Links of the week


A New Kind of Science: A 15-Year View – BackChannel
Stephen Wolfram celebrates 15 years after publishing A New Kind of Science with a long article elucidating the computational paradigm introduced in his 1000+-pages book. If one manages to withstand the Wolfram’s self-celebratory tone and prolix writing, there’s a deep idea to be savoured: what if the fundamental descriptions of nature are not elegant mathematical equations, but simple programs? What can we then say about these programs? Do they all have the same irreducible complexity?

Inside One Founder’s Personal Fast Club – BackChannel
Five years ago, it was meditation, now it’s fasting. Read about the new Silicon Valley, but not only, craze about not eating, and it’s superlative health benefits. Research is positive, but still very scant.

How Much Do You Really Understand? – Scott Young
Excellent explanation about checking your understanding of anything, and why we often underestimate our ignorance. Plus some tips on how to learn to learn.

JupyterLab: the next generation of the Jupyter Notebook – Jupyter
What are the promises of JupyterLab? Pretty impressive!

JupyterLab: The evolution of the Jupyter web interface – O’Reilly
A short, but insightful, interview of Brian Granger, one of the creators of Jupyter Notebook and its evolution, JupyterLab: What issues is JupyterLab addressing and what are the new features?

Links of the week

Morning mist rolling through beech forest in Monte Amiata, Val d’Orcia, Tuscany, Italy.

Conscious exotica: From algorithms to aliens, could humans ever understand minds that are radically unlike our own? – Aeon
A philosophical attempt to map minds other than human, with implications to what it means to be conscious. Is consciousness an intrinsic, inscrutable subjective phenomenon or a fact of matter that can be known? Read on.

Crash Space – Scott Bakker
What would happen if we engineered our brains to be able to tweak our personality and emotional responses as we experience life? What would life look like? Scott Bakker gives us a glimpse in this short story.

AlphaGo, in context – Andrej Karpathy
A short, but comprehensive explanation of why the recent AlphaGo victories do not represent a big breakthrough in artificial intelligence, and how real-world problems differ, from an algorithmic point of view, from the game of Go.

Multiply or Add? – Scott Young
In many business and personal projects, factors multiply, meaning that the performance you get is heavily influenced by the performance of weakest factor. In some other cases, e.g., learning a language, factors add. The strategy to take in developing factors/skills depends by which context, add or multiply, you’re in. For more insights, read the original article.

Human Resources Isn’t About Humans – BackChannel
Often, HR is not there to help us or solve people’s problems, it is just another corporate division with its own strict rules. But it can be changed for the better. Read on.

Links of the week


Using Machine Learning to Explore Neural Network Architecture – Google
Designing Neural Network Architectures using Reinforcement Learning – MIT
How neural networks can generate successful offsprings and alleviate the burden from human designers using reinforcement learning.

Data as Agriculture’s New Currency: The Farmer’s Perspective – AgFunder News
A classification of three types of agricultural data and how they related to the farmer’s needs.

The AI Cargo Cult: The Myth of a Superhuman AI – Kevin Kelly
The founding executive editor of Wired explains why he believes superhuman AI is very unlikely. Instead, we already see many form of extra-human new species of intelligence.

Everything that Works Works Because it’s Bayesian: Why Deep Nets Generalize? – inFERENCe
Finally, Bayesian can also say that they can explain why Deep Learning works! Jokes apart, this article overviews several recent useful interpretations of Deep Learning from a Bayesian perspective.

Links of the week

Balda_200608_Trek_156.jpgSunset on the north face of the Cima del Cantun (m. 3354) reflecting on a small lake, Val Bregaglia, Switzerland.

How could we do this? – SUM
A concise summary of Yuval Harari’s Sapiens, which I read last year and left me profoundly impressed about our history.

Deep, Deep Trouble – Micheal Elad
Elad reflects on the impact of deep learning on image processing. Should we throw away rigorous mathematical models for the improved, but black-box, performance of deep learning?

Can the brain do back-propagation? – Geoffrey Hinton
A seminar from last year by Geoffrey Hinton at Stanford on why he thinks that the brain can actually do back-propagation, addressing four obstacles raised by neuroscientists.

A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN – Dhruv Parthasarathy
Well written post about the development from AlexNet to Mask R-CNN for pixel-level image segmentation.

Should You Listen to Music While Studying, The Pi Model and Learning How to Learn w/ Dr. Barbara Oakley – Scott Young
Interesting 20mins conversation about learning techniques and tips.

Escaping The 24-hour Dystopia – Unlimited
“Busyiness has become a global cult”. We cannot keep pace with the online onslaught of information. What’s the cure? This article overviews some technological solutions: brain enhancement, supersonic travel, Neuralink and others. My take is that we must first consider behavioral solutions instead.

Links of the week

Arches onto high cliff over the Mediterranean. Portovenere, Italy.

Deep Habits: The Importance of Planning Every Minute of Your Work Day – Study Hacks
How to increase your productivity by taking control of your time via time blocking.

Chaos, Ignorance and Newton’s Great Puzzle – Scott Young
Luck, chaos or ignorance? Understanding this mixture for your projects may help to better allocate resources.

Garry Kasparov on AI, Chess, and the Future of Creativity – Mercatus Center
A very interesting conversation with Garry Kasparov on chess, AI, Russian politics, education and creativity.

If everything is measured, can we still see one another as equals? – Justice Everywhere
The dangers of measuring everything and ranking ourselves on different scales, neglecting those human skills and experiences that cannot and should not quantified.

Links of the week

Close-up of a gall on oak leaf.Close-up of a gall on oak leaf.

The Attention Paradox: Winning By Slowing Down – Unlimited
Time and attention are limited resources that most cognitive workers waste in unnecessary behaviour. Some useful advice on how to think about cognitive resources and plan your working day accordingly.

The Problem of Happiness – Scott Young
Have we evolved to be unhappy? What are the pros and cons of some of the proposed solutions to be happier? Read this concise summary to know more.

The Dark Secret at the Heart of AI – MIT Technology Review
Machine learning and, in particular deep learning, are notoriously inscrutable. This may be an issue in deploying them to mission critical applications, such as health care and military. But are humans much more transparent? Or are they just capable of providing ad-hoc a-posteriori explanations?

Academia to Data Science – Airbnb
Some insights on how to shift from academia to industry from the perspective of Airbnb.

Scaling Knowledge at Airbnb – Airbnb
How does a company effectively disseminate new knowledge across their teams. Airbnb proposes and open-sources the Knowledge Repository to facilitate this process across their data teams.


Links of the week

Ski-mountaineers climbing the last steep meters to the summit of the Bishorn (4153m) in Valais, Switzerland

Time And Tide Wait For No Economist – UNLIMITED
The changing market of time and how the leisure time gap is widening between skilled and unskilled labour.

The Simple Economics of Machine Intelligence – Harvard Business Review
AI-based prediction tasks will get cheaper and cheaper, but the value of still-to-be-automatized complementary tasks, such as judgement will increase. A simple, but effective, economic perspective on the impact of AI.

Do you need a Data Engineer before you need a Data Scientist? – Michael Young
How Data Engineer and Data Architects can make your Data Science team more effective and satisfied.

The Art of the Finish: How to Go From Busy to Accomplished – Cal Newport
How task-based planning makes you productive, but not accomplished. A simple strategy to change that.

Data Science jargon buster – for Data Scientists – Guerrilla Analytics
Do your data scientists confuse your customers. Here’s a useful translating table.