Reykjavik University logo   Intelligence is the foundation of all human activity.
The pursuit of intelligent machines
may be the most important endeavor
humanity can undertake.
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My M.I.T. pages
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Kristinn R. Thórisson, Ph.D.

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Founding Director of the
Icelandic Institute
for Intelligent Machines

IIIM introductory talk

IIIM introduction on YouTube

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Co-founder of Radar Networks, Inc., San Francisco (funded by Paul Allen's Vulcan Ventures), and inventor of the Twine technology (with Nova Spivack and Jim Wissner), the first large-scale Semantic Web site. Acquired by Evri in 2010.
Paper describing the technology behind the Twine Semantic Web portal:

Either Way (Á annan veg, 2011) features synthpop I authored and recorded at age 17.
The US remake, Prince Avalance, came out in January 2013. Alas, it does not use my music. But it's a fun movie.


ASIMO playing card games with kids

Humanoid Cognitive Robotics

I collaborated with the brilliant guys at Honda Research Institute USA and Communicative Machines in developing the Cogntive Map architecture which enables Honda ASIMO to play board games with kids.

My focus is on how a mind can be implemented in an artificial substrate – how we can build machines that think – and understand – the world around them.

Natural intelligence is the result of multiple systems and subsystems, implementing complex information flow and control that produce learning, reasoning, intuition, attention, insight, creativity, and understanding. How can we implement such a system in a machine? Our artificial general intelligence (AGI) work focuses on how the architecture of a mind works as a whole. By building working, running models implemented in software we aim to both understand the mind and build a practical AGI system.

To this end my team and I developed the AERA system. AERA demonstrates numerous operational features necessary to achieve AGI: Domain-independent learning, cumulative incremental learning, transfer learning, time-sensitive resource management, and life-long scalability. The system is currently being used by myself and my collaborators to study machine understanding, teaching methodologies for artificial learners, even the development of ethical values. You can read about AERA in my numerous publications — some of which have received best paper awards — and a 56-page technical report.

Con - struct - iv - ist AI: Self-constructive artificial intelligence system with general knowledge acquisition and integration skills. Systems capable of architectural self-modification and self-directed growth; develop from a seed specification; capable of learning to perceive, think and act in a wide range of novel situations and domains and learning to perform a number of different tasks.

Not to be confused with:

Con - struct - ion - ist AI: Artificially intelligent manmade system built by hand; learning is restricted to combining predefined situations and tasks, based on detailed specifications provided by a human programmer. While the system may automatically improve performance in some limited domain, the domain itself is decided and defined by the programmer.

Where Does Intelligence Come From?

The evidence gathered so far on the nature of intelligence makes it highly unlikely that mind appears from a single or simple principle. Even a small set of key principles seems unlikely; after all, if it takes a myriad of closely coordinated mechanisms for an automobile engine to run, why should a mind be any different? At the level of the brain a mind results from interaction among a vast amount of components, hooked up in complex, clever ways according to largely unknown principles. Although a mind might be constructed on different principles than neurons, the key operating principles responsible for producing human-like thought are still likely to count in the dozens if not hundreds. This means that if we want to build very smart machines, rivaling the human mind, we need to build more integrated and complete systems than achieved to date, demonstrating a large number of operating principles.

The mind is a system, and my research to date indicates that its operation needs to be captured holistically to achieve truly intelligent machines.

My approach has followed two main traditions in systems thinking. On the one hand is the familiar modular decomposition from cognitive science and software development. Modularization, object-orientation being one expression, is the most accepeted method at present to construct complex software systems by hand, including AI systems – what I call constructionist AI Unfortunately for the field of AI, this method has severe limitations in the size of the systems that can be built, but until recently there really wasn't a viable alternative available. There is now; keep reading.

Have you ever seen a child take apart a favorite toy? Did you then see the little one cry after realizing he could not put all the pieces back together again? Well, here is a secret that never makes the headlines: We have taken apart the universe and have no idea how to put it back together. After spending trillions of research dollars to dissasemble nature in the last century, we are just now acknowledging that we have no clue how to continue - except to take it apart further.

Albert-Lásló Barbasi
Linked - The New Science of Networks
(bold: KRTh)

As the proponents of the holistic systems approach have pointed out (e.g. Varela, Maturana, Simon), many complex systems have the elusive property that local interactions between their parts are not sufficient to explain, understand or predict the operation of the whole system of which they are part. Software methodologies employing traditional modular decomposition will not be sufficient to allow us to construct such systems in the lab.

If we are ever to see generally intelligent artificial systems we must look towards methodologies that more directly allow us to model and study complex phenomena, calling for an investigation of the principles of self-organization and meta-control. In short, we must employ methods that allow the system to develop on its own, through self-constructive principles. This is constructivist A.I.

A new constructivist AI methodology was the subject of my 2009 AAAI Fall Symposium on Biologically-Inspired Cognitive Architectures keynote speech, and much of my writing in the past years, summarized in my transparently-titled paper From Manual Construction to Self-Constructive Systems: A New Constructivist AI (2012).

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Constructivist AI

S1 agent

Seed-GMI: Virtual Humans That Learn Complex Skills Through Observation and Self-Programming

The manual construction process employed in standard software development wil not be sufficient to construct the kinds of complex architectures that we require for general intelligence can acquire their own knowledge and grow on their own, without the constant need for re-design. For this our focus must shift towards systems that can program themselves. Without self-programming principles in hand it is unlikely that we will we see systems with architecture-wide integration of learning, attention, analogy making and system growth – i.e. artificial general intelligence. For the past decade we have managed to take significan steps in this direction.

Constructivist Papers

Seed-Programmed Autonomous General Learning
A New Constructivist AI: From Manual Construction to Self-Constructive Systems
About Understanding
Anytime Bounded Rationality

Bounded Seed-AGI
Autonomous Acquisition of Situated Natural Communication
Bounded Recursive Self-Improvement

Resource-Bounded Machines are Motivated to be Effective, Efficient & Curious
A New Constructivist AI: From Manual Methods to Self-Constructive Systems Self-Programming: Operationalizing Autonomy
Achieving Artificial General Intelligence Through Peewee Granularity

Thorisson lecture, AGI 2009

Kristinn R. Thorisson
Lecture, Artificial General Intelligence conference 2009.
Holistic Intelligence: Transversal Skills & Current Methodologies

Eric Nivel lecture, AGI 2009

Eric Nivel
Lecture, Artificial General Intelligence conference 2009.
Self-Programming: Operationalizing Autonomy


©Kristinn R. Thórisson


Patents   |   Media

Kurzweil Award plaque - CA 2012

IADIS Outstanding Paper Award

Kurzweil Award Plaque - Beijing 2013


entertaning essay by Oren Etzioni


IIIM's Newsletter volume 4 issue 1


MIRI logo - small


H-Plus Magazine logo


Interview on Robotspodcast
March 12 2010 (starts@3:50min)

AGI talk, March 2009

Holistic Intelligence: Transversal Skills & Current Methodologies


Proceedings Editor
Artificial General Intelligence 2011

AGI Proceedings 2011

Among the top 25% most downloaded eBooks in the Springer eBook Collection 2012
On Amazon

Editorial Board Member

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Link for submissions

Editorial Board Member

LNCS Transactions
on Computational Collective Intelligence

TCCI Journal

Conference Organizer
Intelligent Virtual Agents

IVA 2011 Proceedings

Member of IEEE Taskforce on
IEEE Taskforce on Huma
Towards Human-like Intelligence