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Richard Feynman and The Connection Machine

Posted on Wednesday, February 8th, 02017 by Ahmed Kabil
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One of the most popular pieces of writing on our site is Long Now co-founder Danny Hillis’ remembrance of building an experimental computer with theoretical physicist Richard Feynman. It’s easy to see why: Hillis’ reminisces about Feynman’s final years as they worked together on the Connection Machine are at once illuminating and poignant, and paint a picture of a man who was beloved as much for his eccentricity as his genius.

Photo by Faustin Bray

Photo by Faustin Bray

Richard Feynman and The Connection Machine

by W. Daniel Hillis for Physics Today

Reprinted with permission from Phys. Today 42(2), 78 (01989). Copyright 01989, American Institute of Physics.

One day when I was having lunch with Richard Feynman, I mentioned to him that I was planning to start a company to build a parallel computer with a million processors. His reaction was unequivocal, “That is positively the dopiest idea I ever heard.” For Richard a crazy idea was an opportunity to either prove it wrong or prove it right. Either way, he was interested. By the end of lunch he had agreed to spend the summer working at the company.

Richard’s interest in computing went back to his days at Los Alamos, where he supervised the “computers,” that is, the people who operated the mechanical calculators. There he was instrumental in setting up some of the first plug-programmable tabulating machines for physical simulation. His interest in the field was heightened in the late 1970’s when his son, Carl, began studying computers at MIT.

I got to know Richard through his son. I was a graduate student at the MIT Artificial Intelligence Lab and Carl was one of the undergraduates helping me with my thesis project. I was trying to design a computer fast enough to solve common sense reasoning problems. The machine, as we envisioned it, would contain a million tiny computers, all connected by a communications network. We called it a “Connection Machine.” Richard, always interested in his son’s activities, followed the project closely. He was skeptical about the idea, but whenever we met at a conference or I visited CalTech, we would stay up until the early hours of the morning discussing details of the planned machine. The first time he ever seemed to believe that we were really going to try to build it was the lunchtime meeting.

Richard arrived in Boston the day after the company was incorporated. We had been busy raising the money, finding a place to rent, issuing stock, etc. We set up in an old mansion just outside of the city, and when Richard showed up we were still recovering from the shock of having the first few million dollars in the bank. No one had thought about anything technical for several months. We were arguing about what the name of the company should be when Richard walked in, saluted, and said, “Richard Feynman reporting for duty. OK, boss, what’s my assignment?” The assembled group of not-quite-graduated MIT students was astounded.

After a hurried private discussion (“I don’t know, you hired him…”), we informed Richard that his assignment would be to advise on the application of parallel processing to scientific problems.

“That sounds like a bunch of baloney,” he said. “Give me something real to do.”

So we sent him out to buy some office supplies. While he was gone, we decided that the part of the machine that we were most worried about was the router that delivered messages from one processor to another. We were not sure that our design was going to work. When Richard returned from buying pencils, we gave him the assignment of analyzing the router.

The Machine

The router of the Connection Machine was the part of the hardware that allowed the processors to communicate. It was a complicated device; by comparison, the processors themselves were simple. Connecting a separate communication wire between each pair of processors was impractical since a million processors would require $10^{12]$ wires. Instead, we planned to connect the processors in a 20-dimensional hypercube so that each processor would only need to talk to 20 others directly. Because many processors had to communicate simultaneously, many messages would contend for the same wires. The router’s job was to find a free path through this 20-dimensional traffic jam or, if it couldn’t, to hold onto the message in a buffer until a path became free. Our question to Richard Feynman was whether we had allowed enough buffers for the router to operate efficiently.

During those first few months, Richard began studying the router circuit diagrams as if they were objects of nature. He was willing to listen to explanations of how and why things worked, but fundamentally he preferred to figure out everything himself by simulating the action of each of the circuits with pencil and paper.

In the meantime, the rest of us, happy to have found something to keep Richard occupied, went about the business of ordering the furniture and computers, hiring the first engineers, and arranging for the Defense Advanced Research Projects Agency (DARPA) to pay for the development of the first prototype. Richard did a remarkable job of focusing on his “assignment,” stopping only occasionally to help wire the computer room, set up the machine shop, shake hands with the investors, install the telephones, and cheerfully remind us of how crazy we all were. When we finally picked the name of the company, Thinking Machines Corporation, Richard was delighted. “That’s good. Now I don’t have to explain to people that I work with a bunch of loonies. I can just tell them the name of the company.”

