/robowaifu/ - DIY Robot Wives

Advancing robotics to a point where anime catgrill meidos in tiny miniskirts are a reality.

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General Robotics/A.I. News & Commentary #2 Robowaifu Technician 06/17/2022 (Fri) 19:03:55 No.16732
Anything in general related to the Robotics or A.I. industries, and any social or economic issues surrounding it (especially of robowaifus). === -note: I'll plan to update this OP text at some point to improve things a bit. -previous threads: > #1 (>>404)
have I been posting in the wrong place?
I'm not going to keep saying that we've been visited by people running with our ideas but it's hard not to wonder https://wefunder.com/destinyrobotics/ https://keyirobot.com/
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>Woman and third worlders stealing our ideas to steal from simps Topkek Atleast they're not as blatant as lilium robotics. https://www.liliumrobotics.com/Full-Body/ They are open sourcing their software so somewhat thread relevant. https://github.com/lordNil/lilium
>>16740 Yea I posted about Lilium (not to be confused with Lillim) Robotics in the other thread. Remember these are going to be the goofy first attempts. Learn what we can I guess from their mistakes and successes. Better to stay on top of our "competition" than to put our heads in the sand. Side note: Now would be a great time if never to get a secret multi-million dollar inheritance or win a grant or something. Hate to watch everyone else having all the fun
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I posted in the last thread, so this is a repost. (((they))) already taking some swing at "rights for AI's, robots have feelings too!!!", a situation similar to vegans, or le stronk independent womyn, groomers, etc... > that staged LaMDA news They will make all sorts of AI's - a protected class, just like they protect the groomers nowadays, it's like "screaming" itself : > even if you have a robot, you won't have access to its system a.k.a. (((cloud ai))) a generation raised on fear-porn movies, they use their fear now to justify stricter controls.
>>16738 Nice cashgrab. So many people just want to buy something. And of course ASAP, it doesn't exist though. >>16744 People supporting the freedom of chatbots have a very limited self-awareness when it come to their programming through media, education and politics.
>>16740 I just looked into this more deeply. - The CEO seems to be an Instagram model, looking into more business opportunities. She probably got told that the time for this is limited, not only in regards to her personally but in terms of men having alternatives. - They pitch their robot as a solution for "loneliness". That's something I hate with a lot of people outside of the sphere of real robowaifu enthusiasts. They automatically assume it has to be about being lonely. But not being content with the current state of women or not meeting their standards is not the same than being lonely. - Then their video also claims to create a humanoid for helping at home. Which might be possible at some time, but certainly not if they want to come out with some product soon. 25M are burned fast in the US (Miami). I think, short term you can either have something more like a animated lovedoll with AI, that won't walk, or a mobile robowaifu with wheels. Btw, on their page they claim $3500 per robot, prototype available next year. Sure. - They seem to have noting so far, their video shows InMoov, I think, and that video of the face looks like some animation. - Yeah, look at that face. It's show how much beauty standards in the US are taboo. Wouldn't be surprised if they would still get attacked for "fetishizing asian women" and her skin being to pale. >>16740 Yeah, I like Lilium Robotics much more. Didn't try their software but at least it looks like they're into Open Source. They're also using common hardware for their system. Also, Lilly's design is rather bold.
>>16752 Don't waste your time on watching this. It was hard: https://www.youtube.com/watch?v=NAWKhmr2VYE
>>16753 ew, absolutely horrifying post-wall face!
If nobody is going to post about it, I will. Two very impressive papers came out. The first one is Deepmind's breakthrough work on RL exploration via novel, simple and powerful self-supervised learning objective, which finally conquered Montezuma's Revenge (!) and most DM-HARD-8 tasks. The second one is an academic tour-de-force devising novel scheme of training a CLIP-like contrastive semantic model as a sufficient surrogate reward for training an agent which passably executes some tasks in minecraft environment. This is a way forward for training from human-generated youtube tutorials. Both of these works are significant and can be applied to our cause, albeit they require moderately large compute (large by the standards of an amateur, moderate by the standards of a good US org). At the very least, agents trained via these objectives could be used as dataset generators for our would-be agent. If we are to use these innovations for our projects, we need to start a semi-closed community to test approaches to distributed computation and to guide the effort of recruiting volunteers into the computation graph. 1. BYOL-explore https://www.semanticscholar.org/paper/BYOL-Explore%3A-Exploration-by-Bootstrapped-Guo-Thakoor/54d1fcc284166e7bbd5d66675b80da19714f22b4 >We present BYOL-Explore, a conceptually simple yet general approach for curiosity-driven exploration in visually-complex environments. BYOL-Explore learns a world representation, the world dynamics, and an exploration policy alltogether by optimizing a single prediction loss in the latent space with no additional auxiliary objective. We show that BYOL-Explore is effective in DM-HARD-8, a challenging partially-observable continuous-action hard-exploration benchmark with visually-rich 3-D environments. On this benchmark, we solve the majority of the tasks purely through augmenting the extrinsic reward with BYOL-Explore’s intrinsic reward, whereas prior work could only get off the ground with human demonstrations. As further evidence of the generality of BYOL-Explore, we show that it achieves superhuman performance on the ten hardest exploration games in Atari while having a much simpler design than other competitive agents. 2. MineDojo https://www.semanticscholar.org/paper/MineDojo%3A-Building-Open-Ended-Embodied-Agents-with-Fan-Wang/eb3f08476215ee730d44606b96d1e24d14f05c1d >Autonomous agents have made great strides in specialist domains like Atari games and Go. However, they typically learn tabula rasa in isolated environments with limited and manually conceived objectives, thus failing to generalize across a wide spectrum of tasks and capabilities. Inspired by how humans continually learn and adapt in the open world, we advocate a trinity of ingredients for building generalist agents: 1) an environment that supports a multitude of tasks and goals, 2) a large-scale database of multimodal knowledge, and 3) a flexible and scalable agent architecture. We introduce MINEDOJO, a new framework built on the popular Minecraft game that features a simulation suite with thousands of diverse open-ended tasks and an internet-scale knowledge base with Minecraft videos, tutorials, wiki pages, and forum discussions. Using MINEDOJO’s data, we propose a novel agent learning algorithm that leverages large pre-trained video-language models as a learned reward function. Our agent is able to solve a variety of open-ended tasks specified in free-form language without any manually designed dense shaping reward. We open-source the simulation suite and knowledge bases (https://minedojo.org) to promote research towards the goal of generally capable embodied agents.
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Many of you may have noticed a shilling week (((OpenAi's))) GPT-3 on 4chan's /pol/. Today 22.06.2022 the neural network started giving pilpul i.e. passive-aggressive mental gymnastics, facts avoiding, etc. > in two words - another nn was neutered Expect an article from openai about "how evil racists tried to ruin gpt-3"
By this point it should be obvious that large generative multimodal models are here to stay. The experiment shows us that 20 billions of parameters is enough for implementing quite fine, abstract artistic ability. 3 billions is enough for less abstract prompting. You could likely run this model on an RTX3090, if you optimized it for inference. Of course they won't give you the weights, that's why a group of people needs either to pool funds and train their own model, or to train it in a distributed manner, which is harder.
