Category Archives: Learning

Turing’s Cathederal

Source: IAS, 2012

The history of digital computing can be divided into an Old Testament whose prophets, led by Gottfried Wilhelm Leibniz, supplied the logic, and a New Testament whose prophets, led by John von Neumann, built the machines.

Alan Turing, whose “On Computable Numbers, with an Application to the Entscheidungsproblem” was published shortly after his arrival in Princeton as a twenty-four-year-old graduate student in October 1936, formed the bridge between the two.

In this talk, George Dyson, a Director’s Visitor in 2002–03 and the author of Turing’s Cathedral: The Origins of the Digital Universe (Pantheon, 2012), discusses the role of the Institute’s Electronic Computer Project as modern stored-program computers were developed after WWII. Turing’s one-dimensional model of universal computation led directly to von Neumann’s two-dimensional implementation, and the world has never been the same since.

Super Hubs

Source: Daily Beast, Mar 2017

Professionally, networks are the ultimate competitive advantage. But on a more basic level they are a fundamental precondition for social mobility. Network science mathematically substantiates that in all networks a greater number of connections increases the chances of individual survival. Our fates are determined by the place we occupy within networks, and that place depends on the number and the quality of our connections. “Nodes” with the most connections and the most influence—including human ones—are “superhubs.” Nodes at the fringes are the least connected and suffer the greatest risk of failure.

At elite schools, they receive the best education and, even more important, are introduced to top-tier professional networks. “At Yale,” Vance writes, “networking power is like the air we breathe—so pervasive it’s easy to miss.” These networks allow superhubs to create circumstances favorable to advancing their interests. To optimally scale and capitalize on the system, they continuously build ever more interlinkages.


Source: The New Yorker, Apr 2017

The most powerful element in these clinical encounters, I realized, was not knowing that or knowing how—not mastering the facts of the case, or perceiving the patterns they formed. It lay in yet a third realm of knowledge: knowing why.

nowing why—asking why—is our conduit to every kind of explanation, and explanation, increasingly, is what powers medical advances.

“A deep-learning system doesn’t have any explanatory power,” as Hinton put it flatly. A black box cannot investigate cause. Indeed, he said, “the more powerful the deep-learning system becomes, the more opaque it can become. As more features are extracted, the diagnosis becomes increasingly accurate. Why these features were extracted out of millions of other features, however, remains an unanswerable question.” The algorithm can solve a case. It cannot build a case.

If more and more clinical practice were relegated to increasingly opaque learning machines, if the daily, spontaneous intimacy between implicit and explicit forms of knowledge—knowing how, knowing that, knowing why—began to fade, is it possible that we’d get better at doing what we do but less able to reconceive what we ought to be doing, to think outside the algorithmic black box?

The word “diagnosis,” he reminded me, comes from the Greek for “knowing apart.” Machine-learning algorithms will only become better at such knowing apart—at partitioning, at distinguishing moles from melanomas. But knowing, in all its dimensions, transcends those task-focussed algorithms. In the realm of medicine, perhaps the ultimate rewards come from knowing together.

Para Limes 2017 Videos and Presentation Slides

Source: Para Limes website, Mar 2017

Videos and Presentation Slides

Opening address by Bertil Andersson
President, Nanyang Technological University
Welcome remarks by Jan W. Vasbinder
Director, Para Limes, Nanyang Technological University

Chair: Cheong Siew Ann
School of Physical and Mathematical Sciences, Nanyang Technological University

Speaker: George Rzevski
Emeritus Professor, Complexity and Design Research Group, The Open University, Milton Keynes, United Kingdom
Biography & AbstractVideoPresentation

