If you are familiar with complex systems theorist Dr. Stuart Kauffman’s ideas you know he covers a broad range of disciplines and concepts, many in considerable depth, and with a keen eye for isomorphic and integrative principles. If you peruse some of his writings and other communications, please share with us how you see Kauffman’s ideas informing our focal interests: brain, mind, intelligence (organic and inorganic), and self-aware consciousness.
Do you find Kauffman’s ideas well supported by empirical research? Which are more scientific and which, if any, more philosophical? What intrigues, provokes, or inspires you? Do any of his perspectives or claims help you better orient or understand your own interests in our focal topics?
Following are a few reference links to get the conversation going. Please add your own in the comments to this post. If you are a member and have a lot to say on a related topic, please create a new post, tag it with ‘Stuart Kauffman,’ and create a link to your post in the comments to this post.
Psychologist Robert Epstein, the former editor of Psychology Today, challenges anyone to show the brain processing information or data. The IP metaphor, he says, is so deeply embedded in thinking about thinking it prevents us from learning how the brain really works. Epstein also takes on popular luminaries including Ray Kurzweil and Henry Markram, seeing both exemplifying the extremes of wrongness we get into with the IP metaphor and the notion mental experience could persist outside the organic body.
This article was originally published at Aeon and has been republished under Creative Commons.
Cassandra woke up to the rays of the sun streaming through the slats on her blinds, cascading over her naked chest. She stretched, her breasts lifting with her arms as she greeted the sun. She rolled out of bed and put on a shirt, her nipples prominently showing through the thin fabric. She breasted boobily to the stairs, and titted downwards.
This particular hyperbolic gem has been doing the rounds on Tumblr for a while. It resurfaced in April 2018, in response to a viral Twitter challenge posed by the US podcaster Whitney Reynolds: women, describe yourself the way a male writer would.
The dare hit a sweet spot. Many could summon up passages from books containing terrible, sexualised descriptions of women. Some of us recalled Haruki Murakami, whose every novel can be summarised as: ‘Protagonist is an ordinary man, except lots of really beautiful women want to sleep with him.’ Others remembered J M Coetzee, and his variations on the plot: ‘Tenured male professor in English literature sleeps with beautiful female undergraduate.’ It was a way for us to joke about the fact that so much great literature was written by men who could express perfectly detailed visual descriptions of the female body, and yet possessed such an impoverished understanding of the female mind.
This is why the philosophical project of trying to map the contours of other minds needs a reality check. If other humans are beyond our comprehension, what hope is there for understanding the experience of animals, artificial intelligence or aliens?
I am a literature scholar. Over thousands of years of literary history, authors have tried and failed to convey an understanding of Others (with a capital ‘O’). Writing fiction is an exercise that stretches an author’s imagination to its limits. And fiction shows us, again and again, that our capacity to imagine other minds is extremely limited.
It took feminism and postcolonialism to point out that writers were systematically misrepresenting characters who weren’t like them. Male authors, it seems, still struggle to present convincing female characters a lot of the time. The same problem surfaces again when writers try to introduce a figure with a different ethnicity to their own, and fail spectacularly.
I mean, ‘coffee-coloured skin’? Do I really need to find out how much milk you take in the morning to know the ethnicity you have in mind? Writers who keep banging on with food metaphors to describe darker pigmentation show that they don’t appreciate what it’s like to inhabit such skin, nor to have such metaphors applied to it.
Conversely, we recently learnt that some publishers rejected the Korean-American author Leonard Chang’s novel The Lockpicker (2017) – for failing to cater to white readers’ lack of understanding of Korean-Americans. Chang gave ‘none of the details that separate Koreans and Korean-Americans from the rest of us’, one publisher’s letter said. ‘For example, in the scene when she looks into the mirror, you don’t show how she sees her slanted eyes …’ Any failure to understand a nonwhite character, it seems, was the fault of the nonwhite author.
Fiction shows us that nonhuman minds are equally beyond our grasp. Science fiction provides a massive range of the most fanciful depictions of interstellar space travel and communication – but anthropomorphism is rife. Extraterrestrial intelligent life is imagined as Little Green Men (or Little Yellow or Red Men when the author wants to make a particularly crude point about 20th-century geopolitics). Thus alien minds have been subject to the same projections and assumptions that authors have applied to human characters, when they fundamentally differ from the authors themselves.
