Posts Tagged ‘Babbage’
“There are only two ways to live your life. One is as though nothing is a miracle. The other is as though everything is a miracle.”*…
… Indeed, the same might be said of life itself. David Krakauer and Chris Kempes of the Santa Fe Institute suggest that life is starting to look a lot less like an outcome of chemistry and physics, and more like a computational process…
… Today, doubts about conventional explanations of life are growing and a wave of new general theories has emerged to better define our origins. These suggest that life doesn’t only depend on amino acids, DNA, proteins and other forms of matter. Today, it can be digitally simulated, biologically synthesised or made from entirely different materials to those that allowed our evolutionary ancestors to flourish. These and other possibilities are inviting researchers to ask more fundamental questions: if the materials for life can radically change – like the materials for computation – what stays the same? Are there deeper laws or principles that make life possible?
Our planet appears to be exceptionally rare. Of the thousands that have been identified by astronomers, only one has shown any evidence of life. Earth is, in the words of Carl Sagan, a ‘lonely speck in the great enveloping cosmic dark.’ This apparent loneliness is an ongoing puzzle faced by scientists studying the origin and evolution of life: how is it possible that only one planet has shown incontrovertible evidence of life, even though the laws of physics are shared by all known planets, and the elements in the periodic table can be found across the Universe?
The answer, for many, is to accept that Earth really is as unique as it appears: the absence of life elsewhere in the Universe can be explained by accepting that our planet is physically and chemically unlike the many other planets we have formally identified. Only Earth, so the argument goes, produced the special material conditions conducive to our rare chemistry, and it did so around 4 billion years ago, when life first emerged.
In 1952, Stanley Miller and his supervisor Harold Urey provided the first experimental evidence for this idea through a series of experiments at the University of Chicago. The Miller-Urey experiment, as it became known, sought to recreate the atmospheric conditions of early Earth through laboratory equipment, and to test whether organic compounds (amino acids) could be created in a reconstructed inorganic environment. When their experiment succeeded, the emergence of life became bound to the specific material conditions and chemistry on our planet, billions of years ago.
However, more recent research suggests there are likely countless other possibilities for how life might emerge through potential chemical combinations. As the British chemist Lee Cronin, the American theoretical physicist Sara Walker and others have recently argued, seeking near-miraculous coincidences of chemistry can narrow our ability to find other processes meaningful to life. In fact, most chemical reactions, whether they take place on Earth or elsewhere in the Universe, are not connected to life. Chemistry alone is not enough to identify whether something is alive, which is why researchers seeking the origin of life must use other methods to make accurate judgments.
Today, ‘adaptive function’ is the primary criterion for identifying the right kinds of biotic chemistry that give rise to life, as the theoretical biologist Michael Lachmann (our colleague at the Santa Fe Institute) likes to point out. In the sciences, adaptive function refers to an organism’s capacity to biologically change, evolve or, put another way, solve problems. ‘Problem-solving’ may seem more closely related to the domains of society, culture and technology than to the domain of biology. We might think of the problem of migrating to new islands, which was solved when humans learned to navigate ocean currents, or the problem of plotting trajectories, which our species solved by learning to calculate angles, or even the problem of shelter, which we solved by building homes. But genetic evolution also involves problem-solving. Insect wings solve the ‘problem’ of flight. Optical lenses that focus light solve the ‘problem’ of vision. And the kidneys solve the ‘problem’ of filtering blood. This kind of biological problem-solving – an outcome of natural selection and genetic drift – is conventionally called ‘adaptation’. Though it is crucial to the evolution of life, new research suggests it may also be crucial to the origins of life.
This problem-solving perspective is radically altering our knowledge of the Universe…
The idea of life asa kind of computational process has roots that go back to the 4th century BCE, when Aristotle introduced his philosophy of hylomorphism in which functions take precedence over forms. For Aristotle, abilities such as vision were less about the biological shape and matter of eyes and more about the function of sight. It took around 2,000 years for his idea of hylomorphic functions to evolve into the idea of adaptive traits through the work of Charles Darwin and others. In the 19th century, these naturalists stopped defining organisms by their material components and chemistry, and instead began defining traits by focusing on how organisms adapted and evolved – in other words, how they processed and solved problems. It would then take a further century for the idea of hylomorphic functions to shift into the abstract concept of computation through the work of Alan Turing [and here] and the earlier ideas of Charles Babbage [here].
