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Paradigm Shift in AI

Oren EtzioniTuring Center

Note: some half-baked thoughts, please don’t circulate or cite

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Two Apologies

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Basic Premises (I’m a…)

Materialist everything is made of atoms Functionalist if you can instantiate it in

neurons, you can also instantiate in silicon.

(what is ‘it’?)

This makes me an AI Optimist (long term) We are very far from the boundary of

“machine intelligence” medium term optimist too!

Are we studying AI, though?!?

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Outline

1. A bit of philosophy of science2. Critique of Present AI

1. “Whenever you find yourself on the side of the majority, it is time to pause and reflect” – Mark Twain

3. Hints at a new paradigm

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Science is done within paradigms

paradigm = set of shared assumptions, ideas, methods.

Cathedral view: we keep accumulating bricks

over 100’s of years until

we have….

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Paradigm Shift, (The Duck View) Thomas Kuhn ’62

Anomalies are explained away

When they accumulate

The paradigm is deemed inadequate

Change is unexpected, and revolutionary!

Or is it a rabbit?

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Example from Physics

19th Century (and earlier) light is a wave How does light move in space? Through the “luminferous ether”

But on one has observed it… 1897: Michelson-Morley experiment

ingenious way to detect ether, but… No ether was detected.. Other “cracks” in the Newtonian paradigm 1905: theory of relativity

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Critique of Current AI Paradigm

Subtask-driven research (e.g., parsing, concept learning, planning)

Formulate a narrow subtask spend way too many years solving it better & better

One shot systems (e.g., run learning algorithm once on single data set, single concept)

Where is the intelligence in an AI system? target concept, learning algorithm,

representation, bias, pruning, training set all chosen by expert

labor-intensive, iterative refinement

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Critique of AI cont. Focus of the field is on:

Modules, not “complete systems” Desired I/O is assumed and invented

Where do target concepts, goals come from?

Experimental metrics are surrogates for real performance (e.g., search-tree depth versus chess rating).

precision/recall of KnowItAll. How well can the robot pick up cups?

“How” instead of “what” Most papers describe a

mechanism/algorithm/system/enhancement Only a few tackle issue/question (why does NB work?)

This makes me an AI Pessimist (short term)

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So What do we Need?

Rod Brooks (1991): “Complete systems” “Real sensing, real action” (Drosophila is a real

creature!) Pitfall: low level/engineering overhead For me, this led to softbots (1991 - 1997) Pitfall: low level/engineering overhead Pitfall: Need background knowledge to succeed

Ed Figenbaum/Doug Lenat: Machines that can learn/represent/utilize massive

bodies of knowledge Cyc, KnowItAll, MLNs are pieces of this Lesson from Cyc/KnowItAll: “writing down” bits is easy Lesson from MLNs: reasoning is still hard

Question: how to make progress? How to measure it? Need bona fide, external performance metrics

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External Measure of Performance

IQ score, SAT score, chess rating, Turing test

This is surprisingly tricky: Peter Turney’s SAT analogy test Demo Halo Project

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HALO project

Build a Scientific KB “Digital Aristotle”  Measure performance on AP science tests  The Hype: “computer passes AP test” The Reality: “goals in this project are

further than they appear” Slides courtesy of Peter Clark (KCAP ’07)

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A solution of nickel nitrate and sodium hydroxide are mixed together. Which of the following statements is true?a. A precipitate will not form.b. A precipitate of sodium nitrate will be produced.c. Nickel hydroxide and sodium nitrate will be produced.d. Nickel hydroxide will precipitate.e. Hydrogen gas is produced from the sodium hydroxide.

Example question (physics)

Example question (chemistry)

An alien measures the height of a cliff by dropping a boulder from rest and measuring the time it takes to hit the ground below. The boulder fell for 23 seconds on a planet with an acceleration of gravity of 7.9 m/s2. Assuming constant acceleration and ignoring air resistance, how high was the cliff?

?

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Formallanguage

Unrestrictednatural

languageCPL

“A boulder is dropped”“Consider the following possible situation in which a boulder first…”

“xy B(x)R(x,y)C(y)”

too hard for the user

too hard for the computer

to understand

There lies a “sweet spot” between logic and full NL which is both human-usable and machine-understandable

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Example of a CPL encoding of a qn

A boulder is dropped.The initial speed of the boulder is 0 m/s.The duration of the drop is 23 seconds.The acceleration of the drop is 7.9 m/s^2.What is the distance of the drop?

An alien measures the height of a cliff by dropping a boulder from rest and measuring the time it takes to hit the ground below. The boulder fell for 23 seconds on a planet with an acceleration of gravity of 7.9 m/s2. Assuming constant acceleration and ignoring air resistance, how high was the cliff?

?

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The Interface (Posing Questions)

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Controlled Language for Question-Asking…

Controlled Language: Not a panacea! Not just a matter of grammatical simplification Only certain linguistic forms are understood

Many concepts, many ways of expressing each one Huge effort to encode these in the interpreter User has to learn acceptable forms

User needs to make common sense explicit Man pulls rope, rope attached to sled → force on sled 4 wheels support a car → ¼ weight on each wheel

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Lessons from HALO Project

Setting an ambitious, externally-defined target is exciting but challenging

Grammatical simplification (via CL) is helpful, but only one layer of the onion!

