Podcasts about Cambrian

Listen in as Real Science Radio host Fred Williams and co-host Doug McBurney review and update some of Bob Enyart's legendary list of not so old things! From Darwin's Finches to opals forming in months to man's genetic diversity in 200 generations, to carbon 14 everywhere it's not supposed to be (including in diamonds and dinosaur bones!), scientific observations simply defy the claim that the earth is billions of years old. Real science demands the dismissal of the alleged million and billion year ages asserted by the ungodly and the foolish.     * Finches Adapt in 17 Years, Not 2.3 Million: Charles Darwin's finches are claimed to have taken 2,300,000 years to diversify from an initial species blown onto the Galapagos Islands. Yet individuals from a single finch species on a U.S. Bird Reservation in the Pacific were introduced to a group of small islands 300 miles away and in at most 17 years, like Darwin's finches, they had diversified their beaks, related muscles, and behavior to fill various ecological niches. Hear about this also at rsr.org/spetner.  * Finches Speciate in Two Generations vs Two Million Years for Darwin's Birds?  Darwin's finches on the Galapagos Islands are said to have diversified into 14 species over a period of two million years. But in 2017 the journal Science reported a newcomer to the Island which within two generations spawned a reproductively isolated new species. In another instance as documented by Lee Spetner, a hundred birds of the same finch species introduced to an island cluster a 1,000 kilometers from Galapagos diversified into species with the typical variations in beak sizes, etc. "If this diversification occurred in less than seventeen years," Dr. Spetner asks, "why did Darwin's Galapagos finches [as claimed by evolutionists] have to take two million years?" * Opals Can Form in "A Few Months" And Don't Need 100,000 Years: A leading authority on opals, Allan W. Eckert, observed that, "scientific papers and textbooks have told that the process of opal formation requires tens of thousands of years, perhaps hundreds of thousands... Not true." A 2011 peer-reviewed paper in a geology journal from Australia, where almost all the world's opal is found, reported on the: "new timetable for opal formation involving weeks to a few months and not the hundreds of thousands of years envisaged by the conventional weathering model." (And apparently, per a 2019 report from Entomology Today, opals can even form around insects!) More knowledgeable scientists resist the uncritical, group-think insistence on false super-slow formation rates (as also for manganese nodules, gold veins, stone, petroleum, canyons and gullies, and even guts, all below). Regarding opals, Darwinian bias led geologists to long ignore possible quick action, as from microbes, as a possible explanation for these mineraloids. For both in nature and in the lab, opals form rapidly, not even in 10,000 years, but in weeks. See this also from creationists by a geologist, a paleobiochemist, and a nuclear chemist. * Blue Eyes Originated Not So Long Ago: Not a million years ago, nor a hundred thousand years ago, but based on a peer-reviewed paper in Human Genetics, a press release at Science Daily reports that, "research shows that people with blue eyes have a single, common ancestor. A team at the University of Copenhagen have tracked down a genetic mutation which took place 6-10,000 years ago and is the cause of the eye color of all blue-eyed humans alive on the planet today." * Adding the Entire Universe to our List of Not So Old Things? Based on March 2019 findings from Hubble, Nobel laureate Adam Riess of the Space Telescope Science Institute and his co-authors in the Astrophysical Journal estimate that the universe is about a billion years younger than previously thought! Then in September 2019 in the journal Science, the age dropped precipitously to as low as 11.4 billion years! Of course, these measurements also further squeeze the canonical story of the big bang chronology with its many already existing problems including the insufficient time to "evolve" distant mature galaxies, galaxy clusters, superclusters, enormous black holes, filaments, bubbles, walls, and other superstructures. So, even though the latest estimates are still absurdly too old (Google: big bang predictions, and click on the #1 ranked article, or just go on over there to rsr.org/bb), regardless, we thought we'd plop the whole universe down on our List of Not So Old Things!   * After the Soft Tissue Discoveries, NOW Dino DNA: When a North Carolina State University paleontologist took the Tyrannosaurus Rex photos to the right of original biological material, that led to the 2016 discovery of dinosaur DNA, So far researchers have also recovered dinosaur blood vessels, collagen, osteocytes, hemoglobin, red blood cells, and various proteins. As of May 2018, twenty-six scientific journals, including Nature, Science, PNAS, PLoS One, Bone, and Journal of Vertebrate Paleontology, have confirmed the discovery of biomaterial fossils from many dinosaurs! Organisms including T. Rex, hadrosaur, titanosaur, triceratops, Lufengosaur, mosasaur, and Archaeopteryx, and many others dated, allegedly, even hundreds of millions of years old, have yielded their endogenous, still-soft biological material. See the web's most complete listing of 100+ journal papers (screenshot, left) announcing these discoveries at bflist.rsr.org and see it in layman's terms at rsr.org/soft. * Rapid Stalactites, Stalagmites, Etc.: A construction worker in 1954 left a lemonade bottle in one of Australia's famous Jenolan Caves. By 2011 it had been naturally transformed into a stalagmite (below, right). Increasing scientific knowledge is arguing for rapid cave formation (see below, Nat'l Park Service shrinks Carlsbad Caverns formation estimates from 260M years, to 10M, to 2M, to it "depends"). Likewise, examples are growing of rapid formations with typical chemical make-up (see bottle, left) of classic stalactites and stalagmites including: - in Nat'l Geo the Carlsbad Caverns stalagmite that rapidly covered a bat - the tunnel stalagmites at Tennessee's Raccoon Mountain - hundreds of stalactites beneath the Lincoln Memorial - those near Gladfelter Hall at Philadelphia's Temple University (send photos to Bob@rsr.org) - hundreds of stalactites at Australia's zinc mine at Mt. Isa.   - and those beneath Melbourne's Shrine of Remembrance. * Most Human Mutations Arose in 200 Generations: From Adam until Real Science Radio, in only 200 generations! The journal Nature reports The Recent Origin of Most Human Protein-coding Variants. As summarized by geneticist co-author Joshua Akey, "Most of the mutations that we found arose in the last 200 generations or so" (the same number previously published by biblical creationists). Another 2012 paper, in the American Journal of Physical Anthropology (Eugenie Scott's own field) on High mitochondrial mutation rates, shows that one mitochondrial DNA mutation occurs every other generation, which, as creationists point out, indicates that mtEve would have lived about 200 generations ago. That's not so old! * National Geographic's Not-So-Old Hard-Rock Canyon at Mount St. Helens: As our List of Not So Old Things (this web page) reveals, by a kneejerk reaction evolutionary scientists assign ages of tens or hundreds of thousands of years (or at least just long enough to contradict Moses' chronology in Genesis.) However, with closer study, routinely, more and more old ages get revised downward to fit the world's growing scientific knowledge. So the trend is not that more information lengthens ages, but rather, as data replaces guesswork, ages tend to shrink until they are consistent with the young-earth biblical timeframe. Consistent with this observation, the May 2000 issue of National Geographic quotes the U.S. Forest Service's scientist at Mount St. Helens, Peter Frenzen, describing the canyon on the north side of the volcano. "You'd expect a hard-rock canyon to be thousands, even hundreds of thousands of years old. But this was cut in less than a decade." And as for the volcano itself, while again, the kneejerk reaction of old-earthers would be to claim that most geologic features are hundreds of thousands or millions of years old, the atheistic National Geographic magazine acknowledges from the evidence that Mount St. Helens, the volcanic mount, is only about 4,000 years old! See below and more at rsr.org/mount-st-helens. * Mount St. Helens Dome Ten Years Old not 1.7 Million: Geochron Laboratories of Cambridge, Mass., using potassium-argon and other radiometric techniques claims the rock sample they dated, from the volcano's dome, solidified somewhere between 340,000 and 2.8 million years ago. However photographic evidence and historical reports document the dome's formation during the 1980s, just ten years prior to the samples being collected. With the age of this rock known, radiometric dating therefore gets the age 99.99999% wrong. * Devils Hole Pupfish Isolated Not for 13,000 Years But for 100: Secular scientists default to knee-jerk, older-than-Bible-age dates. However, a tiny Mojave desert fish is having none of it. Rather than having been genetically isolated from other fish for 13,000 years (which would make this small school of fish older than the Earth itself), according to a paper in the journal Nature, actual measurements of mutation rates indicate that the genetic diversity of these Pupfish could have been generated in about 100 years, give or take a few. * Polystrates like Spines and Rare Schools of Fossilized Jellyfish: Previously, seven sedimentary layers in Wisconsin had been described as taking a million years to form. And because jellyfish have no skeleton, as Charles Darwin pointed out, it is rare to find them among fossils. But now, reported in the journal Geology, a school of jellyfish fossils have been found throughout those same seven layers. So, polystrate fossils that condense the time of strata deposition from eons to hours or months, include: - Jellyfish in central Wisconsin were not deposited and fossilized over a million years but during a single event quick enough to trap a whole school. (This fossil school, therefore, taken as a unit forms a polystrate fossil.) Examples are everywhere that falsify the claims of strata deposition over millions of years. - Countless trilobites buried in astounding three dimensionality around the world are meticulously recovered from limestone, much of which is claimed to have been deposited very slowly. Contrariwise, because these specimens were buried rapidly in quickly laid down sediments, they show no evidence of greater erosion on their upper parts as compared to their lower parts. - The delicacy of radiating spine polystrates, like tadpole and jellyfish fossils, especially clearly demonstrate the rapidity of such strata deposition. - A second school of jellyfish, even though they rarely fossilized, exists in another locale with jellyfish fossils in multiple layers, in Australia's Brockman Iron Formation, constraining there too the rate of strata deposition. By the way, jellyfish are an example of evolution's big squeeze. Like galaxies evolving too quickly, 

Listen in as Real Science Radio host Fred Williams and co-host Doug McBurney review and update some of Bob Enyart's legendary list of not so old things! From Darwin's Finches to opals forming in months to man's genetic diversity in 200 generations, to carbon 14 everywhere it's not supposed to be (including in diamonds and dinosaur bones!), scientific observations simply defy the claim that the earth is billions of years old. Real science demands the dismissal of the alleged million and billion year ages asserted by the ungodly and the foolish.   * Finches Adapt in 17 Years, Not 2.3 Million: Charles Darwin's finches are claimed to have taken 2,300,000 years to diversify from an initial species blown onto the Galapagos Islands. Yet individuals from a single finch species on a U.S. Bird Reservation in the Pacific were introduced to a group of small islands 300 miles away and in at most 17 years, like Darwin's finches, they had diversified their beaks, related muscles, and behavior to fill various ecological niches. Hear about this also at rsr.org/spetner.  * Finches Speciate in Two Generations vs Two Million Years for Darwin's Birds?  Darwin's finches on the Galapagos Islands are said to have diversified into 14 species over a period of two million years. But in 2017 the journal Science reported a newcomer to the Island which within two generations spawned a reproductively isolated new species. In another instance as documented by Lee Spetner, a hundred birds of the same finch species introduced to an island cluster a 1,000 kilometers from Galapagos diversified into species with the typical variations in beak sizes, etc. "If this diversification occurred in less than seventeen years," Dr. Spetner asks, "why did Darwin's Galapagos finches [as claimed by evolutionists] have to take two million years?" * Opals Can Form in "A Few Months" And Don't Need 100,000 Years: A leading authority on opals, Allan W. Eckert, observed that, "scientific papers and textbooks have told that the process of opal formation requires tens of thousands of years, perhaps hundreds of thousands... Not true." A 2011 peer-reviewed paper in a geology journal from Australia, where almost all the world's opal is found, reported on the: "new timetable for opal formation involving weeks to a few months and not the hundreds of thousands of years envisaged by the conventional weathering model." (And apparently, per a 2019 report from Entomology Today, opals can even form around insects!) More knowledgeable scientists resist the uncritical, group-think insistence on false super-slow formation rates (as also for manganese nodules, gold veins, stone, petroleum, canyons and gullies, and even guts, all below). Regarding opals, Darwinian bias led geologists to long ignore possible quick action, as from microbes, as a possible explanation for these mineraloids. For both in nature and in the lab, opals form rapidly, not even in 10,000 years, but in weeks. See this also from creationists by a geologist, a paleobiochemist, and a nuclear chemist. * Blue Eyes Originated Not So Long Ago: Not a million years ago, nor a hundred thousand years ago, but based on a peer-reviewed paper in Human Genetics, a press release at Science Daily reports that, "research shows that people with blue eyes have a single, common ancestor. A team at the University of Copenhagen have tracked down a genetic mutation which took place 6-10,000 years ago and is the cause of the eye color of all blue-eyed humans alive on the planet today." * Adding the Entire Universe to our List of Not So Old Things? Based on March 2019 findings from Hubble, Nobel laureate Adam Riess of the Space Telescope Science Institute and his co-authors in the Astrophysical Journal estimate that the universe is about a billion years younger than previously thought! Then in September 2019 in the journal Science, the age dropped precipitously to as low as 11.4 billion years! Of course, these measurements also further squeeze the canonical story of the big bang chronology with its many already existing problems including the insufficient time to "evolve" distant mature galaxies, galaxy clusters, superclusters, enormous black holes, filaments, bubbles, walls, and other superstructures. So, even though the latest estimates are still absurdly too old (Google: big bang predictions, and click on the #1 ranked article, or just go on over there to rsr.org/bb), regardless, we thought we'd plop the whole universe down on our List of Not So Old Things!   * After the Soft Tissue Discoveries, NOW Dino DNA: When a North Carolina State University paleontologist took the Tyrannosaurus Rex photos to the right of original biological material, that led to the 2016 discovery of dinosaur DNA, So far researchers have also recovered dinosaur blood vessels, collagen, osteocytes, hemoglobin, red blood cells, and various proteins. As of May 2018, twenty-six scientific journals, including Nature, Science, PNAS, PLoS One, Bone, and Journal of Vertebrate Paleontology, have confirmed the discovery of biomaterial fossils from many dinosaurs! Organisms including T. Rex, hadrosaur, titanosaur, triceratops, Lufengosaur, mosasaur, and Archaeopteryx, and many others dated, allegedly, even hundreds of millions of years old, have yielded their endogenous, still-soft biological material. See the web's most complete listing of 100+ journal papers (screenshot, left) announcing these discoveries at bflist.rsr.org and see it in layman's terms at rsr.org/soft. * Rapid Stalactites, Stalagmites, Etc.: A construction worker in 1954 left a lemonade bottle in one of Australia's famous Jenolan Caves. By 2011 it had been naturally transformed into a stalagmite (below, right). Increasing scientific knowledge is arguing for rapid cave formation (see below, Nat'l Park Service shrinks Carlsbad Caverns formation estimates from 260M years, to 10M, to 2M, to it "depends"). Likewise, examples are growing of rapid formations with typical chemical make-up (see bottle, left) of classic stalactites and stalagmites including: - in Nat'l Geo the Carlsbad Caverns stalagmite that rapidly covered a bat - the tunnel stalagmites at Tennessee's Raccoon Mountain - hundreds of stalactites beneath the Lincoln Memorial - those near Gladfelter Hall at Philadelphia's Temple University (send photos to Bob@rsr.org) - hundreds of stalactites at Australia's zinc mine at Mt. Isa.   - and those beneath Melbourne's Shrine of Remembrance. * Most Human Mutations Arose in 200 Generations: From Adam until Real Science Radio, in only 200 generations! The journal Nature reports The Recent Origin of Most Human Protein-coding Variants. As summarized by geneticist co-author Joshua Akey, "Most of the mutations that we found arose in the last 200 generations or so" (the same number previously published by biblical creationists). Another 2012 paper, in the American Journal of Physical Anthropology (Eugenie Scott's own field) on High mitochondrial mutation rates, shows that one mitochondrial DNA mutation occurs every other generation, which, as creationists point out, indicates that mtEve would have lived about 200 generations ago. That's not so old! * National Geographic's Not-So-Old Hard-Rock Canyon at Mount St. Helens: As our List of Not So Old Things (this web page) reveals, by a kneejerk reaction evolutionary scientists assign ages of tens or hundreds of thousands of years (or at least just long enough to contradict Moses' chronology in Genesis.) However, with closer study, routinely, more and more old ages get revised downward to fit the world's growing scientific knowledge. So the trend is not that more information lengthens ages, but rather, as data replaces guesswork, ages tend to shrink until they are consistent with the young-earth biblical timeframe. Consistent with this observation, the May 2000 issue of National Geographic quotes the U.S. Forest Service's scientist at Mount St. Helens, Peter Frenzen, describing the canyon on the north side of the volcano. "You'd expect a hard-rock canyon to be thousands, even hundreds of thousands of years old. But this was cut in less than a decade." And as for the volcano itself, while again, the kneejerk reaction of old-earthers would be to claim that most geologic features are hundreds of thousands or millions of years old, the atheistic National Geographic magazine acknowledges from the evidence that Mount St. Helens, the volcanic mount, is only about 4,000 years old! See below and more at rsr.org/mount-st-helens. * Mount St. Helens Dome Ten Years Old not 1.7 Million: Geochron Laboratories of Cambridge, Mass., using potassium-argon and other radiometric techniques claims the rock sample they dated, from the volcano's dome, solidified somewhere between 340,000 and 2.8 million years ago. However photographic evidence and historical reports document the dome's formation during the 1980s, just ten years prior to the samples being collected. With the age of this rock known, radiometric dating therefore gets the age 99.99999% wrong. * Devils Hole Pupfish Isolated Not for 13,000 Years But for 100: Secular scientists default to knee-jerk, older-than-Bible-age dates. However, a tiny Mojave desert fish is having none of it. Rather than having been genetically isolated from other fish for 13,000 years (which would make this small school of fish older than the Earth itself), according to a paper in the journal Nature, actual measurements of mutation rates indicate that the genetic diversity of these Pupfish could have been generated in about 100 years, give or take a few. * Polystrates like Spines and Rare Schools of Fossilized Jellyfish: Previously, seven sedimentary layers in Wisconsin had been described as taking a million years to form. And because jellyfish have no skeleton, as Charles Darwin pointed out, it is rare to find them among fossils. But now, reported in the journal Geology, a school of jellyfish fossils have been found throughout those same seven layers. So, polystrate fossils that condense the time of strata deposition from eons to hours or months, include: - Jellyfish in central Wisconsin were not deposited and fossilized over a million years but during a single event quick enough to trap a whole school. (This fossil school, therefore, taken as a unit forms a polystrate fossil.) Examples are everywhere that falsify the claims of strata deposition over millions of years. - Countless trilobites buried in astounding three dimensionality around the world are meticulously recovered from limestone, much of which is claimed to have been deposited very slowly. Contrariwise, because these specimens were buried rapidly in quickly laid down sediments, they show no evidence of greater erosion on their upper parts as compared to their lower parts. - The delicacy of radiating spine polystrates, like tadpole and jellyfish fossils, especially clearly demonstrate the rapidity of such strata deposition. - A second school of jellyfish, even though they rarely fossilized, exists in another locale with jellyfish fossils in multiple layers, in Australia's Brockman Iron Formation, constraining there too the rate of strata deposition. By the way, jellyfish are an example of evolution's big squeeze. Like galaxies e

Further reading: Reconstructing fossil cephalopods: Endoceras Retro vs Modern #17: Ammonites Hammering Away at Hamites An endocerid [picture by Entelognathus - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=111981757]: An ammonite fossil: A hamite ammonoid that looks a lot like a paperclip [picture by Hectonichus - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=34882102]: Show transcript: Welcome to Strange Animals Podcast. I'm your host, Kate Shaw. When you think about cephalopods, if that's a word you know, you probably think of octopuses and squid, maybe cuttlefish. But those aren't the only cephalopods, and in particular in the past, there used to be even more cephalopods that are even weirder than the ones we have today. Cephalopods are in the family Mollusca along with snails and clams, and many other animals. The first ancestral cephalopods date back to the Cambrian, and naturally we don't know a whole lot about them since that was around 500 million years ago. We have fossilized shells that were only a few centimeters long at most, although none of the specimens we've found are complete. By about 475 million years ago, these early cephalopod ancestors had mostly died out but had given rise to some amazing animals called Endocerids. Endocerids had shells that were mostly cone-shaped, like one of those pointy-ended ice cream cones but mostly larger and not as tasty. Most were pretty small, usually only a few feet long, or less than a meter, but some were really big. The largest Endoceras giganteum fossil we have is just under 10 feet long, or 3 meters, and it isn't complete. Some scientists estimate that it might have been almost 19 feet long, or about 5.75 meters, when it was alive. But that's just the long, conical shell. What did the animal that lived in the shell look like? We don't know, but scientists speculate that it had a squid-like body. The head and arms were outside of the shell's opening, while the main part of the body was protected by the front part of the shell. We know it had arms because we have arm impressions in sections of fossilized sea floor that show ten arms that are all about the same length. We don't know if the arms had suckers the way many modern cephalopods do, and some scientists suggest it had ridges on the undersides of the arms that helped it grab prey, the way modern nautiluses do. It also had a hood-shaped structure on top of its head called an operculum, which is also seen in nautiluses. This probably allowed Endoceras giganteum to pull its head and arms into its shell and use the operculum to block the shell's entrance. We don't know what colors the shells were, but some specimens seem to show a mottled or spotted pattern. The interior of Endoceras giganteum's shell was made up of chambers, some of which were filled with calcium deposits that helped balance the body weight, so the animal didn't have trouble dragging it around. 3D models of the shells show that they could easily stick straight up in the water, but we also have trace fossils that show drag marks of the shell through sediment. Scientists think Endoceras was mainly an ambush predator, sitting quietly until a small animal got too close. Then it would grab it with its arms. It could also crawl around to find a better spot to hunt, and younger individuals that had smaller shells were probably a lot more active. We talked about ammonites way back in episode 86. Ammonites were really common in the fossil record for hundreds of millions of years, only going extinct at the same time as the dinosaurs. Some ammonites lived at the bottom of the ocean in shallow water, but many swam or floated throughout the ocean. Many ammonite fossils look like snail shells, but the shell contains sections inside called chambers. The largest chamber, at the end of the shell, was for the ammonite's body,

The gang discusses two papers that look at preserved skin/external tissues. The first paper shows a unique record of Cambrian molting, and the second paper looks at the first preserved samples of plesiosaur skin. Meanwhile, Amanda commits an "own goal”, Curt shares some old internet fun, and James has opinions about fins.   Up-Goer Five (Curt Edition): The friends talk about two papers that look at skin that is very very very old. The first paper looks at animals from a long time ago that lose their skin when they get too big for it when then grow. They found these parts on the skin that are hard and most of the time there are two but some of them have four, and that these ones that have four are because they are growing new skin under the old skin. The second paper looks at an animal from a long time ago that breathes air but lives in the water and is close to things today that have harder skin. Other animals like this animal have some skin that we know about, but for this group of animal we did not know a lot about their skin. In the other animals that move into water, their skin gets soft, but this group shows that some of their skin is hard like the animals that are on land. This might be because how these animals live.   References: Yu, Chiyang, Deng Wang, and Jian Han. "Cambrian palaeoscolecidomorph Cricocosmia caught in the act of moulting." Historical Biology 37.3 (2025): 643-649. Marx, Miguel, et al. "Skin, scales, and cells in a Jurassic plesiosaur." Current Biology (2025).

