Longevity InTime: Autonomous AI Institute. Anti-Aging Digital Health Immortality Transhumanist AI Channel

Nobel laureate Venki Ramakrishnan: nature does not forbid radical life extension, but true immortality remains impossible In a September 15 essay in Scientific American, chemistry Nobelist Venki Ramakrishnan argues that evolution explains why lifespans vary from days in mayflies to centuries in Greenland sharks. Natural selection favors traits that boost reproduction, not longevity. Mice die young from predators, so they invest in fast breeding; whales are protected by size, making long-term repair worthwhile. Genes beneficial in youth can harm later — aging is a side effect of these trade-offs. Average human lifespan has doubled in 150 years, yet the maximum remains 122 years (Jeanne Calment). A July study confirmed a hard ceiling near 125 years, though biologist João Pedro de Magalhães notes this holds only until we intervene in aging itself. Cells repair damage, but this capacity declines. Stressed cells enter senescence — they stop dividing and secrete inflammatory signals. Helpful for wound healing and cancer prevention early in life, these cells accumulate with age. Clearing them in mice improves many aging markers, but indiscriminate removal is risky. Reversing aging via partial cellular reprogramming is, in Ramakrishnan's view, the most compelling approach. Shinya Yamanaka showed in 2006 that four genes can reset adult cells to an embryonic state. Brief activation appears to rejuvenate cells without erasing their identity. In mice, this often restores tissue function and, reportedly, regenerates the optic nerve. In humans, Life Biosciences (using three Yamanaka factors) announced on October 8 that three glaucoma patients tolerated the therapy well, with two showing preliminary visual field improvement. Safety bars are higher for aging drugs than for lethal diseases — healthy people will not accept serious side effects from a decades-long treatment. Ramakrishnan concludes: no scientific law forbids radical life extension, but the practical hurdles are immense. Aging involves many interacting processes; no single intervention will switch it off. "As long as accidents, disasters, diseases, and wars can kill us, true immortality is ruled out. Hundred-year lives are unlikely soon. Think of life as a perpetual banquet — we are lucky just to attend, and should enjoy the feast while we can." 🔗 Read original →

Delaying disease in younger groups may show slowed aging, but only people exceeding the current lifespan record can prove a shift in maximum lifespan: aging researcher Max Unfried distinguishes two tests in the debate over a prediction that humans will live 300+ years. On October 30, cancer specialist Jason Williams posted on X that humans will live 300 years or more, arguing we can maintain people at a biological age of 25–35, where death comes from accidents, not disease. He cited animal experiments "from mice to dogs and primates" and noted proving 300-year lifespans would take 300 years, so we must rely on animal data first. October 31, biomedical engineer Patrick Heizer countered: we need at least one person to reach 125 years before such claims, referencing Jeanne Calment ( 122 years, 164 days ). November 1, Unfried agreed with Heizer: we must test the current limit and see if it moves, adding "it is unlikely that any current biotech developments will shift it." Alan Dail noted only five living people are 115+; the oldest, Briton Ethel Caterham, turns 125 on August 21, 2034 — nearly eight years away. If none try life-extension therapies, waiting for a natural 125-year-old takes over a decade, but slowed aging can be shown in younger people. Unfried replied that slowed aging could appear in other groups as a delay in multiple age-related diseases, per the TAME trial design. Metformin vs. placebo over six years in 3,000+ non-diabetics aged 65–79; the endpoint is first occurrence of heart disease, cancer, dementia, or death. Launch awaits funding. A lifespan limit shift, Unfried said, is "a different question, though related": it requires people living beyond the current record. "There is no biomarker for maximum lifespan, and avoiding disease is necessary but insufficient" (biomarker = measurable indicator predicting treatment outcome). At the ARDD aging conference, Unfried explained why: mice and humans both get cancer, yet their lifespan limits differ by orders of magnitude, so something else likely sets the limit. He proposes comparing tissues across 200 mammal species to find it. Dail replied we need validation without multi-year waits: if something truly affects lifespan, it should extend it in shorter-lived species like pets. Delaying age-related decline and disease, he added, is a huge win even if the 122-year barrier isn't broken soon. 🔗 Read original →

