Neurosymbolic AI—David Cox, Ph.D.
IBM Director, MIT-IBM Watson AI Lab
IBM Research

MIT-IBMWATSONAI LAB

“Artificial Intelligence”

The evolution of AI

General AI

RevolutionaryBroad AI

Disruptive andPervasive
Narrow AIEmerging

IBM Research AI © 2018 IBM Corporation▼ We are here

2050 and beyond

3

The evolution of AI
Narrow AI

Broad AI

General AI

Single task, single domain

Multi-task, multi-domain

Superhuman accuracy andspeed for certain tasks

Multi-modal

Cross-domain
learning and reasoning

Distributed AI

Broad autonomy

Explainable

IBM Research AI © 2018 IBM Corporation

4

The evolution of AI
Narrow AI

Broad AI

General AISingle task, single domain

Multi-task, multi-domain

Superhuman accuracy andspeed for certain tasks

Multi-modal

Cross-domain
learning and reasoning

Distributed AI

Broad autonomy

Explainable

IBM Research AI © 2018 IBM Corporation

5

The evolution of AI
Narrow AI

Broad AIGeneral AI

Single task, single domain

Multi-task, multi-domain

Superhuman accuracy andspeed for certain tasks

Multi-modal

Cross-domain
learning and reasoning

Distributed AI

Broad autonomy

Explainable

IBM Research AI © 2018 IBM Corporation

7

The path to a “Broad AI” toolbox
Security

Explainability

Ethics+Infrastructure

Learn more from small datarestaurant
Follow
recipecook+

personsurvivebakesatisfy
hungerovenswallow

dessertsweetcakeeatcheesecake

Learns to transfer

Physics of AI

Reasoning

Platform for AI Lifecycle
ComputeData & Models

Applications

WorkflowMIT-IBM
WATSONAI LAB

The evolution of AI

General AI

Revolutionary
Broad AI

Disruptive andPervasive
Narrow AI

Emerging

IBM Research AI © 2018 IBM Corporation▼ We are here

2050 and beyond

14

So what’s “narrow” about today’s AI toolbox?

Karpathy and Li, 2015

Gatys et al. 2015

Brock et al. 2018

“Teddy Bear”

Meret Oppenheim, Le Déjeuner en fourrureWang et al. 2018

Karpathy and Li, 2015

Lake, Ullman, Tenenbaum & Gershman, 2016

What’s this?

ObjectNet
Boris Katz
MITAndrei Barbu
MITDan Gutfreund
IBM#MITIBM #AI

ObjectNet• ~50K images• ~300 object
classes• 4 different room
types
#MITIBM #AI

Testing ImageNet-trained models on ObjectNet#MITIBM #AI

Pin-yu Chen
IBM

Chen et al. 2018

Xu et al. 2019

Neural Networks / Deep Learning“Apple”

Symbolic AI

apple

originapple tree

shaperoundkind

structure

bodyfruit

stem

size

color

hand

redtaste
green

apple
Reproduced from Minksy, 1991

MIT-IBM Watson AI Lab

Neural-symbolic AI
Disentangling reasoning from vision and language understanding

Jiajun Wu

Chuang Gan

Joshua Tenenbaum

Question: Are there an equal number of large
things and metal spheres?

Small green
rubbercylinderLarge red
metalsphere

Small blue
rubbercube

Program: equal_number(count(filter_size(Scene, Large)),count(filter_material(filter_shape(Scene,Sphere), Metal)))Answer: Yes

End-to-End Visual Reasoning
Visual Question Answering
Q: What’s the shape of the red object?

End-to-End
Neural Network
NMN [Andreas et al., 2016]
IEP [Johnson et al., 2017]
FiLM [Perez et al., 2018],
MAC [Hudson & Manning, 2018]
Stack-NMN [Hu et al., 2018]
TbD [Mascharka et al. 2018]

A: Sphere.

End-to-End Visual Reasoning
Visual Question Answering
Q: What’s the shape of the red object?

Concept
End-to-End(e.g., colors, shapes)

Neural Network

NMN [Andreas et al., 2016]
IEP [Johnson et al., 2017]
FiLM [Perez et al., 2018],
MAC [Hudson & Manning, 2018]
Stack-NMN [Hu et al., 2018]
TbD [Mascharka et al. 2018]

Reasoning(e.g., count)
A: Sphere.

End-to-End Visual Reasoning
Visual Question Answering
Q: What’s the shape of the red object?