In the 1930s, Turing became the first to connect the classical Greek idea of function to the modern idea of computation, but his ideas were impossible without the work of Babbage, a century before. Important for Turing was the way Babbage had marked the difference between calculating devices that follow fixed laws of operation, which Babbage called ‘Difference Engines’, and computing devices that follow programmable laws of operation, which he called ‘Analytical Engines.’
Using Babbage’s distinction, Turing developed the most general model of computation: the universal Turing Machine…
Turing did not describe any of the materials out of which such a machine would be built. He had little interest in chemistry beyond the physical requirement that a computer store, read and write bits reliably. That is why, amazingly, this simple (albeit infinite) programmable machine is an abstract model of how our powerful modern computers work. But the theory of computation Turing developed can also be understood as a theory of life. Both computation and life involve a minimal set of algorithms that support adaptive function. These ‘algorithms’ help materials process information, from the rare chemicals that build cells to the silicon semiconductors of modern computers. And so, as some research suggests, a search for life and a search for computation may not be so different. In both cases, we can be side-tracked if we focus on materials, on chemistry, physical environments and conditions.
In response to these concerns, a set of diverse ideas has emerged to explain life anew, through principles and processes shared with computation, rather than the rare chemistry and early Earth environments simulated in the Miller-Urey experiment. What drives these ideas, developed over the past 60 years by researchers working in disparate disciplines – including physics, computer science, astrobiology, synthetic biology, evolutionary science, neuroscience and philosophy – is a search for the fundamental principles that drive problem-solving matter. Though researchers have been working in disconnected fields and their ideas seem incommensurable, we believe there are broad patterns to their research on the origins of life. However, it can be difficult for outsiders to understand how these seemingly incommensurable ideas are connected to each other or why they are significant. This is why we have set out to review and organise these new ways of thinking.
Their proposals can be grouped into three distinct categories, three hypotheses, which we have named Tron, Golem and Maupertuis…
[The authors unpack all three proposals…]
… Is life problem-solving matter? When thinking about our biotic origins, it is important to remember that most chemical reactions are not connected to life, whether they take place here or elsewhere in the Universe. Chemistry alone is not enough to identify life. Instead, researchers use adaptive function – a capacity for solving problems – as the primary evidence and filter for identifying the right kinds of biotic chemistry. If life is problem-solving matter, our origins were not a miraculous or rare event governed by chemical constraints but, instead, the outcome of far more universal principles of information and computation. And if life is understood through these principles, then perhaps it has come into existence more often than we previously thought, driven by problems as big as the bang that started our abiotic universe moving 13.8 billion years ago.
The physical account of the origin and evolution of the Universe is a purely mechanical affair, explained through events such as the Big Bang, the formation of light elements, the condensation of stars and galaxies, and the formation of heavy elements. This account doesn’t involve objectives, purposes, or problems. But the physics and chemistry that gave rise to life appear to have been doing more than simply obeying the fundamental laws. At some point in the Universe’s history, matter became purposeful. It became organised in a way that allowed it to adapt to its immediate environment. It evolved from a Babbage-like Difference Engine into a Turing-like Analytical Engine. This is the threshold for the origin of life.
In the abiotic universe, physical laws, such as the law of gravitation, are like ‘calculations’ that can be performed everywhere in space and time through the same basic input-output operations. For living organisms, however, the rules of life can be modified or ‘programmed’ to solve unique biological problems – these organisms can adapt themselves and their environments. That’s why, if the abiotic universe is a Difference Engine, life is an Analytical Engine. This shift from one to the other marks the moment when matter became defined by computation and problem-solving. Certainly, specialised chemistry was required for this transition, but the fundamental revolution was not in matter but in logic.
In that moment, there emerged for the first time in the history of the Universe a big problem to give the Big Bang a run for its money. To discover this big problem – to understand how matter has been able to adapt to a seemingly endless range of environments – many new theories and abstractions for measuring, discovering, defining and synthesising life have emerged in the past century. Some researchers have synthesised life in silico. Others have experimented with new forms of matter. And others have discovered new laws that may make life as inescapable as physics…
Eminently worth reading in full: “Problem-solving matter,” from @sfiscience and @aeonmag.
Pair with “At the limits of thought” (also by Krakauer).
* Albert Einstein
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As we obsess on ontology, we might spare a thought for someone concerned with life as it is lived: Sigismund Schlomo “Sigmund” Freud; he died on this date in 1939. A neurologist, he was the founder of psychoanalysis– a clinical method for evaluating and treating pathologies seen as originating from conflicts in the psyche, through dialogue between patient and psychoanalyst, and the distinctive theory of mind and human agency derived from it.
