࡳ ☺Պֶͱػցֶशͷ͍͋ͩɿมྔͷઃܭɾม׵ɾબ୒ɾަޓ࡞༻ɾઢܗੑ
 https://www.slideshare.net/itakigawa/ss-69269618 ༧ଌ͍ͨ͠ݕࠪೖྗ఺ͷۙ͘ʹݟຊ఺͕શવͳ͍(or ඇৗʹ গͳ͍)৔߹͸جຊతʹ֎ૠత

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73 ػցֶशͰ͖ͨͱ͖ͷ̎छྨͷظ଴ 1. ಘΒΕͨม׵աఔ(ؔ਺)ʹΑΔ༧ଌΛ৭ʑͳ໨తʹ࢖͏ 2. ಘΒΕͨม׵աఔ(ؔ਺)Λ෼ੳͯ͠എܠաఔͷ࢓૊ΈΛ஌Δ 1.ʹ͍ͭͯ • ໰୊͕಺ૠతʹͳΔΑ͏޻෉ (දݱֶशɾӅΕߏ଄ಉఆ) • ಺ૠɾ֎ૠ൑ఆ (༧ଌͷ৴པ౓ܭࢉ) • Ϟσϧϕʔε࠷దԽͱ୳ࡧ (࠷ద࣮ݧܭը) 2.ʹ͍ͭͯ • ϙετϗοΫղੳͱղऍੑϞσϧ (ֶशࡁΈϞσϧ෼ੳ)

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ػցֶशͷར׆༻ʹΑΔ࠷ద࣮ݧܭըʁ 74 ط஌ͷ஌ݟɾ ؍ଌ(σʔλ) ݁Ռͷ֬ೝͱ ݕূ ߴ଎ɾߴਫ਼౓ͳ Data-Driven༧ଌ ࣍ͷ࣮ݧܭը΁feedback Ծઆܗ੒ (γϛϡϨʔγϣϯ+࣮ݧ) • ࠶ݱੑΛ୲อ͢Δߴਫ਼౓ɾߴ଎࣮ݧܥ • Ծ૝Խݕূ͕ՄೳͳҼࢠͷγϛϡϨʔ γϣϯ(ܭࢉՊֶ)ʹΑΔ୳ࡧ
 → ๬·͍͠ର৅ͷ͞ΒͳΔߜΓࠐΈ Ծઆݕূ (ػցֶशɾσʔλϚΠχϯά) • Ͳ͏͍͏࣮ݧɾγϛϡϨʔγϣϯΛ
 ࣍ʹߦ͏͔ͷܭըཱҊ • ࣌ؒͷ͔͔Δܭࢉͷߴਫ਼౓ߴ଎ۙࣅ • ᐆດͳҼࢠ΍࣮ݧ৚݅ͷ࠷దԽ • Multilevelͷ৘ใ౷߹

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2000-2010೥ࠒ͔Β૑ༀ/ੜ໋ՊֶͰઌߦ 75

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ࡐྉ։ൃ΁΋ల։ 76 ݚڀ։ൃମ੍ ੜ࢈ମ੍ ࡐྉ։ൃɺηϧσβΠϯʹ͓͍ͯ
 ࠷ઌ୺γϛϡϨʔγϣϯٕज़Λ׆༻ ׬શࣗಈԽʹΑΔϑϨΩγϒϧͳੜ࢈
 IoT΍ϏοάσʔλΛ׆༻ͨ͠ੜ࢈؅ཧ ↓඼࣭Λ୲อ͢Δେن໛Խͷٕज़తඞਢཁૉɿ ੡଄ϥΠϯʹਓ͕(΄ͱΜͲ)͍ͳ͍

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ࡐྉ։ൃ΁΋ల։ 76 ݚڀ։ൃମ੍ ੜ࢈ମ੍ ࡐྉ։ൃɺηϧσβΠϯʹ͓͍ͯ
 ࠷ઌ୺γϛϡϨʔγϣϯٕज़Λ׆༻ ׬શࣗಈԽʹΑΔϑϨΩγϒϧͳੜ࢈
 IoT΍ϏοάσʔλΛ׆༻ͨ͠ੜ࢈؅ཧ ↓඼࣭Λ୲อ͢Δେن໛Խͷٕज़తඞਢཁૉɿ ੡଄ϥΠϯʹਓ͕(΄ͱΜͲ)͍ͳ͍

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Խֶʹ΋೾ٴ 77

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Keyɿ஝ੵ͞Εͨʮܭࢉɾ࣮ݧσʔλʯͷར׆༻ 78 (ػցֶशɾσʔλϚΠχϯά) Ծઆܗ੒ ݕূ (γϛϡϨʔγϣϯ+࣮ݧ) • ࠶ݱੑΛ୲อ͢Δߴਫ਼౓ɾߴ଎࣮ݧܥ • Ծ૝Խݕূ͕ՄೳͳҼࢠͷγϛϡϨʔ γϣϯ(ܭࢉՊֶ)ʹΑΔ୳ࡧ
 → ๬·͍͠ର৅ͷ͞ΒͳΔߜΓࠐΈ ࣮ݧσʔλɾܭࢉσʔλɾϑΝΫτͷ஝ੵ In-Houseσʔλ + Publicσʔλ + ஌ࣝϕʔε 
 + ͦͷQuality Control / Annotations • Ͳ͏͍͏࣮ݧɾγϛϡϨʔγϣϯΛ
 ࣍ʹߦ͏͔ͷܭըཱҊ • ࣌ؒͷ͔͔Δܭࢉͷߴਫ਼౓ߴ଎ۙࣅ • ᐆດͳҼࢠ΍࣮ݧ৚݅ͷ࠷దԽ • Multilevelͷ৘ใ౷߹

