This week’s render was probably the toughest ever to produce. I built the set before knowing what was going in it and then decided to return to my attempt at making a fully rigged robot arm.
The model is actually a beefed-up version of a desktop arm with added hydraulics and cabling with my own materials added. The tricky bit was doing all the rigging. I decided to learn inverse kinematics after avoiding it my whole life. This means that the rig follows a leader, with all the rest of the armature automatically adjusting based on a series of weights and limits rather than forward kinematics where you animate from the base outwards. The difference is easily explained in how you pick things up; if you reach out your hand to pick up a cup, your arm automatically follows without you having to direct it. If we were a forward kinematic system you would first have to position your upper arm, then your lower arm, then your hand, then your fingers.
I even rigged the cables to work this way so that they move and flex realistically. It was definitely one of the most grinding renders I’ve done because it takes a lot more forward-thinking about workflow, if you set things up in the wrong order or if one piece is just slightly misaligned you can end up with a broken and glitchy system. Having spent two or three days carefully and intricately setting up an enormously complicated system I then decided to chuck in an element of danger by using cloth simulation to anchor a balloon to the claw. You can set different parts of a cloth mesh to behave differently so I kept the string floppy and the balloon pressured, inverted gravity and weakened it for the whole scene (which is a lot easier than telling something how to float) and then ran the simulation about three dozen times. My only advice here: ramp the simulation quality steps way up – sky’s the limit. I think the final version is something like 300 against the default of 5.
Fallacies
This week’s Exponential View focussed on quantum computing with an interview with a founder of one of the companies developing it. The science is fascinating and it’s something I know blissfully little about. What was interesting was the way the interviewee, Chad Rigetti, trod the line between the mundanity of technological innovation and the existential premise of computing at the quantum level. For example, when asked about application he talked about the quantum theory of gravity, something that we cannot really experiment with on Earth with our current computers. However, when pushed on the everyday application he defaulted to national security, intelligence and finance, citing Moore’s law and saying that ‘computing technology has always been a fundamental driver of economic development… an inevitable march of better computing power.’ I fully believe him when he says the science is what’s most interesting, it’s just predictably tragic that something as incredible a unifying theory of gravity isn’t going to drum up as much funding as crypto.
There are two fundamental interconnected fallacies in technological innovation. Both of which have been explored extensively by scholars of STS: First, that technological ‘evolution’ is inevitable – that the next thing will be better – and secondly that everything will be modellable or simulatable. The narrative of tech innovation is that through these duelling paradoxes, some supremacy over the ‘messiness’ of ‘nature’ (both human and non-human) can be achieved. But these are fallacies. Every new innovation is never quite good enough to model things with enough accuracy and so the next innovation is the promissory one with the current one being an exception for its failures: ‘…past failures are often isolated as special or peculiar cases with little technically or organizationally in common with the newly proposed promissory solution.’ (Borup et al.)
This is not to say that innovation is pointless, it’s more complex than that. Is there a way to present new technological innovation as neither inevitable nor final? Rather than an all-or-nothing approach something that is more about presenting technology in a constant state of imperfect flux? Open-source stuff has some of this. Blender’s development pipeline is fascinating because it’s totally open and done by volunteers. There’s some fanfare around releases but there’s never any promissory rhetorics of finality; it’s treated as incomplete (and all the more charming for it) and in constant development, which is a useful way to inspire the community to contribute. I’m sure there are loads of other examples.
Short Stuff
- Venkatash Rao is literally giving away his OODA loop work for anyone to use. OODA loops were all the rage about five or six years ago but Rao has stuck with them and made something really rich. Also, I’m super in to giving stuff away and it’s great to see such an influential character leading there.
- Meredith Whittaker’s Steep Cost of Capture – a pretty concise overview of the big-tech-so-called-AI research industry nexus.
- Piece on Vox here talking about that idea of inevitability more and it’s ties to the American manifest destiny worldview.
- Everyday Robots seems like a complex project – to build robots that can perform everyday chores. I’m always torn on these types of projects. On the one hand, it’s a super interesting and remarkably complicated set of technical goals to be able to teach robots to do things that we take for granted like folding sheets and wiping surfaces. On the other hand it feels like something we don’t really need robots for – we’re good at household chores already and the house was built around the able human body in most cases, so why adapt robots for it? They cite economic productivity as a rationale but again, there are better reasons for robots; see the ever-citable Paro.
- An expolanet found where there should be none: 11 times the size of Jupiter found orbiting a binary star system 10 times larger and hotter than the Sun.
- Brief into from IGN on speedrunning and tools. It’s a bit hyperbolic in places and skips over some of the interesting things in specific controversial runs. For more on the exact maths of that 1 in 7 trillion Minecraft speedrun check out this Standup Maths.
I’m migrating blogging to Monday because of my new training schedule. It’s best to get a solid block in Tuesday-Friday. Ok, love you, love you, love you. Have an amazing week.