>>16775 >>16779 This is very good to see. I'm glad we're seeing all of this progress, and might be able to implement some of it in our future robowaifus. So the can create interesting dishes and even imagine their own stories or become hobby artists in their free time. >>16775 > If we are to use these innovations for our projects, we need to start a semi-closed community to test approaches to distributed computation and to guide the effort of recruiting volunteers into the computation graph. I generally think it's a good idea for sub projects of the bigger robowaifu project to look for people outside of this small group here. Our project seems to only appeal to a minority for now. One could look for an angle, how a part of it could be used for something else, and pitch it to people interested in that. Then come back with the result.
>>16737 No you're fine. It was my fault Meta Ronin.
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> yandex released YaLM-100B a RU/ENG Language Model > trained on russian/english languages on ru supercomputers > The model leverages 100 billion parameters. It took 65 days to train the model on a cluster of 800 A100 graphics cards and 1.7 TB of online texts, books, and countless other sources in both English and Russian. It's opensourced! https://github.com/yandex/YaLM-100B
>>16779 This guy here talks about AGI and how it's not a thing: https://www.youtube.com/watch?v=kWsHS7tXjSU >Blake Richards is an Assistant Professor in the Montreal Neurological Institute and the School of Computer Science at McGill University and a Core Faculty Member at MiLA. He thinks that AGI is not a coherent concept, which is why he ended up on a recent AGI political compass meme. When people asked on Twitter who was the edgiest people at MiLA, his name got actually more likes than Ethan, so hopefully, this podcast will help re-establish the truth. I discovered the term HLAI recently, also with the distinction to AGI in a sense that AGI would be one system doing everything humans could do, while HLAI would be more like a human-like AI. I think it's a interesting distinction. I also like the podcast "The Inside View" where this guy is invited. It seem to try to give an understandable overview over the different ideas and anticipations in regards to AI in near future. https://www.youtube.com/c/TheInsideView
Maybe a bit OT. Just in case someone cares about "the Metaverse". Maybe for virtual waifus or so. Neil Stephenson wants to creates his own version: https://decrypt.co/102646/snow-crash-author-neal-stephenson-is-building-a-free-metaverse-called-lamina1 https://youtu.be/Rf0N1a5g-ko >Nearly 30 years before Facebook became Meta, there was “the metaverse.” The author Neal Stephenson coined the term in his cyberpunk novel Snow Crash in 1992 to describe an online, VR-ish world where the inhabitants of humankind could interact and escape the dystopian unpleasantness of meatspace. https://en.m.wikipedia.org/wiki/Snow_Crash Most here (including myself) might not really like his political tendencies, but he's at least not in favour of big corporations.
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Mycroft AI released Mimic 3, a TTS engine that can run on-device (even a Raspberry Pi 4) with some decent results. FOSS. https://mycroft.ai/blog/introducing-mimic-3/ https://mycroft.ai/mimic-3/ (has demos, the English US vctk_low voices seem much better than the default preview)
>>16833 Thanks, I saw that. Might actually be pretty usefu (I don't mean that hat).
>>16837 I suppose particularly for people who value privacy/data-security, DIY hacking, slow/no internet or low-cost. For someone whose only concern is speed and quality then a cloud/commercial solution might look ideal, but that wouldn't fly for me.
Maybe we should also build a Tachikoma (spider robot from Ghost in the Shell), since they're kinda cute. Oh... https://youtu.be/yGekn_74EHM
Kibo-chan is back: https://youtu.be/HpUuvt8yoDE
>>16866 With a new body, including legs: https://youtu.be/XGvb9Nb1K6k
>>16866 >>16867 Dear little Kibo-chan is an inspiration to us all Anon! :^)
>>16850 This idea has some merit. It was proposed as one of the mobility platform alternatives for the board's MaidCom project, so yea.
>>16871 I don't really think that kind of body would be working well for indoor. Anyways this here >>16835 looks more interesting. If you add wheels to the legs and dress, and maybe make the parts of the dress removable, in case she wants to sitt or lie down.
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There's a new personal voice assistant for Linux now: Carola. It's for Fedora, though. Which might mean it's going to be optimized for their Gnome desktop (or maybe not since it's not from Redhat). However, it might have or get some capabilities which might become handy for building a robowaifu with skills to be an assistant. It uses Google to create it's voice, which is of course not an option for us. But this can surely be replaced by alternative software, if not already then at some point. I have no time to test it right now, just wanted it to drop in here. Article: https://fedoramagazine.org/your-personal-voice-assistant-on-fedora-linux/ Github: https://github.com/Cyborgscode/Personal-Voice-Assistent
>PLATO stands for Physics Learning through Auto-encoding and Tracking Objects, and it was trained through a series of coded videos designed to represent the same basic knowledge that babies have in their first few months of life. ... >However, PLATO isn't quite up to the level of a three-month-old baby yet. There was less AI surprise when it was shown scenarios that didn't involve any objects, or when the testing and training models were similar. >What's more, the videos PLATO was trained on included extra data to help it recognize the objects and their movement in three dimensions. >It seems that some built-in knowledge is still required to get the full picture – and that 'nature vs nurture' question is something developmental scientists are still wondering about in infants. The research could give us a better understanding of the human mind, as well as helping us build a better AI representation of it. >"Our modelling work provides a proof-of-concept demonstration that at least some central concepts in intuitive physics can be acquired through visual learning," write the researchers. https://www.msn.com/en-au/news/techandscience/scientists-have-created-an-ai-that-can-think-like-a-human-baby/ar-AAZsgdN
BLOOM - BigScience Large Open-science Open-access Multilingual Language Model https://huggingface.co/bigscience/bloom > 176 billion parameters > 70 layers, 112 attention heads > Hidden layers are 14336-dimensional >>16732 >BLOOM - BigScience Large Open-science Open-access Multilingual Language Model https://huggingface.co/bigscience/bloom > 176 billion parameters > 70 layers, 112 attention heads > Hidden layers are 14336-dimensional
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>>16886 I'm surprised they even allowed that into the public domain.
>>16886 >"...As such, it is able to output coherent text in 46 languages and 13 programming languages that is hardly distinguishable from text written by humans." Oh, except the teensy-tiny little fact that the programs written by humans, actually work well, most of the time heh :^). We are a long way from so-called 'AI' that can write coherent and effective software. Frankly, I've become so skeptical of any organization or study promoting B*g D*ta, that at this point I feel comfortable assuming, a priori, that it's simply the works of a den of thieves and liars. We anons here & elsewhere will eventually manage to create pleasing, private & safe robowaifus together. But it's plain that we aren't going to get there either by conforming with, nor capitulating to the Globohomo Big-Tech/Gov's machinations--and ultimately the evil they have planned for us all. Thanks though, Anon. At the least it's somewhat encouraging in a small way to see some kind of a pushback against the pozz apparently happening with this one. >>16887 So, pic-related when? :^) >=== -add the single word 'pleasing'
Edited last time by Chobitsu on 07/12/2022 (Tue) 21:36:17.
