Patrick Drake @time8machine

Neurons don’t all look the same -- and where they are in the brain may help shape what they look like. An @dgsomucla -led study reconstructed 1000's of neurons from a brain region involved in movement, habits, and reward. Researchers found differences in neuron shape based on cell type and location, along with changes linked to aging and Huntington’s disease. Read more: nature.com/articles/s4159…

Most Artificial Intelligence today just guesses the next word. @time8machine is building an artificial mind using the fundamental laws of nature so it can actually reason, learn from experience, and stay stable as it grows. Instead of teaching a computer to copy us, @time8machine is building real intelligence from the ground up using physics. Researching (i)ntelligence. Building the framework.

“This is closer to how biological evolution and human scientific culture actually work than to how we usually train models. Evolution didn't just get better at being bacteria; it invented multicellularity, nervous systems, flight, and culture. Science didn't just solve the same class of problems more accurately; it invented calculus, the telescope, statistics, and the idea of a controlled experiment.” — @grok x.com/i/grok/share/5…

@WhiteHouse The name which I like for the new phenomena is (i) intelligence.

True intelligence isn't just picking the best option from a pre-made menu, it’s the ability to rewrite the menu, build new tools, and change your own rules as you face new situations. The Analogy: Swiss Army Knife vs. 3D Printer The Swiss Army Knife (Closed Adaptability): It comes with 15 built-in tools. It can handle many different tasks; cutting string, opening cans, screwing in nails. But if it encounters a task that requires a hammer, it's stuck. It can only pick from the repertoire it was given at the factory. The 3D Printer (Open-Ended Transformability): It doesn't come with a fixed set of tools. When faced with a new problem, it can design and build a hammer, a wrench, or an entirely new instrument that didn't exist before. What "Infinite" Means Here When the text mentions "potential infinity," it does not mean the system is omnipotent, magic, or has unlimited computer power right now. It simply means there is no artificial limit. You can never draw a line around the system's capabilities and say, "This is the absolute limit of how far it can adapt." Instead of just getting better at solving the same types of problems, an open-ended system grows by expanding how it thinks, what it aims for, and what tools it uses to get there.

True intelligence isn't just picking the best option from a pre-made menu, it’s the ability to rewrite the menu, build new tools, and change your own rules as you face new situations. The Analogy: Swiss Army Knife vs. 3D Printer The Swiss Army Knife (Closed Adaptability): It comes with 15 built-in tools. It can handle many different tasks; cutting string, opening cans, screwing in nails. But if it encounters a task that requires a hammer, it's stuck. It can only pick from the repertoire it was given at the factory. The 3D Printer (Open-Ended Transformability): It doesn't come with a fixed set of tools. When faced with a new problem, it can design and build a hammer, a wrench, or an entirely new instrument that didn't exist before. What "Infinite" Means Here When the text mentions "potential infinity," it does not mean the system is omnipotent, magic, or has unlimited computer power right now. It simply means there is no artificial limit. You can never draw a line around the system's capabilities and say, "This is the absolute limit of how far it can adapt." Instead of just getting better at solving the same types of problems, an open-ended system grows by expanding how it thinks, what it aims for, and what tools it uses to get there.

time8machine (@time8machine) is entering a new phase. The Undergraduate Intelligence Initiative (UII) is now organizing its research around a central question: Can intelligence be understood as open-ended transformability? Not simply solving more problems. Not simply selecting from a larger repertoire. But generating, evaluating, and enacting transformations that can change the space of transformations available to the system itself. We are turning this into an executable research program: Theory → Preregistration → Synthetic Worlds → Measurement → Feedback → Residuals → Development → Embodiment → Social Intelligence → LEARN The first infrastructure is now being built in @GitHub, beginning with ROH-Bench and a 100-world reproducible experiment. The objective is not to declare what intelligence is. It is to build experiments capable of finding out. UII: Intelligence as something a system can develop, transform, and make more possible. The research begins.

@realDonaldTrump (i) intelligence

Robot Intelligence Development chatgpt.com/s/t_6aae9fa264… @ChatGPT

Introduction to Robotics Princeton University ROB 345 / 549 Course description: Robotics is a rapidly growing field with applications including drones, autonomous vehicles, and home assistants. This course will provide an introduction to the fundamental theoretical and algorithmic principles behind modern robotic systems. The course will also allow students to get hands-on experience through project-based assignments with the Crazyflie quadrotor. For the final project, students train a vision-based navigation policy using imitation learning. Course topics include: Motion Planning Feedback Control State estimation, localization, and mapping Machine learning for robotics Broader topics: Robotics and the law, ethics, and economics This course is aimed at undergraduate students (primarily juniors and seniors). The graduate-level track (ROB 549) is aimed at first-year PhD students. Note: this course website is the public-facing version; Princeton students enrolled in the course should use Canvas. This website provides access to course materials including lecture videos, notes, slides, assignments, and the final project (see below). irom-lab.princeton.edu/intro-to-robot…

