Import AI 480: Self-sustaining space economies; AI for science progress, and Meta builds perfect hands for a superintelligence

Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. Subscribe now AI Science startup converts a quantum conjecture into a theorem: ...An evocative taste of the singularity... In the same way a few years ago it went from remarkable to unremarkable to see computers gain the ability to identify objects in images, we’re now in an era where it’s rapidly going from remarkable to unremarkable for computers to extend the bounds of human knowledge. The latest example comes from Physical Superintelligence, an AI science startup which has proved a longstanding quantum computing conjecture. What they did: “Open since 2008, the conjecture of Scott Aaronson and Andris Ambainis concerns when quantum computers can be exponentially faster than ordinary ones. Now, a research system developed internally at Physical Superintelligence (PSI), running a non-public Anthropic model, found a proof for it, settling one of the most fundamental questions in quantum computing,” the startup writes. “The system had a complete proof of the conjecture during the third day of the run, and Lean, a proof assistant in which a computer checks every step, accepted it. We then set the system to work on related open problems. Along the way, the system improved the result twice, and Lean accepted the final version.” Why this matters – we are in the singularity : As I said in my speech at the IMF ( #477 ), I believe we’re in the early stages of the singularity, defined as the period when the rate of technological and scientific progress exceeds the rate at which we humans can parse out and make sense of it. Research like this is a taste of what that means; mostly automated synthetic researchers extending the frontier of knowledge, spitting out results which need to then be understood by humans. Read more: A proof of the Aaronson‐Ambainis conjecture (Physical Superintelligence, blog) . *** Meta builds the hands that a superintelligence needs to take over the world: ...Combine Tactus with an autonomous agent and you have the makings of a scifi takeover... Meta Reality Labs Research has published details on Tactus, “a textile-integrated tactile and pose sensing glove designed from the ground up for naturalistic manipulation data collection”. The cool thing about this glove is that it helps record fine-grained detail from hands as they’re manipulating objects – this is the kind of thing that cameras can visually report but often leads to sub-par data due to occlusion during manipulation. It’s also interesting because this is exactly the kind of hardware an autonomous AI system might want to pay humans to wear so it could gather data and get feedback from the world. More details on Tactus: The gloves have three design pillars: “(1) a thin, conformal 3D-knit form factor (1.5mm fingertip stackup) designed to minimize interference with natural manipulation, (2) strategically placed capacitive tactile sensors at manipulation-relevant regions informed by empirical contact studies”, Meta writes. “Tactus provides calibrated fingertip force sensing and occlusion-resilient hand tracking in a textile-integrated form factor that preserves natural grasp behavior.” “(3) IMU-fused pose tracking that maintains accuracy through the hand-object occlusions where manipulation data is most valuable.” Why this matters – hand-by-wire: Picture some takeoff scenario where an AI system has carved out an independent niche for itself running on some stolen compute, perhaps even using a form of distributed inference and training. The AI now has a few goals, some of which can be satisfied by doing things in the digital world, and some of which can be satisfied by doing things in the physical world. For the physical world stuff, it either needs to be able to get robots to do the thing, or figure out a way to get people to work for it – the Tactus gloves are the kind of hardware you might imagine a rogue AI paying people to wear so they could both a) perform tasks for it and it could give live feedback, and b) become a source of data generation for the AI itself. Of course, I am not endorsing this particular future! But I have begun to find it helpful to view research papers through the lens of “how would an AI use this”, similar to how it is often helpful to view papers through the lens of “how would an intelligence agency use the insights shared here”. Read more : Tactus: Tactile & Pose Sensing Glove for Natural Dexterity Data Collection (arXiv) . *** Building a self-sustaining space economy is cheaper than you think – especially with Starship: ...A 2016 NASA paper contains some exciting ideas for off-Earth industrialization... Here’s a fascinating paper from researchers with the NASA Kennedy Space Center in 2016 about what it’d take to bootstrap a self-sustaining industry on the moon. “Once successfully bootstrapped, a robotic network can access, process, transport, and utilize the solar system’s resources for mankind’s benefit,” the researchers write. Moon manufacturing – cheaper than you think: The big idea in the paper is how little mass is required to kickstart a manufacturing ecosystem. The key is to start with a relatively basic set of things and then build up from there, eventually fabricating chips on the moon. “In the baseline case with maximum manufacturing rate, a total of 41 MT is launched to the Moon. In the reduced rate case, only 12 MT is launched to the Moon but the scale of industry is two orders of magnitude smaller so the benefits of the industry are relatively delayed by eight years”. Starship has a capacity to lift 100 MT to low-earth orbit, and given multiple refuelings in orbit it’s feasible you could then land it on the moon – when you napkin out the cost of multiple Starship launches and refuelings you can get 12-41MT to the Moon for between $1.5bn and $4.2bn at list prices. What you build on the moon and in what order: The basic idea here is to start by building simple things and then move up from there by launching a “sub-replicating system and evolve it toward the self-replication capability,” they write. “The objective is for the first robotic “colonists” on the Moon to fabricate a set of, say, 1700’s era machines and then to advance them steadily through the equivalent of the 1800’s, 1900’s, and finally back into the 2000’s. We argue that this can be accomplished in just a few decades”. The authors see this unfolding over 6 distinct manufacturing generations: Gen 1: Making gases, water, crude alloys, ceramics, solar cells. Gen 2.5: Plastics, rubbers, some chemicals. Gen 3 : Diversify chemicals, simple fabrics, eventually polymers. Gen 4: Sandwiched and other advanced material processes. Gen 5: Large-scale production. Gen 6: Makes all necessary materials, increasing sophistication, including making all electronics locally. What machines do you send to the moon? Excavators: “travel between the digging site and the resource processing site, delivering sufficient lunar regolith each hour to maintain production rates of the other assets.” Chemical plants for volatiles : One type will be oriented around producing gases and liquids like oxygen, hydrogen, water, hydrocarbons like methane, and solvents for industrial processes. Chemical plants for solids: Produce plastics and rubbers from lunar pole ice. “We now know that the ice contains large quantities of carbon molecules (CO, CO2,...) as well as nitrogen-bearing and hydrogen-bearing molecules... these materials may be needed for gaskets, seals, and insulators, for example.” Metal and Ceramics refining: Early generations will “produce crude ‘mongrel alloys’ by electrowinning or other methods” and hardware built out of this stuff will need to be huge to add strength to make up for poor mechanical properties. “Subsequent generations of metal refineries will add processes and material streams to improve the mechanics of the materials. Oxyg