Entangled

Concept
End-to-End(e.g., colors, shapes)

Neural Network

NMN [Andreas et al., 2016]
IEP [Johnson et al., 2017]
FiLM [Perez et al., 2018],
MAC [Hudson & Manning, 2018]
Stack-NMN [Hu et al., 2018]
TbD [Mascharka et al. 2018]

Reasoning(e.g., count)
A: Sphere.

End-to-End Visual Reasoning
Visual Question Answering
Q: What’s the shape of the red object?

Entangled

Concept
End-to-End(e.g., colors, shapes)

Neural Network

NMN [Andreas et al., 2016]
IEP [Johnson et al., 2017]
FiLM [Perez et al., 2018],
MAC [Hudson & Manning, 2018]
Stack-NMN [Hu et al., 2018]
TbD [Mascharka et al. 2018]

Reasoning(e.g., count)
A: Sphere.

Hard to transfer
Image Captioning
Instance Retrieval

Task: Visual Reasoning
Question: Are there an equal number oflarge things and metal spheres?

Task: Visual Reasoning
Question: Are there an equal number oflarge things and metal spheres?

Task: Visual Reasoning
Question: Are there an equal number oflarge things and metal spheres?

3 largethings!

Task: Visual Reasoning
Question: Are there an equal number oflarge things and metal spheres?

3 largethings!

3 metalspheres!

Task: Visual Reasoning
Question: Are there an equal number oflarge things and metal spheres?

Equal? Yes!
3 largethings!

3 metalspheres!

Task: Visual Reasoning
Question Understanding
Question: Are there an equal number oflarge things and metal spheres?

Equal? Yes!
3 largethings!

Visual Perception

Logic Reasoning

3 metalspheres!

Vision (CNN)

Structured
Representation

Language (RNN)

SymbolicProgramNS-VQA [Yi et al. 2018]

Incorporate Concepts in Visual Reasoning
VisionSceneParsing

Language
Q: What’s the shape ofthe red object?

NS-VQA [Yi et al. 2018]

Incorporate Concepts in Visual Reasoning
Vision1Language
Q: What’s the shape ofthe red object?

SceneParsing

ID

ColorShape

Material

1

GreenCube

Metal

NS-VQA [Yi et al. 2018]

Incorporate Concepts in Visual Reasoning
VisionScene
Parsing12
Language
Q: What’s the shape ofthe red object?

SemanticParsing

ID

Color

Shape

Material

1

Green

Cube

Metal

2

Red

SphereRubberFilter(Red)Query(Shape)
ProgramNS-VQA [Yi et al. 2018]

Incorporate Concepts in Visual Reasoning
VisionScene
Parsing12
Language
Q: What’s the shape ofthe red object?

ID

Color

Shape

Material

1

Green

Cube

Metal

2

Red

SphereRubberSymbolic
Reasoning
Semantic
Parsing

Filter(Red)Query(Shape)
ProgramNS-VQA [Yi et al. 2018]

Incorporate Concepts in Visual Reasoning
VisionScene
Parsing12
Language
Q: What’s the shape ofthe red object?

ID

Color

Shape

Material

1

Green

Cube

Metal

2

Red

SphereRubberSymbolic
Reasoning
Semantic
Parsing

Filter(Red)Query(Shape)
ProgramNS-VQA [Yi et al. 2018]

Incorporate Concepts in Visual Reasoning
VisionScene
Parsing12
Language
Q: What’s the shape ofthe red object?

ID

Color

Shape

Material

1

Green

Cube

Metal

2

Red

SphereRubberSymbolic
Reasoning
Semantic
Parsing

Filter(Red)Query(Shape)
ProgramNS-VQA [Yi et al. 2018]

Incorporate Concepts in Visual Reasoning
VisionScene
Parsing12
Language
Q: What’s the shape ofthe red object?

ID

Color

Shape

Material

1

Green

Cube

Metal

2

Red

SphereRubberSymbolic
Reasoning
Semantic
Parsing

Filter(Red)Query(Shape)
ProgramSphereAdvantage 1: High Accuracy
MethodAccuracy (%)

Human

92.6

RN

95.5

IEP

96.9

FiLM

97.6

MAC

98.9

TbD

99.1

NS-VQA(Ours)

99.8

Effectively
perfect!