The technical side of the project was definitely stretching our capacities. We had decided to simplify things by starting with only 64,000 processors, but even then the amount of work to do was overwhelming. We had to design our own silicon integrated circuits, with processors and a router. We also had to invent packaging and cooling mechanisms, write compilers and assemblers, devise ways of testing processors simultaneously, and so on. Even simple problems like wiring the boards together took on a whole new meaning when working with tens of thousands of processors. In retrospect, if we had had any understanding of how complicated the project was going to be, we never would have started.

‘Get These Guys Organized’

I had never managed a large group before and I was clearly in over my head. Richard volunteered to help out. “We’ve got to get these guys organized,” he told me. “Let me tell you how we did it at Los Alamos.”

Every great man that I have known has had a certain time and place in their life that they use as a reference point; a time when things worked as they were supposed to and great things were accomplished. For Richard, that time was at Los Alamos during the Manhattan Project. Whenever things got “cockeyed,” Richard would look back and try to understand how now was different than then. Using this approach, Richard decided we should pick an expert in each area of importance in the machine, such as software or packaging or electronics, to become the “group leader” in this area, analogous to the group leaders at Los Alamos.

Part Two of Feynman’s “Let’s Get Organized” campaign was that we should begin a regular seminar series of invited speakers who might have interesting things to do with our machine. Richard’s idea was that we should concentrate on people with new applications, because they would be less conservative about what kind of computer they would use. For our first seminar he invited John Hopfield, a friend of his from CalTech, to give us a talk on his scheme for building neural networks. In 1983, studying neural networks was about as fashionable as studying ESP, so some people considered John Hopfield a little bit crazy. Richard was certain he would fit right in at Thinking Machines Corporation.

What Hopfield had invented was a way of constructing an [associative memory], a device for remembering patterns. To use an associative memory, one trains it on a series of patterns, such as pictures of the letters of the alphabet. Later, when the memory is shown a new pattern it is able to recall a similar pattern that it has seen in the past. A new picture of the letter “A” will “remind” the memory of another “A” that it has seen previously. Hopfield had figured out how such a memory could be built from devices that were similar to biological neurons.

Not only did Hopfield’s method seem to work, but it seemed to work well on the Connection Machine. Feynman figured out the details of how to use one processor to simulate each of Hopfield’s neurons, with the strength of the connections represented as numbers in the processors’ memory. Because of the parallel nature of Hopfield’s algorithm, all of the processors could be used concurrently with 100\% efficiency, so the Connection Machine would be hundreds of times faster than any conventional computer.

An Algorithm For Logarithms

Feynman worked out the program for computing Hopfield’s network on the Connection Machine in some detail. The part that he was proudest of was the subroutine for computing logarithms. I mention it here not only because it is a clever algorithm, but also because it is a specific contribution Richard made to the mainstream of computer science. He invented it at Los Alamos.

Consider the problem of finding the logarithm of a fractional number between 1.0 and 2.0 (the algorithm can be generalized without too much difficulty). Feynman observed that any such number can be uniquely represented as a product of numbers of the form $1 + 2^{-k]$, where $k$ is an integer. Testing each of these factors in a binary number representation is simply a matter of a shift and a subtraction. Once the factors are determined, the logarithm can be computed by adding together the precomputed logarithms of the factors. The algorithm fit especially well on the Connection Machine, since the small table of the logarithms of $1 + 2^{-k]$ could be shared by all the processors. The entire computation took less time than division.

Concentrating on the algorithm for a basic arithmetic operation was typical of Richard’s approach. He loved the details. In studying the router, he paid attention to the action of each individual gate and in writing a program he insisted on understanding the implementation of every instruction. He distrusted abstractions that could not be directly related to the facts. When several years later I wrote a general interest article on the Connection Machine for [Scientific American], he was disappointed that it left out too many details. He asked, “How is anyone supposed to know that this isn’t just a bunch of crap?”

Feynman’s insistence on looking at the details helped us discover the potential of the machine for numerical computing and physical simulation. We had convinced ourselves at the time that the Connection Machine would not be efficient at “number-crunching,” because the first prototype had no special hardware for vectors or floating point arithmetic. Both of these were “known” to be requirements for number-crunching. Feynman decided to test this assumption on a problem that he was familiar with in detail: quantum chromodynamics.

Quantum chromodynamics is a theory of the internal workings of atomic particles such as protons. Using this theory it is possible, in principle, to compute the values of measurable physical quantities, such as a proton’s mass. In practice, such a computation requires so much arithmetic that it could keep the fastest computers in the world busy for years. One way to do this calculation is to use a discrete four-dimensional lattice to model a section of space-time. Finding the solution involves adding up the contributions of all of the possible configurations of certain matrices on the links of the lattice, or at least some large representative sample. (This is essentially a Feynman path integral.) The thing that makes this so difficult is that calculating the contribution of even a single configuration involves multiplying the matrices around every little loop in the lattice, and the number of loops grows as the fourth power of the lattice size. Since all of these multiplications can take place concurrently, there is plenty of opportunity to keep all 64,000 processors busy.

