Ben Goertzel - Singularity Summit Australia talk in 2011

Artiﬁcial General Intelligence:
1950-2050
Dr. Ben Goertzel
CEO, Novamente LLC and Biomind LLC
CTO, Genescient Corp
Co-Founder, OpenCog Project
Vice Chairman, Humanity+
External Research Professor, Xiamen University
Advisor, Singularity Institute and Singularity University
Wednesday, September 7, 2011

1950-
1970

1970-
1990

1990s

2001

2011

The Robot Revolution
is Accelerating

“Narrow AI”, rather than
“Artiﬁcial General
Intelligence” (AGI)
still dominates the AI ﬁeld

Artiﬁcial General Intelligence (AGI)
Humans are, in a certain sense, general-purpose rather than
narrowly specialized intelligences…
General intelligence may be loosely conceived as
“The ability of a system to achieve a variety of complex
goals in a variety of complex environments using limited
computational resources -- including goals and
environments that were not anticipated at the time the
system was created.”

General intelligence
in regard to the
environments &
goals for which
humans evolved,
and in which
humans now
operate, involves
certain key
competencies

2009 AGI Roadmap
Workshop

Current virtual world platforms have some fairly severe
limitations, which fortunately can be remedied with effort
Object-object interactions
are oversimplified, making
tool use difficult
Agent control relies on animations
and other simplified mechanisms,
rather than having virtual
servomotors associated with each
joint of an agent’s skeleton

Partial solution: Integration of a robot simulator
with a virtual world engine
Player / Gazebo: 3D robot
control + simulation
framework
OpenSim: open-source
virtual world
It seems feasible to replace OpenSim’s physics engine with appropriate components of
Player/Gazebo, and make coordinated OpenSim client modifications
+

Current Virtual Worlds lack ﬂuids, powders,
pastes, fabrics … they donʼt completely implement
“naïve physics”

One possible solution:
bead physics
Spherical beads with
specially designed
adhesion properties can
emulate fluids, fabrics,
pastes, strings, rubber
bands, etc.
Bead physics can be
added to virtual world
physics engines

Possible solution: build robots from “macrocells” --
ﬂexible ball-like units that can stretch into different
shapes, sense the environment, and pass power and
information to each other
Current robots are made mainly of inert parts --
very few of their parts are sensors or actuators...
claytronics, etc.??

2020

2020:
Rise of the
Robot
Children

animal/
childlike
AGI
+
useful
narrow AI
=
useful
early-stage AGI,
such as
virtual AI scientists
2020:
Rise of the
AI Scientists

one path to a powerful global brain:
AGI + narrow-AI + humans
+ communication networks
2020:
Emergence of
a more reﬂective,
deliberative global brain
spanning AGIs,
narrow AIs and humans

2030

2030:
One Big
AI Network

2030:
Jacked into
the AI
Network

2050

Kurzweil projects
technological Singularity
around 2045

Text

A Very Hard Problem
Goal Invariance Under Radical Self-
Modification
How to architect an AGI system so as
to maximize the odds that, as it
radically self-modifies and self-
improves, it will not lose track of its
originally programmed/taught goal
system?
And ... what are the goals/values that
we want the AGI system to preserve
as it evolves and grows and learns??

Another Very Hard Problem
Making Sure the “Good Guys” Win
Suppose we eventually do understand
how to build a safe, powerful AGI,
pursuing a positive value system...
How do we guarantee that this is the first
kind that gets built and achieves wide
influence?

Does Humanity Need an AI Nanny?
Humanity’s Dilemma
The rapid advance of technology seems
effectively unstoppable
Advanced tech lets people with less
education and resources cause more
damage more easily
Solving the problem of engineering
relatively safe, beneficial uber-God-
minds is difficult and may take a while

What Might an AGI Nanny Be Like?
•General intelligence somewhat above the human level, but
not too dramatically so
•Interconnection to powerful worldwide surveillance
systems, online and in the physical world
•Control of a massive contingent of robots (e.g. service
robots, teacher robots, etc.) and connectivity to the
world’s home and building automation systems, robot
factories, self-driving cars, and so on and so forth
•A cognitive architecture featuring an explicit set of goals,
and an action selection system that causes it to choose
those actions that it rationally calculates will best help it
achieve those goals