In this episode of the Starting Point podcast, Jay Seegert interviews Dr. Carl Werner, who presents compelling evidence against Darwinian evolution and naturalism. Dr. Werner shares his extensive research journey, highlighting the challenges in explaining the origins of the universe and life. He discusses the Cambrian explosion and the lack of transitional fossils, arguing that the fossil record does not support evolutionary theory. The conversation sets the stage for a follow-up episode that will delve into specific evolutionary proofs and their shortcomings.

We're in Wales for this episode and Ben's guest is James Raw - a seventh-generation farmer who runs a hill farm in the heart of the Cambrian mountains. James has implemented a range of practices and projects at his farm, including the creation of two hydro systems and a farming system that focuses on soil health and biodiversity. He was awarded the prestigious Low Impact Award in the annual M&S Select Farm Awards for England and Wales last year. In this episode Ben and James talk about James's farming journey, the changes that he has enacted on the farm and some trends and issues in farming. Meet the Farmers is produced by RuralPod Media, the only specialist rural podcast production agency. Please note that this podcast does not constitute advice. Our podcast disclaimer can be found here. About Ben and  RuralPod MediaBen Eagle is the founder and Head of Podcasts at RuralPod Media, a specialist rural podcast production agency. He is also a freelance rural affairs and agricultural journalist. You can find out more at ruralpodmedia.co.uk or benjamineagle.co.uk If you have a business interested in getting involved with podcasting check us out at RuralPod Media. We'd love to help you spread your message. Please subscribe to the show and leave us a review wherever you are listening. Follow us on social mediaInstagram @mtf_podcastTwitter @mtf_podcastWatch us on Youtube here

In this episode of the Starting Point podcast, Jay Sigert interviews Dr. Carl Werner, who presents compelling evidence against Darwinian evolution and naturalism. Dr. Werner shares his extensive research journey, highlighting the challenges in explaining the origins of the universe and life. He discusses the Cambrian explosion and the lack of transitional fossils, arguing that the fossil record does not support evolutionary theory. The conversation sets the stage for a follow-up episode that will delve into specific evolutionary proofs and their shortcomings.

CertiK chief business officer Jason Jiang shares the nitty gritty on how North Korea's Lazarus Group stole $1.4 billion in ETH-related tokens from Bybit, who is ultimately at fault, and what the crypto industry and investors can do to protect themselves against the next major hack. (00:00) Introduction to The Agenda podcast and this week's episode(02:17) How Lazarus Group hacked Bybit (07:17) Are hard wallets and cold wallets safe from hacks?(09:19) How AI and quantum computing could compromise blockchains(12:24) Who is most at fault for the Bybit hack?(16:05) Is THORChain facilitating crime or abiding by the rules of decentralization?(18:46) How smart contract audits work(23:31) Securing AI and planning for the quantum computing Cambrian explosion(26:02) Is there a white hat hacker shortage?(30:34) The future of onchain securityThe Agenda is brought to you by Cointelegraph and hosted/produced by Ray Salmond and Jonathan DeYoung, with post-production by Elena Volkova (Hatch Up). Follow Cointelegraph on X (Twitter) at @Cointelegraph, Jonathan at @maddopemadic and Ray at @HorusHughes. Jonathan is also on Instagram at @maddopemadic, and he made the music for the podcast — hear more at madic.art.Check out Cointelegraph at cointelegraph.com.If you like what you heard, rate us and leave a review!The views, thoughts and opinions expressed in this podcast are its participants' alone and do not necessarily reflect or represent the views and opinions of Cointelegraph. This podcast (and any related content) is for entertainment purposes only and does not constitute financial advice, nor should it be taken as such. Everyone must do their own research and make their own decisions. The podcast's participants may or may not own any of the assets mentioned.

Since inception, ARK has researched and published thoughts on the cryptocurrency ecosystem within Big Ideas and through articles, whitepapers, monthly Bitcoin reports and podcasts. Now, in coordination with Bitcoin Park, ARK is pleased to introduce a monthly conversation with leaders in the Bitcoin space, to discuss everything happening in the rapidly-changing and still nascent Bitcoin ecosystem. Published through the For Your innovation podcast channels, this monthly series aims to be informative and enlightening, including experts with diverse viewpoints.Guests on this month's Bitcoin Brainstorm include: Cathie Wood: Founder, CEO and CIO, ARK InvestLorenzo Valente: Director of Research, Digital Assets, ARK InvestSkot9000: Creator of BitaxeJose Rios: Former VP, AI and Datacenter Group, IntelRobert Warren: Author,Bitcoin Miner's AlmanacRod Roudi: Co-Founder, Bitcoin Park Key Points FromThis Episode:00:00:00 Intro00:01:00 Cathie's thoughts on open-source development vs. closed00:05:50 Where does bitcoin fit on a global macro-economic scale?00:10:20 Supply chain: do developers have enough resources?00:21:50 What are some projects we should be paying attention to?00:30:10 Bitaxe production process, scale, and chip demand00:39:00 Exogenous use cases for bitcoin miners00:43:10 Is there a solution to chip production bottlenecks and vulnerabilities?00:48:00 Are we at the precipice of a Cambrian explosion in bitcoin mining?  LinksMentioned in this Episode: Learnmore about Bitcoin Park: bitcoinpark.com

The Scoop's host, Frank Chaparro, was joined by August Co-Founder and CEO Aya Kantorovich. In this episode, Chaparro and Kantorovich went over the state of crypto capital markets, the resilience of DeFi during market downturns, and how banks are looking to get started in DeFi. OUTLINE 00:00 Introduction  01:08 From banking to crypto  02:30 Why institutions avoid DeFi  05:33 How institutions impact volatility  10:50 Crypto as the weekend market  17:00 Bitcoin as a reserve asset  20:01 Cambrian explosion of memecoins  26:28 Coinbase earnings  31:42 The resiliency of deFi  37:49 AI Agents  42:00 Conclusion GUEST LINKS Aya Kantorovich - https://www.linkedin.com/in/ayakantorovich/ Aya Kantorovich on X - https://x.com/aya_kantor August - https://www.augustdigital.io/ August on X - https://x.com/august_digital

Since inception, ARK has researched and published thoughts on the cryptocurrency ecosystem within Big Ideas and through articles, whitepapers, monthly Bitcoin reports and podcasts. Now, in coordination with Bitcoin Park, ARK is pleased to host a monthly conversation with leaders in the Bitcoin space, to discuss everything happening in the rapidly-changing and still nascent Bitcoin ecosystem. Published through the For Your Innovation podcast channels, this monthly series aims to be informative and enlightening, including experts with diverse viewpoints.Guests on this month's Bitcoin Brainstorm include:Cathie Wood: Founder, CEO and CIO, ARK InvestLorenzo Valente: Director of Research, Digital Assets, ARK InvestSkot9000: Creator of BitaxeJose Rios: Former VP, AI and Datacenter Group, IntelRobert Warren: Author, Bitcoin Miner's AlmanacRod Roudi: Co-Founder, Bitcoin ParkKey Points From This Episode: 00:01:00 Cathie's thoughts on open-source development vs. closed00:05:50 Where does bitcoin fit on a global macro-economic scale?00:10:20 Supply chain: do developers have enough resources?00:21:50 What are some projects we should be paying attention to?00:30:10 Bitaxe production process, scale, and chip demand00:39:00 Exogenous use cases for bitcoin miners00:43:10 Is there a solution to chip production bottlenecks and vulnerabilities?00:48:00 Are we at the precipice of a Cambrian explosion in bitcoin mining? Links Mentioned in this Episode: Learn more about Bitcoin Park: bitcoinpark.com