“Machines take me by surprise with great frequency”*…
In search of universals in the 17th century, Gottfried Leibniz imagined the calculus ratiocinator, a theoretical logical calculation framework aimed at universal application, that led Norbert Wiener to suggest that Leibniz should be considered the patron saint of cybernetics. In the 19th century, Charles Babbage and Ada Lovelace took a pair of whacks at making it real.
Ironically, it was confronting the impossibility of a universal calculator that led to modern computing. In 1936 (the same year that Charlie Chaplin released Modern Times) Alan Turing (following on Godel’s demonstration that mathematics is incomplete and addressing Hilbert‘s “decision problem,” querying the limits of computation) published the (notional) design of a “machine” that elegantly demonstrated those limits– and, as Sheon Han explains, birthed computing as we know it…
… [Hilbert’s] question would lead to a formal definition of computability, one that allowed mathematicians to answer a host of new problems and laid the foundation for theoretical computer science.
The definition came from a 23-year-old grad student named Alan Turing, who in 1936 wrote a seminal paper that not only formalized the concept of computation, but also proved a fundamental question in mathematics and created the intellectual foundation for the invention of the electronic computer. Turing’s great insight was to provide a concrete answer to the computation question in the form of an abstract machine, later named the Turing machine by his doctoral adviser, Alonzo Church. It’s abstract because it doesn’t (and can’t) physically exist as a tangible device. Instead, it’s a conceptual model of computation: If the machine can calculate a function, then the function is computable.
…
With his abstract machine, Turing established a model of computation to answer the Entscheidungsproblem, which formally asks: Given a set of mathematical axioms, is there a mechanical process — a set of instructions, which today we’d call an algorithm — that can always determine whether a given statement is true?…
… in 1936, Church and Turing — using different methods — independently proved that there is no general way of solving every instance of the Entscheidungsproblem. For example, some games, such as John Conway’s Game of Life, are undecidable: No algorithm can determine whether a certain pattern will appear from an initial pattern.
…
Beyond answering these fundamental questions, Turing’s machine also led directly to the development of modern computers, through a variant known as the universal Turing machine. This is a special kind of Turing machine that can simulate any other Turing machine on any input. It can read a description of other Turing machines (their rules and input tapes) and simulate their behaviors on its own input tape, producing the same output that the simulated machine would produce, just as today’s computers can read any program and execute it. In 1945, John von Neumann proposed a computer architecture — called the von Neumann architecture — that made the universal Turing machine concept possible in a real-life machine…
As Turing said, “if a machine is expected to be infallible, it cannot also be intelligent.” On the importance of thought experiments: “The Most Important Machine That Was Never Built,” from @sheonhan in @QuantaMagazine.
* Alan Turing
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As we sum it up, we might spare a thought for Martin Gardner; he died on this date in 2010. Though not an academic, nor ever a formal student of math or science, he wrote widely and prolifically on both subjects in such popular books as The Ambidextrous Universe and The Relativity Explosion and as the “Mathematical Games” columnist for Scientific American. Indeed, his elegant– and understandable– puzzles delighted professional and amateur readers alike, and helped inspire a generation of young mathematicians.
Gardner’s interests were wide; in addition to the math and science that were his power alley, he studied and wrote on topics that included magic, philosophy, religion, and literature (c.f., especially his work on Lewis Carroll– including the delightful Annotated Alice— and on G.K. Chesterton). And he was a fierce debunker of pseudoscience: a founding member of CSICOP, and contributor of a monthly column (“Notes of a Fringe Watcher,” from 1983 to 2002) in Skeptical Inquirer, that organization’s monthly magazine.

“We may say most aptly that the Analytical Engine weaves algebraical patterns just as the Jacquard loom weaves flowers and leaves”*…
Lee Wilkins on the interconnected development of digital and textile technology…
I’ve always been fascinated with the co-evolution of computation and textiles. Some of the first industrialized machines produced elaborate textiles on a mass scale, the most famous example of which is the jacquard loom. It used punch cards to create complex designs programmatically, similar to the computer punch cards that were used until the 1970s. But craft work and computation have many parallel processes. The process of pulling wires is similar to the way yarn is made, and silkscreening is common in both fabric and printed circuit board production. Another of my favorite examples is rubylith, a light-blocking film used to prepare silkscreens for fabric printing and to imprint designs on integrated circuits.