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ʮσʔλར׆༻ٕज़ʯ͸Պֶݚڀͷಓ۩ͷҰͭʹ 79 REVIEW Inverse molecular design using machine learning: Generative models for matter engineering Benjamin Sanchez-Lengeling1 and Alán Aspuru-Guzik2,3,4* The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials. Many of the challenges of the 21st century (1), from personalized health care to energy production and storage, share a common theme: materials are part of the solution (2). In some cases, the solu- tions to these challenges are fundamentally limited by the physics and chemistry of a ma- terial, such as the relationship of a materials bandgap to the thermodynamic limits for the generation of solar energy (3). Several important materials discoveries arose by chance or through a process of trial and error. For example, vulcanized rubber was prepared in the 19th century from random mixtures of com- pounds, based on the observation that heating with additives such as sulfur improved the rubber’s durability. At the molecular level, in- dividual polymer chains cross-linked, forming bridges that enhanced the macroscopic mechan- ical properties (4). Other notable examples in this vein include Teflon, anesthesia, Vaseline, Perkin’s mauve, and penicillin. Furthermore, these materials come from common chemical compounds found in nature. Potential drugs either were prepared by synthesis in a chem- ical laboratory or were isolated from plants, soil bacteria, or fungus. For example, up until 2014, 49% of small-molecule cancer drugs were natural products or their derivatives (5). In the future, disruptive advances in the dis- covery of matter could instead come from unex- plored regions of the set of all possible molecular and solid-state compounds, known as chemical space (6, 7). One of the largest collections of molecules, the chemical space project (8), has mapped 166.4 billion molecules that contain at most 17 heavy atoms. For pharmacologically rele- vant small molecules, the number of structures is estimated to be on the order of 1060 (9). Adding consideration of the hierarchy of scale from sub- nanometer to microscopic and mesoscopic fur- ther complicates exploration of chemical space in its entirety (10). Therefore, any global strategy for covering this space might seem impossible. Simulation offers one way of probing this space without experimentation. The physics and chemistry of these molecules are governed by quantum mechanics, which can be solved via the Schrödinger equation to arrive at their ex- act properties. In practice, approximations are used to lower computational time at the cost of accuracy. Although theory enjoys enormous progress, now routinely modeling molecules, clusters, and perfect as well as defect-laden periodic solids, the size of chemical space is still overwhelming, and smart navigation is required. For this purpose, machine learning (ML), deep learning (DL), and artificial intelligence (AI) have a potential role to play because their computational strategies automatically improve through experience (11). In the context of materials, ML techniques are often used for property prediction, seeking to learn a function that maps a molecular material to the property of choice. Deep generative models are a special class of DL methods that seek to model the underlying probability distribution of both structure and property and relate them in a nonlinear way. By exploiting patterns in massive datasets, these models can distill average and salient features that characterize molecules (12, 13). Inverse design is a component of a more complex materials discovery process. The time scale for deployment of new technologies, from discovery in a laboratory to a commercial pro- duct, historically, is 15 to 20 years (14). The pro- cess (Fig. 1) conventionally involves the following steps: (i) generate a new or improved material concept and simulate its potential suitability; (ii) synthesize the material; (iii) incorporate the ma- terial into a device or system; and (iv) characterize and measure the desired properties. This cycle generates feedback to repeat, improve, and re- fine future cycles of discovery. Each step can take up to several years. In the era of matter engineering, scientists seek to accelerate these cycles, reducing the FRONTIERS IN COMPUTATION 1Department of Chemistry and Chemical Biology, Harvard University 12 Oxford Street, Cambridge, MA 02138, USA. 2Department of Chemistry and Department of Computer Science, University of Toronto, Toronto Ontario, M5S 3H6, Canada. 3Vector Institute for Artificial Intelligence, Toronto, Ontario M5S 1M1, Canada. 4Canadian Institute for Advanced Fig. 1. Schematic comparison of material discovery paradigms. The current paradigm is APTED BY K. HOLOSKI on July 26, 2018 http://science.sciencemag.org/ Downloaded from REVIEW https://doi.org/10.1038/s41586-018-0337-2 Machine learning for molecular and materials science Keith T. Butler1, Daniel W . Davies2, Hugh Cartwright3, Olexandr Isayev4* & Aron Walsh5,6* Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence. The Schrödinger equation provides a powerful structure– property relationship for molecules and materials. For a given spatial arrangement of chemical elements, the distribution of electrons and a wide range of physical responses can be described. The development of quantum mechanics provided a rigorous theoretical foundation for the chemical bond. In 1929, Paul Dirac famously proclaimed that the underlying physical laws for the whole of chemistry are “completely known”1. John Pople, realizing the importance of rapidly developing computer technologies, created a program—Gaussian 70—that could perform ab initio calculations: predicting the behaviour, for molecules of modest size, purely from the fundamental laws of physics2. In the 1960s, the Quantum Chemistry Program Exchange brought quantum chemistry to the masses in the form of useful practical tools3. Suddenly, experi- mentalists with little or no theoretical training could perform quantum calculations too. Using modern algorithms and supercomputers, systems containing thousands of interacting ions and electrons can now be described using approximations to the physical laws that govern the world on the atomic scale4–6. The field of computational chemistry has become increasingly pre- dictive in the twenty-first century, with activity in applications as wide ranging as catalyst development for greenhouse gas conversion, materials discovery for energy harvesting and storage, and computer-assisted drug design7. The modern chemical-simulation toolkit allows the properties of a compound to be anticipated (with reasonable accuracy) before it has been made in the laboratory. High-throughput computational screening has become routine, giving scientists the ability to calculate the properties of thousands of compounds as part of a single study. In particular, den- sity functional theory (DFT)8,9, now a mature technique for calculating the structure and behaviour of solids10, has enabled the development of extensive databases that cover the calculated properties of known and hypothetical systems, including organic and inorganic crystals, single molecules and metal alloys11–13. The emergence of contemporary artificial-intelligence methods has the potential to substantially alter and enhance the role of computers in science and engineering. The combination of big data and artificial intel- ligence has been referred to as both the “fourth paradigm of science”14 and the “fourth industrial revolution”15, and the number of applications in the chemical domain is growing at an astounding rate. A subfield of artificial intelligence that has evolved rapidly in recent years is machine learning. At the heart of machine-learning applications lie statistical algo- rithms whose performance, much like that of a researcher, improves with training. There is a growing infrastructure of machine-learning tools for generating, testing and refining scientific models. Such techniques are suitable for addressing complex problems that involve massive combi- natorial spaces or nonlinear processes, which conventional procedures either cannot solve or can tackle only at great computational cost. As the machinery for artificial intelligence and machine learning matures, important advances are being made not only by those in main- stream artificial-intelligence research, but also by experts in other fields (domain experts) who adopt these approaches for their own purposes. As we detail in Box 1, the resources and tools that facilitate the application of machine-learning techniques mean that the barrier to entry is lower than ever. In the rest of this Review, we discuss progress in the application of machine learning to address challenges in molecular and materials