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Curious AI Proprietary and Confidential
Digital co-worker
A digital co-worker can filter out
irrelevant information, manage multiple
connections and interfaces and do some
tasks autonomously, all with a
high bandwidth
Digital
co-worker
Search
engine
ERP
ArchiveCalendar
Digital
co-worker
It learns while working with you
and then works for you.
Curious AI Proprietary and Confidential
High bandwidth enables an “Internet of Minds”
CEO’s
Digital
co-worker
Assistant’s
Digital
co-worker
Engineer’s
Digital
co-worker
Digital co-workers cross-communicate
with superior bandwidth.
Curious AI Proprietary and Confidential
Handcrafted SW
Handcrafted concepts,
useful in narrow problems
– Perception
Learning
Autonomy
Reasoning
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Three waves of AI
Deep learning
Classification and prediction,
lacks object representations
Advanced AI
Autonomous learning and
symbolic reasoning
The current AI boom
Perception
Learning
Autonomy
Reasoning–
Perception
Learning
Autonomy
Reasoning
+
–
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Curious AI solves this
Wave 1: 1980s- Wave 2: 2000s- Wave 3: 2020s-
Adapted from DARPA’s 3-wave model
Curious AI Proprietary and Confidential
General Intelligence needs a rich World Model
Scripts and hand-crafted model
Hand-
crafted
software
Learned rich world modelCurious AI
Core
algorithms
Existing Wave 1 + Wave 2 hybrid system
• Rigid narrow domains
• Scripted decision making
Wave 3 system by Curious AI
• Meaningful communication
• Autonomous intelligent decision-making
• Learn new domains flexibly
Neural language modelDeep
Learning
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if distance < 100:
cmd = BREAK
else:
if distance >= 800:
cmd =
ACCELERATE
Curious AI Proprietary and Confidential
Autonomous learning
Semi-supervised recognition
Neural networkInput Label
Class X
Problem:
Action hierarchies defined
manually with specific pre-
defined discrete goal types
Example: Atlas does not learn
Decision-making and control
Task planning
Task coordination
Torso
contro
l
Head
contro
l
Hands
contro
l
Torso
subtas
k
Head
subtas
k
Hands
subtas
k
Semi-supervised segmentation Relevance
Problem:
Humans provide
segmentation which
is very laborious Problem:
Humans select and clean up
training data sets
Problem:
Humans provide abstractions
Curious AI Proprietary and Confidential
CAI: world leader in autonomous learning
Brain-inspired
learning principles
Strong research tradition
in Helsinki since 1970’s
CAI has achieved state-of-the-art performance in
autonomous learning in MNIST and SVHN
classification and autonomous segmentation
CAI
autonomous
learning
Error:
2.76%
labels
images
500
570 000
labels
images
70 000
70 000
Standard
deep learning
Error:
2.81%
Example: SVHN dataset
Google Street View
House Numbers
dataset
-99%
Curious AI Proprietary and Confidential
Current deep learning networks have trouble representing objects and their interactions
Easy to represent objects and
structured relations, but discrete
and handcrafted categories
Objects and relations
Real-world problems require both
types of representations but they
are fundamentally incompatible
Neural networks learn, but lack
representational power for objects
and their interactions
Handcrafted software Neural representationsHybrid systems
Neural representations
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3 17 19
4 3 2
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if distance < 100:
cmd = BREAK
else:
if distance >= 800:
cmd =
ACCELERATE
Discrete categories Coding and rules
Segment pixels to objects
Detect object bounding boxes
Curious AI Proprietary and Confidential
Neuro-symbolic deep learning
Brain-inspired
neuro-symbolic
representation
CAI Tagger does autonomous
segmentation and natively
supports representing objects
Same principle can be used to
autonomously learn
neuro-symbolic reasoning
See video: tinyurl.com/taggervideo
Curious AI Proprietary and Confidential
Thank you
HarriValpola, CEO
The Curious AI Company