I've thought a lot about this, because my state is heavily gerrymandered, and the legislature keeps voting to undermine several recently passed amendments (abortion rights, marijuana) to our state constitution. They also tried to change the threshold for passing a referendum from 50% + 1 vote to 60%, when it was clear that there was enough support to pass the abortion rights amendment.
You do it via constitutional amendment, which is a popular referendum.
Fuck what the professional politicians think of sortition; do an end run around them, because professional politicians are the problem.
Get this accomplished in enough states, and then you have a level chance of doing an amendment to the US Constitution.
> You do it via constitutional amendment, which is a popular referendum.
Aka...voting.
If you truly believe "voting doesn't work", as you first said (EDIT: that was someone else whose username starts with a "b" sorry), then this referendum won't go anywhere either.
I saw 'medical physicist' and wondered what you do. Thank you for 'a bit more context', I care! Very interesting stuff. Did you attend medical school + a physics program?
>"it just works", completely by luck
What does your validation function look like for this? Whenever stuff "just works" for me I get a little nervous until I determine why.
> I saw 'medical physicist' and wondered what you do. Thank you for 'a bit more context', I care! Very interesting stuff. Did you attend medical school + a physics program?
That's a whole separate long answer. I'm not a qualified doctor (and nor would I claim to be), but after a masters' degree in particle physics I moved into an explicitly interdisciplinary training programme that led to a doctorate and at other places in the country I did it in, a separate MPhil. During that initial year I spent a fair amount of time in the dissection room, learning anatomy, as well as most of the first three years (the foundational, preclinical part) of a medical degree combined into one (which contained lots of molecular biology, frankly). My final doctorate was between the departments of condensed matter physics (nominally my awarding institution), biochemistry, radiation oncology, and "the department of physiology anatomy and genetics", which is basically preclinical medicine. The people I work with are 50/50 recovering engineers or physicists, and qualified clinical medics who are trying to learn things like perturbation theory in their time off…
>"it just works", completely by luck What does your validation function look like for this? Whenever stuff "just works" for me I get a little nervous until I determine why.
Ah. I do know why: the relevant Damköhler numbers [0] are either very small (chemistry is much quicker than flow) or large (flow is much quicker than chemistry). So the approximations I am building in are justified and an awkward middle region is excluded; we also are only interested in small concentrations in a carrier fluid (e.g. blood, lymph) where the presence or absence of the species in question does not change its rheology.
I am lucky because we have evolved this way. If our circulatory system and its approach to metabolism was more similar to e.g. a reacting polymer foam ("can of expanding foam") which completely consumes its reactants as it goes, this implicit Lagrangian approach would likely not work.
Funny, I've been interested in mathematical rheology modeling (especially hemorheology). Do you know places journals or books to read to get in touch with the field ? (Note: I'm just a dev, few numerical analysis skills)
From my experience, the safeguards only come up during exploit development. You are free to do reverse engineering and even the first half of vulnerability research (i.e. vulnerability discovery) and it only stops once you want it to actually write the exploit.
Context: I did not study CS in college & wished I had so I read the python data science handbook, K&R's C, and Stroustrup's Programming Practice and Principles. I spend a significant amount of time bashing my head against getting code for personal projects to work (it usually did, eventually!).
I am way way way more capable with AI agents for this experience than if I had not invested the time. I can look at the code they wrote & decide if it's good enough or a bad idea. I have instincts about how to do things. I am much more able to plan things with them. My coworkers with degrees are wildly productive.
If you think you will be interacting with code in any significant way, I would at least read those three books, they will help you understand "how computers really work". If you want to have a career in making software, then definitely learn to write programs without AI.
I think what you've said here is unfair and overly cynical; nowhere have I read Chris or any of the Anthropic people make the claim that LLMs definitively are conscious. What they say is that there is increasing uncertainty, and evidence - for which they show receipts - backing up that uncertainty.
Personally, I have been unconvinced by any of these definitive yes or no answers to "are LLMs conscious?" - in both directions. The "they are not / can not be conscious" side relies too heavily on mechanistic reductionist arguments that can apply equally to neurochemical processes in the human brain - and yet, it seems that humans, with brains made of these neurochemical processes, are conscious. At the same time, the "they are definitely conscious" answers seem to generally rely too heavily on self-deception and lack of reality testing.
To be able to say something definitive here, it seems to me that one would need to say definitively what this experience is. I have not heard any of the loud voices in the arena - not Chiang, not Giulio Tonini, not Karl Friston, etc - do so. Therefore I find Anthropic's uncertainty, and careful, caring investigative process, well grounded given the evidence.