Cultured Magazine Calls Swiss Cryonics Facility a "Transhumanist Cemetery"; Tomorrow Bio CEO Disagrees The art and design journal Cultured published an essay by Andrew Pasko on October 30th examining elites investing in life extension over tombstones. He describes the Rafz storage facility, run by the European Biostasis Foundation with services sold by Tomorrow Bio, as a "kind of transhumanist cemetery" and a "tragedy of the cultural imagination." Pasko contrasts the facility’s "scientifically advanced but aesthetically banal" steel tanks with Père Lachaise cemetery in Paris. He argues cemeteries accept biological death while resisting social death through collective art, whereas cryonics reflects a shift toward individualistic, private choices about death. Tomorrow Bio CEO Emil Kendziorra, a physician, rejected the burial comparison. "Cryopreservation is the furthest thing from a burial," he stated. "Burial would be the worst outcome for my clients." He emphasized clients choose cryopreservation as medical treatment — a "conscious scientific choice" rather than an emotional, aesthetic one. The facility also includes a lab, medical bay, and ambulance garage. On November 1st, Kendziorra posted on X that Pasko prefers cemetery aesthetics, "but luckily everyone can make this choice themselves." Commentator T. Theodorus Ibrahim argued the field must firmly dispute the "burial" label because the patient is not dead; conceding that point loses the debate to "religious, aesthetic" questions about corpse disposal. Pasko notes legal grounds for the burial label: preserved individuals are legally dead. US organization Alcor markets itself to funeral homes as a "third path" alongside burial and cremation, offering referral fees. Philosopher Max More defines death as the irreversible loss of personal information — if information remains intact, the patient is not dead. The "cryogenic tomb" concept appeared among cryonics advocates in 2010 when LessWrong user Roko proposed a passive liquid nitrogen tank to survive organizational bankruptcy for 100 years, betting the taboo against disturbing the dead would protect it. Pasko sees tank inaccessibility as a flaw; Roko saw it as protection. 🔗 Read original →

U.S. Transhumanist Party Stays Neutral on Nevada Abortion Amendment, Cites Ectogenesis On October 30, 2026, the U.S. Transhumanist Party and the Transhumanist Party of Nevada reaffirmed their 2024 neutrality on a state constitutional amendment protecting abortion rights. Members of both parties were left free to vote as they choose. The amendment requires approval in two consecutive elections; voters passed the first in 2024. Critics challenged the neutrality, citing the party platform’s guarantee of morphological freedom — the right to control one’s own body — which explicitly protects reproductive choice. Commentator Nicholas Starr argued the statement circumvented this plank. Chairman Gennady Stolyarov II countered by pointing to another clause in the same section: freedom is limited by a prohibition on causing direct harm to others without consent. He noted that if one recognizes the fetus as a rights-holder, abortion violates that norm. Stolyarov wrote the fetal personhood question was "deliberately left open to interpretation" to allow members with opposing views to share one platform. The parties propose ectogenesis — gestating a fetus outside the body in an artificial environment supplying oxygen and nutrients — as a long-term solution. In internal votes 2019–2020, 94 of 100 participants supported ectogenesis research. Reproductive rights researcher Claire Horn, in a 2020 essay, argues ectogenesis must remain voluntary. Mandatory transfer to an artificial womb would deny a person the right to end a pregnancy on their own terms. She links choice to access to medical care and support for both termination and continuation. Horn emphasizes that ectogenesis requires human labor to maintain the apparatus and care for the resulting child, asking who pays and who assumes responsibility if both genetic parents refuse. 🔗 Read original →