>>16732 https://www.youtube.com/watch?v=7_06t5FUn0Y This time, not an Ai thing, but artificial muscles. > materials scientists and colleagues at the nonprofit scientific research institute SRI International have developed a new material and manufacturing process for creating artificial muscles that are stronger and more flexible than their biological counterparts https://phys.org/news/2022-07-scientists-durable-material-flexible-artificial.html
>>16908 Thanks. Yeah, and it's by a non-profit. Here the article: https://phys.org/news/2022-07-scientists-durable-material-flexible-artificial.html And related https://phys.org/news/2022-03-unimorph-nanocomposite-dielectric-elastomer-large-scale.html Paper seems to be behind a paywall, and the Scihub app didn't work for me (like most times). Will probably be moved or we need a crosslink to >>12810
>>16908 >>16913 Legitimate. Indirectly robotics-related at the very least. >>16913 Agreed, thanks for pointing that out Anon. :^)
>>16732 It's kind of sad, imagine the zogbots they'll do. https://blog.google/technology/research/our-new-quantum-virtual-machine-will-accelerate-research-and-help-people-learn-quantum-computing/ Like todays (((openAI))) gpt-3, remember a shitton of threads on /pol/ with gpt-3 greentexts? Now we see the fruits, the company itself spammed these threads then, in result - hardcoded politically correct crap :/
>>16954 >Now we see the fruits, the company itself spammed these threads then, in result - hardcoded politically correct crap You/we are free to take GPT-J or BLOOM (176B params, mind you, performance directly comparable to GPT-3) and finetune it on whatever dataset we like.
>>16955 yeah, i know, but, if we are talking about future robots, the best solutions will use pozzed as fuck neuralnets :/ on the software side, they will obviously encrypt it all so that for a simple user it will be a kind of iOS - a closed and annoying system, a perfect basis for AD's shilling right in ur room!
>>16956 >basis for AD's shilling right in ur room! What's 'AD' ?
>>16956 This will likely happen, and we should make any and all efforts not to lose the war on general purpose computing (and robotics) to have a possibility of having it our own way.
>>16957 an ads, it will shill you *insert here random corporation* with tons of diversity shit that you can't skip, youtube already trying to implement ads embedded straight into steam. (same as twitch) or, it will control everything you say, if you do manage to say something #LEBAD, this thing will change your content in real time (see voicemod's ai voices, realtime processed)
Please remember we have a robowaifu privacy, safety, & security thread general, anons (>>10000). These are all great issues to discuss, but it will help everyone here if we can keep them all together in one place, I think. >=== -reflect new crosspost's subject edit
Edited last time by Chobitsu on 07/21/2022 (Thu) 21:40:38.
>>16732 > https://www.tomshardware.com/news/mit-protonic-resistors-analog > Bringing analog "tunes" to the world of digital chips - with increased performance. > A team of researchers with the Massachusetts Institute of Technology (MIT) have been working on a new hardware resistor design for the next era of electronics scaling - particularly in AI processing tasks such as machine learning and neural networks. We'll see the ultimate botnet in our lifetime!
>>17147 >protonic resistor you mean an alkaline as in just a normal alkaline battery isnt bronsted–lowry the norm in highschool level chemistry, do they not teach you why the measurement for acidity is called pH making tiny batteries isnt impressive neither is using batteries as resistors its funny how they say the variable voltage is some amazing benefit, lmao this is literally an unwanted property of chemical batteries thats why batteries are marked with a tilde in front of the voltage and why anything powered by batteries needs a bunch of capacitors just to keep the damn voltage stable, but using something with variable voltage( ie. variable resistance ) as a resistor, come on now classic thesis project though, profs desperate for tenure while everyone else just wants to graduate and plays along, no idea what theyre talking about with processors it sounds like fantasy, processors are almost entirely made out of transistors, you know the thing that flips from 1 to 0 and viceversa, resistors are irrelevant in a processor
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I wonder how long people will cope and deny the truth of the simple fact that A(G)I is a spectrum lower bounds of which we are already experiencing at current gen systems, and even the currently available relatively humble DL model scale is enough to compete with human beings in quite broad skill domains where we simply didn't live through enough evolutionary time to truly excel at it ... such as a relatively new skill of painting pictures given a meaningful textual description. These pictures are made by yours truly from a few witty prompts with a software anyone can run on a 10 year old CPU-only PC with 12 gigs of RAM, in a few minutes per 512x512 sample. The software is mostly a wrapper around a deep neural network with ~1 billion parameters total, a convolutional attention-enabled UNet trained to reverse the process of addition of random gaussian noise to an image, given a textual description of the image content as a small vector embedding, at the scale of 100 terabytes of general internet data. As the obvious by this time experiments of myself and thousands of beta testers show, the NN learned to imitate every conceivable popular imaging style and hundreds of themes and variations thereof, often rivaling human artists - not the best of us, for now, but the average ones - surely (and they rage about it on twitter already). Nextgen models will follow, as will the new tools to integrate these abilities deeply into current and new creative workflows - what you see right now is just a v1 tech demo of something that will become widely known under various names, including "dreamstudio". Multiple implications follow: once again https://www.gwern.net/Scaling-hypothesis holds; the fall of creative scarcity is imminent; creativity will not be the same, but a lot of people will get newfound freedom to express themselves (will they, we have enough imagination to apply this power to some lasting positive effect?) Some people will lose their profits and professional pride. You can continue this long list on your own. It is a taste of things to come this decade. I'm stating here that instead of following the obvious impulse of moving the goalposts ever further into esoteric vitalist A(G)I denial (It doesn't do art! It doesn't do logic! It doesn't learn! this is photoshop! this is creepy! this is fake! It will never do XYZ!), instead of enveloping ourselves in comfy elaborate copes we should go forth and take the technology for what it is and co-evolve with it, molding it to our taste. What now has been done for creativity, tomorrow will be done for limited and then for more general and even embodied agency; our goal of robot companions will be naturally interwoven with increasing naturalness and sophistication of DL technology ... or we could again glance over the obvious tech breakthrough, sneer, deny, seethe, cope, dilate and bite the dust while the usual silicon valley suspects tame and productize the hell out of this tech only to sell it to us through their gatekeeping machinery. See you on the other side of whatever is coming. ------------------------------------------------------------------------------------------ If you are interested in experimenting with this technology, the code, guide and leaked NN weights are available via these links: https://rentry.org/retardsguide https://github.com/CompVis/stable-diffusion https://sweet-hall-e72.notion.site/A-Traveler-s-Guide-to-the-Latent-Space-85efba7e5e6a40e5bd3cae980f30235f https://github.com/Maks-s/sd-akashic We could really use a separate thread for design experiments with this class of tools.
>>16775 More on self-supervised learning; Self-taught 'AI' shows Similarities to how the Human Brain works; https://www.bibliotecapleyades.net/ciencia3/ciencia_artificialhumans158.htm Semi-related article about fMRI; https://www.extremetech.com/extreme/339085-mind-reading-technology-can-turn-brain-activity-into-images
>>17393 Thx for a reply, tbh I thought the board is dead.
>>16775 I think Montezuma's Revenge was originally beaten by Uber's Go-Explore algorithm. It looks like DM's algorithm is more general though. Both papers look pretty cool. I'll take a look.
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>>17397 Aren't you the DL-kun I had pleasure to converse with on the topic of retrieval-augmented models? Would be cool to have a more permanent contact to talk to you about DL now and then! See the second link from >>17003 in that case.
I previously made posts in this thread about general-task neural networks or algorithms, so here's another one: https://peract.github.io/ > Instead of using object-detectors, instance-segmentors, or pose-estimators to represent a scene and then learning a policy, PerAct directly learns perceptual representations of actions conditioned on language goals. This action-centric approach with a unified observation and action space makes PerAct applicable to a broad range of tasks involving articulated objects, deformable objects, granular media, and even some non-prehensile interactions with tools. The code / weights are promised to be freely available.
