Posts Tagged ‘artificial intelligence’
“In science, it happens every few years that something that was previously considered a mistake suddenly reverses all views, or that an inconspicuous and despised idea becomes the ruler of a new realm of thought.”*…
Today’s post is, in essence, the recommendation of the current issue of of a publication referenced here often, Noema. It’s editor, Nathan Gardels, previews its contents…
When a concept that organizes our reality is replaced by an entirely different and incommensurate worldview, it is called a “paradigm shift.”
The theme of this edition of Noema was conceived in early 2024. At that time, we had in mind the epochal shift from the paradigm of globalization, in which markets, trade and technology cross borders, to “the Planetary,” where we recognize that the whole Earth system embeds and entangles human civilization in its habitat.
This deeper awareness has been enabled by the emergence of a technological exoskeleton of satellites, sensors and cloud computation that expands the heretofore limited scope of human understanding of the world, repositioning our place in the natural order. Neither above nor apart from nature, we have now come to realize we are part and parcel of one interdependent organism comprised of multiple intelligences striving for sustainable equilibrium.
The disclosure of climate change as a destabilizing consequence of human endeavor was enabled in the first place by planetary-scale computation. This capacity holds out the evolutionary prospect that human, machine and Earth intelligence might one day merge into a kind of planetary sapience that restores and maintains the ecological balance.
As we have written often in Noema, this conceptual reorientation would entail a redefinition of what realism means in geopolitics. This new condition calls not for the old “realpolitik” that seeks to secure the interests of nation-states against each other but for a “Gaiapolitik” aimed at securing a livable biosphere for all.
As logically compelling as this case for planetary realism may be, the paradigm shift underway is going in the opposite direction. Instead of the global interconnectivity forged in recent decades maturing into a planetary perspective, it is breaking up into a renewed nationalism more emphatically sovereigntist than before the advent of globalization.
In short, the prevailing political temperament around the world today is out of sync with the planetary imperative. This does not diminish its reality but, for the moment, eclipses and derails its emergence as the conscious organizing principle of human civilization.
The paradigm shift we are witnessing today not only marks a move away from a planetary awareness but also signals the last sigh of liberal universalism as the dominant governing philosophy of the postwar order since 1945.
The rules-based liberal international order, underwritten and guaranteed for decades by American might, has been consigned to the ash can of history by the summary defection of its founding architect from its terms and premises.
Under President Donald Trump and his allies, America has effectively joined the revisionist powers of China and Russia by baldly asserting sovereigntist self-interest unencumbered by rules that also encompass the interests of others.
Tariff walls, outright trade wars and unraveling alliances are supplanting the expansive web of global commerce, Western unity and cultural cross-fertilization that characterized times only recently. In a further break from the established order, Team Trump openly contemplates its own Anschluss of other people’s territory in Greenland, the Panama Canal and even Canada, instead of expressing outrage at China’s desire to take Taiwan, Russia’s bloody attempt to seize Ukraine or Israel’s increasing occupation of the Palestinian territories.
As Francis Fukuyama and Niall Ferguson discuss in a collage of commentary in this Noema edition, these developments portend the return to a world not unlike that of the 19th century, when the great powers carved out exclusive domains of influence.
The obvious great powers that would constitute a world apportioned in this way are China and Russia, both grasping at Eurasia, plus the United States and India. Whether Europe falls within the American sphere of influence depends on its capacity to cohere as a continental entity and find its identity as an alternative within a West that is fracturing under the strain of America’s revisionist turn.
Since the future appears to be taking us back to the 19th century, one cannot say we are in “uncharted territory.” On the contrary, we’ve been down this path before and know how it led to world wars that the global rules-based order, for all its well-known faults, was meant to avoid repeating.
On the American home front, and increasingly elsewhere in the West, it appears the “strong gods” of family, faith and nation are prevailing over the culturally liberal sentiments of an open society.
When there is no common agreement on what constitutes the good life, culture is politicized. As Alexandre Lefebvre argues in Noema, who gets to define “the good life” has become the central political question of our time. As in China, Russia, Iran or Turkey, governing authorities in the West are increasingly seeking to assign the moral substance of their vision to the state in place of the neutral proceduralism of liberal regimes that, at least in theory, embrace the diversity of all values without favor.
As the ascendant traditionalists see it, this rights-based liberalism grants a kind of converse moral substance to the state by virtue of the permissive openness it invites, nourishes and protects.
