Introduction to
Advanced AI Topics
|
|
|
Vision |
|
Natural Language Processing |
|
Robotics |
Homework #2
|
|
|
We are not yet ready to hand out
Homework #2. We will probably have it
ready for you by Friday. |
|
You will be grouping yourselves into
teams of three students via a web form.
We will announce the URL for this on Friday. |
|
|
Advanced Topics Overview
|
|
|
|
|
|
|
Agents have sensors and actuators |
|
|
|
Sensors: |
|
Seeing (visual input) Ž Image
Processing and Computer Vision |
|
Hearing (audio input) Ž Natural
Language Processing |
|
Actuators: |
|
Moving and manipulating Ž Robotics |
Computer Vision
Definition: versus
graphics
|
|
|
|
|
|
|
Graphics |
|
Have world model W |
|
Generate the sensory stimulus from the
model
S = f(W) |
|
|
|
Vision |
|
Generate the model from the sensors: W
= f-1(S) |
|
To think about: f() doesn’t have a
proper inverse. Why? |
Ambiguity in sensory
input
|
|
|
Girls playing with dollhouses |
|
Or giants playing with people? |
Definition: versus image
processing
|
|
|
|
Image Processing |
|
A transformation of data to other data |
|
e.g., smoothing |
|
|
|
Computer Vision |
|
Reduction in data to a (more useful)
abstraction |
|
e.g., digit / face recognition |
Applications
|
|
|
Surveillance – can we detect objects or
people as they move around our field of vision? |
|
Handwriting recognition – from
handwritten addresses to barcodes |
|
Content based Image Retrieval – query
for images using without any text features.
“Show me similar pictures” |
|
Automated Driving – speaks for itself |
Natural Language
Processing
Definition of NLP
|
|
|
|
|
Examines communication in human
languages. |
|
Theoretical and practical aspects. |
|
Similar to vision, has production and
understanding affects |
|
Understanding: speech / text to meaning |
|
Generation: meaning to speech / text |
|
Both processes have inherent ambiguity |
Not so great newspaper
headlines
|
|
|
Squad helps dog bite victim. |
|
Helicopter powered by human flies |
|
Portable toilet bombed; police have
nothing to go on. |
|
British left waffles on Falkland
Islands. |
|
Teacher strikes idle kids. |
Sample Applications
|
|
|
Restaurant Query converts English
queries into SQL. |
|
MS Dictation converts speech into text |
|
Babelfish translates Web pages to
different languages |
|
Summarizing multiple news articles from
the web |
Robotics
|
|
|
Planning in the real world environment |
Getting around
|
|
|
|
Effectors |
|
Sensors on effectors? Is the output
noisy? |
|
Low-level: need to build higher-level
abstractions |
|
|
Problems
|
|
|
|
Localization – where am I? |
|
Mobile robots but also robotic arms |
|
Mapping – what does my environment look
like? |
|
Moving – how do I get from here to my
goal? What type of plan do I have execute? |
Applications of robotics
|
|
|
Robotic Flight – robotic helicopter,
unmanned piloting |
|
Path planning for exploration |
|
Rock climbing, perhaps difficult even
for some of us |
Summary
|
|
|
|
|
All three areas deal with search: |
|
Vision: search for most likely world w
given input sensor s |
|
Natural Language Processing: given an
input utterance / text i, find most likely meaning m |
|
Robotics: |
|
Localization: given unknown input
configuration / location, determine configuration. |
|
Planning: given goal g and state s
output plan p to reach g from s |
Summary
|
|
|
|
All three areas use heuristics : |
|
Vision: trihedral structure |
|
Natural Language Processing: grammars
of language, most frequent meanings |
|
Robotics: decomposition of problems
into cells, maximizing distance between obstacles |
|
Many of these heuristics involve
probability, which we will return to at the end of the semester. |