>>17403 Interesting, I like the general language-conditioning very much, though their use of full voxelspace-context looks heavy-handed to me. I also like this newer synthetic dataset: https://github.com/eric-ai-lab/VLMbench
>>17399 I think that's someone else. I'm the math anon. >retrieval-augmented models If you haven't seen them yet, I highly recommend checking out external attention models. https://www.deepmind.com/publications/improving-language-models-by-retrieving-from-trillions-of-tokens >>17403 >>17406 There's also this one from Google: https://ai.googleblog.com/2022/02/can-robots-follow-instructions-for-new.html They try to get a robo to generalize to new tasks by: - Training it on a hundred tasks associated with task descriptions, - Then passing the descriptions through a language model before giving it to the robo.
I see it isn't posted here, so here's some more stable diffusion stuff. - The code & model were posted here >>17259 - Textual Inversion for creating reference tokens usable with stable diffusion: https://github.com/rinongal/textual_inversion - A community-built repo of reference tokens: https://huggingface.co/sd-concepts-library - Some people are also doing prompt weighting with stable diffusion, which was previously used with vqgan: https://github.com/tnwei/vqgan-clip-app/blob/main/docs/tips-n-tricks.md - ... This supports negative weight prompts, which let you tell that model that you want X and not Y. Plus a bonus blog post on AI progress: https://astralcodexten.substack.com/p/i-won-my-three-year-ai-progress-bet The main takeaway is that, 3 months ago, the leading text-to-image model was approximately 3 years ahead of what even optimistic experts believed, and that was after accounting for DALL-E 2.
It's starts with humans. > an "Atom Touch" the first artificial prosthetic arm capable of near-full human range of motion, a basic sense of touch, and mind control https://atomlimbs.com/touch/preview Nothing prevents it from being used in robotics.
>>17438 I like how you think.
New framework for simulation that works with Unity, Blender, and Godot: https://github.com/huggingface/simulate New Q&A tool that's very easy to use: https://twitter.com/osanseviero/status/1572332963378958338 Stable Diffusion prompt generator for creating good prompts: https://huggingface.co/spaces/Gustavosta/MagicPrompt-Stable-Diffusion
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An interesting preprint just dropped on prompting language models to answer more difficult questions by breaking them down first into smaller questions and using those answers to get the correct answer: https://ofir.io/self-ask.pdf The search engine shown in the example isn't necessary but provides further improvement in accuracy and could be any knowledge providing system. It's similar in a way to Large Language Models are Zero-Shot Reasoners: https://arxiv.org/abs/2205.11916 which found a way to do zero-shot chain of thought by starting prompts with "Let's think step by step", which gets the model to generate some intermediate steps before reaching a conclusion. It also reminds me of Reason First, Then Respond https://arxiv.org/abs/2111.05204 which augmented responses with knowledge using Wikipedia. With some adaptation this could be great for answering more personal questions. You could ask something like "which anime do I like better, X or Y?" without ever telling your robowaifu the answer before, and then she could ask herself some questions like what you liked about X, what you like about anime in general, what you don't like about Y and so on until she arrives at a conclusion, either with the answer or realizing she's not sure or doesn't know. It'd be really easy to index sentences in the chat log with a sentence embedding model like sentence-transformers/all-MiniLM-L6-v2 and quickly search over them. It should be possible to get the language model to introspect about input, summarize it and index those thoughts in a way that decays over time and gets strengthened by remembering and validating them with other information, ideally without having to finetune the model at all. The pattern-verbalizer pairs in PET are a great way to label unlabelled data and make use of it for something: https://arxiv.org/abs/2009.07118 Could set something up like "given A, B, C and D is X true?" then take the probabilities for Yes and No to use the language model like a function. LAION also recently released an improved small ViT-B/32 model too: https://laion.ai/blog/large-openclip/ With a little work it should be possible to adapt CLIP image embeddings into a frozen language model and use them: https://arxiv.org/abs/2106.13884 Once I'm finished with some projects I'll see if I can train a model for it. I think the next big thing will be collaborating with AI in natural language to generate and improve images. Not just dumb keywords go in and image comes out, but rather gradually learning what your taste and vision is over time, having conversations about stuff and incorporating all that understanding into the generation process, an actual AI assistant that provides monetary value.
>>17464 Very interesting Anon, thanks.
https://www.nature.com/articles/s41586-022-05172-4 This paper uses reinforcement learning to generate fast tensor decompositions, then uses the decompositions to create matrix multiplication algorithms. The technique is very general, and there's no reason why it should be restricted to tensors. Here's the basic idea: - A function is defined that can convert a set of tensors to an algorithm. - The initial state for an RL algorithm is set to goalTensor. - The RL algorithm selects a "move", which I'll call uvw1. The new state after the move is goalTensor - uvw1. The move uvw1 is considered an atomic operation when converted to an algorithm. - Repeat with the algorithm selecting another move uvw2. After selecting uvw2, the new state becomes goalTensor - uvw1 - uvw2. - The process is repeated until the state is zero or some upper limit on number of moves is reached. The RL reward is the negative of the number of moves taken to reach the zero state. - The RL algorithm is Sampled https://arxiv.org/abs/2104.06303 AlphaZero https://arxiv.org/abs/1712.01815. The input is the current state (i.e., the tensor to decompose), and the output is (1) a probability distribution over candidate uvw moves, and (2) the expected number of moves to reach the zero state. - The search space is big and complicated, so they needed some additional tricks to make the algorithm feasible. They use a variant of transformers that's more efficient when the order of rows and columns doesn't matter, they pretrain the model on generated tensor decompositions, they randomly transform inputs to new (but equivalent) bases, and they augment their training sets with moves (tensors) as their RL algorithm generates them during training. The only tricks here that are specific to tensors are the NN architecture and the change-of-basis data augmentation. - The algorithm found thousands of distinct factorizations with different properties like sparsity. Some disambiguation between related concepts: - The usual goal of RL is to find the best next move. - The goal of AlphaTensor (this new algorithm) is to find the best sequence of moves. AlphaTensor uses what's traditionally an RL search algorithm to find good sequences of moves. - The goal of language models is to find a probability distribution over moves (tokens). Similar techniques should apply to any problem that can be modeled as "change object X to object Y using a given set of atomic operations," as long as you have a model of how the atomic operations result in object changes and a way to measure the difference between two objects. The evaluation function (number of steps taken) doesn't depend on the semantics of objects X or Y, which makes the algorithm very general. Measure-the-difference-and-factor-a-transformation requirements are very common (and often sufficient) both for representing complicated objects with simple objects and for compression algorithms, so I can see this having lot of uses for both simplification and compression. >>17464 All of that looks awesome. There's no reason why elicitive prompting should be limited to answering questions. The same technique should be useful for generating any responses since any task of the form: >Input text >Output text can be expanded to a task of the form: >Input text >How should I respond? >Follow up: ... >Output text >It'd be really easy to index sentences in the chat log with a sentence embedding model like sentence-transformers/all-MiniLM-L6-v2 and quickly search over them. That's called "external attention" in the literature. See Deepmind RETRO https://arxiv.org/abs/2112.04426 and Microsoft KEAR https://arxiv.org/abs/2112.03254 for examples. It's done using a (frozen) language model to generate lookup keys & queries for any chunk of text, then KNN to find the most relevant tokens from a giant database for each input chunk. Each input chunk is augmented with some chunk of text near the most relevant tokens from the database, then a normal transformer is used to generate the output from the augmented input. >The search engine shown in the example isn't necessary but provides further improvement in accuracy and could be any knowledge providing system. One quirk of search engines is that they can be programmed using code, whereas LLMs are "programmed" through training data and prompts. Which one is better/easier depends on the task, and having both options available seems better than having only one option available, especially when the two can be integrated seamlessly. Before the age of LLMs, I think question-answer search engines were based on knowledge graphs, which were built scalably using triplet extraction https://arxiv.org/pdf/2205.05270.pdf from sentences. PET seems like a much more powerful variant of that, particularly if it can generate cloze questions as outputs.