In many ways, liberalism was bound to fail just as Marxism did, and for the same reason. Marxism lacked a theory of politics that accommodated diverse constituencies because it assumed the universality of the interests of one class. Similarly, liberalism has falsely assumed its own universality, believing that there can be a consensus on only one conception of “the good life.” In reality, where some see declaring gender identity as the positive freedom to pursue self-realization, others see it as the corrosion of traditional Christian morality.
Like the British philosopher John Gray, Lefebvre suggests that the liberalism of the future may well entail a constitutionally grounded “modus vivendi” of autonomous jurisdictions as one way to keep the civil peace in diverse societies.
What is stunning in this context is how rapidly the America that elected Trump has tilted toward illiberal democracy under his tumultuous reign. Team Trump has robustly pursued retribution against political enemies, scorned universities as “the enemy,” moved to dismantle the administrative state and climate policies, demeaned the judicial system and cultivated crony corruption. Moreover, in the Orwellian name of free speech, Trump insists on ideological conformity across the board, from college students to corporate law firms.
To base the idea of democracy solely on elections invites this kind of illiberalism because it implies that majoritarian rule is all that is necessary for legitimacy. But, as the American founding fathers well understood, the will of the majority does not embrace all interests in a society, which must be protected equally. That is the reason for constitutional rule as the founding principle of a liberal polity.
In constitutional theory, the imposition of limitations and restraints — the “negative” — is what prevents the majority from absolute domination. It is the negative that makes the Constitution and the “positive” that makes government. One is the power of acting, the other the power of amending or arresting action. The two combined make a constitutional government.
It is this governing arrangement that made America great. The biggest danger of Making America Great Again is that a movement that believes it is the embodiment of the will of the majority will cast aside any constraints on its power as a contrivance by the elites of the ancien régime to keep the masses down.
In Niall Ferguson’s contribution to Noema, the historian raises the specter that “history was always against any republic lasting 250 years. This republic is in its late republican phase, with the intimations of empire much more visible.”…
… As politicized cultural battles and the churning geopolitical economy further unfold, a paradigm shift of a significance similar to planetary awareness is taking place that will redefine what it means to be human.
Across the sciences, we are coming to understand the self-organizing principle of “computation” as the building block of all forms of budding intelligence, from primitive cells to generative AI. This process involves learning from the environment, assembling information and arranging it by sharing functional instructions through “copying and pasting” code, so that an organism can develop, reproduce and sustain itself.
As Google’s Blaise Agüera y Arcas and James Manyika write in this issue, “computing existed in nature long before we built the first ‘artificial computers.’ … Understanding computing as a natural phenomenon will enable fundamental advances not only in computer science and AI, but also in physics and biology.”
More than half a century ago, they note, pioneering computer scientists had the intuition that organic and inorganic intelligence follow the same set of rules for development. “John von Neumann,” write the authors, “realized that for a complex organism to reproduce, it would need to contain instructions for building itself, along with a machine for reading and executing those instructions.” The technical requirements for that “universal constructor” in nature — the tape-like instructions of DNA — “correspond precisely to the technical requirements for the earliest computers.”
“Life,” they continue, “is computational because its existence over time depends on growth, healing or reproduction, and computation itself must evolve to support these essential functions.”
Grasping the correspondence with natural computation and learning from it, they believe, will render AI “brainlike” as it further evolves along the path from mimicking neural computation to predictive intelligence, general intelligence and, ultimately, collective intelligence. “Brains, AI agents and societies can all become more capable through increased scale. However, size alone is not enough. Intelligence is fundamentally social, powered by cooperation and the division of labor among many agents.”
In short, as philosopher of technology Tobias Rees also argues in this issue, the evolution of computation as a symbiosis of human and machine will cause us to rethink what it means to be human as, for the first time in history, a “more than human” intelligence emerges on our planet.
These contradictions and crosscurrents of the profound paradigm shifts we are living through all at once mark what future historians will surely describe as the Age of Upheaval…
FWIW, I worry that the diagnosis of our current political/cultural morass is maybe not dark enough. And as to AI, I’m no wide-eyed believer in the current cycle of hype. Indeed, I worry that AI could contribute to our social ills in the short term both by increasing and amplying the atomization and misinformation that we suffer and by challenging the economy if, as seems all too plausible, current over-enthusisam/over investment occasions a crash. That said, I honor the wisdom of Roy Amara: “We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.”
In any case, every link above is eminently worth clicking/reading; better yet, buy the issue.