To find out how well this would work in practice, Feynman had to write a computer program for QCD. Since the only computer language Richard was really familiar with was Basic, he made up a parallel version of Basic in which he wrote the program and then simulated it by hand to estimate how fast it would run on the Connection Machine.

He was excited by the results. “Hey Danny, you’re not going to believe this, but that machine of yours can actually do something [useful]!” According to Feynman’s calculations, the Connection Machine, even without any special hardware for floating point arithmetic, would outperform a machine that CalTech was building for doing QCD calculations. From that point on, Richard pushed us more and more toward looking at numerical applications of the machine.

By the end of that summer of 1983, Richard had completed his analysis of the behavior of the router, and much to our surprise and amusement, he presented his answer in the form of a set of partial differential equations. To a physicist this may seem natural, but to a computer designer, treating a set of boolean circuits as a continuous, differentiable system is a bit strange. Feynman’s router equations were in terms of variables representing continuous quantities such as “the average number of 1 bits in a message address.” I was much more accustomed to seeing analysis in terms of inductive proof and case analysis than taking the derivative of “the number of 1’s” with respect to time. Our discrete analysis said we needed seven buffers per chip; Feynman’s equations suggested that we only needed five. We decided to play it safe and ignore Feynman.

The decision to ignore Feynman’s analysis was made in September, but by next spring we were up against a wall. The chips that we had designed were slightly too big to manufacture and the only way to solve the problem was to cut the number of buffers per chip back to five. Since Feynman’s equations claimed we could do this safely, his unconventional methods of analysis started looking better and better to us. We decided to go ahead and make the chips with the smaller number of buffers.

Fortunately, he was right. When we put together the chips the machine worked. The first program run on the machine in April of 1985 was Conway’s game of Life.

Cellular Automata

The game of Life is an example of a class of computations that interested Feynman called [cellular automata]. Like many physicists who had spent their lives going to successively lower and lower levels of atomic detail, Feynman often wondered what was at the bottom. One possible answer was a cellular automaton. The notion is that the “continuum” might, at its lowest levels, be discrete in both space and time, and that the laws of physics might simply be a macro-consequence of the average behavior of tiny cells. Each cell could be a simple automaton that obeys a small set of rules and communicates only with its nearest neighbors, like the lattice calculation for QCD. If the universe in fact worked this way, then it presumably would have testable consequences, such as an upper limit on the density of information per cubic meter of space.

The notion of cellular automata goes back to von Neumann and Ulam, whom Feynman had known at Los Alamos. Richard’s recent interest in the subject was motivated by his friends Ed Fredkin and Stephen Wolfram, both of whom were fascinated by cellular automata models of physics. Feynman was always quick to point out to them that he considered their specific models “kooky,” but like the Connection Machine, he considered the subject sufficiently crazy to put some energy into.

There are many potential problems with cellular automata as a model of physical space and time; for example, finding a set of rules that obeys special relativity. One of the simplest problems is just making the physics so that things look the same in every direction. The most obvious pattern of cellular automata, such as a fixed three-dimensional grid, have preferred directions along the axes of the grid. Is it possible to implement even Newtonian physics on a fixed lattice of automata?

Feynman had a proposed solution to the anisotropy problem which he attempted (without success) to work out in detail. His notion was that the underlying automata, rather than being connected in a regular lattice like a grid or a pattern of hexagons, might be randomly connected. Waves propagating through this medium would, on the average, propagate at the same rate in every direction.

Cellular automata started getting attention at Thinking Machines when Stephen Wolfram, who was also spending time at the company, suggested that we should use such automata not as a model of physics, but as a practical method of simulating physical systems. Specifically, we could use one processor to simulate each cell and rules that were chosen to model something useful, like fluid dynamics. For two-dimensional problems there was a neat solution to the anisotropy problem since [Frisch, Hasslacher, Pomeau] had shown that a hexagonal lattice with a simple set of rules produced isotropic behavior at the macro scale. Wolfram used this method on the Connection Machine to produce a beautiful movie of a turbulent fluid flow in two dimensions. Watching the movie got all of us, especially Feynman, excited about physical simulation. We all started planning additions to the hardware, such as support of floating point arithmetic that would make it possible for us to perform and display a variety of simulations in real time.