[Yi et al. NeurIPS 2018, Johnson et al. ICCV 2017, Santoro et al. NIPS 2017, Perez et al. AAAI 2018, Hudson et al. ICLR 2018, Mascharka et al. CVPR 2018]

Advantage 2: Data Efficiency

High accuracy whentrained with just 1% the of
the data that othermethods require[Yi et al. NeurIPS 2018]

Advantage 3: Transparency and Interpretability
Question: What number of cylinders
are gray objects or tiny brown matte
objects?

umber of cylinders
tiny brown matte

Question: Are there more yellow
matte things that are right of the grayball than cyan metallic objects?

Ours

IEP

Ours

IEP

scene
filter_small
filter_brown
filter_rubber
scene
filter_grayunionfilter_cylindercountfilter_small
filter_brown
filter_large
filter_cyan...(25 modules)
filter_metalunion
filter_cylinder
count

scene
filter_cyan
filter_metalcount...(4 modules)
scene
filter_yellow
filter_rubber
countgreater_thanfilter_small
filter_cyan
unionfilter_brown...(25 modules)
filter_small
filter_yellow
filter_rubber
countgreater_than

Answer: noAnswer: no

Question: Are there more yellow
matte
things thatthe gray 2Answer:
1 are right ofAnswer:ball than cyan metallic objects?

IEPOurs

IEP

filter_small
filter_brown
filter_large
filter_cyan...(25 modules)
filter_metalunion
filter_cylinder
count

scene
filter_cyan
filter_metalcount...(4 modules)
scene
filter_yellow
filter_rubber
countgreater_thanfilter_small
filter_cyan
unionfilter_brown...(25 modules)
filter_small
filter_yellow
filter_rubber
countgreater_than[Yi et al. NeurIPS 2018, Johnson et al. ICCV 2017]

NeurIPS 2018: Neurosymbolic VQA:
Properties (e.g. “color”) and values (“red”) predefined

ICLR 2019: Neurosymbolic Concept Learner:
Properties predefined, can learn new values autonomously
NeurIPS 2019: Neurosymbolic Metaconcept Learner:
Autonomously learns new concepts
ICML 2020 (target submission):
Real world images

less predefined, more autonomous →62

Neuro-Symbolic Concept Learning
Visual Representation

1

Obj 1
Obj 2

2
Concept Embeddings
red......

Q: What’s the shapeof the red object?

Filter
redQueryshapeNeuro-Symbolic Concept Learning
General Representation Space
Visual Representation

1

Obj 1

Obj 1
Obj 22Concept Embeddings
red......

Color
Proj.

Color Space

Q: What’s the shapeof the red object?

Filterred

QueryshapeColor(Obj 1)

Neuro-Symbolic Concept Learning
General Representation Space
Visual Representation

1

Obj 1
Obj 2

Obj 1
Obj 2

2
Concept Embeddings
red......

Color
Proj.

Color Space

Q: What’s the shapeof the red object?

Filterred

QueryshapeColor(Obj 1)

Color(Obj 2)

Neuro-Symbolic Concept Learning
General Representation Space
Visual Representation

1

Obj 1
Obj 2

Obj 1
Obj 2

2
Concept Embeddings
red......
Color Space

Q: What’s the shapeof the red object?

redFilter
red

Query
shapeColor(Obj 1)

Color(Obj 2)

Symbolic AI

apple

originapple tree

shaperoundkindstructure

bodyfruit

stem

size

color

hand

redtaste
green

apple
Reproduced from Minksy, 1991

Meta-concept Learning
Visual reasoning questions

Han et al. NeurIPS 2019

+ Metaconcept questions

color:red

Q: Is there any red cube?
A: Yes.

Q: Is red a same kind of concept as green?
A: Yes.

color:green

Q: Is there any green block?
A: Yes

Q: Is cube a synonym of block?
A: Yes.

CLEVR(Johnson et al. 2017)

IvoryGull
LaridaeBlackTernCUB(Wah et al. 2011)

Q: Is there any Ivory Gull?
A: Yes.
Q: Is there any Laridae?
A: Yes.
Q: Is there any Black Tern?
A: Yes.
Q: Is there any Laridae?
A: Yes.

Q: Is Laridae a hypernym of Ivory gull?
A: Yes.

Augmenting VQA with Metaconcepts
Visual reasoning questions+ Metaconcept questions

color:red

Q: Is there any red cube?
A: Yes.

Q: Is red a same kind of concept as green?
A: Yes.

color:green

Q: Is there any green block?
A: Yes

Q: Is cube a synonym of block?
A: Yes.

