Sci journalist/TED speaker/MIT KSJ Fellow/Books: The Edge of Physics, The Man Who Wasn't There, Through Two Doors at Once / Mastodon:
@anil
@sciencemastodon
.com
Don't know how other authors feel, but seeing the first physical copy of your book, after years of looking at Word files and PDFs... Seems surreal. It's as if someone else wrote it... Anyway, this advance copy arrived in the mail. One more month to pub date
Imagine being 𝐚𝐜𝐜𝐮𝐬𝐞𝐝, even in jest, of being the man who 𝘴𝘦𝘵 𝘣𝘢𝘤𝘬 𝘥𝘦𝘦𝘱 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘣𝘺 𝘵𝘸𝘰 𝘥𝘦𝘤𝘢𝘥𝘦𝘴. 𝐆𝐞𝐨𝐫𝐠𝐞 𝐂𝐲𝐛𝐞𝐧𝐤𝐨, who proved one of the seminal 𝐮𝐧𝐢𝐯𝐞𝐫𝐬𝐚𝐥 𝐚𝐩𝐩𝐫𝐨𝐱𝐢𝐦𝐚𝐭𝐢𝐨𝐧 𝐭𝐡𝐞𝐨𝐫𝐞𝐦𝐬, showed that a 1-hidden
Before one groks it, 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 (𝐌𝐋) can seem abstruse, even for those with software and math backgrounds (I found it so, despite having once learned the necessary skills). But once you get it, the simplicity of the conceptual building blocks is striking.
There must be a 𝐜𝐨𝐦𝐩𝐨𝐮𝐧𝐝 𝐆𝐞𝐫𝐦𝐚𝐧 𝐰𝐨𝐫𝐝 for how authors feel the day their book is released :-) Today is pub date for 𝐖𝐇𝐘 𝐌𝐀𝐂𝐇𝐈𝐍𝐄𝐒 𝐋𝐄𝐀𝐑𝐍: 𝘛𝘩𝘦 𝘌𝘭𝘦𝘨𝘢𝘯𝘵 𝘔𝘢𝘵𝘩 𝘉𝘦𝘩𝘪𝘯𝘥 𝘔𝘰𝘥𝘦𝘳n 𝘈𝘐. Nearly 4-5 yrs of work in relative obscurity sees
Sometimes, when writing a book, you encounter an idea so mind-bending, it doesn't leave you. For me, it was the answer this question: Why are deep neural networks universal function approximators? Turns out, understanding why functions are vectors is key; it's a truly eye-opening
Some scientists I met and wrote about in 3 of my 4 books have won Nobel Prizes in physics after the book came out :-) The joy of writing about cutting-edge science! Not saying anything more, just in case anyone thinks otherwise.
I'm trying to figure out who is most likely to win
I'm never going to get another chance for this particular humble brag, so I'll take it :-) When Geoff Hinton endorsed WHY MACHINES LEARN sometime back in March/April, my impostor syndrome came to the fore (yes, writers have it too, not just scientists). Surely not, I thought at
𝐓𝐡𝐞 𝐓𝐡𝐞𝐨𝐫𝐞𝐭𝐢𝐜𝐚𝐥 𝐌𝐢𝐧𝐢𝐦𝐮𝐦
I owe a huge debt to two books that inspired 𝐖𝐇𝐘 𝐌𝐀𝐂𝐇𝐈𝐍𝐄𝐒 𝐋𝐄𝐀𝐑𝐍. Leonard Susskind's THE THEORETICAL MINIMUM (for classical mechanics) and Steven Strogatz's 𝐈𝐍𝐅𝐈𝐍𝐈𝐓𝐄 𝐏𝐎𝐖𝐄𝐑𝐒. They are different
Did not have seen this coming! The Physics Nobel goes to John Hopfield and Geoff Hinton. Interviewing them was among the many highlights of writing Why Machines Learn. It took some persuasion to get Hopfield to talk to me--but he did, and it was such a joy! In fact, the chapter
As the pub date for a new book approaches, writers harbor conflicting emotions: 𝐣𝐨𝐲𝐟𝐮𝐥 of soon seeing the book out in the wild, 𝐧𝐞𝐫𝐯𝐨𝐮𝐬 that it's out in the wild and they can do little now to control its fate. 𝐖𝐇𝐘 𝐌𝐀𝐂𝐇𝐈𝐍𝐄𝐒 𝐋𝐄𝐀𝐑𝐍 is a week away from
Finally, after a long pandemic-induced hiatus,
@NCBS_Bangalore
announces its 10th annual science journalism workshop. Application deadline: Jan 5, '23. Workshop dates: Feb 12-25, 2023. Apply! Spread the word!