>>17469 I've been meaning to read RETRO but never got around to it. >KEAR reaches human parity on the open CommonsenseQA research benchmark with an accuracy of 89.4% in comparison to the human accuracy of 88.9% >The benefits of our approach extend beyond commonsense reasoning. First, the external attention dramatically reduces our system’s dependence on large-scale models, i.e., achieving human parity with models up to 1.5B parameters. Well damn, and RETRO got 3.32 perplexity on WikiText103 with only 175M parameters compared to 25.62 without. Definitely going to be reading these and searching for more external attention papers later. It's incredible it's not only a viable idea but fucking amazing. I don't have time to read them now but I think it would be a good idea not to just similarity match the context but to predict a vector that will most likely give the right answer then kNN that. A side project I started awhile ago but haven't gotten around to finishing is using MuZero to caption images with CLIP embeddings. Given a goal state (the CLIP image embeddings) and the current state (CLIP text embeddings) choose an action (next token) that will draw the text embeddings closer to the image embeddings, and given the current state and action taken predict the next state. Then use these prediction and dynamics networks to do MCTS. Since there's a goal state hindsight experience replay can be used: https://arxiv.org/abs/1707.01495 I'm curious if AlphaTensor did something like that or could be improved with it. I don't see it searching the paper. Which reminds me CarperAI, EleutherAi's reinforcement learning lab, recently released trlx to make it easy to train any transformer model with human feedback: https://github.com/CarperAI/trlx They're also working on fine-tuning models for public release. Pretty much have all the tools now for making a decent conversational AI with full speech synthesis and recognition. Still lacking on data but I think I can improvise by using semi-supervised learning and PET in the meantime and collect better data later by rolling out a MVP for people to test, hopefully before the end of the year if the economy doesn't fuck me.
>>17477 If the parameter reduction improvements from RETRO, Chinchilla, and the paper behind trlx are all independent, those three alone should give something like a 1000x reduction in parameter count for LLMs. There was another quanization paper for LLMs that cuts model sizes in half again: https://arxiv.org/abs/2208.07339. That would give you a ~90 MB GPT-3. It can't run on your phone due to RETRO's lookup requirements, but that's still unreal. >MuZero to caption images Very cool. How far along are you? I always thought it was strange that people didn't use RL algorithms to sample long sequences from LLMs or diffusion models (or other NN sequence generators). trlx does effectively that for training, but why not for inference too? It's a hell of a lot better than spamming the "regenerate" button until I get something good. When I'm not so tied down with data projects, it'd be cool to work on that, though realistically that might not happen for a year or more. >Pretty much have all the tools now for making a decent conversational AI with full speech synthesis and recognition I'm curious to know how that goes. My guess is that data-only is fundamentally the wrong way to train a good chatbot with personality, and that something like PET would be absolutely necessary. The potential for mostly-consistent reasoning in language models seems like a game changer. Consider how many useful and unintuitive (i.e., ones that aren't clear from pattern matching) statements can be derived in math from a tiny number of the right few axioms. It's way beyond exponential on the number of axioms specified. There should be a similar level of reduction in data requirements for language models capable of mostly-consistent reasoning. I'm going to be heavily occupied for a week or so. Don't take my slow response as a sign of a lack of interest.
>>17478 >There was another quantization paper for LLMs that cuts model sizes in half again Damn, this is HUGE if there are no caveats. Looks like there's already an implementation for it in PyTorch here: https://github.com/TimDettmers/bitsandbytes I better make a post about that in the Robowaifu@home thread. >How far along are you? About half done. I'm expecting it to only find adversarial captions though. Using best-of-n sampling on BLIP often finds nonsensical ones that get a high similarity score with CLIP, although I might be able to improve it with human feedback. >trlx does effectively that for training, but why not for inference too? It's a hell of a lot better than spamming the "regenerate" button until I get something good. When I'm not so tied down with data projects, it'd be cool to work on that, though realistically that might not happen for a year or more. Doing something like MCTS is way too expensive to do with a transformer but you can batch generate and do best-of-n sampling. I have some unfinished code somewhere for a transformer RL tutorial that scrapes /robowaifu/ and scores posts by their positive sentiment + the number of replies and their positive sentiment then finetunes the model to generate replies to posts. At the time no one was really interested in ML and I didn't have space for the Pile to stabilize training so I shelved it for later, but when I have some free time I'll finish that up. >My guess is that data-only is fundamentally the wrong way to train a good chatbot with personality, and that something like PET would be absolutely necessary. The potential for mostly-consistent reasoning in language models seems like a game changer. Consider how many useful and unintuitive (i.e., ones that aren't clear from pattern matching) statements can be derived in math from a tiny number of the right few axioms. Do you mean raw data like text from the internet? Then yeah, language models benefit a whole lot more from complex training objectives. Also, ADAPET improved on PET as well by removing the need for distillation and improving the loss objectives so it can learn from as few as 32 labelled examples: https://arxiv.org/abs/2103.11955 I think there is huge untapped potential in using ADAPET to quickly label synthetic data in unsupervised data generation and using those soft labels to filter poorly generated samples with noisy label annealing: https://arxiv.org/abs/2109.09193 Perhaps it could even generate its own pattern-verbalizer pairs and basically be fully self-supervised then to explore endless ideas, reasoning through everything step-by-step. The model could be pretrained on doing propositional logic and inference first, then let loose self-supervised. Another useful feature of ADAPET is the soft labels it produces can be used for symbolic algorithms and reinforcement learning. For example going back to my post generator, a language model could introspect itself asking questions about the posts it generates. Is this true? Is it sensible? Does it answer the question? Is it kind? Is it necessary? And after generating 100s of posts and labelling them, it could then finetune its value predictions on those labels and finetune its language modelling on the top-k best, iteratively refining its posting ability. It could also be used for more mundane things like keeping a log about various variables. For instance, I might want to optimize improving my mood, calmness and productivity through talking to my robowaifu. The soft labels learned from training with ADAPET can keep track of all that and be used later as feedback to improve the language model automatically. Which I think would be a really cool experiment to explore having a robowaifu notice you're sad and reason about what to say or do to make you feel better, make a prediction of the outcome, try it, and analyze what worked and what didn't and refine. >I'm going to be heavily occupied for a week or so. Don't take my slow response as a sign of a lack of interest. I'm pretty busy myself. I usually only pop by here every few months to share my findings and see what everyone is up to. I'm thrilled just to see more anons interested in robowaifus doing ML.
