Ken Steiglitz on The Discrete Charm of the Machine

SteiglitzA few short decades ago, we were informed by the smooth signals of analog television and radio; we communicated using our analog telephones; and we even computed with analog computers. Today our world is digital, built with zeros and ones. Why did this revolution occur? The Discrete Charm of the Machine explains, in an engaging and accessible manner, the varied physical and logical reasons behind this radical transformation. Ken Steiglitz examines why our information technology, the lifeblood of our civilization, became digital, and challenges us to think about where its future trajectory may lead.

What is the aim of the book?

The subtitle: To explain why the world became digital. Barely two generations ago our information machines—radio, TV, computers, telephones, phonographs, cameras—were analog. Information was represented by smoothly varying waves. Today all these devices are digital. Information is represented by bits, zeros and ones. We trace the reasons for this radical change, some based on fundamental physical principles, others on ideas from communication theory and computer science. At the end we arrive at the present age of the internet, dominated by digital communication, and finally greet the arrival of androids—the logical end of our current pursuit of artificial intelligence. 

What role did war play in this transformation?

Sadly, World War II was a major impetus to many of the developments leading to the digital world, mainly because of the need for better methods for decrypting intercepted secret messages and more powerful computation for building the atomic bomb. The following Cold War just increased the pressure. Business applications of computers and then, of course, the personal computer opened the floodgates for the machines that are today never far from our fingertips.

How did you come to study this subject?

I lived it. As an electrical engineering undergraduate I used both analog and digital computers. My first summer job was programming one of the few digital computers in Manhattan at the time, the IBM 704. In graduate school I wrote my dissertation on the relationship between analog and digital signal processing and my research for the next twenty years or so concentrated on digital signal processing: using computers to process sound and images in digital form.

What physical theory played—and continues to play—a key role in the revolution?

Quantum mechanics, without a doubt. The theory explains the essential nature of noise, which is the natural enemy of analog information; it makes possible the shrinkage and speedup of our electronics (Moore’s law); and it introduces the possibility of an entirely new kind of computer, the quantum computer, which can transcend the power of today’s conventional machines. Quantum mechanics shows that many aspects of the world are essentially discrete in nature, and the change from the classical physics of the nineteenth century to the quantum mechanics of the twentieth is mirrored in the development of our digital information machines.

What mathematical theory plays a key role in understanding the limitations of computers?

Complexity theory and the idea of an intractable problem, as developed by computer scientists. This theme is explored in Part III, first in terms of analog computers, then using Alan Turing’s abstraction of digital computation, which we now call the Turing machine. This leads to the formulation of the most important open question of computer science, does P equal NP? If P equals NP it would mean that any problem where solutions can just be checked fast can be solved fast. This seems like asking a lot and, in fact, most computer scientists believe that P does not equal NP. Problems as hard as any in NP are called NP-complete. The point is that NP-complete problems, like the famous traveling problem, seem to be intrinsically difficult, and cracking any one of them cracks them all.  Their essential difficulty manifests itself, mysteriously, in many different ways in the analog and digital worlds, suggesting, perhaps, that there is an underlying physical law at work. 

What important open question about physics (not mathematics) speaks to the relative power of digital and analog computers?

The extended Church-Turing thesis states that any reasonable computer can be simulated efficiently by a Turing machine. Informally, it means that no computer, even if analog, is more powerful (in an appropriately defined way) than the bare-boned, step-by-step, one-tape Turing machine. The question is open, but many computer scientists believe it to be true. This line of reasoning leads to an important conclusion: if the extended Church-Turing thesis is true, and if P is not equal to NP (which is widely believed), then the digital computer is all we need—Nature is not hiding any computational magic in the analog world.

What does all this have to do with artificial intelligence (AI)?

The brain uses information in both analog and digital form, and some have even suggested that it uses quantum computing. So, the argument goes, perhaps the brain has some special powers that cannot be captured by ordinary computers.

What does philosopher David Chalmers call the hard problem?

We finally reach—in the last chapter—the question of whether the androids we are building will ultimately be conscious. Chalmers calls this the hard problem, and some, including myself, think it unanswerable. An affirmative answer would have real and important consequences, despite the seemingly esoteric nature of the question. If machines can be conscious, and presumably also capable of suffering, then we have a moral responsibility to protect them, and—to put it in human terms—bring them up right. I propose that we must give the coming androids the benefit of the doubt; we owe them the same loving care that we as parents bestow on our biological offspring.

Where do we go from here?

A funny thing happens on the way from chapter 1 to 12. I begin with the modest plan of describing, in the simplest way I can, the ideas behind the analog-to-digital revolution.  We visit along the way some surprising tourist spots: the Antikythera mechanism, a 2000-year old analog computer built by the ancient Greeks; Jacquard’s embroidery machine with its breakthrough stored program; Ada Lovelace’s program for Babbage’s hypothetical computer, predating Alan Turing by a century; and B. F. Skinner’s pigeons trained in the manner of AI to be living smart bombs. We arrive at a collection of deep conjectures about the way the universe works and some challenging moral questions.

