[AI@50]
Filene Auditorium, Moore Hall
Dartmouth College
Hanover, NH
Conference notes by Meg Houston Maker
Ron Brachman, Yahoo! Research
A Large Part of Human Thought
From the original summer project proposal -- the proposition that "a large part of human thought" was bound up in language, but there is also a conflation of language and concepts that language replaces:
"It may be speculated that a large part of human thought consists of manipulating words according to rules of reasoning and rules of conjecture. From this point of view, forming a generalization consists of admitting a new word and some rules whereby sentences containing it imply and are implied by others. This idea has never been very precisely formulated nor have examples been worked out." (From the original proposal.)
Cognitive Penetrability: language can produce widely ranging human behavior.
Knowledge Matters: "the city council refused to grant the protesters a permit becuase they feared violence" vs. "the city council refused to grant the protesters a permit because they advocated violence." In each case we understand a different reference for the pronoun "they." Background knowledge of the universe changes our interpretation of linguistic inputs, even if the inputs are, grammatically, nearly identical.
A good portion of human thought involves a store of represented beliefs, and procedures that operate on them to produce new beliefs (inferences) that impact decisions about what we do. The focus is on what can be known, and how, and what follows from what is known. Knowledge representation and procedures for manipulating knowledge became an object of study in early AI.
The internal conceptual monologue: From the simplest idea (formed from a simple syllogism such as "Socrates is a man and all men are mortal therefore Socrates is mortal") you get to daydreaming, imagining other possibilities, envisioning and caring about your new vision, planning about it, and teaching or preserving it as an idea. And all the way out, this action begets writing, reading, culture, and science. Having knowledge representations in our heads that are disconnected from concrete reality begets intelligence, culture, and society [MHM: some editorial liberty in this last bit].
You can use knowledge structures to do forward-looking inference, come to a mental conclusion, and make a decision about what to do. This was an original idea of the conference 50 years ago, and since then, it's had a great impact on:
- Database management systems
- Expert systems
- Descripton logics, and the semantic web (or OWL = web ontology language)
- Cognitive robotics
- Planning systems (e.g. SIPE)
- Model-based reasoning (e.g. at NASA)
Language and perception create usable memories, useful for making decisions about what to do now, or to plan future moves in the abstract. It's about taking advantage of past action, or learning. The explicit formal representation of what a machine knows and believes is at the heart of creating a machine that can do what humans can do.
To sum:
- Actions are conditioned by what we know and believe;
- At least part of this knowledge arrives in linguistic form;
- This allows us to focus on what needs to be known;
- And gives rise to a 'knowledge-based" approach.
This idea defined AI, as opposed to approaches like computational neuroscience.
Question: Much of the AI community has either forgotten this or would disagree with this focus. E.g. the natural language community.
Answer: These insights mentioned are still relevant, and essential to knit the whole of AI together.
Question: (actually a comment from John McCarthy): In some ways our proposal for a revolution was a proposal for a counter-revolution against the behaviorists that advocate a stimulus-response approach.
David Mumford, Brown University
What Is The Right Model for "Thought?"
Is thought logic/rule-based or is it stochastic models/statistical inference? This is an old question, but still being discussed. Logical deduction and statistical inference are really different, and maybe even incompatible models. Logic comes in many flavors: traditional, modal, temporal, etc. Stochastic models are differently structured. Logical deduction is brittle and unforgiving, whereas statistical inference is more adaptive and flexible.
"Logic/rule-based thinking is fun, but is it real? To grammarians, rules are the deep and very real way to understand natural language grammars, but language is a maze of exceptions within exceptions."
People think in a Bayesian (probablistic) fashion. People make inferences based on a collection of hypotheses about the context. E.g., given the following test:
Actual sound (missing consonant): "The ?eel is on the shoe."
Perceived sound "The heel is on the shoe."
People will not report they thought the word was wheel or steel. They know that shoes have heels, and so assume, absent other data, that the missing consonant was "h."
Implementing statistical inference: there are vast amounts of noisy data and many variables present. Statistical tables won't help you. So you must sample the entire probability distribution and do 'particle filtering' to create a result. But how does the brain do this? The brain isn't ever doing just one thing. There is no "grandmother cell" that fires when you think about your grandmother. The brain has to manage hundreds of variables at once, and filter. So choice is postponed.
Stuart Russell, UC Berkeley
The Approach of Modern AI
Russell would love to see the various branches of AI research merge; they have been quite divergent (cognitive science, mathematics, other research projects).
Formalisms or algorithms for probablistic knowledge bases: focus on concrete syntax, semantics, and completeness. A key component of any formalism is expressiveness. E.g. the rules of chess can be stated in about a page of first-order logic, whereas it takes 100,000 pages to do so in propositional logic. Humans operate on the 1-page version. The question is how we get there.
The focus of research projects should involve issues of various time scales, extended deliberation, a varied environment. A human-scale, dexterous 4-legged, seeing robot would be exceptionally valuable to the project.
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