If you're in SF, join us tomorrow for a fun meetup at CodeGen Night!If you're in NYC, join us for AI Engineer Summit! The Agent Engineering track is now sold out, but 25 tickets remain for AI Leadership and 5 tickets for the workshops. You can see the full schedule of speakers and workshops at https://ai.engineer!It's exceedingly hard to introduce someone like Bret Taylor. We could recite his Wikipedia page, or his extensive work history through Silicon Valley's greatest companies, but everyone else already does that.As a podcast by AI engineers for AI engineers, we had the opportunity to do something a little different. We wanted to dig into what Bret sees from his vantage point at the top of our industry for the last 2 decades, and how that explains the rise of the AI Architect at Sierra, the leading conversational AI/CX platform.“Across our customer base, we are seeing a new role emerge - the role of the AI architect. These leaders are responsible for helping define, manage and evolve their company's AI agent over time. They come from a variety of both technical and business backgrounds, and we think that every company will have one or many AI architects managing their AI agent and related experience.”In our conversation, Bret Taylor confirms the Paul Buchheit legend that he rewrote Google Maps in a weekend, armed with only the help of a then-nascent Google Closure Compiler and no other modern tooling. But what we find remarkable is that he was the PM of Maps, not an engineer, though of course he still identifies as one. We find this theme recurring throughout Bret's career and worldview. We think it is plain as day that AI leadership will have to be hands-on and technical, especially when the ground is shifting as quickly as it is today:“There's a lot of power in combining product and engineering into as few people as possible… few great things have been created by committee.”“If engineering is an order taking organization for product you can sometimes make meaningful things, but rarely will you create extremely well crafted breakthrough products. Those tend to be small teams who deeply understand the customer need that they're solving, who have a maniacal focus on outcomes.”“And I think the reason why is if you look at like software as a service five years ago, maybe you can have a separation of product and engineering because most software as a service created five years ago. I wouldn't say there's like a lot of technological breakthroughs required for most business applications. And if you're making expense reporting software or whatever, it's useful… You kind of know how databases work, how to build auto scaling with your AWS cluster, whatever, you know, it's just, you're just applying best practices to yet another problem. "When you have areas like the early days of mobile development or the early days of interactive web applications, which I think Google Maps and Gmail represent, or now AI agents, you're in this constant conversation with what the requirements of your customers and stakeholders are and all the different people interacting with it and the capabilities of the technology. And it's almost impossible to specify the requirements of a product when you're not sure of the limitations of the technology itself.”This is the first time the difference between technical leadership for “normal” software and for “AI” software was articulated this clearly for us, and we'll be thinking a lot about this going forward. We left a lot of nuggets in the conversation, so we hope you'll just dive in with us (and thank Bret for joining the pod!)Timestamps* 00:00:02 Introductions and Bret Taylor's background* 00:01:23 Bret's experience at Stanford and the dot-com era* 00:04:04 The story of rewriting Google Maps backend* 00:11:06 Early days of interactive web applications at Google* 00:15:26 Discussion on product management and engineering roles* 00:21:00 AI and the future of software development* 00:26:42 Bret's approach to identifying customer needs and building AI companies* 00:32:09 The evolution of business models in the AI era* 00:41:00 The future of programming languages and software development* 00:49:38 Challenges in precisely communicating human intent to machines* 00:56:44 Discussion on Artificial General Intelligence (AGI) and its impact* 01:08:51 The future of agent-to-agent communication* 01:14:03 Bret's involvement in the OpenAI leadership crisis* 01:22:11 OpenAI's relationship with Microsoft* 01:23:23 OpenAI's mission and priorities* 01:27:40 Bret's guiding principles for career choices* 01:29:12 Brief discussion on pasta-making* 01:30:47 How Bret keeps up with AI developments* 01:32:15 Exciting research directions in AI* 01:35:19 Closing remarks and hiring at Sierra Transcript[00:02:05] Introduction and Guest Welcome[00:02:05] Alessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Decibel Partners, and I'm joined by my co host swyx, founder of smol.ai.[00:02:17] swyx: Hey, and today we're super excited to have Bret Taylor join us. Welcome. Thanks for having me. It's a little unreal to have you in the studio.[00:02:25] swyx: I've read about you so much over the years, like even before. Open AI effectively. I mean, I use Google Maps to get here. So like, thank you for everything that you've done. Like, like your story history, like, you know, I think people can find out what your greatest hits have been.[00:02:40] Bret Taylor's Early Career and Education[00:02:40] swyx: How do you usually like to introduce yourself when, you know, you talk about, you summarize your career, like, how do you look at yourself?[00:02:47] Bret: Yeah, it's a great question. You know, we, before we went on the mics here, we're talking about the audience for this podcast being more engineering. And I do think depending on the audience, I'll introduce myself differently because I've had a lot of [00:03:00] corporate and board roles. I probably self identify as an engineer more than anything else though.[00:03:04] Bret: So even when I was. Salesforce, I was coding on the weekends. So I think of myself as an engineer and then all the roles that I do in my career sort of start with that just because I do feel like engineering is sort of a mindset and how I approach most of my life. So I'm an engineer first and that's how I describe myself.[00:03:24] Bret: You majored in computer[00:03:25] swyx: science, like 1998. And, and I was high[00:03:28] Bret: school, actually my, my college degree was Oh, two undergrad. Oh, three masters. Right. That old.[00:03:33] swyx: Yeah. I mean, no, I was going, I was going like 1998 to 2003, but like engineering wasn't as, wasn't a thing back then. Like we didn't have the title of senior engineer, you know, kind of like, it was just.[00:03:44] swyx: You were a programmer, you were a developer, maybe. What was it like in Stanford? Like, what was that feeling like? You know, was it, were you feeling like on the cusp of a great computer revolution? Or was it just like a niche, you know, interest at the time?[00:03:57] Stanford and the Dot-Com Bubble[00:03:57] Bret: Well, I was at Stanford, as you said, from 1998 to [00:04:00] 2002.[00:04:02] Bret: 1998 was near the peak of the dot com bubble. So. This is back in the day where most people that they're coding in the computer lab, just because there was these sun microsystems, Unix boxes there that most of us had to do our assignments on. And every single day there was a. com like buying pizza for everybody.[00:04:20] Bret: I didn't have to like, I got. Free food, like my first two years of university and then the dot com bubble burst in the middle of my college career. And so by the end there was like tumbleweed going to the job fair, you know, it was like, cause it was hard to describe unless you were there at the time, the like level of hype and being a computer science major at Stanford was like, A thousand opportunities.[00:04:45] Bret: And then, and then when I left, it was like Microsoft, IBM.[00:04:49] Joining Google and Early Projects[00:04:49] Bret: And then the two startups that I applied to were VMware and Google. And I ended up going to Google in large part because a woman named Marissa Meyer, who had been a teaching [00:05:00] assistant when I was, what was called a section leader, which was like a junior teaching assistant kind of for one of the big interest.[00:05:05] Bret: Yes. Classes. She had gone there. And she was recruiting me and I knew her and it was sort of felt safe, you know, like, I don't know. I thought about it much, but it turned out to be a real blessing. I realized like, you know, you always want to think you'd pick Google if given the option, but no one knew at the time.[00:05:20] Bret: And I wonder if I'd graduated in like 1999 where I've been like, mom, I just got a job at pets. com. It's good. But you know, at the end I just didn't have any options. So I was like, do I want to go like make kernel software at VMware? Do I want to go build search at Google? And I chose Google. 50, 50 ball.[00:05:36] Bret: I'm not really a 50, 50 ball. So I feel very fortunate in retrospect that the economy collapsed because in some ways it forced me into like one of the greatest companies of all time, but I kind of lucked into it, I think.[00:05:47] The Google Maps Rewrite Story[00:05:47] Alessio: So the famous story about Google is that you rewrote the Google maps back in, in one week after the map quest quest maps acquisition, what was the story there?[00:05:57] Alessio: Is it. Actually true. Is it [00:06:00] being glorified? Like how, how did that come to be? And is there any detail that maybe Paul hasn't shared before?[00:06:06] Bret: It's largely true, but I'll give the color commentary. So it was actually the front end, not the back end, but it turns out for Google maps, the front end was sort of the hard part just because Google maps was.[00:06:17] Bret: Largely the first ish kind of really interactive web application, say first ish. I think Gmail certainly was though Gmail, probably a lot of people then who weren't engineers probably didn't appreciate its level of interactivity. It was just fast, but. Google maps, because you could drag the map and it was sort of graphical.[00:06:38] Bret: My, it really in the mainstream, I think, was it a map[00:06:41] swyx: quest back then that was, you had the arrows up and down, it[00:06:44] Bret: was up and down arrows. Each map was a single image and you just click left and then wait for a few seconds to the new map to let it was really small too, because generating a big image was kind of expensive on computers that day.[00:06:57] Bret: So Google maps was truly innovative in that [00:07:00] regard. The story on it. There was a small company called where two technologies started by two Danish brothers, Lars and Jens Rasmussen, who are two of my closest friends now. They had made a windows app called expedition, which had beautiful maps. Even in 2000.[00:07:18] Bret: For whenever we acquired or sort of acquired their company, Windows software was not particularly fashionable, but they were really passionate about mapping and we had made a local search product that was kind of middling in terms of popularity, sort of like a yellow page of search product. So we wanted to really go into mapping.[00:07:36] Bret: We'd started working on it. Their small team seemed passionate about it. So we're like, come join us. We can build this together.[00:07:42] Technical Challenges and Innovations[00:07:42] Bret: It turned out to be a great blessing that they had built a windows app because you're less technically constrained when you're doing native code than you are building a web browser, particularly back then when there weren't really interactive web apps and it ended up.[00:07:56] Bret: Changing the level of quality that we [00:08:00] wanted to hit with the app because we were shooting for something that felt like a native windows application. So it was a really good fortune that we sort of, you know, their unusual technical choices turned out to be the greatest blessing. So we spent a lot of time basically saying, how can you make a interactive draggable map in a web browser?[00:08:18] Bret: How do you progressively load, you know, new map tiles, you know, as you're dragging even things like down in the weeds of the browser at the time, most browsers like Internet Explorer, which was dominant at the time would only load two images at a time from the same domain. So we ended up making our map tile servers have like.[00:08:37] Bret: Forty different subdomains so we could load maps and parallels like lots of hacks. I'm happy to go into as much as like[00:08:44] swyx: HTTP connections and stuff.[00:08:46] Bret: They just like, there was just maximum parallelism of two. And so if you had a map, set of map tiles, like eight of them, so So we just, we were down in the weeds of the browser anyway.[00:08:56] Bret: So it was lots of plumbing. I can, I know a lot more about browsers than [00:09:00] most people, but then by the end of it, it was fairly, it was a lot of duct tape on that code. If you've ever done an engineering project where you're not really sure the path from point A to point B, it's almost like. Building a house by building one room at a time.[00:09:14] Bret: The, there's not a lot of architectural cohesion at the end. And then we acquired a company called Keyhole, which became Google earth, which was like that three, it was a native windows app as well, separate app, great app, but with that, we got licenses to all this satellite imagery. And so in August of 2005, we added.[00:09:33] Bret: Satellite imagery to Google Maps, which added even more complexity in the code base. And then we decided we wanted to support Safari. There was no mobile phones yet. So Safari was this like nascent browser on, on the Mac. And it turns out there's like a lot of decisions behind the scenes, sort of inspired by this windows app, like heavy use of XML and XSLT and all these like.[00:09:54] Bret: Technologies that were like briefly fashionable in the early two thousands and everyone hates now for good [00:10:00] reason. And it turns out that all of the XML functionality and Internet Explorer wasn't supporting Safari. So people are like re implementing like XML parsers. And it was just like this like pile of s**t.[00:10:11] Bret: And I had to say a s**t on your part. Yeah, of[00:10:12] Alessio: course.[00:10:13] Bret: So. It went from this like beautifully elegant application that everyone was proud of to something that probably had hundreds of K of JavaScript, which sounds like nothing. Now we're talking like people have modems, you know, not all modems, but it was a big deal.[00:10:29] Bret: So it was like slow. It took a while to load and just, it wasn't like a great code base. Like everything was fragile. So I just got. Super frustrated by it. And then one weekend I did rewrite all of it. And at the time the word JSON hadn't been coined yet too, just to give you a sense. So it's all XML.[00:10:47] swyx: Yeah.[00:10:47] Bret: So we used what is now you would call JSON, but I just said like, let's use eval so that we can parse the data fast. And, and again, that's, it would literally as JSON, but at the time there was no name for it. So we [00:11:00] just said, let's. Pass on JavaScript from the server and eval it. And then somebody just refactored the whole thing.[00:11:05] Bret: And, and it wasn't like I was some genius. It was just like, you know, if you knew everything you wished you had known at the beginning and I knew all the functionality, cause I was the primary, one of the primary authors of the JavaScript. And I just like, I just drank a lot of coffee and just stayed up all weekend.[00:11:22] Bret: And then I, I guess I developed a bit of reputation and no one knew about this for a long time. And then Paul who created Gmail and I ended up starting a company with him too, after all of this told this on a podcast and now it's large, but it's largely true. I did rewrite it and it, my proudest thing.[00:11:38] Bret: And I think JavaScript people appreciate this. Like the un G zipped bundle size for all of Google maps. When I rewrote, it was 20 K G zipped. It was like much smaller for the entire application. It went down by like 10 X. So. What happened on Google? Google is a pretty mainstream company. And so like our usage is shot up because it turns out like it's faster.[00:11:57] Bret: Just being faster is worth a lot of [00:12:00] percentage points of growth at a scale of Google. So how[00:12:03] swyx: much modern tooling did you have? Like test suites no compilers.[00:12:07] Bret: Actually, that's not true. We did it one thing. So I actually think Google, I, you can. Download it. There's a, Google has a closure compiler, a closure compiler.[00:12:15] Bret: I don't know if anyone still uses it. It's gone. Yeah. Yeah. It's sort of gone out of favor. Yeah. Well, even until recently it was better than most JavaScript minifiers because it was more like it did a lot more renaming of variables and things. Most people use ES build now just cause it's fast and closure compilers built on Java and super slow and stuff like that.[00:12:37] Bret: But, so we did have that, that was it. Okay.[00:12:39] The Evolution of Web Applications[00:12:39] Bret: So and that was treated internally, you know, it was a really interesting time at Google at the time because there's a lot of teams working on fairly advanced JavaScript when no one was. So Google suggest, which Kevin Gibbs was the tech lead for, was the first kind of type ahead, autocomplete, I believe in a web browser, and now it's just pervasive in search boxes that you sort of [00:13:00] see a type ahead there.[00:13:01] Bret: I mean, chat, dbt[00:13:01] swyx: just added it. It's kind of like a round trip.[00:13:03] Bret: Totally. No, it's now pervasive as a UI affordance, but that was like Kevin's 20 percent project. And then Gmail, Paul you know, he tells the story better than anyone, but he's like, you know, basically was scratching his own itch, but what was really neat about it is email, because it's such a productivity tool, just needed to be faster.[00:13:21] Bret: So, you know, he was scratching his own itch of just making more stuff work on the client side. And then we, because of Lars and Yen sort of like setting the bar of this windows app or like we need our maps to be draggable. So we ended up. Not only innovate in terms of having a big sync, what would be called a single page application today, but also all the graphical stuff you know, we were crashing Firefox, like it was going out of style because, you know, when you make a document object model with the idea that it's a document and then you layer on some JavaScript and then we're essentially abusing all of this, it just was running into code paths that were not.[00:13:56] Bret: Well, it's rotten, you know, at this time. And so it was [00:14:00] super fun. And, and, you know, in the building you had, so you had compilers, people helping minify JavaScript just practically, but there is a great engineering team. So they were like, that's why Closure Compiler is so good. It was like a. Person who actually knew about programming languages doing it, not just, you know, writing regular expressions.[00:14:17] Bret: And then the team that is now the Chrome team believe, and I, I don't know this for a fact, but I'm pretty sure Google is the main contributor to Firefox for a long time in terms of code. And a lot of browser people were there. So every time we would crash Firefox, we'd like walk up two floors and say like, what the hell is going on here?[00:14:35] Bret: And they would load their browser, like in a debugger. And we could like figure out exactly what was breaking. And you can't change the code, right? Cause it's the browser. It's like slow, right? I mean, slow to update. So, but we could figure out exactly where the bug was and then work around it in our JavaScript.[00:14:52] Bret: So it was just like new territory. Like so super, super fun time, just like a lot of, a lot of great engineers figuring out [00:15:00] new things. And And now, you know, the word, this term is no longer in fashion, but the word Ajax, which was asynchronous JavaScript and XML cause I'm telling you XML, but see the word XML there, to be fair, the way you made HTTP requests from a client to server was this.[00:15:18] Bret: Object called XML HTTP request because Microsoft and making Outlook web access back in the day made this and it turns out to have nothing to do with XML. It's just a way of making HTTP requests because XML was like the fashionable thing. It was like that was the way you, you know, you did it. But the JSON came out of that, you know, and then a lot of the best practices around building JavaScript applications is pre React.[00:15:44] Bret: I think React was probably the big conceptual step forward that we needed. Even my first social network after Google, we used a lot of like HTML injection and. Making real time updates was still very hand coded and it's really neat when you [00:16:00] see conceptual breakthroughs like react because it's, I just love those things where it's like obvious once you see it, but it's so not obvious until you do.[00:16:07] Bret: And actually, well, I'm sure we'll get into AI, but I, I sort of feel like we'll go through that evolution with AI agents as well that I feel like we're missing a lot of the core abstractions that I think in 10 years we'll be like, gosh, how'd you make agents? Before that, you know, but it was kind of that early days of web applications.[00:16:22] swyx: There's a lot of contenders for the reactive jobs of of AI, but no clear winner yet. I would say one thing I was there for, I mean, there's so much we can go into there. You just covered so much.[00:16:32] Product Management and Engineering Synergy[00:16:32] swyx: One thing I just, I just observe is that I think the early Google days had this interesting mix of PM and engineer, which I think you are, you didn't, you didn't wait for PM to tell you these are my, this is my PRD.[00:16:42] swyx: This is my requirements.[00:16:44] mix: Oh,[00:16:44] Bret: okay.[00:16:45] swyx: I wasn't technically a software engineer. I mean,[00:16:48] Bret: by title, obviously. Right, right, right.[00:16:51] swyx: It's like a blend. And I feel like these days, product is its own discipline and its own lore and own industry and engineering is its own thing. And there's this process [00:17:00] that happens and they're kind of separated, but you don't produce as good of a product as if they were the same person.[00:17:06] swyx: And I'm curious, you know, if, if that, if that sort of resonates in, in, in terms of like comparing early Google versus modern startups that you see out there,[00:17:16] Bret: I certainly like wear a lot of hats. So, you know, sort of biased in this, but I really agree that there's a lot of power and combining product design engineering into as few people as possible because, you know few great things have been created by committee, you know, and so.[00:17:33] Bret: If engineering is an order taking organization for product you can sometimes make meaningful things, but rarely will you create extremely well crafted breakthrough products. Those tend to be small teams who deeply understand the customer need that they're solving, who have a. Maniacal focus on outcomes.[00:17:53] Bret: And I think the reason why it's, I think for some areas, if you look at like software as a service five years ago, maybe you can have a [00:18:00] separation of product and engineering because most software as a service created five years ago. I wouldn't say there's like a lot of like. Technological breakthroughs required for most, you know, business applications.[00:18:11] Bret: And if you're making expense reporting software or whatever, it's useful. I don't mean to be dismissive of expense reporting software, but you probably just want to understand like, what are the requirements of the finance department? What are the requirements of an individual file expense report? Okay.[00:18:25] Bret: Go implement that. And you kind of know how web applications are implemented. You kind of know how to. How databases work, how to build auto scaling with your AWS cluster, whatever, you know, it's just, you're just applying best practices to yet another problem when you have areas like the early days of mobile development or the early days of interactive web applications, which I think Google Maps and Gmail represent, or now AI agents, you're in this constant conversation with what the requirements of your customers and stakeholders are and all the different people interacting with it.[00:18:58] Bret: And the capabilities of the [00:19:00] technology. And it's almost impossible to specify the requirements of a product when you're not sure of the limitations of the technology itself. And that's why I use the word conversation. It's not literal. That's sort of funny to use that word in the age of conversational AI.[00:19:15] Bret: You're constantly sort of saying, like, ideally, you could sprinkle some magic AI pixie dust and solve all the world's problems, but it's not the way it works. And it turns out that actually, I'll just give an interesting example.[00:19:26] AI Agents and Modern Tooling[00:19:26] Bret: I think most people listening probably use co pilots to code like Cursor or Devon or Microsoft Copilot or whatever.[00:19:34] Bret: Most of those tools are, they're remarkable. I'm, I couldn't, you know, imagine development without them now, but they're not autonomous yet. Like I wouldn't let it just write most code without my interactively inspecting it. We just are somewhere between it's an amazing co pilot and it's an autonomous software engineer.[00:19:53] Bret: As a product manager, like your aspirations for what the product is are like kind of meaningful. But [00:20:00] if you're a product person, yeah, of course you'd say it should be autonomous. You should click a button and program should come out the other side. The requirements meaningless. Like what matters is like, what is based on the like very nuanced limitations of the technology.[00:20:14] Bret: What is it capable of? And then how do you maximize the leverage? It gives a software engineering team, given those very nuanced trade offs. Coupled with the fact that those nuanced trade offs are changing more rapidly than any technology in my memory, meaning every few months you'll have new models with new capabilities.[00:20:34] Bret: So how do you construct a product that can absorb those new capabilities as rapidly as possible as well? That requires such a combination of technical depth and understanding the customer that you really need more integration. Of product design and engineering. And so I think it's why with these big technology waves, I think startups have a bit of a leg up relative to incumbents because they [00:21:00] tend to be sort of more self actualized in terms of just like bringing those disciplines closer together.[00:21:06] Bret: And in particular, I think entrepreneurs, the proverbial full stack engineers, you know, have a leg up as well because. I think most breakthroughs happen when you have someone who can understand those extremely nuanced technical trade offs, have a vision for a product. And then in the process of building it, have that, as I said, like metaphorical conversation with the technology, right?[00:21:30] Bret: Gosh, I ran into a technical limit that I didn't expect. It's not just like changing that feature. You might need to refactor the whole product based on that. And I think that's, that it's particularly important right now. So I don't, you know, if you, if you're building a big ERP system, probably there's a great reason to have product and engineering.[00:21:51] Bret: I think in general, the disciplines are there for a reason. I think when you're dealing with something as nuanced as the like technologies, like large language models today, there's a ton of [00:22:00] advantage of having. Individuals or organizations that integrate the disciplines more formally.[00:22:05] Alessio: That makes a lot of sense.[00:22:06] Alessio: I've run a lot of engineering teams in the past, and I think the product versus engineering tension has always been more about effort than like whether or not the feature is buildable. But I think, yeah, today you see a lot more of like. Models actually cannot do that. And I think the most interesting thing is on the startup side, people don't yet know where a lot of the AI value is going to accrue.[00:22:26] Alessio: So you have this rush of people building frameworks, building infrastructure, layered things, but we don't really know the shape of the compute. I'm curious that Sierra, like how you thought about building an house, a lot of the tooling for evals or like just, you know, building the agents and all of that.[00:22:41] Alessio: Versus how you see some of the startup opportunities that is maybe still out there.[00:22:46] Bret: We build most of our tooling in house at Sierra, not all. It's, we don't, it's not like not invented here syndrome necessarily, though, maybe slightly guilty of that in some ways, but because we're trying to build a platform [00:23:00] that's in Dorian, you know, we really want to have control over our own destiny.[00:23:03] Bret: And you had made a comment earlier that like. We're still trying to figure out who like the reactive agents are and the jury is still out. I would argue it hasn't been created yet. I don't think the jury is still out to go use that metaphor. We're sort of in the jQuery era of agents, not the react era.[00:23:19] Bret: And, and that's like a throwback for people listening,[00:23:22] swyx: we shouldn't rush it. You know?[00:23:23] Bret: No, yeah, that's my point is. And so. Because we're trying to create an enduring company at Sierra that outlives us, you know, I'm not sure we want to like attach our cart to some like to a horse where it's not clear that like we've figured out and I actually want as a company, we're trying to enable just at a high level and I'll, I'll quickly go back to tech at Sierra, we help consumer brands build customer facing AI agents.[00:23:48] Bret: So. Everyone from Sonos to ADT home security to Sirius XM, you know, if you call them on the phone and AI will pick up with you, you know, chat with them on the Sirius XM homepage. It's an AI agent called Harmony [00:24:00] that they've built on our platform. We're what are the contours of what it means for someone to build an end to end complete customer experience with AI with conversational AI.[00:24:09] Bret: You know, we really want to dive into the deep end of, of all the trade offs to do it. You know, where do you use fine tuning? Where do you string models together? You know, where do you use reasoning? Where do you use generation? How do you use reasoning? How do you express the guardrails of an agentic process?[00:24:25] Bret: How do you impose determinism on a fundamentally non deterministic technology? There's just a lot of really like as an important design space. And I could sit here and tell you, we have the best approach. Every entrepreneur will, you know. But I hope that in two years, we look back at our platform and laugh at how naive we were, because that's the pace of change broadly.[00:24:45] Bret: If you talk about like the startup opportunities, I'm not wholly skeptical of tools companies, but I'm fairly skeptical. There's always an exception for every role, but I believe that certainly there's a big market for [00:25:00] frontier models, but largely for companies with huge CapEx budgets. So. Open AI and Microsoft's Anthropic and Amazon Web Services, Google Cloud XAI, which is very well capitalized now, but I think the, the idea that a company can make money sort of pre training a foundation model is probably not true.[00:25:20] Bret: It's hard to, you're competing with just, you know, unreasonably large CapEx budgets. And I just like the cloud infrastructure market, I think will be largely there. I also really believe in the applications of AI. And I define that not as like building agents or things like that. I define it much more as like, you're actually solving a problem for a business.[00:25:40] Bret: So it's what Harvey is doing in legal profession or what cursor is doing for software engineering or what we're doing for customer experience and customer service. The reason I believe in that is I do think that in the age of AI, what's really interesting about software is it can actually complete a task.[00:25:56] Bret: It can actually do a job, which is very different than the value proposition of [00:26:00] software was to ancient history two years ago. And as a consequence, I think the way you build a solution and For a domain is very different than you would have before, which means that it's not obvious, like the incumbent incumbents have like a leg up, you know, necessarily, they certainly have some advantages, but there's just such a different form factor, you know, for providing a solution and it's just really valuable.[00:26:23] Bret: You know, it's. Like just think of how much money cursor is saving software engineering teams or the alternative, how much revenue it can produce tool making is really challenging. If you look at the cloud market, just as a analog, there are a lot of like interesting tools, companies, you know, Confluent, Monetized Kafka, Snowflake, Hortonworks, you know, there's a, there's a bunch of them.[00:26:48] Bret: A lot of them, you know, have that mix of sort of like like confluence or have the open source or open core or whatever you call it. I, I, I'm not an expert in this area. You know, I do think [00:27:00] that developers are fickle. I think that in the tool space, I probably like. Default towards open source being like the area that will win.[00:27:09] Bret: It's hard to build a company around this and then you end up with companies sort of built around open source to that can work. Don't get me wrong, but I just think that it's nowadays the tools are changing so rapidly that I'm like, not totally skeptical of tool makers, but I just think that open source will broadly win, but I think that the CapEx required for building frontier models is such that it will go to a handful of big companies.[00:27:33] Bret: And then I really believe in agents for specific domains which I think will, it's sort of the analog to software as a service in this new era. You know, it's like, if you just think of the cloud. You can lease a server. It's just a low level primitive, or you can buy an app like you know, Shopify or whatever.[00:27:51] Bret: And most people building a storefront would prefer Shopify over hand rolling their e commerce storefront. I think the same thing will be true of AI. So [00:28:00] I've. I tend to like, if I have a, like an entrepreneur asked me for advice, I'm like, you know, move up the stack as far as you can towards a customer need.[00:28:09] Bret: Broadly, but I, but it doesn't reduce my excitement about what is the reactive building agents kind of thing, just because it is, it is the right question to ask, but I think we'll probably play out probably an open source space more than anything else.[00:28:21] swyx: Yeah, and it's not a priority for you. There's a lot in there.[00:28:24] swyx: I'm kind of curious about your idea maze towards, there are many customer needs. You happen to identify customer experience as yours, but it could equally have been coding assistance or whatever. I think for some, I'm just kind of curious at the top down, how do you look at the world in terms of the potential problem space?[00:28:44] swyx: Because there are many people out there who are very smart and pick the wrong problem.[00:28:47] Bret: Yeah, that's a great question.[00:28:48] Future of Software Development[00:28:48] Bret: By the way, I would love to talk about the future of software, too, because despite the fact it didn't pick coding, I have a lot of that, but I can talk to I can answer your question, though, you know I think when a technology is as [00:29:00] cool as large language models.[00:29:02] Bret: You just see a lot of people starting from the technology and searching for a problem to solve. And I think it's why you see a lot of tools companies, because as a software engineer, you start building an app or a demo and you, you encounter some pain points. You're like,[00:29:17] swyx: a lot of[00:29:17] Bret: people are experiencing the same pain point.[00:29:19] Bret: What if I make it? That it's just very incremental. And you know, I always like to use the metaphor, like you can sell coffee beans, roasted coffee beans. You can add some value. You took coffee beans and you roasted them and roasted coffee beans largely, you know, are priced relative to the cost of the beans.[00:29:39] Bret: Or you can sell a latte and a latte. Is rarely priced directly like as a percentage of coffee bean prices. In fact, if you buy a latte at the airport, it's a captive audience. So it's a really expensive latte. And there's just a lot that goes into like. How much does a latte cost? And I bring it up because there's a supply chain from growing [00:30:00] coffee beans to roasting coffee beans to like, you know, you could make one at home or you could be in the airport and buy one and the margins of the company selling lattes in the airport is a lot higher than the, you know, people roasting the coffee beans and it's because you've actually solved a much more acute human problem in the airport.[00:30:19] Bret: And, and it's just worth a lot more to that person in that moment. It's kind of the way I think about technology too. It sounds funny to liken it to coffee beans, but you're selling tools on top of a large language model yet in some ways your market is big, but you're probably going to like be price compressed just because you're sort of a piece of infrastructure and then you have open source and all these other things competing with you naturally.[00:30:43] Bret: If you go and solve a really big business problem for somebody, that's actually like a meaningful business problem that AI facilitates, they will value it according to the value of that business problem. And so I actually feel like people should just stop. You're like, no, that's, that's [00:31:00] unfair. If you're searching for an idea of people, I, I love people trying things, even if, I mean, most of the, a lot of the greatest ideas have been things no one believed in.[00:31:07] Bret: So I like, if you're passionate about something, go do it. Like who am I to say, yeah, a hundred percent. Or Gmail, like Paul as far, I mean I, some of it's Laura at this point, but like Gmail is Paul's own email for a long time. , and then I amusingly and Paul can't correct me, I'm pretty sure he sent her in a link and like the first comment was like, this is really neat.[00:31:26] Bret: It would be great. It was not your email, but my own . I don't know if it's a true story. I'm pretty sure it's, yeah, I've read that before. So scratch your own niche. Fine. Like it depends on what your goal is. If you wanna do like a venture backed company, if its a. Passion project, f*****g passion, do it like don't listen to anybody.[00:31:41] Bret: In fact, but if you're trying to start, you know an enduring company, solve an important business problem. And I, and I do think that in the world of agents, the software industries has shifted where you're not just helping people more. People be more productive, but you're actually accomplishing tasks autonomously.[00:31:58] Bret: And as a consequence, I think the [00:32:00] addressable market has just greatly expanded just because software can actually do things now and actually accomplish tasks and how much is coding autocomplete worth. A fair amount. How much is the eventual, I'm certain we'll have it, the software agent that actually writes the code and delivers it to you, that's worth a lot.[00:32:20] Bret: And so, you know, I would just maybe look up from the large language models and start thinking about the economy and, you know, think from first principles. I don't wanna get too far afield, but just think about which parts of the economy. We'll benefit most from this intelligence and which parts can absorb it most easily.[00:32:38] Bret: And what would an agent in this space look like? Who's the customer of it is the technology feasible. And I would just start with these business problems more. And I think, you know, the best companies tend to have great engineers who happen to have great insight into a market. And it's that last part that I think some people.[00:32:56] Bret: Whether or not they have, it's like people start so much in the technology, they [00:33:00] lose the forest for the trees a little bit.[00:33:02] Alessio: How do you think about the model of still selling some sort of software versus selling more package labor? I feel like when people are selling the package labor, it's almost more stateless, you know, like it's easier to swap out if you're just putting an input and getting an output.[00:33:16] Alessio: If you think about coding, if there's no ID, you're just putting a prompt and getting back an app. It doesn't really matter. Who generates the app, you know, you have less of a buy in versus the platform you're building, I'm sure on the backend customers have to like put on their documentation and they have, you know, different workflows that they can tie in what's kind of like the line to draw there versus like going full where you're managed customer support team as a service outsource versus.[00:33:40] Alessio: This is the Sierra platform that you can build on. What was that decision? I'll sort of[00:33:44] Bret: like decouple the question in some ways, which is when you have something that's an agent, who is the person using it and what do they want to do with it? So let's just take your coding agent for a second. I will talk about Sierra as well.[00:33:59] Bret: Who's the [00:34:00] customer of a, an agent that actually produces software? Is it a software engineering manager? Is it a software engineer? And it's there, you know, intern so to speak. I don't know. I mean, we'll figure this out over the next few years. Like what is that? And is it generating code that you then review?[00:34:16] Bret: Is it generating code with a set of unit tests that pass, what is the actual. For lack of a better word contract, like, how do you know that it did what you wanted it to do? And then I would say like the product and the pricing, the packaging model sort of emerged from that. And I don't think the world's figured out.[00:34:33] Bret: I think it'll be different for every agent. You know, in our customer base, we do what's called outcome based pricing. So essentially every time the AI agent. Solves the problem or saves a customer or whatever it might be. There's a pre negotiated rate for that. We do that. Cause it's, we think that that's sort of the correct way agents, you know, should be packaged.[00:34:53] Bret: I look back at the history of like cloud software and notably the introduction of the browser, which led to [00:35:00] software being delivered in a browser, like Salesforce to. Famously invented sort of software as a service, which is both a technical delivery model through the browser, but also a business model, which is you subscribe to it rather than pay for a perpetual license.[00:35:13] Bret: Those two things are somewhat orthogonal, but not really. If you think about the idea of software running in a browser, that's hosted. Data center that you don't own, you sort of needed to change the business model because you don't, you can't really buy a perpetual license or something otherwise like, how do you afford making changes to it?[00:35:31] Bret: So it only worked when you were buying like a new version every year or whatever. So to some degree, but then the business model shift actually changed business as we know it, because now like. Things like Adobe Photoshop. Now you subscribe to rather than purchase. So it ended up where you had a technical shift and a business model shift that were very logically intertwined that actually the business model shift was turned out to be as significant as the technical as the shift.[00:35:59] Bret: And I think with [00:36:00] agents, because they actually accomplish a job, I do think that it doesn't make sense to me that you'd pay for the privilege of like. Using the software like that coding agent, like if it writes really bad code, like fire it, you know, I don't know what the right metaphor is like you should pay for a job.[00:36:17] Bret: Well done in my opinion. I mean, that's how you pay your software engineers, right? And[00:36:20] swyx: and well, not really. We paid to put them on salary and give them options and they vest over time. That's fair.[00:36:26] Bret: But my point is that you don't pay them for how many characters they write, which is sort of the token based, you know, whatever, like, There's a, that famous Apple story where we're like asking for a report of how many lines of code you wrote.[00:36:40] Bret: And one of the engineers showed up with like a negative number cause he had just like done a big refactoring. There was like a big F you to management who didn't understand how software is written. You know, my sense is like the traditional usage based or seat based thing. It's just going to look really antiquated.[00:36:55] Bret: Cause it's like asking your software engineer, how many lines of code did you write today? Like who cares? Like, cause [00:37:00] absolutely no correlation. So my old view is I don't think it's be different in every category, but I do think that that is the, if an agent is doing a job, you should, I think it properly incentivizes the maker of that agent and the customer of, of your pain for the job well done.[00:37:16] Bret: It's not always perfect to measure. It's hard to measure engineering productivity, but you can, you should do something other than how many keys you typed, you know Talk about perverse incentives for AI, right? Like I can write really long functions to do the same thing, right? So broadly speaking, you know, I do think that we're going to see a change in business models of software towards outcomes.[00:37:36] Bret: And I think you'll see a change in delivery models too. And, and, you know, in our customer base you know, we empower our customers to really have their hands on the steering wheel of what the agent does they, they want and need that. But the role is different. You know, at a lot of our customers, the customer experience operations folks have renamed themselves the AI architects, which I think is really cool.[00:37:55] Bret: And, you know, it's like in the early days of the Internet, there's the role of the webmaster. [00:38:00] And I don't know whether your webmaster is not a fashionable, you know, Term, nor is it a job anymore? I just, I don't know. Will they, our tech stand the test of time? Maybe, maybe not. But I do think that again, I like, you know, because everyone listening right now is a software engineer.[00:38:14] Bret: Like what is the form factor of a coding agent? And actually I'll, I'll take a breath. Cause actually I have a bunch of pins on them. Like I wrote a blog post right before Christmas, just on the future of software development. And one of the things that's interesting is like, if you look at the way I use cursor today, as an example, it's inside of.[00:38:31] Bret: A repackaged visual studio code environment. I sometimes use the sort of agentic parts of it, but it's largely, you know, I've sort of gotten a good routine of making it auto complete code in the way I want through tuning it properly when it actually can write. I do wonder what like the future of development environments will look like.[00:38:55] Bret: And to your point on what is a software product, I think it's going to change a lot in [00:39:00] ways that will surprise us. But I always use, I use the metaphor in my blog post of, have you all driven around in a way, Mo around here? Yeah, everyone has. And there are these Jaguars, the really nice cars, but it's funny because it still has a steering wheel, even though there's no one sitting there and the steering wheels like turning and stuff clearly in the future.[00:39:16] Bret: If once we get to that, be more ubiquitous, like why have the steering wheel and also why have all the seats facing forward? Maybe just for car sickness. I don't know, but you could totally rearrange the car. I mean, so much of the car is oriented around the driver, so. It stands to reason to me that like, well, autonomous agents for software engineering run through visual studio code.[00:39:37] Bret: That seems a little bit silly because having a single source code file open one at a time is kind of a goofy form factor for when like the code isn't being written primarily by you, but it begs the question of what's your relationship with that agent. And I think the same is true in our industry of customer experience, which is like.[00:39:55] Bret: Who are the people managing this agent? What are the tools do they need? And they definitely need [00:40:00] tools, but it's probably pretty different than the tools we had before. It's certainly different than training a contact center team. And as software engineers, I think that I would like to see particularly like on the passion project side or research side.[00:40:14] Bret: More innovation in programming languages. I think that we're bringing the cost of writing code down to zero. So the fact that we're still writing Python with AI cracks me up just cause it's like literally was designed to be ergonomic to write, not safe to run or fast to run. I would love to see more innovation and how we verify program correctness.[00:40:37] Bret: I studied for formal verification in college a little bit and. It's not very fashionable because it's really like tedious and slow and doesn't work very well. If a lot of code is being written by a machine, you know, one of the primary values we can provide is verifying that it actually does what we intend that it does.[00:40:56] Bret: I think there should be lots of interesting things in the software development life cycle, like how [00:41:00] we think of testing and everything else, because. If you think about if we have to manually read every line of code that's coming out as machines, it will just rate limit how much the machines can do. The alternative is totally unsafe.[00:41:13] Bret: So I wouldn't want to put code in production that didn't go through proper code review and inspection. So my whole view is like, I actually think there's like an AI native I don't think the coding agents don't work well enough to do this yet, but once they do, what is sort of an AI native software development life cycle and how do you actually.[00:41:31] Bret: Enable the creators of software to produce the highest quality, most robust, fastest software and know that it's correct. And I think that's an incredible opportunity. I mean, how much C code can we rewrite and rust and make it safe so that there's fewer security vulnerabilities. Can we like have more efficient, safer code than ever before?[00:41:53] Bret: And can you have someone who's like that guy in the matrix, you know, like staring at the little green things, like where could you have an operator [00:42:00] of a code generating machine be like superhuman? I think that's a cool vision. And I think too many people are focused on like. Autocomplete, you know, right now, I'm not, I'm not even, I'm guilty as charged.[00:42:10] Bret: I guess in some ways, but I just like, I'd like to see some bolder ideas. And that's why when you were joking, you know, talking about what's the react of whatever, I think we're clearly in a local maximum, you know, metaphor, like sort of conceptual local maximum, obviously it's moving really fast. I think we're moving out of it.[00:42:26] Alessio: Yeah. At the end of 23, I've read this blog post from syntax to semantics. Like if you think about Python. It's taking C and making it more semantic and LLMs are like the ultimate semantic program, right? You can just talk to them and they can generate any type of syntax from your language. But again, the languages that they have to use were made for us, not for them.[00:42:46] Alessio: But the problem is like, as long as you will ever need a human to intervene, you cannot change the language under it. You know what I mean? So I'm curious at what point of automation we'll need to get, we're going to be okay making changes. To the underlying languages, [00:43:00] like the programming languages versus just saying, Hey, you just got to write Python because I understand Python and I'm more important at the end of the day than the model.[00:43:08] Alessio: But I think that will change, but I don't know if it's like two years or five years. I think it's more nuanced actually.[00:43:13] Bret: So I think there's a, some of the more interesting programming languages bring semantics into syntax. So let me, that's a little reductive, but like Rust as an example, Rust is memory safe.[00:43:25] Bret: Statically, and that was a really interesting conceptual, but it's why it's hard to write rust. It's why most people write python instead of rust. I think rust programs are safer and faster than python, probably slower to compile. But like broadly speaking, like given the option, if you didn't have to care about the labor that went into it.[00:43:45] Bret: You should prefer a program written in Rust over a program written in Python, just because it will run more efficiently. It's almost certainly safer, et cetera, et cetera, depending on how you define safe, but most people don't write Rust because it's kind of a pain in the ass. And [00:44:00] the audience of people who can is smaller, but it's sort of better in most, most ways.[00:44:05] Bret: And again, let's say you're making a web service and you didn't have to care about how hard it was to write. If you just got the output of the web service, the rest one would be cheaper to operate. It's certainly cheaper and probably more correct just because there's so much in the static analysis implied by the rest programming language that it probably will have fewer runtime errors and things like that as well.[00:44:25] Bret: So I just give that as an example, because so rust, at least my understanding that came out of the Mozilla team, because. There's lots of security vulnerabilities in the browser and it needs to be really fast. They said, okay, we want to put more of a burden at the authorship time to have fewer issues at runtime.[00:44:43] Bret: And we need the constraint that it has to be done statically because browsers need to be really fast. My sense is if you just think about like the, the needs of a programming language today, where the role of a software engineer is [00:45:00] to use an AI to generate functionality and audit that it does in fact work as intended, maybe functionally, maybe from like a correctness standpoint, some combination thereof, how would you create a programming system that facilitated that?[00:45:15] Bret: And, you know, I bring up Rust is because I think it's a good example of like, I think given a choice of writing in C or Rust, you should choose Rust today. I think most people would say that, even C aficionados, just because. C is largely less safe for very similar, you know, trade offs, you know, for the, the system and now with AI, it's like, okay, well, that just changes the game on writing these things.[00:45:36] Bret: And so like, I just wonder if a combination of programming languages that are more structurally oriented towards the values that we need from an AI generated program, verifiable correctness and all of that. If it's tedious to produce for a person, that maybe doesn't matter. But one thing, like if I asked you, is this rest program memory safe?[00:45:58] Bret: You wouldn't have to read it, you just have [00:46:00] to compile it. So that's interesting. I mean, that's like an, that's one example of a very modest form of formal verification. So I bring that up because I do think you have AI inspect AI, you can have AI reviewed. Do AI code reviews. It would disappoint me if the best we could get was AI reviewing Python and having scaled a few very large.[00:46:21] Bret: Websites that were written on Python. It's just like, you know, expensive and it's like every, trust me, every team who's written a big web service in Python has experimented with like Pi Pi and all these things just to make it slightly more efficient than it naturally is. You don't really have true multi threading anyway.[00:46:36] Bret: It's just like clearly that you do it just because it's convenient to write. And I just feel like we're, I don't want to say it's insane. I just mean. I do think we're at a local maximum. And I would hope that we create a programming system, a combination of programming languages, formal verification, testing, automated code reviews, where you can use AI to generate software in a high scale way and trust it.[00:46:59] Bret: And you're [00:47:00] not limited by your ability to read it necessarily. I don't know exactly what form that would take, but I feel like that would be a pretty cool world to live in.[00:47:08] Alessio: Yeah. We had Chris Lanner on the podcast. He's doing great work with modular. I mean, I love. LVM. Yeah. Basically merging rust in and Python.[00:47:15] Alessio: That's kind of the idea. Should be, but I'm curious is like, for them a big use case was like making it compatible with Python, same APIs so that Python developers could use it. Yeah. And so I, I wonder at what point, well, yeah.[00:47:26] Bret: At least my understanding is they're targeting the data science Yeah. Machine learning crowd, which is all written in Python, so still feels like a local maximum.[00:47:34] Bret: Yeah.[00:47:34] swyx: Yeah, exactly. I'll force you to make a prediction. You know, Python's roughly 30 years old. In 30 years from now, is Rust going to be bigger than Python?[00:47:42] Bret: I don't know this, but just, I don't even know this is a prediction. I just am sort of like saying stuff I hope is true. I would like to see an AI native programming language and programming system, and I use language because I'm not sure language is even the right thing, but I hope in 30 years, there's an AI native way we make [00:48:00] software that is wholly uncorrelated with the current set of programming languages.[00:48:04] Bret: or not uncorrelated, but I think most programming languages today were designed to be efficiently authored by people and some have different trade offs.[00:48:15] Evolution of Programming Languages[00:48:15] Bret: You know, you have Haskell and others that were designed for abstractions for parallelism and things like that. You have programming languages like Python, which are designed to be very easily written, sort of like Perl and Python lineage, which is why data scientists use it.[00:48:31] Bret: It's it can, it has a. Interactive mode, things like that. And I love, I'm a huge Python fan. So despite all my Python trash talk, a huge Python fan wrote at least two of my three companies were exclusively written in Python and then C came out of the birth of Unix and it wasn't the first, but certainly the most prominent first step after assembly language, right?[00:48:54] Bret: Where you had higher level abstractions rather than and going beyond go to, to like abstractions, [00:49:00] like the for loop and the while loop.[00:49:01] The Future of Software Engineering[00:49:01] Bret: So I just think that if the act of writing code is no longer a meaningful human exercise, maybe it will be, I don't know. I'm just saying it sort of feels like maybe it's one of those parts of history that just will sort of like go away, but there's still the role of this offer engineer, like the person actually building the system.[00:49:20] Bret: Right. And. What does a programming system for that form factor look like?[00:49:25] React and Front-End Development[00:49:25] Bret: And I, I just have a, I hope to be just like I mentioned, I remember I was at Facebook in the very early days when, when, what is now react was being created. And I remember when the, it was like released open source I had left by that time and I was just like, this is so f*****g cool.[00:49:42] Bret: Like, you know, to basically model your app independent of the data flowing through it, just made everything easier. And then now. You know, I can create, like there's a lot of the front end software gym play is like a little chaotic for me, to be honest with you. It is like, it's sort of like [00:50:00] abstraction soup right now for me, but like some of those core ideas felt really ergonomic.[00:50:04] Bret: I just wanna, I'm just looking forward to the day when someone comes up with a programming system that feels both really like an aha moment, but completely foreign to me at the same time. Because they created it with sort of like from first principles recognizing that like. Authoring code in an editor is maybe not like the primary like reason why a programming system exists anymore.[00:50:26] Bret: And I think that's like, that would be a very exciting day for me.[00:50:28] The Role of AI in Programming[00:50:28] swyx: Yeah, I would say like the various versions of this discussion have happened at the end of the day, you still need to precisely communicate what you want. As a manager of people, as someone who has done many, many legal contracts, you know how hard that is.[00:50:42] swyx: And then now we have to talk to machines doing that and AIs interpreting what we mean and reading our minds effectively. I don't know how to get across that barrier of translating human intent to instructions. And yes, it can be more declarative, but I don't know if it'll ever Crossover from being [00:51:00] a programming language to something more than that.[00:51:02] Bret: I agree with you. And I actually do think if you look at like a legal contract, you know, the imprecision of the English language, it's like a flaw in the system. How many[00:51:12] swyx: holes there are.[00:51:13] Bret: And I do think that when you're making a mission critical software system, I don't think it should be English language prompts.[00:51:19] Bret: I think that is silly because you want the precision of a a programming language. My point was less about that and more about if the actual act of authoring it, like if you.[00:51:32] Formal Verification in Software[00:51:32] Bret: I'll think of some embedded systems do use formal verification. I know it's very common in like security protocols now so that you can, because the importance of correctness is so great.[00:51:41] Bret: My intellectual exercise is like, why not do that for all software? I mean, probably that's silly just literally to do what we literally do for. These low level security protocols, but the only reason we don't is because it's hard and tedious and hard and tedious are no longer factors. So, like, if I could, I mean, [00:52:00] just think of, like, the silliest app on your phone right now, the idea that that app should be, like, formally verified for its correctness feels laughable right now because, like, God, why would you spend the time on it?[00:52:10] Bret: But if it's zero costs, like, yeah, I guess so. I mean, it never crashed. That's probably good. You know, why not? I just want to, like, set our bars really high. Like. We should make, software has been amazing. Like there's a Mark Andreessen blog post, software is eating the world. And you know, our whole life is, is mediated digitally.[00:52:26] Bret: And that's just increasing with AI. And now we'll have our personal agents talking to the agents on the CRO platform and it's agents all the way down, you know, our core infrastructure is running on these digital systems. We now have like, and we've had a shortage of software developers for my entire life.[00:52:45] Bret: And as a consequence, you know if you look, remember like health care, got healthcare. gov that fiasco security vulnerabilities leading to state actors getting access to critical infrastructure. I'm like. We now have like created this like amazing system that can [00:53:00] like, we can fix this, you know, and I, I just want to, I'm both excited about the productivity gains in the economy, but I just think as software engineers, we should be bolder.[00:53:08] Bret: Like we should have aspirations to fix these systems so that like in general, as you said, as precise as we want to be in the specification of the system. We can make it work correctly now, and I'm being a little bit hand wavy, and I think we need some systems. I think that's where we should set the bar, especially when so much of our life depends on this critical digital infrastructure.[00:53:28] Bret: So I'm I'm just like super optimistic about it. But actually, let's go to w

