Selmer Bringsjord, Nick Cassimatis, Kostas Arkoudas, and Bettina Schimanski

Department of Cognitive ScienceDepartment of Computer Science

Rensselaer Polytechnic Institute (RPI)Troy NY 12180 USA

December 9 2004 @ NSF

Human-Level Machine Learning

RAIR Lab Sponsors

-Cracking Project;“Superteaching”

Slate (Intelligence Analysis)

test generation

synthetic characters/psychological time

Deontic/DoxasticReasoning

QuickTime™ and aTIFF (Uncompressed) decompressor

are needed to see this picture.

hypothesis generation;AI in support of IA

advanced synthetic charactrs

“Poised-For” Learning

Overview• The Problem:

– Machine learning is dominated by forms of learning that are impoverished relative to the human case.

– Humans often learn by leveraging an ensemble of “pre-established” heterogeneous reasoning mechanisms and vast amounts of prior knowledge.

• Solution/Goal:– Formalize human learning and rich cognitive mechanisms that

underlie and enable it.– Implement these formalizations to produce “human-level”

machine learning, and corresponding applications.

• Applications:– Software and robotic applications; in our case, specifically

• Homeland defense/intelligence analysis tools• Elder-care robots that are quickly adapt to their owners

– Improve learning in humans:• Intelligent tutoring systems in math,/logic/computer science• More precise understanding of learning disabilities for less

traumatic interventions

Formal Models of Human-Level Learning Can Help Close Learning Gaps• Learning gaps (esp in math) between:

– US and other countries• The latest PISA and TIMSS point to an outright crisis!

– 12.7.04 WSJ

– High-achieving and low-achieving students within US– High-achieving and low-achieving schools within US

• A precise, formal understanding of learning would enable us to – pinpoint the factors that enable rapid, explosive learning;– build machines able to augment human teaching (which for

various reasons is failing) in the math/logic/comp sci area

Machine Learning Today:Costly Trial and Error

• Traditional machine learning:– Learn only after many repetitions of trial and error– Stuck on function-based model– E.g., Language: WSJ Corpus, 1987-1989, with 39 million words– Explanation-Based Learning uses only primitive reasoning/knowledge

compared to the full human-level arsenal of heterogeneous reasoning and knowledge

• Hurts with applications:– Trial and error not good in cases where errors kill

• Medical robotics– Thousands of learning trials can be expensive

• Acquainting a robot with a new hospital would take days• Teaching people new software makes them less productive in the short-term.

Machines train us now instead of us training them.– Learning trials often not available

• Homeland security: Not thousands of people in flight schools– Robots and software therefore limited to narrow tasks and inflexible– We are forced to assemble machine knowledge manually

• CYC has over a million facts and is not even remotely complete

Some Motivating Examples...

Millions of students are currently learning primarily by reading -- and ditto e.g. for adult researchers like us!

Example 1: Suppose You Were Tasked to Learn About Astronomy!

The scorpion lies between Libra and Sagittarius in the Milky Way. It is not hard to imagine this pattern of starts resembling a scorpion,with its claws and stinging tail. An arc of stars marks the curve of itsraised tail and the fiery red star Antares lies at is heart...

Example 2: Human One-shot Learning(a simple example)

USB

CONVERTORCUP

Insert movie here (Nick has a copy)

The traditional machine learning approach...

Behavior of Micro-PERI

Implications of One-Shot Learning and Learning by Reading

• Learning by reading and one-shot learning examples require:– Rich set of representation and reasoning abilities early on

• Where was speaker looking when he said “USB Converter”.• Social reasoning to track where speaker was looking.• Spatial and temporal reasoning to infer what he was looking at.• Diagrammatic reasoning

– Existing machine learning algorithms have no notion of space, time or human attention.

– Statistical generalization just one of several learning strategies; also need:• Inference (deductive, abductive, inductive, ...) from single group of percepts• Analogy• Imitation• Instruction

– Learning much more socially and physically interactive.• Ask questions: Why? How? What if? Physically test their own hypotheses about

the world.• And, in learning by reading...

– the best learners are those who “pre-test” themselves, and hence acquire “poised-for” knowledge that marks true learning

To Solve the Problem:A New (5-step) Research Program

1 Without flinching, study the human case -- humans (including kids) who learn rapidly, including learning by reading

– Developmental psychology has shown that even infants and toddlers have rich notions of:• Time, place, causality, belief, desire, attention, number, etc., and of inference over these

concepts

2 Develop formal theories that show how to use these factors to make learning faster and more effective

3 Develop machine learning algorithms using these formalizations that learn by:– Explicit reading and instruction– Analogical reasoning– Deduction, Abduction, etc.– Imitation– Visual reasoning

4 Build applications from these algorithms that have broad impact– Elder care– Homeland security

5 Trace out the implications of these algorithms for better teaching/learning in the human sphere, particularly in mathematics/logic instruction

– address “Math Gap”– including intelligent tutoring systems and synthetic characters

Our Approach Forges a Bridge

SBE

Behavioral &Cognitive Sciences

CISE

Artificial Intelligence andCognitive Science

? ?

