1、Intelligence in ContextDouglas H. Fisher Vanderbilt University,Intelligence is subordinate to ?Synthesis/abstraction and compartmentalization/specializationClassroom: teaching the context tooResearch: synthesis and metaphorA proposal for placing intelligent system research in contextFinal thoughts:
2、balanced communities,Compartmentalization/fragmentation Is it true? (analyses of textbooks, curricula, citation patterns, etc) Stems from a pursuit of depth and breadth Stems from a lack of synthesis Stems from a desire and need to manage complexity (breadth/selectivity tradeoff) Stems from a lack o
3、f sufficiently rich reward system,AI,DTs,SVMs,Compartmentalization in research,ML,Hierarchical,Discrepancy Search,Concept Learning,Real-time Search,Classifiers,Planning,Decision Theoretic,NLP,DTs,SVMs,Classifiers,Quality,Time,Hunt and Quinlan,InfoGain (Quinlan),GainRatio (Quinlan),DeMantaras,Qual_1,
4、Qual_2,Time,Research-result fan effect (the best measure may get lost),Compartmentalization and synthesis in research,DTs,DLs,SVMs,The big win was recursive partitioning,Where is the “basic level” for most researchers?,Concept Learning,Embodied Cognitive Architectures,AI,DTs,SVMs,Classifiers,Compart
5、mentalization and synthesis in research,Concept Learning,ML,AI,DTs,SVMs,Classifiers,Concept Learning,ML,If fragmentation is analogous to evolutionary specialization,Then how to introduce “competition”?Break up, reorganize the superordinatesand introduce new superordinatesPromote a view that “applica
6、tions”, while not necessary for each researcher, are not secondary and are critical to the health of AI(come to this in final proposal),Embedded Systems,Counting PublicationsCounting CitationsCounting data sets defined, collected, released, and used Counting educational materials developed, released
7、, and used (next)Tutorials, surveys, synthesisA many dimensional community should have many rewards,Richer Reward Systems,Research and academic communities,UCIICMLs and IWMLsAIStatsIDAsAAAIs and IJCAIsMLJJMLR,Classroom: teaching the context too,Compartmentalization in the curriculumInformal analysis
8、 of textbooksABET accreditationIEEE Code of ConductNSF Ethics Education in Science and EngineeringTwo models for embedding ethics and contemporary issues,Embedding ethics and contemporary issues,Synthesis at curriculum level with separate classes covering ethics and contemporary issues,Materials exi
9、st to support this level of synthesis, but they are incomplete,Synthesis at course level Materials rare to support this level of synthesis,Ultimately, we want synthesis in the mind of the student. Which organization best supports familiarity with possible ethical and societal consequences and opport
10、unities?Appeal to whole person (e.g., data security, privacy, identity theft),Embedding ethics and contemporary issues,Evaluate the pedagogical models and if appropriate encourage and reward the development and deployment of materials that integrate ethics and contemporary issues at the course level
11、 for information science courses,Research: synthesis and metaphor,Cobweb (Fisher, 1987): a diverse ancestryBasic level, fan and typicality effects(Silber 2006),Embodied Cognitive Architectures,AI,DTs,SVMs,Classifiers,Compartmentalization and synthesis in research,Concept Learning,ML,AI,DTs,SVMs,Clas
12、sifiers,Concept Learning,ML,If fragmentation is analogous to evolutionary specialization,Then how to introduce “competition”?Break up, reorganize the superordinatesand introduce new superordinatesPromote a view that “applications”, while not necessary for each researcher, are not secondary and are c
13、ritical to the health of AI,Embedded Systems,A proposal for placing intelligent system research in context:A taxonomy of climate change issues and tasks,Climate Change,Conflicts,Modeling,Adaptation,Marketing,Ramifications,Alternative Energy,Resources,Biodiversity,Invasive Species,Mitigation,Efficien
14、cy,DesignOpt,Directed,Migration,Fecundity,Extinction,Public Transit,SmartTech,Retirement Planning,Infrastructure maintenance,AI,CC,A survey of Climate Change (CC) and AI work,Integrated intelligence,Alternative methods,Loosely-coupled collaboration,Examples,CC/Modeling/ModelAdjustment/ “Here we asse
15、ss the range of warming rates over the coming 50 years that are consistent with the observed near-surface temperature record as well as with the overall patterns of response predicted by several general circulation models.” Quantifying the uncertainty in forecasts of anthropogenic climate change All
16、en, M. R., Stott, P. A., Mitchell, J. F. B., Schnur, R. & Delworth, T. L. Nature 407, 617620 (2000).“Regression analysis is used to estimate the scaling factor a that produces the best match between observations and the simulated climate-change signal.” Weaver, A. J. & Zwiers, F. W., Nature 407, 571
17、-572 (5 October 2000) Suggestive of the use of other methods of combining models and/or experts: Each models prediction becomes a feature that augments the dataand can be used by inductive learning (e.g., SVMs, regression trees,ANNs) (Cox, 1999, Ortega & Fisher, 1995) Can be used for regional modeli