research. We review the basics of machine-learning approaches, iden- tify areas in which existing methods have the potential to accelerate research and consider the developments that are required to enable more wide-ranging impacts. Nuts and bolts of machine learning With machine learning, given enough data and a rule-discovery algo- rithm, a computer has the ability to determine all known physical laws (and potentially those that are currently unknown) without human input. In traditional computational approaches, the computer is little more than a calculator, employing a hard-coded algorithm provided by a human expert. By contrast, machine-learning approaches learn the rules that underlie a dataset by assessing a portion of that data and building a model to make predictions. We consider the basic steps involved in the construction of a model, as illustrated in Fig. 1; this constitutes a blueprint of the generic workflow that is required for the successful application of machine learning in a materials-discovery process. Data collection Machine learning comprises models that learn from existing (train- ing) data. Data may require initial preprocessing, during which miss- ing or spurious elements are identified and handled. For example, the Inorganic Crystal Structure Database (ICSD) currently contains more than 190,000 entries, which have been checked for technical mistakes but are still subject to human and measurement errors. Identifying and removing such errors is essential to avoid machine-learning algorithms being misled. There is a growing public concern about the lack of reproducibility and error propagation of experimental data DNA to be sequences into distinct pieces, parcel out the detailed work of sequencing, and then reassemble these independent ef- forts at the end. It is not quite so simple in the world of genome semantics. Despite the differences between genome se- quencing and genetic network discovery, there are clear parallels that are illustrated in Table 1. In genome sequencing, a physical map is useful to provide scaffolding for assembling the fin- ished sequence. In the case of a genetic regula- tory network, a graphical model can play the same role. A graphical model can represent a high-level view of interconnectivity and help isolate modules that can be studied indepen- dently. Like contigs in a genomic sequencing project, low-level functional models can ex- plore the detailed behavior of a module of genes in a manner that is consistent with the higher level graphical model of the system. With stan- dardized nomenclature and compatible model- ing techniques, independent functional models can be assembled into a complete model of the cell under study. To enable this process, there will need to be standardized forms for model representa- tion. At present, there are many different modeling technologies in use, and although models can be easily placed into a database, they are not useful out of the context of their specific modeling package. The need for a standardized way of communicating compu- tational descriptions of biological systems ex- tends to the literature. Entire conferences have been established to explore ways of mining the biology literature to extract se- mantic information in computational form. Going forward, as a community we need to come to consensus on how to represent what we know about biology in computa- tional form as well as in words. The key to postgenomic biology will be the computa- tional assembly of our collective knowl- edge into a cohesive picture of cellular and organism function. With such a comprehen- sive model, we will be able to explore new types of conservation between organisms and make great strides toward new thera- peutics that function on well-characterized pathways. References 1. S. K. Kim et al., Science 293, 2087 (2001). 2. A. Hartemink et al., paper presented at the Pacific Symposium on Biocomputing 2000, Oahu, Hawaii, 4 to 9 January 2000. 3. D. Pe’er et al., paper presented at the 9th Conference on Intelligent Systems in Molecular Biology (ISMB), Copenhagen, Denmark, 21 to 25 July 2001. 4. H. McAdams, A. Arkin, Proc. Natl. Acad. Sci. U.S.A. 94, 814 ( 1997 ). 5. A. J. Hartemink, thesis, Massachusetts Institute of Technology, Cambridge (2001). V I E W P O I N T Machine Learning for Science: State of the Art and Future Prospects Eric Mjolsness* and Dennis DeCoste Recent advances in machine learning methods, along with successful applications across a wide variety of fields such as planetary science and bioinformatics, promise powerful new tools for practicing scientists. This viewpoint highlights some useful characteristics of modern machine learn- ing methods and their relevance to scientific applications. We conclude with some speculations on near-term progress and promising directions. Machine learning (ML) (1) is the study of computer algorithms capable of learning to im- prove their performance of a task on the basis of their own previous experience. The field is closely related to pattern recognition and statis- tical inference. As an engineering field, ML has become steadily more mathematical and more successful in applications over the past 20 years. Learning approaches such as data clus- tering, neural network classifiers, and nonlinear regression have found surprisingly wide appli- cation in the practice of engineering, business, and science. A generalized version of the stan- dard Hidden Markov Models of ML practice have been used for ab initio prediction of gene structures in genomic DNA (2). The predictions correlate surprisingly well with subsequent gene expression analysis (3). Postgenomic bi- ology prominently features large-scale gene ex- pression data analyzed by clustering methods (4), a standard topic in unsupervised learning. Many other examples can be given of learning and pattern recognition applications in science. Where will this trend lead? We believe it will lead to appropriate, partial automation of every element of scientific method, from hypothesis generation to model construction to decisive experimentation. Thus, ML has the potential to amplify every aspect of a working scientist’s progress to understanding. It will also, for better or worse, endow intelligent computer systems with some of the general analytic power of scientific thinking. Machine Learning at Every Stage of the Scientific Process Each scientific field has its own version of the scientific process. But the cycle of observing, creating hypotheses, testing by decisive exper- iment or observation, and iteratively building up comprehensive testable models or theories is shared across disciplines. For each stage of this abstracted scientific process, there are relevant developments in ML, statistical inference, and pattern recognition that will lead to semiauto- matic support tools of unknown but potentially broad applicability. Increasingly, the early elements of scientific method—observation and hypothesis genera- tion—face high data volumes, high data acqui- sition rates, or requirements for objective anal- ysis that cannot be handled by human percep- tion alone. This has been the situation in exper- imental particle physics for decades. There automatic pattern recognition for significant events is well developed, including Hough transforms, which are foundational in pattern recognition. A recent example is event analysis for Cherenkov detectors (8) used in neutrino oscillation experiments. Microscope imagery in cell biology, pathology, petrology, and other fields has led to image-processing specialties. So has remote sensing from Earth-observing satellites, such as the newly operational Terra spacecraft with its ASTER (a multispectral thermal radiometer), MISR (multiangle imag- ing spectral radiometer), MODIS (imaging Machine Learning Systems Group, Jet Propulsion Lab- oratory/California Institute of Technology, Pasadena, CA, 91109, USA. *To whom correspondence should be addressed. E- mail: [email protected] Table 1. Parallels between genome sequencing and genetic network discovery. Genome sequencing Genome semantics Physical maps Graphical model Contigs Low-level functional models Contig reassembly Module assembly Finished genome sequence Comprehensive model www.sciencemag.org SCIENCE VOL 293 14 SEPTEMBER 2001 2051 C O M P U T E R S A N D S C I E N C E on August 29, 2018 http://science.sciencemag.org/ Downloaded from Nature, 559
 pp. 547–555 (2018) Science, 293 pp. 2051-2055 (2001) Science, 361 pp. 360-365 (2018) Science is changing, the tools of science are changing. And that requires different approaches. ─── Erich Bloch, 1925-2016 ҰํͰɺੜ໋ՊֶͰ΋ಘΒΕͨڭ܇ͱͯ͠ɺɺɺ(͓ۚ΋͔͔ΔͷͰɺɺɺ)
 ࣮ޮੑΛͱ΋ͳ͏ํࣜͷཱ֬ʹ͸·ͩ·ͩཁૉٕज़ͷվྑͱʮྑ͍ʯσʔλͷ஝ੵ͕ඞཁ "low input, high throughput, no output science." (Sydney Brenner) → ࡶͳઃఆɾܥͰ໢ཏతͳϋΠεϧʔϓοτ࣮ݧΛ͍͘Βͯ͠΋Կ΋ಘΒΕͳ͍