Feynman the Explainer

In the meantime, we were having a lot of trouble explaining to people what we were doing with cellular automata. Eyes tended to glaze over when we started talking about state transition diagrams and finite state machines. Finally Feynman told us to explain it like this,

“We have noticed in nature that the behavior of a fluid depends very little on the nature of the individual particles in that fluid. For example, the flow of sand is very similar to the flow of water or the flow of a pile of ball bearings. We have therefore taken advantage of this fact to invent a type of imaginary particle that is especially simple for us to simulate. This particle is a perfect ball bearing that can move at a single speed in one of six directions. The flow of these particles on a large enough scale is very similar to the flow of natural fluids.”

This was a typical Richard Feynman explanation. On the one hand, it infuriated the experts who had worked on the problem because it neglected to even mention all of the clever problems that they had solved. On the other hand, it delighted the listeners since they could walk away from it with a real understanding of the phenomenon and how it was connected to physical reality.

We tried to take advantage of Richard’s talent for clarity by getting him to critique the technical presentations that we made in our product introductions. Before the commercial announcement of the Connection Machine CM-1 and all of our future products, Richard would give a sentence-by-sentence critique of the planned presentation. “Don’t say `reflected acoustic wave.’ Say [echo].” Or, “Forget all that `local minima’ stuff. Just say there’s a bubble caught in the crystal and you have to shake it out.” Nothing made him angrier than making something simple sound complicated.

Getting Richard to give advice like that was sometimes tricky. He pretended not to like working on any problem that was outside his claimed area of expertise. Often, at Thinking Machines when he was asked for advice he would gruffly refuse with “That’s not my department.” I could never figure out just what his department was, but it did not matter anyway, since he spent most of his time working on those “not-my-department” problems. Sometimes he really would give up, but more often than not he would come back a few days after his refusal and remark, “I’ve been thinking about what you asked the other day and it seems to me…” This worked best if you were careful not to expect it.

I do not mean to imply that Richard was hesitant to do the “dirty work.” In fact, he was always volunteering for it. Many a visitor at Thinking Machines was shocked to see that we had a Nobel Laureate soldering circuit boards or painting walls. But what Richard hated, or at least pretended to hate, was being asked to give advice. So why were people always asking him for it? Because even when Richard didn’t understand, he always seemed to understand better than the rest of us. And whatever he understood, he could make others understand as well. Richard made people feel like a child does, when a grown-up first treats him as an adult. He was never afraid of telling the truth, and however foolish your question was, he never made you feel like a fool.

The charming side of Richard helped people forgive him for his uncharming characteristics. For example, in many ways Richard was a sexist. Whenever it came time for his daily bowl of soup he would look around for the nearest “girl” and ask if she would fetch it to him. It did not matter if she was the cook, an engineer, or the president of the company. I once asked a female engineer who had just been a victim of this if it bothered her. “Yes, it really annoys me,” she said. “On the other hand, he is the only one who ever explained quantum mechanics to me as if I could understand it.” That was the essence of Richard’s charm.

A Kind Of Game

Richard worked at the company on and off for the next five years. Floating point hardware was eventually added to the machine, and as the machine and its successors went into commercial production, they were being used more and more for the kind of numerical simulation problems that Richard had pioneered with his QCD program. Richard’s interest shifted from the construction of the machine to its applications. As it turned out, building a big computer is a good excuse to talk to people who are working on some of the most exciting problems in science. We started working with physicists, astronomers, geologists, biologists, chemists — everyone of them trying to solve some problem that it had never been possible to solve before. Figuring out how to do these calculations on a parallel machine requires understanding of the details of the application, which was exactly the kind of thing that Richard loved to do.

For Richard, figuring out these problems was a kind of a game. He always started by asking very basic questions like, “What is the simplest example?” or “How can you tell if the answer is right?” He asked questions until he reduced the problem to some essential puzzle that he thought he would be able to solve. Then he would set to work, scribbling on a pad of paper and staring at the results. While he was in the middle of this kind of puzzle solving he was impossible to interrupt. “Don’t bug me. I’m busy,” he would say without even looking up. Eventually he would either decide the problem was too hard (in which case he lost interest), or he would find a solution (in which case he spent the next day or two explaining it to anyone who listened). In this way he worked on problems in database searches, geophysical modeling, protein folding, analyzing images, and reading insurance forms.