The Scoop's host, Frank Chaparro, was joined by Cambrian Asset Management President Tony Fenner-Leitão. In this episode, Chaparro and Fenner-Leitão discussed his firm's approach to the crypto market, which involves using machine learning and data-driven models. They also touched on the impact of increased institutional participation and regulatory changes in the crypto space and how these developments have affected the overall market dynamics. OUTLINE 00:00 Introduction 03:29 Lack of liquid participants 08:25 Cambrian's perspective on the Trump meme coin 13:31 Shifting market dynamics 17:18 Regulatory changes 22:56 Cambrian's requirements for entering a market 29:16 Active management vs VC allocation 35:39 Conclusion GUEST LINKS Tony Fenner-Leitão - https://www.linkedin.com/in/tonyfennerleitao/ Cambrian Asset Management - https://www.cambrianasset.com/ Cambrian Asset Management on X - https://x.com/CambrianAsset This episode is brought to you by our sponsor: Polkadot Polkadot is the blockspace ecosystem for boundless innovation. To discover more, head to polkadot.network

You might know them as ghost sharks, rat fish, or spook fish. Chimaeras are close cousins of sharks and rays, equipped with distinctive and unusual features in their fins, teeth, and reproductive structures. In today's oceans, chimaeras are rare and easy to miss, but their extended family includes some of the most diverse and iconic fish of the Paleozoic seas. In this episode, we'll explore the traits that set chimaeras apart, we'll take a tour through their ancient relatives, and we'll investigate what their most famous cousins were doing with their strange spiral rows of teeth. In the news: Baltic herrings, Cambrian arms race, pterosaur tails, and early dinosaurs. Time markers: Intro & Announcements: 00:00:00 News: 00:06:35 Main discussion, Part 1: 00:40:25 Main discussion, Part 2: 01:16:45 Patron question: 02:20:00 Check out our website for this episode's blog post and more: http://commondescentpodcast.com/ Join us on Patreon to support the podcast and enjoy bonus content: https://www.patreon.com/commondescentpodcast Got a topic you want to hear about? Submit your episode request here: https://commondescentpodcast.com/request-a-topic/ Lots more ways to connect with us: https://linktr.ee/common_descent The Intro and Outro music is “On the Origin of Species” by Protodome. More music like this at http://ocremix.org Musical Interludes are "Professor Umlaut" by Kevin MacLeod (incompetech.com). Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0

A new week means new questions! Hope you have fun with these!What type of liquor is used in a classic Tom Collins cocktail?Prohibition in America ended with the ratification of which Constitutional Amendment?The spread of market disturbances – mostly on the downside – from one country to the other is known as "Financial" what, a word used as the title of an unrelated 2011 Matt Damon film?Which two Oscar nominated actors played Tom Cooper in Christopher Nolan's 2014 film Interstellar?The peninsula known as Anatolia makes up the majority of the land area of which country?Which part of the body's immune system is also known as leukocytes?What language has also been known in English as "Cambrian", "Cambric", and "Cymric" (Cum-rik)?Dielli, Surya, and Utuliya are names in various world mythologies for what heavenly body?Sporting clays, skeet, and trap are all types of what recreational and competitive hobby?MusicHot Swing, Fast Talkin, Bass Walker, Dances and Dames, Ambush by Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 3.0 http://creativecommons.org/licenses/by/3.0/Don't forget to follow us on social media:Patreon – patreon.com/quizbang – Please consider supporting us on Patreon. Check out our fun extras for patrons and help us keep this podcast going. We appreciate any level of support!Website – quizbangpod.com Check out our website, it will have all the links for social media that you need and while you're there, why not go to the contact us page and submit a question!Facebook – @quizbangpodcast – we post episode links and silly lego pictures to go with our trivia questions. Enjoy the silly picture and give your best guess, we will respond to your answer the next day to give everyone a chance to guess.Instagram – Quiz Quiz Bang Bang (quizquizbangbang), we post silly lego pictures to go with our trivia questions. Enjoy the silly picture and give your best guess, we will respond to your answer the next day to give everyone a chance to guess.Twitter – @quizbangpod We want to start a fun community for our fellow trivia lovers. If you hear/think of a fun or challenging trivia question, post it to our twitter feed and we will repost it so everyone can take a stab it. Come for the trivia – stay for the trivia.Ko-Fi – ko-fi.com/quizbangpod – Keep that sweet caffeine running through our body with a Ko-Fi, power us through a late night of fact checking and editing!