What Might an AGI Nanny’s Goals Include?
•A strong inhibition against modifying its preprogrammed
goals
•A strong inhibition against rapidly modifying its general
intelligence
•A mandate to cede control of the world to a more intelligent
AI within N years (where N could be, say, 10, 100 or 5000)
•A mandate to help abolish human disease, involuntary
human death, and the practical scarcity of common
humanly-useful resources like food, water, housing,
computers, etc.
•A mandate to prevent the development of technologies that
would threaten its ability to carry out its other goals
•A strong inhibition against carrying out actions with a result
that a strong majority of humans would oppose, if they
knew about the action in advance
•A mandate to be open-minded toward suggestions by
intelligent, thoughtful humans about the possibility that it
may be misinterpreting its initial, preprogrammed goals

Argument for Creating an AI Nanny
1.It’s impracticable to halt the exponential advancement of
technology (even if one wanted to)
2. As technology advances, it becomes possible for individuals
or groups to wreak greater and greater damage using less
and less intelligence and resources
3. As technology advances, humans will more and more acutely
lack the capability to monitor global technology development
and forestall radically dangerous technology-enabled events
4. Creating an AI Nanny is a significantly less difficult
technological problem than creating an AI or other
technology with a predictably high probability of launching a
full-scale positive Singularity
5.Imposing a permanent or very long term constraint on the
development of new technologies is undesirable

OpenCog:
An Open Source Software Framework
&
A Design &Vision for Advanced AGI
• 2011-2012:A Proto-AGIVirtual Agent in aVideogame type world
• 2013-2014:A Complete, Integrated Proto-AGI Mind ... virtual world +
humanoid robot
• 2015-2016:Advanced Learning and Reasoning
• 2017-2018: AGI Experts: biology, ﬁnance, service robotics,???
• 2019-2021: Full-On Human Level AGI
• 2021-2023: Advanced Self-Improvement
Extremely tentative
schedule,
assuming the design/
theory is basically right
and funding is adequate

biomedical
informatics
ﬁnancial
prediction /
analytics
video games /
virtual worlds
robot toys
service robots
information
retrieval
etc. !!!

memory
prediction
perception
action
goals
a mind uses
and
to do
of what
will achieve its
core operating principle of a general intelligence

Should we implement AGI via...
-- A single core complex cognitive process,
supported by others as needed
-- Multiple simple processes interacting together,
emergently yielding intelligence... ?
-- A number of complex cognitive processes, interacting
together in a speciﬁc way? Glued together perhaps
using probabilistic & economic semantics?
The human brain seems involve a number of complex processes (modelable as algorithmic),
interacting together in speciﬁed ways...

Which is critical?
-- The overall cognitive architecture?
-- The power of the learning algorithms?
-- Both? and they have to synergize well?


Which is critical?
-- The particulars of the system’s explicitly implemented
processes and representations?
-- The structures and dynamics that emerge via
complex self-organization, as the system grows and
learns?
-- Both? and they have to synergize well?


a mind is an evolving, autopoietic, self-referring set of patterns,
associated with a system that’s interpreted as goal-achieving --
including patterns in the system and the world (and emergent
therebetween), and patterns regarding goal-achievement
2006 2010 2012 (?)

MotorSensory
Declarative Episodic AttentionalIntentional Procedural
OpenPsi
DeSTIN
(vision / audition)
Reward
Hierarchy
Motor
Control
Hierarchy
robot
proxy
game
proxy
memory
PLN
(inference)
Pattern
mining
concept
blending
dimensional
embedding
MOSES Hillclimbing Internal
simulation
ECAN
(economic
attention
allocation
Language
processing

Declarative
Probabilistic Logic
Networks,
concept blending,
language
comprehension &
generation
Episodic
internal world
simulation engine
Attentional/
Intentional
economic attention
networks, adaptive
goal hierarchy
Sensory
hierarchy of
memory/
processing units
Procedural
MOSES
(probabilistic
evolutionary
learning),
hillclimbing
cognitive
synergy in
OpenCog
In OpenCog, multiple cognitive
processes act concurrently on the
same knowledge store

