In which we discover that discovery requires a new kind of cognitive architecture.

1. Perception, Parcellation, and Programming “Signposts to a Bridge Between Connectionist and Symbolic Systems” • In which we discover that discovery requires a new kind of cognitive architecture. • In which we discover that enabling scientists to use new kinds of cognitive architectures requires a new kind of computational architecture. • In which we discover that even simple cognition requires a new kind of cognitive architecture.

2. Rationality (i.e., reason/symbolic computing) is the hallmark of intelligence. (The parable of the google car.) The challenge is how to compute flexibly with symbolic representations. One approach is to synchronize via “grounded” representations. The problem with this is that abstractions can overlap on the ground. We need to be able to program with Self-Organizing Probabilistic Partially-Overlapping Abstraction Hierarchies

3. Thanks to dozens of colleagues, esp. Jeff Elhai, Tim Finin, Arthur Grossman, Mark Johnson, David Klahr, Pat Langley, JP Massar, Al Newell, Andrew Pohorille, Bob Siegler, Herb Simon, Marty Tenenbaum, Mike Travers, and numerous students. Thanks too for support from CIWDPB, CMU, IBM, NASA, NIH, NSF, Stanford, and Xerox PARC.

4. Rationality (i.e., reason/symbolic computing) is the hallmark of intelligence. (The parable of the google car.) The challenge is how to compute flexibly with symbolic representations. One approach is to synchronize via “grounded” representations. The problem with this is that abstractions can overlap on the ground. We need to be able to program with Self-Organizing Probabilistic Partially-Overlapping Abstraction Hierarchies

5. Perception, Parcellation, and Programming “Signposts to a Bridge Between Connectionist and Symbolic Systems” • In which we discover that discovery requires a new kind of cognitive architecture. • In which we discover that enabling scientists to use new kinds of cognitive architectures requires a new kind of computational architecture. • In which we discover that even simple cognition requires a new kind of cognitive architecture.

6. Perception, Parcellation, and Programming “Signposts to a Bridge Between Connectionist and Symbolic Systems” • In which we discover that discovery requires a new kind of cognitive architecture. • In which we discover that enabling scientists to use new kinds of cognitive architectures requires a new kind of computational architecture. • In which we discover that even simple cognition requires a new kind of cognitive architecture.

7. Perception, Parcellation, and Programming “Signposts to a Bridge Between Connectionist and Symbolic Systems” • In which we discover that discovery requires a new kind of cognitive architecture. • In which we discover that enabling scientists to use new kinds of cognitive architectures requires a new kind of computational architecture. • In which we discover that even simple cognition requires a new kind of cognitive architecture.

8. Rationality (i.e., reason/symbolic computing) is the hallmark of intelligence. (The parable of the google car.) The challenge is how to compute flexibly with symbolic representations. One approach is to synchronize via “grounded” representations. The problem with this is that abstractions can overlap on the ground. We need to be able to program with Self-Organizing Probabilistic Partially-Overlapping Abstraction Hierarchies

9. Partially-Overlapping Abstraction Hierarchies

10. Partially-Overlapping Abstraction Hierarchies

11. Partially-Overlapping Abstraction Hierarchies

12. Probabilistic Partially-Overlapping Abstraction Hierarchies P=.031 P=.031 P=.031 P=.031 P=.031 P=.031 P=.031 P=.031 P=.031

13. In which we discover that discovery requires a new kind of cognitive architecture. • In which we discover that enabling scientists to use new kinds of cognitive architectures requires a new kind of computational architecture. • In which we discover that even simple cognition requires a new kind of cognitive architecture.

14. The First Computer Wizard The Goal: Novices make errors, or know they need help, and are therefore helped by help systems or error reports. Experts don’t need help (or use help and references). Intermediate users don’t make errors, but can stagnate because they don’t know what they don’t know! Shrager, J. & Finin, T. (1982). An expert systems that volunteers advice. AAAI 82, Pittsburgh, PA. pp. 339-340.

15. The First Computer Wizard

16. Partially-Overlapping Abstraction Hierarchies

17. The First Computer Wizard

18. The First Computer Wizard Modeling An Expert Consultant: Traces of Users’ Action Expert Analysis Library of “Bad plans” “KeyHole Goal Recognition” (Bad) Plan Recognizer Advice Generator Advice

19. The First Computer Wizard

20. The First Computer Wizard Modeling An Expert Consultant: Traces of Users’ Action Expert Analysis Library of “Bad plans” “KeyHole Goal Recognition” (Bad) Plan Recognizer Advice Generator Advice

21. Instructionless Learning The Problem: People are really good at figuring out how fairly complex things work without either training or reading the manual. How do people pull this off, and can we figure out how to design devices that are easy to learn instructionlessly? Shrager, J. & Klahr, D. (1986). Instructionless learning about a complex device: The paradigm and observations. IJMMS, 25.

22. Instructionless Learning The program: RIGHT 1, FORWARD 2 What the subject expected: 2 1

23. Instructionless Learning The program: RIGHT 1, FORWARD 2 What the subject expected…. What the BigTrak did: 2 2 1: 1 min right turn 1

24. Instructionless Learning The program: RIGHT 1, FORWARD 2 What the subject expected…. What the BigTrak did: Her interpretation: “Oh, I see, it’s like doing the resultant or something….” 2 2 1: 1 min right turn 1

25. View Application: The Process: Instructionless Learning 1. Observations are interpreted by current model. 2. If there are discrepancies, one new view is selected. 3. The model is updated by mixing in the view. 4. Coercion is carried out as needed in accord with new concepts introduced by the view. 5. The updated model may demand various actions and observations to be completed.