All change: “Paradigm Shifts,” from @noemamag.com.
[Image above: source]
* Robert Musil, The Man Without Qualities
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As we buckle up, we might recall that it was on ths date in 1944 that IBM dedicated the first program-controlled calculator, the Automatic Sequence Controlled Calculator (known best as the Harvard Mark I)– one of the earliest, if not the earliest, general-purpose electromechanical computers, and the one that laibd the base for subsequent development… and thus a catalyst for the string of developments– technical, social, and political with which we’re wrestling now.
Designer Howard Aiken had enlisted IBM as a partner in 1937; company chairman Thomas Watson Sr. personally approved the project and its funding. On completion it was put to work on a set war-related tasks, including calculations– overseen by John von Neumann— for the Manhattan Project.
The Mark I was the industry’s largest electromechanical calculator… and it was large: 51 feet long, 8 feet high, and 2 feet deep; it weighed about 9,445 pounds The basic calculating units had to be synchronized and powered mechanically, so they were operated by a 50-foot (15 m) drive shaft coupled to a 5 horsepower electric motor, which served as the main power source and system clock. It could do 3 additions or subtractions in a second; a multiplication took 6 seconds; a division took 15.3 seconds; and a logarithm or a trigonometric function took over a minute… ridiculously slow by today’s standards, but a huge advance in its time.
“Mathematics is the music of reason”*…
New technologies, most centrally AI, are arming scientists with tools that might not just accelerate or enhance their work, but altogether transform it. As Jordana Cepelewicz reports, mathematicians have started to prepare for a profound shift in what it means to do math…
Since the start of the 20th century, the heart of mathematics has been the proof — a rigorous, logical argument for whether a given statement is true or false. Mathematicians’ careers are measured by what kinds of theorems they can prove, and how many. They spend the bulk of their time coming up with fresh insights to make a proof work, then translating those intuitions into step-by-step deductions, fitting different lines of reasoning together like puzzle pieces.
The best proofs are works of art. They’re not just rigorous; they’re elegant, creative and beautiful. This makes them feel like a distinctly human activity — our way of making sense of the world, of sharpening our minds, of testing the limits of thought itself.
But proofs are also inherently rational. And so it was only natural that when researchers started developing artificial intelligence in the mid-1950s, they hoped to automate theorem proving: to design computer programs capable of generating proofs of their own. They had some success. One of the earliest AI programs could output proofs of dozens of statements in mathematical logic. Other programs followed, coming up with ways to prove statements in geometry, calculus and other areas.
Still, these automated theorem provers were limited. The kinds of theorems that mathematicians really cared about required too much complexity and creativity. Mathematical research continued as it always had, unaffected and undeterred.
Now that’s starting to change. Over the past few years, mathematicians have used machine learning models (opens a new tab) to uncover new patterns, invent new conjectures, and find counterexamples to old ones. They’ve created powerful proof assistants both to verify whether a given proof is correct and to organize their mathematical knowledge.
They have not, as yet, built systems that can generate the proofs from start to finish, but that may be changing. In 2024, Google DeepMind announced that they had developed an AI system that scored a silver medal in the International Mathematical Olympiad, a prestigious proof-based exam for high school students. OpenAI’s more generalized “large language model,” ChatGPT, has made significant headway on reproducing proofs and solving challenging problems, as have smaller-scale bespoke systems. “It’s stunning how much they’re improving,” said Andrew Granville, a mathematician at the University of Montreal who until recently doubted claims that this technology might soon have a real impact on theorem proving. “They absolutely blow apart where I thought the limitations were. The cat’s out of the bag.”
Researchers predict they’ll be able to start outsourcing more tedious sections of proofs to AI within the next few years. They’re mixed on whether AI will ever be able to prove their most important conjectures entirely: Some are willing to entertain the notion, while others think there are insurmountable technological barriers. But it’s no longer entirely out of the question that the more creative aspects of the mathematical enterprise might one day be automated.
Even so, most mathematicians at the moment “have their heads buried firmly in the sand,” Granville said. They’re ignoring the latest developments, preferring to spend their time and energy on their usual jobs.
Continuing to do so, some researchers warn, would be a mistake. Even the ability to outsource boring or rote parts of proofs to AI “would drastically alter what we do and how we think about math over time,” said Akshay Venkatesh, a preeminent mathematician and Fields medalist at the Institute for Advanced Study in Princeton, New Jersey.