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ʮ(ےͷྑ͍)Ծઆܗ੒ʯͱػցֶशɾσʔλϚΠχϯά 80 • ࣗಈԽͷ΋͏ҰͭͷԸܙ͸ɺʮ࠶ݱੑʯʮ݁Ռͷ࣭ʯͷ୲อ
 ଐਓੑ͕࢒͍ͬͯΔͱσʔλͷ࣭ʹ΋(༧ଌʹ΋)͹Β͖͕ͭੜ͡Δ • ۙ೥ͷࣗಈԽͰʮߴ଎ʹͰ͖Δ͚ͩͨ͘͞Μࢼ͢ʯͷ͸
 ୳ࡧΛޮ཰Խ͢ΔԦಓ͕ͩɺߟ͑ΒΕΔީิ͕΄΅ແݶʹ͋Γ͑Δ
 ͷͰɺʮԿΛࢼ͔͢ʯͷબ୒ͷ໰୊͸࢒Δ (શ෦͸ࢼͤͳ͍...) • ࣗಈԽΛ͢Δ͔͠ͳ͍͔ʹΑΒͣɺ࣮ݧͰ΋ܭࢉͰ΋ɺےͷྑ͍
 λʔήοτɺ࣮ݧ৚݅ɺύϥϝλΛܾΊΔεςοϓ͸ϘτϧωοΫ ࣮ݧσʔλɾܭࢉσʔλɾϑΝΫτͷ஝ੵ In-Houseσʔλ + Publicσʔλ + ஌ࣝϕʔε 
 + ͦͷQuality Control / Annotations) ػցֶशɾσʔλϚΠχϯά +