Ken Steiglitz is professor emeritus of computer science and senior scholar at Princeton University. His books include Combinatorial OptimizationA Digital Signal Processing Primer, and Snipers, Shills, and Sharks (Princeton). He lives in Princeton, New Jersey.

Matthew Salganik: Invisibilia, the Fragile Families Challenge, and Bit by Bit

Salganik

This week’s episode of Invisibilia featured my research on the Fragile Families Challenge. The Challenge is a scientific mass collaboration that combines predictive modeling, causal inference, and in-depth interviews to yield insights that can improve the lives of disadvantaged children in the United States. Like many research projects, the Fragile Families Challenge emerged from a complex mix of inspirations. But, for me personally, a big part of the Fragile Families Challenge grew out of writing my new book Bit by Bit: Social Research in the Digital Age. In this post, I’ll describe how Bit by Bit helped give birth to the Fragile Families Challenge.

Bit by Bit is about social research in the age of big data. It is for social scientists who want to do more data science, data scientists who want to do more social science, and anyone interested in the combination of these two fields. Rather than being organized around specific data sources or machine learning methods, Bit by Bit progresses through four broad research designs: observing behavior, asking questions, running experiments, and creating mass collaboration. Each of these approaches requires a different relationship between researchers and participants, and each enables us to learn different things.

As I was working on Bit by Bit, many people seemed genuinely excited about most of the book—except the chapter on mass collaboration. When I talked about this chapter with colleagues and friends, I was often greeted with skepticism (or worse). Many of them felt that mass collaboration simply had no place in social research. In fact, at my book manuscript workshop—which was made up of people that I deeply respected—the general consensus seemed to be that I should drop this chapter from Bit by Bit.  But I felt strongly that it should be included, in part because it enabled researchers to do new and different kinds of things. The more time I spent defending the idea of mass collaboration for social research, the more I became convinced that it was really interesting, important, and exciting. So, once I finished up the manuscript for Bit by Bit, I set my sights on designing the mass collaboration that became the Fragile Families Challenge.

The Fragile Families Challenge, described in more detail at the project website and blog, should be seen as part of the larger landscape of mass collaboration research. Perhaps the most well known example of a mass collaboration solving a big intellectual problem is Wikipedia, where a mass collaboration of volunteers created a fantastic encyclopedia that is available to everyone.

Collaboration in research is nothing new, of course. What is new, however, is that the digital age enables collaboration with a much larger and more diverse set of people: the billions of people around the world with Internet access. I expect that these new mass collaborations will yield amazing results not just because of the number of people involved but also because of their diverse skills and perspectives. How can we incorporate everyone with an Internet connection into our research process? What could you do with 100 research assistants? What about 100,000 skilled collaborators?

As I write in Bit by Bit, I think it is helpful to roughly distinguish between three types of mass collaboration projects: human computation, open call, and distributed data collectionHuman computation projects are ideally suited for easy-task-big-scale problems, such as labeling a million images. These are projects that in the past might have been performed by undergraduate research assistants. Contributions to human computation projects don’t require specialized skills, and the final output is typically an average of all of the contributions. A classic example of a human computation project is Galaxy Zoo, where a hundred thousand volunteers helped astronomers classify a million galaxies. Open call projects, on the other hand, are more suited for problems where you are looking for novel answers to clearly formulated questions. In the past, these are projects that might have involved asking colleagues. Contributions to open call projects come from people who may have specialized skills, and the final output is usually the best contribution. A classic example of an open call is the Netflix Prize, where thousands of scientists and hackers worked to develop new algorithms to predict customers’ ratings of movies. Finally, distributed data collection projects are ideally suited for large-scale data collection. These are projects that in the past might have been performed by undergraduate research assistants or survey research companies. Contributions to distributed data collection projects typically come from people who have access to locations that researchers do not, and the final product is a simple collection of the contributions. A classic example of a distributed data collection is eBird, in which hundreds of thousands of volunteers contribute reports about birds they see.

Given this way of organizing things, you can think of the Fragile Families Challenge as an open call project, and when designing the Challenge, I draw inspiration from the other open call projects that I wrote about such as the Netflix Prize, Foldit, and Peer-to-Patent.

If you’d like to learn more about how mass collaboration can be used in social research, I’d recommend reading Chapter 5 of Bit by Bit or watching this talk I gave at Stanford in the Human-Computer Interaction Seminar. If you’d like to learn more about the Fragile Families Challenge, which is ongoing, I’d recommend our project website and blog.  Finally, if you are interested in social science in the age of big data, I’d recommend reading all of Bit by Bit: Social Research in the Digital Age.

Matthew J. Salganik is professor of sociology at Princeton University, where he is also affiliated with the Center for Information Technology Policy and the Center for Statistics and Machine Learning. His research has been funded by Microsoft, Facebook, and Google, and has been featured on NPR and in such publications as the New Yorker, the New York Times, and the Wall Street Journal.

