Category Archives: epistemology

Threads

I’ve been using Twitter (@awojdyla) more frequently over the last 3 years, finding a lot value in this tool which allows to address a worldwide audience and reach out to people in a very effective way.

Straight goals

Twitter is a very strange medium, in that it can be extremely helpful to reach out to people (the six degrees of separation collapse to one, basically), but whose rules and purpose are hard to understand.

Here’s a few remarks on my experience, and some resources if you’re interested in engaging the tweet game!

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Kolmogorov Access

Back in undergrad, I remember being fascinated by the notion of Kolmogorov complexity in computer science.

Put simply, the Kolmogorov complexity is the minimal length (number of lines) of the code needed to generate a signal, would it be a mathematical sequence (such as one listed in the OEIS) or an image, irrespective to the size needed to store it. It bears deep relations with the notion of entropy (a great book on the topic is Information Theory, Inference, and Learning Algorithms by the late David MacKay.)

For example, a series of eight billion ones in a row would require 1GB of memory, but can be written in a few lines of code:

for i in 1:1e9; print 1; end

(To some extent, this is why computer science is often problematic, since one of the goal of a good code is sometimes to reduce its Kolmogorov complexity, but the final code does not show all the lines that have been erased to get there…)

In the field of arts, culture and science, this description seems naive: can you really generate a book based on a script, or has it infinite entropy?

Science is organized knowledge. Wisdom is organized life.
– Immanuel Kant

In the age of the Internet, can we do better?

update 6/10/2019: I’ve seen recently on Twitter the embodiment of these ideas, see Nicole R.‘s thread. Way to go!

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On truth and its perils

What is true, what is false, what is wrong?

With the rise of large scale misinformation, this question has become more and more important, as it seems political parties around the world have reached the escape velocity of facts.

polarization in US politics really goes only one way…

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Hyperloonies

Mr Musk is having a hard time, and even though I have great appreciation for his making engineering look cool again, I won’t relent  as I believe his efforts are misguided.

Nothing can be so amusingly arrogant as a young man who has just discovered an old idea and thinks it is his own. – Sidney J. Harris

Today, I’ll talk about Hyperloop.

edit 12/18/2018: Hyperloop startup Arrivo is shutting down -The Verge — lol

edit 10/10/2019: Was the Ocean Cleanup Just a Pipe Dream? – Outside online
Jenny Allen: “Male privilege in science is a 24 year old guy with no formal training being called a ‘boy genius’, receiving millions of dollars in funding, and referring to qualified female oceanographers as ‘Ms’ instead of ‘Dr.’ when they critique his project.”

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Quick’n’dirty

Over the years I’ve collected quotes from people who are.

I always like quotes, because they are atoms of knowledge, quick and dirty ways to understand the world we only have one life to explore. To some extent, they axioms of life in that they are true and never require an explanation (otherwise they wouldn’t be quotations.)

Here’s a bunch of quotes that I found particularly interesting, starting with my absolute favorite quote comes from the great Paul Valery:

The folly of mistaking a paradox for a discovery, a metaphor for a proof, a torrent of verbiage for a spring of capital truths, and oneself for an oracle, is inborn in us. – Paul Valery

On research — trial and error

Basic research is like shooting an arrow into the air and, where it lands, painting a target.
-Homer Burton Adkins

A thinker sees his own actions as experiments and questions–as attempts to find out something. Success and failure are for him answers above all.
– Friedrich Nietzsche
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Learning Deep

In the past four years, there’s been a lot of progress in the field of machine learning, and here’s a story seen from the outskirts.

Eight years ago, for a mock start-up project, we tried to do some basic headtracking. At that time, my professor Stéphane Mallat told us that the most efficient way to do this was the Viola-Jones algorithm, which was still based on hard-coded features (integral images and Haar features) and a hard classifier (adaboost.)

(I was thrilled when a few years later Amazon Firephone was embedding similar features; unfortunately, this was a complete bomb — better technologies now exist and will make a splash pretty soon.)

By then, the most advanced book on machine learning was “Information Theory, Inference, and Learning” by David McKay, a terrific book to read, and also “Pattern Recognition and Machine Learning” by Chris Bishop (which I never read past chapter 3, lack of time.)

Oh boy, how things have changed!

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Julia Language

A few years ago, I got interested in the then-nascent Julia language (julialang.org), a new open source language based on Matlab syntax with C-like performances, thanks to its just-in-time compiler.

Large Synoptic Survey Telescope (LSST, Chile) data being processed with Julia on super computers with 225x speedup

Large Synoptic Survey Telescope (LSST, Chile) data being processed with Julia on super computers with 200x speedup (from https://arxiv.org/pdf/1611.03404.pdf)

It now seems that the language is gaining traction, with many available packages, lots of REPL integration (it works with Atom+Hydrogen, and I suspect Jupyter gets its first initial from Julia and Python) and delivering on performances.