He and a relatively small group of other mathematicians are now starting to examine what an AI-powered mathematical future might look like, and how it will change what they value. In such a future, instead of spending most of their time proving theorems, mathematicians will play the role of critic, translator, conductor, experimentalist. Mathematics might draw closer to laboratory sciences, or even to the arts and humanities.
Imagining how AI will transform mathematics isn’t just an exercise in preparation. It has forced mathematicians to reckon with what mathematics really is at its core, and what it’s for…
Absolutely fascinating: “Mathematical Beauty, Truth, and Proof in the Age of AI,” from @jordanacep.bsky.social in @quantamagazine.bsky.social. Eminently worth reading in full.
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As we wonder about ways of knowing, we might spare a thought for a man whose work helped trigger an earlier iteration of this enhance/transform discussion and laid the groundwork for the one unpacked in the article linked above above: J. Presper Eckert; he died on this day in 1995. An electrical engineer, he co-designed (with John Mauchly) the first general purpose computer, the ENIAC (see here and here) for the U.S. Army’s Ballistic Research Laboratory. He and Mauchy went on to found the Eckert–Mauchly Computer Corporation, at which they designed and built the first commercial computer in the U.S., the UNIVAC.
“‘When I use a word,’ Humpty Dumpty said in rather a scornful tone, ‘it means just what I choose it to mean — neither more nor less.'”*…
Like today’s large language models, some 16th-century humanists (like Erasmus) had techniques to automate writing. But as Hannah Katznelson explains, others (like Rabelais) called foul…
The Renaissance scholar and educator Erasmus of Rotterdam opens his polemical treatise The Ciceronian (1528) by describing the utterly dysfunctional writing process of a character named Nosoponus. The Ciceronianis structured as a dialogue, withtwo mature writers, Bulephorus and Hypologus, trying to talk Nosoponus out of his paralysing obsession with stylistic perfection. Nosoponus explains that it would take him weeks of fruitless writing and rewriting to produce a casual letter in which he asks a friend to return some borrowed books. He says that writing requires such intense concentration that he can do it only at night, when no one else is awake to distract him, and even then his perfectionism is so intense that a single sentence becomes a full night’s work. Nosoponus goes over what he’s written again and again, but remains so dissatisfied with the quality of his language that eventually he just gives up.
Nosoponus’s problem might resonate. Who has not spent too long going over the wording of a simple email, at some point or another? Today there is an easy fix: we have large language models (LLMs) to write our letters for us, helpfully proffering suggestions as to what we might say, and how we might phrase it. When I input Nosoponus’s intended request into GPT-4, it generated the following almost instantly:
Hey [Friend’s Name],
Hope you’re doing well! I just realised I never got those books back that I lent you a while ago. No rush, but whenever you get a chance, I’d love to get them back. Let me know what works for you! Thanks!
Nosoponus
But there was a solution in the 16th century, too. A humanist education on the Erasmian model could train its students to produce letters of any length, on any topic – quickly, easily and eloquently. The French humanist François Rabelais, a contemporary of Erasmus, appears to have understood these compositional techniques as automating the creating of text in a way that, retrospectively, looks a lot like how LLMs function. If we want to understand LLMs, and what they are and aren’t capable of, we can look at earlier versions of the same technology – like Erasmian humanism. We can also read authors like Rabelais, who is already thinking about automatic text-generation along these lines, as someone who appreciates the effectiveness of Erasmian generative technology, but at the same time sees it as vitiating the social force of language and, ultimately, ruining language as a tool for moral and political life…
[Katznelson recounts Erasmus’s efforts, Rabelais’s response, and unpacks the important differences between our own authentic speech language created to speak for us and their practical and moral implications…]
What lessons from the 16th century can tell us about AI and LLMs: “Methodical banality,” from @aeon.co.
* Lewis Carroll, Through the Looking Glass
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As we honor authenticity, we might recall that it was on this date in 1886 that three U.S. patents were issued to Alexander Graham Bell’s Volta Labs for “recording and reproducing speech and other sounds.” The Graphophone, was an improved (and the first practical) version of the Edison phonograph (from 1877), and became the foundation on which the speech recording (e.g., dictaphone) and recorded music (and spoken word) industries began to grow.
“I think the next century will be the century of complexity”*…
… and as Philip Ball reports, a team of scientists at Carnegie Science agrees…
In 1950 the Italian physicist Enrico Fermi was discussing the possibility of intelligent alien life with his colleagues. If alien civilizations exist, he said, some should surely have had enough time to expand throughout the cosmos. So where are they?
