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Nick Bostrom: What Happens When AI Evolves Faster Than Humans?

The journey “Up from Eden” could involve humanity’s growth in understanding, comprehending and appreciating with greater love true and wisdom, shaping a future worth living for.


AI is accelerating faster than human biology. What happens to humanity when the future moves faster than we can evolve?

Oxford philosopher Nick Bostrom, author of Superintelligence, says we are entering the biggest turning point in human history — one that could redefine what it means to be human.

In this talk, Bostrom explains why AI might be the last invention humans ever make, and how the next decade could bring changes that once took thousands of years in health, longevity, and human evolution. He warns that digital minds may one day outnumber biological humans — and that this shift could change everything about how we live and who we become.

Superintelligence will force us to choose what humanity becomes next.

Unlocking the Potential of the Microbiome in Cancer Therapy

Colorectal cancer (CRC) is closely linked to gut microbiota dysbiosis. We synthesize evidence that carcinogenic microbes promote CRC through chronic inflammation, bacterial genotoxins, and metabolic imbalance, highlighting key pathways involving Fusobacterium nucleatum, pks+Escherichia coli, and enterotoxigenic Bacteroides fragilis (ETBF). Building on these mechanisms, we propose a minimal diagnostic signature that integrates multi-omics with targeted qPCR, and a pathway–therapy–microbiome matching framework to guide individualized treatment. Probiotics, fecal microbiota transplantation (FMT), and bacteriophage therapy show promise as adjunctive strategies; however, standardization, safety monitoring, and regulatory readiness remain central hurdles. We advocate a three-step path to clinical implementation—stratified diagnosis, therapy matching, and longitudinal monitoring—supported by spatial multi-omics and AI-driven analytics. This approach aims to operationalize microbiome biology into deployable tools for risk stratification, treatment selection, and surveillance, advancing toward microbiome-informed precision oncology in CRC.

Colorectal cancer (CRC) is one of the most prevalent malignant tumors worldwide. According to the latest data released by the International Agency for Research on Cancer (IARC), the global incidence of CRC is expected to exceed 3.2 million new cases in 2040, with nearly 1.6 million deaths, ranking third among all cancers after breast and lung cancer (Morgan et al., 2022). While early detection rates are relatively high in some developed countries, such as the United States and European nations, due to well-established screening programs, the situation remains critical in developing regions including India and Africa, where screening coverage is limited and over 60% of cases are diagnosed at advanced stages (Lee and Holmes, 2023). This “high-incidence and high-mortality” pattern not only poses a significant threat to public health but also imposes a considerable burden on global healthcare systems.

With the rapid development of high-throughput sequencing, metagenomics, and metabolomics, the role of the gut microbiota in human health and disease has drawn increasing attention (Fan and Pedersen, 2020). Gut microbes maintain intestinal homeostasis and host immunity. They also contribute to CRC via chronic inflammation, bacterial genotoxins, oxidative stress, and dysregulated microbial metabolites (Dougherty and Jobin, 2023; White and Sears, 2023). Given that the colon and rectum harbor a highly dense microbial ecosystem, gut microbiota dysbiosis is now considered a pivotal environmental factor contributing to CRC onset and progression.

LLMs choose friends and colleagues like people, researchers find

When large language models (LLMs) make decisions about networking and friendship, the models tend to act like people, across both synthetic simulations and real-world network contexts.

Marios Papachristou and Yuan Yuan developed a framework to study network formation behaviors of multiple LLM agents and compared these behaviors against human behaviors. The paper is published in the journal PNAS Nexus.

Computers Made From Human Brain Tissue Are Coming. Are We Prepared?

As prominent artificial intelligence (AI) researchers eye limits to the current phase of the technology, a different approach is gaining attention: using living human brain cells as computational hardware.

These “biocomputers” are still in their early days. They can play simple games such as Pong, and perform basic speech recognition.

But the excitement is fuelled by three converging trends.

Philip Goff — Can AI Become Conscious?

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AI consciousness, its possibility or probability, has burst into public debate, eliciting all kinds of issues from AI ethics and rights to AI going rogue and harming humanity. We explore diverse views; we argue that AI consciousness depends on theories of consciousness.

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Philip Goff is a British author, panpsychist philosopher, and professor at Durham University whose research focuses on philosophy of mind and consciousness. Specifically, it focuses on how consciousness can be part of the scientific worldview.

Closer To Truth, hosted by Robert Lawrence Kuhn and directed by Peter Getzels, presents the world’s greatest thinkers exploring humanity’s deepest questions. Discover fundamental issues of existence. Engage new and diverse ways of thinking. Appreciate intense debates. Share your own opinions. Seek your own answers.

China’s ‘Darwin Monkey’ is the world’s largest brain-inspired supercomputer

Scientists in China have unveiled a supercomputer built on brain-like architecture — specifically, that of a monkey.

Called Darwin Monkey or “Wukong”, the system features over 2 billion artificial neurons and more than 100 billion synapses, putting it roughly on par with the neural structure of a macaque.

Machine learning reveals how disordered protein regions contribute to cancer-causing condensates

Fusion oncoproteins arise when a gene fuses with another gene and acquires new abilities. Such abilities can include the formation of biomolecular condensates, “droplets” of concentrated proteins, DNA or RNA.

The abnormal molecular condensates formed by fusion oncoproteins can disrupt cellular functions and drive cancer development, but the specific protein features behind this process remain unclear.

Scientists at St. Jude Children’s Research Hospital studied intrinsically disordered regions, unstructured protein segments that are often involved in condensate formation, to determine if they drive fusion oncoproteins to form condensates. They trained a machine learning model, called IDR-Puncta ML, with experimental data from intrinsically disordered regions in fusion oncoproteins to predict the behavior of other such regions.

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