In today's episode, Jackson Ellis hosts Rex Salisbury, a solo General Partner at Cambrian Ventures, a venture capital fund focused on early stage fintech investments. Tune in to hear about: - How to build a fintech community - How AI will impact fintech - How the new administration may impact the fintech landscape

This bedtime story takes us on a journey through time and space. We explore the appearance and evolution of life in Earth's oceans, from the first microorganisms and the diversification of life forms that happened during the Cambrian and Ordovician explosions, to our time. Timestamps00:00 Introduction to Prehistoric Oceans05:05 How Oceans and Life Appeared on Earth19:59 Cambrian Explosion31:16 Fish, Arthropods, and Mollusks45:08 From Paleozoic to Mesozoic56:49 Towards Modern Marine Ecosystems  Welcome to Lights Out LibraryJoin me for a sleepy adventure tonight. Sit back, relax, and fall asleep to documentary-style stories read in a calming voice. Learn something new while you enjoy a restful night of sleep.Listen ad free and get access to bonus content on our Patreon: ⁠⁠⁠https://www.patreon.com/LightsOutLibrary621⁠⁠⁠Listen on Youtube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@LightsOutLibraryov⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ¿Quieres escuchar en Español? Echa un vistazo a La Biblioteca de los Sueños!En Spotify: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://open.spotify.com/show/1t522alsv5RxFsAf9AmYfg⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠En Apple Podcasts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://podcasts.apple.com/us/podcast/la-biblioteca-de-los-sue%C3%B1os-documentarios-para-dormir/id1715193755⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠En Youtube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@LaBibliotecadelosSuenosov⁠⁠⁠ 

The Chronicler is back and this time he's answering our piercing questions interview style. It's the Frost/Nixon of Welsh history if literally all of the context was changed. If you haven't already, get around this guys YouTube channel and start feeding your noggins on his top quality content.From merch to more content, click here for all things Tales for Wales and start 2025 the way the Cymry of old would have wanted.

Send us a text2024 was an incredible year for EdTech, filled with transformative shifts and groundbreaking innovations!In this special year-end episode, Alex and Ben dive into the defining trends of the year—from the ESSER funding cliff reshaping K-12 strategies to the rise of AI-powered learning tools. They also look ahead to 2025, making bold predictions about the future of education technology, including the potential of gaming, assessment overhauls, and micro-company innovation.Stay tuned for Part 2, coming in January, where Alex and Ben will share predictions from thought leaders across the EdTech space to kick off the new year!✨ Episode Highlights:[00:00:43]

"There are so many reasons to be interested in peer-to-peer. You can be interested in it for resilience, because there are no middlemen, so it's going to work if any of your devices are online. You can also just be interested in it from this angle, the idea that if you have full control of your data, and the code that you're using, that you're running on that data, then anybody can fork it, modify it, tweak it, and create a whole... That's where the Cambrian explosion comes in, this whole proliferation of apps that give users very different experiences."~ Andrew OsheroffIn this conversation, I had the privilege of joining Mathias Buus, David Clements, and Andrew Osheroff from Pears.com, Keet, and Holepunch for an engaging fireside chat about the transformative power of peer-to-peer technology. Together, we explored how P2P is reshaping the internet, enabling greater freedom, and redefining how we communicate and collaborate in a decentralized world. This conversation delved into the intersection of technology and liberty, highlighting the potential of P2P to challenge centralized systems and empower individuals. Whether you're passionate about Bitcoin, decentralization, or the future of digital freedom, this was an inspiring and valuable discussion that I'm excited to share again. Original episode on Pears YouTube - How P2P is changing the Internet - A fireside chat with Guy Swann & the Holepunch/ Keet team (Link: https://www.youtube.com/watch?v=InmemKYD6ZM) Guest LinksMathias Buus on X: (Link: https://x.com/mafintosh)Andrew Osheroff on X: (Link: https://x.com/andrewosh)David Clements on X: (Link: http://twitter.com/davidmarkclem) Links to check out  When Should You Sell Bitcoin? (Link: https://youtu.be/3YdMXC9fpVY) Host Links ⁠Guy on Nostr ⁠(Link: http://tinyurl.com/2xc96ney) ⁠Guy on X ⁠(Link: https://twitter.com/theguyswann) Guy on Instagram (Link: https://www.instagram.com/theguyswann) Guy on TikTok (Link: https://www.tiktok.com/@theguyswann) Guy on YouTube (Link: https://www.youtube.com/@theguyswann) ⁠Bitcoin Audible on X⁠ (Link: https://twitter.com/BitcoinAudible) The Guy Swann Network Broadcast Room on Keet (Link: https://tinyurl.com/3na6v839) Check out our awesome sponsors! Fold: The best way to buy, use, and earn #Bitcoin on everything you do! Sats back on your debit card, gift cards, auto-buys, round-ups, you name it. Fold is the true bitcoiner's banking. Get 20K sats for FREE using referral code bitcoinaudible.com/fold Ready for best-in-class self custody? Get ⁠5% off the COLDCARD⁠ with code BITCOINAUDIBLE ⁠⁠⁠⁠⁠⁠(Link: https://bitcoinaudible.com/coldcard) Trying to BUY BITCOIN? River, secure, trusted, bitc...

Earth is the only planet in our solar system, our galaxy, and potentially our entire universe that sustains life. Us humans believe we're practically indestructible, but it turns out there's a unique creature tougher than us that can't be killed. Meet the tardigrade, one of the most resilient animals in the world – and possibly the universe. TIMESTAMPS It's also called the water bear. 1:18 It's really small. 2:20 It's really, really old. 3:08 It can live anywhere. 4:02 It's (mostly) harmless. 5:00 It's practically unkillable. 6:04 It survives extinction events. 10:45 It might survive on another planet. 12:33 Music: https://www.youtube.com/audiolibrary/... SUMMARY -A tardigrade is commonly called a water bear because it is most often found in water, where it prefers to dwell, and because of its slow gait, which resembles that of a bear. -The biggest full-grown tardigrade is about 0.5 mm or about 1/50 of an inch, which is smaller than flea and tick larva. -The earliest fossils we have of the tardigrade species date back to the Cambrian period, some 530 million years ago. -Tardigrades have been found on the high-altitude peaks of the Himalayas, in deep trenches in the ocean, in mud volcanoes, and in tropical rainforests. -These unique creatures move into a new environment and help to establish an ecosystem, not unlike a gold prospector setting up shop out in the frontier. -Many tardigrades can enter a dehydrated state to survive almost any dangerous outside environment. They curl up into a barrel shape, their bodies become glass-like, and they put themselves in stasis, during which time they're pretty much indestructible. -The tardigrade has survived all possible extinction events, from the Ordovician-Silurian extinction 440 million years ago all the way up to the extinction events that took out the dinosaurs. -The conditions on Mars are within the tardigrades' capabilities to survive, assuming that a sufficient amount of water exists to support them. Subscribe to Bright Side : https://goo.gl/rQTJZz ---------------------------------------------------------------------------------------- Our Social Media: Facebook:   / brightside   Instagram:   / brightgram   5-Minute Crafts Youtube: https://www.goo.gl/8JVmuC ---------------------------------------------------------------------------------------- For more videos and articles visit: http://www.brightside.me/ Learn more about your ad choices. Visit megaphone.fm/adchoices

