MAS 966: Meaning Machines (Spring 2004)
Mondays 1-3 PM, E15-468H (first meeting on Feb 9)
Credits: 3-0-9 (H)
Computers don’t grasp meaning in any deep, human sense. From a computer’s point of view, words are meaningless bits of information processed with speed and precision, but without any genuine interpretation of content. The interpretation of words is left to humans. The absence of meaning in computers is not limited to the domain of words -- it is a widespread limitation of current computing systems.
We will study new approaches to creating language processing systems that grasp meanings in human-like ways. In particular, we will explore interactions between physically grounded knowledge representations and goal-directed behavior that provide the foundation for meaningful language use by machines. Readings from semiotics, philosophy of mind, artificial intelligence, and cognitive science.
MAS 963: Curious Machines (Spring 2003) (Co-taught with Profs. Bruce Blumberg and Cynthia Breazeal)
It is ironic that we are witness today to an abundance of machines
that learn, yet we would not consider any of them to be
curious. Curiosity is a trait of those natural learning systems, such
as people and animals, that learn what they ought to learn when they
ought to learn it. It implies a pro-active system that is motivated to
learn. It also implies a reflective aspect to the learning process:
when to learn, what to learn, from whom, how, and why? How can we
build machines that are as curious learners as natural systems? How
can we build systems that have a deeper understanding of the learning
process beyond turning the statistical crank of a learning algorithm?
How can synthetic systems interact with and leverage rich environments
that include other agents?
This course examines the issues, principles, and challenges toward building curious machines through lecture, lively discussion, critique of course readings, and student projects. Both natural and synthetic systems are explored to investigate how to build machines that are: (1) Curious, self-motivated and pro-active learners, (2) Reflect upon their learning process in order to initiate learning what they ought to learn when they ought to learn it, and (3) Have learning behaviors that are transparent and understandable to a human instructor. They must be easy to train and teach based on their observable behavior.
MAS 962: Computational Semantics (Fall 2002)
How do words get their meanings? How can word meanings be represented
and used by machines? We will explore various approaches to these
questions from a computational perspective. Relational / structural
methods such as semantic networks represent the meaning of words in
terms of their relations to other words. Knowledge of the world
through perception and action leads to the notion of external
grounding, a process by which word meanings are 'attached' to the
world. How an agent theorizes about, and conceptualizes its world
provides yet another foundation for word meanings. We will examine
each of these perspectives, and consider ways to integrate them.
Topics in
Natural Language Processing (Fall 2001)
This course introduces principles and algorithms of naturallanguage
processing relevant for both text and spoken language
processing.Topics include statistical language modeling, part of
speech tagging, robustparsing, frame-based language understanding,
language acquisition, andinformation retrieval. Emphasis will be
placed on semantic analysis andrepresentations. Selected topics from
current research in spoken languageprocessing will also be covered.
MAS 964: Concepts, Language, Embodiment and Learning (Spring 2001)
This seminar explores the role of embodiment and perception in the
acquisition of concepts and language by studying and building
computational models. Recent trends in cognitive science point towards
human physiology as a key to understanding how humans develop
conceptual knowledge. These trends have been complemented in the
artificial intelligence community with a growing focus on the role of
robotics and perceptual computing in developing knowledge
representations. This seminar will be structured around (1) a set of
case studies of computational/robotic efforts to build embodied
communication machines, and (2) a group project aimed at building an
embodied language learning system.
MAS 622: Pattern Recognition (Fall 2000)
Introduction to pattern recognition, feature detection, classification;
Review of probability theory, conditional probability and Bayes rule;
Random vectors, expectation, correlation, covariance; Review of linear
algebra, linear transformations; Decision theory, ROC curves,
Likelihood ratio test; Linear and quadratic discriminants, Fisher
discriminant; Sufficient statistics, coping with missing or noisy
features; Template-based recognition, eigenvector analysis, feature
extraction; Training methods, Maximum likelihood and Bayesian
parameter estimation; Linear discriminant/Perceptron learning,
optimization by gradient descent; k-nearest-neighbor classification;
Non-parametric classification, density estimation, Parzen estimation;
Unsupervised learning, clustering, vector quantization, K-means;
Mixture modeling, optimization by Expectation-Maximization; Hidden
Markov models, Viterbi algorithm, Baum-Welch algorithm; Linear
dynamical systems, Kalman filtering and smoothing, system
identification; Bayesian networks, independence diagrams; Decision
trees, Multi-layer Perceptrons;
MAS 967: Multilingual Computing
(Spring 2000)
How might we build computers which can be used by everyone around the
planet? The problems are complex. Thousands of languages are in active
use worldwide. Almost a billion people are unable to read or
write. Many languages don?t even have a written form. Yet most
interface technologies are tied to specific written languages. This
course explores aspects of natural language and language technologies
related to the theme of multilingual computing. Topics to be covered
include: text, speech, and visual input and output; semantic
representations; language translation and trans-lingual communication;
language learning by humans and machines; open system development of
language technologies; and applications of multilingual computing in
developing nations. Readings will draw from a broad number of areas
including artificial intelligence, speech processing, linguistics, and
media studies.