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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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.

Humanoid Cognitive Robotics
For the past few years I have been collaborating with the brilliant guys at Honda Research Institute USA and Communicative Machines in developing integrated cognitive architectures for humanoid robots; our Cogntive Map architecture enables Honda ASIMO to play board games with kids.

With hard work and a little bit of luck we can make the age-old dream of machines
with human-level intelligence come true in our lifetime.
And as Hans Moravec once pointed out, luck depends on having enough lottery tickets.
The rest calls for research. We're on it



Kris Thórisson – Towards True AI: Artificial General Intelligence

Artificial General Intelligence

My focus is on howa 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 - ion - ist A.I.: 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.

Con - struct - iv - ist A.I.: 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.

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? t 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. 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.

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 A.I. systems – what I call constructionist A.I. Unfortunately 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 A.I. methodology is the subject of my 2009 AAAI Fall Symposium on Biologically-Inspired Cognitive Architectures keynote speech, and much of my writing in the past 7 years, summarized in my transparently-titled paper From Manual Construction to Self-Constructive Systems: A New Constructivist AI (2012). The topic was also the subject of the HUMANOBS Workshop From Constructionist to Constructivist AI held in the fall of 2011, and one of the key topics of our two Summer School on Constructivist AI and Artificial General Intelligence, the first in Reykjavik, Iceland, and our Summer School on AGI in Beijing, China (part 1, part 2, part 3, part 4, part 5, part 6).

I have written two papers arguing for why we need constructivist AI and why constructionist AI will never give us AGI. In the coming years I will be writing and publishing papers on the specifics of constructivist AI and how to use it to build real systems that can do so.


Constructivist A.I.

humanobs header image

A.I. research on applying principles of self-organization in the design and implementation of A.I. systems is called constructivist A.I. As it is becoming clear that the manual construction process employed in most of software development wil not be sufficient to construct the kinds of complex architectures that we require for general intelligence, our focus must shift towards using techniques that allow systems to acquire their own knowledge and grow on their own. Without such principles in hand it is unlikely that we will we see systems with architecture-wide integration of learning, attention, analogy making and system growth. Our recently-awarded HUMANOBS project grant from the EU will enable us to take notable steps in this direction.


Constructivist Papers
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


Eric Nivel
Artificial Gengeral Intelligence conference 2009: Self-Programming: Operationalizing Autonomy

S1 agent
Seed AGI: A Virtual Human That Learns Complex Skills From Scratch


Constructionist A.I.

Ymir architecture built out in LEGO blocks Ymir architecture built out in LEGO blocks

Constructionist A.I. (not to be confused with constructivist A.I. - see above) is a moniker given to the bulk of A.I. research being performed around the world, where traditional software development methods form the basis of the work.

In this tradition we developed the Constructionist Design Methodology (CDM), which takes the best from the existing such methodologies. CDM has the goal of easing the creation of modular, complex machines that incorporate some aspects of a world-mind interaction loop – perception-action loop. We have used it on the HONDA Asimo humanoid robot and Mirage autonomus virtual agent [Quicktime icon watch movie]. Mirage inhabits an augmented reality; this complex system of integrated heterogeneous components was designed and implemented in as little as 2 mind-months using the CDM. We think it's directly due to the application of CDM in the project [published in A.I. Magazine, winter 2004]. We have also used CDM for a live performance of the Robot Opera in Reykjavik, 2006 [Quicktime icon watch movie] and 2007.

Constructionist Papers
Cognitive Map Architecture for Honda ASIMO
Constructionist Design Methdology for Interactive Intelligences
From Constructionist to Constructivist A.I.


©Kristinn R. Thórisson


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IIIM's Newsletter volume 4 issue 1




------------Kurzweil Award plaque - CA 2012
IADIS Outstanding Paper Award
Kurzweil Award Plaque - Beijing 2013

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

AGI talk, March 2009



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Artificial General Intelligence 2011

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