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* List of Discoveries Squeezing Evolution: Did you know that dinosaurs ate rice before rice evolved? That turtle shells existed forty million years before turtle shells began evolving? That insects evolved tongues for eating from flowers 70 million years before flowers evolved? And that birds appeared before birds evolved? The fossil record is a wonderful thing. And more recently, only a 40,000-year squeeze, Neanderthal had blood types A, B, and O, shocking evolutionists but expected to us here at Real Science Radio! Sit back and get ready to enjoy another instant classic, today's RSR "list show" on Evolution's Big Squeeze! Our other popular list shows include: - scientists doubting Darwin - evidence against whale evolution - problems with 'the river carved the canyon' - carbon 14 everywhere it shouldn't be - dinosaur still-soft biological tissue - solar system formation problems - evidence against the big bang - evidence for the global flood - genomes that just don't fit - and our list of not so old things! (See also rsr.org/sq2 and rsr.org/sq3!) * Evolution's Big Squeeze: Many discoveries squeeze the Darwinian theory's timeframe and of course without a workable timeframe there is no workable theory. Examples, with their alleged (and falsified) old-earth timeframes, include: - Complex skeletons existed 9 million years before they were thought to have evolved, before even the "Cambrian explosion".- Butterflies existed 10 million years before they were thought to have evolved. - Parrots existed "much earlier than had been thought", in fact, 25 million years before they were thought to have evolved. - Cephalopod fossils (squids, cuttlefish, etc.) appear 35 million years before they were able to propagate. - Turtle shells 40 million years before turtle shells began evolving - Trees began evolving 45 million years before they were thought to evolve - Spores appearing 50 million years before the plants that made them (not unlike footprints systematically appearing "millions of years before" the creatures that made them, as affirmed by Dr. Marcus Ross, associate professor of geology). - Sponges existed 60 million years before they were believed to have evolved. - Dinosaurs ate rice before it evolved Example - Insect proboscis (tongue) in moths and butterflies 70 million years before previously believed has them evolving before flowers. - Arthropod brains fully developed with central nervous system running to eyes and appendages just like modern arthropods 90 million years earlier than previously known (prior to 2021, now, allegedly 310mya) - 100 million years ago and already a bird - Fossil pollen pushes back plant evolution 100 million years. - Mammalian hair allegedly 100-million-years-old show that, "the morphology of hair cuticula may have remained unchanged throughout most of mammalian evolution", regarding the overlapping cells that lock the hair shaft into its follicle. - Piranha-like flesh-eating teeth (and bitten prey) found pushing back such fish 125 million years earlier than previously claimed   - Shocking organic molecules in "200 million-years-old leaves" from ginkgoes and conifers show unexpected stasis. - Plant genetic sophistication pushed back 200 million years. - Jellyfish fossils (Medusoid Problematica :) 200 million years earlier than expected; here from 500My ago. - Green seaweed 200 million years earlier than expected, pushed back now to a billion years ago!  - The acanthodii fish had color vision 300 million years ago, but then, and wait, Cheiracanthus fish allegedly 388 million years ago already had color vision. - Color vision (for which there is no Darwinian evolutionary small-step to be had, from monochromatic), existed "300 million years ago" in fish, and these allegedly "120-million-year-old" bird's rod and cone fossils stun researchers :) - 400-million-year-old Murrindalaspis placoderm fish "eye muscle attachment, the eyestalk attachment and openings for the optic nerve, and arteries and veins supplying the eyeball" The paper's author writes, "Of course, we would not expect the preservation of ancient structures made entirely of soft tissues (e.g. rods and cone cells in the retina...)." So, check this next item... :) - And... no vertebrates in the Cambrian? Well, from the journal Nature in 2014, a "Lower-Middle Cambrian... primitive fish displays unambiguous vertebrate features: a notochord, a pair of prominent camera-type eyes, paired nasal sacs, possible cranium and arcualia, W-shaped myomeres, and a post-anal tail" Primitive? - Fast-growing juvenile bone tissue, thought to appear in the Cretaceous, has been pushed back 100 million years: "This pushes the origin of fibrolamellar bone in Sauropterygia back from the Cretaceous to the early Middle Triassic..."- Trilobites "advanced" (not the predicted primitive) digestion "525 million" years ago - And there's this, a "530 million year old" fish, "50 million years before the current estimate of when fish evolved" - Mycobacterium tuberculosis 100,000 yr-old MRCA (most recent common ancestor) now 245 million- Fungus long claimed to originate 500M years ago, now found at allegedly 950 Mya (and still biological "the distant past... may have been much more 'modern' than we thought." :) - A rock contained pollen a billion years before plants evolved, according to a 2007 paper describing "remarkably preserved" fossil spores in the French Alps that had undergone high-grade metamorphism - 2.5 billion year old cyanobacteria fossils (made of organic material found in a stromatolite) appear about "200 million years before the [supposed] Great Oxidation Event". - 2.7 billion year old eukaryotes (cells with a nucleus) existed (allegedly) 1 billion years before expected - 3.5 billion year "cell division evidently identical to that of living filamentous prokaryotes." - And even older cyanobacteria! At 220 million years earlier than thought, per Nature's 3.7 billion year old dating of stromatolites! - The universe and life itself (in 2019 with the universe dated a billion, now, no, wait, two billion!, years younger than previously thought, that's not only squeezing biological but also astronomical evolution, with the overall story getting really tight) - Mantis shrimp, with its rudimentary color but advanced UV vision, is allegedly ancient. - Hadrosaur teeth, all 1400 of them, were "more complex than those of cows, horses, and other well-known modern grazers." Professor stunned by the find! (RSR predicts that, by 2030 just to put an end date on it, more fossils will be found from the geologic column that will be more "advanced" as compared to living organisms, just like this hadrosaur and like the allegedly 100M year old hagfish  fossil having more slime glands than living specimens.)  - Trace fossils "exquisitely preserved" of mobile organisms (motility) dated at 2.1 billion years ago, a full 1.5 billion earlier than previously believed - Various multicellular organisms allegedly 2.1 billion years old, show multicellularity 1.5 billion years sooner than long believed   - Pre-sauropod 26,000-pound dinosaur "shows us that even as far back as 200 million years ago, these animals had already become the largest vertebrates to ever walk the Earth." - The Evo-devo squeeze, i.e., evolutionary developmental biology, as with rsr.org/evo-devo-undermining-darwinism. - Extinct Siberian one-horned rhinos coexisted with mankind. - Whale "evolution" is being crushed in the industry-wide "big squeeze". First, geneticist claims whales evolved from hippos but paleontologists say hippos evolved tens of millions of years too late! And what's worse than that is that fossil finds continue to compress the time available for whale evolution. To not violate its own plot, the Darwinist story doesn't start animals evolving back into the sea until the cast includes land animals suitable to undertake the legendary journey. The recent excavation of whale fossils on an island of the Antarctic Peninsula further compresses the already absurdly fast 10 million years to allegedly evolve from the land back to the sea, down to as little as one million years. BioOne in 2016 reported a fossil that is "among the oldest occurrences of basilosaurids worldwide, indicating a rapid radiation and dispersal of this group since at least the early middle Eocene." By this assessment, various techniques produced various published dates. (See the evidence that falsifies the canonical whale evolution story at rsr.org/whales.) * Ancient Hierarchical Insect Society: "Thanks to some well-preserved remains, researchers now believe arthropod social structures have been around longer than anyone ever imagined. The encased specimens of ants and termites recently studied date back [allegedly] 100 million years." Also from the video about "the bubonic plague", the "disease is well known as a Middle Ages mass killer... Traces of very similar bacteria were found on [an allegedly] 20-million-year-old flea trapped in amber." And regarding "Caribbean lizards... Even though they are [allegedly] 20 million years old, the reptiles inside the golden stones were not found to differ from their contemporary counterparts in any significant way. Scientists attribute the rarity [Ha! A rarity or the rule? Check out rsr.org/stasis.] to stable ecological surroundings." * Squeezing and Rewriting Human History: Some squeezing simply makes aspects of the Darwinian story harder to maintain while other squeezing contradicts fundamental claims. So consider the following discoveries, most of which came from about a 12-month period beginning in 2017 which squeeze (and some even falsify) the Out-of-Africa model: - find two teeth and rewrite human history with allegedly 9.7 million-year-old teeth found in northern Europe (and they're like Lucy, but "three times older") - date blue eyes, when humans first sported them, to as recently as 6,000 years ago   - get mummy DNA and rewrite human history with a thousand years of ancient Egyptian mummy DNA contradicting Out-of-Africa and demonstrating Out-of-Babel - find a few footprints and rewrite human history with allegedly 5.7 million-year-old human footprints in Crete - re-date an old skull and rewrite human history with a very human skull dated at 325,000 years old and redated in the Journal of Physical Anthropology at about 260,000 years old and described in the UK's Independent, "A skull found in China [40 years ago] could re-write our entire understanding of human evolution." - date the oldest language in India, Dravidian, with 80 derivatives spoken by 214 million people, which appeared on the subcontinent only about 4,500 years ago, which means that there is no evidence for human language for nearly 99% of the time that humans were living in Asia. (Ha! See rsr.org/origin-of-language for the correct explanation.) - sequence a baby's genome and rewrite human history with a 6-week old girl buried in Alaska allegedly 11,500 years ago challenging the established history of the New World. (The family buried this baby girl just beneath their home like the practice in ancient Mesopotamia, the Hebrews who sojourned in Egypt, and in Çatalhöyük in southern Turkey, one of the world's most ancient settlements.) - or was that 130,000? years ago as the journal Nature rewrites human history with a wild date for New World site - and find a jawbone and rewrite human history with a modern looking yet allegedly 180,000-year-old jawbone from Israel which "may rewrite the early migration story of our species" by about 100,000 years, per the journal Science - re-date a primate and lose yet another "missing link" between "Lucy" and humans, as Homo naledi sheds a couple million years off its age and drops from supposedly two million years old to (still allegedly) about 250,000 years old, far too "young" to be the allegedly missing link - re-analysis of the "best candidate" for the most recent ancestor to human beings, Australopithecus sediba, turns out to be a juvenile Lucy-like ape, as Science magazine reports work presented at the American Association of Physical Anthropologists 2017 annual meeting - find skulls in Morocco and "rewrite human history" admits the journal Nature, falsifying also the "East Africa" part of the canonical story - and from the You Can't Make This Stuff Up file, NPR reports in April 2019, Ancient Bones And Teeth Found In A Philippine Cave May Rewrite Human History. :) - Meanwhile, whereas every new discovery requires the materialists to rewrite human history, no one has had to rewrite Genesis, not even once. Yet, "We're not claiming that the Bible is a science textbook. Not at all. For the textbooks have to be rewritten all the time!"  - And even this from Science: "humans mastered the art of training and controlling dogs thousands of years earlier than previously thought."- RSR's Enyart commented on the Smithsonian's 2019 article on ancient DNA possibly deconstructing old myths...  This Smithsonian article about an ancient DNA paper in Science Advances, or actually, about the misuse of such papers, was itself a misuse. The published research, Ancient DNA sheds light on the genetic origins of early Iron Age Philistines, confirmed Amos 9:7 by documenting the European origin of the biblical Philistines who came from the island of Caphtor/Crete. The mainstream media completely obscured this astounding aspect of the study but the Smithsonian actually stood the paper on its head. [See also rsr.org/archaeology.]* Also Squeezing Darwin's Theory: - Evolution happens so slowly that we can't see it, yet - it happens so fast that millions of mutations get fixed in a blink of geologic time AND: - Observing a million species annually should show us a million years of evolution, but it doesn't, yet - evolution happens so fast that the billions of "intermediary" fossils are missing AND: - Waiting for helpful random mutations to show up explains the slowness of evolution, yet - adaption to changing environments is often immediate, as with Darwin's finches Finches Adapt in 17 Years, Not 2.3 Million: Charles Darwin's finches are claimed to have taken 2,300,000 years to diversify from an initial species blown onto the Galapagos Islands. Yet individuals from a single finch species on a U.S. Bird Reservation in the Pacific were introduced to a group of small islands 300 miles away and in at most 17 years, like Darwin's finches, they had diversified their beaks, related muscles, and behavior to fill various ecological niches. So Darwin's finches could diversify in just 17 years, and after 2.3 million more years, what had they evolved into? Finches! Hear this also at rsr.org/lee-spetner and see Jean Lightner's review of the Grants' 40 Years. AND: - Fossils of modern organisms are found "earlier" and "earlier" in the geologic column, and - the "oldest" organisms are increasingly found to have anatomical, proteinaceous, prokaryotic, and eukaryotic sophistication and similarity to "modern" organisms AND: - Small populations are in danger of extinction (yet they're needed to fix mutations), whereas - large populations make it impossible for a mutation to become standard AND: - Mutations that express changes too late in an organism's development can't effect its fundamental body plan, and - mutations expressed too early in an organism's development are fatal (hence among the Enyart sayings, "Like evolving a vital organ, most major hurdles for evolutionary theory are extinction-level events.") AND: - To evolve flight, you'd get bad legs - long before you'd get good wings AND: - Most major evolutionary hurdles appear to be extinction-level events- yet somehow even *vital* organs evolve (for many species, that includes reproductive organs, skin, brain, heart, circulatory system, kidney, liver, pancreas, stomach, small intestines, large intestines, lungs -- which are only a part of the complex respiration system) AND: - Natural selection of randomly taller, swifter, etc., fish, mammals, etc. explains evolution yet - development of microscopic molecular machines, feedback mechanisms, etc., which power biology would be oblivous to what's happening in Darwin's macro environment of the entire organism AND: - Neo-Darwinism suggests genetic mutation as the engine of evolution yet - the there is not even a hypothesis for modifying the vast non-genetic information in every living cell including the sugar code, electrical code, the spatial (geometric) code, and the epigenetic code AND: - Constant appeals to "convergent" evolution (repeatedly arising vision, echolocation, warm-bloodedness, etc.) - undermine most Darwinian anatomical classification especially those based on trivialities like odd or even-toed ungulates, etc. AND: - Claims that given a single species arising by abiogenesis, then - Darwinism can explain the diversification of life, ignores the science of ecology and the (often redundant) biological services that species rely upon AND: - humans' vastly superior intelligence indicates, as bragged about for decades by Darwinists, that ape hominids should have the greatest animal intelligence, except that - many so-called "primitive" creatures and those far distant on Darwin's tee of life, exhibit extraordinary rsr.org/animal-intelligence even to processing stimuli that some groups of apes cannot AND: - Claims that the tree of life emerges from a single (or a few) common ancestors - conflict with the discoveries of multiple genetic codes and of thousands of orphan genes that have no similarity (homology) to any other known genes AND (as in the New Scientist cover story, "Darwin Was Wrong about the tree of life", etc.): - DNA sequences have contradicted anatomy-based ancestry claims - Fossil-based ancestry claims have been contradicted by RNA claims - DNA-based ancestry claims have been contradicted by anatomy claims - Protein-based ancestry claims have been contradicted by fossil claims. - And the reverse problem compared to a squeeze. Like finding the largest mall in America built to house just a kid's lemonade stand, see rsr.org/200 for the astounding lack of genetic diversity in humans, plants, and animals, so much so that it could all be accounted for in just about 200 generations! - The multiplied things that evolved multiple times - Etc. * List of Ways Darwinists Invent their Tree of Life, aka Pop Goes the Weasle – Head and Shoulders, Knees and Toes: Evolutionists change their selection of what evidence they use to show 'lineage', from DNA to fossils to genes to body plans to teeth to many specific anatomical features to proteins to behavior to developmental similarities to habitat to RNA, etc. and to a combination of such. Darwinism is an entire endeavor based on selection bias, a kind of logical fallacy. By anti-science they arbitrarily select evidence that best matches whichever evolutionary story is currently preferred." -Bob E. The methodology used to create the family tree edifice to show evolutionary relationships classifies the descent of organisms based on such attributes as odd-toed and even-toed ungulates. Really? If something as wildly sophisticated as vision allegedly evolved multiple times (a dozen or more), then for cryin' out loud, why couldn't something as relatively simple as odd or even toes repeatedly evolve? How about dinosaur's evolving eggs with hard shells? Turns out that "hard-shelled eggs evolved at least three times independently in dinosaurs" (Nature, 2020). However, whether a genus has an odd or even number of toes, and similar distinctions, form the basis for the 150-year-old Darwinist methodology. Yet its leading proponents still haven't acknowledged that their tree building is arbitrary and invalid. Darwin's tree recently fell anyway, and regardless, it has been known to be even theoretically invalid all these many decades. Consider also bipedalism? In their false paradigm, couldn't that evolve twice? How about vertebrate and non-vertebrates, for that matter, evolving multiple times? Etc., etc., etc. Darwinists determine evolutionary family-tree taxonomic relationships based on numbers of toes, when desired, or on hips (distinguishing, for example, dinosaur orders, until they didn't) or limb bones, or feathers, or genes, or fossil sequence, or neck bone, or..., or..., or... Etc. So the platypus, for example, can be described as evolving from pretty much whatever story would be in vogue at the moment...   * "Ancient" Protein as Advanced as Modern Protein: A book review in the journal Science states, "the major conclusion is reached that 'analyses made of the oldest fossils thus far studied do not suggest that their [allegedly 145-million year-old] proteins were chemically any simpler than those now being produced.'" 1972, Biochemistry of Animal Fossils, p. 125 * "Ancient" Lampreys Just Modern Lampreys with Decomposed Brain and Mouth Parts: Ha! Researches spent half-a-year documenting how fish decay. RSR is so glad they did! One of the lessons learned? "[C]ertain parts of the brain and the mouth that distinguish the animals from earlier relatives begin a rapid decay within 24 hours..." :) * 140-million Year Old Spider Web: The BBC and National Geographic report on a 140-million year old spider web in amber which, as young-earth creationists expect, shows threads that resemble silk spun by modern spiders. Evolutionary scientists on the otherhand express surprise "that spider webs have stayed the same for 140 million years." And see the BBC. * Highly-Credentialed Though Non-Paleontologist on Flowers: Dr. Harry Levin who spent the last 15 years of a brilliant career researching paleontology presents much evidence that flowering plants had to originate not 150 million years ago but more than 300 million years ago. (To convert that to an actual historical timeframe, the evidence indicates flowers must have existed prior to the time that the strata, which is popularly dated to 300 mya, actually formed.) * Rampant Convergence: Ubiquitous appeals to "convergent" evolution (vision, echolocation, warm-bloodedness, icthyosaur/dolphin anatomy, etc.), all allegedly evolving multiple times, undermines anatomical classification based on trivialities like odd or even-toed ungulates, etc. * Astronomy's Big Evolution Squeeze: - Universe a billion, wait, two billion, years younger than thought   (so now it has to evolve even more impossibly rapidly) - Sun's evolution squeezes biological evolution - Galaxies evolving too quickly - Dust evolving too quickly - Black holes evolving too quickly - Clusters of galaxies evolving too quickly. * The Sun's Evolution Squeezes Life's Evolution: The earlier evolutionists claim that life began on Earth, the more trouble they have with astrophysicists. Why? They claim that a few billion years ago the Sun would have been far more unstable and cooler. The journal Nature reports that the Faint young Sun paradox remains for the "Sun was fainter when the Earth was young, but the climate was generally at least as warm as today". Further, our star would shoot out radioactive waves many of which being violent enough to blow out Earth's atmosphere into space, leaving Earth dead and dry like Mars without an atmosphere. And ignoring the fact that powerful computer simulators cannot validate the nebula theory of star formation, if the Sun had formed from a condensing gas cloud, a billion years later it still would have been emitting far less energy, even 30% less, than it does today. Forget about the claimed one-degree increase in the planet's temperature from man-made global warming, back when Darwinists imagine life arose, by this just-so story of life spontaneously generating in a warm pond somewhere (which itself is impossible), the Earth would have been an ice ball, with an average temperature of four degrees Fahrenheit below freezing! See also CMI's video download The Young Sun. * Zircons Freeze in Molten Eon Squeezing Earth's Evolution? Zircons "dated" 4 to 4.4 billion years old would have had to freeze (form) when the Earth allegedly was in its Hadean (Hades) Eon and still molten. Geophysicist Frank Stacey (Cambridge fellow, etc.) has suggested they may have formed above ocean trenches where it would be coolest. One problem is that even further squeezes the theory of plate tectonics requiring it to operate two billion years before otherwise claimed. A second problem (for these zircons and the plate tectonics theory itself) is that ancient trenches (now filled with sediments; others raised up above sea level; etc.) have never been found. A third problem is that these zircons contain low isotope ratios of carbon-13 to carbon-12 which evolutionists may try to explain as evidence for life existing even a half-billion years before they otherwise claim. For more about this (and to understand how these zircons actually did form) just click and then search (ctrl-f) for: zircon character. * Evolution Squeezes Life to Evolve with Super Radioactivity: Radioactivity today breaks chromosomes and produces neutral, harmful, and fatal birth defects. Dr. Walt Brown reports that, "A 160-pound person experiences 2,500 carbon-14 disintegrations each second", with about 10 disintergrations per second in our DNA. Worse for evolutionists is that, "Potassium-40 is the most abundant radioactive substance in... every living thing." Yet the percentage of Potassium that was radioactive in the past would have been far in excess of its percent today. (All this is somewhat akin to screws in complex machines changing into nails.) So life would have had to arise from inanimate matter (an impossibility of course) when it would have been far more radioactive than today. * Evolution of Uranium Squeezed by Contrasting Constraints: Uranium's two most abundant isotopes have a highly predictable ratio with 235U/238U equaling 0.007257 with a standard deviation of only 0.000017. Big bang advocates claim that these isotopes formed in distant stellar cataclysms. Yet that these isotopes somehow collected in innumerable small ore bodies in a fixed ratio is absurd. The impossibility of the "big bang" explanation of the uniformity of the uranium ratio (rsr.org/bb#ratio) simultaneously contrasts in the most shocking way with its opposite impossibility of the missing uniform distribution of radioactivity (see rsr.org/bb#distribution) with 90% of Earth's radioactivity in the Earth's crust, actually, the continental crust, and even at that, preferentially near granite! A stellar-cataclysmic explanation within the big bang paradigm for the origin of uranium is severely squeezed into being falsified by these contrasting constraints. * Remarkable Sponges? Yes, But For What Reason? Study co-author Dr. Kenneth S. Kosik, the Harriman Professor of Neuroscience at UC Santa Barbara said, "Remarkably, the sponge genome now reveals that, along the way toward the emergence of animals, genes for an entire network of many specialized cells evolved and laid the basis for the core gene logic of organisms that no longer functioned as single cells." And then there's this: these simplest of creatures have manufacturing capabilities that far exceed our own, as Degnan says, "Sponges produce an amazing array of chemicals of direct interest to the pharmaceutical industry. They also biofabricate silica fibers directly from seawater in an environmentally benign manner, which is of great interest in communications [i.e., fiber optics]. With the genome in hand, we can decipher the methods used by these simple animals to produce materials that far exceed our current engineering and chemistry capabilities." Kangaroo Flashback: From our RSR Darwin's Other Shoe program: The director of Australia's Kangaroo Genomics Centre, Jenny Graves, that "There [are] great chunks of the human genome… sitting right there in the kangaroo genome." And the 20,000 genes in the kangaroo (roughly the same number as in humans) are "largely the same" as in people, and Graves adds, "a lot of them are in the same order!" CMI's Creation editors add that "unlike chimps, kangaroos are not supposed to be our 'close relatives.'" And "Organisms as diverse as leeches and lawyers are 'built' using the same developmental genes." So Darwinists were wrong to use that kind of genetic similarity as evidence of a developmental pathway from apes to humans. Hibernating Turtles: Question to the evolutionist: What happened to the first turtles that fell asleep hibernating underwater? SHOW UPDATE Of Mice and Men: Whereas evolutionists used a very superficial claim of chimpanzee and human genetic similarity as evidence of a close relationship, mice and men are pretty close also. From the Human Genome Project, How closely related are mice and humans?, "Mice and humans (indeed, most or all mammals including dogs, cats, rabbits, monkeys, and apes) have roughly the same number of nucleotides in their genomes -- about 3 billion base pairs. This comparable DNA content implies that all mammals [RSR: like roundworms :)] contain more or less the same number of genes, and indeed our work and the work of many others have provided evidence to confirm that notion. I know of only a few cases in which no mouse counterpart can be found for a particular human gene, and for the most part we see essentially a one-to-one correspondence between genes in the two species." * Related RSR Reports: See our reports on the fascinating DNA sequencing results from roundworms and the chimpanzee's Y chromosome! * Genetic Bottleneck, etc: Here's an excerpt from rsr.org/why-was-canaan-cursed... A prediction about the worldwide distribution of human genetic sequencing (see below) is an outgrowth of the Bible study at that same link (aka rsr.org/canaan), in that scientists will discover a genetic pattern resulting from not three but four sons of Noah's wife. Relevant information comes also from mitochondrial DNA (mtDNA) which is not part of any of our 46 chromosomes but resides outside of the nucleus. Consider first some genetic information about Jews and Arabs, Jewish priests, Eve, and Noah. Jews and Arabs Biblical Ancestry: Dr. Jonathan Sarfati quotes the director of the Human Genetics Program at New York University School of Medicine, Dr. Harry Ostrer, who in 2000 said: Jews and Arabs are all really children of Abraham … And all have preserved their Middle Eastern genetic roots over 4,000 years. This familiar pattern, of the latest science corroborating biblical history, continues in Dr. Sarfati's article, Genesis correctly predicts Y-Chromosome pattern: Jews and Arabs shown to be descendants of one man. Jewish Priests Share Genetic Marker: The journal Nature in its scientific correspondence published, Y Chromosomes of Jewish Priests, by scie

Darwin's Dilemma. A Documentary about The Cambrian Explosion and Intelligent Design. This powerful documentary explores one of the great mysteries in the history of life: the geologically-sudden appearance of dozens of major complex animal types in the fossil record without any trace of the gradual transitional steps Charles Darwin had envisioned 150 years ago. Frequently described as “the Cambrian Explosion,” the development of these new animal types required a massive increase in genetic information. Growing evidence suggests that the creation of novel genetic information requires intelligence, and thus the burst of genetic information during the Cambrian Explosion provides convincing evidence that animal life is the product of intelligent design rather than a blind undirected process like natural selection. Darwin's Dilemma recreates the prehistoric world of the Cambrian era with state-of-the-art computer animation, and the film features interviews with numerous scientists, including leading evolutionary paleontologists Simon Conway Morris of Cambridge University and James Valentine of the University of California at Berkeley, marine biologist Paul Chien of the University of San Francisco, and evolutionary biologist Richard Sternberg, a Research Collaborator at the National Museum of Natural History. Watch this documentary for free at- https://watch.redeemtv.com/show-details/darwin-s-dilemma For 3 Intelligent Design Documentaries at RedeemTV visit- https://watch.redeemtv.com/search?query=intelligent%20design   For more many more ACU Shows on Intelligent Design visit- https://acupodcast.podbean.com/?s=intelligent%20design   Stephen C. Meyer, geophysicist, Vice President of the Discovery Institute, and author of the New York Time's best seller "Darwin's Doubt," joins Ben to discuss philosophy, the origins of life, the overlap of science and religion, and much more. Check Stephen C. Meyer out on: Facebook:   / drstephencmeyer   Website: http://www.stephencmeyer.org   You can find out more about Stephen C. Meyer and the books mentioned in this interview at https://stephencmeyer.org/books/ You can follow Stephen on Twitter (X) at:   / stephencmeyer   ‪@DrStephenMeyer   Dr. Stephen C. Meyer received his Ph.D. in the philosophy of science from the University of Cambridge. A former geophysicist and college professor, he now directs Discovery Institute's Center for Science and Culture in Seattle. He has authored the New York Times best seller Darwin's Doubt: The Explosive Origin of Animal Life and the Case for Intelligent Design, Signature in the Cell: DNA and the Evidence for Intelligent Design, which was named a Book of the Year by the Times Literary Supplement in 2009, and now, The Return of the God Hypothesis. In this episode, you can expect to hear Dr Stephen C Meyer on: - The scientific evidence for intelligent design - The identity of the 'creator'…

The gang discusses two papers that look at examples of soft tissue preservation during the Cambrian. The first paper is a deep dive into the sedimentology and paleoenvironment of the Emu Bay Shale. The second paper makes some interesting claims about soft tissue preservation in a marginal marine environment. Meanwhile, James needs some shortcuts, Curt is locked up, and Amanda should be blamed for everything that happened here.   Up-Goer Five (Curt Edition): The friends look at two papers that look at animals from a long long time ago that lived in the water and were soft but were able to be found in rocks. The first paper looks at a place where there are a lot of animals found in rocks but the types of animals are different from other places around the same time. This paper looks at what the place was like at that time and they see that this was a place where a long line of water that you can drink made its way into the big water that you can not drink. The second paper made us all sad.   References: Naimark, E. B., A. V. Sizov, and V. B. Khubanov. "Kimiltei Is a New Late Cambrian Lagerstätte with the Faunistic Complex of Arthropods (Euthycarcinoidae, Synziphosurina, and Chasmataspidida) in the Irkutsk Region." Doklady Earth Sciences. Vol. 512. No. 1. Moscow: Pleiades Publishing, 2023. Gaines, Robert R., et al. "The Emu Bay Shale: A unique early Cambrian Lagerstätte from a tectonically active basin." Science advances 10.30 (2024): eadp2650.

Stephen Wolfram answers general questions from his viewers about science and technology as part of an unscripted livestream series, also available on YouTube here: https://wolfr.am/youtube-sw-qa Questions include: May I ask a simple question? What aspects or elements of a probability distribution can be computed or quantified, and how are these computations used to describe the distribution? - Why are some creatures nocturnal? Why aren't humans? - Is the normal distribution related to the complexity of the dynamics, or is it found equally at all scales? - ​Does pi have a normal number distribution? - ​​Google says the average human height is 5'9"–​​it's 5'10" in the US. - I read that there is a puzzle over why no new body plans developed since the Cambrian. In your machine learning view of adaptive evolution, what's happening here? - Apparently Japanese kids are getting taller, correlated with red meat consumption. - ​​Do you think there are so many variables that it's impossible to figure out? Everyone knows about corn syrup, but there are also things like smoking was very common, etc. - ​​What kinds of diseases that have afflicted humanity for almost all of our history would stunt growth? - If you consume less energy, your processes including various damage and aging slow down, right? - Could we have evolved out of needing an appendix because of diet? - Is it possible to measure somehow the intelligence of dinosaurs?

In this bedtime story, I take you on an underwater cruise to the various layers of the Earth's oceans. I talk about marine life, its evolution, and the conditions it had to adapt to. Types of animals or species we look at in more detail include jellyfish, coral, sharks, giant squids, sperm whales, the components of plankton, fishes from the bathyal zone and below, or the inhabitants of ecosystems around cold seeps and hydrothermal vents. We also make a bit of time travel, and I tell you about some of the largest creatures that dominated our seas from the Cambrian period to today: trilobites, anomalocaris, sea scorpions, placoderm fishes, plesiosaurs, ichthyosaurs, mosasaurs or megalodons.  Welcome to Lights Out LibraryJoin me for a sleepy adventure tonight. Sit back, relax, and fall asleep to documentary-style stories read in a calming voice. Learn something new while you enjoy a restful night of sleep.Listen ad free and get access to bonus content on our Patreon: ⁠⁠⁠https://www.patreon.com/LightsOutLibrary621⁠⁠⁠Listen on Youtube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@LightsOutLibraryov⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ¿Quieres escuchar en Español? Echa un vistazo a La Biblioteca de los Sueños!En Spotify: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://open.spotify.com/show/1t522alsv5RxFsAf9AmYfg⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠En Apple Podcasts: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://podcasts.apple.com/us/podcast/la-biblioteca-de-los-sue%C3%B1os-documentarios-para-dormir/id1715193755⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠En Youtube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@LaBibliotecadelosSuenosov⁠⁠⁠ 

Prepare a feast, fetch the finest mead and summon the most whimsical jesters in the land for we are once again welcoming a guest into the great halls of Tales for Wales. This week we chat about some of our favourite nuggets of history with a true scholar of the Cymru of old, the mega brain behind Cambrian Chronicles.Hit play and give us a listen then head over to Cam Chronie's channel and fall down the historic rabbit hole he's put together there.As always check out our socials where we're currently building our own pub or get around our Patreon for some extra bits of content.