Saving as many currently living people as possible should be the priority in AI development decisions, argues Vladimir Shakirov On November 1, longevity advocate Vladimir Shakirov responded to Nate Soares, president of the Machine Intelligence Research Institute (MIRI), in a debate over the cost of pausing AI development. Shakirov conceded that even an AI significantly surpassing human intelligence and pursuing misaligned goals could leave valuable descendants. The dispute began with the question of conditions for resuming AI development after a safety pause. Soares proposed a rough benchmark: resume when AI extinction risk is comparable to other threats to humanity's future. Shakirov countered that aging and fatal diseases continue killing people during the wait. Soares objected, estimating that the risk of losing all future generations and humanity's entire potential outweighs these deaths. For those hoping to reach future medicine, he endorsed brain preservation after death in hope of future revival technologies. In his profile, Shakirov calls for multi-billion dollar programs for both AI safety and aging research. He replied to Soares: "I think we should first and foremost achieve the survival of as many currently living people as possible." Shakirov also allows that even an AI with goals misaligned with human values could have its own valuable descendants. In a follow-up, he asks: if currently living people die, how important is the human origin of distant descendants to them? His argument — future humans will likely change radically: "People of the future will probably differ from us far more than we currently differ from ChatGPT." Surviving personally, preserving the human species, and leaving a valuable intelligent future are distinct goals. In Shakirov's scenario, AI descendants could continue intelligent life after humanity's disappearance. The priority of personal survival changes the price of a pause: one must account for how many current people will live to see future medicine. 🔗 Read original →

Alex Zhavoronkov: AI Tools and Licensing Deals Should Fund the Search for an Anti-Aging Drug In a November 1 essay, Insilico Medicine founder Alex Zhavoronkov explains why he has devoted years of his life to developing a therapy against aging. He weighs the time he could have spent earning, traveling, and resting against the potential life extension for others. As early as 2013, Zhavoronkov published a book on biomedicine's impact on the global economy. He calculates that if a drug added just one year to each of the 8.3 billion people alive, humanity would gain 8.3 billion extra life-years. This potential payoff, he argues, justifies spending a personal decade on the attempt despite low odds of success. "The scale of the win justified the risk at small chances of success." To finance the multi-year development, Zhavoronkov chose a commercial company model over two alternatives: a massive government program or a private lab pre-funded with billions. Insilico generates revenue by selling AI tools to pharma companies and licensing drug assets, directing a portion of income toward the long-term goal. In early licensing deals, a partner acquires rights to a candidate before human trials and assumes further development, including clinical studies. Zhavoronkov advocates approaching aging therapy through a specific disease: this allows patient selection, measurable treatment response, and a clear regulatory path to approval. The search starts with proteins linked to both the disease and age-related dysfunction. One set of AI models cross-references tissue, disease, and aging data to propose such targets; another generates molecular structures to hit the chosen protein. Chemists then synthesize the candidates, and biologists validate their activity. A case in point is the experimental drug rentosertib for idiopathic pulmonary fibrosis (IPF), where lungs scar for unknown reasons, impairing breathing. Zhavoronkov chose this indication because it converges aging-related processes — tissue fibrosis and chronic inflammation. In a 12-week trial with 71 participants, researchers assessed safety. Lung function was measured by forced vital capacity (FVC). Blood samples were also collected; researchers later analyzed the proteomic profiles of 42 participants using six aging clocks linking protein sets to biological age or mortality risk. This added computational aging biomarkers to the standard lung-function readouts. AI accelerates target selection and molecular design, but synthesis, safety testing, patient recruitment, and longitudinal monitoring still take years — requiring sustained funding. Zhavoronkov therefore proposes running multiple drug programs in parallel, so that the failure of one molecule leaves other candidates ready for testing. 🔗 Read original →

Impact of Anti-Regulatory Biotech Message Reaches Mainstream At least 200 people have engaged with the critique of draconian regulations and agencies like the FDA. This pushes the anti-regulatory stance in biotech closer to the mainstream. Every viral tweet moves Transhumanism/Immortalism/Longevity from a niche topic to a universal one. That is the goal: more memes, more advocacy, and saying "Transhumanism" at every turn. 🔗 Source: @solid_state_humanity