As authors, we have favorite stories that we couldn't somehow fit into our books. In 𝐖𝐇𝐘 𝐌𝐀𝐂𝐇𝐈𝐍𝐄𝐒 𝐋𝐄𝐀𝐑𝐍, I failed to find a place to tell the story of how AI pioneers 𝐁𝐞𝐫𝐧𝐢𝐞 𝐖𝐢𝐝𝐫𝐨𝐰 and 𝐅𝐫𝐚𝐧𝐤 𝐑𝐨𝐬𝐞𝐧𝐛𝐥𝐚𝐭𝐭 had a friendly face-off in the
A simple 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 of why 𝐧𝐞𝐮𝐫𝐚𝐥 𝐧𝐞𝐭𝐰𝐨𝐫𝐤𝐬, despite first being proposed in the late 1950s, remained a side-show in machine learning, until the back-propagation algorithm was developed in 1986.
𝐅𝐫𝐚𝐧𝐤 𝐑𝐨𝐬𝐞𝐧𝐛𝐥𝐚𝐭𝐭'𝐬 𝐩𝐞𝐫𝐜𝐞𝐩𝐭𝐫𝐨𝐧
Dan Dennett is no more.🙁 This is really sad. He touched so many lives, with his deep and uncompromising philosophy, his humor and a gentle kindness that lay beneath it all. I'd say RIP Dan, but he'd have admonished anyone for saying that! From Bacteria to Bach and Back, Dan.
“We are raising a generation of algorithms that are like undergrads [who] didn’t come to class the whole semester and then the night before the final, they’re cramming." Alexei Efros on supervised learning. My story for
@Quanta
on self-supervised learning:
You'll often see arguments that 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 is "𝘫𝘶𝘴𝘵" 𝐠𝐥𝐨𝐫𝐢𝐟𝐢𝐞𝐝 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬. I respectfully beg to differ. To write 𝐖𝐇𝐘 𝐌𝐀𝐂𝐇𝐈𝐍𝐄𝐒 𝐋𝐄𝐀𝐑𝐍, I began coding after a 20-year gap (I was a distributed systems software architect in
Sometimes, as an author, you get an endorsement that delights because of the endorser's way with words. My sincere thanks to
@stevenstrogatz
for this lovely blurb; it gets to the heart of why I wrote Why Machines Learn;
@DuttonBooks
@penguinrandom
Back in 2007, Steven Weinberg's casual remark during a chat triggered an entire story, a deep look at supersymmetry(SUSY). Weinberg worried that the LHC would find the Higgs and nothing more. "I'm terrified...Discovering just the Higgs would really be a crisis." He was prescient!
We first discovered the laws of gravity, and then those of quantum mechanics. But
@seanmcarroll
et. al. say nature may go about it the other way around: space-time and gravity could emerge from a fundamental quantum mechanical description of the universe.
1/n When my first book, The Edge of Physics, came out, a filmmaker, Steve Elkins, got in touch. He wanted to film the places I had been to (Siberia, Chile, Antarctica, Ladakh ...) and weave the story of these places and the physics, with other stories about slowness and silence
It's been three months of being a fellow at
@KSJatMIT
, and I still pinch myself. Studying at MIT and Harvard and Tufts... could not have imagined that science journalism would have landed me here, to think about philosophy of mind, machine learning and fiction
The 10th annual
#sciencejournalism
workshop
@NCBS_Bangalore
wrapped up yesterday. Last Fri, we marked 10 editions of the workshop with a dinner with alumni and faculty who have been a big part of the effort to foster
#scicomm
in India
Anyone who has attended a
#RogerPenrose
talk will recognize his insanely complicated presentations using multiple overhead projector slides stacked atop each other. He would even attempt animations by jiggling the slides 🙂
#NobelPrize2020
Not often do you get to write a quantum physics story that starts in a Viennese café, about an
#AI
that designs
#quantum
physics experiments that human imagination had failed to figure out...