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Ϟσϧϕʔε࠷దԽ (୅ཧϞσϧ࠷దԽ) 81 ߤۭӉ஦ػͷΑ͏ͳྲྀମػցઃܭͳͲɺܭࢉ͕͔͔࣌ؒΔ
 γϛϡϨʔγϣϯΛ༻͍ͨઃܭ࠷దԽٕज़ͱͯ͠ൃల ܭࢉ͕͔͔࣌ؒΔγϛϡϨʔγϣϯͷ୅ཧ(surrogate)
 ͱͯ͠ɺػցֶशɹɹɹΛ׆༻͢Δ x!y AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 1. Initial Sampling 2. Loop: 1. Construct a Surrogate Model. 2. Search Infill Criterion. 3. Add new samples. ػցֶश దԠαϯϓϦϯά ࣮ݧܭը e.g.
 Latin hypercube sampling (LHS) e.g.
 Expected improvement (EI) ☺Recent advances in surrogate-based optimization (Forrester & Keane, 2009)
 https://doi.org/10.1016/j.paerosci.2008.11.001

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Infillج४ͱ࠷ద࣮ݧܭը 82 x AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= ML༧ଌ 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 Ͳ͏΍ͬͯόϥϯε੍ޚ͢Δͷ͔ʹ͸৭ʑͳ΍Γ͔͕ͨ͋ΓHot Topics • ΞΫςΟϒϥʔχϯά • ଟ࿹όϯσΟοτ • ਐԽܭࢉ • ήʔϜཧ࿦ (CFRͳͲ) "exploitation" ͱ "exploration" ͷόϥϯε੍ޚʹ΋MLΛ༻͍Δ هड़ࢠ(Ұൠʹ͸ߴ࣍ݩ) ׆ੑ • ڧԽֶश + ୳ࡧ • ϒϥοΫϘοΫε࠷దԽ • ϕΠζ࠷దԽ • ஞ࣮࣍ݧܭը

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Infillج४ͱ࠷ద࣮ݧܭը 82 x AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= ML༧ଌ 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 ༧ଌͷʮෆ࣮֬͞ʯ ྫ) ༧ଌ෼ࢄ, ༧ଌ෼෍ Ͳ͏΍ͬͯόϥϯε੍ޚ͢Δͷ͔ʹ͸৭ʑͳ΍Γ͔͕ͨ͋ΓHot Topics • ΞΫςΟϒϥʔχϯά • ଟ࿹όϯσΟοτ • ਐԽܭࢉ • ήʔϜཧ࿦ (CFRͳͲ) "exploitation" ͱ "exploration" ͷόϥϯε੍ޚʹ΋MLΛ༻͍Δ هड़ࢠ(Ұൠʹ͸ߴ࣍ݩ) ׆ੑ • ڧԽֶश + ୳ࡧ • ϒϥοΫϘοΫε࠷దԽ • ϕΠζ࠷దԽ • ஞ࣮࣍ݧܭը

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Infillج४ͱ࠷ద࣮ݧܭը 82 x AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= AAAB/XicbVDLSgMxFL2pr1pfVZdugkVwVWZE0GXRjcsK9gHtUDJppo1NMkOSEctQ/AW3uncnbv0Wt36JaTsLbT1w4XDOvZzLCRPBjfW8L1RYWV1b3yhulra2d3b3yvsHTROnmrIGjUWs2yExTHDFGpZbwdqJZkSGgrXC0fXUbz0wbXis7uw4YYEkA8UjTol1UrMbyuxx0itXvKo3A14mfk4qkKPeK393+zFNJVOWCmJMx/cSG2REW04Fm5S6qWEJoSMyYB1HFZHMBNns2wk+cUofR7F2oyyeqb8vMiKNGcvQbUpih2bRm4r/eqFcSLbRZZBxlaSWKToPjlKBbYynVeA+14xaMXaEUM3d75gOiSbUusJKrhR/sYJl0jyr+l7Vvz2v1K7yeopwBMdwCj5cQA1uoA4NoHAPz/ACr+gJvaF39DFfLaD85hD+AH3+ADzJlfc= ML༧ଌ 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 ༧ଌͷʮෆ࣮֬͞ʯ ྫ) ༧ଌ෼ࢄ, ༧ଌ෼෍ e.g.
 "expected improvement Ͳ͏΍ͬͯόϥϯε੍ޚ͢Δͷ͔ʹ͸৭ʑͳ΍Γ͔͕ͨ͋ΓHot Topics • ΞΫςΟϒϥʔχϯά • ଟ࿹όϯσΟοτ • ਐԽܭࢉ • ήʔϜཧ࿦ (CFRͳͲ) "exploitation" ͱ "exploration" ͷόϥϯε੍ޚʹ΋MLΛ༻͍Δ هड़ࢠ(Ұൠʹ͸ߴ࣍ݩ) ׆ੑ • ڧԽֶश + ୳ࡧ • ϒϥοΫϘοΫε࠷దԽ • ϕΠζ࠷దԽ • ஞ࣮࣍ݧܭը

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ػցֶश෼໺ࣗମͰ΋Hot Research Topic 83 AlphaGo
 (Nature, Jan 2016) "VUP.- શࣗಈػցֶश AlphaGo Zero
 (Nature, Oct 2017) AlphaZero
 (Science, Dec 2018) w "MHPSJUIN$POpHVSBUJPO w )ZQFSQBSBNFUFS0QUJNJ[BUJPO )10 w /FVSBM"SDIJUFDUVSF4FBSDI /"4 w .FUB-FBSOJOH-FBSOJOHUP-FBSO Amazon SageMaker MuZero
 (arXiv, Nov 2019)

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ྫ) Model-based RL (Toward sample-efficient RL) ࠷ద੍ޚ (Optimal Control) ର৅ͷಈతγεςϜͷڍಈ(෺ཧ๏ଇͳͲ)͕Θ͔͍ͬͯΔ৔߹ɺ࠷ྑߦಈΛܾఆՄೳ Կ΋Θ͔Βͳ͍৔߹ (Model-free RL) ࣮ࡍʹ؀ڥ͔ΒಘΒΕΔߦಈɾঢ়ଶܥྻ͔Β௚઀తʹํࡦ΍Ձ஋ؔ਺Λਪఆ͢Δ গ͠౰ͨΓ͕͚ͭΒΕΔ(?)৔߹ (Model-based RL or ݹయతͳγεςϜಉఆͷઃఆ) ࣮ࡍͷߦಈɾঢ়ଶܥྻ͔Β·ͣಈతγεςϜͷڍಈΛਪఆ͠ɺͦͷਪఆͨ͠Ϟσϧ
 Λ༻͍ͯ࠷దߦಈΛܭը͢Δ (e.g. কع͢Δͱ͖૬खͷखΛ಄ʹதͰγϛϡϨʔτ͢Δ) Planning ํࡦ ֶश ํࡦ+Ձ஋ؔ਺
 ֶश Ձ஋ؔ਺
 ֶश