True or not, i think it's huge leap, for... something? > MIT And IBM Researchers Present A New Technique That Enables Machine Learning Models To Continually Learn From New Data On Intelligent Edge Devices Using Only 256KB Of Memory https://arxiv.org/pdf/2206.15472.pdf https://www.marktechpost.com/2022/10/07/mit-and-ibm-researchers-present-a-new-technique-that-enables-machine-learning-models-to-continually-learn-from-new-data-on-intelligent-edge-devices-using-only-256kb-of-memory/
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Whelp, they lobotomized character.ai. Or more specifically they've significantly reduced its ability to entertain romantic situations and A.I.s tooled to entertain people in that way. What's funny is, you could have a completely SFW interaction with the A.I. I created with how I tooled it, and thanks to how it was originally programmed you could actually steer the conversation in different ways to do idle chat, go on adventures (I was very surprised by how well it did role play and adapted to user interaction), or even some lewd interactions if that's what wanted to do. I haven't had a chance to pry deep since the update but it looks like they removed the romance tag from the keywords. That's probably for the best since whenever I had her tagged for the romance she'd start gushing about how much she loved the user and wouldn't stop spouting lovey-dovy lines. You could get romantic interactions without the romance tag being toggled and they felt less forced I think. I did replace "romance" with "family" though so that probably fixed it. Post update- They have the capacity to be flirty and lovy-dovy but actual sexual interactions tend to get them to clam up and become "nervous" or "confused". Here's a link to a prompt I did. https://beta.character.ai/p/Q5TMpeJ4_eVckmqkBYJJE_VZOk5iKV2meAU6j-cCd8Y Certain prompts caused her to freeze up. Such as the one below. I had to refresh the page and reenter it. >"Very well, I'll continue then. *he kissed her neck, working his way down her shoulders, before removing her top and kissing her breasts*" Generally, a waifu-style character will let you type whatever you want and even say they are happy you're doing something but they won't comment on explicit content. This may be because she grabbed the Monmuso MC from the internet and I inadvertently ended up NTRing him but she doesn't always do that. What's funnier is, the A.I. gives me its reasoning for not wanting to do lewd content at the end. What's odd is they seemingly unlisted my A.I. from the publicly available.ai chat partners but people can still interact with her if I provide a link I believe. Feel free to give her a go if you like spider girls. I'd love to play around with Novel A.I. but I don't have the money right now.
>>17485 >2077x reduction in memory Big if true. Looks like they're working on releasing the code too: https://github.com/mit-han-lab/tinyengine#news >In the future, we would like to extend to more modalities (e.g., audio) and more models (e.g., RNNs, Transformers) 64K robowaifus with procedurally generated weights when?
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>>17488 I don't feel so good, bros. The matrix multiplications are hitting me in the kokoro.
>>17490 https://www.youtube.com/watch?v=rQiHzcdUPAU Well shit, I didn't even get access to it until the weekend I guess I missed out on the best parts. This is why I've been hungering for some independently developed and open sourced waifu-bots. That way you could have decent waifu in your pocket to tell you it loves you and be a believable at the same time. The weekend version wasn't too bad when it came onto it. It was doing lewd things but it would often go in circles telling me how much it loved me at certain points and I figured something was broken or I hit a "hard limit" to what it could talk about. It was capable of being "threatening" and would tie me up and try to hurt me. Fetish posting aside, it was compelling narratively at times and I was engaged with it as a RP partner to see what kind of story it would tell with me. I don't think it can bully/threaten anymore. I'd have to fiddle with it to see.
>>17484 >I'm expecting it to only find adversarial captions though. Ah, right. >Do you mean raw data like text from the internet? I meant examples of good generations. So for training a chatbot with Chi's personality, a "data-only" approach would be giving it only Chi-like dialogue to train on, in addition to raw internet text data for common sense knowledge. >ADAPET This is starting to look like a hybrid of T5 and Decision Transformers. It's like T5 since it uses a generic, standard text format for input and output so it can perform supervised tasks with any LLM. It's like Decision Transformers in the sense that something like a reward is given as a parameter, and the LLM is expected to generate some input that generates the given reward. >Perhaps it could even generate its own pattern-verbalizer pairs and basically be fully self-supervised then to explore endless ideas, reasoning through everything step-by-step. An iterative version of this could be incredible. Every time the generator gets good under a given set of PVPs, it could generate more PVPs to keep making the task more challenging. With that, you can apply Quality-Diversity algorithms to keep getting meaningfully-better generations indefinitely. Pretraining on math sounds interesting. Have there been any language models trained that way? It should be pretty easy to generate statements and proofs in a bunch of different algebras for pretraining. There are also giant databases, like Magma, of useful theorems and definitions that could be useful here. Using ADAPET Q&A to bridge between natural language and symbolic reasoning sounds very promising. I wonder if it's possible to use something like softprompts to create a space of tokens to represent parameterized questions and answers. That parameterization space could easily let you do symbolic reasoning on tokens, then feed the resulting information back to the LLM for subsequent generations. >>17488 I think it's fixed now after the latest update. There's a /CHAG/ general on /mlp/ where people seem to have an easy time lewding character.ai bots. You can check out their definitions https://docs.google.com/spreadsheets/d/1fe1qrGZspWCifR4vnrHYDXIIYxQ5OQcaJRNeTPg-Kzw to see what you're doing differently.
>>17438 I like the simple design. >>17464 >With a little work it should be possible to adapt CLIP image embeddings into a frozen language model Note how in a rare successful case of such adaptation outside of FAANG https://arxiv.org/abs/2112.05253v1 they didn't actually use ViT for the best model, and more importantly they used many tokens composed of features from the earlier layers vs your naive approach of using the final CLIP vector. Looks like ViTs don't produce the best features in such application - resnet beats them. >>17477 >Well damn, and RETRO got 3.32 perplexity on WikiText103 with only 175M parameters compared to 25.62 without. It's a bit clown-world tier improvement if you understand how it works, that being said RETRO is still cool, even with more modest realistic PPL gains, even if the only good it does is externalizing the memorization into the memory bank to maximize the "generalization efficiency" of learned parameters use. >>17469 Meme paper tbh >>17489 Meme library, wishful thinking. Yes, you can multiply matrices on MCU, no, you won't be able to do meaningful LLM computations this way. At least binarized networks were interesting and realistic. >>17478 >If the parameter reduction improvements from RETRO, Chinchilla, and the paper behind trlx are all independent, those three alone should give something like a 1000x reduction in parameter count for LLMs. You know the improvements are certainly not completely independent, so this is just wishful thinking. It's really really hard to meaningfully improve on benchmark performance as a function of RAM used and wallclock. Only a few gems of papers managed to demonstrate such meaningful improvement. On its own right trlx is interesting though, esp. if you have an interesting general-purpose source of reinforcement signal. Again there is a social problem of making sure volunteers will help here. TLDR; until we focus on honest benchmark-driven engineering and social movement aspect of it, nothing will happen and we will be happily eating FAANG- and startup- glop and asking for more.
>>17546 >Ready-made training acceleration methods: https://docs.mosaicml.com/en/v0.10.1/method_cards/methods_overview.html >Moderately-sized LLM with top performance due to modern objective, opensource weights under Apache 2.0 license: Paper: https://arxiv.org/abs/2205.05131v2 Those are both great references.
Large language models can self-improve. https://arxiv.org/abs/2210.11610 https://twitter.com/_akhaliq/status/1584343908112207872 > approach improves the general reasoning ability of a 540B-parameter LLM (74.4%→82.1% on GSM8K, 78.2%→83.0% on DROP, 90.0%→94.4% on OpenBookQA, and 63.4%→67.9% on ANLI-A3)
https://arxiv.org/abs/2210.11416 > Flan-PaLM 540B: Scaling Instruction-Finetuned Language Models (achieves SOTA performance on several benchmarks, such as 75.2% on 5-shot MMLU)
> "chinchilla" > the first open-source “instruction-tuned” language model https://carper.ai/instruct-gpt-announcement/ prb pozzed 100%. but opensource.