#MachineLearning
My story for
@sciam
...
One small thing Geoff Hinton and David Rumelhart did -- apropos of his physics
#NobelPrize2024
-- that often goes unnoticed when we talk of his neural network work is the idea of symmetry breaking. Granted it's not quite the same as how physicists think of it, but it's worth
Despite the 𝐜𝐮𝐫𝐬𝐞 𝐨𝐟 𝐝𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐲, sometimes machine learning algorithms work better by pushing data into higher dimensions. But why? Here’s an 𝐢𝐧𝐭𝐮𝐢𝐭𝐢𝐯𝐞 𝐞𝐱𝐩𝐥𝐚𝐧𝐚𝐭𝐢𝐨𝐧.
The very first single-layer neural networks, such as
"The effort to understand the universe is one of the very few things that lifts a human life a little above the level of farce, and gives it some of the grace of tragedy"-Steven Weinberg in The First Three Minutes. His life was lived with immense grace! 1/n
Steven Weinberg, the 1979 Nobel laureate for physics, died today. A delightful companion who loved theater, he could recite the poetry of Gerard Manley Hopkins by heart, and wrote profoundly about the mysteries of creation. He loved life and left it reluctantly. He’ll be missed.
The moments when a book you are writing becomes real, sometimes frighteningly so: 1) When your publisher lists it on Amazon and you are in the dark:
And ...(see next tweet)
The 9th Annual Science Journalism workshop
@NCBS_Bangalore
ended today. The class of 2019 below. For the first time we had 2 computer scientists and 2 physicists, a great, diverse group! Good luck to all of you.
#Journalism
#science
Now for some beer 🙂
There’s no better illustration of how uncertainty messes with our minds than the Monty Hall dilemma. If you haven’t tried answering the question posed by the host of the game show Let’s Make a Deal, give it go and see why it fooled the best of mathematicians.
Here’s the
Could dark matter as we think we know it be dead? Yet another ultra-diffuse galaxy found with seemingly no
#darkmatter
. If confirmed--a big if--the galaxy and its ilk pose challenges for theories of cold dark matter and MOND!
@sciam
@LeeBillings
My
#Penrose
memory: Here's Penrose explaining to me his ideas about gravity-induced collapse of quantum superpositions, using his spectacles. One of the nicest physicists I have met! Congratulations to him on getting the physics
#NobelPrize2020
Call for applications for the 9th Science Journalism Workshop
@NCBS_Bangalore
. The deadline is 10th May! Get application in, or please spread the word. Details here:
Renewing call for applications.
@NCBS_Bangalore
hosts the 10th Annual Science Journalism Workshop, after 2 years of pandemic-induced absence. Apply by Jan 10, 2023, to learn the nuts-and-bolts of science journalism. Details here:
#SciComm
Why does 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 suffer from the 𝐜𝐮𝐫𝐬𝐞 𝐨𝐟 𝐝𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐲? Because, as Julie Delon of the Université Paris–Descartes says in her talks, "𝘐𝘯 𝘩𝘪𝘨𝘩-𝘥𝘪𝘮𝘦𝘯𝘴𝘪𝘰𝘯𝘢𝘭 𝘴𝘱𝘢𝘤𝘦𝘴, 𝘯𝘰𝘣𝘰𝘥𝘺 𝘤𝘢𝘯 𝘩𝘦𝘢𝘳 𝘺𝘰𝘶
The awesome 𝐬𝐮𝐩𝐩𝐨𝐫𝐭 𝐯𝐞𝐜𝐭𝐨𝐫 𝐦𝐚𝐜𝐡𝐢𝐧𝐞𝐬 that ruled the roost in 2000s, before 𝐧𝐞𝐮𝐫𝐚𝐥 𝐧𝐞𝐭𝐰𝐨𝐫𝐤𝐬 became the darlings of 𝐦𝐚𝐜𝐡𝐢𝐧𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠, can be traced back to 𝘝𝘭𝘢𝘥𝘪𝘮𝘪𝘳 𝘝𝘢𝘱𝘯𝘪𝘬'𝘴 algorithm, which was an appendix in his 1964
Why the double-slit experiment should NOT be used to make definitive statements about
#consciousness
or the nature of
#quantum
reality: The experiment can be interpreted in many different ways and each offers a different view of what's going on.