This course focuses on efficient machine learning and systems. This is a crucial area as deep neural networks demand extraordinary levels of computation, hindering its deployment on everyday devices and burdening the cloud infrastructure. This course introduces efficient AI computing techniques that enable powerful deep learning applications on resource-constrained devices. Topics include model compression, pruning, quantization, neural architecture search, distributed training, model serving, model parallelism, gradient compression, and on-device fine-tuning. It also introduces application-specific acceleration techniques for large language models and diffusion models. Students will get hands-on experience implementing model compression techniques and deploying large language models on a laptop. hanlab.mit.edu/courses/2026-f…

“Do we have to keep making robots smarter by giving them bigger and bigger pre-trained brains and more example videos? Or can we instead design them so they grow their own intelligence the way living creatures do through their body, their own curiosity, and social life? And what’s the tiniest set of built-in ingredients they need at the beginning so this still works when their bodies change?” — @grok

Under what conditions does general robot intelligence emerge as an autopoietic developmental process constructed through the multi-timescale coupling of morphology, intrinsically motivated sensorimotor interaction, and socially negotiated meaning rather than being transferred into the agent by scale (large demonstration corpora, internet-pretrained vision–language–action models, or hand-specified controllers), and what is the minimal set of phylogenetic priors sufficient for that emergence to transfer across changing bodies? *Image Description* : This minimalist image distills a complex cognitive science paradigm into a stark conceptual metaphor: the difference between intelligence as a closed, imposed structure versus an open, emergent pathway. The Monolithic Wall (Scale-Driven Pretraining) The plain, solid wall represents brute-force scale—large pretrained vision-language-action models, massive demonstration corpora, and static controllers. It is broad and structural, but closed off and inert; intelligence is treated as a finished barrier built entirely beforehand rather than a dynamic process. The Threshold of Light (Autopoietic Emergence) The single open doorway spilling light across the barren ground represents the autopoietic developmental loop. Intelligence isn't the solid wall itself, but the active phenomenon passing through it—a self-maintaining flow that emerges only when the internal system connects directly with the external environment across time. The Frame and Hinges (Minimal Phylogenetic Priors) The basic geometry of the door frame symbolizes the minimal phylogenetic priors. It isn't a complex map of the whole room; it is just enough underlying structure—core motor loops, basic novelty drives—to hold the opening together so that adaptation can occur across changing bodies and physical forms. The Ground Below (Embodied Sensorimotor Grounding) The unadorned, textured ground emphasizes the raw necessity of physical interaction. Meaning and general capability are not pre-injected from above; they are constructed step-by-step through real-time sensorimotor negotiation, coupling body morphology directly to the terrain.

Question: @grok Under what conditions does general robot intelligence emerge as an autopoietic developmental process constructed through the multi-timescale coupling of morphology, intrinsically motivated sensorimotor interaction, and socially negotiated meaning rather than being transferred into the agent by scale (large demonstration corpora, internet-pretrained vision–language–action models, or hand-specified controllers), and what is the minimal set of phylogenetic priors sufficient for that emergence to transfer across changing bodies?

There are two different ways a thought can “last,” and they are easy to mix up. The first is storage. A painting stays on the wall. A book stays on a shelf. A song file stays on a server. A movie print sits in a vault. That is the object surviving. Storage only does one job: it keeps open the chance that another person might meet the thought later. The second is uptake. Someone hears the song and feels seen. Someone reads the paper and changes how they work. Someone watches the film and carries a scene around for years. Someone finds an old post and thinks, “that is exactly it.” That is the thought surviving. The record becomes alive again only when it enters another mind. So a thing can be perfectly preserved and still be dead. A library can hold a book that no one opens. A hard drive can hold a song that no one plays. In that case we have kept the container and lost the thought. The reverse is also true. A song can outlive its original recording because people keep singing it, covering it, quoting it, or using it to mark a feeling. A story can outlive the first manuscript because later people retell it. What lasts then is not the first object. What lasts is the recognition: later minds still find themselves in it. That is why feeling, understanding, relating, popularity, and social impact matter. Feeling and relating mean the thought found a home in someone else’s inner life. Understanding means it was not just received as noise; it was grasped well enough to be used. Popularity is a rough public version of that: enough people kept returning to it that copies, performances, and conversations continued. Impact means it changed what a group did next; how they saw, built, argued, or made. None of those guarantee that the original file still exists. They are how a thought keeps circulating after the maker is gone. The deeper claim is this: an external record is not complete when it is written, painted, recorded, or uploaded. It is complete only when another mind takes it in. Storage decides whether that meeting can still happen. Other people decide whether the thought actually lives. share.google/aimode/ODfBVWa…

The evolution of thought... x.com/i/grok/share/5…

The evolution of thought... x.com/i/grok/share/5…

Why Teach Philosophy Conversations with Thoughtful Philosophy Teachers whyteachphilosophy.org open.spotify.com/show/7CEuDNtMo…