CLEVR(Johnson et al. 2017)

IvoryGullLaridae
BlackTernCUB(Wah et al. 2011)

Q: Is there any Ivory Gull?
A: Yes.
Q: Is there any Laridae?
A: Yes.
Q: Is there any Black Tern?
A: Yes.
Q: Is there any Laridae?
A: Yes.

Q: Is Laridae a hypernym of Ivory gull?
A: Yes.

Program Execution AnimatedVisual reasoning questions
Feature Obj. 1
Extraction

ObjectDetection

Similarity

Semantic
Parsing

Semantic
Q: Is red a same kind of Parsing

Similarity

score=0.9score=0.9
Answer: Yes

P: Exist(Exist( Filter(Filter(Filter( redredred))))))

Metaconcept questions
concept as yellow?

MaxredObj. 2

Q: Is there any red object?

score=0.1

redP:
P: MetaVerify(MetaVerify(red,same-kind
red,yellow,yellow,same-kind))same-kind

MetaVerify

score=0.9

yellow
Answer: Yes

Generalization
Metaconcept Generalization
Q: Is there any airplane?
A: Yes

Q: Is there any kid?
A: Yes

Q: Is airplane a synonym ofplane?
A: Yes

Q: Is there any plane?
A: Yes

Q: Is there any child?
A: Yes

Q: Is kid a synonym ofchild?
A: YesTesting: metaconcepts on
unseen pairs of concepts

Training

airplanesynonymplanekidsynonym?
childGeneralization
Metaconcept Generalization: Results
Q: Is there any airplane?
A: Yes

Q: Is there any kid?
A: Yes

Q: Is airplane a synonym ofplane?
A: Yes

Q: Is there any plane?
A: Yes

Q: Is there any child?
A: Yes

Q: Is kid a synonym ofchild?
A: YesTraining

Testing

CLEVERER: CoLlision Events forVideo REpresentation and Reasoning• Descriptive
Q: What is the material of thelast object to collide with thecyan cylinder?
A: Metal

Chuang Gan w/ Kevin Xi, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba & Josh Tenenbaum• Explanatory
Q: What is responsible for thecollision between the rubber
and metal cylinder?
A. The presence of the yellowsphere
B. The collision between the rubber
cylinder and the red rubber sphere• Counterfactual
Q: What will happen without
the cyan cylinder?
A. The red rubber sphere and themetal sphere collide
B. The red rubber sphere and thegray object collide

Looking Ahead
How many employees haveover 10 years experience but
have moved location in thelast year?
What factors might contributeto better output from Factory Avs. Factory B?
Why is our database down?

Neurosymbolic
Generative Models

Srivastava et al. 2020 (submitted)

Neurosymbolic
Planning

Asai et al. AAAI 2018

Neurosymbolic
Safe ML/RL

Fulton et al AAAI 2018

Neurosymbolic
Code Optimization

Shi et al. ICLR 2019

Neurosymbolic
NLU

Wilcox et al. NAACL 2019

Neurosymbolic
Machine Common Sense

Smith et al. NeurIPS 2019

MIT-IBM Watson AI Lab

Inducing Behavioral InsightInferring flexible behavioral plans/policies from temporal observation data
Julie Shah
MITChristian MuiseIBM

(:action pickup:parameters (?b1 ?b2 - block):precondition (and (on ?b1 ?b2)(hand-clear))):effect (and (not (hand-clear))(not (on ?b1 ?b2))(holding ?b1))

Task: Induce the action theory of anenvironment through observations

MIT-IBM Watson AI Lab

LatPlanMixing symbolic planning with neural networks
Masataro AsaiIBMMIT-IBM Watson AI Lab

LatPlanMixing symbolic planning with neural networks
Masataro AsaiIBMMIT-IBM Watson AI Lab

Verifiably Safe Reinforcement Learning
φNathan Fulton
IBM{accel,brake,turn}

Safe?
PolicyObserve Reward

Use a theorem prover to prove:init→[{{accel∪brake};ODEs}*]safe

is correctly monitored by φ.

82

appleoriginapple tree

body

shape

round

kind

structure

size

hand

fruit

stem

color

red

taste

green

apple

+NEURAL NETWORKSSYMBOLIC AI

MIT-IBM Watson AI Lab

Causal Inference

Beyond Correlation—inferring and testing for causal
relationships in complex systems

Caroline Uhler
MITGuy Bresler
MITKarthikeyan
ShanmugamIBM

http://tylervigen.com/spurious-correlations

MIT-IBM Watson AI Lab

S4
S1

S5
S3

S2

S6
S7