Many answers to Fermi’s “paradox” have been proposed: Maybe alien civilizations burn out or destroy themselves before they can become interstellar wanderers. But perhaps the simplest answer is that such civilizations don’t appear in the first place: Intelligent life is extremely unlikely, and we pose the question only because we are the supremely rare exception.
A new proposal by an interdisciplinary team of researchers challenges that bleak conclusion. They have proposed nothing less than a new law of nature, according to which the complexity of entities in the universe increases over time with an inexorability comparable to the second law of thermodynamics — the law that dictates an inevitable rise in entropy, a measure of disorder. If they’re right, complex and intelligent life should be widespread.
In this new view, biological evolution appears not as a unique process that gave rise to a qualitatively distinct form of matter — living organisms. Instead, evolution is a special (and perhaps inevitable) case of a more general principle that governs the universe. According to this principle, entities are selected because they are richer in a kind of information that enables them to perform some kind of function.
This hypothesis, formulated by the mineralogist Robert Hazen [here] and the astrobiologist Michael Wong [here] of the Carnegie Institution in Washington, D.C., along with a team of others, has provoked intense debate. Some researchers have welcomed the idea as part of a grand narrative about fundamental laws of nature. They argue that the basic laws of physics are not “complete” in the sense of supplying all we need to comprehend natural phenomena; rather, evolution — biological or otherwise — introduces functions and novelties that could not even in principle be predicted from physics alone. “I’m so glad they’ve done what they’ve done,” said Stuart Kauffman, an emeritus complexity theorist at the University of Pennsylvania. “They’ve made these questions legitimate.”…
[Ball explains the origin and outline of Hazen’s and Wong’s conjecture, explores the critiques– among them, that it’s not clear how to test the hypothesis– and examines the resonant work on Assembly Theory being done by Lee Cronin and Sara Walker…]
… Wong said there is more work still to be done on mineral evolution, and they hope to look at nucleosynthesis and computational “artificial life.” Hazen also sees possible applications in oncology, soil science and language evolution. For example, the evolutionary biologist Frédéric Thomas of the University of Montpellier in France and colleagues have argued that the selective principles governing the way cancer cells change over time in tumors are not like those of Darwinian evolution, in which the selection criterion is fitness, but more closely resemble the idea of selection for function from Hazen and colleagues.
Hazen’s team has been fielding queries from researchers ranging from economists to neuroscientists, who are keen to see if the approach can help. “People are approaching us because they are desperate to find a model to explain their system,” Hazen said.
But whether or not functional information turns out to be the right tool for thinking about these questions, many researchers seem to be converging on similar questions about complexity, information, evolution (both biological and cosmic), function and purpose, and the directionality of time. It’s hard not to suspect that something big is afoot. There are echoes of the early days of thermodynamics, which began with humble questions about how machines work and ended up speaking to the arrow of time, the peculiarities of living matter, and the fate of the universe…
A new suggestion that complexity increases over time, not just in living organisms but in the nonliving world, promises to rewrite notions of time and evolution: “Why Everything in the Universe Turns More Complex,” from @philipcball.bsky.social and @quantamagazine.bsky.social.
See also: Benjamin Bratton‘s explantion of the work he and his collegues are doing at a new institute at UCSD: “Antikythera.” See his recent Long Now Foundation talk on this same subject here.
* Stephen Hawking
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As we celebrate complication, we might spare a thought for G. N. Ramachandran (Gopalasamudram Narayanan Ramachandran); he died on this date in 2001. A biophysicist, he discovered the triple helical “coiled coil” structure of the collagen molecule, among other remarkable contributions to structural biology.
Ramachandran was a master of X-ray crystallography, and with his colleagues, constructed space filling models of protein molecules. He devised the Ramachandran Plot, a method to diagram the conformation of polypeptides, polysaccharides and polynucleotides– which remains the international standard to describe protein structures.
Ramachandran, inspired by the ancient Syaad Nyaaya (doctrine of “may be”), also explored artificial intelligence. He developed the Boolean Vector Matrix Formulation which has important application in writing software for AI.
“Look before you ere you leap; / For as you sow, y’ are like to reap”*…
Further in a fashion to Saturday’s post, Robert Wright on the recent AI Summit in Paris…
[Last] week at the Paris AI summit, Vice President JD Vance stood before heads of state and tech titans and said, “When conferences like this convene to discuss a cutting edge technology, oftentimes, I think, our response is to be too self-conscious, too risk-averse. But never have I encountered a breakthrough in tech that so clearly calls us to do precisely the opposite.”