The last project that I worked on with Richard was in simulated evolution. I had written a program that simulated the evolution of populations of sexually reproducing creatures over hundreds of thousands of generations. The results were surprising in that the fitness of the population made progress in sudden leaps rather than by the expected steady improvement. The fossil record shows some evidence that real biological evolution might also exhibit such “punctuated equilibrium,” so Richard and I decided to look more closely at why it happened. He was feeling ill by that time, so I went out and spent the week with him in Pasadena, and we worked out a model of evolution of finite populations based on the Fokker Planck equations. When I got back to Boston I went to the library and discovered a book by Kimura on the subject, and much to my disappointment, all of our “discoveries” were covered in the first few pages. When I called back and told Richard what I had found, he was elated. “Hey, we got it right!” he said. “Not bad for amateurs.”

In retrospect I realize that in almost everything that we worked on together, we were both amateurs. In digital physics, neural networks, even parallel computing, we never really knew what we were doing. But the things that we studied were so new that no one else knew exactly what they were doing either. It was amateurs who made the progress.

Telling The Good Stuff You Know

Actually, I doubt that it was “progress” that most interested Richard. He was always searching for patterns, for connections, for a new way of looking at something, but I suspect his motivation was not so much to understand the world as it was to find new ideas to explain. The act of discovery was not complete for him until he had taught it to someone else.

I remember a conversation we had a year or so before his death, walking in the hills above Pasadena. We were exploring an unfamiliar trail and Richard, recovering from a major operation for the cancer, was walking more slowly than usual. He was telling a long and funny story about how he had been reading up on his disease and surprising his doctors by predicting their diagnosis and his chances of survival. I was hearing for the first time how far his cancer had progressed, so the jokes did not seem so funny. He must have noticed my mood, because he suddenly stopped the story and asked, “Hey, what’s the matter?”

I hesitated. “I’m sad because you’re going to die.”

“Yeah,” he sighed, “that bugs me sometimes too. But not so much as you think.” And after a few more steps, “When you get as old as I am, you start to realize that you’ve told most of the good stuff you know to other people anyway.”

We walked along in silence for a few minutes. Then we came to a place where another trail crossed and Richard stopped to look around at the surroundings. Suddenly a grin lit up his face. “Hey,” he said, all trace of sadness forgotten, “I bet I can show you a better way home.”

And so he did.

Edge Question 02017

Posted on Friday, January 20th, 02017 by Ahmed Kabil
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Spiders 2013 by Katinka Matson

It’s been an annual tradition since 01998: with a new year comes a new Edge question.

Every January, John Brockman presents the members of his online salon with a question that elicits discussion about some of the biggest intellectual and scientific issues of our time. Previous iterations have included prompts such as “What should we be worried about?” or  “What do you think about machines that think?” The essay responses – in excess of a hundred each year – offer a wealth of insight into the direction of today’s cultural forces, scientific innovations, and global trends.

This year, Brockman asks:

What scientific term or concept ought to be more widely known?

The extensive collection of answers includes contributions by several Long Now Board members, fellows, and past (and future!) SALT speakers:

George Dyson, who spoke at Long Now in 02013, says the Reynolds Number from fluid dynamics can be applied to non-traditional domains to understand why things might go smoothly for a while, and then all of a sudden don’t.

Long Now Board Member Stewart Brand says genetic rescue can help threatened wildlife populations by restoring genetic diversity.

Priyamvada Natarajan, who spoke at Long Now in 02016, describes how the bending of light, or gravitational lensing, is a consequence of Einstein’s re-conceptualization of gravity in his theory of relativity.

Samuel Arbesman, who spoke at the Interval in 02016, says “magical” self-replicating computer programs known as quines underscore the limits of mathematics and computer science while demonstrating that reproduction isn’t limited to the domain of the biological.

Michael Shermer, who spoke at Long Now in 02015, says the very human tendency to be “preternaturally pessimistic” has an evolutionary basis. Negativity bias, which can be observed across all domains of life, is a holdover from an evolutionary past where existence was more dangerous, so over-reacting to threats offered more of a pay-off than under-reacting.

Long Now Board Member Brian Eno sets his sights on confirmation bias after a particularly divisive election season playing out on social media revealed that more information does not necessarily equal better decisions.

George Church of Long Now’s Revive and Restore says that while DNA may be one of the most widely known scientific terms, far too few people understand the DNA in their own bodies. With DNA tests as low as $499, Church says there’s no reason not to get your DNA tested, especially when it could allow for preventative measures when it comes to genetic diseases.

Brian Christian, who spoke at Long Now in 02016, argues that human culture progresses via the retention of youthful traits into adulthood, a process known as neoteny.

Long Now Board Member Kevin Kelly argues that the best way to steer clear of failure is by letting go of success once it is achieved, thereby avoiding premature optimization.

