Artificial Intelligence to Win the Nobel Prize and Beyond: Creating the Engine for Scientific Discovery

This article proposes a new grand challenge for AI reasearch: to develop AI system to make major scientific discoveries in biomedical sciences that worth Nobel Prize. There are a series of human cognitive limitations that prevents us from making accerlated scientific discoveries, particularity in biomedical sciences. As a result, scientific discoveries are left behind at the level of cottage industry. AI systems can transform scientific discoveries into highly efficient practice, thereby enable us to expand our knowledge in unprecedented way. Such system may out-compute all possible hypotheses and may redefine the nature of scientific intuition, hence scientific discovery process.

[1]  L. Lenert,et al.  EHR Big Data Deep Phenotyping , 2014, Yearbook of Medical Informatics.

[2]  Peter N. Robinson,et al.  Deep phenotyping for precision medicine , 2012, Human mutation.

[3]  Yolanda Gil,et al.  Discovery Informatics: AI Opportunities in Scientific Discovery , 2012, AAAI Fall Symposium: Discovery Informatics.

[4]  J. Hendler,et al.  Amplify scientific discovery with artificial intelligence , 2014, Science.

[5]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[6]  H. Kitano,et al.  Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology , 2013, PloS one.

[7]  H. Kitano,et al.  A comprehensive map of the mTOR signaling network , 2010, Molecular systems biology.

[8]  H. Kitano,et al.  Computational systems biology , 2002, Nature.

[9]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[10]  H. Kitano,et al.  Software for systems biology: from tools to integrated platforms , 2011, Nature Reviews Genetics.

[11]  Erik T. Mueller,et al.  Watson: Beyond Jeopardy! , 2013, Artif. Intell..

[12]  Raj Reddy,et al.  Automatic Speech Recognition: The Development of the SPHINX System , 2013 .

[13]  Yan Liu,et al.  Deep Computational Phenotyping , 2015, KDD.

[14]  Paul Wicks,et al.  Increasing patient participation in drug development , 2015, Nature Biotechnology.

[15]  F. Prinz,et al.  Believe it or not: how much can we rely on published data on potential drug targets? , 2011, Nature Reviews Drug Discovery.

[16]  H. Kitano,et al.  A comprehensive pathway map of epidermal growth factor receptor signaling , 2005, Molecular systems biology.

[17]  P. Bryan Heidorn,et al.  Shedding Light on the Dark Data in the Long Tail of Science , 2008, Libr. Trends.

[18]  Z. Popovic,et al.  Crystal structure of a monomeric retroviral protease solved by protein folding game players , 2011, Nature Structural &Molecular Biology.

[19]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  E. Feigenbaum,et al.  Computers and Thought , 1963 .

[21]  Leonard Eisenbud.  Scientific Discovery: Computational Explorations of the Creative Processes.Pat Langley , Herbert A. Simon , Gary L. Bradshaw , Jan M. Zytkow , 1988 .

[22]  Dietrich Rebholz-Schuhmann,et al.  Biological network extraction from scientific literature: state of the art and challenges , 2014, Briefings Bioinform..

[23]  Hiroaki Kitano,et al.  RoboCup: A Challenge Problem for AI , 1997, AI Mag..

[24]  Joshua Lederberg,et al.  DENDRAL: A Case Study of the First Expert System for Scientific Hypothesis Formation , 1993, Artif. Intell..

[25]  Christopher H. Bryant,et al.  Functional genomic hypothesis generation and experimentation by a robot scientist , 2004, Nature.

[26]  H. Kitano.  Systems Biology: A Brief Overview , 2002, Science.

[27]  M. Massagli,et al.  Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm , 2011, Nature Biotechnology.

[28]  Ken E. Whelan,et al.  The Automation of Science , 2009, Science.

[29]  Samik Ghosh,et al.  A comprehensive map of the influenza A virus replication cycle , 2013, BMC Systems Biology.

[30]  Peter J. Haas,et al.  Automated hypothesis generation based on mining scientific literature , 2014, KDD.

[31]  Samik Ghosh,et al.  Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks , 2013, PLoS Comput. Biol..

[32]  Yolanda Gil,et al.  Wings for Pegasus: Creating Large-Scale Scientific Applications Using Semantic Representations of Computational Workflows , 2007, AAAI.

[33]  Matthias Peter,et al.  Nuclear sequestration of the exchange factor Cdc24 by Far1 regulates cell polarity during yeast mating , 2000, Nature Cell Biology.

[34]  Geoffrey E. Hinton.  A better way to learn features , 2011, Commun. ACM.

[35]  Douglas B. Lenat,et al.  Why AM and EURISKO Appear to Work , 1984, Artif. Intell..

[36]  Hiroaki Kitano,et al.  Challenges of massive parallelism , 1993, IJCAI 1993.

[37]  Samik Ghosh,et al.  Social engineering for virtual 'big science' in systems biology. , 2011, Nature chemical biology.

[38]  H. Kitano,et al.  A comprehensive map of the toll-like receptor signaling network , 2006, Molecular systems biology.

[39]  Jennifer Chu-Carroll,et al.  Building Watson: An Overview of the DeepQA Project , 2010, AI Mag..

[40]  Maria Liakata,et al.  Make way for robot scientists. , 2009, Science.

[41]  Alfred Korzybski.  Selections from Science and Sanity: An Introduction to Non-Aristotelian Systems and General Semantics , 2013 .