Ben Peters on Keywords: Digital & Analog

This post appears concurrently at Culture Digitally.

The popular opposition between “analog” and “digital,” to put it in a nutshell, is wrong.

Two essays in the Digital Keywords volume—Jonathan Sterne’s “Analog” and my own “Digital”—frame this fundamental point: the analog and the digital are not a pair (itself a rehearsal of that tired digital binary, 0 and 1). Nor are they necessarily separate. Neither mutually exclusive nor embedded, digital and analog techniques should be understood by and independent of their fundamental non-relation. The digital is no simple realm of artificial and discrete symbols nor is the analog everything made of natural and continuous real waves, and certainly is the analog no opposite of digital. For Sterne, the analog is both narrower than we thought, compatible with, and subsequent to the digital. For me, the digital has roots in the extension of human hands.

When we talk about the digital, the analog, or other technical processes, are we sure we know what we are talking about? What, if anything, might these two essays have to offer the conversation?

Peters: Digital

Sterne: Analog

This comment may have been adapted from the introduction to Benjamin Peters’ Digital Keywords: A Vocabulary of Information Society and Culture. 25% discount code in 2016: P06197

Peters

Announcing Digital Keywords (at a discount) and a Call for More Keywords at #dkw

This post appears concurrently at Culture Digitally.

I’m thrilled to announce the official publication, by Princeton University Press, of Digital Keywords: A Vocabulary of Information Society and Culture — on the fortieth anniversary of the publication of Raymond Williams’ classic Keywords: A Vocabulary of Culture and Society.

Princeton University Press is offering a discount of 25% on the book to all Culture Digitally readers. Enter the discount code P06197 at any time, until December 31, 2016.

Check out the table of contents, featuring 25 essays from a great group of scholars, or join the Twitter-verse fun at #dkw:

Also, consider indulging in three minutes with the editor Benjamin Peters (me).

The book offers an immensely teachable collection of 25 short essays from leading scholars, set to change the conversation about our contemporary information society and culture. It also represents a conversation begun two years ago with the readers of Culture Digitally and continued thanks to the support of Fred Appel at Princeton University Press. I would like to continue that conversation today.

The volume covers just 25 terms that the contributors felt were important to contemporary scholarly thinking around the information age. So many more terms warrant similar attention. What are some of the other words you think are key to understanding the modern world and its media, and why? Help out now by tweeting your own keyword of interest with the hashtag #dkw.

(If you do not tweet, your welcome to submit your keywords suggestions into this Google form. If you’d like others to be able to follow up with you, please add your name and institutional affiliation; please do not include bot-readable email addresses, since the file will be public.)

Next week, a list of candidate digital keywords will be drawn from the #dkw Twitter hashtag and the Google form, and then posted to Culture Digitally as a public reference and basis for future work. This open resource will also feature a list of the keywords we arrived at well as more than 200 candidate keywords we listed in the Digital Keywords appendix. The resource is intended as a first step toward building a rolling Rolodex of keywords and their scholars and students. The hope is that this exercise will stimulate future Digital Keywords volumes, teaching, and conversations.

Please come join the conversation in print and online, stay tuned as sample keyword essays follow this month, and enjoy!

In Celebration of Mathematicians

This week San Diego, California is home to the largest mathematics meeting in the world. Hosted by the Mathematical Association of America (MAA) and the American Mathematical Society (AMS), the 2013 Joint Mathematics Meeting is more than just panels and presentations—it is a mass gathering of people who are passionate about mathematics.

Mathematicians come from diverse backgrounds, maintain varying interests, and have their own unique journeys. In Fascinating Mathematical People: Interviews and Memoirs, Fern Hunt describes what it was like to be among the first black women to earn a PhD in mathematics, Harold Bacon makes trips to Alcatraz to help a prisoner learn calculus, and Thomas Banchoff, who first became interested in the fourth dimension while reading a Captain Marvel comic, relates his fascinating friendship with Salvador Dalí and their shared passion for art, mathematics, and the profound connection between the two. But whether they view mathematics as reason, art, or something else, all mathematicians are in search of truth.

This week is not only an endeavor in furthering the pursuit of knowledge, but a celebration of the gifted mathematical intellectuals who shape society, culture, and our awareness and understanding of ourselves and the world in which we live. Browse our website or latest mathematics catalog to see more by and about mathematicians, such as Paul J. Nahin’s The Logician and the Engineer: How George Boole and Claude Shannon Created the Information Age. If you’re at the Joint Mathematics Meeting, you may even visit us at booth 311. As Underwood Dudley wrote in “What Is Mathematics For?” included in The Best Writing on Mathematics: 2011 (The Best Writing on Mathematics: 2012 also available.), “What mathematics education is for is not for jobs. It is to teach the race to reason,” and we’ve all got room to learn.