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ྫ) Model-based RL or Planning with Models Deep Planning Network (PlaNet) Hafner+ Learning Latent Dynamics for Planning from Pixels. arXiv:1811.04551 (Jun 2019) MuZero Schrittwieser,+ Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model. arXiv:1911.08265 (Nov, 2019) Simulated Policy Learning (SimPLe) Kaiser+ Model-Based Reinforcement Learning for Atari. arXiv:1903.00374 (Jun 2019) Stochastic Latent Actor-Critic (SLAC) Lee+ Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model. arXiv:1907.00953 (Jul 2019)

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86 ػցֶशͰ͖ͨͱ͖ͷ̎छྨͷظ଴ 1. ಘΒΕͨม׵աఔ(ؔ਺)ʹΑΔ༧ଌΛ৭ʑͳ໨తʹ࢖͏ 2. ಘΒΕͨม׵աఔ(ؔ਺)Λ෼ੳͯ͠എܠաఔͷ࢓૊ΈΛ஌Δ 1.ʹ͍ͭͯ • ໰୊͕಺ૠతʹͳΔΑ͏޻෉ (දݱֶशɾӅΕߏ଄ಉఆ) • ಺ૠɾ֎ૠ൑ఆ (༧ଌͷ৴པ౓ܭࢉ) • Ϟσϧϕʔε࠷దԽͱ୳ࡧ (࠷ద࣮ݧܭը) 2.ʹ͍ͭͯ • ϙετϗοΫղੳͱղऍੑϞσϧ (ֶशࡁΈϞσϧ෼ੳ)

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(ҼՌͷཧղ͸ఘΊͯ?)ղऍ/આ໌/Ծઆੜ੒΁ 87 Explainable AI (XAI), Interpretable ML, Causal ML ʮղऍʯvsʮཧղʯ ਓ(ख๏)ͷ ਺͚ͩ͋Δ ਅ࣮͸
 ͻͱͭʁ എޙͷਅͷ๏ଇʹؔ͢Δ৘ใ͕ಘΒΕΔͱ͸ݶΒͳ͍ͷͰ஫ҙ ☺ࢲͷϒοΫϚʔΫɿػցֶशʹ͓͚Δղऍੑ (ݪ ૱, ਓ޻஌ೳ 33(3), 366-369, 2018೥5݄) • ถDARPAͷExplainable AI (XAI)ϓϩάϥϜ • ػցֶशۀքʹ͓͚ΔInterpretable ML • CausalML: ػցֶश for Causal Inference, Counterfactual Prediction, and Autonomous Action ֤ख๏ʹΑͬͯҟͳΔԾઆܗ੒΍ࣔࠦͷఏڙ

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ػցֶशϞσϧͷղऍɺػցֶशʹΑΔղऍ 88 • ਂ૚ֶश
 ܭࢉάϥϑͱͯ͠දݱ (൚༻తύϥϝλਪఆɿٯϞʔυࣗಈඍ෼) • ֬཰తϓϩάϥϛϯά (ੜ੒త౷ܭϞσϦϯά)
 ֬཰ม਺ͷ֊૚తੜ੒ؔ܎Ͱදݱ (൚༻తύϥϝλਪఆ: MCMC/ࣗಈVI) • ճؼ෼ੳʹ͓͚ΔཁҼ෼ੳ 
 (ճؼ܎਺ͷ༗ҙੑݕఆ) • ϕΠζ༧ଌ෼෍ • ม਺ॏཁ౓ɾ෦෼ैଐ౓plot • άϩʔόϧ/ϩʔΧϧײ౓ղੳ (Sobol'๏, FAST๏, etc) • ਂ૚ֶशʹ͓͚ΔSaliency΍Attentionͷར༻ • ہॴઆ໌ੜ੒: LIME (KDD16), SHAP (NIPS17) • ͷ֊૚త෼ղʹΑΔӅΕҼࢠ΍֊૚ߏ଄ͷಉఆ • ͷؔ਺ͷPost-hocղੳ ౷ܭֶख๏͸ཁલఏͷݕূ • આ໌ม਺ͷબ୒ • ઢܗͷԾఆ • ଟॏڞઢܗੑ "Ϛϧνί" • ࢒ࠩͷݕ౼
 : x!y AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 x!y AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5

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ࠓ೔ͷ಺༰ 89 1. Πϯτϩ
 ػցֶशͱՊֶ(͋Δ͍͸"΋ͷͮ͘Γ") 2. ػցֶशͰԿ͔ΛʮཧղʯͰ͖Δ͔ʁ
 Answer: ௚઀తʹ͸ݪཧ্ࠔ೉ 3. ػցֶशͰԿ͔ΛʮൃݟʯͰ͖Δ͔ʁ
 Answer: ௚઀తʹ͸ݪཧ্ࠔ೉ 4. ͡Ό͋Ͳ͏͢ΜͷʂʁԿ͕͍Δͷʂʁ
 Answer:ʮදݱʯͱʮհೖʯ
 2ͱ3Λલఏʹػցֶश෼໺ͷτϐοΫΛ؆୯ʹ঺հ