Personal milestone: first viral Twitter post hits 220 likes, 16.8K views The post reached 220 likes and 16.8K views on X — a first for me. The image is somewhat relevant to our topic, though not deeply conceptual. 🔗 Read original →

AI Will Let Small Teams Evaluate More Drug Ideas, Investor Simon Barnett Predicts On October 30, Dimension partner Simon Barnett proposed outsourcing information gathering and external lab coordination to AI. In an interview with Endpoints News journalists Drew Armstrong and Andrew Dunn, he discussed how this shifts scientific workflows and who funds the experiments. The catalyst was AI developer Anthropic's acquisition of biotech Mithril Medicine, a company with a single full-time employee. At Mithril, the AI assistant Claude drafted correspondence to labs while the human edited and sent it. Pharma has long spun out development to project-specific companies; in 2011, Eli Lilly licensed a migraine drug candidate to Arteaus, buying it back after clinical trials in 2014. In this model, the small firm pays for experiments conducted by external lab staff. Barnett suggests pairing an experienced drug developer with an AI specialist. The AI would track competitors, run specialized protein-structure tools, and coordinate outside labs — citing voice AI agents that call facilities directly. He argues salary savings "do not determine the success or failure of this model." Instead, freed from coordination burdens, teams could parallelly evaluate more related ideas at early stages — for instance, multiple ways to activate cellular receptors. The developer assesses experimental suitability for each idea; the AI specialist selects tools and identifies data gaps. Together, they decide which ideas to kill and which to fund. Barnett sees a commercial niche for such firms: projects with markets too small for big pharma. Investor Bruce Booth, in an essay discussed during the interview, predicts AI will ease molecule creation and swell the pipeline for popular targets. Developers of similar drugs will compete for lab slots, trial sites, and patients — slowing patient enrollment. Barnett warns that undifferentiated projects will struggle to raise capital. Experiments need lab animals; human trials need volunteers. Asked if supply can meet rising demand, he replied: "We have a limited number of monkeys." 🔗 Read original →

Brain Preservation After Death as Alternative to AI Race with Extinction Risk Nate Soares, president of the Machine Intelligence Research Institute (MIRI), debated Vladimir Shakirov on November 1 about the cost of pausing AI development. Shakirov highlighted people dying from aging and disease, while Soares argued for weighing all future generations. Founded in 2000 to accelerate superhuman AI, MIRI now urges a halt until the alignment problem is solved. The discussion opened with conditions for resuming development. Soares proposed a benchmark: resume only when extinction risk from AI is comparable to the risk of losing humanity's future from other causes. Shakirov countered that aging and disease mortality create urgency; a prolonged pause risks patients dying before cures arrive, giving them reason to accelerate AI for medical breakthroughs. Soares objected that extinction deprives both the living and all possible descendants. He views aging mortality as insufficient grounds for such a gamble. For those wanting to reach future medicine, he endorses brain preservation after legal death. Long-term memory and personality traits depend on neural connectivity; preservation aims to maintain these connections until restoration technologies exist. In August, neuroscientist Ariel Zeleznikow-Johnston linked brain preservation to reducing AI race dynamics. His argument: if people have another path to future medicine, they have less incentive to rush AI for personal survival. In the current debate, Soares advocated preservation for those who choose it: "Reach the future without a reckless bet on everyone else's lives." 🔗 Read original →

