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## “Darwinian Evolution is a form of PAC (Machine) Learning”

Changing or increasing functionality of circuits in biological evolution is a form of computational learning. – Leslie Valiant

The title of this post comes from Prof. Leslie Valiant‘s The ACM Alan M. Turing award lecture titled “The Extent and Limitations of Mechanistic Explanations of Nature”.

Prof. Leslie G. Valiant

Click on the image above to watch the lecture

[Image Source: CACM “Beauty and Elegance”]

Short blurb: Though the lecture came out sometime in June-July 2011, and I have shared it (and a paper that it quotes) on every online social network I have presence on, I have no idea why I never blogged about it.

The fact that I have zero training (and epsilon knowledge of) in biology that has not stopped me from being completely fascinated by the contents of the talk and a few papers that he cites in it. I have tried to see the lecture a few times and have also started to read and understand some of the papers he mentions. Infact, the talk has inspired me enough to know more about PAC Learning than the usual Machine Learning graduate course might cover. Knowing more about it is now my “full time side-project” and it is a very exciting side-project to say the least!

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It is widely accepted that Darwinian Evolution has been the driving force for the immense complexity observed in life or how life evolved. In this beautiful 10 minute video Carl Sagan sums up the timeline and the progression:

There is however one problem: While evolution is considered the driving force for such complexity, there isn’t a satisfactory explanation of how 13.75 billion years of it could have been enough. Many have often complained that this reduces it to a little more than an intuitive explanation. Can we understand the underlying mechanism of Evolution (that can in turn give reasonable time bounds)? Valiant makes the case that this underlying mechanism is of computational learning.

There have been a number of computational models that have been based on the general intuitive idea of Darwinian Evolution. Some of these include: Genetic Algorithms/Programming etc. However, people like Valiant amongst others find such methods useful in an engineering sense but unsatisfying w.r.t the question.

In the talk Valiant mentions that this question was asked in Darwin’s day as well. To which Darwin proposed a bound of 300 million years for such evolution to occur. This immediately fell into a problem as Lord Kelvin, one of the leading physicists of the time put the figure of the age of Earth to be 24 million years. Now obviously this was a problem as evolution could not have happened for more than 24 million years according to Kelvin’s estimate. The estimate of the age of the Earth is now much higher. ;-)

The question can be rehashed as: How much time is enough? Can biological circuits evolve in sub-exponential time?

For more I would point out to his paper:

Evolvability: Leslie Valiant (Journal of the ACM – PDF)

Towards the end of the talk he shows a Venn diagram of the type usually seen in complexity theory text books for classes P, NP, BQP etc but with one major difference: These subsets are fact and not unproven:

$Fact: Evolvability \subseteq SQ Learnable \subseteq PAC Learnable$

*SQ or Statistical Query Learning is due to Michael Kearns (1993)

Coda: Valiant claims that the problem of evolution is no more mysterious than the problem of learning. The mechanism that underlies biological evolution is “evolvable target pursuit”, which in turn is the same as “learnable target pursuit”.

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## The Emerging Mind

About two months back I came across a series of Reith lectures given by professor Vilayanur Ramachandran, Dr Ramachandran holds a MD from Stanley Medical College and a PhD from Trinity College, Cambridge University and is presently the director of the center for Brain and cognition at the University of California at San Diego and an adjunct professor of biology at the Salk Institute. Dr Ramachandran is known for his work on behavioral neurology, which promises to greatly enhance our understanding of the human brain, which could be the key in my opinion in making “truly intelligent” machines.

[Dr VS Ramachandran: Image Source- TED]

I heard these lectures two three times and really enjoyed them and was intrigued by the cases he presents. Though these are old lectures (they were given in 2003), they are new to me and I think they are worth sharing anyway.

For those who are not aware, the Reith lectures were started by the British Broadcasting Corporation radio in 1948. Each year a person of high distinction gives these lectures. The first were given by mathematician Bertrand Russell. They were named so in the honor of the first director general of the BBC- Lord Reith. Like most other BBC presentations on science, politics and philosophy they are fantastic. Dr Ramachandran became the first from the medical profession to speak at Reith.

The 2003 series named The Emerging Mind has five lectures, each being roughly about 28-30 minutes. Each are a trademark of Dr Ramachandran with funny anecdote, witty arguments, very intersting clinical cases, the best pronunciation of “billions” since Carl Sagan, and let me not mention the way he rolls the RRRRRRRs while talking. Below I don’t intend to write what the lectures are about, I think they should be allowed to talk for themselves.

Lecture 1: Phantoms in the Brain

Lecture 2: Synapses and the Self

Lecture 3: The Artful Brain

Lecture 4: Purple Numbers and Sharp Cheese

Lecture 5: Neuroscience the new Philosophy

[Images above courtesy of the BBC]

Note: Real Player required to play the above.

As a bonus to the above I would also advice to those who have not seen this to have a look at the following TED talk.

In a wide-ranging talk, Vilayanur Ramachandran explores how brain damage can reveal the connection between the internal structures of the brain and the corresponding functions of the mind. He talks about phantom limb pain, synesthesia (when people hear color or smell sounds), and the Capgras delusion, when brain-damaged people believe their closest friends and family have been replaced with imposters.

Again he talks about curious disorders. One that he talks about in the above video, the Capgras Delusion is only one among the many he talks about in the Reith lectures. Other things that he talks about here is the origin of language and synesthesia.

Now look at the picture below and answer the following question: Which of the two figures is Kiki and which one is Bouba?

If you thought that the one with the jagged shape was Kiki and the one with the rounded one was Bouba then you belong to the majority. The exceptions need not worry.

These experiments were first conducted by the German gestalt psychologist Wolfgang Kohler and were repeated with the names “Kiki” and “Bouba” given to these shapes by VS Ramachandran and Edward Hubbard. In their experiments, they found a very strong inclination in their subjects to name the jagged shape Kiki and the rounded one Bouba. This happened with about 95-98 percent of the subjects. The experiments were repeated in Tamil speakers and then in babies of about 3 years of age. (who could not write) The results were similar. The only exceptions being in people having autistic disorders where the percentage reduced to only 60.

Dr Ramachandran and Dr Hubbard went on to suggest that this could have implications in our understanding of how language evolved as it suggests that naming of objects is not a random process as held by a number of views but depends on the appearance of the object under consideration. The strong “K” in Kiki had a direct correlation with the jagged shape of that object, thus suggesting a non-arbitrary mapping of objects with the sounds associated with them.

In the above talk and also the lectures, he talks about Synesthesia, a condition wherein the subject associates a color on seeing black and white numbers and letters with each.

His method of studying rare disorders to understand what in the brain does what is very interesting and is giving insights much needed to understand the organ that drives innovation and well, almost everything.

I highly recommend all the above lectures and the video above.

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## Video Lectures

Via DataWrangling, Here is one of my best finds since i took to blogging.

While browsing i came across this post that makes up a comprehensive list of publicly available video lectures on various topics on Physics, Mathematics, Computer Science, Neuro-Science etc.

Peter Skomoroch almost writes my story at the start of the post i made a reference to above. There is just too much to do these days, but i like it.

His blog is also highly recommended. It is one of the best i have come across. Though he writes at a lesser frequency, his posts are very high quality.

Pay a visit here, to find updated links for complete courses.

Here is a complete list of all the videos Peter has compiled:

### Open Courseware Directories and Other Video Lecture Roundup Posts

The full post can be viewed here>>

Please note that i have not opened each and every link from the above. If there is any lecture that is not in the public domain, then please notify me. I will remove it (them) with immediate effect. I do not intend to post / propagate stuff which is NOT in the public domain at all.

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