08.10.12 Artificial Intelligence and Cognition - A.I.
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08.10.12 Artificial Intelligence and Cognition - A.I.

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Drs Nick Hawes (computer sciences) and Jackie Chappell (biosciences) presented on the topic of intelligence and how studies of natural and artificial systems can help each other.

Drs Nick Hawes (computer sciences) and Jackie Chappell (biosciences) presented on the topic of intelligence and how studies of natural and artificial systems can help each other.

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08.10.12 Artificial Intelligence and Cognition - A.I. 08.10.12 Artificial Intelligence and Cognition - A.I. Presentation Transcript

  • Natural Cognition and Artificial IntelligenceWhat can AI learn from Biology Nick Hawes http://www.cs.bham.ac.uk/~nah
  • “It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.” John McCarthy http://www-formal.stanford.edu/jmc/whatisai/http://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist)
  • “It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.” John McCarthy http://www-formal.stanford.edu/jmc/whatisai/http://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist)
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  • worldperception action cognition
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  • 1254 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 11, NOVEMBER 1998Short PapersA Model of Saliency-Based Visual Attention for Rapid Scene Analysis Laurent Itti, Christof Koch, and Ernst NieburAbstract—A visual attention system, inspired by the behavior and theneuronal architecture of the early primate visual system, is presented.Multiscale image features are combined into a single topographicalsaliency map. A dynamical neural network then selects attendedlocations in order of decreasing saliency. The system breaks down thecomplex problem of scene understanding by rapidly selecting, in acomputationally efficient manner, conspicuous locations to be analyzedin detail.Index Terms—Visual attention, scene analysis, feature extraction,target detection, visual search. ———————— F ————————1 INTRODUCTIONPRIMATES have a remarkable ability to interpret complex scenes inreal time, despite the limited speed of the neuronal hardware avail- Fig. 1. General architecture of the model.able for such tasks. Intermediate and higher visual processes appearto select a subset of the available sensory information before furtherprocessing [1], most likely to reduce the complexity of scene analysis bottom-up saliency and does not require any top-down guidance[2]. This selection appears to be implemented in the form of a spa- to shift attention. This framework provides a massively paralleltially circumscribed region of the visual field, the so-called “focus of method for the fast selection of a small number of interesting im-attention,” which scans the scene both in a rapid, bottom-up, sali- age locations to be analyzed by more complex and time-ency-driven, and task-independent manner as well as in a slower, consuming object-recognition processes. Extending this approachtop-down, volition-controlled, and task-dependent manner [2]. in “guided-search,” feedback from higher cortical areas (e.g., Models of attention include “dynamic routing” models, in knowledge about targets to be found) was used to weight the im-
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  • paintin painting bookcase g cabinet lamp books sofa table teapot stand chair sofa laptop table table table chair chair birds cupboard building reflectors bannermicrowave globe street window lamp 12 street pipe lamp plate bus bus poster chair washing chair machine people people Ales Leonardis, 2012
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  • Ales Leonardis, 2012
  • Ales Leonardis, 2012
  • 1646 J.G. Taylor et al. / Image and Vision Computing 27 (2009) 1641–1657 VENTRAL DORSAL IFG_no_goal FEF_2_no_goal IFG TPJ SPL FEF_2 TE FEF Object 1 TEO Space 1 LIP V4 Object 2 V5 Space 2 V2 V1 V1 (ventral) (dorsal) Object goal signal Spatial goal signal LGN LGN (ventral) (dorsal)Fig. 4. The architecture of the hierarchical neural network used in the visual perception/concept simulation in the GNOSYS brain. There is a hierarchy of modules simulatingthe known hierarchy of the ventral route of V1 ? V2 ? V4 ? TEO ? TE ? PFC(IFG) in the human brain. The dorsal route is represented by V1 ? V5 ? LIP ? FEF, with alateral connectivity from LIP to V4 to allow for linking the spatial position of an object with its identity (as known in the human brain). There are two sets of sigma–pi weights,one from TPJ in the ventral stream which acts on the inputs from V2 to V4, the other from SPL which acts on the V5 to LIP inputs. This allows for the multiplicative control ofattention.
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  • Andrew Spence & Dan Koditschek
  • Andrew Spence & Dan Koditschek
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  • worldperception action cognition
  • worldperception action cognition