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࠶ߟ ౷ܭతཧղͱՊֶͷจ๏ 90 Պֶͷจ๏ (1892) 
 "Statistics is the grammar of science." (Karl Pearson) Պֶ͸ʮσʔλͷݟํʯͱແԑͰ͸͍ΒΕͳ͍ʂ ݱࡏͷՊֶతٙ໰ͷଟ͘͸100%YES/NOͳ౴͕͑ແ͍໰͍ʂ • ͜ͷༀΛҿΊ͹ࢲͷපؾ͸࣏Δͷʁ • ͜ͷ݈߁৯඼৯΂͍ͯΕ͹௕ੜ͖Ͱ͖Δͷʁ • ͜ͷԽহ඼͚͍ͭͯΕ͹গ͠Ͱ΋ए͍͘ΒΕΔͷʁ • ͜ͷ৯඼ͨ΂Ε͹μΠΤοτͰ͖Δͷʁ • ݪࢠྗ͸҆શͳͷʁ YES and NOͷ ؒʹ͖ͬͱਅ࣮͕ Ն໨ᕸੴΑΓ10࠽೥্ͷେ౷ܭֶऀ Պֶͱ͍͏΋ͷʹ͸ɺຊདྷݶք͕͋ͬͯɺ޿͍ҙຯͰͷ࠶ݱՄೳͷݱ৅Λɺ ࣗવք͔Βൈ͖ग़ͯ͠ɺͦΕΛ౷ܭֶతʹڀ໌͍ͯ͘͠ɺͦ͏͍͏ੑ࣭ͷ
 ֶ໰ͳͷͰ͋ΔɻʮՊֶͷํ๏ (த୩Ӊ٢࿠)ʯ

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Impossible to model everything...? 91 • ਅͷ๏ଇ͕ਓؒʹཧղՄೳͳ΄ͲγϯϓϧͳϞσϧʹཁૉؐݩͰ͖Δ
 อূ͸Ͳ͜ʹ΋ͳ͍ɻ • σʔλ͕༗ݶͳΒͦΕΛઆ໌Ͱ͖ΔϞσϧ͸Ұൠʹແ਺ʹ͋Δɻ Ϟσϧͱ͸Կ͔Λࣺ৅ͨ͠΋ͷͰ͋Γ࣮ੈք(ෳࡶܥ)ͱ͸ҧ͏ɻ ࿦ड़తཧղ/ཁૉؐݩ͔Βෳࡶܥͷ"౷ܭతཧղ"΁? "one of the great statistical minds of the 20th century" େ౷ܭֶऀ George E. P. Box (1919-2013) https://en.wikipedia.org/wiki/All_models_are_wrong "Essentially, all models are wrong, but some are useful"

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ల๬ɿTheory-driven vs Data-drivenͷղফͱ༥߹ 92 Theory-driven Data-driven • ର৅ݱ৅ͷෳࡶԽ • γϛϡϨʔγϣϯٕ๏΋ෳࡶԽ • "ܦݧతʹܾΊΔ"ύϥϝλ΍ॳظ஋ • ൚ؔ਺ɺަ׵૬߲ؔͷઃܭ • খαϯϓϧɾ௿Χ΢ϯτͷ໰୊ • ֎ૠͷෆՄೳੑͷ໰୊ • ؼೲόΠΞεͷϞσϧΤϯίʔυ • Blackboxੑɾղऍੑͷ໰୊ • ஌ࣝϕʔεͱ࿦ཧਪ࿦(ه߸AI)ͷݶք • ݫີਪ࿦΍୳ࡧͷܭࢉരൃ(NPࠔ೉ੑ) • େྔσʔλͷ஌ࣝԽͷ໰୊ • ੍໿ϓϩάϥϛϯά΍૊߹ͤ࠷దԽ (ਓ޻஌ೳ෼໺) (ਓ޻஌ೳ෼໺) • Data-Drivenख๏(ػցֶश)ͱਓؒͷ
 ࿦ཧతࢥߟͱͷେ͖ͳΪϟοϓ • Data͕ͳ͍ྖҬͷ୳ࡧ΍ʮͻΒΊ͖ʯ • Ϟσϧద༻ൣғͱ৴པੑɾ҆શੑ ৽ͨͳํ๏࿦΁ʁ σʔλಉԽɺ໛฿ֶशɺ࿦ཧ߹੒ɺetc Ϟσϧϕʔε࠷దԽɺڧԽֶशɺϝλ ֶशɺυϝΠϯదԠɺੜ੒Ϟσϧɺetc ʲ߹ཧ࿦ʳ ʲܦݧ࿦ʳ

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ੈքτοϓϨϕϧݚڀڌ఺ϓϩάϥϜʢWPIʣ 93 ࠷େԯԁ೥º೥

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๺ւಓେֶ Խֶ൓Ԡ૑੒ݚڀڌ఺(ICReDD) 94

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ڌ఺ͷػٕؔज़ɿԽֶ൓Ԡܦ࿏ͷࣗಈ୳ࡧ 95 θ1 θ2 O H H Energy θ = 104.45° 1 θ = 95.84 pm 2 θ1 Schrödinger equation Potential Energy Surface EQ EQ T ADDF Ohno & Maeda, Chem Phys Lett, 2004 Reaction Path AFIR Maeda & Morokuma, J Chem Phys, 2010 θ2

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Խֶ൓Ԡͷઃܭͱ୳ࡧ 96 Chemical reactions = recombinations of atoms and chemical bonds subjected to the laws of nature • Intractably large chemical space: A intractably large number of "theoretically possible" candidates for reactions and compounds... • Scalability issue: Simulating an Avogadro-constant number of atoms is utterly infeasible... (After all, we need some compromise here) • Complexity and uncertainty of real-world systems: Many uncertain factors and arbitrary parameters are involved... • Known and unknown imperfections of currently established theories: Current theoretical calculations have many exceptions and limitations...