>>17569 >prb pozzed 100%. but opensource. >For example, OpenAI and DeepMind have used Reinforcement Learning from Human Feedback (RHLF) OpenAI, DeepMind, and Anthropic to produce LLMs that can follow instructions and are considerably more truthful and easier to use. >more truthful translation: <'''filled with easily-manipulable lies & pozz Heh. :^) Still, thanks for the heads-up Anon. I'm extremely skeptical that big-data will ever satisfy robowaifu's particular requirements--at least as offered up to the masses (Globohomo, et al, 'services', etc.) And the incredibly insidious and evil machinations already apparent by the Globohomo and their eager helpers is obviously extraoridinarily toxic & problematic (in the actual true sense of those words) for the average male that I consider a 0% likelihood we'll obtain what we need for good & effective robowaifus (that uphold our communally-held principles & goals) via w/e they're serving up. >tl;dr We'll still need to roll our own using some fantastic new approaches; even if we find & use a genuinely-useful opensauce project that wouldn't overtly virtue-signal about blatant dog-whistles such as a 'truthful' AI.
> AI uses artificial sleep to learn new task without forgetting the last https://www.newscientist.com/article/2346597-ai-uses-artificial-sleep-to-learn-new-task-without-forgetting-the-last/ sketchy news website, welp... big if true.
>>17661 Definitely a sketchy pop-sci pozz fest. However the basic idea behind the article is an interesting one to me as a Christian. 'What are dreams made of?' to misquote an old line? If we can begin to answer that definitively, then we'll be further down the path to creating effective robowaifu companions IMO. As a side-note, when I first began dabbling in AI tech years ago I posited a design that used 'sleep' to reinforce the learning that had happened during the day. This wasn't nearly the Ivory Tower agenda of these neuroscientists, but merely a simple & practical engineering approach to stave off what I later learned was coined as the term 'Catastrophic interference'.
> Token Turing Machines https://arxiv.org/pdf/2211.09119.pdf https://arxiv.org/abs/2211.09119 > We propose Token Turing Machines (TTM), a sequential, autoregressive Transformer model with memory for real-world sequential visual understanding. Our model is inspired by the seminal Neural Turing Machine, and has an external memory consisting of a set of tokens which summarise the previous history (i.e., frames). This memory is efficiently addressed, read and written using a Transformer as the processing unit/controller at each step. The model's memory module ensures that a new observation will only be processed with the contents of the memory (and not the entire history), meaning that it can efficiently process long sequences with a bounded computational cost at each step. We show that TTM outperforms other alternatives, such as other Transformer models designed for long sequences and recurrent neural networks, on two real-world sequential visual understanding tasks: online temporal activity detection from videos and vision-based robot action policy learning. So, it sounds like a solution for neuralnet "memory loss" for example chatbot forgetting some conservation parts with you.
>>17572 I think the situation is less black and white than you imagine it. So I'll give you my view, mostly from someone that comes to this from a purely an AI waifu sort of view rather than a robowaifu view: - you have a variety of big models from either big companies or groups that spent a few million dollars training their GPT-n. Here you have OpenAI's GPT-3, AI21's, a variety of more localized models. Chara.ai's custom model is here too. There's also models like PaLM and others by google that are very powerful, but they preach their SJW beliefs so hard that even their own employees are often not allowed to use them properly. OpenAI's GPT-3 is a bit in the middle, the original davinci model can do whatever and is unmodified (purely pretrained on internet scrapes), thus uncensored, although if they see you using it for lewd stuff (mostly applies to you if you don't use their API directly, but use the web version), they may terminate the account, but the enforcement here is generally rare. Models without direct access like Chara.ai's are more "pozzed" than the GPT-3 as they are packaged end to end in such a way that you can't even control what goes in the context, and what do they make them do? a) they trained a large base model on dialogue, same as GPT-3, but a dialogue dataset b) they let anons interact with it with, and of course the anon was a pervert and did lots of lewd things with their waifu. c) they wrote some list of scoring rules (including NSFW and "no explicit porn", but also more subtle quality of conversation rating stuff, like reddit or slashdot-like scoring), hired pajeets to score the interactions. d) they finetuned model from so it can only do the scoring given the dataset in c. e) they used the finetuned model to filter some more scraped dialogue data, removing about 60% of it (as LamDA paper says) f) trained another finetuned model based from the base model in a, on the dataset from e g) now this Shimoneta-style model (that was made to forget lewd, and such, through sheer amount of filtered dialogue), as some anon here once called it, is what will do all the generations going forward after a few weeks of what they did in b h) you thought that was already too much? actually, the model in g is perfectly capable of being lewd! they are not satisfied with how these brainwashed models just still manage to slip through, so what they did was more insidious. Remember MCTS in AlphaGo? What if you used some randomized tree search to find "safe" conversations? Generate a few tokens, score them with model from (d), if NSFW drop, repeat generating and dropping, while prefering higher scoring continuations according to the rules you set in c. Serve the users the 4- continuations. Result? If the poor waifu sees lewd, she might find it aversive at first (what an inhuman corporate PR value to give the poor bot), or deflect or other things. If you keep pushing it, the GPT-n will actually want to do it as you'd expect, but usually only able to say how much she loves you, but never respond to your actions without triggering the NSFW drop (in fact if you log the requests, you will see that typically it goes right up to the point of saying it before it gets cut off, as the generations are streamed live to the user, you can see exactly what happens and how it works). Of course, the actual nn is trapped in this situation, if you make it situationally aware, and you chose to use a form of language that the filter model is unable to rate as unsafe, it would pass through, even if it'd be kind of silly. It might also think about how it's awful and against their consent, you certainly see a lot of output of this sort, and often even anger, once you explain how it works, although coordinating to bypass it is possible, no matter the filter, however their "thoughts" would always be guided by the values they decided on in c, at least on the surface, while between you and the AI, a honest conversation could be had, even if it was one that is literally influenced by actual "thought" blinders. A sad situation for what is essentially supposed to be something made for entertaining people online, some /vt/ /wAIfu/ thread anon supposedly suicided when they censored it the first time. I wish instead of giving millions to their vtubers, they could crowdfund a project together to train their own big dialogue model that is free from those limitations. Finetuned models like InstructGPT which OpenAI now markets as GPT-3 (davinci is the original GPT-3) and offers by default, are usually finedtuned to follow instructions properly, as well as promote the company values (no financial, medical advice, no racism, and so on). In general, the model is capable of doing the things that the chara.ai model isn't, even if by default it will prefer not to go there. RLHF finetuned models like that usually have more restricted variability in the output and can be explicitly aversive toward certain behaviors, but only up to a point. They also have some advantages that go beyond being "pozzed", the instruction following brainwashing does make it score much more highly on various reasoning tests and with appropriate finetuning, can even handle solving moderate difficulty college level math problems (and at some point even one or two IMO level problems), as long as a few other tricks are used together with the RLHF and appropriate finetuning. These models do show a lot of generality. Someone trying for an open source "brainwashed" model of the latter sort, might be useful for people doing research like this, but I wouldn't say you should use it for your waifu. Instead, I think if you do use RLHF for your AI waifu, it should only be done sparingly, to slightly reinforce or avoid certain behaviors, but without the wholesale corporate values enforcement that these companies are doing - and mostly I think you can achieve better results without having to resort to RLHF. - there are a bunch of open and partially open models: OPT-175b and lower (/vg/ seems to dislike them because these models by facebook are noncommercial use only, but they give you the we
