The First Conference on Artificial General Intelligence (AGI-08)

I will not be there, but I am looking forward to seeing what comes out of it.

The First Conference on Artificial General Intelligence (1st-3rd March, Memphis, US):

http://www.agi-08.org

Note that you can already read the submitted papers in the website.

Again on Measuring Machine Intelligence

I have recently found two tech reports written by Shane Legg and Marcus Hutter (IDSIA, Lugano, Switzerland) in which they make very interesting reviews on the definitions of machine intelligence and ways to measure it.
Have a look at:

Tests of Machine Intelligence
http://www.idsia.ch/idsiareport/IDSIA-11-07.pdf

Universal Intelligence: A Definition of Machine Intelligence
http://www.idsia.ch/idsiareport/IDSIA-10-07.pdf

It's a pleasure to see that some people face the fundamental problems of AI from the front!

NARS: Non-axiomatic Reasoning System

Pei-Wang's well-defined approach to Artificial General Intelligence takes as basic premises the fact that the agent has limited time and memory resources. He then develops a reasoning system that learns from experience and is able to deal with uncertainty and contradictory data.

http://nars.wang.googlepages.com

The project has become open-source, so you can even have a look at the code. There is also an free e-book on the webpage.

Brain Science Podcast

Another suggestion of a podcast, this time in the field of neuroscience. It features interviews and book reviews. Have a look at:

http://brainsciencpodcast.wordpress.com

John Searle: Beyond Dualism

It's now time to make the first suggestion about philosophy of mind.

And who else could it be if not the well-known american philosopher John Searle (the one from the Chinese-room argument against strong AI). 

Check out this animated talk at IBM Almaden Institute on Cognitive Computing (2006).

On Intelligence by Jeff Hawkins

I would like to recommend this book by Jeff Hawkins, in which the author tries to create a theory about the neocortex. 

He claims that the neocortex is basically a hierarchical memory system able to detect temporal and spatial patterns. Jeff Hawkins, and his company Numenta, are now trying to move forward and implementing this "neocortical algorithm" as software running on a computer.

I enjoyed a lot reading it and I am trying now to read the technical papers. So far it looks like a good model, specially for computer vision systems, but it's not yet clear to me how to solve problems from other cognitive areas such as language processing or planning.

More posts on that for the coming weeks!

Measuring Intelligence

In order to develop artificial intelligence further, it would be important to have a formal and quantitative way to measure intelligence of an agent, being it a human or a machine.
The most famous test for artificial intelligence is the so-called Turing Test, in which "a human judge engages in a natural language conversation with one human and one machine, each of which try to appear human; if the judge cannot reliably tell which is which, then the machine is said to pass the test". There is even a competition, the Loebner Prize which really evaluates different chatbots and choses the one who most resembles a human.

However, this test is nowadays considered to be anthropomorphically biased, because an agent can be intelligent and still not be able to respond exactly like a human.

Marcus Hutter as recently proposed a new way of measuring intelligence, based on the concepts of Kolmogorov Complexity and Minimum Description Length, in which compression = learning = intelligence. The Hutter Prize measures how much one can compress the first 100MB of wikipedia. The idea is that intelligence is the ability to detect patterns and make predictions, which in turn allows one to compress data a lot.

In my opinion this is not yet a totally satisfactory way of measuring general intelligence, for at least two reasons:

- the fact that method A compressed the dataset more than method B, does not necessarily mean that method A is more intelligent. It may simply mean that the developer of the method exploited some characteristic of the (previously known) data. Or it can mean that the method is good to find regularities in such dataset, but not being able to learn other structures in other environments.

- it can not be applied to humans (or animals).

For these reasons, I guess measuring intelligence is still a fundamental open problem in AI.

Artificial General Intelligence

Back in 1956, the founders of the new AI research field (John McCarthy, Marvin Minsky, Allen Newell and Hebert Simon) were deeply convinced that in a period of one generation we would have human-level intelligent computers.
However, after more than 50 years, we are still not able to solve some tasks that humans do without any apparent effort (such as distinguishing a dog from a cat or a horse in any kind of picture). Many frustrating results mark the history of AI: low quality of (early) machine translation systems, lack of robustness of speech recognition and computer vision systems, etc.
The so called "AI winter" is generally perceived to be finished by now, since many researchers have new hopes on building Artificial General Intelligence. Recent contributions from both neuroscience and theoretical computer science were decisive to create this optimism.
Here is a book edited by Ben Goertzel and Cassio Pennachin putting together several of the different renewed ideas.


As I read it, I will post comments on individual chapters concerning different approaches to AGI.

Talking Robots

Talking Robots is a "podcast featuring interviews with high-profile professionals in Robotics and Artificial Intelligence for an inside view on the science, technology, and business of intelligent robotics".

This podcast is produced at the Laboratory of Intelligent Systems, EPFL, Lausanne, Switzerland and it comes out every two weeks.

In future posts we will comment some of the episodes. Stay tunned!

Welcome to "About Intelligence"

Welcome to the blog where you can find ideas, comments and reviews about Artificial Intelligence, Robotics, Neuroscience, Consciousness and Philosophy of Mind.
Looking forward to having your feedback!