Seth Lloyd, who spoke at Long Now in 02016, explains the accelerating spread of digital information using a centuries-old scientific concept from classical mechanics called the virial theorem.

Long Now Board Member Danny Hillis unpacks impedance matching, or adding elements to a system so that it accepts energy more efficiently. He predicts a future where impedance matching could help cool the earth by adding tiny particles of dust to our stratosphere that would reflect away the sun’s infrared waves.

Steven Pinker, who spoke at Long Now in 02012, argues that the meaning of life and human purpose lies in the second law of thermodynamics. Pinker believes our deeply-engrained habit of under-appreciating the universe’s tendency towards disorder is “a major source of human folly.”

Long Now Board Member Paul Saffo says that at the heart of today’s biggest challenges, from sustaining mega-cities to overpopulation to information overload, are hidden laws of scale described by Haldane’s Rule of the Right Size.

Martin Rees, who spoke at Long Now in 02010, says we may be living in a multiverse.

These are just a few of this year’s thought-provoking answers; you can read the full collection here.

50% Long Now Member Discount for REAL2016 Conference Event at Fort Mason Center

Posted on Tuesday, March 1st, 02016 by Andrew Warner
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Bay-Lights-REAL2016

On March 8th & 9th at Fort Mason Center, Autodesk will be hosting their annual conference event REAL2016, which focuses on new 3D technologies, including 3D modeling, 3D printing, laser scanning, augmented reality, and fabrication. Autodesk has generously offered Long Now Members a 50% discount for the event, please check your email for instructions on how to redeem this discount.

The programming over 2 days features talks, panels, demonstrations and a startup competition – all centered around capture, compute and create technologies and their increasing convergence.

Alexander Rose, Long Now’s Executive Director, will also be speaking on the Futures Panel with designer Syd Mead and others on Wednesday March 9 starting at 11:20am.

Do stop by and visit The Interval while you are at the event, we’ll be open from 10am to midnight offering thoughtful coffee and cocktails. Private events and ticketed lectures are noted on The Interval website and our Twitter.

We hope that many of you will be able to attend!

Meet Otto — The Interval’s Chalkboard Drawing Machine

Posted on Tuesday, January 26th, 02016 by Andrew Warner
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One of The Interval’s most distinctive features is “Otto”, our chalkboard drawing machine. Otto is a vector drawing machine that was built by Swiss artist Jürg Lehni. In this short video piece by Fusion media, you get to see Otto making a drawing and hear directly from Jürg about his work. To see Otto drawing in person, get tickets to one of our Conversations at The Interval talks on Tuesday nights.

Everyone watching Otto the chalkboard drawing machine at the Interval St George Spirits event, 02015

Edge Question 02016

Posted on Tuesday, January 12th, 02016 by Andrew Warner
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double_tulips_2016.640

It’s been an annual tradition since 01998: with a new year comes a new Edge question.

Every January, John Brockman presents the members of his online salon with a question that elicits discussion about some of the biggest intellectual and scientific issues of our time. Previous iterations have included prompts such as “What should we be worried about?” or “What scientific concept would improve everybody’s cognitive toolkit?” The essay responses – in excess of a hundred each year – offer a wealth of insight into the direction of today’s cultural forces, scientific innovations, and global trends.

This year, Brockman asks:

WHAT DO YOU CONSIDER THE MOST INTERESTING RECENT [SCIENTIFIC] NEWS? WHAT MAKES IT IMPORTANT?

Scientific topics receiving prominent play in newspapers and magazines over the past several years include molecular biology, artificial intelligence, artificial life, chaos theory, massive parallelism, neural nets, the inflationary universe, fractals, complex adaptive systems, superstrings, biodiversity, nanotechnology, the human genome, expert systems, punctuated equilibrium, cellular automata, fuzzy logic, space biospheres, the Gaia hypothesis, virtual reality, cyberspace, and teraflop machines. … Unlike previous intellectual pursuits, the achievements of the third culture are not the marginal disputes of a quarrelsome mandarin class: they will affect the lives of everybody on the planet.

You might think that the above list of topics is a preamble for the Edge Question 2016, but you would be wrong. It was a central point in my essay, “The Third Culture,” published 25 years ago in The Los Angeles Times, 1991 (see below). The essay, a manifesto, was a collaborative effort, with input from Stephen Jay Gould, Murray Gell-Mann, Richard Dawkins, Daniel C. Dennett, Jared Diamond, Stuart Kauffman, Nicholas Humphrey, among other distinguished scientists and thinkers. It proclaimed:

The third culture consists of those scientists and other thinkers in the empirical world who, through their work and expository writing, are taking the place of the traditional intellectual in rendering visible the deeper meanings of our lives, redefining who and what we are.