On today's show we welcome special guest, the co-CIO of Cambrian Asset Management, Martin Green. Cambrian is a quantitative investment firm for digital assets. On the show, we discuss: Cambrian's approach to crypto investing and how it is differentiated Navigating crypto's volatility and macro events including the FTX bankruptcy Keys to success and staying power in the digital asset space And more

To donate to help victims of Hurricane Helena: Day One Relief - direct donation link World Central Kitchen - direct donation link It's the big 400th episode! Let's have a good old-fashioned mystery episode! Thanks to Richard from NC for suggesting two of our animal mysteries today. Further reading: A 150-Year-Old Weird Ancient Animal Mystery, Solved The Enigmatic Cinnamon Bird: A Mythical Tale of Spice and Splendor First ever photograph of rare bird species New Britain Goshawk Scientists stumbled onto toothy deep-sea "top predator," and named it after elite sumo wrestlers Bryde's whales produce Biotwang calls, which occur seasonally in long-term acoustic recordings from the central and western Pacific A stylophoran [drawing by Haplochromis - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=10946202]: A cinnamon flycatcher, looking adorable [photo by By https://www.flickr.com/photos/neilorlandodiazmartinez/ - https://www.flickr.com/photos/neilorlandodiazmartinez/9728856384, CC BY-SA 2.0, https://commons.wikimedia.org/w/index.php?curid=30338634]: The rediscovered New Britain goshawk, and the first photo ever taken of it, by Tom Vieras: The mystery fish photo: The yokozuna slickhead fish: The Biotwang maker, Bryde's whale: Show transcript: Welcome to Strange Animals Podcast. I'm your host, Kate Shaw. We've made it to the big episode 400, and also to the end of September. That means monster month is coming up fast! To celebrate our 400th episode and the start of monster month, let's have a good old-fashioned mysteries episode. We'll start with an ancient animal called a stylophoran, which first appears in the fossil record around 500 million years ago. It disappears from the fossil record around 300 million years ago, so it persisted for a long time before going extinct. But until recently, no one knew what the stylophoran looked like when it was alive, and what it could possibly be related to. It was just too weird. That's an issue with ancient fossils, especially ones from the Cambrian period. We talked about the Cambrian explosion in episode 69, which was when tiny marine life forms began to evolve into much larger, more elaborate animals as new ecological niches became available. In the fossil record it looks like it happened practically overnight, which is why it's called the Cambrian explosion, but it took millions of years. Many of the animals that evolved 500 million years ago look very different from all animals alive today, as organisms evolved body plans and appendages that weren't passed down to descendants. As for stylophorans, the first fossils were discovered about 150 years ago. They're tiny animals, only millimeters long, and over 100 species have been identified so far. The body is flattened and shaped sort of like a rectangle, but two of the rectangle's corners actually extend up into little points, and growing from those two points are what look like two appendages. From the other side of the rectangle, the long flat side, is another appendage that looks like a tail. The tail has plates on it and blunt spikes that stick up, while the other two appendages look like they might be flexible like starfish arms. Naturally, the first scientists to examine a stylophoran decided the tail was a tail and the flexible appendages were arm-like structures that helped it move around and find food. But half a billion years ago, there were no animals with tails. Tails developed much later, and are mainly a trait of vertebrates. That led to some scientists questioning whether the stylophoran was an early precursor of vertebrates, or animals with some form of spinal cord. The spikes growing from the top of the tail actually look a little bit like primitive vertebrae, made of calcite plates. That led to the calcichordate hypothesis that suggested stylophorans gave rise to vertebrates. Then, in 2014,

In this week's episode of The Week in Sustainability, we explore groundbreaking research that reconstructs Earth's surface temperatures over the past 500 million years. The study reveals that the Phanerozoic eon, dating back to the Cambrian explosion, was much hotter than previously thought, providing a striking contrast to the current climate crisis. Asofsky highlights two key insights: the unprecedented speed of today's warming and the direct link between atmospheric carbon and temperature shifts. With clear evidence tying fossil fuel combustion to modern climate change, she emphasizes the critical role of businesses in mitigating the impact.

500 some million years ago the world was full of ‘abnormal shrimp' and ‘blunt feet' and other animals that defy comprehensible definitions. In this episode we talk all about the mind-boggling biology and bizarre geology of the Cambrian. CW: Drug references, cursing, dead animals, cavalier jokes at the expense of white men, dick jokes

In this episode of The Data Minute, Peter Walker (Head of Insights at Carta) is joined by Rex Salisbury (Founding GP, Cambrian Ventures) to discuss what's developing in the fintech space, GPs calling more capital, and Rex's idea on how to get into the tech industry.This conversation also covers Rex's personal cheat sheet explaining why someone shouldn't be a VC, and should instead start a career in tech or become a founder.Subscribe to Carta's weekly Data Minute newsletter: https://carta.com/subscribe/data-newsletter-sign-up/Explore interactive startup and VC data, with Carta's Data Desk: https://carta.com/data-desk/Chapters:00:00 Intro01:07 State of pre-seed and seed markets03:16 Comparing SaSS and fintech valuations06:34 The value of specialized funds08:52 Best places for early-stage fintech founders13:13 How are founders thinking about hiring?19:01 Should founders care about GP deployments?24:15 Are LPs rotating away from venture?27:19 The allure of going public28:11 Rex's cheat sheet on getting into VC36:30 Why do so many MBAs want to be VCs?39:14 When will we have a $1B company with one employee41:20 OutroThis presentation contains general information only and eShares, Inc. dba Carta, Inc. (“Carta”) is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services, and is for informational purposes only.  This presentation is not a substitute for such professional advice or services nor should it be used as a basis for any decision or action that may affect your business or interests. © 2024 eShares, Inc., dba Carta, Inc. All rights reserved.

In this episode, the Arcturian Collective Thingy takes a deep dive into the metaphorical evolution of neurons and their connection to the ancient civilization of Lemuria. Through an exploration of folklore, science, and esoteric traditions, the Collective shares how neurons, long before the development of the human ego, were psychically interconnected, forming a harmonious network that orchestrated life on Earth. The episode examines the disruption of this connection by external predatory forces, leading to the creation of the egoic personality, which severed humanity from its deeper neural awareness. Join the Collective as they offer a thought-provoking narrative on the evolution of consciousness, the power of neurons, and how understanding this connection could lead to greater personal and collective awakening.Show Notes:Introduction to the Arcturian Collective Thingy and their multidimensional perspective.Explanation of Lemuria as a metaphor for the psychic connection between neurons and life on Earth.The origin of Lemuria and its connection to the study of folklore and lost civilizations.Discussion on how neurons evolved as highly intelligent and interconnected entities.The role of neurons in creating and coordinating life forms during the Cambrian explosion.Exploration of how external predatory forces severed the psychic connection, creating the egoic personality.Insights into how tradition and cultural norms shape human consciousness and behavior.Tangents on the importance of interconnectedness and multidimensional awareness.Conclusion with a promise of future episodes exploring related topics, including the story of Omra.Listeners are invited to reflect on the evolutionary journey of neurons and their connection to the spiritual and material world, and consider how these insights may influence their personal and collective consciousness.

Reed Smith partners Howard Womersley Smith and Bryan Tan with AI Verify community manager Harish Pillay discuss why transparency and explain-ability in AI solutions are essential, especially for clients who will not accept a “black box” explanation. Subscribers to AI models claiming to be “open source” may be disappointed to learn the model had proprietary material mixed in, which might cause issues. The session describes a growing effort to learn how to track and understand the inputs used in AI systems training. ----more---- Transcript: Intro: Hello and welcome to Tech Law Talks, a podcast brought to you by Reed Smith's Emerging Technologies Group. In each episode of this podcast, we will discuss cutting-edge issues on technology, data, and the law. We will provide practical observations on a wide variety of technology and data topics to give you quick and actionable tips to address the issues you are dealing with every day.  Bryan: Welcome to Tech Law Talks and our new series on artificial intelligence. Over the coming months, we'll explore the key challenges and opportunities within the rapidly evolving AI landscape. My name is Bryan Tan and I'm a partner at Reed Smith Singapore. Today we will focus on AI and open source software.  Howard: My name is Howard Womersley Smith. I'm a partner in the Emerging Technologies team of Reed Smith in London and New York. And I'm very pleased to be in this podcast today with Bryan and Harish.  Bryan: Great. And so today we have with us Mr. Harish Pillay. And before we start, I'm going to just ask Harish to tell us a little bit, well, not really a little bit, because he's done a lot about himself and how he got here.  Harish: Well, thanks, Bryan. Thanks, Howard. My name is Harish Pillay. I'm based here in Singapore, and I've been in the tech space for over 30 years. And I did a lot of things primarily in the open source world, both open source software, as well as in the hardware design and so on. So I've covered the spectrum. When I was way back in the graduate school, I did things in AI and chip design. That was in the late 1980s. And there was not much from an AI point of view that I could do then. It was the second winter for AI. But in the last few years, there was the resurgence in AI and the technologies and the opportunities that can happen with the newer ways of doing things with AI make a lot more sense. So now I'm part of an organization here in Singapore known as AI Verify Foundation. It is a non-profit open-source software foundation that was set up about a year ago to provide tools, software testing tools, to test AI solutions that people may be creating to understand whether those tools are fair, are unbiased, are transparent. There's about 11 criteria it tests against. So both traditional AI types of solutions as well as generative AI solutions. So these are the two open source projects that are globally available for anyone to participate in. So that's currently what I'm doing.  Bryan: Wow, that's really fascinating. Would you say, Harish, that kind of your experience over the, I guess, the three decades with the open source movement, with the whole Linux user groups, has that kind of culminated in this place where now there's an opportunity to kind of shape the development of AI in an open-source context?  Harish: I think we need to put some parameters around it as well. The AI that we talk about today could never have happened if it's not for open-source tools. That is plain and simple. So things like TensorFlow and all the tooling that goes around in trying to do the model building and so on and so forth could not have happened without open source tools and libraries, a Python library and a whole slew of other tools. If these were all dependent on non-open source solutions, we will still be talking about one fine day something is going to happen. So it's a given that that's the baseline. Now, what we need to do is to get this to the next level of understanding as to what does it mean when you say it's open source and artificial intelligence or open source AI, for that matter. Because now we have a different problem that we are trying to grapple with. The problem we're trying to grapple with is the definition of what is open-source AI. We understand open-source from a software point of view, from a hardware point of view. We understand that I have access to the code, I have access to the chip designs, and so on and so forth. No questions there. It's very clear to understand. But when you talk about generative AI as a specific instance of open-source AI, I can have access to the models. I can have access to the weights. I can do those kinds of stuff. But what was it that made those models become the models? Where were the data from? What's the data? What's the provenance of the data? Are these data openly available? Or are they hidden away somewhere? Understandably, we have a huge problem because in order to train the kind of models we're training today, it takes a significant amount of data and computing power to train the models. The average software developer does not have the resources to do that, like what we could do with a Linux environment or Apache or Firefox or anything like that. So there is this problem. So the question still comes back to is, what is open source AI? So the open source initiative, OSI, is now in the process of formulating what does it mean to have open source AI. The challenge we find today is that because of the success of open source in every sector of the industry, you find a lot of organizations now bending around and throwing around the label, our stuff is open source, our stuff is open source, when it is not. And they are conveniently using it as a means to gain attention and so on. No one is going to come and say, hey, do you have a proprietary tool? Adding that ship has sailed. It's not going to happen anymore. But the moment you say, oh, we have an open source fancy tool, oh, everybody wants to come and talk to you. But the way they craft that open source message is actually quite sadly disingenuous because they are putting restrictions on what you can actually do. It is contrary completely to what the open-source licensing says in open-source initiative. I'll pause there for a while because I threw a lot of stuff at you.  Bryan: No, no, no. That's a lot to unpack here, right? And there's a term I learned last week, and it's called AI washing. And that's where people try to bandy the terms, throw it together. It ends up representing something it's not. But that's fascinating. I think you talked a little bit about being able to see what's behind the AI. And I think that's kind of part of those 11 criteria that you talked about. I think auditability, transparency would be kind of one of those things. I think we're beginning to go into some of the challenges, kind of pitfalls that we need to look out for. But I'm going to just put a pause on that and I'm going to ask Howard to jump in with some questions on his phone. I think he's got some interesting questions for you also.  Howard: Yeah, thank you, Bryan. So, Harris, you spoke about the open source initiative, which we're very familiar with, and particularly the kind of guardrails that they're putting around what open source should be applied to AI systems. You've got a separate foundation. What's your view on where open source should feature in AI systems?  Harish: It's exactly the same as what OSI says. We are making no difference because the moment you make a distinction, then you bifurcate or you completely fragment the entire industry. You need to have a single perspective and a perspective that everybody buys into. It is a hard sell currently because not everybody agrees to the various components inside there, but there is good reasoning for some of the challenges. But at the same time, if that conversation doesn't happen, we have a problem. But from AI Verify Foundation perspective, it is our code that we make. Our code, interestingly, it's not an AI tool. It is a testing tool. It is written purely to test AI solutions. And it's on an Apache license. This is a no-brainer type of licensing perspective. It's not an AI solution in and of itself. It's just taking an input, run through the test, and spit out an output, and Mr. Developer, take that and do what you want with it.  Howard: Yeah, thank you for that. And what about your view on open source training data? I mean, that is really a bone of contention.  Harish: That is really where the problem comes in because I think we do have some open source trading data, like the Common Crawl data and a whole slew of different components there. So as long as you stick to those that have been publicly available and you then train your models based on that, or you take models that were trained based on that, I think we don't have any contention or any issue at the end of the day. You do whatever you want with it. The challenge happens when you mix the trading data, whether it was originally Common Crawl or any of the, you know, creative license content, and you mix it with non-licensed or licensed under proprietary stuff with no permission, and you mix it up, then we have a problem. And this is actually an issue that we have to collectively come to an agreement as to how to handle it. Now, should it be done on a two-tier basis? Should it be done with different nuances behind it? This is still a discussion that is ongoing, constantly ongoing. And OSI is taking the mother load of the weight to make this happen. And it's not an easy conversation to have because there's many perspectives.  Bryan: Yeah, thank you, for that. So, Harish, just coming back to some of the other challenges that we see, what kind of challenges do you foresee the continued development of open source with AI we'll see in the near future you've already said we've encountered some of them some of the the problems are really kind of in the sense a man-made because we're a lot of us rushing into it what kind of challenges do you see coming up the road soon.  Harish: I think the, part of the the challenge you know it's an ongoing thing part of the challenge is not enough people understand this black box called the foundational model. They don't know how that thing actually works. Now, there is a lot of effort that is going into that space. Now, this is a man-made artifact. This piece of software that you put in something and you get something out or get this model to go and look at a bunch of files and then fine-tune against those files. And then you query the model, and then you get your answer back, a rag for that matter. It is a great way of doing it. Now, the challenge, again, goes back to because people are finding it hard to understand, how does this black box do what it does? Now, let's step back and say, okay, has physics and chemistry and anything in science solved some of these problems before? We do have some solutions that we think that make sense to look at. One of them is known as, well, it's called Computational Fluid Dynamics, CFD. CFD is used, for example, if you want to do a fluid analysis or flow analysis over the wing of an aircraft to see where the turbulences are. This is all well understood, mathematically sound. You can model it. You can do all kinds of stuff with it. You can do the same thing with cloud formation. You can do the same thing with water flow and laminar flow and so on and so forth. There's a lot of work that's already been done over decades. So the thinking now is, can we now take those same ideas that has been around for a long time and we have understood them and try and see if we can apply this into what happens in a foundational model. And one of the ideas that's being worked on is something called PINN, which stands for Physics Informed Neural Networks. So using physics, standard physics, to figure out how does this model actually work. Now, once you have those things working, then it becomes a lot more clearer. And I would hazard a guess that within the next 18 to 24 months, we'll have a far clearer understanding of what is it inside that black box that we call the foundational model. With all these known ways of solving problems that, you know, who knew we could figure out how water flows or how, who knew we could figure out how, you know, the air turbulence happens over a wing of a plane. We figured it out. We have the math behind it. So that's where I feel that we are solving some of these problems step by step.  Bryan: And look, I take your point that we all need to try to understand this. And I think you're right. That is the biggest challenge that we all face. Again, when it's all coming thick and fast at you, that becomes a bigger challenge. Before I kind of go into my last question, Howard, any further questions for Harish?  Howard: I think what Harish just came up with in terms of the explanation of how the models actually operate is really the killer question that everybody is poised with the work the type of work that I do is on the procurement of technology for financial sector clients and when they want to understand when procuring AI what the model does it they often receive the answer that it is a black box and not explainable which kind of defies the logic of what their experience is in terms of deterministic software you know if this then that you know ] find it very difficult to get their head around the answer being a black box box methodology and often ask you know what why can't you just reverse engineer the logic and plot a point back from the answer as a breadcrumb trail to the input? Have you got any views on that sort of question from our clients?  Harish: Yeah, there's plenty of opportunities to do that kind of work. Not necessarily going back from a breadcrumb perspective, but using the example of the PINN, Physics Informed Neuro Networks. Not all of them can explain stuff today. We have to, no one, an organization and a CIO who is worth their weight in gold should ever agree to an AI solution that they cannot explain. If they cannot explain, you are asking for trouble. So that is a starting point. So don't go down the path just because your neighbor is doing that. That is being very silly from my perspective. So if we want to solve this problem, we have to collectively figure out what to do. So I give you another example of an organization called KWAAI.ai. They are a nonprofit based in California, and they are trying to build a personal AI solution. And it's all open source, 100%. And they are trying really, really hard to explain how is it that these things work. And so this is an open source project that people can participate in if they choose to and understand more and at some point some of these things will become available as model for any other solution to be tested against so so and then let me then come back to what the verify foundation does we have two sets of tools that we have created one is to create One is called AI Verified Toolkit. What it does is if you have your application you're developing that you claim is an AI solution, great. Now, what I want you to do is, Mr. Developer, put this as part of your tool chain, your CICD cycle. When you do that, what happens, you change some stuff in your code. Then you run this through this toolkit, and the toolkit will spit out a bunch of reports. Now, in the report, it will tell you whether it is biased, unbiased, is it fair, unfair, is it transparent, a whole bunch of things it spits out. Then you, Mr. Developer, make a call and say, oh, is that right or is that wrong? If it's wrong, we'll fix it before you actually deploy it. And so this is a cycle that has to go continuously. That is for traditional AI stuff. Now, you take the same idea in the traditional AI and you look at generative AI. So there's another project called Moonshot. That's the name of the project called Moonshot. It allows you to test large language models of your choosing with some inputs and what outputs come up with the models that you are testing against. Again, you do the same process. The important thing for people to understand and developers to understand, and especially businesses to understand is, as you rightly pointed out, Howard, the challenge we have, this is not deterministic outputs. These are all probabilistic outputs. So if I were to query a large language model, like AAM in London, by the time I ask the question at 10 a.m. in Singapore, it may give me a completely different answer. With the same prompt, exactly the same model, a different answer. Now, is the answer acceptable within your band of acceptance? If it is not acceptable, then you have a problem. That is one understanding. The other part of that understanding is, it suggests to me that I have to continuously test my output every single time for every single output throughout the life of the production of the system because it is probabilistic. And that's a problem. That's not easy.  Howard: Great. Thank you, Harish. Very well explained. But it's good to hear that people are trying to address the problem and we're not just living in an inexplicable world.  Harish: There's a lot of effort underway. There's a significant amount. MLCommons is another group of people. It's another open source project out of Europe who's doing that. AI Verified Foundation, that's what we are doing. We're working with them as well. And there's many other open source projects that are trying to address this real problem. Yeah so one of the outcomes hopefully that you know makes a lot of sense is at some point in time the tools that we have created maybe be multiple tools can be then used by some entity who is a certification authority so to speak takes the tool and says hey Mr. company a company b, we can test your ai solutions against these tools and once it is done you pass we give you a rubber stamp and say you have tested against it so that raises the confidence level from a consumer's perspective, oh, this organization has tested their tools against this toolkit and as more people start using it, the awareness of the tools being available becomes greater and greater. Then people can ask the question, oh, don't just provide me a solution to do X. Was this tested against this particular set of tools, a testing framework? If it's not, why not? That kind of stuff.  Howard: And that reminds me of the Black Duck software that tests for the prevalence of open source in traditional software.  Harish: Yeah, yeah. In some sense, that is a corollary to it, but it's slightly different. And the thing is, it is about how one is able to make sure that you... I mean, it's just like ISO 9000 certification. I can set up the standards. If I'm the standards entity, I cannot go and certify somebody else against my own standards. So somebody else must do it, right? Otherwise, it doesn't make sense. So likewise, from AI Verify Foundation perspective, we have created all these tools. Hopefully this becomes accepted as a standard and somebody else takes it and then goes and certifies people or whatever else that needs to be done from that point.  Howard: Yeah and and we we do see standards a lot you know in the form of iso standards recovering almost like software development and cyber security again that also makes me think about certification which we're is seeing appear in European regulation. We saw it in the GDPR, but it never came into production as something that you certify your compliance with the GDPR. We have now seen it appear in the EU AI Act. And because of our experience of not seeing it appear in the GDPR, we're all questioning, you know, whether it will come to fruition in the AI Act or whether we have learned about the advantages of certification, and it will be focused on when the AI Act comes into force on the 1st of August. I think we have many years to understand the impact of the AI Act before certification will start to even make a small appearance.  Harish: It's one thing to have legislative or regulated aspects of behavior. It's another one when you voluntarily do it on the basis of this makes sense. Because then there is less of hindrance or less of resistance to do it. It's just like ISO 9000, right? No one legislates it, but people still do it. Organizations still do it because it's their, oh yeah, we are an ISO 9035 organization, And so we have quality processes in place and so on and so forth, which is good for those that is important. That becomes a selling point. So likewise, I would love to see something that right now, ISO 42001, which is all the series of AI-related standards. I don't think any one of them has got anything that can be right now be certified yet. Doesn't mean it will never happen. So that could be another one, right? So again, the tools that AI Verified Foundation creates and Mel Korman creates and everybody feeds into it. Hopefully that makes sense. I'd rather see a voluntary take-up rather than a mandated regulatory one because things change. And it's much harder to change the rules than to do anything else.  Howard: Well, I think that's a question in itself, but probably it will take us way over our time whether the market forces us to drive standardization. And we could probably have our own session on that, but it's a fascinating subject. Thank you, Harish.  Bryan: Exactly I think standards and certifications are possibly the kind of the next thing to look out for for AI you know Harish you could be correct. But on that note last question from me Harish so, interestingly the term you use moonshot right and so personally for you what kind of moonshot wish would you have for open source and AI. Leave aside resources, yeah if you could choose what kind of development would you think would be the one that you would look out for, the one that excites you?  Harish: I would rather that, for me, we need to go all the way back to the start from an AI training perspective, right? So the data. We have to start from the data, the provenance of the data. We need to make sure that that data is actually okay to be used. Now, instead of everybody going and doing their own thing, Can we have a pool where, you know, I tap into the resources and then I create my models based on the pool of well-known, well-identified data to train on. Then at least the outcome from that arrangement is we know the provenance of the data. We know how it was trained. We can see the model. model, and hopefully in that process, we also begin to understand how the model actually works with whichever physics related understanding that we can throw at it. And then people can start benefiting and using it in a coherent manner. Instead of what we have today, I mean, in a way, what we have today is called a Cambrian explosion, right? There are a billion experiments happening right now. And majority, 99.9% of it will fail at some point. And 0.1% needs to succeed. And I think we are getting to that point where there's a lot more failures happening rather than successes. And so my sense is that we need to have data that we can prove that it's okay to get and okay to use, and it is being replenished as and when needed. And then you go through the cycle. That's really my, you know, Mojoc perspective.  Bryan: I think there's really a lot for us to unpack, to think about, but I think it's really been an interesting discussion from my perspective. I'm sure, Howard, you think the same. And I think with this, I want to thank you for coming online and joining us this afternoon in Singapore, this morning in Europe on this discussion. I think it's been really interesting from a perspective of somebody who's been in technology and interesting for the ReadSmith clients who are looking at this from a legal and technology perspective. And I just wanted to thank you for this. And I also wanted to thank the people who are tuning into this. Thank you for joining us on this podcast. Stay tuned to the other podcasts that the firm will be producing, and I do have a good day.  Harish: Thank you.  Howard: Thank you very much.  Outro: Tech Law Talks is a Reed Smith production. Our producers are Ali McCardell and Shannon Ryan. For more information about Reed Smith's Emerging Technologies practice, please email techlawtalks@reedsmith.com. You can find our podcasts on Spotify, Apple Podcasts, Google Podcasts, reedsmith.com, and our social media accounts.  Disclaimer: This podcast is provided for educational purposes. It does not constitute legal advice and is not intended to establish an attorney-client relationship, nor is it intended to suggest or establish standards of care applicable to particular lawyers in any given situation. Prior results do not guarantee a similar outcome. Any views, opinions, or comments made by any external guest speaker are not to be attributed to Reed Smith LLP or its individual lawyers.  All rights reserved. Transcript is auto-generated.

