This document discusses the potential for open source artificial intelligence to help understand molecular biology data. It argues that capturing common sense knowledge computationally has been challenging, but knowledge about molecular biology exists explicitly. An open source AI focused on molecular biology could help explain genomic data by developing a comprehensive knowledge base and using abductive inference. However, explaining biological phenomena is difficult and requires judgment. The document advocates for open source development to gain productivity advantages and build trust through transparency. It outlines challenges and opportunities for facilitating an open source AI community focused on understanding life.

1. The Role of Openness in Creating a Mind for Life

2. Open Source, AI, & Biology An AI breakthrough can come from an application in biology It is imperative that this be open source Some steps toward (and questions about) creating an open source AI for understanding life

3. The first artificial mind will think about molecular biology “You can’t think about thinking without thinking about thinking about something.” Seymour Papert, 1974 “A thorough study of Human Physiology is, in itself, an education broader and more comprehensive than much that passes under that name. There is no side of the intellect which it does not call into play, no region of human knowledge into which either its roots, or its branches, do not extend.” Thomas Huxley,1893

4. Why AI hasn’t succeeded (yet) People know a lot about the world implicitly Conversing with a partnerwho doesn’t know these basic things is very frustrating 50 years of failing to capture this “common sense” information computationally suggests: Lack of explicit enumeration makes capture very expensive (encyclopedias don’t have it!) Still no idea of the extent of this knowledge

6. X J.J. Hornberg et al. / BioSystems 83 (2006) 81–90 Homeostatic networks foil single markers and drugs outcome target

7. Networks change through time Mjolsness, Sharp, Reinitz, A Connectionist Model of Development J. Theoretical Bio 1991

8. Understanding the data “We are close to having a $1,000 genome sequence, but this may be accompanied by a $1,000,000 interpretation.” - Bruce Korf, president American College of Medical Genetics Not only is the cost of sequencing essentially free, but big computers and big storage are cheap, too. What will keep us busy for the next 50 years is understanding the data” - Russ Altman, chair of Biomedical Engineering at Stanford

9. The Hard Problem Given a set of genomic regions, variants, gene products, and/or concentrations empirically involved in a defined phenotype… Produce: An explanation of the reasons that those genomic regions / variants / products / concentrations are (or are not) relevant to the phenotype Evidence to support the explanation(s) Alternative explanations Reasons to prefer one explanation over another

10. Answering Why? questions Fundamental to human cognitive development Amazing human facility Even to confabulation Causal explanation is central to science The only question “big data”doesn’t seem to be enoughto answer (cfRamachandran & Hovy, 2002)

11. Abductive inference “However man may have acquired his faculty of divining the ways of Nature, it has certainly not been by a self-controlled and critical logic. Even now he cannot give any exact reason for his best guesses…. For though it goes wrong oftener than right, yet the relative frequency with which it is right is on the whole the most wonderful thing in our constitution.” The Essential Peirce: Selected Philosophical Writings v. 2 p. 217

12. “Two paradoxes are better than one; they may even suggest a solution” –Edward Teller Molecular Systems Biology + Artificial Intelligence

13. Explanation is hard Not just about the connection between an explanation and the thing explained, but must also be “consonant” with other explanations. Knowledge is key Have to know many other explanations. Need “judgment” to compare the qualities of alternative explanations. Racunas & Shah’s HyBrow system, but required extensive manually represented knowledge A “complete enough” knowledge-base?

14. Knowledge-based Computational Biology Widespread use, e.g. Simulation systems (e.g. BioCyc) Question answering systems (e.g. AskHermes or Watson Medicine) High-throughput result analysis (e.g. GOEAST, Ontologizer) Hypothesis generation / testing (e.g. HyQue) Anything that uses an ontology Annotations (e.g. GOA) Cross-species comparisons NCBO

15. KB for explanation Knowledge base quality Correctness, timeliness (tracking changes) Completeness A constantly receding goal, that obviously cannot be achieved, but is important anyway Need to cover the material in Textbooks Journal articles Databases

16. Explanatory inference Even if all the relevant knowledge were available in computationally tractable form… We need inferential methods to Identify possible explanations of complex biological phenomena (symbolic?) Compare alternative explanations in the light of existing evidence (numeric?) History of explanatory inference in AI is suggestive, but key open problems remain

17. Why does openness matter? Productivity: Attacking hard problems efficiently Rapid assimilation of effective methods Building on (not ignoring) each other’s results Equity: Access to scientists with low budgets Distribution to the widest possible community Ethics: Transparency for AI is a moral value

18. Transparency is a moral value AI matters – lots of social concerns about loss of control, etc. 2001, Robopocolypse AI is cheap to replicate, and will diverge (if you can build one mind, building millions more is easy). Too important to be private Technological development in the face of such broad social concern requires earning the trust of the society

19. Getting there Build on track records of openness OBO &Community-curated Ontologies Semantic Web / OWL / SPARQL / SWRL Open Access Publishing Linked Life Data Breaking down barriers Infrastructure Incentives

20. Opening a Bazzar To get the productivity advantage, infrastructure matters Technical infrastructure to share, compare and integrate code Social infrastructure to work together to solve hard problems Motivation Competition Cooperation

21. Confronting the temptations of being proprietary The temptations: Potential future payoff Avoid effort to conform to the infrastructure Fear of not being able to improve in the future Competition errors Wrong task / evaluation / supplied data Poor process (timing, execution, infrastructure) Doesn’t evolve toward worthy end

22. Goals Participation from many, previously disparate communities Bio focused: BioCreative, BioNLP, Comp Ling: ACL Shared Tasks, CONLL NIST: TREC, TAC A living, open source collection of useful, modular, repurposable, state of the art software for understanding biomedical texts Major advances in AI

23. Facilitating an OS community Providing Resources Software (UIMA, U-COMPARE) Compute power Training data (CRAFT, Analysis of analysts) Signal Events Series of competitions based on CRAFT Incentives Prizes for significant achievements

24. http://bionlp-corpora.sourceforge.net/CRAFT/http://bionlp.sourceforge.net

25. Remaining challenges Pubmed Central and open access Corporate ownership (Ontotext & LLD) Semantic compatibility of various sources UMLS breadth vs. BFO logic Sharing inference methods & rules Rule syntax (SWRL) is not enough. DL inference is not enough UIMA equivalent?

26. How to participate Help design CRAFT competitions Confront publishers about PMC bulk downloads Help define inferential benchmarks