Curious AI Proprietary and Confidential

The Future of AI

The Curious AI CompanyHarri Valpola, CEO

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

E-mail

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

–

–

+

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

+

–

+

+

+

+

+

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

Existing systems are Narrow AI:Wave 1 + Wave 2 hybrid

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

A NB N DD AN H

1 33 1

2 12 22

3 17 19

4 3 2

5 3

6 15 13

7 2 5

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

A NB N DD AN H

1 33 1

2 12 22

3 17 19

4 3 2

5 3

6 15 13

7 2 5

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

Segmentation to determine what belongs together

Curious AI Proprietary and Confidential

Binding problems in video pixel network

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

Unsupervised Texture Segmentation

Curious AI Proprietary and Confidential

Unsupervised Texture Segmentation

Curious AI Proprietary and Confidential

Thank you

HarriValpola, CEO

The Curious AI Company

harri@cai.fi