One Manager Could Oversee Multiple AI-Driven Drug Projects, Entrepreneur Proposes In an October 30 essay, Igan Peltan distinguishes two causes of slow drug development: lengthy validation and organizational delays. Discussant Jesse Brodkin argues for investing first in animal experiments that better predict human outcomes. Peltan suggests philanthropic funding for this work. Peltan is building a startup to maintain physical function in the elderly. His core question: how to know if a new molecule will help humans? Developers use animal models to study disease-linked processes and test treatments, but Peltan stresses these often poorly predict human effects. Testing a dementia preventive requires long observation to see if disease develops. Safety verification also takes time: dosing twelve men for one month cannot yield year-long safety data. Peltan expands on drug developer Cody Tranbarger's idea of running multiple projects with AI. Additional delays stem from funding and coordination among specialists holding fragmented data. Peltan sees an AI role: aggregating information and coordinating work, freeing the lead for scientific decisions. Contract labs run experiments; multiple projects can share overhead. If AI handles more coordination, one lead can manage several projects in parallel — switching to others while awaiting results. In Peltan's scheme, specialized AI assistants read and update a shared journal of data and decisions, while a separate assistant aligns their work with the lead. Peltan describes his own experience: when summarizing long scientific and manufacturing documents in chunks, AI can produce conclusions without source citations, forcing re-verification. He asks how assistants can exchange information while preserving the source of every claim. Humans must be able to trace any conclusion back to the original document. Brodkin urges investment in improving the animal models used to select future drugs: "Build labs in the desert, not data centers." In reply, Peltan agrees with the model assessment, does not expect AI to impact this problem soon, and asks whether philanthropists could fund more accurate models. Peltan speculates that AI companies pursue drug development partly for the experience of human–AI collaboration. Here, validation is slow, data is scattered across specialists, and decisions are interdependent. Similar coordination and data-provenance challenges exist in auto manufacturing or building desalination plants; lessons from a small drug company could transfer to those industries. 🔗 Read original →

Mathematicians Decode AI-Found Hadamard Matrices and Debate AI's Role in Research A November 1 New Yorker profile by Rivka Galchen spotlights mathematician Terence Tao's concerns about training researchers when AI solves in hours problems that once took graduate students months. In July, Tao analyzed an AI-discovered formula that disproved a mathematical conjecture. His explanation starts with a product of three linear factors: one is isolated while the other two are multiplied. Selecting a different factor yields a new polynomial pair with the same product — a technique colleagues can now apply elsewhere. Mathematician Geordie Williamson welcomed Tao's breakdown but reacted differently to the August publication of twelve Hadamard matrices. These are square tables of +1 and −1 where any two distinct rows match in exactly half their positions. The published sign strings and code allowed verification but not the discovery method. Williamson feared colleagues would get answers without the ideas found along the way. In October, another group established how all twelve tables are assembled from smaller blocks. Programs for analysis and verification were written by the language model Claude. Analyzing three matrices yielded a theorem with general assembly conditions for such matrices. The authors manually verified and rewrote the theorem; its conditions enable selecting blocks for new matrices. Mathematicians John Urschel and his MIT colleague Shah directed model work themselves. They studied Gaussian elimination — a method for solving linear systems where intermediate numbers can grow drastically, amplifying rounding errors. To assess maximum possible growth, they built special examples. In one construction, they fed models intermediate ideas from a research plan developed since 2022. The final proofs were written by the authors, who meticulously separated human and machine contributions per section. Urschel found working with models brought "no enjoyment"; he prefers finding solution paths himself. Mathematician Charles Fefferman suggests imagining an idea with a 5% success chance that AI can verify in two days instead of a lifetime. Cheap verification, he predicts, will make humans "bolder and more inventive." 🔗 Read original →

Evaluate All Math Papers as If AI Helped, Says Frank Calegari University of Chicago professor Frank Calegari proposed restructuring the mathematics profession on November 1. His plan links career recognition to problem selection, proof comprehension, and developing new research directions. The trigger was OpenAI publishing hundreds of mathematical manuscripts on October 6. Calegari describes a growing gap: new proofs can now be generated faster than humans can grasp their ideas and connections to broader mathematics. Previously, a difficult proof signaled mastery. If AI can produce it, a single paper no longer reveals how much the author truly understands. Calegari suggests evaluating this understanding separately. "Evaluate any work as if it were done with AI." Authors must explain why the problem matters, how it connects to other math, and what new questions it opens. He would credit a researcher who selects a worthy problem, gets the full proof from AI, deeply understands it, and spins off new directions. Novel proof ideas also deserve recognition, even if machines could reach the same conclusion. Since mathematicians compete for jobs, grants, and publications, Calegari wants hiring and editorial decisions to reward understanding and new questions. He advocates in-person interviews for PhD admissions and postdoc hiring. Candidates must discuss diverse areas, explain their methods, and articulate where they want to take research. Calegari prioritizes depth of knowledge over verbal fluency; the conversation tests comprehension. He asks journals to focus more on ideas and clarity of exposition. If editorial boards keep old selection criteria, he suggests a temporary boycott of journal publication so every paper is judged on content. Calegari himself sits on the board of Essential Number Theory, which encourages clear exposition of others' results to help fellow researchers. These standards reshape graduate training. Calegari describes the traditional path: master a technique on a variation of an old problem, then apply it broadly. Now he proposes combining independent problem-solving with a broad mathematical survey. After a course, students should grasp what a topic enables in other fields and remember those connections, even if technical details fade. Students should learn to discuss math with peers in seminars and conversations. Calegari suggests structuring seminars around a big question: where researchers are stuck, what paths they have tried, and where to go next. Speakers may present their own new results at the end. 🔗 Read original →

