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.
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 — loledit 10/10/2019: Was the Ocean Cleanup Just a Pipe Dream? – Outside online
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
Basic research is like shooting an arrow into the air and, where it lands, painting a target.
-Homer Burton Adkins
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.)
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! Continue reading
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!
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).
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!)
“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.
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.
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. Continue reading
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.
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
— Thanks to Martin Burkhardt for sharing some of these pieces ! Continue reading
Recent events in the scienfic community – I’m thinking of the detection of primordial B-mode signal in the CMB polarization by BICEP2 (probable), the discovery of Higgs Boson (Nobel-prized) and of the faster-than-light neutrinos (ruled out as an experimental error) – invite us to draw a line between what is reasonable science and what is not.
Hi peeps !
Dear English-speaking readers :
this post is about a French translation of Hamming’s
“The Unreasonable Effectiveness of Mathematics“
You can readily enjoy this text in English language!
C’est ainsi qu’il y a des odeurs que les chiens peuvent sentir et que nous ne pouvons sentir, des sons que les chiens peuvent entendre et que nous ne pouvons entendre, et encore des couleurs que nous nous ne pouvons voir et de saveurs dont nous ne pouvons nous délecter.Des lors pourquoi, compte tenu de la façon dont nos cerveaux sont câblés, la remarque “Peut-être y a-t-il des pensées que nous ne pouvons pas concevoir” vous surprendrait-elle ? L’évolution, jusqu’à présent, pourrait nous avoir empêché de penser suivant certaines directions ; il se pourrait qu’il y ait des pensées impensables.