26. Instructionless Learning A Model Library of “Views” Current “Mental Model” “SimTrak” View Application Experiment Planner Instructionless Experimenter J Shrager (1987) Theory change via view application in instructionless learning. Machine Learning, 2: 247-276.

27. View Application Update the theory in terms of Views. Conceptually coherent, possibly complex, units of partially abstract knowledge that can be incrementally “mixed into” an existing model (by “View Application”), updating the model in accord with the principles represented in the view. Some Views in BigTrak Learning: Toy Deterministic Electrical device Non-deterministic Electronic … Vehicle Instruction following Clock face Vector addition Memory (remembering) and clearing the memory …

28. View Application A Problem: The program: RIGHT 1, FORWARD 2 What the subject expected: 2 1

29. View Application A Problem: The program: RIGHT 1, FORWARD 2 What the subject expected…. What the BigTrak did: 2 2 1: 1 min right turn 1

30. View Application A Problem: The program: RIGHT 1, FORWARD 2 What the subject expected…. What the BigTrak did: Her interpretation: “Oh, I see, it’s like doing the resultant or something….” 2 2 1: 1 min right turn 1

31. View Application: The Problem: Instructionless Learning The original SYMBOLIC model has NO RELEVANT CONTENT through which to recognize nor on to which to hang the new view! Her interpretation: “Oh, I see, it’s like doing the resultant or something….” 2 2 1: 1 min right turn 1

32. Selfridge’s (1959) Pandemonium Paradigm

33. Commonsense Perception (Symbolic “Cognitive” Level) Finders Generators Quasi-Symbolic “Perceptual” Level Representation Specific Computation 2.5D “Sensory” Level

35. Commonsense Perception Symbolic “Wizard” Reasoner Finders Generators Quasi-Symbolic “Goal/Plan” Level Representation Specific Computation Shell Command Log

36. Commonsense Perception “Perception” is an active process that binds (synchronizes) cognition with the sensory-motor systems, and thus the real world. This binding enables cognition the flexibility to discover and reason about novel features.

37. Commonsense Perception J Shrager (1990c) Commonsense perception and the psychology of theory formation. In Shrager & Langley (Eds.) Computational models of scientific discovery and theory formation. San Mateo, CA: Morgan Kaufmann.

38. Commonsense Perception

39. Commonsense Perception

40. Commonsense Perception

41. Commonsense Perception

42. In which we discover that discovery requires a new kind of cognitive architecture. • In which we discover that enabling scientists to use new kinds of cognitive architectures requires a new kind of computational architecture. • In which we discover that even simple cognition requires a new kind of cognitive architecture.

43. BioBike The BioLingua Vision:Biologist as Programmer Give biologists a program and they’ll make you program more and more. But give them anintegratedknowledge andprogramming environment, and teach them to use it, and you’ll change their lives! (Not to mention saving yourself a lot of boring programming!)

44. BioBike The BioLingua Vision:Biologist as Programmer • Integrate Genomic and Data Analysis Tools • Unify All Important Knowledge Bases • Integrate the Most Advanced Analytical Tools • Provide a Universal Programming Methodology • Provide Community Extensibility

45. BioBike P=.031 P=.031 P=.031 P=.031 P=.031 P=.031 P=.031 P=.031 P=.031

46. BioBike Important Algae Cyanobacteria are 3.5 billion years old. They created the oxygen atmosphere. Algae and cyanobacteria create most of of the current oxygen atmosphere, and fix most of the greenhouse CO2. Algae form the base of the marine ecosystem. Where they go; The planet follows!

47. BioBike How do cells control light response? I.e., What genes are related to the adaptation to high light? Look for: • Gene present in Prochlorococcus MED4 MED4 is naturally adapted to grow in high light. • Ortholog absent in Prochlorococcus MIT9313 MIT9313 is naturally adapted to grow in low light • Ortholog present in Synechocystis PCC 6803 In order to make contact with annotation and microarray data • Synechocystis PCC 6803 ortholog responds to high light Gene turns on by factor > 2 in response to high light

48. BioBike How do cells control light response? I.e., What genes are related to the adaptation to high light? For each gene in ProMed4, Find all the gene’s functional orthologs, Find those from Syny6803, When there are not any Pro9313 genes in the orthologs, and there are any the 6803 orthologs and the expression ratio for the 6803 orthologs in the experimental data is >= 2, collect the 6803 orthologs in a list, called light-specific-genes.

49. BioBike The BioLingua Vision:Biologist as Programmer • Integrate Genomic and Data Analysis Tools • Unify All Important Knowledge Bases • Integrate the Most Advanced Analytical Tools • Provide a Universal Programming Methodology • Provide Community Extensibility

50. BioBike BioLingua Computational Biology Workbench BioLisp Scripting Layer Standard analytic tools, plus discovery tools that combine know- ledge and data under user control. A simple programming language to be used by biologists to answer specific questions regarding the integration of their data with the concepts below. Computed Concepts Layer An ever-expanding library of computations that produce complex, virtual, biological concepts, such as pathways, complexes, regulons, etc. Unified Basic Concepts Layer Structures provided for important biological concepts: e.g., reactions, molecules, enzymes, experiments, expression-levels, etc. Integrated K/DB Layer KEGG BioCyc GO Remote Access Other K/DBs SMD Locally mirror important K/DBs