This TED talk discusses how tech innovation is driven by those with diverse experience that syntegrate a variety of genres instead of specialists that are limited to a few. They call it ‘lateral’ thinking but that term sets up a dichotomy with hierarchical thinking, which the syntegral approach is certainly much more than. The hierarchical complexity approach would limit that way of thinking merely to what it calls horizontal complexity, again missing the boat entirely of the sort of cross-paradigmatic…
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Here’s an interesting infographic of the main concepts and thinkers in complexity science across time. Notice S. Kauffman is slated in the 1980s column, suggesting the graphic depicts when influential thinkers first make their marks. https://www.art-sciencefactory.com/complexity-map_feb09.html
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Neuroscientist Larry Cahill takes issue with a Feb 2019 Nature favorable book review of Gina Rippon’s The Gendered Brain: The New Neuroscience That Shatters The Myth Of The Female Brain. Cahill’s response prompted an interview by Medium Neuroscience writer Meghan Daum. Scientific findings have a way of upsetting apple carts, especially when we consider our oft-demonstrated human capacity to bend science to advantage some power-coveting groups over others. Valid research amply shows there are real differences in male and female…
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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…
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Good quick summary of some of Deacon’s ideas. Deacon: “We need to stop thinking about hierarchic evolution in simple Darwinian terms. We need to think about it both in terms of selection and the loss of selection or the reduction of selection. And that maybe it’s the reduction of selection that’s responsible for the most interesting features” (9:40).
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Ideally, automation would yield a Star Trek reality of increasing leisure and quality of choice and experience. Why isn’t this our experience? An article on Medium offers insight into why this is not occurring on any significant scale. Evolved behavioral strategies explained by the prisoner’s dilemma damn the majority of humans to a constant doubling down. We exchange the ‘leisure dividend’ (free time) granted by automation for opportunities to outcompete others. Apparently, the sort of reciprocal social learning that could…
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Article in Scientific American. One point. The article sees energetic fields underlying matter as if they are separate things, one the cause of the other. Whereas a naturalistic, postmetaphysical view might be that they mutually entail and co-generate each other within an ecological frame. The cause/effect frame still clings to a form of dualism.
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This McKinsey article is an excellent overview of this more extensive article (3 MB PDF) enumerating the ways in which varieties of deep learning can improve existence. Worth a look. The articles cover the following: Mapping AI use cases to domains of social good AI capabilities that can be used for social good Overcoming bottlenecks, especially around data and talent Risks to be managed Scaling up the use of AI for social good
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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…
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Given the human brain’s approximately 80 billion neurons, it would take tens of thousands of these devices to record a substantial volume of neuron-level activities. Still, this is a remarkable achievement. The system would simultaneously acquire data from more than 1 million neurons in real time. It would convert the spike data (using bit encoding) and send it via an effective communication format for processing and storage on conventional computer systems. It would also provide feedback to a subject in…
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