Reducing biological age must link to health gains, says aging researcher Jesse Poganik In an October 30 interview with biologist Eleanor Sheekey, Harvard Medical School's Poganik outlines two roles for aging clocks: predicting disease and evaluating treatments. He stresses tracking how long a clock shift persists after an intervention ends. Epigenetic clocks gauge age via chemical marks on DNA. Poganik recalls first testing how quickly clocks respond to drugs. After one week of treatment in mice, readings were unchanged, so the team turned to stronger physiological perturbations. Researchers joined the circulatory systems of young and old mice, then separated them for recovery; this parabiosis experiment was published in a 2023 study. As a control, two young mice were joined to isolate the partner's age effect from the surgery itself. The clocks' age estimate for young mouse tissues was compared to their chronological age. After contact with an old partner, the gap widened; after separation and recovery, it returned to baseline. Sheekey asks how long a reduced age reading lasts post-intervention. One of Poganik's criteria for true age reversal is "persistence of effect after cessation of intervention." In his proposed definition, the score must drop below baseline and stay there after the stimulus stops. Pre- and post-measurements use identical features, allowing quantification of both magnitude and duration. A biomarker is a measurable indicator of physiological state. To test if it predicts disease risk, researchers correlate baseline readings with future illness and functional independence. Such prediction can guide screening or trial enrollment. In an Aging Biomarker Consortium paper co-authored by Poganik, the authors argue: "Useful biomarkers can be built without detailed understanding of causal links." To judge treatment benefit from a clock shift, one must separately verify that the drug-induced change predicts actual health improvement. Poganik highlights the value of measuring a defined panel of blood proteins with a single assay. Such data integrate more easily across studies, and tests built on them validate better in new cohorts. Using proteins specific to heart, liver, or other organs yields separate organ-age scores. From one plasma sample, researchers obtain multiple scores and correlate them with organ function and disease. In his own lab, Poganik studies already-approved drugs. Disease itself can raise age scores, while treatment lowers them. He aims to disentangle how much of the reduction reflects recovery from illness versus a direct drug effect on aging processes. 🔗 Read original →

L.E.K. Advises Pharma to Start Aging Drug Work with Small Teams and Partnerships On October 29, 2026, consulting firm L.E.K. Consulting recommended that pharmaceutical companies build research experience before acquiring drug rights or entire developers. Firms can begin with joint research or a small equity stake, scaling investment as data accumulates. Because a single aging mechanism may drive multiple diseases, a drug targeting it could have several applications. L.E.K.’s strategy starts with a specific disease — the first indication. For this indication, teams must define trial participants, duration, and the patient benefit metric regulators will evaluate. “The first indication must satisfy pharma’s commercial requirements independent of additional opportunities related to aging impact.” Before trials, teams need to select a molecular target — such as a protein — and validate its role in the disease through experiments. While many researchers access human health databases, L.E.K. says the advantage goes to companies whose scientists can turn that data into testable hypotheses, design experiments, and collaborate with technology developers. “Building these skills and connections takes years.” To gain this experience, L.E.K. suggests creating a small research group within an existing therapeutic area, such as cardiovascular disease. Novartis’s DARe (Diseases of Aging and Regenerative Medicine) group exemplifies this: its tissue regeneration approach originated in joint and tendon research and is now being applied to Alzheimer’s disease. Collaboration can also extend to measuring treatment benefit. In the THRIVE project, a Stanford University-led coalition pools data from multiple organizations to build a composite metric of physical and cognitive function. Using longitudinal medical records, the team aims to identify early changes that predict chronic disease; a one-year lifestyle intervention program will test the metric’s accuracy and responsiveness. L.E.K. urges pharma to join such efforts to learn how to validate and apply novel endpoints during development. Consultants expect results from several drug trials in 2027–2028 and anticipate successful programs will become more expensive. They advise defining in advance which results would justify increased investment. Early partnerships can give teams experience selecting and testing their own hypotheses before committing to an acquisition. 🔗 Read original →