“The wide appeal of the third-culture thinkers,” I wrote, “is not due solely to their writing ability; what traditionally has been called ‘science’ has today become ‘public culture.’Stewart Brand writes that ‘Science is the only news. When you scan through a newspaper or magazine, all the human interest stuff is the same old he-said-she-said, the politics and economics the same sorry cyclic dramas, the fashions a pathetic illusion of newness, and even the technology is predictable if you know the science. Human nature doesn’t change much; science does, and the change accrues, altering the world irreversibly.’ We now live in a world in which the rate of change is the biggest change.” Science has thus become a big story, if not the big story: news that will stay news.

This is evident by the continued relevance today of the scientific topics in the 1991 essay that were all in play before the Web, social media, mobile communications, deep learning, big data. Time for an update. …

Contributors include: Long Now President Stewart Brand, Long Now Board Members Kevin Kelly, Peter Schwartz, Paul Saffo, and many of our past Seminar speakers.

 

 

 

 

DOTS—Long-Term, Human-Readable Archival Data Storage

Posted on Sunday, December 27th, 02015 by Andrew Warner
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Via Alexis Madrigal’s TinyLetter, Real FutureDOTS is a Digital Optical Technology System developed by Eastman Kodak in the 01990s, and abandoned in 02002. In 02008, a team of digital imaging experts and former Kodak employees founded Group 47 to buy the DOTS patents and continue development. They succeeded in 2011.

Unlike magnetic and optical storage solutions, which must be protected from data corruption, physical degradation, and environmental damage, DOTS physically encodes data on an archival tape coated in a phase-change alloy. The alloy is resistant to temperature extremes, electromagnetic pulses, and other common environmental hazards (though it is vulnerable to acids—including, apparently, Sprite), while the tape itself is made of archival materials.

Just as importantly, DOTS is also meant to withstand the onset of a digital dark age. The data, which may include words and images as well as digitally encoded information, is transferred to the alloy using a laser, which changes the alloy’s index of refraction. In other words, the blank portions of the tape are shiny, while the data-bearing portions are dull. The result, though written at a microscopic scale, is visible under normal magnification.

Each tape begins with a Rosetta Leader(TM)—a human-readable, microfiche-scale, page that explains the storage format, and includes instructions for building a DOTS reader. According to Group 47 president Rob Hummel, even if there were no way to build the reader, it would be possible to decode the data using nothing more than the instructions in the Rosetta Leader and a camera. “While it would be very tedious,” he says in a video describing the system, “at least it wouldn’t be impossible.”

In tests, DOTS has been shown to remain archival for at least 100 years—short enough, as long-term thinking goes, but far longer than magnetic tape storage or the always-evolving hardware needed to read current digital storage media. As the Group 47 website notes, “Competing technologies (magnetic tape, hard drives) require a complex and expensive system of data migration within five years, as, beyond this, there is an unacceptably high probability of data loss due to catastrophic media and/or data degradation or outright failure.”

“100 Years of Robot Art and Science in the Bay Area” Long Conversation November 20th 02015

Posted on Monday, November 16th, 02015 by Andrew Warner
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Running Machine and Dual Mule

On November 20, 02015, our Executive Director Alexander Rose is helping organize a free “Long Conversation” about the history of robots with UC Berkeley’s Ken Goldberg at “Friday Nights at the DeYoung”.

The event starts at 6:30, with doors at 6:00pm in the Koret Auditorium of the De Young Museum.

A “Long Conversation” is a relay style speaking event. In this case, it is a 2 hour relay of 10 minute public conversations between 11 pairs of speakers who will be speaking on “100 Years of Robot Art and Science in the Bay Area”. The conversation is part of a larger exhibit honoring the 100 year anniversary of the 1915 Panama-Pacific International Exposition. The participants of this conversation include:

  • Josette Melchor (Grey Area Foundation for the Arts)
  • Dorothy R. Santos (writer, curator)
  • Tim Roseborough (artist, musician, former Kimball Artist-in-Residence)
  • John Markoff (author of Machines of Loving Grace)
  • Karen Marcelo (dorkbotSF)
  • David Pescovitz (Institute for the Future)
  • Catharine Clark (Catharine Clark Gallery)
  • Alexander Rose (director, Long Now Foundation)
  • Pieter Abbeel (professor, Computer Sciences, UC Berkeley)
  • Terry Winograd (Computer Science department, Stanford Univeristy)
  • Kal Spelletich (Seeman)
  • Artist Jenny Odell, who will be providing live images (VJing)

Friday Nights at the de Young are after-hours art happenings that include a mix of live music, dance and theater performances, film screenings, panel discussions, lectures, artist demonstrations, hands-on art activities, and exhibition tours. Local artists conduct drop-in workshops, debut new commissions, display their art in the Kimball Education Gallery, and take part in conversations about the creative process. The café offers a delicious prix-fixe menu and specialty cocktails, and the Hamon Tower observation level is open until 8 pm. Artists-in-Residence, curators, scholars, and arts educators play active roles in making Friday Nights an engaging museum experience.