Psi Architecture Overview
ﬁgure from “Principles of Synthetic Intelligence” by Joscha Bach

Explicit knowledge
representation:
Nodes and links (collectively
“Atoms”) that explicitly
encode individual pieces of
knowledge
Implicit knowledge
representation:
Knowledge that is encoded in the
coordinated structure or
activity of a large set of nodes
and links
Knowledge Representation in OpenCog

MindAgents
are objects that act on the Atomspace: modifying, adding and/or removing Atoms
MindAgents’ activities are scheduled by a Scheduler: the simplest Scheduler
would just cycle through all available MindAgents
OpenCog involves a carefully constructed combination of MindAgents, intended
to reinforce rather than confuse each other!

Currently the OpenCog “core
(AtomSpace, scheduler, comms,
saving to disk, etc.) is C++ for Unix,
with an STL-based API
MindAgents are currently
coded in C++ or Python.
Some MindAgents also invoke
external Java processes

Economic Attention Allocation
Each node or link in the AtomSpace is tagged with a
probabilistic truth value, and also with an “attention value”,
containing Short-Term Importance and Long-Term
Importance components.
An artiﬁcial-economics-based process is used to update
these attention values dynamically -- a complex, adaptive
nonlinear process.

MOSES Probabilistic Evolutionary
Learning
Combines the power of two leading AI
paradigms: evolutionary and probabilistic
learning
Extremely broad applicability. Successful track
record in bioinformatics, text and data mining,
and virtual agent control.
Moshe Looks 2006 PhD thesis: metacog.org
Probabilistic Logic Networks
A highly general, practical integration of
probability theory and symbolic logic.
Extremely broad applicability. Successful track
record in bio text mining, virtual agent control.
Based on mathematics described in Probabilistic
Logic Networks, published by Springer in 2008
Two Key Algorithms for Procedural and Declarative
Knowledge Creation

adviceinput
below
belowbelow
between
aligned on axis
perpendicular to
mouth-nose-eyes axis
...
visual patterns (e.g.
DeSTIN centroids)
corresponding to
the semantic
perception node
linked to “human
eye”
concept node
phrase node
“human eye”
reference
link
PERCEPTUAL CSDLN
SEMANTIC-PERCEPTUAL
CSDLN
COGNITIVE
SEMANTIC
NETWORK
reference
link
reference
link
semantic HTM nodes/links
formed by pattern mining
perceptual HTM
semantic HTM provides
probabilistic biasing to
perceptual HTM
aligned on axis
perpendicular to
eyes axis
CSDLN = “Compositional
Spatiotemporal Deep Learning
Network, e.g. HTM, DeSTIN,...

grasp object
The motoric hierarchy node corresponding to a
particular set of servomotors, might for instance contain
clusters of paths through conﬁguration space that the
servomotors have historically followed.
MOTORIC CSDLN
SEMANTIC- MOTORIC CSDLN
rotate arm toward object
raise or lower arm toward object
raise or lower arm toward default
position
after
after
before/after/
simultaneous
get object
advice
input
COGNITIVE
SEMANTIC
NETWORK
“get”
phrase nodeconcept node
reference
link
reference
link
reference
link
motor patterns (e.g.
centroids) corresponding
to the semantic motoric
node “raise or lower
arm toward object”

This shows OpenCog oscillating between the robot's "home" and batteries, as a result of its quests to fulﬁll integrity and
energy demands respectively. After a few movements back and forth, it also shows the Psi monitor updating graphs of the
Psi variables (note the updates are synchronized between graphs).

Piagetan Stages of Development

Ben Goertzel - Singularity Summit Australia talk in 2011

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Andere mochten auch

Andere mochten auch (10)

Ähnlich wie Ben Goertzel - Singularity Summit Australia talk in 2011

Ähnlich wie Ben Goertzel - Singularity Summit Australia talk in 2011 (20)

Mehr von Adam Ford

Mehr von Adam Ford (19)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Ben Goertzel - Singularity Summit Australia talk in 2011