Machine Learning Summer School

Held in Ile de Ré (France), 1-15th September, this school counted with some famous names within the Machine Learning and Artificial Intelligence communities: Rich Sutton (co-author of the widely adopted book on Reinforcement Learning), Isabelle Guyon (co-author of the first paper on Support Vector Machines) and Yann LeCun (known for the convolutional neural network, energy based models and the DjVu image compression technique).

You can check the (almost) complete list of lecturers here. I found the course given by Shai Ben-David, on the Theoretical Foundations of Clustering" quite interesting and intriguing. Clustering seems to be *really* lacking solid theoretical support, which is surprising, given the importance of the problem. Some atempts are being done to axiomatize it, but there are a lot of open questions: what exactly is the class of clustering algorithms? how can you compare different clustering algorithms? why is a partition better than other?
Hope to see more developments in this area in the coming years.

ICVSS 2008

Last week I attended the International Computer Vision Summer School in Sicily, Italy. The main topics were Reconstruction and Recognition. I think the quality of the lectures, organization and location were all quite good, therefore I would recommend it to other PhD students.

Here is a short summary of some of the things we heard about:

Andrew Zisserman (Oxford, UK) - gave an overview of object recognition and image classification, with focus on methods that use "bag of visual words" models. Quite nice for newcomers like me!

Silvio Savarese (UIUC, USA) - talked about 3D representations for object recognition. There is actually a Special Issue of the "Computer Vision and Image Understanding" on the topic at
http://vangogh.ai.uiuc.edu/cviu/home.html

Luc Van Gool (ETH Zurich, Switzerland) - Lots of cool and fancy demos about 3D reconstruction. They are starting to use some recognition to help reconstruction (opposite direction of S. Savarese).

Stefano Soatto (UCLA, USA) - gave an "opinion talk" on the foundations of Computer Vision and how it can be distinguished from Machine Learning. I would have to read his papers to understand better, but he seems to claim that the existence of non-invertible operations such as
occlusions would support the need for image analysis instead of just "brute-force machine learning".

We also had Bill Triggs (CNRS) talking about human detection, Jan Koendrick (Utrecht, Netherlands) on "shape-from-shade" and a few tutorials touching stuff as diverse as: SIFT, object tracking, multi-view stereo and photometric methods for 3D reconstruction or
randomized decision forests.


To summarize, I think the message was:

- Traditionally, recognition uses lots of Machine Learning but models keep few 3D information about objects;
- Traditionally, reconstruction uses ideas from geometry, optics and optimization but not learning;
- The future trend is to merge them: use 3D reconstruction to help in recognition tasks and use recognition to help in 3D reconstruction.

Moved to Switzerland

Since the 1st of July, I am a PhD student at Idiap Research Institute and the Ecole Polytechnique Fédérale de Lausanne.
I am working in Machine Learning and Computer Vision under the supervision of Dr. François Fleuret.

Generating all possible pictures

Think of an image of 800 x 600 pixel and 24 bit of color (8 bit per each RGB component). Its trivial binary representation is a sequence of 11520000 bits (800 x 600 x 24) and we can think of each picture as being a natural number.

Imagine now that we write an computer program that generates all these pictures one by one, incrementing the natural number by one in each round.

Running this algorithm for enough time you would eventually get:

- a picture of your face
- a picture of you in the Moon
- a picture of you with Marlin Monroe and James Dean
- pictures of ancient Earth, with dinosaurs
- pictures of all the paintings of Leonardo da Vinci, Van Gogh or Picasso
- pictures of all the pages of Shakespeare's writings
- pictures of proofs of all relevant mathematical theorems (already proved or not)
- pictures of all great music compositions (already written or not)
- pictures of Microsoft Office and Windows source code
- pictures/printscreens of all pages in the World Wide Web, including all the versions of Wikipedia


Warning: don't do this at home unless you can wait for some billion years between each pair of interesting pictures you would get!

Still, it's interesting to realize that you can compress all the world's information to a short and trivial program, all you have to do is add enough useless data to it!

Monkey with robotic arm

I'm not sure it's recent news, because there is a public release from as back as 2005, but I just came across this video of a monkey eating using a robotic arm directly controlled by his brain. Researchers are from the Pittsburgh University.

Really impressive, although probably a bit tough for the monkey.

The amazing intelligence of crows

In this 10min TED talk, Joshua Klein talks about crows and how they are incredibly good learners.



They seem to have a powerful memory, use vision effectively, have problem solving skills, use tools and even learn from examples of other crows. I guess AGI is more than achieved at "crow-level Artificial Intelligence"!!

How Ant Colonies Get Things Done

Here you have a nice and very informative Google Tech Talk by Dr. Deborah Gordon on how ant colonies work without any central control:






It seems that ants make most of their decisions just based on the frequency they encounter other ants (which have a specific smell according to their role in the colony).

Science in Summer time

If everything goes as planned this year I am attending two Summer Schools.

The first one, the International Computer Vision Summer School 2008 , will be hosted in Sicily, Italy in 14-19 July. The program seems to be quite good and it will cover topics like object detection, tracking or 3D reconstruction, among others. There's also a reading group on "how to conduct a literature review and discover the context of an idea". The challenge is to see how far back in the past one can track the origins of a scientific idea. For example, the AdaBoost is a well known machine learning meta-algorithm, in which a sequence of classifiers is progressively trained focusing on the instances misclassified by previous classifiers. The set of classifiers is then combined by a weighted average. It was introduced by Freund and Schapire in 1996. This is easy to track, the question however is: can you find the same or similar core idea, or intution, somewhere else back in the past? Possibly from a different domain?
It's gonna be fun!

The second one is the 10th Machine Learning Summer School, 1-15 September, Ile de Re, France. The program is also quite nice, but I still don't have the confirmation I can attend it.
I would be specially interested in Rich Sutton's lecture on "Reinforcement Learning and Knowledge Representation" although hearing about Active Learning, Bayesian Learning, Clustering, Kernel Methods, etc. also sounds quite appealing.

Looking forward to science in summer time!

How difficult is Vision?

Lately I have been wondering about the problem of Vision and how difficult it should be compared to problem of Artificial General Intelligence.
It seems to me that, given the order that it happened in Nature, processing visual input should be much simpler than using language or reasoning. I say this because there are quite simple animals with eyes, say a fish, a frog or a mouse... As I am not a biologist or neurologist, I am not sure what kind of visual tasks these animals are able to perform. For example, can a mouse tell if there is a cat in a picture or not?
In any case, I guess that these neuronal systems, much simpler than the human brain, are able to solve tasks that we have not yet achieved with Computer Vision algorithms.

If that's the case, I have two questions to my readers, who hopefully can help me clarify these issues:

- What is the "perfect" biological system to understand vision? It should be powerful enough to solve problems that we are interested in, such as distinguishing between different objects, but it should also have a relatively simple brain. Any ideas?

- If animals without human-level intelligence use vision quite effectively, does this mean that Artificial Intelligence will follow the same order of achievements? Or given the properties of computers, it will turn out to be easier to do reasoning, planning or even language processing?

Looking forward to reading your comments.

Videolectures.net

A fast recommendation to the people interested in assisting to video talks in their computers:

http://www.videolectures.net

The website is specially interesting for the Machine Learning community, given that it has currently almost 600 videos on the topic, however you may find many other nice subjects. In fact if you work on Machine Learning, most likely I am not telling you anything new. In this case you could perhaps post a comment pointing to a video lecture that you found specially relevant. Thanks!

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!