SUM is a dazzling exploration of funny and unexpected afterlives that have never been considered -- each presented as a vignette that offers us a stunning lens through which to see ourselves here and now.
In one afterlife you may find that God is the size of a microbe and is unaware of your existence. In another, your creators are a species of dim-witted creatures who built us to figure out what they could not. In a different version of the afterlife you work as a background character in other people's dreams. Or you may find that God is a married couple struggling with discontent, or that the afterlife contains only those people whom you remember, or that the hereafter includes the thousands of previous gods who no longer attract followers. In some afterlives you are split into your different ages; in some you are forced to live with annoying versions of yourself that represent what you could have been; in others you are re-created from your credit card records and Internet history. David Eagleman proposes many versions of our purpose here; we are mobile robots for cosmic mapmakers, we are reunions for a scattered confederacy of atoms, we are experimental subjects for gods trying to understand what makes couples stick together.
These wonderfully imagined tales -- at once funny, wistful, and unsettling -- are rooted in science and romance and awe at our mysterious existence: a mixture of death, hope, computers, immortality, love, biology, and desire that exposes radiant new facets of our humanity.
President and William H. Miller Professor of Complex Systems
For all of you waiting with bated breath! ALife 2020 Keynote Speaker Announcement #2: Melanie Mitchell @MelMitchell1 Who is a living legend in Complex Systems & has a brand new book out, "Artificial Intelligence: A Guide for Thinking Humans." http://vermontcomplexsystems.org/events/ALIFE-2020/ #Alife2020 pic.twitter.com/WIRQbuuZ2G— ALIFE Conference 2020 (@ALifeConf) December 3, 2019
A cat is alive, a sofa is not: that much we know. But a sofa is also part of life. Information theory tells us why
Decades of early research on the genetics of depression were built on nonexistent foundations. How did that happen?
I’m glad I managed to sit in on the class and still have the audio recordings and notes. While I can’t say that Newton taught me calculus, I can say I learned combinatorics from Golomb.
The Most-Used Mathematical Algorithm Idea in History
An octillion. A billion billion billion. That’s a fairly conservative estimate of the number of times a cellphone or other device somewhere in the world has generated a bit using a maximum-length linear-feedback shift register sequence. It’s probably the single most-used mathematical algorithm idea in history. And the main originator of this idea was Solomon Golomb, who died on May 1—and whom I knew for 35 years.
Solomon Golomb’s classic book Shift Register Sequences, published in 1967—based on his work in the 1950s—went out of print long ago. But its content lives on in pretty much every modern communications system. Read the specifications for 3G, LTE, Wi-Fi, Bluetooth, or for that matter GPS, and you’ll find mentions of polynomials that determine the shift register sequences these systems use to encode the data they send. Solomon Golomb is the person who figured out how to construct all these polynomials.
Many of the fantastical seeming stories here, as well as Sol’s personality read very true to me with respect to the man I knew for almost two decades.
Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology to engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. Finally, we discuss the usefulness of self-organization within ALife studies, point to perspectives for future research, and list open questions.
Organisms live and die by the amount of information they acquire about their environment. The systems analysis of complex metabolic networks allows us to ask how such information translates into fitness. A metabolic network transforms nutrients into biomass. The better it uses information on available nutrient availability, the faster it will allow a cell to divide.
I here use metabolic flux balance analysis to show that the accuracy I (in bits) with which a yeast cell can sense a limiting nutrient's availability relates logarithmically to fitness as indicated by biomass yield and cell division rate. For microbes like yeast, natural selection can resolve fitness differences of genetic variants smaller than 10-6, meaning that cells would need to estimate nutrient concentrations to very high accuracy (greater than 22 bits) to ensure optimal growth. I argue that such accuracies are not achievable in practice. Natural selection may thus face fundamental limitations in maximizing the information processing capacity of cells.
The analysis of metabolic networks opens a door to understanding cellular biology from a quantitative, information-theoretic perspective.
Received: 01 March 2007 Accepted: 30 July 2007 Published: 30 July 2007
Self-replication is a capacity common to every species of living thing, and simple physical intuition dictates that such a process must invariably be fueled by the production of entropy. Here, we undertake to make this intuition rigorous and quantitative by deriving a lower bound for the amount of heat that is produced during a process of self-replication in a system coupled to a thermal bath. We find that the minimum value for the physically allowed rate of heat production is determined by the growth rate, internal entropy, and durability of the replicator, and we discuss the implications of this finding for bacterial cell division, as well as for the pre-biotic emergence of self-replicating nucleic acids.
So far there’s nothing new for me here. He’s encapsulating a lot of prior books I’ve read. (Though he’s doing an incredible job of it.) There are a handful of references that I’ll want to go take a look at though.
Developed during the first half of the 20th century, in three different fields, theoretical physics, statistics applied to agronomy and telecommunication engineering, the notion of information has become a scientific concept in the context of the Second War World. It is in this highly interdisciplinary environment that “information theory” emerged, combining the mathematical theory of communication and cybernetics. This theory has grown exponentially in many disciplines, including biology. The discovery of the genetic “code” has benefited from the development of a common language based on information theory and has fostered a almost imperialist development of molecular genetics, which culminated in the Human Genome Project. This project however could not fill all the raised expectations and epigenetics have shown the limits of this approach. Still, the theory of information continues to be applied in the current research, whether the application of the self-correcting coding theory to explain the conservation of genomes on a geological scale or aspects the theory of evolution.