The Right Time:Resurrection of Human-Level AI

• Recognition of need for human-level AI and integrated cognitive systems growing:– Dedicated issue of AAAI’s journal of record (AI Magazine) to be devoted to human-level

AI• Cassimatis editor, Bringsjord, Arkoudas, Schimanski contributors

– AAAI Fall Symposium on Integrated Cognition (Cassimatis led)

– “Grand Cognitive Challenges” under discussion @ DARPA’s Learning-Focused IPTO• “Psychometric AI” a candidate

– Hundreds of studies in infant cognition give us a good idea of what the right substrate is.

• Integrated cognitive models exist and are advancing every day

• Computational infrastructure there:– Abundant computational power for multiple methods in one system

– Formal methods exploding with new power (e.g., Athena)

– Robot and machine vision infrastructure in place:• Object recognition

• Face recognition, eye-tracking

• Mobility and navigation

• Robot manipulation

So the time is ripe for human-level machine learning.

Applications

Some Applications• High-stakes applications where trial and error too dangerous.

– Homeland security.– Hazardous waste removal.

• Robots and software for less sophisticated or learning-challenged humans use them.– Disabled.– Elder care.

• Elder-care robots easier to use by the older set.• Emerging Robotics Technologies & Applications Conference Proceedings, March 9-10, 2004,

Cambridge, MA– Rodney Brooks mentioned Elderly Care as one of the current future trends in robotics:

» Currently: None» Future: Robotic Assistants in Millions of Households

• Less brittle, more general, easier-to-learn and use robots and software.• Better learning environments:

– Direct/instruct robots (PERI)– More accurate pinpoint causes of problem learning.

A catalyst grant for ...?

• Carry out proof-of-concept version of entire 5-step research agenda• Build team to implement this sequence

– part of team that would presumably power full SLC on Human-Level Machine Learning

• Build proof-of-concept– p-o-c would run all the way through our proposed 5-step R&D sequence,

start to finish– application/implementation:

• homeland defense• Elder care robot• ITS for math/logic/comp sci

• Workshops/Symposia• Conference presentations• Publications• Web site from the very start

END

Objection

• How is this an improvement over GOFAI? i.e., Why isn’t this the 1970s all over again?– Less knowledge of human learning then– Formal methods in their infancy

• Nothing like Athena (used to prove a good part of Unix sound)!• Like two-layer neural networks compared to bigger ones

– Formal infrastructure was fragmented. Not known how to combine logical and probabilistic knowledge?

– So researchers were either using no representation and reasoning substrate or they were using the wrong one.

– Integrated cognitive models for combining methods not developed, • Polyscheme, ACT-R, ...

– These techniques were not interactive.• No question asking• No tracking or reasoning about human intent• No experimentation

PERIPsychometric Experimental Robotic Intelligence

• Scorbot-ER IX • Sony B&W XC55 Video

Camera• Cognex MVS-8100M

Frame Grabber• Dragon Naturally

Speaking Software• NL (Carmel & RealPro?)• BH8-260 BarrettHand

Dexterous 3-Finger Grasper System

Our Assets• Background in intersection of reasoning and

formal methods, and learning– Bringsjord, Cassimatis, Arkoudas, and Schimanski

• Prior R&D in logic-based machine learning.– Bringsjord, Arkoudas

• Background in child development.– Cassimatis

• Integrated cognitive models– All four

• Background in robotics– Cassimatis, Bringsjord, Schimanski

Prior Related Work on One-Shot Learning

• There isn’t anything that maches up perfectly.

• But, related, we have:

Prior Related Work on Learning by Reading

• Ask for pointers from Ken Forbus...

Impact on Machine Learning and AI

• More flexible and resourceful learning and reasoning algorithms

• Intellectually flexible robots (again, e.g., PERI)• Quantum leap in machine learning• Learning in situations that were impossible before• Integration of reasoning community back into

learning community• Impact back on education, including machine-assisted

education (e.g., intelligent tutoring systems & synthetic characters)

Impact on Study of Human Learning

• Existing empirical work hampered by vague theories that make results of simple experiments controversial. – Formal theory should help this

• Develop better understanding of which instruction or learning techniques are best in which circumstances.

• More specifically:– Will produce new pedagogy linking learning to reasoning

(mathematics/logic a beneficiary)

– Will produce revolutionary advances in intelligent tutoring systems, synthetic characters/simulation)