Transcript AnalogySpace - MIT Media Lab

Adding Common Sense into
Artificial Intelligence
Common Sense Computing Initiative
Software Agents Group
MIT Media Lab
Why do computers need
common sense?
• Conversation works because of
unspoken assumptions
• People tend not to provide information
they consider extraneous (Grice, 1975)
• Understanding language requires
understanding connections
What can computers do with
common sense?
• Understand the context of what the user
wants
• Fill in missing information using
background knowledge
• Discover trends in what people mean,
not just what they say
But how do we collect it?
A Brief Outline
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What is OMCS?
What is ConceptNet?
Using AnalogySpace for Inference
Using Blending for Intuition
OMCS Applications
Open Mind Common
Sense Project
• Collecting common sense from internet
volunteers since 2000
• We have over 1,000,000 pieces of English
language knowledge from 15,000 contributors
• Multilingual
– Additional resources in Chinese, Portuguese,
Korean, Japanese, and Dutch
– In-progress: Spanish and Hungarian
• Users consider 87% of statements used in
ConceptNet to be true
What kind of knowledge?
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“A coat is used for keeping warm.”
“People want to be respected.”
“The sun is very hot.”
“The last thing you do when you cook
dinner is wash your dishes.”
• “People want good coffee.”
Where does the knowledge
come from?
• Contributors on our Web site
(openmind.media.mit.edu)
• Games that collect knowledge
What is ConceptNet?
• A semantic network representation of the
OMCS database (Liu and Singh, 2004)
• Over the years, used for:
affect sensing, photo and video storytelling, text
prediction, goal-oriented interfaces, speech
recognition, task prediction, …
• ConceptNet 4.0
– Over 300,000 connections between ~80,000
concepts
– Natural language processing tools to help line up
your data with ConceptNet
An Example
Creation of ConceptNet
• A shallow parser turns natural language
sentences into ConceptNet assertions
• 59 top-level patterns for English, such
as “You would use {NP} to {VP}”
• {NP} and {VP} candidates identified by
a chart parser
Representation
• Statement: expresses a fact in natural
language
• Assertion: asserts that a relation exists
between two concepts
• Concepts: sets of related phrases
– identified by lemmatizing (or stemming) and
removing stop words
• Relations: one of 25:
– IsA, UsedFor, HasA, CapableOf, Desires,
CreatedBy, AtLocation, CausesDesire, …
Example
Reliability
• Reliability increases when more users
affirm that a statement is true
– by entering equivalent statements
independently
– by rating existing statements on the Web
• Each assertion gets a weight according
to how many users support it
Polarity
• Allows predicates that express true,
negative information: “Pigs cannot fly”
• Negated assertions are represented by
negative weights
• Reliability and polarity are independent
AnalogySpace
• Technique for learning, reasoning, and analyzing
using common sense
• AnalogySpace can:
– generalize from sparsely-collected knowledge
– confirm or question existing knowledge
– classify information in a knowledge base in a variety of ways
• Can use the same technique in other domains:
businesses, people, communities, opinions
AnalogySpace Overview
• Finds patterns in knowledge
• Builds a representation in terms of
those patterns
• Finds additional knowledge using the
combination of those patterns
• Uses dimensionality reduction via
Singular Value Decomposition
Input to the SVD
• Input to SVD: matrix of concepts vs.
features
• Feature: a concept, a relation, and an
open slot, e.g., (. . . , MadeOf, metal)
• Concepts × features = assertions
The Input Matrix
• For consistency, we scale each concept
to unit Euclidean magnitude
Running the SVD
The Truncated SVD
Truncating the SVD smoothes over
sparse data.
Good vs. Bad
QuickTime™ and a
decompressor
are needed to see this picture.
Reasoning with AnalogySpace
• Similarity represented by dot products
of concepts (AAT)
– Approximately the cosine of their angle
Reasoning with AnalogySpace
• Predictions represented by dot products
of concepts with features
Contributors are in the loop
Ad-hoc Categories
What can we use common
sense for?
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A “sanity check” on natural language
Text prediction
Affect sensing
Recommender systems
“Knowledge management”
Common Sense in Context
• We don’t just use common sense to
make more common sense
• Helps a system make sense of
everyday life
– Making connections in domain-specific
information
– Understanding free text
– Bridging different knowledge sources
Digital Intuition
• Add common sense intuition
• Using similar techniques to make
connections and inference between
data sets
• Create a shared “Analogy”Space from
two data sets using Blending
Blending
• Two data sets are combined in a way to
maximize the interaction between the
data sets
• They are weighted by a factor:
C = (1 – f)A + fB
Blending Creates a
New Representation
• With f = 0 or 1, equivalent to projecting
one dataset into the other’s space
• In the middle, representation
determined by both datasets.
No overlap = no interaction
A’s singular values
B’s singular values
Overlap -> Nonlinear
Interaction (Veering)
Overlap -> Nonlinear
Interaction
SVD over Multiple Data Sets
• Convert all data sets to matrices
• Find a rough alignment between the
matrices
– Some rows or features
• Find a blending factor
– Maximize veering or interaction
• Run the AnalogySpace process jointly
Blends of Multiple Data Sets
• You can blend more than two
things
– Simple blending heuristic: scale all
your data so that their largest singular
vectors are equal
Applications
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Inference over domain specific data
Word sense disambiguation
Data visualization and analysis
Finance
Tools we Distribute
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The OMCS database
ConceptNet
Divisi
In development: the Luminoso visualizer
The Common Sense
Computing Initiative
Web: http://csc.media.mit.edu/
Email: [email protected]
Thank you!