1. Human-Level Machine Learning
Selmer Bringsjord, Nick Cassimatis, Kostas Arkoudas, and Bettina Schimanski Department of Cognitive Science Department of Computer Science Rensselaer Polytechnic Institute (RPI) Troy NY USA December 9 NSF

2. RAIR Lab Sponsors “Poised-For” Learning Deontic/Doxastic Reasoning
-Cracking Project;
“Superteaching”
hypothesis generation;
AI in support of IA
“Poised-For” Learning
Slate (Intelligence Analysis)
test generation
advanced synthetic charactrs
synthetic characters/psychological time

3. Overview The Problem: Solution/Goal: Applications:
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

4. 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!
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

5. 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, , with 39 million words
Explanation-Based Learning uses only primitive reasoning/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
Can stay pretty much as is.

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

7. 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 its
raised tail and the fiery red star Antares lies at is heart...

8. Example 2: Human One-shot Learning (a simple example)
USB
CONVERTOR
CUP

9. Insert movie here (Nick has a copy)

10. The traditional machine learning approach...

11. Behavior of Micro-PERI

12. 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
Can stay pretty much as is.

13. 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

14. Our Approach Forges a Bridge
SBE
CISE
Behavioral &
Cognitive Sciences
Artificial Intelligence and
Cognitive Science ? ?

15. 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 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.
Need to put the AAAI logo in this slide. And here is where a shot of PERI succeeding on the convertor/cup task can slide in, or be used in a subsequent slide.

16. Applications

17. 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.

18. 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

19. END

20. 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

21. PERI Psychometric 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

22. 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

23. Prior Related Work on One-Shot Learning
There isn’t anything that maches up perfectly.
But, related, we have:

24. Prior Related Work on Learning by Reading
Ask for pointers from Ken Forbus...

25. 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)

26. 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)