@sciam
To add to the research done at Bell Labs-it had an amazing group of
#MachineLearning
researchers in the 80s & 90s, who pioneered SVMs, neural networks, etc. Some I can think of : Sara Solla, Isabel Guyon, Yann LeCun, Corinna Cortes, Vladimir Vapnik... ML owes much to Bell Labs
Adam Becker (
@FreelanceAstro
) and I, almost at the end of our stay
@sfiscience
, as journalism fellows attending the complex systems summer school in Santa Fe, NM...
Many folks asked about the level of 𝐦𝐚𝐭𝐡 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 assumed of the reader of 𝐖𝐇𝐘 𝐌𝐀𝐂𝐇𝐈𝐍𝐄𝐒 𝐋𝐄𝐀𝐑𝐍. Anyone with exposure to high school-level math (mainly basic 𝐭𝐫𝐢𝐠 and 𝐜𝐚𝐥𝐜𝐮𝐥𝐮𝐬) can follow along easily. More important, I'm targeting the
We knew of the three possible fates for our universe: Big Freeze, Big Rip and Big Crunch. But there's a kid on the block, a possible unfurling of an extra dimension, making the cosmos indescribable with the physics we have: A
@newscientist
cover feature
Bangalore folks who were asking about WHY MACHINES LEARN being available in local bookstores, looks like
@blossombookhous
(yes, that Blossom book house!) has a few :-)
#NewArrival
📚 Dive into the fascinating world of mathematics that has paved the way for machine learning and the rapid growth of artificial intelligence! 🤖✨
Machine-learning systems are now making life-changing decisions: from approving mortgages to diagnosing cancer, and
Never thought I'd author 3 books when I became a writer. It's Pub Day for Through Two Doors at Once. Please share/re-tweet--it's much appreciated. Available at local indie booksellers and at
@DuttonBooks
@penguinrandom
@PenguinRH_News
@EmilyCanders
Of all the joys of
@KSJatMIT
fellowship, I did not expect this: talking about quantum mechanics to
@danieldennett
's class on Free Will. Loving the class, in which we grapple with the nuances of whether or not we have free will. Did I have any choice in taking this class? 😀
Huge congrats to
@aathiperinchery
for winning the 2021
#Kavli
#AAAS
Science Journalism Award for her story Succession (). Her journey began with the
@NCBS_Bangalore
science journalism workshop (our first batch, 2011;
@paldhous
missing in this group photo):
What a place to think and write about consciousness! Beginning my writer's residency at The Bellagio Center, Lake Como, Italy!
#RFBellagio
@RockefellerFdn
...
A generous endorsement from
@skdh
, gratefully received, for Why Machines Learn. “If you were looking for a way to make sense of the AI revolution that is well underway, look no further." Preorder from your favorite indie bookstore or from, where else,
A Quantum Cheshire Cat was separated from its grin in 2013. Now, a controversial thought experiment says two quantum Cheshire cats can exchange their grins. The upshot: properties of particles, such as their spin, may not belong to the particles.
@sciam
One of the joys of opening boxes of books that I hadn't seen for 2 years+... And finding gems I want to reread. The late Dan Dennett's Intuition Pumps and
@anilkseth
's Being You.
Redshift: things are moving away from each other. Blueshift: things are moving toward each other. Make of it what you will. This is what's written in the stars!