Precisely the opposite of “too risk-averse” would seem to be “not risk-averse enough.” Or maybe, as both ChatGPT and Anthropic’s Claude said when asked for the opposite of “too risk-averse”: “too risk-seeking” or “reckless.” In any event, most people in the AI safety community would agree that such terms capture the Trump administration’s approach to AI regulation. And that includes people who generally share Trump’s and Vance’s laissez faire intuitions. AI researcher Rob Miles posted a video of Vance’s speech on X and commented, “It’s so depressing that the one time when the government takes the right approach to an emerging technology, it’s for basically the only technology where that’s actually a terrible idea.”
The news for AI safety advocates gets worse: The summit’s overall vibe wasn’t all that different from Vance’s. The host, French President Emmanuel Macron, after announcing a big AI infrastructure investment, said that France is “back in the AI race” and that “Europe and France must accelerate their investments.” European Commission President Ursula von der Leyen vowed to “accelerate innovation” and “cut red tape” that now hobbles innovators. China and the US may be the world’s AI leaders, she granted, but “the AI race is far from being over.” All of this sat well with the corporate sector. As Axios reported, “A range of tech leaders, including Google CEO Sundar Pichai and Mistral CEO Arthur Mensch, used their speeches to push the acceleration mantra.”
Seems like only yesterday Sundar Pichai was emphasizing the need for international regulation, saying that AI, for all its benefits, holds great dangers. But, actually, that was back in 2023, when people like Open AI’s Sam Altman were also saying such things. That was the year world leaders convened in Britain’s Bletchley Park to discuss ways to collectively address AI risks, including catastrophic ones. The idea was to hold annual global summits on the international governance of AI. In theory, the Paris summit was the third of these (after the 2024 summit in Seoul). But you should always read the fine print: Whereas the official name of the first summit was “AI Safety Summit,” this year’s version was “AI Action Summit.” The headline over the Axios story was: “Don’t miss out” replaces “doom is nigh” at Paris’ AI summit.
The statement that came out of the summit did call for AI “safety” (along with “sustainable development, innovation,” and many other virtuous things). But there was no elaboration. Nothing, for example, about preventing people from using AIs to help make bioweapons—the kind of problem you’d think would call for international regulation, since pandemics don’t recognize national borders (and the kind of problem that some knowledgeable observers worry has been posed by OpenAI’s recently released Deep Research model).
MIT physicist Max Tegmark tweeted on Monday that a leaked draft of the summit statement seemed “optimized to antagonize both the US government (with focus on diversity, gender and disinformation) and the UK government (completely ignoring the scientific and political consensus around risks from smarter-than-human AI systems that was agreed at the Bletchley Park Summit).” And indeed, Britain and the US refused to sign the statement. The other 60 attending nations, including China, signed it.
Journalist Shakeel Hashim wrote about the world’s journey from Bletchley Park to Paris: “What was supposed to be a crucial forum for international cooperation has ended as a cautionary tale about how easily serious governance efforts can be derailed by national self-interest.” But, he said, the Paris Summit may have value “as a wake-up call. It has shown, definitively, that the current approach to AI governance is broken. The question now is whether we have time to fix it.”…
The ropes are down; the brakes are off: “AI Accelerationism Goes Global,” from @robertwrighter.bsky.social.
Apposite: the always-illuminating (and amusing) Matt Levine on Elon Musk’s bid to purchase Open AI (gift link to Bloomberg).
* Samuel Butler, Hudibras
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As we prioritize prudence, we might spare a thought for Giordano Bruno; he died on this date in 1600. A philosopher, poet, alchemist, astrologer, cosmological theorist, and esotericist (occultist), his theories anticipated modern science. The most notable of these were his theories of the infinite universe and the multiplicity of worlds, in which he rejected the traditional geocentric (or Earth-centred) astronomy and intuitively went beyond the Copernican heliocentric (sun-centred) theory, which still maintained a finite universe with a sphere of fixed stars. Although one of the most important philosophers of the Italian Renaissance, Bruno’s various passionate utterings led to intense opposition. In 1592, after a trial by the Roman Inquisition, he was kept imprisoned for eight years and interrogated periodically. When, in the end, he refused to recant, he was burned at the stake in Rome for heresy.
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