Of course, textiles and computation have diverged on their evolutionary paths, but I love finding the places where they do converge – or inventing them myself. Recently, I’ve had the opportunity to work with a gigantic Tajima digital embroidery machine [see above]. This room-sized machine, affectionately referred to as The Spider Queen by the technician, loudly sews hundreds of stitches per minute – something that would take me months to make by hand. I’m using it to make large soft speaker coils by laying conductive fibers on a thick woven substrate. I’m trying to recreate functional coils – for use as radios, speakers, inductive power, and motors – in textile form. Given the shared history, I can imagine a parallel universe where embroidery is considered high-tech and computers a crafty hobby…
Notes, in @the_prepared.
* Ada Lovelace, programmer of the Analytical Engine, which was designed and built by her partner Charles Babbage
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As we investigate intertwining, we might recall that it was on this date in 1922 that Frederick Banting and Charles Best announced their discovery of insulin the prior year (with James Collip). The co-inventors sold the insulin patent to the University of Toronto for a mere $1. They wanted everyone who needed their medication to be able to afford it.
Today, Banting and his colleagues would be spinning in their graves: their drug, one on which many of the 30 million Americans with diabetes rely, has become the poster child for pharmaceutical price gouging.
The cost of the four most popular types of insulin has tripled over the past decade, and the out-of-pocket prescription costs patients now face have doubled. By 2016, the average price per month rose to $450 — and costs continue to rise, so much so that as many as one in four people with diabetes are now skimping on or skipping lifesaving doses…

Best (left) and Bantling with with one of the diabetic dogs used in their experiments with insulin
“Plans are worthless, but planning is everything”*…

We’re living through a real-time natural experiment on a global scale. The differential performance of countries, cities and regions in the face of the COVID-19 pandemic is a live test of the effectiveness, capacity and legitimacy of governments, leaders and social contracts.
The progression of the initial outbreak in different countries followed three main patterns. Countries like Singapore and Taiwan represented Pattern A, where (despite many connections to the original source of the outbreak in China) vigilant government action effectively cut off community transmission, keeping total cases and deaths low. China and South Korea represented Pattern B: an initial uncontrolled outbreak followed by draconian government interventions that succeeded in getting at least the first wave of the outbreak under control.
Pattern C is represented by countries like Italy and Iran, where waiting too long to lock down populations led to a short-term exponential growth of new cases that overwhelmed the healthcare system and resulted in a large number of deaths. In the United States, the lack of effective and universally applied social isolation mechanisms, as well as a fragmented healthcare system and a significant delay in rolling out mass virus testing, led to a replication of Pattern C, at least in densely populated places like New York City and Chicago.
Despite the Chinese and Americans blaming each other and crediting their own political system for successful responses, the course of the virus didn’t score easy political points on either side of the new Cold War. Regime type isn’t correlated with outcomes. Authoritarian and democratic countries are included in each of the three patterns of responses: authoritarian China and democratic South Korea had effective responses to a dramatic breakout; authoritarian Singapore and democratic Taiwan both managed to quarantine and contain the virus; authoritarian Iran and democratic Italy both experienced catastrophe.
It’s generally a mistake to make long-term forecasts in the midst of a hurricane, but some outlines of lasting shifts are emerging. First, a government or society’s capacity for technical competence in executing plans matters more than ideology or structure. The most effective arrangements for dealing with the pandemic have been found in countries that combine a participatory public culture of information sharing with operational experts competently executing decisions. Second, hyper-individualist views of privacy and other forms of risk are likely to be submerged as countries move to restrict personal freedoms and use personal data to manage public and aggregated social risks. Third, countries that are able to successfully take a longer view of planning and risk management will be at a significant advantage…
From Steve Weber and @nils_gilman, an argument for the importance of operational expertise, plans for the long-term, and the socialization of some risks: “The Long Shadow Of The Future.”
* Dwight D. Eisenhower
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As we make ourselves ready, we might recall that it was on this date in 1822 that Charles Babbage [see almanac entry here] proposes a Difference Engine in a paper to the Royal Astronomical Society (which he’d helped found two years earlier).
In Babbage’s time, printed mathematical tables were calculated by human computers… in other words, by hand. They were central to navigation, science, and engineering, as well as mathematics– but mistakes occurred, both in transcription and in calculation. Babbage determined to mechanize the process and to reduce– indeed, to eliminate– errors. His Difference Engine was intended as precisely that sort of mechanical calculator (in this instance, to compute values of polynomial functions).
In 1833 he began his programmable Analytical Machine (AKA, the Analytical Engine), the forerunner of modern computers, with coding help from Ada Lovelace, who created an algorithm for the Analytical Machine to calculate a sequence of Bernoulli numbers— for which she is remembered as the first computer programmer.

A portion of the difference engine




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