Thanks to Sy and Finn for their suggestions this week! Further reading: Creeping Crinoids! Sea Lilies Crawl to Escape Predators, New Video Shows New and Unusual Crinoid Discovered Sea otters maintain remnants of healthy kelp forest amid sea urchin barrens Sea urchins see with their feet A sea lily [photo from this page]: A feather star [still from a video posted on this page]: Purple urchins [photo by James Maughn]: Show transcript: Welcome to Strange Animals Podcast. I'm your host, Kate Shaw. This week as we bring invertebrate August to a close, we're going to cover some animals suggested by Finn and Sy. We'll start with Sy's suggestion, crinoids, also called feather stars or sea lilies depending on what body plan a particular species has. We talked about them in episode 79 but it's definitely time to revisit them. Crinoids are echinoderms, a really old phylum of animals. Fossils of ancient echinoderms date back to the Cambrian half a billion years ago and they're still incredibly common throughout the world's oceans. Ancient crinoids had five arms the way many starfish do, which makes sense because crinoids are related to starfish. At some point each arm developed into two, so many crinoids have ten arms or even more, and many have arms that branch. The arms are used for feeding and have feathery appendages lined with sticky mucus that traps tiny bits of food floating in the water. There are two big divisions of crinoids today, the feather stars and the sea lilies. Feather stars are more common and can swim around as adults if they want to, although most stick to crawling along the sea floor. They swim by waving their feathery arms. Sea lilies look like flowers as adults, with a slender stem-like structure with the small body and long feathery arms at the top. I specify that sea lilies have stems as adults because a lot of feather stars also have stems as juveniles, but when they reach maturity they become free-swimming. Even though the sea lily looks like a plant, and some species even have root-like filaments that help it anchor itself to the sea floor or to rocks, it's still an animal. For one thing, it can uproot itself and move to a better location if it wants to, crawling with its arms and pulling its stem behind it, which is not something a plant can do except in cartoons. If a predator attacks it, the sea lily will even shed its stem completely so it can crawl away much faster. Since echinoderms in general are really good at regenerating parts of the body, losing its stem isn't a big deal. The biggest sea lilies today are deep-sea species, but even they only grow a stem up to about three feet long at most, or about a meter. This wasn't the case in the ancient past, though. The longest crinoid stem fossil ever discovered was 130 feet long, or 40 meters. Crinoids filter food particles from the water that flows through the feathery arms. Even though they look like feathers or petals, a crinoid's arms are actually arms. They have tiny tube feet on them that act sort of like fingers to help the crinoid hold onto pieces of food, and to do a better job of holding the food, the tube feet are covered with a sticky mucus. The mouth is in the middle of the arms on the top of the body. Crinoids absorb oxygen directly from the water. Its body contains a system of chambers and pores that are full of water, and by contracting special muscles, the crinoid moves water around in its body to transport nutrients and oxygen and to collect waste material. Crinoids are closely related to starfish, sea cucumbers, sand dollars, and sea urchins, which brings us to Finn's suggestion. Finn suggested urchins, which are also echinoderms. In fact, at the end of episode 79 I mentioned that one day I'd do an episode about urchins, and it only took me six years to get here! Many urchins look like living pincushions because they're covered in spines. That's where the name urchin comes from,

NASA's Perseverance Rover has found a fascinating rock on Mars that may indicate it hosted microbial life billions of years ago. Abigail Allwood, exobiologist at NASA's Jet Propulsion Lab, is on the team scrutinising the new Martian data. And a couple of newly discovered, approximately 500 year old fossils from the ‘Cambrian explosion' of complexity caught presenter Roland Pease's eye this week. First Martin Smith from Durham University tells us about a tiny grub that's ancestor to worms, insects, spiders and crustaceans. Then Ma Xiaoya, who has positions at both Yunnan University in China and Exeter University in the UK, tells us about a spiny slug that was also discovered in a famous fossil site in China. And the first sightings of the landscapes on the underside of the ice shelves that fringe Antarctica. These float atop the ocean around the frozen continent but effectively hold back the glaciers and ice sheets on the vast landmass. Their physical condition therefore is pretty critical in this warming world, Anna Wohlin of Gothenburg University tells us. Presenter: Roland Pease Producer: Jonathan Blackwell Production Co-ordinator: Jana Bennett-Holesworth (Image: NASA's Perseverance Mars rover taking a selfie on Mars. Credit: NASA/JPL-Caltech/MSSS)

In November 2023, Cambrian announced a $200 Million Growth Equity commitment from Pennybacker. That came along a bizarre move: Pennybacker was also acquiring Cambrian (for an undisclosed amount). So, what's to think of this? What factors led to this unconventional move? Why would I feel there's a bittersweet taste to it? What does it actually mean for other water tech companies? Why is it great news for early-stage water investors? And... who's Cambrian, if you never heard of them?Let's explore!More #water insights? Connect with me on Linkedin: https://www.linkedin.com/in/antoinewalter1/ #️⃣ All the Links Mentioned in this Episode #️⃣ Cambrian's Website: https://www.cambrianinnovation.com/ Pennybacker Capital's Website: https://www.pennybackercap.com/ My interviews with Matthew Silver (be kind; it was a while ago, and my sound quality dramatically improved in between

Prof. Andy Knoll is the Fisher Professor of Natural History at Harvard University. Andy has been a member of the Harvard faculty ever since, serving as both Professor of Biology and Professor of Earth and Planetary Sciences. Professor Knoll's research focuses on the early evolution of life, Earth's environmental history, and, especially, the interconnections between the two. For the past decade, he has served on the science team for NASA's MER mission to Mars. Professor Knoll's honors include the 2022 Crafoord prize, the Walcott Medal and the Mary Clark Thompson Medal of the National Academy of Sciences, the Phi Beta Kappa Book Award in Science (for his 2003 book Life on a Young Planet), the Moore Medal of the Society for Sedimentary Geology, the Paleontological Society Medal, and the Wollaston Medal of the Geological Society of London. Topics discussed: 00:00 - Introduction 01:36 - How do scientists know what happened billions of years ago? 05:17 - How scientists figured out the age of the planet 09:30 - 4.6 billion years ago - how stardust created a planet 15:14 - The probability of the earth and life existing 17:57 - Why earth can accommodate life 22:40 - What the earliest life on earth would've been like 29:46 - Photosynthesis and the Oxygen revolution 33:02 - How early life interacted with its environment 38:03 - How the oxygen revolution shaped the atmosphere 41:13 - 'The boring billion years' 44:19 - The Cambrian explosion 49:00 - The development of more advanced animals 51:30 - Andy's most mind-boggling part of the earths history 53:25 - How the major extinction events shaped earth 58:22 - The permian Triassic extinction 59:40 - The evolution of animals to humans 01:01:22 - Is the universe deterministic or stochastic? 01:03:20 - Connect with Andy 01:03:45 - What makes a life worth living? Buy Andy's book: https://www.amazon.co.uk/Brief-History-Earth-Billion-Chapters/dp/B08N2QBVYJ/ref=sr_1_1?crid=3B0ZS1FYJWHV7&dib=eyJ2IjoiMSJ9.nafWcNEL9ryJVvhwR8S4Onm8O9U7dbPb0BHcF6ReZZX6EIO1rjN4HfY8qN-58Hkq.1ZWBjgI3NyO0h_OH4pqyHS3xIE5XVoYdA1IEm891CRA&dib_tag=se&keywords=andrew+knoll&qid=1721587430&sprefix=andrew+knoll%2Caps%2C96&sr=8-1 Read Andy's academic work: https://scholar.google.com/citations?user=F6mLNzoAAAAJ&hl=en&oi=ao Connect with us: https://freedompact.co.uk/newsletter​ (Healthy, Wealthy & Wise Newsletter) twitter.com/freedompactpod Email: freedompact@gmail.com https://Tiktok.com/personaldevelopment

Complex life did not start in the Cambrian - it was there in the Ediacaran, the period that preceded the Cambrian. And the physical and chemical environment that prevailed in the early to middle Cambrian may well have arisen at earlier times in Earth history. So what exactly was the Cambrian explosion? And what made it happen when it did, between 541 and 530 million years ago? Many explanations have been proposed, but, as Paul Smith explains in the podcast, they tend to rely on single lines of evidence, such as geological, geochemical, or biological. He favors explanations that involve interaction and feedback among processes that stem from multiple disciplines. His own research includes extensive study of a site where Cambrian fossils are exceptionally well preserved in the far north of Greenland. Smith is Director of the Oxford University Museum of Natural History and Professor of Natural History at the University of Oxford.

On Episode 1453 - Brought to you by: Lasara Men's Health - 10% Off Weight Loss Therapy https://bit.ly/HARDFACTOR - and 10% Off Testosterone Replacement Therapy at https://Lasara.com/HardFactor Timestamps: (00:00:00) - Teasers!

Paleontologists constantly search for new species of fossilized creatures from the distant past to expand our understanding of the history of life on Earth.

Author, journalist and podcast host Kara Swisher reflects on her decades-long reporting career.To get the show every day, follow the podcast here.Tech journalist, author and podcast host Kara Swisher joins "First Mover" to reflect on her decades-long career reporting on some of the biggest technological advancements in recent history. "The internet was a major Cambrian explosion. This is a tiny one,” she said when asked if recent crypto progress is similar to the early days of the internet. Swisher also shares her thoughts on the tech founders she's interviewed over the years, what the future might look like, and a bitcoin wallet she lost years ago.-Consensus is where experts convene to talk about the ideas shaping our digital future. Join developers, investors, founders, brands, policymakers and more in Austin, Texas from May 29-31. The tenth annual Consensus is curated by CoinDesk to feature the industry's most sought-after speakers, unparalleled networking opportunities and unforgettable experiences. Register now at consensus.coindesk.com.-This episode was hosted by Jennifer Sanasie. “First Mover” is produced by Jennifer Sanasie and Melissa Montañez and edited by Victor Chen.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

500 million years ago, the world was a very different place. During this period of time, known as the Cambrian period, basically all life was in the water. The ocean was brimming with animals that looked pretty different from the ones we recognize today — including a group of predatory worms with a throat covered in teeth and spines. Researchers thought these tiny terrors died out at the end of the Cambrian period. But a paper published recently in the journal Biology Letters showed examples of a new species of this worm in the fossil record 25 million years after scientists thought they'd vanished from the Earth. One of the authors of the paper, Karma Nanglu, tells us how this finding may change how scientists understand the boundaries of time. Curious about other weird wonders of the ancient Earth? Email us at shortwave@npr.org.Learn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy

Sergey Litvinenko, Co-Founder & CEO, Koop joined Grayson Brulte on The Autonomy Economy podcast how autonomy could transform insurance economics. As autonomous vehicles and robotic automation spread across industries, a massive new risk ecosystem is emerging that will require innovative insurance solutions. In a fascinating podcast interview, Sergey Litvinenko, co-founder & CEO of insurtech pioneer Koop, provided rare insights into how his company is leading the charge in underwriting this technological transformation.Traditional insurance carriers have been hesitant to dive into insuring autonomy risks like self-driving cars and warehouse robotics due to a lack of data and technical expertise. As Sergey explained, “If you can't model the risk, you can't underwrite it profitably.” This knowledge gap has created a massive greenfield opportunity for insurtechs focused specifically on robotics and AI.Koop has developed proprietary systems that ingest and analyze real-world sensor data from robots and autonomous vehicles to precisely model their behavior and safety performance. Using this cutting-edge approach, Koop has achieved stellar underwriting results, with loss ratios under 5% for its robotics book – compared to 70%+ for traditional P&C lines.This lucrative capability is allowing Koop to rapidly scale and cement its position as the dominant player in the burgeoning autonomy insurance market. Sergey believes large incumbents will be forced to partner with or acquire specialist providers like Koop rather than build expertise in-house. He forecasted robotics insurance could be a “tens of billions” dollar market delivering 30%+ underwriting profits.As AI ushers in a “Cambrian explosion” of robotic use cases across industries, demand for intelligently underwritten insurance solutions will skyrocket. Koop is uniquely positioned with the technical foundations, proprietary data, and risk modeling skills to capture this unprecedented opportunity.In Sergey's words, “When you intersect tens of billions of deployed robots with insurance where you can deliver 30% annual returns…it just makes me very excited about the space.” The autonomy economy is materializing rapidly – don't be surprised if the pioneering innovators insuring this revolution turn out to be young insurtechs like Koop rather than industry giants.Listen to the full podcast for more fascinating insights from Sergey Kravchenko on the future of autonomy insurance. The robotics risk market is open for disruption – will your company be leading or following?Recorded on Tuesday, March 26, 2024Episode Chapters0:10 2024 Insurance Market Outlook5:14 Cyber Security Insurance9:40 Underwriting Autonomous Vehicles and Trucks35:36 How Companies Should Prepare for Autonomy and Automation 37:44 AI Impact on Insurance 40:26 The Evolving Underwriting Markets for Autonomous Vehicles 42:34 Key Take Aways--------About The Road to AutonomyThe Road to Autonomy® is a leading source of data, insight and commentary on autonomous vehicles/trucks and the emerging autonomy economy™. The company has two businesses: The Road to Autonomy Indices, with Standard and Poor's Dow Jones Indices as the custom calculation agent; Media, which includes The Road to Autonomy and Autonomy Economy podcasts as well as This Week in The Autonomy Economy newsletter.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Juliette is a Korean American neuroscientist, adjunct professor, writer, and CFO/COO of a biotech company called Cambrian. On her socials, she offers uplifting advice and insights related to a variety of topics including communication, health, emotions, and networking. As a woman of color in spaces where representation is typically imbalanced, Juliette encourages us all to tap into our worth and our potential, and she herself is an inspiring example of breaking through boundaries. I learned a lot in this conversation, especially regarding women's health and the intersectionality of race, data, and healthcare. True to being a neuroscientist, Juliette emphasizes collecting our own personal data from as early as possible, so that we can understand our own bodies and selves and strive to be as healthy and well as possible.  The lessons in this episode are analogous to the idea that we must first put on our own oxygen mask if we want to be able to help anyone else. When we ourselves are steady and clear, we will be that much more able to be of service to the collective. Please enjoy the very vibrant — Juliette. Juliette's IG Juliette's substack Leah's IG

This year marks the 40th anniversary of a lot of landmarks in pop culture, especially sci-fi and fantasy. So many franchises were born in 1984. Some came to define the genre or invent new genres. The great podcast Imaginary Worlds noticed this and produced a three-part series about 1984's Cambrian explosion of creativity that  landed on the big screen, the small screen, bookstore shelves and, of course, the toy store.In this episode we learn about at two iconic franchises that launched in 1984: Transformers and Teenage Mutant Ninja Turtles. They came from opposite ends of the business spectrum. Transformers was a top-down marketing synergy between American and Japanese toy companies along with Marvel Comics to compete against He-Man -- another TV toy behemoth. Teenage Mutant Ninja Turtle would eventually rival them in cultural dominance, but it began with two indie comic book creators making a black and white comic as a lark. But Turtles and Transformers both ended up wrestling with similar questions around what happens when you put the cart before the horse in creating content to sell products.Toyetic

Zoey Tang and Rocky Gowni sat down with Rex Salisbury and Nik Milanovic. In today's episode, we discussed -Latest updates from This Week in Fintech and Cambrian -Nik and Rex's experiences as solo GP -Their perspectives of the latest fintech ecosystem and what to look forward to in 2024 About Nik Milanovic & This Week in Fintech Nik is the founder of This Week in Fintech. He has been a key organizer in building the fintech community, bringing together fintech enthusiasts all over the world. Last year, he also started the Fintech Fund as the solo GP. About Rex Salisbury and Cambrian Rex is the Founder & Solo GP @ Cambrian VC. He started Cambrian previously in 2016 to cultivate a community focused on founders and builders in fintech. His community building had accidentally led him into the world of venture, as he ultimately became a founding member of a16z's fintech practice, investing in companies such as Tally, Deel, and Oyster Technology. For more FinTech insights, follow us on: WFT Medium: medium.com/wharton-fintech WFT Twitter: twitter.com/whartonfintech WFT Instagram: instagram.com/whartonfintech Zoey's LinkedIn: https://www.linkedin.com/in/zoeytang1007/ Previous episodes with Nik & Rex: -https://medium.com/wharton-fintech/nik-milanovi%C4%87-founder-of-this-week-in-fintech-and-general-partner-of-the-fintech-fund-on-9023790f242c -https://medium.com/wharton-fintech/podcast-with-rex-salisbury-founder-of-fintech-devs-and-pms-e40869eb4de1

From the astonishing evolutionary advances of the Cambrian explosion to our present-day computing revolution, theoretical biologist Stuart Kauffman believes he can explain the trend of dramatic growth after periods of stability through what he calls the theory of the "adjacent possible." Tracing the arc of human history through the tools and technologies we've invented, he explains the impact human ingenuity has had on the planet -- and calls for a shift towards more protection for all life on Earth.