Julia is now used on supercomputers, such as Berkeley Lab’s NERSC, taught at MIT (by no less than Steven G Johnson, the guy who brought us FFTW and MEEP!), and I’ve noticed that some of the researchers from Harvard’s RoLi Lab I’ve invited to SPIE DCS 2018 are sharing their Julia code from their paper “Pan-neuronal calcium imaging with cellular resolution in freely swimming zebrafish“. Pretty cool!

Julia used for code-sharing in a Nature publication. I wish I could see that every day!

Julia used for code-sharing in a Nature publication. I wish I could see that every day!

I got a chance to attend parts of Julia Con 2017 in Berkeley. I was amazed by how dynamic was the the community, in part supported by Moore’s foundation (Carly Strasser, now head of Coko Foundation), and happy to see Chris Holdgraf (my former editor at the Science Review) thriving at the Berkeley Institute for Data Science (BIDS).

Julia picking up speed at Intel (picture taken dusing JuliaCon 2017)

Julia picking up speed at Intel (picture taken dusing JuliaCon 2017)

I started sharing some code for basic image processing (JLo) on Github. Tell me what you think!

(by the way, I finally shared my meep scripts on github, and it’s here!)

Open Access

My article for the Berkeley Science Review on Open Access is out, and it is available here (for free, of course!): Science to the people.

“Astronomers and physicists have been sharing pre-prints since before the web existed,” says Alberto Pepe, founder of the authoring and pre-printing platform Authorea. “Pre-prints are an effective (and fully legal) way to make open access a reality in all scholarly fields.” Within hours, articles are available online, and scientists can interact with the author, leaving comments and feedback. Importantly, submission, storage, and access are all free. The pre-printing model ensures that an author’s work is visible and properly indexed by a number of tools, such as Google Scholar.

Special thanks to Rachael Samberg from thee UC Library and Alberto Pepe from Authorea.

Things seems to change quickly in that field, thanks to institutional efforts:

Here’s a list of resources that I’ve compiled from the talk by Laurence Bianchini from MyScienceWork when I invited at LBL, and a piece written by Nils Zimmerman on Open Access at LBNL: Open Access publishing at Berkeley Lab.

Ubris

Living in the Bay Area has a lots of perks, notably the climate and the people who live here– some are driven, and some maybe too much.

The Silicon Valley became fertile and successful when entrepreneurs started bringing hardcore scientific advances to the masses, with companies such as Fairchild Semiconductors, or innovative technologies, with companies such as Xerox and its Palo Alto Research Center.

Nowadays, the silicon in the valley is mostly gone (I often joke that among my friends living in the Silicon Valley, I am the only one actually working on silicon… yet I don’t live in the valley:), and tech companies that have nothing to do with actual τέχνη. Yet the dreams of technology to save us all are still pretty alive. But it seems that it all has to do with hubris, or PR at best, and it is hurting actual science and those who make it.

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Information in optics

Dear reader,

I’ve compiled here, just for you, three old article that I’ve OCRed and revamped a little bit. They are article with a lot of style and insight, that people sometimes cite without ever reading them… mostly because they’re not readily available. That’s the silly situation I wanted to fix.
– Enjoy !

Zernike – Physica IX, no 7 (1942)
Phase Contrast, A New Method for the Microscopic Observation of Transparent Object

Gerchberg and Saxton – Optik Vol. 35, No.2 (1972)
A Practical Algorithm for the Determination of Phase from Image and Diffraction Plane Pictures

Gabor – Progress in Optics Vol. 1 (1951)
Light and information” (still under revision)

Here’s a blissful excerpt from Gabor’s piece :

Light is our most powerful source of information on the physical world. Anthropologists have often emphasized that the privileged position of Man is due as much to his exceptionally perfect eye, as to his large brain. I was much impressed by a remark of Aldous Huxley, that we owe our civilization largely to the fact that vision is an objective sense. An animal with an olfactory sense or with hearing, however well developed, could never have created science. A smell is either good or bad, and even hearing is never entirely neutral; music can convey emotions with an immediateness of which the sober visual arts are incapable. No wonder that the very word “objective” has been appropriated by optics. But on the other hand it is probably the peculiar character of vision which is chiefly responsible for one of the most deep-rooted of scientific prejudices; that the world can be divided into an outer world and into an “objective” observer, who observes “what there is”, without influencing the phenomena in the slightest.

Tukey – Annals of Statistical Mathematics
The future of data analysis” (1961) — still working on it

Zernike – Physica I, pp. 689-704 (1932)
Diffraction theory of the knife-edge test and its improved form“,
translation by Anthony Yen (who went all in by redrawing all the figures !)

gabor_perpetual

A gedankenexperiment by Gabor, to discuss the nature of information contained in light

 

— Thanks to Martin Burkhardt for sharing some of these pieces !

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