Speaker: Stuart Kauffman
Emeritus Professor – Biochemistry, The University of Pennsylvania, United States
Biography & AbstractVideoPresentation
Speaker: De Kai
Professor of Computer Science and Engineering, Hong Kong University of Science and Technology
Biography & AbstractVideo
Speaker: Michael Puett
Walter C. Klein Professor of Chinese History, Department of East Asian Languages and Civilizations, Harvard University, United States
Biography & AbstractVideo
Chair: Mikhail Filippov
School of Physical and Mathematical Sciences, Nanyang Technological University
Speaker: James Bailey
Independent Scholar, United States
Biography & AbstractVideoPresentation
Speaker: Nick Obolensky
Chief Executive Officer, Complex Adaptive Leadership, United Kingdom
Biography & AbstractVideoPresentation
Speaker: Ernst Pöppel
Ludwig-Maximilians-University Munich, Germany
Biography & AbstractVideoPresentation
Speaker: Stefan Thurner
Professor for Science of Complex Systems at the Medical University of Vienna and President of Complexity Science Hub, Vienna
Biography & AbstractVideoPresentation
Chair: Jan W. Vasbinder
Director, Para Limes, Nanyang Technological University
Speaker: Ilan Chabay
Head of Strategic Research Initiatives and Fellowships, Institute for Advanced Sustainability Studies (IASS) and Chair of the Knowledge, Learning, and Societal Change International Research Alliance (KLASICA), Potsdam, Germany
Biography & Abstract VideoPresentation
Speaker: Peter Edwards
Director, Singapore-ETH Centre
Biography & AbstractVideoPresentation
Speaker: Mile Gu
Nanyang Assistant Professor and National Research Foundation Fellow, Complexity Institute and School of Mathematical and Physical Sciences, Nanyang Technological University
Biography & Abstract VideoPresentation
Speaker: Sydney Brenner
Senior Fellow, A*STAR, Singapore
Closing remarks by Jan W. Vasbinder
Director, Para Limes, Nanyang Technological University

Conference: Causality – Reality

We seek to manage and control our world by establishing causalities. And we try to use science to help us. However one of the biggest challenges for science is to untangle or better understand the relationship between causality and reality. This is especially true for complexity science that deals with the real world, or with complex systems like our brains or our immune system,
Causality is the agency or efficacy that connects one process (the cause) with another (the effect),  where the first is understood to be partly responsible for the second1.
Reality is the state of things as they actually exist, rather than as they may appear or might be imagined2.
Once we have met this challenge we have the key to finding ways to sustainably manage our lives, our systems, our science, our education, our laws, our healthcare and our cities in a world that is becoming more complex and interconnected than ever before. It is also key to finding new breakthroughs in the sciences that seek to understand “the human” and its relations.
The sixth Para Limes complexity conference is about “Causality – Reality”3.
The twelve speakers in this conference are uniquely qualified to address the issue of Causality – Reality. To hear them share their insights and partake in their discussions will be a great experience for all participants to the conference.

Seeing Good in Others

Source: Fast Company, Apr 2017

people who tend to trust others at work score higher on a range of measure than those who don’t, from job performance to commitment to the team. And since we know that it’s our relationships—particularly with our bosses and colleagues—that determine how happy and successful we are as our careers progress, it may be worth asking some new questions.

Instead of, “How can I improve?” the better question might be, “How can I start seeing more of the good in people, more often?”

One of the biggest opportunities for growth at work comes from the way you solicit feedback and what you do with it afterward. Research demonstrates that while employees who speak up tend to improve how well teams function, many tend to be afraid to do so, worrying that their input won’t be well-received. Simply assuming the best in others can lay the foundation for managers and their team members alike to learn and improve without wounding egos.

when we think others are capable of changing their attitudes, we’re more likely to advance our own views. But when we think others’ beliefs are fixed, we don’t try too hard to persuade them—what would be the point?

Choosing Between a Startup and a Tech Giant

Source: Business Insider, Apr 2017

1. ‘Do I want to eventually found my own tech startup?’