For instance, let’s look at a meeting of human minds and alien minds. The Chinese science fiction author Liu Cixin is best known for his trilogy starting with The Three-Body Problem (2008). It appeared in English in 2014 and, in that edition, each book has footnotes – because there are some concepts that are simply not translatable from Chinese into English, and English readers need these footnotes to understand what motivates the characters. But there are also aliens in this trilogy. From a different solar system. Yet their motivations don’t need footnoting in translation.
Splendid as the trilogy is, I find that very curious. There is a linguistic-cultural barrier that prevents an understanding of the novel itself, on this planet. Imagine how many footnotes we’d need to really grapple with the motivations of extraterrestrial minds.
Our imaginings of artificial intelligence are similarly dominated by anthropomorphic fantasies. The most common depiction of AI conflates it with robots. AIs are metal men. And it doesn’t matter whether the press is reporting on swarm robots invented in Bristol or a report produced by the House of Lords: the press shall plaster their coverage with Terminator imagery. Unless the men imagining these intelligent robots want to have sex with them, in which case they’re metal women with boobily breasting metal cleavage – a trend spanning the filmic arts from Fritz Lang’s Metropolis (1927) to the contemporary TV series Westworld (2016-). The way that we imagine nonhumans in fiction reflects how little we, as humans, really get each other.
All this supports the idea that embodiment is central to the way we understand one another. The ridiculous situations in which authors miss the mark stem from the difference between the author’s own body and that of the character. It’s hard to imagine what it’s like to be someone else if we can’t feel it. So, much as I enjoyed seeing a woman in high heels outrun a T-Rex in Jurassic World (2015), I knew that the person who came up with that scene clearly has no conception of what it’s like to inhabit a female body, be it human or Tyrannosaurus.
Because stories can teach compassion and empathy, some people argue that we should let AIs read fiction in order to help them understand humans. But I disagree with the idea that compassion and empathy are based on a deep insight into other minds. Sure, some fiction attempts to get us to understand one another. But we don’t need any more than a glimpse of what it’s like to be someone else in order to empathise with them – and, hopefully, to not want to kill and destroy them.
As the US philosopher Thomas Nagel claimed in 1974, a human can’t know what it is like to be a bat, because they are fundamentally alien creatures: their sensory apparatus and their movements are utterly different from ours. But we can imagine ‘segments’, as Nagel wrote. This means that, despite our lack of understanding of bat minds, we can find ways to keep a bat from harm, or even nurse and raise an orphaned baby bat, as cute videos on the internet will show you.
The problem is that sometimes we don’t realise this segment of just a glimpse of something bigger. We don’t realise until a woman, a person of colour, or a dinosaur finds a way to point out the limits of our imagination, and the limits of our understanding. As long as other human minds are beyond our understanding, nonhuman ones certainly are, too.
Kanta Dihal is a postdoctoral research assistant and the research project coordinator of the Leverhulme Centre for the Future of Intelligence at the University of Cambridge.
This article was originally published at Aeon and has been republished under Creative Commons.
Our discussions all, to some extent, relate to cognition. An important area of inquiry concerns whether some form of physical embodiment is required for a brain to support cognition in general and the self-aware sort of cognition we humans possess.
Philosophy In The Flesh: The Embodied Mind And Its Challenge To Western Thought, by George Lakoff and Mark Johnson. Please note, while the title includes “Philosophy,” we are not a philosophy group and the book and discussion will revolve around scientific concepts and implications, not spiritualistic or metaphysical ideas.
– Amazon (used copies in the $6 range, including shipping)
RSVP by email to email@example.com if you plan to attend our discussion on the afternoon of Saturday, November 3, 2018.
While our group enjoys socializing and will plan other events to that end, this meeting is for focused discussion among people who invest the time in advance to inform themselves on the topic. As a courtesy to those who will do their ‘homework,’ before the meeting please read and consider Part 1 (the first eight chapters) of the book. As you read, jot down your thoughts and questions on the book’s claims, supporting evidence, and implications for our core topics–brain, mind, and artificial intelligence. If you are not able to invest this effort prior to the meeting, please do not attend. Thank you for your understanding.