We hope to see you there.

Live audio stream for John Markoff at The Interval on September 29, 02015

Posted on Monday, September 28th, 02015 by Mikl Em
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Long Now members can tune in for a live audio simulcast of this sold out event starting at 7:15 PT, September 29

Veteran technology writer John Markoff speaks in Long Now’s “Conversations at The Interval” series this Tuesday. He will discuss his new book Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots which covers the birth of artificial intelligence in the 1950s all the way up to the consumer and industrial robotics innovations of today. Long Now’s Paul Saffo will interviewed Markoff onstage.

John Markoff at The Interval, September 29, 02015

Tickets to this talk sold out very quickly, as our Interval events often do. Due to the huge interest in this event, Long Now will be live audio-streaming Tuesday’s talk for members.

You can join Long Now for just $8/month which includes tickets to Seminars, HD video of 12 years of Long Now talks, and many other benefits.

Current Long Now members, just login on the member site. The stream will begin at 7:15pm Pacific.

Machines of Loving Grace is the first comprehensive study to place [robots] in the context of the cloud-based intelligence

—George Dyson, author of Turing’s Cathedral: The Origins of the Digital Universe

In recent years, the pace of technological change has accelerated dramatically, posing an ethical quandary. If humans delegate decisions to machines, who will be responsible for the consequences? Drawing on his forty years covering the tech industry, Markoff conducted numerous interviews and extensive research to assemble this history and poise key questions about how we will cohabitate with our robotic creations.

Long Now members can tune in for a live audio simulcast at 7:15 PT on September 29

This will be the third time we have live streamed an Interval event. Due to our limited resources, it is not possible to do so for most talks. We do plan to release Interval talks as podcasts and video on the Long Now site (similarly to our Seminar series).

We also plan to stream the talk by Andy Weir author of The Martian which takes place at The Interval on October 27, 02015. Tickets will go on sale for that talk two weeks beforehand and we expect it will sell out quickly.

Andy Weir at The Interval, October 27, 02015

Long Now is looking for a major sponsor to fund the cost of producing the series to the standard of our Seminar media. We are also seeking a sponsor to support more regular streaming of Interval events. Sponsorship inquiries are welcome.

2,000-Year Old Termite Mounds Found in Central Africa

Posted on Friday, August 28th, 02015 by Charlotte Hajer
link   Categories: Long Term Science, Millennial Precedent, Technology, The Big Here   chat 0 Comments

Much like ants, termites are a testament to the adage that a whole is greater than the sum of its parts. A single termite is an almost translucent creature, no more than a few millimeters long. But put several thousand of them together, and they become capable of building expansive structures, some reaching up as high as 17 feet.

Moreover, a recent discovery suggests that some termite mounds are not only very tall, but also very old. A joint Belgian-Congolese team of geologists carbon-dated a set of four mounds in the Congo’s Miombo Woods, and found them to be between 680 and 2200 years old. Though the oldest of these had been abandoned centuries ago, the researchers infer from their findings that some species of termites can inhabit one and the same structure for several hundreds of years. This far exceeds the lifespan of any one colony (which matches that of its queen), suggesting that a kind of intergenerational inheritance passes the mound from one queen to the next.

Swarm intelligence, it seems, leads not only to highly organized labor and solid engineering, but also to long-term thinking.

Paul Saffo Featured on Singularity Hub’s Ask An Expert Series

Posted on Monday, August 17th, 02015 by Charlotte Hajer
link   Categories: Futures, Long Term Thinking, Technology   chat 0 Comments

This week’s episode of Singularity Hub’s Ask an Expert features Long Now Board member Paul Saffo.

Ask an Expert is a new web series in which, well, experts answer tweeted questions about the future of technology. In this episode, Paul discusses virtual reality, weighs in on the word ‘disrupt’, and considers the possibility of having a wooly mammoth for a pet – with a quick shout-out to Long Now’s Revive & Restore project.

To see more videos in the Ask an Expert series, you can visit this page. And if you have a question of your own, you can tweet it to @singularityu with the hashtag #AskSU.