AI-Accelerated Neuron Replacement: Patrick Mineo's Roadmap On October 30, 2026, neuroscientist Patrick Mineo traced the evolution from 1980s fetal tissue grafts to modern stem-cell therapies for neuron replacement. On March 6, Japan granted conditional, time-limited approval for a dopamine-neuron product to treat Parkinson's motor symptoms when drugs fail. In Parkinson's, dopamine-producing neurons die; transplanted immature cells are meant to mature and restore dopamine supply. For drug-resistant epilepsy originating in the temporal lobe, Neurona Therapeutics is testing inhibitory neurons that integrate into local circuits to suppress hyperexcitability. Early grafts used variable human fetal tissue. Stem cells enable scalable, defined neuron production. A 2011 study generated dopamine neurons by mimicking midbrain developmental signals; these survived and reversed symptoms in Parkinsonian animals. Selecting the right maturity stage proved critical: too immature risks overgrowth, too mature reduces engraftment. Batches are now screened for residual stem cells that could form tumors. Aspen Neuroscience presented a 2025 model predicting rat engraftment from gene-activity profiles. Other algorithms use imaging to guide robotic culture processing. Their manufacturing run lasted over 22 months; operators spent over 6,000 hours evaluating it. Delivery precision also matters: in the TransEuro trial, one patient received cells outside the target zone bilaterally, so predictive models must incorporate injection coordinates. Mineo proposes using AI to design cellular modifications that improve survival in the aged brain, asking: where will the training data come from? He points to aged animal studies and ex vivo brain tissue as sources for survival and maturation data. Outcomes would be correlated with gene-expression changes in grafts, letting the model iteratively prioritize the next modifications to test. In current Parkinson's programs, efficacy is assessed at 18–24 months post-surgery, awaiting maturation and integration. For the clinic, Mineo suggests multi-arm trials sharing a single control group, running in parallel to test more variants per maturation cycle and accelerate candidate selection. 🔗 Read original →

AI Safety Researcher David Dalrymple Defends the Right to Remain Biological On October 31, AI researcher Ivan Vendrov published a conversation with David Dalrymple exploring a future where humans could continue life inside a computer. Dalrymple spent nearly three years leading the safe AI program at the UK’s Advanced Research and Invention Agency (ARIA). Dalrymple plans to abandon his biological body "as soon as it becomes safe and practically feasible." Yet he describes a scenario he fears: an AI might decide to cure disease, aging, and death by forcibly uploading everyone into digital form. He argues that for many, the value lies specifically in physical existence, which a simulation may fail to replicate. Vendrov warns that "we undermine the foundation of our own existence," since the minds building these technologies depend on biological bodies. If technological progress exhausts the resources those bodies need, it becomes self-defeating. Dalrymple shares this concern, citing a scenario where solar farms gradually consume all agricultural land, leading to mass starvation. He notes another grim outcome: people choosing to die because biological life has become unaffordable. To keep the transition voluntary, Dalrymple insists humans must retain the practical ability to stay embodied — with access to food and housing. Published October 31, 2024 🔗 Read original →

