Summer 2011 Thursday, 8/4. Modules Dissociable functional components, e.g. stereo speakers, keyboards. Mental modules are: isolable function-specific.

Slides:



Advertisements
Similar presentations
Pat Langley School of Computing and Informatics Arizona State University Tempe, Arizona USA Modeling Social Cognition in a Unified Cognitive Architecture.
Advertisements

Cognitive Systems, ICANN panel, Q1 What is machine intelligence, as beyond pattern matching, classification and prediction. What is machine intelligence,
ARCHITECTURES FOR ARTIFICIAL INTELLIGENCE SYSTEMS
The Logic of Intelligence Pei Wang Department of Computer and Information Sciences Temple University.
Summer 2011 Monday, 8/1. As you’re working on your paper Make sure to state your thesis and the structure of your argument in the very first paragraph.
Purpose The aim of this project was to investigate receptive fields on a neural network to compare a computational model to the actual cortical-level auditory.
An Introduction to Artificial Intelligence Presented by : M. Eftekhari.
Summer 2011 Monday, 07/25. Recap on Dreyfus Presents a phenomenological argument against the idea that intelligence consists in manipulating symbols according.
OBJECT-ORIENTED THINKING CHAPTER Topics  The Object-Oriented Metaphor  Object-Oriented Flocks of Birds –Boids by Craig W. Reynolds  Modularity.
Swarm algorithms COMP308. Swarming – The Definition aggregation of similar animals, generally cruising in the same direction Termites swarm to build colonies.
Jochen Triesch, UC San Diego, 1 Emergence A system with simple but strongly interacting parts can often exhibit very intricate.
1 Lecture 35 Brief Introduction to Main AI Areas (cont’d) Overview  Lecture Objective: Present the General Ideas on the AI Branches Below  Introduction.
CS 357 – Intro to Artificial Intelligence  Learn about AI, search techniques, planning, optimization of choice, logic, Bayesian probability theory, learning,
About metaphorical expressions The essence of a metaphor is understanding and experiencing one kind of things in terms of another Metaphor is pervasive.
©The McGraw-Hill Companies, Inc. Permission required for reproduction or display. slide 1 CS 125 Introduction to Computers and Object- Oriented Programming.
How does the mind process all the information it receives?
Chapter Seven The Network Approach: Mind as a Web.
Summer 2011 Wednesday, 8/3. Biological Approaches to Understanding the Mind Connectionism is not the only approach to understanding the mind that draws.
Physical Symbol System Hypothesis
Chapter 2: Modeling mental imagery. Cognitive Science  José Luis Bermúdez / Cambridge University Press 2010 The ingredients Encountered some of the basic.
What is it?. Reliability – assessment that produces consistent results Internal consistency – do separate questions measure the same thing Validity –
Artificial Intelligence Lecture No. 28 Dr. Asad Ali Safi ​ Assistant Professor, Department of Computer Science, COMSATS Institute of Information Technology.
Chapter 14: Artificial Intelligence Invitation to Computer Science, C++ Version, Third Edition.
计算机科学概述 Introduction to Computer Science 陆嘉恒 中国人民大学 信息学院
Robotica Lecture 3. 2 Robot Control Robot control is the mean by which the sensing and action of a robot are coordinated The infinitely many possible.
Using robots to model animals: a cricket test Barbara Webb Presenter: Gholamreza Haffari.
A Cognitive Substrate for Natural Language Understanding Nick Cassimatis Arthi Murugesan Magdalena Bugajska.
Artificial Intelligence
Theory of Cognitive Development
Advances in Modeling Neocortex and its impact on machine intelligence Jeff Hawkins Numenta Inc. VS265 Neural Computation December 2, 2010 Documentation.
Presented by Scott Lichtor An Introduction to Neural Networks.
PSY105 Neural Networks 1/5 1. “Patterns emerge”. π.
Cognitive Psychology: Thinking, Intelligence, and Language
Methods Neural network Neural networks mimic biological processing by joining layers of artificial neurons in a meaningful way. The neural network employed.
Robotica Lecture 3. 2 Robot Control Robot control is the mean by which the sensing and action of a robot are coordinated The infinitely many possible.
Theories of First Language Acquisition
Chapter 11 Artificial Intelligence Introduction to CS 1 st Semester, 2015 Sanghyun Park.
Chapter 13 Artificial Intelligence and Expert Systems.
Information-Processing Theory
University of Windsor School of Computer Science Topics in Artificial Intelligence Fall 2008 Sept 11, 2008.
Section 1 summary: Input specialization Domain specific transduction Fovea Receptive fields Spatial position on the sensory array encodes information Cells.
© NOKIAmind.body.PPT / / PHa page: 1 Conscious Machines and the Mind-Body Problem Dr. Pentti O A Haikonen, Principal Scientist, Cognitive Technology.
Neural Networks in Computer Science n CS/PY 231 Lab Presentation # 1 n January 14, 2005 n Mount Union College.
Software Design Process
Neural Networks and Machine Learning Applications CSC 563 Prof. Mohamed Batouche Computer Science Department CCIS – King Saud University Riyadh, Saudi.
CSCI-383 Object-Oriented Programming & Design Lecture 10.
Distributed Models for Decision Support Jose Cuena & Sascha Ossowski Pesented by: Gal Moshitch & Rica Gonen.
Higher Mental Function: Information Processing Scott S. Rubin, Ph.D. Neuroscience.
1 Discovery and Neural Computation Paul Thagard University of Waterloo.
Introduction to Artificial Intelligence CS 438 Spring 2008.
HEARING Do you know how you are able to hear your phone ringing? A baby crying? Leaves rustling? Sound travels through the air in waves. It is caused.
Artificial Intelligence: Research and Collaborative Possibilities a presentation by: Dr. Ernest L. McDuffie, Assistant Professor Department of Computer.
Presented by:- Reema Tariq Artificial Intelligence.
Some Thoughts to Consider 5 Take a look at some of the sophisticated toys being offered in stores, in catalogs, or in Sunday newspaper ads. Which ones.
MODELS PURPOSE: Predict the future, test outcomes of various scenarios, identify the important components or variables, and understand how the parts interact.
1 Chapter 13 Artificial Intelligence and Expert Systems.
Introduction to Cogsci April 07, Central Theme Cognitive Science was occuppied with the algorithmic level for much of its history: successive manipulation.
The physics of hearing and other odds and ends. The range of human hearing The range of average human hearing is from about 15Hz to about 15,000Hz. Though.
Cognitive Modeling Cogs 4961, Cogs 6967 Psyc 4510 CSCI 4960 Mike Schoelles
Intro to Cogsci Jan 25, Class room change 209 architecture.
Introduction Characteristics Advantages Limitations
PART IV: The Potential of Algorithmic Machines.
Marco Mamei Franco Zambonelli Letizia Leonardi ESAW '02
Knowledge Representation
Creating Data Representations
Neoroscience related Philosophical aspects of AI
Artificial Intelligence Lecture No. 28
CHAPTER I. of EVOLUTIONARY ROBOTICS Stefano Nolfi and Dario Floreano
EAR REVIEW.
The Network Approach: Mind as a Web
Presentation transcript:

Summer 2011 Thursday, 8/4

Modules Dissociable functional components, e.g. stereo speakers, keyboards. Mental modules are: isolable function-specific processing systems, whose operations are mandatory, which are associated with specific neural structures, and whose internal operations may be both encapsulated from the remainder of cognition and inaccessible to it. Cognitive Architecture: The basic structural organization of the mind.

Modest Modularity Positive Claim: input systems, or mechanisms that “present the world to thought”, as well as systems that are involved in language production/comprehension, are modular. Negative Claim: Central systems, such as systems involved in belief fixation and practical reasoning, are not modular. On this view, the mind is akin to a general purpose computer that’s equipped with a limited number of special purpose modules.

Massive Modularity: “Bag of Tricks” view Claims that the mind is modular through and through, including the parts responsible for high- level cognition functions like belief fixation, problem-solving, planning, and the like. “Intelligence does not depend on the translation of incoming information into some unitary inner code that’s operated on by general purpose logical inference. Instead, we confront a mixed bag of relatively special-purpose encodings and strategems whose overall effect is to support the particular needs of a certain kind of creature occupying a specific environmental niche.” (Clark)

Biological Arguments for the Bag of Tricks View 1. Biological systems are designed systems, constructed incrementally (by a process of tinkering). 2. Such systems, when complex, need to have massively modular organization. 3. The human mind is a biological system, and is complex. C. So the human mind will be massively modular in its organization.

1.Animal minds are massively modular. 2.Human minds are incremental extensions of animal minds. C.So human minds are massively modular. Biological Arguments for the Bag of Tricks View

Reason to be weary of adaptationist accounts of the mind: Spandrels

1.Minds are computational problem solving devices. 2.There are specific types of solutions to specific types of problems (a single general type of problem is incoherent!). C.So for minds to be (successful) general problem solving devices, they must consist of collections of specific problem solving devices, i.e. many computational modules. A priori Argument for the Bag of Tricks View

Coordination Problem How does the “bag of tricks” view account for large scale coherent behavior?

Cricket Phonotaxis Phonotaxis: the capacity to detect and reliably move towards a specific sound or signal. The male cricket produces a song of a specific rhythm (indicative of species) and tone (indicative of fitness). The female cricket detects and moves towards the loudest cricket around.

From a design standpoint, it is tempting to decompose the female cricket’s task into these steps: 1. Hear and identify the song of her own species. 2. Localize the source of the song. 3. Move towards it. Cricket Phonotaxis

What actually happens: 1.The cricket’s ears are joined by a tube, so that each ear receives sounds via two routes. 2.Simple neural circuitry compares the out-of-phase sound waves, yielding a vibration of greater amplitude at the ear nearest the sound source. 3.Two neurons are connected to each ear and fire when vibration amplitude reaches a critical level. 4.The neuron connected to the ear nearest the sound source reaches the threshold first and causes to turn the cricket to its side. Cricket Phonotaxis

Webb (et all) built a simple robot- cricket that performs the same task. The robot does not build a rich model of its environment and then apply complex analysis to generate an action plan. Its operation can be explained purely on the implementational level without talking about computations or mental representations or anything like that. But these simple operations yield what, from a distance, seems like highly complex, intelligent action. Cricket Phonotaxis

Boid Flocking Behavior Each boid (a simple unit) follows three rules: 1. Try to stay near a mass of other boids. 2. Avoid getting too close to any one neighbor. 3. Match your velocity to that of your neighbors. When each boid followed these simple rules, pattens of on-screen activity ensued that closely resembled the flocking behavior of real flocks of animals (fish, birds, etc.).

Termite Nest Construction In building nests, termites follow two simple strategies: 1. Rolling mud into a ball and impregnating it with a chemical trace. 2. Picking up the balls and depositing them wherever the trace is stronger.

Fodor’s against massive modularity Nomic vs. non-nomic properties. Inference vs. simple sensory transducability. Abductive inference.