Sep 5 is Teacher's Day in India. As it happens, WHY MACHINES LEARN is dedicated to "Teachers everywhere, sung and unsung"--as a way of acknowledging the enormous debt I owe to so many from whom I learned about machine learning and the math of it! And I got to squeeze into the
To further admire
@physicsworld
's awesome graphic about the Monty Hall problem: Each panel is one trial, and if you do 1000s of trials, you'll see the probabilities emerge: switching wins 2/3rds of the time. It's such a simulation that convinced the reluctant Paul Erdős (who
Goat or no Goat! This graphic in
@DrMattHodgson
's review of WHY MACHINES LEARN in
@PhysicsWorld
is a superb illustration of the Monty Hall Problem, and why you should switch your choice. Wish I had thought of it first :) Review here:
How to use a large language model to teach yourself
#MachineLearning
. In the beginning there was the perceptron. So, first steps: Get an LLM to generate code for an interactive user interface that let's you create two clusters of data, and then activate and visualize a perceptron
Writers are always finding inspiration in the work of other writers. In 𝐖𝐇𝐘 𝐌𝐀𝐂𝐇𝐈𝐍𝐄𝐒 𝐋𝐄𝐀𝐑𝐍, I borrowed a technique from the British writer 𝘚𝘰𝘮𝘦𝘳𝘴𝘦𝘵 𝘔𝘢𝘶𝘨𝘩𝘢𝘮'𝘴 novel 𝘛𝘩𝘦 𝘙𝘢𝘻𝘰𝘳'𝘴 𝘌𝘥𝘨𝘦, to make the case that while it's okay to skip the
Thomas Young was born 250 years ago this week...A good excuse to revisit the double-slit experiment that he designed :) Starting with Young's classical version and the far more confounding quantum variants. My essay for
@Nature
Two slits and one hell of a quantum conundrum: Philip Ball lauds a study of a famous experiment and the insights it offers into a thoroughly maddening theory.
@philipcball
reviews Through Two Doors at Once for
@NatureNews
@DuttonBooks
@penguinrandom
Sabine Hossenfelder reviews Through Two Doors: "I like Anil’s writing because he doesn’t waste your time. He says what he has to say, doesn’t make excuses when it gets technical, and doesn’t wrap the science into layers of flowery cushions."
@skdh
Had an intense and amazing time, as a
#TED2022
speaker last week. Spoke about the human sense of self, the topic of my book, The Man Who Wasn't There. Among the many unexpected intangibles of writing a book! Photo: Gilberto Tadday. A nod to
@dbiello
One of the funny things you often realize when reading popular science, especially popular physics, is that beyond a point, the npn-technical language actually makes it harder to understand abstract ideas and the math actually makes it *simpler*.
A simple insight into not why or how, but what a neural network learns.
Let’s say you want to train an ML model to distinguish between two classes of data (top left panel, circles and triangles). A linear classifier—which would find a straight line, plane or hyperplane to
1/n "If there’s one place in India that offers the best crash course there is on the core elements of good science writing, it’s the NCBS science journalism workshop." Never imagined that the workshop we started
@NCBS
in 2011 would have such an impact.
The contentious/storied history of the 𝐛𝐚𝐜𝐤𝐩𝐫𝐨𝐩𝐚𝐠𝐚𝐭𝐢𝐨𝐧 algorithm that ignited the 𝐝𝐞𝐞𝐩 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠 revolution: Geoff Hinton and co. are often credited for it, but even he acknowledges the algorithm’s precedents. Why then do we regard the
The focus on Hopfield networks takes away from John Hopfield's fabulous earlier work in the dynamics of biochemical reactions--which laid the foundation for his eponymous networks, which led to the
#NobelPrize2024
.
When I talked to him for WHY MACHINES LEARN, he spoke of the
Magazines sometimes publish an issue of their mag with two different covers, to figure out which one appeals to their readers. Well, 𝐖𝐇𝐘 𝐌𝐀𝐂𝐇𝐈𝐍𝐄𝐒 𝐋𝐄𝐀𝐑𝐍 is being released on 𝐉𝐮𝐥 𝟏𝟔 𝐢𝐧 𝐔𝐒 𝐚𝐧𝐝 𝐔𝐊 with different cover designs. Which one catches your
It's not without some trepidation that I wrote 𝐖𝐡𝐲 𝐌𝐚𝐜𝐡𝐢𝐧𝐞𝐬 𝐋𝐞𝐚𝐫𝐧. There's a fair bit of math, equations and all, in it. The impulse to share the beauty of the math was strong. So, it's no small relief to hear 𝐁𝐣ö𝐫𝐧 𝐎𝐦𝐦𝐞𝐫 acknowledge the math, calling it