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97 Cause-and-Effect
 Relationship Related factors
 (and their states) Outcome Reactions Some mechanism [Inputs] [Outputs] Theory-driven methods try to explicitly model the inner workings of a target phenomenon (e.g. through first-principles simulations) Data-driven methods try to precisely approximate its outer behavior (the input-output relationship) observable as "data". 
 (e.g. through machine learning from a large collection of data) governing equation? Խֶ൓Ԡͷઃܭͱ୳ࡧ

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98 Խֶ൓Ԡͷઃܭͱ୳ࡧ Brc1cncc(Br)c1 C[O-] CN(C)C=O Na+ COc1cncc(Br)c1 SMILES Structural Formla Steric Structures Electronic States Reactants Reagents Products As pattern languages (e.g. known facts in textbooks/databases) As physical entities (e.g. quantum chemical calculations)

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99 Խֶ൓Ԡͷઃܭͱ୳ࡧ Computer-assisted synthetic planning (path search on knowledge bases) or AI-Assisted Synthesis? (with Machine Learning)

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https://www.chemistryworld.com/machine-learning/1616.tag

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ML-based chemical reaction predictions 3N-MCTS/AlphaChem Segler+ Nature 2018 Molecular Transformer Schwaller+ ACS Cent Sci 2019 seq2seq Liu+ ACS Cent Sci 2017 WLDN Jin+ NeurIPS 2017 ELECTRO Bradshaw+ICLR 2019 WLN Coley+ Chem Sci 2019 GPTN Do+ KDD 2019 Graph NN Sequence NN Combined or Other IBM RXN Schwaller+ Chem Sci 2018 Molecule Chef Bradshaw+ DeepGenStruct (ICLR WS) 2019 Neural-Symbolic ML Segler+ Chemistry 2017 Similarity-based Coley+ ACS Cent Sci 2017 Fermionic Neural Network Pfau+ Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks. 
 arXiv:1909.02487, Sep 2019. ML + First-principle simulations Hamiltonian Graph Networks with ODE Integrators Sanchez-Gonzalez+ Hamiltonian Graph Networks with ODE Integrators. 
 arXiv:1909.12790, Sep 2019. Both from Խֶ൓Ԡͷઃܭͱ୳ࡧ

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Խֶ൓Ԡͷઃܭͱ୳ࡧ 102

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ࠢ͸ࡉ෦ʹ॓Δɿಓ۩ͱͯ͠ͷػցֶश 103 • ϓϩʹͱͬͯ࢖͏ಓ۩͸໋ɻ • ಓ۩ͷಛੑʹਫ਼௨͠ɺஸೡʹѻ͍ɺ खೖΕΛଵΒͳ͍ɻ • ಓ۩ശͷதΛͻͱΊݟΔ͚ͩͰͦͷ ৬ਓͷؾ࣭ͱϨϕϧ͕෼͔Δɻ ৬ਓࠢ (ٕज़ऀࠢ) ΑΓཧղ͢ΔͨΊʹٕ๏(ಓ۩)Λ੔උ͢Δ ʮྑ͍࢓ࣄ͸ྑ͘खೖΕ͞Εͨಓ۩͔Βʯ http://www.900910.com/mies.php ʮػցֶशʯݚڀͷҙٛ

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࠷ޙʹɿհೖɾ࣮ݧ΋ؚΉ࠷దԽ΁ 104 ݅ͷBoxͷ1966೥ͷ࿦จʮUse and Abuse of regressionʯ͸
 ඇৗʹ༗໊ͳ͜ΜͳҰจͰకΊ͘͘ΒΕΔɻ ཧ۶͔Βݴͬͯ΋ػցֶश԰ͱσʔλʮ͚ͩʯͰ͸Կ΋Ͱ͖ͳ͍ ͱ͍͏͜ͱͰ͢ɻ To find out what happens to a system when you interfere with it you have to interfere with it (not just passively observe it). υϝΠϯ஌ࣝΛ࣋ͬͨઐ໳Ոͱͷڠಇ͕ඞਢͰ͢ʂ Ͳ͏ͧΑΖ͓͘͠ئ͍͠·͢(?)

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Take Home Message 105 Պֶ͕ٻΊΔ͜ͱ: ෼͔Βͳ͍͜ͱ͕෼͔Δ(Պֶతൃݟ) ൃݟ ཧղ ݪҼͱ݁Ռ(ҼՌؔ܎)Λݟग़͢ ࠓ·Ͱݟग़͞Ε͍ͯͳ͍ྑ͍ର৅Λݟग़͢ ࠓ೔఻͍͑ͨͨͬͨ3ͭͷ͜ͱ 1. ୯७ʹػցֶशΛ࢖͏͚ͩͰ͸͍ͣΕ΋ղ͚ͳ͍ 2. ػցֶशҎ֎ͷ΋ͷ(հೖ΍υϝΠϯ஌ࣝ)͕ݪཧ্ඞਢ 3. ࠷ۙ·͞ʹݚڀ͕ਐߦதͷະղܾྖҬ͕ͩݚڀ͸৭ʑ͋Δ ͷաఔΛཧղ͠(ਓ͕ؒ)ൃݟ͢Δ x!y AAACBXicbVC7SgNBFL0bXzG+opY2E4NgFXZF0DJoYxnBPCC7htnJJBkys7PMzIrLktpfsNXeTmz9Dlu/xEmyhSYeuHA4517O5YQxZ9q47pdTWFldW98obpa2tnd298r7By0tE0Vok0guVSfEmnIW0aZhhtNOrCgWIaftcHw99dsPVGkmozuTxjQQeBixASPYWOneD0X2OPErvpF+Je2Vq27NnQEtEy8nVcjR6JW//b4kiaCRIRxr3fXc2AQZVoYRTiclP9E0xmSMh7RraYQF1UE2+3qCTqzSRwOp7EQGzdTfFxkWWqcitJsCm5Fe9Kbiv14oFpLN4DLIWBQnhkZkHjxIODISTStBfaYoMTy1BBPF7O+IjLDCxNjiSrYUb7GCZdI6q3luzbs9r9av8nqKcATHcAoeXEAdbqABTSCg4Ble4NV5ct6cd+djvlpw8ptD+APn8wfECJj5 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