SOME RANDOM THOUGHTS … ON SOME RANDOM

TOPICS ABOUT AI David Israel

Artificial Intelligence CenterSRI InternationalAugust 8, 2013

Two Big Methodological Dimensions

AI as Cognitive Science by Other Means How do people do what they do

AI as focused on emulating intelligence artificially… by whatever means necessary AI as a design and engineering discipline, not an

empirical science

AI as Applied Logic (in weird disguise?) Central focus: Representation & Reasoning

AI as Applied Probability Theory/Statistics Central focus: (Machine) Learning from data

(Natural Language) Processing Central Application Area

Distinctively Human (?) Cognitive Achievement In any case, a central human cognitive achievement

We (not I!) know something about how we do it – about underlying processes and mechanisms of language use

And we know something about how we (our infant selves) come to be able to do it – how we learn our first language(s)

BUT I DON’T CARE !!*And more important, neither does DARPA* Beyond finding “inspiration” in theories of

actual cognitive mechanisms/processes

The Machine Reading Program

Goal: To make the knowledge expressed in (English) texts accessible by formal (artificial) reasoning systems Translation(?): To make the (information)

content expressed, e.g., in news stories available as input to “downstream” AI-systems

For, e.g., Intelligence Analysts, trying to put together an analytic picture of what was going on in some region during some time period.

The Second Dimension

Applied Linguistics & Logic vs. (versus???)

Machine Learning: Applied Probability Theory and Statistics

What does this really come to, in our case (Machine Reading)?

The Good Old-Fashioned Picture

Hand-built grammars: sets of rules governing the ways in which sentences could be constructed out of sub-sentential elements (ultimately, of words/morphemes)

Often quite directly inspired by work in linguistics Rules linking syntactic elements and structures

with structures of symbols from formal languages

Often directly inspired by the languages developed and studied by logicians, typically for representing mathematical structures

The Statistical ML Revolution of the ‘90s

Availability of large annotated data-sets and of huge quantities of “raw” (unlabeled) text data

Growth of the practice of community-wide open evaluations and of

A metrics-focused research community Moore’s Law; huge advances in processing speeds,

memory capacity, etc., etc. Resulted in moving toward a-theoretical,

statistically-trained, ML-induced NLP modules (e.g., POS-taggers, NamedEntityExtractors, SemanticRoleLabelers, Parsers)

Til recently: sentence-/clause-level semantics was ignored

Our (My?) Vision for Machine Reading

A New Synthesis:

Probabilistic Representation of Non-linguistic information + state-of-the-art Statistically-based ML-induced NLProcessing Modules Analogous developments in Computer Vision

How to operationalize that `+’ ??

Many different possibilities to be explored So little time … and nowhere near enough $$