“I meet a lot of people who say, ‘Oh eventually I want to start my own company, but I’ll join Google now,’” he says. “My advice there is to always to just go and join a startup. That’s where you’ll actually learn how to start a new company. That’s where you will see a lot of mistakes made, and a lot of successes as well.”

A smaller company might provide you with a broader experience, which you’ll need if you plan to strike out on your own.

2. ‘What drives me?’

“If you are someone who gets a lot of ideas, like you’re showering in the morning and you just have an idea, in a startup, you can have that idea live and serving users by that afternoon,” Otasevic says. “In bigger companies like Google or Facebook, you’ll probably need a month to roll that out.”

So if you’re driven by speed and constant, fast innovations, go for a smaller team. That being said, Otasevic says that your fast changes may go unappreciated by users, if your startup lacks a big reach.

“Everything you release in Google or Facebook will have millions of eyeballs on it,” he says.

In order to figure out where you should take your talents, consider where you’re more motivated by speed or impact.

3. ‘What do I want to learn?’

“People often just settle for conventional wisdom like, ‘Oh, Google has a great engineering team and therefore I will learn a lot there,’” he says. “Yeah, but what do you want to learn? Go deep.”

He says that companies like Google offer excellent learning experience in terms of large-scale systems, while startups can provide more education on building things up from scratch.

4. ‘In what environment do I work best?’

Many tech giants like Google come with great perks and strong company values.

“Google has a great culture, in terms of engineering,” he says. “Intellectual curiosity is a value. That’s been Google’s philosophy in hiring forever. You want to hire people who are extremely curious and passionate about the world’s problems.”

On the other hand, tiny startups can also provide you with a close, fun environment, if you’re on a great team.

“You really feel that people on the team are like your family,” Otasevic says. “You’re pulling in the same direction. Everything that goes good or bad, you’ll get through it together.”

Applying Scientific Method to Scientific Research

Source: Breitbart, Mar 2017

Fewer than 1 percent of papers published in scientific journals follow the scientific method, according to research by Wharton School professor and forecasting expert J. Scott Armstrong.

Professor Armstrong, who co-founded the peer-reviewed Journal of Forecasting in 1982 and the International Journal of Forecasting in 1985, made the claim in a presentation about what he considers to be “alarmism” from forecasters over man-made climate change.

“We also go through journals and rate how well they conform to the scientific method. I used to think that maybe 10 percent of papers in my field … were maybe useful. Now it looks like maybe, one tenth of one percent follow the scientific method” said Armstrong in his presentation, which can be watched in full below. “People just don’t do it.

Armstrong defined eight criteria for compliance with the scientific method, including full disclosure of methods, data, and other reliable information, conclusions that are consistent with the evidence, valid and simple methods, and valid and reliable data.

According to Armstrong, very little of the forecasting in climate change debate adheres to these criteria. “For example, for disclosure, we were working on polar bear [population] forecasts, and we were asked to review the government’s polar bear forecast. We asked, ‘could you send us the data’ and they said ‘No’… So we had to do it without knowing what the data were.”

According to Armstrong, forecasts from the Intergovernmental Panel on Climate Change (IPCC) violate all eight criteria.

“Why is this all happening? Nobody asks them!” said Armstrong, who says that people who submit papers to journals are not required to follow the scientific method. “You send something to a journal and they don’t tell you what you have to do. They don’t say ‘here’s what science is, here’s how to do it.’”

“They’re rewarded for doing non-scientific research. One of my favourite examples is testing statistical significance – that’s invalid. It’s been over 100 years we’ve been fighting the fight against that. Even its inventor thought it wasn’t going to amount to anything. You can be rewarded then, for following an invalid [method].”

“They cheat. If you don’t get statistically significant results, then you throw out variables, add variables, [and] eventually you get what you want.”

“My big thing is advocacy. People are asked to come up with certain answers, and in our whole field that’s been a general movement ever since I’ve been here, and it just gets worse every year. And the reason is funded research.”