18、ng,Examples,CC/Modeling/ModelAdjustment/ “One or more experts are used to define a Bayesian prior distribution to each of the selected attributes, and the interattribute links, of the system under study. Posterior probabilities are calculated interactively, indicating consistency of the assessment a
19、nd allowing iterative analysis of the system. Illustration is given by 2 impact studies of surface waters. In addition to climatic change studies, the approach has been designed to be applicable to conventional EIA. Insufficient attention has thus far been devoted to the probabilistic nature of the
20、assessment and potential inconsistencies in expert judgment.” BENE-EIA: A BAYESIAN APPROACH TO EXPERT JUDGMENT ELICITATION WITH CASE STUDIES ON CLIMATE CHANGE IMPACTS ON SURFACEWATERS, VARIS, O. & KUIKKA, S. Climatic Change 37: 539563, 1997.,Additionally, AI search-based methods might be profitably
21、applied in many circumstances associated with high uncertainty, looking for conditional outcomes with less conditional uncertainty.,CC/Ramifications/Biodiversity/ “Here we forecast the potential distribution of zebra mussels in the United States by applying a machine-learning algorithm for nonparame
22、tric prediction of species distributions (genetic algorithm for rule-set production, or GARP) to data about the current distribution of zebra mussels in the United States and 11 environmental and geological covariates. Our results suggest that much of the American West will be uninhabitable for zebr
23、a mussels.” (p. 931). The Potential Distribution of Zebra Mussels in the United States, DRAKE, J. M. & BOSSENBROEK, J. M. BioScience Vol. 54 No. 10 931-941,Examples,“Unification of predictive analyses across these two phenomena (invasions and climate change) is completely feasible, yielding predicti
24、ons of opportunities for invasions in the face of global climate change. Integrating projections of invasions with other scenarios of change, such as human-induced changes in land use and land cover, is equally feasible. A limitation of these explorations, however, is the lack of appropriate baselin
25、e data sets to permit quantitative statistical validation of predictivity across multiple scenarios of change.” (p. 429). PREDICTING THE GEOGRAPHY OF SPECIES INVASIONS VIA ECOLOGICAL NICHE MODELING Volume 78, No. 4 December 2003 THE QUARTERLY REVIEW OF BIOLOGY, 419-433.,Ramifications and adaptation“
26、A Machine Learning (ML) System known as ROAMS (Ranker for Open-Auto Maintenance Scheduling) was developed to create failure-susceptibility rankings for almost one thousand 13.8kV-27kV energy distribution feeder cables that supply electricity to the boroughs of New York City.” (p. 1) “We have a numbe
27、r of theories as to why performance was better during the summer. The first is that many of the input features to our machine learning algorithm were developed by Con Edison with a specific focus on modeling the electric distribution system during heat waves. The second is that distribution system f
28、ailures may have more deterministic causes during heat waves, as the load and stress contribute directly to cable, joint, and transformer problems, while in the cooler months, failures tend to be more random and difficult to model.” (p. 5) Predicting Electricity Distribution Feeder Failures Using Ma
29、chine Learning Susceptibility Analysis, Gross, P. et al. AAAI (2006),Examples,Some researchers may not be conscious that they are working on climate change problems,“The domain for our experimental investigation is a popular computer war strategy game called FreeCiv. FreeCiv is a multiple-player gam
30、e in which a player competes either against several software agents that come with the game or against other human players. Each player controls a civilization that becomes increasingly modern as the game progresses. As the game progresses, each player explores the world, learns more about it, and e
31、ncounters other players. Each player can make alliances with other players, attack the other players, and defend their own assets from them. In the course of a game (that can take a few hours to play) each player makes a large number of decisions for his civilization ranging from when and where to b
32、uild cities on the playing field, to what sort of infrastructure to build within the cities and between the civilizations cities, to how to defend the civilization. FreeCiv provides a highly complex, extremely large, non-deterministic, partially-observable domain in which the agent must operate.” Us
33、ing Model-Based Reflection to Guide Reinforcement Learning, Ulam, P., Goel, A., et al “The need for decomposition in learning problems has been widely recognized. One approach to making learning in large state spaces tractable is to design a knowledge representation composed of small pieces, each of