My aim isn't cynicism so much as an interest in clear an evidence based information about these systems which are clearly important technological developments for the future. It may turn out that there is something analogous to cognition going on and that would be quite significant and require us to think differently about the technology, but we need to be very sure of something like that.
> Therefore I find Anthropic's uncertainty, and careful, caring investigative process, well grounded given the evidence.
I'm inclined to agree mostly, but from the outside its impossible to separate marketing, belief, scientific inquiry, and just plain enthusiasm. Its all to opaque.
And it is worth noting, I think, that Olah's message to the pope is centrally making that point: that Olah and his peers are within incentive structures that distort their actions and understanding, and therefore that dialog with those outside of such incentive structures is necessary (not sufficient) for their outputs to be trustworthy.
I really wantt to get into splatting and I have the tools: good camera, v comfy in blender, comfy with graphics programming ideas, 4080. But I haven't found a good 'all in one intro' to it yet. Possibly because I'm foss-biased and have dismissed proprietary options. But does anyone know of a good 'vertical tutorial' on this stuff?
I recently got into splatting. I looked for some good all-in-one tutorials, but didn't find any, and mostly muddled through through trial and error and LLM assistance. I present this workflow as a straight-line pipeline, though in practice it took a lot of iteration and backtracking and rework to get the final result. Here's what worked for me:
I captured a video on a smartphone camera, using the OpenCamera app. Specifically, this video was captured with exposure locked, framerate locked, focus locked, fairly high framerate and resolution. I walked slowly and carefully around an outdoor scene, trying to get fairly good coverage from multiple angles. I took roughly 20 minutes of video, weighing 19GB.
This video was sampled into individual image frames at about 5fps using ffmpeg. There's room for experimentation and improvement here, an adaptive, coverage-aware sampling strategy would be better. But fixed 5fps was Good Enough (tm). This resulted in roughly 8,000 images at 4k. This was a pretty hefty dataset for my limited 1080, but I made it work.
I then generated masks for these images, to ignore transient objects during the splat training. (i.e. to cut out people who transiently walked through the scene). For this I used Cutie (https://github.com/hkchengrex/Cutie). For outdoor scenes, it can also make sense to mask out low-parallax areas like faraway mountains or especially the sky, as these are difficult to train correctly. If masks are generated for some images, you'll need at least placeholder masks for the all of them. In the end I've got about 8,000 PNGs that are monochrome black/white masks.
Then the images are handed to COLMAP (https://github.com/colmap/colmap), using the 'global mapper' option. This registers the camera positions in 3D space, and generates a crude point cloud that's good for sanity-checking. This step required a fair bit of iteration to get right. The full reconstructed output from COLMAP is not necessary, only the pose-estimate .bin files. The output directory here was about 500MB for this step for me.
With COLMAP registration done, the next step is the actual training. I found two useful pieces of software for this, with different tradeoffs.
Brush (https://github.com/ArthurBrussee/brush). Was very straightforward to install and use, requiring very little in external dependencies and setup. It was also pretty speedy on training, and gave good results. Minor modifications to the training process were possible by editing source, though I didn't get too wild here. Brush takes the *.bin files from COLMAP, plus the original images directory, and the masks directory if it exists. Run on its own, this could produce gaussian splat .ply files, 500-800MB in size, containing 1-10M splats. More than that and my poor little 8GB of VRAM OOM'd.
nerfstudio (https://github.com/nerfstudio-project/nerfstudio) Was also useful, as many research papers get implemented in its framework. In particular, for this outdoor scene, I used wild-gaussians (https://github.com/jkulhanek/wild-gaussians/) to generate just a sky sphere (to help seed low-parallax areas in my particular dataset), stopped training, and used this as an init.ply to pass to brush.
I then set up a very simple viewer website, using SuperSplat (https://github.com/playcanvas/supersplat). I used supersplat's editor to align the splat's coordinate system with the rotation and scaling that I wanted, and then exported an optimized .sog file, roughly 1/10th the size. .sog is nominally open-standards, though I'm not aware of any other projects using the format. This gave fairly good framerates and adequate controls across a variety of platforms.
As a little bit extra, supersplat's splat-transform CLI tool was used to generate a crude collision mesh for the scene, enabling a walking mode that respected object boundaries.
If there's interest I can post my results, I got a bit sidetracked with other projects and other splats, and this particular one I got fiddling with some more cleanup. I can get it up with a few more hours work. But hopefully that's a good start, all of these are fully FOSS, and resulted in a good-looking splat.
We need sortition.
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