Text leaves key information implicit Need “common sense” to understand text Need massive body of “background

knowledge” and ability to reason over it Need to articulate clear lessons

What have we learned from Soar? Cyc? KnowItAll?

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Appealing Hypothesis?

AI will emerge from evolution, from neural soup,…

AI will emerge from scale up. Let’s just continue doing what we’re doing Perhaps gear it up to use massive data

sets/machine cycles (VLSAI) Then, we will “ride” Moore’s Law to

success

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Banko & Brill ’01 (case study)

Example problem: confusion set disambiguation {principle, principal} {then, than} {to, two, too} {whether, weather}

Approaches include: Latent semantic analysis Differential grammars Decision lists A variety of Bayesian classifiers

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Banko & Brill ‘01

Collected a 1-billion-word English training corpus 3 orders of magnitude > than largest corpus

used previously for this problem Consisted of:

News articles Scientific abstracts Government transcripts Literature Etc.

Test set: 1 million words of WSJ text (non used in training)

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Training on a Huge Corpus

Each learner trained at several cutoff points First 1 million, then 5M, etc. Items drawn by probabilistically sampling

sentences from the different sources, weighted by source size.

Learners: Naïve bayes, perceptron, winnow, memory-based

Results: Accuracy continues to increase log-linearly even

out to 1 billion words of training data BUT the size of the trained model also increases

log-linearly as a function of training set size.

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Banko & Brill ‘01

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Lessons from Banko & Brill

Relative performance changes with data set size

Performance continues to climb w. increase in data set

Caveats: there is much more to their paper. I just took

a biased “sample”. the task considered is narrow and simple However, similar phenomena has been shown

in other settings and tasks Lesson: ask what happens if I have 10x

or 100x more data, cycles, memory?

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Computer Chess Case Study

A complete system, in a real/toy domain Simple, external performance metric ~40 years super-human performance

massive databases knowledge engineering to choose features automatic tuning of evaln function

parameters Brute-force search coupled with heuristics

for “selective extensions” Deeper search (scale up!) led to a

qualitative difference in performance

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Achilles Hill of the Scale Up Argument

These were narrow, well-formed problems How do you apply these ideas to broader

problems? Take for example “monkeys at a type

writer” they would eventually produce the world’s

most amazing literature But how would you know?!

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Elements of a New AI Paradigm

Report lessons from major projects Focus on ‘what’ is being computed?

Is it an advance? Build complete systems in real-world test

beds Challenge: avoid engineering “rat holes”

Rely on ‘external’ performance metrics Is AI making progress?

Ask new questions: can this program survive? How does it formulate its goals? Is it conscious?

Is this enough?

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AI = Study of Ill Formed Problems

Conjecture = if you can define it as a search/optimization problem, then computer scientists will figure out how to solve it tractably (if that’s possible)

The fundamental challenge of AI today is to figure out how to map fluid and amazing human capabilities (NLU, Common sense, human navigation of the physical world, etc.) into formal problems.

The amazing thing is how little discussion there is of how to get from here to our goal!!!

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Some Ill-Formed Problems

Softbot: a cyber-assistant with wide ranging capabilities. Would you let it send you email? Would you give it your credit card?

A textbook learner: a program that reads a chapter and then answer

Machine Reading at Web scale: “read” the Web and leverage scale to compensate for limited subtlety

Life-long learner: a program that learns, but also learns how to learn better over time.

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Automatic Formulation of Learning

Learning Problem = (labeled examples, hypothesis space, target concept)

Can the learner Choose a target concept Choose a representation for

examples/hypotheses Label some examples Choose a learning algorithm Evaluate the results

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Life long Learning

Nonstop learning/reasoning/action Is this just a matter of a large enough data

set? Add in “recursive” learning

learning at time T is a function of learning at T-1.

Multiple problems, limited resources Representation change Ability to formulate own goals/learning

problems

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The Future of AI

To borrow from Alan Kay:

“The best way to predict the future of AI is to invent it!”

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My Own View

What is your own goal? write a paper versus “solve AI”

Science is done within paradigms AI’s current paradigm is

“statistical/probabilistic methods” Paradigms shift when they are deemed

inadequate Change is unexpected, and revolutionary!

“The Structure of Scientific Revolutions” by Thomas Kuhn

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Example from Physics

19th Century (and earlier) light is a wave How does light move in space? Through the “luminferous ether”

But on one has observed it… 1897: Michelson-Morley experiment ingenious

way to detect ether, but… No ether was detected.. Other “cracks” in the Newtonian paradigm 1905: theory of relativity

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Cracks in the AI Paradigm

We are building increasingly powerful algorithms for very narrow tasks Learning algorithms are “one shot” we have parsing, but what about

understanding? Much of our progress is due to Moore’s Law It’s time for a revolution…