Taken together, these morphological characteristics suggest that neurons in the elephant cortex may synthesize a wider variety of input than the cortical neurons in other mammals.
In terms of cognition, my colleagues and I believe that the integrative cortical circuitry in the elephant supports the idea that they are essentially contemplative animals. Primate brains, by comparison, seem specialized for rapid decision-making and quick reactions to environmental stimuli.
Kurzweil builds and supports a persuasive vision of the emergence of a human-level engineered intelligence in the early-to-mid twenty-first century. In his own words,
With the reverse engineering of the human brain we will be able to apply the parallel, self-organizing, chaotic algorithms of human intelligence to enormously powerful computational substrates. This intelligence will then be in a position to improve its own design, both hardware and software, in a rapidly accelerating iterative process.
In Kurzweil's view, we must and will ensure we evade obsolescence by integrating emerging metabolic and cognitive technologies into our bodies and brains. Through self-augmentation with neurotechnological prostheses, the locus of human cognition and identity will gradually (but faster than we'll expect, due to exponential technological advancements) shift from the evolved substrate (the organic body) to the engineered substrate, ultimately freeing the human mind to develop along technology's exponential curve rather than evolution's much flatter trajectory.
The book is extensively noted and indexed, making the deep-diving reader's work a bit easier.
If you have read it, feel free to post your observations in the comments below. (We've had a problem with the comments section not appearing. It may require more troubleshooting.)
Two independent teams of scientists from the University of Utah and the University of Massachusetts Medical School have discovered that a gene crucial for learning, called Arc, can send its genetic material from one neuron to another by employing a strategy commonly used by viruses. The studies, both published in Cell, unveil a new way that nervous system cells interact.
Neural learning occurs at dendrite roots, not in synapses.
The newly suggested learning scenario indicates that learning occurs in a few dendrites that are in much closer proximity to the neuron, as opposed to the previous notion. …
The new learning scenario occurs in different sites of the brain and therefore calls for a reevaluation of current treatments for disordered brain functionality. … In addition, the learning mechanism is at the basis of recent advanced machine learning and deep learning achievements. The change in the learning paradigm opens new horizons for different types of deep learning algorithms and artificial intelligence based applications imitating our brain functions, but with advanced features and at a much faster speed.
“This is the first time scientists have been able to identify a patient’s own brain cell code or pattern for memory and, in essence, ‘write in’ that code to make existing memory work better, an important first step in potentially restoring memory loss”
“We showed that we could tap into a patient’s own memory content, reinforce it and feed it back to the patient,” Hampson said. “Even when a person’s memory is impaired, it is possible to identify the neural firing patterns that indicate correct memory formation and separate them from the patterns that are incorrect. We can then feed in the correct patterns to assist the patient’s brain in accurately forming new memories, not as a replacement for innate memory function, but as a boost to it.”
An improvement to the Neural Simulation Tool (NEST) algorithm, the primary tool of the Human Brain Project, expanded the scope of brain neural data management (for simulations) from the current 1% of discrete neurons (about the number in the cerebellum) to 10%. The NEST algorithm can scale to store 100% of BCI-derived or simulated neural data within near-term reach as supercomputing capacity increases. The algorithm achieves its massive efficiency boost by eliminating the need to explicitly store as much data about each neuron’s state.
Abstract of Extremely Scalable Spiking Neuronal Network Simulation Code: From Laptops to Exascale Computers
State-of-the-art software tools for neuronal network simulations scale to the largest computing systems available today and enable investigations of large-scale networks of up to 10 % of the human cortex at a resolution of individual neurons and synapses. Due to an upper limit on the number of incoming connections of a single neuron, network connectivity becomes extremely sparse at this scale. To manage computational costs, simulation software ultimately targeting the brain scale needs to fully exploit this sparsity. Here we present a two-tier connection infrastructure and a framework for directed communication among compute nodes accounting for the sparsity of brain-scale networks. We demonstrate the feasibility of this approach by implementing the technology in the NEST simulation code and we investigate its performance in different scaling scenarios of typical network simulations. Our results show that the new data structures and communication scheme prepare the simulation kernel for post-petascale high-performance computing facilities without sacrificing performance in smaller systems.