34、 which concerns a more compact state space than the overall problem. Techniques that would be intractable for the problem as a whole can then be applied successfully to each of the learning subproblems induced by the set of components.” Knowledge Organization and Structural Credit Assignment, Jones,
35、 J. & Goel, A. (IJCAI 05 Workshop),A survey of Climate Change (CC) and AI work,Survey Prototype developmentPublicly availablePublicly updatableIntegrate with other taxonomiesAscribe utilities for policy makingPromote balanced community,Research results, data sets, educational material, software, art
36、,Other engineering, science, medicine, political, social,Education, Research, Press, Public, engage a whole community,A balanced community of researchers is not (necessarily or even probably) a community of individually-balanced researchersWhat types of scholars should be part of a balanced communit
37、y? Specialists Synthesizers Educators Communicators Ethicists, Artists, Theologians (e.g., MIT), ,Final Thoughts: balanced communities,At what level of community do we want balance and participation of the various types? Within individual? Within research group? Within Institution? Differences withi
38、n small, medium, and large grant teams?How is community defined at NSF: includes RI, IIS, CISE, NSF, other granting agencies? Cross-cutting programs,Balanced research communities,Auxiliary Slides,Why ask? To manage complexityDescriptive, not prescriptiveBenefits of thinking about AI in context Lines
39、 blur and can be redrawn,Intelligence is subordinate to ?,Collaborative Intelligence Interactive Induction and data engineering Asimo can dance, but can Asimo dance with a person? Human-centered to Robust Intelligence and vice versaEmbedded Intelligence Understanding and exploiting domain constraint
40、s for specialized intelligence (e.g., monitoring for accidents in a parking garage)Embodied Intelligence Embodiment on the Internet, a vehicle, a city Emotion and intelligence are a function of the body; whatare characteristics of non-human intelligences and emotionsExpansive Intelligence Intelligen
41、ce isnt greedy search,Benefits of thinking about AI in context,AI Textbook(s) Dont discuss whether intelligent decision support systems in medicine contribute to sloppier and/or more careful physicians? Dont discuss whether intelligent buildings create environmentally stupider or smarter people?Data
42、base Textbook(s) Dont discuss data privacy (illustrated by identity theft)? Dont discuss ethics of decision support?Patterson and Hennessys “Computer Organization and Design” Computing and networking for the Third World, ecological monitoring, and grassroots news Ethics of premature chip release,An
43、Informal Analysis of Textbooks,ABET Accreditation,Outcomes and Program Criteria Assessment(a) an ability to apply knowledge of mathematics, science, and engineering (b) an ability to design and conduct experiments, as well as to analyze and interpret data (c) an ability to design a system, component
44、, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability (d) an ability to function on multi-disciplinary teams (e) an ability to identify, formulate, and solve engineering pr
45、oblems (f) an understanding of professional and ethical responsibility (g) an ability to communicate effectively (h) the broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context (i) a recognition of the need for, and an ab
46、ility to engage in life-long learning (j) a knowledge of contemporary issues (k) an ability to use the techniques, skills, and modern engineering tools necessary for engineering practice Knowledge of . basic sciences, computer science, and engineering sciences necessary to analyze and design (a) com
47、plex electrical and electronic devices, (b) software, and (c) systems containing hardware and software components, as appropriate to program objectives.,1. to accept responsibility in making engineering decisions consistent with the safety, health and welfare of the public, and to disclose promptly
48、factors that might endanger the public or the environment; 2. to avoid real or perceived conflicts of interest whenever possible, and to disclose them to affected parties when they do exist; 3. to be honest and realistic in stating claims or estimates based on available data; 4. to reject bribery in
49、 all its forms; 5. to improve the understanding of technology, its appropriate application, and potential consequences; 6. to maintain and improve our technical competence and to undertake technological tasks for others only if qualified by training or experience, or after full disclosure of pertine
50、nt limitations; 7. to seek, accept, and offer honest criticism of technical work, to acknowledge and correct errors, and to credit properly the contributions of others; 8. to treat fairly all persons regardless of such factors as race, religion, gender, disability, age, or national origin; 9. to avoid injuring others, their property, reputation, or employment by false or malicious action; 10. to assist colleagues and co-workers in their professional development and support them in following this code of ethics.,