Walter Pitts was used to being bullied. He’d been born into a tough family in Prohibition-era Detroit, where his father, a boiler-maker,…
Highlights, Quotes, Annotations, & Marginalia
McCulloch was a confident, gray-eyed, wild-bearded, chain-smoking philosopher-poet who lived on whiskey and ice cream and never went to bed before 4 a.m. ❧
March 03, 2019 at 06:01PM
McCulloch and Pitts were destined to live, work, and die together. Along the way, they would create the first mechanistic theory of the mind, the first computational approach to neuroscience, the logical design of modern computers, and the pillars of artificial intelligence. ❧
March 03, 2019 at 06:06PM
Gottfried Leibniz. The 17th-century philosopher had attempted to create an alphabet of human thought, each letter of which represented a concept and could be combined and manipulated according to a set of logical rules to compute all knowledge—a vision that promised to transform the imperfect outside world into the rational sanctuary of a library. ❧
March 03, 2019 at 06:08PM
Which got McCulloch thinking about neurons. He knew that each of the brain’s nerve cells only fires after a minimum threshold has been reached: Enough of its neighboring nerve cells must send signals across the neuron’s synapses before it will fire off its own electrical spike. It occurred to McCulloch that this set-up was binary—either the neuron fires or it doesn’t. A neuron’s signal, he realized, is a proposition, and neurons seemed to work like logic gates, taking in multiple inputs and producing a single output. By varying a neuron’s firing threshold, it could be made to perform “and,” “or,” and “not” functions. ❧
Based on their meeting date, it would have to be after 1940.And they published in 1943: https://link.springer.com/article/10.1007%2FBF02478259
March 03, 2019 at 06:14PM
McCulloch and Pitts alone would pour the whiskey, hunker down, and attempt to build a computational brain from the neuron up. ❧
March 03, 2019 at 06:15PM
“an idea wrenched out of time.” In other words, a memory. ❧
March 03, 2019 at 06:17PM
McCulloch and Pitts wrote up their findings in a now-seminal paper, “A Logical Calculus of Ideas Immanent in Nervous Activity,” published in the Bulletin of Mathematical Biophysics. ❧
March 03, 2019 at 06:21PM
I really like this picture here. Perhaps for a business card?
March 03, 2019 at 06:23PM
it had been Wiener who discovered a precise mathematical definition of information: The higher the probability, the higher the entropy and the lower the information content. ❧
March 03, 2019 at 06:34PM
By the fall of 1943, Pitts had moved into a Cambridge apartment, was enrolled as a special student at MIT, and was studying under one of the most influential scientists in the world. ❧
March 03, 2019 at 06:32PM
Thus formed the beginnings of the group who would become known as the cyberneticians, with Wiener, Pitts, McCulloch, Lettvin, and von Neumann its core. ❧
March 03, 2019 at 06:38PM
In the entire report, he cited only a single paper: “A Logical Calculus” by McCulloch and Pitts. ❧
March 03, 2019 at 06:43PM
Oliver Selfridge, an MIT student who would become “the father of machine perception”; Hyman Minsky, the future economist; and Lettvin. ❧
March 03, 2019 at 06:44PM
at the Second Cybernetic Conference, Pitts announced that he was writing his doctoral dissertation on probabilistic three-dimensional neural networks. ❧
March 03, 2019 at 06:44PM
In June 1954, Fortune magazine ran an article featuring the 20 most talented scientists under 40; Pitts was featured, next to Claude Shannon and James Watson. ❧
March 03, 2019 at 06:46PM
Lettvin, along with the young neuroscientist Patrick Wall, joined McCulloch and Pitts at their new headquarters in Building 20 on Vassar Street. They posted a sign on the door: Experimental Epistemology. ❧
March 03, 2019 at 06:47PM
“The eye speaks to the brain in a language already highly organized and interpreted,” they reported in the now-seminal paper “What the Frog’s Eye Tells the Frog’s Brain,” published in 1959. ❧
March 03, 2019 at 06:50PM
There was a catch, though: This symbolic abstraction made the world transparent but the brain opaque. Once everything had been reduced to information governed by logic, the actual mechanics ceased to matter—the tradeoff for universal computation was ontology. Von Neumann was the first to see the problem. He expressed his concern to Wiener in a letter that anticipated the coming split between artificial intelligence on one side and neuroscience on the other. “After the great positive contribution of Turing-cum-Pitts-and-McCulloch is assimilated,” he wrote, “the situation is rather worse than better than before. Indeed these authors have demonstrated in absolute and hopeless generality that anything and everything … can be done by an appropriate mechanism, and specifically by a neural mechanism—and that even one, definite mechanism can be ‘universal.’ Inverting the argument: Nothing that we may know or learn about the functioning of the organism can give, without ‘microscopic,’ cytological work any clues regarding the further details of the neural mechanism.” ❧
March 03, 2019 at 06:54PM
Nature had chosen the messiness of life over the austerity of logic, a choice Pitts likely could not comprehend. He had no way of knowing that while his ideas about the biological brain were not panning out, they were setting in motion the age of digital computing, the neural network approach to machine learning, and the so-called connectionist philosophy of mind. ❧
March 03, 2019 at 06:55PM
by stringing them together exactly as Pitts and McCulloch had discovered, you could carry out any computation. ❧
March 03, 2019 at 06:58PM
Henry Quastler (November 11, 1908 – July 4, 1963) was an Austrian physician and radiologist who became a pioneer in the field of information theory applied to biology after emigrating to America. His work with Sidney Dancoff led to the publication of what is now commonly called Dancoff's Law.
Hubert Palmer Yockey, 99, died peacefully under hospice care at his home in Bel Air, MD, on January 31, 2016, with his daughter, Cynthia Yockey, at his side. Born in Alexandria, Minnesota, he was t…