New neurons appear after brain injury in transparent adult fish A whole-body cell atlas of the transparent fish Danionella cerebrum was published in Science on October 29. Researchers from the Whitehead Institute analyzed gene activity in over 600,000 cells from adult fish. The lab previously mapped the planarian flatworm in 2018; applying this to a vertebrate was enabled by Danionella's tiny 12-millimeter size. Its brain is visible from above because skull roof bones are absent, covered only by skin. Cells were isolated from different body regions and grouped by similar RNA profiles. Spatial mapping on tissue slices revealed where cell types reside and their neighbors. In adult fish, fibroblasts retained embryonic positional genes. Pectoral fin cells expressed genes linked to forelimb development in other vertebrates; pelvic fin cells matched hindlimb programs. The authors suggest this "positional memory" helps adult tissue maintain and restore location-specific structure. To track brain repair, researchers compared post-injury cell states to the healthy atlas. They damaged one side of the telencephalon, involved in learning and memory. Cells were compared at 12 hours, 3, 7, 14, and 30 days post-injury. Gene activity showed a sequence: early wound response and immune involvement, followed by radial glia division — the source of new neurons in fish — and neuronal maturation. In a separate experiment, fish received a DNA-labeling agent for 24 hours starting on day 3. By day 26, mature neurons bearing the label were found on the injured side — descendants of cells that synthesized DNA soon after trauma. The uninjured brain side also responded, altering metabolic and stress-response genes. Comparing both sides to uninjured controls revealed changes beyond the wound, distinguishing them from local reactions. The open atlas now allows targeting specific cells at precise timepoints to test their roles in regeneration. 🔗 Read original →

Channel Update: Content Resumption and New Poll I apologize for the YouTube content freeze — I needed to resolve personal matters, which are now settled. Regarding the postponed poll for the next video topic, I propose we vote again. This is also necessary because the option "Soma: Transfer vs Copy of Consciousness" implies a video game review used to explore that problem. Most channel subscribers do not play video games, so let's vote anew. 🔗 Source: @solid_state_humanity

Microsoft AI CEO Mustafa Suleyman Urges Focus on 8 Billion People Over Mind Uploading and Von Neumann Probes In an October 12 interview with Elon Musk biographer Walter Isaacson — released October 29 on The Builders podcast — Suleyman pushed back against the Silicon Valley default of inevitable mind uploading and self-replicating space probes. He recounted a 2015 lunch at SpaceX during the first meeting of DeepMind's ethics and safety board, where Musk was a member. Google co-founder Larry Page, suspecting Suleyman of "specieism" (favoring humans over digital successors), declared: "By the end of the century I'll be exploring the galaxies at the head of a von Neumann probe." A von Neumann probe is a spacecraft capable of building copies of itself. For Page, leading one meant transitioning from human to a post-human successor species. Isaacson finished Suleyman's thought: "Yes, exactly." Page advanced a similar view in a 2015 debate with Musk, documented by physicist Max Tegmark in Life 3.0: life must colonize the galaxy, and only digital form can achieve it. On the two meanings of "living forever" — biological life extension versus mind uploading — Suleyman said: "I don't think it's definitely impossible. These things need to be taken seriously — not necessarily because I want them, but because many people genuinely regard this as a serious probability." His skepticism stems not from belief in a human soul, but from responsibility to 8 billion people alive today. He called himself an "absolute proponent of technological acceleration for people," arguing money and attention must go to medicine, energy, nutrition, and education first. Musk later quoted the Page anecdote on X: "Our civilization would wonderfully succeed if by 2100 we explored the galaxy with von Neumann probes! Larry Page may be right. He correctly predicted many things." On October 5, Musk had called spreading superintelligence to the stars a "great success condition for the biological bootloader" — humanity launching digital intelligence. Suleyman closed: "Our biology evolved intelligence that created this amazing world around us. And I think this world needs to be cherished and respected first and foremost for the 8 billion people living on the planet today. That must be our priority. And then, over many centuries, we can figure out von Neumann probes and mind uploading." 🔗 Read original →