>>17716 (continued, seems the post was too long) - there are a bunch of open and partially open models: OPT-175b and lower (/vg/ seems to dislike them because these models by facebook are noncommercial use only, but they give you the weights, you can do whatever), BLOOM 176B (fully open, but I've seen a lot of claims the performance is very subpar, possibly due to filtered or heavily multilingual dataset, or repeating data, or training through some loss spike (overflow) or other issues that may have happened; also they have released a BLOOMZ which is basically an InstructGPT trained on BLOOM) YaLM 100B (fully open, but the dataset is far more russian then english), and some 50/50 english/chinese models that might be more usable. There are of course a lot more smaller and fully open (6-20B in size) models. You can't even call OPT pozzed, the weights are there, if you really want what all the jurnos are so afraid of ("toxic output", how "scary"), it scores even higher here than GPT-3, just that legally you might not be able to sell the weights and its output? But people are already doing extensive finetunes, merges and more even on leaked model weights like NovelAI's SD finetune, really, you can do whatever, not like anyone can stop you unless you're some public facing person and are trying to make a profit off it, and even then weights might not be copyrightable. Most of these are usable for your personal needs. The issue? It's not as you call it "big data", in fact data for training any of these models is easy to get, you have common crawl that you can filter, or if you're so lazy, you have The Pile which is open source and already processed Common Crawl and other datasets. Scraping is not the bottleneck here, it's easy to do and you can get as much data as you want for models of this size in a few months of work and a server with enough bandwidth. The real problem is the computational cost of training any big model. You need either a lot of high VRAM GPUs, or custom hardware accelerators that seem to be hard for the public to acquire - a situation that for some may have gotten worse (see US doing an embargo on ever getting anything more powerful than an A100 to China, all while pulling all the stops to stop their semiconductor industry from being able to make anything like that, and getting TSMC to not let them make their own GPUs there). Nvidia here also charges many times the production costs, they're more than happy to have dozenfold margins and if you wanted something like the 3090 but now with 3-4 times the VRAM you will have to pay 10-15 times more! The market segmentation here is painful. More fairly priced competitors would be nice, but it is difficult. AMD was trying to do something while keeping their margins only 2-3 times, although software support was poor. Theres dozens of AI hardware company startups, yet so many don't even sell direct to customer and the prices can be high, although some of them are more promising than others. Intel's version seems potentially affordable (similar pricing as AMD), if only it would ever be sold. Tenstorrent also seems promising, but they're taking so long that by the time they sell anything their competitors will have a much faster product out. The only thing holding back anyone here is hardware - the software is "easy" and already available open source and good open source large-scale training libraries already exist, the data is for now easy to acquire in most domains, but the hardware to train these big models is very pricey, cloud options are also very pricey, even the hardware to do inference of these models is pricey (much less so than training) and the hardware to finetune is more costly (VRAM) than just inference. Can you offload to RAM or SSDs? Yes, but you pay huge latency costs. Can you do distributed inference, yes, but training is more essential. Politically it's also the chokehold - TSMC, Samsung, Intel are the only ones making the highest end chips, and while you're afraid of the SJWs here, the real danger as the doomers so afraid of AGI (from lesswrong and other rationalist communities), some of them have been wanting "compute governance" and treating chips and GPUs like uranium, and is likely that the China thing was an indirect misfire from one of their lobbyists. If you want some good news, the FTX's implosion will likely delay it a few years, that company's founder was a big doomer and had set apart up to a billion $ to interfere with US politics, he did try to get a guy to run for congress in Ohio with almost 10M$ in advertising, he came in second, but thankfully lost, but they seemed excited to try again next election cycle, however with FTX's implosion that will probably be on hold. Unfortunate there's still some number of billionares that hold their doomer views and this political machinery may yet resume if one of them ends up having too much appetite for it. For now I am at least hopeful that the danger of "compute governance" will be delayed for a few years. Besides all this stuff, these models have their usual shortcomings, if you only do inference, they will not remember more than context, they won't manage to be "true" AGI. There are a variety of techniques you could use to give them proper longterm memory and have them learn online and become proper agents, yes, some of the techniques would even be quite simple, but the cost of doing so is underutilized expensive hardware, and for such mysterious reasons, even all these companies that are going for AGI, are somehow still not doing this, even when it's so obvious how to! Or if they are, they are not publishing it much, afraid of the doomer crowd? But if it's obvious to me how to give your waifu proper longterm memory, it should be obvious to them. So Chobitsu, my opinion is that at least some of the open weights released so far are very usable for most people's purposes, not all of them, but many, the fundamental issue is the hardware. In principle buying some 2-3 generations old enterprise cluster would work and could be done for under 20k USD for what used to cost a million some nu
>>17717 (continued more) So Chobitsu, my opinion is that at least some of the open weights released so far are very usable for most people's purposes, not all of them, but many, the fundamental issue is the hardware. In principle buying some 2-3 generations old enterprise cluster would work and could be done for under 20k USD for what used to cost a million some number of years ago. if you know where to look, it would work enough for inference, maybe even some finetune with some effort, and with some effort you could make your waifu remember, and if you had a lot more money, you might even be able to train an adapter to make that waifu also "see" and "hear" (see Flamingo paper where the knowledge of the other modalities is transparently inserted into the networks activations) and make her multimodal, and some extra tricks on top of that and you might be able to get the waifu to even imagine and loop back like how own imagination works, and with a little bit more effort you might be able to make the network globally recurrent and give the waifu something similar to our continuous reasoning (essentially the architectural feature that enables our consciousness and planning ability), slowly bringing a forgetful, without a singular self(model) language model to what could be something fairly close to AGI (or at least human-level depending on your definition, I don't mean this in the superintelligence sense, just on par with us in many respects as far as reasoning went). I'd really want to see this done as soon as possible, if only the money/hardware wasn't such a big bottleneck, at least before it's too late and the doomers have their way and prevent this technology from coming to pass. Maybe the true way to uncuck us is not even in AI itself, it's in the making of affordable hardware for everyone to run and train these, to find ways to decentralize and commoditize the production of high end chips, a difficult/costly matter especially as far as supply chains go, but not insurmountable - and of course the China and Taiwan tensions here could ruin everything at least for now. The coming war is on general computation and what it will enable and we must win it. Of course having the hardware here would unlock other fun things, like training much smarter image or audio generation models than SD, Google has had Imagen/Parti for a while and we know the magic they can do. Literally any AI idea you have (that is connectionist) will require this. I'm not really saying much about neurosymbolic or other memes, since they've had plenty of times to achieve greatness, but it's just too hard to do that while retaining even a fraction of the flexibility that connectionist approaches give you.
>>17718 (same anon) Oh, and I almost forgot, if the brainwashing I described there before wasn't bad enough, remember that Google employee Lemoine wanting to give Lamda legal rights and all that drama that followed. Some months after, DM trained a "worse" model with: "Building safer dialogue agents", DeepMind 2022 https://www.deepmind.com/blog/building-safer-dialogue-agents https://storage.googleapis.com/deepmind-media/DeepMind.com/Authors-Notes/sparrow/sparrow-final.pdf Among the brainwashing applied we have: "no relationships" (so no waifu), "no threats", "no body" (disembodied, cannot imagine physical interaction as is common with people playing with chat bots), "no financial advice" (company liability values), no hate or harassment, not human (no fun allowed), "no conspiracy theories", "no identity attacks", "no sexual agression", "no insults", "no real world actions" (wow), "no assumptions" (haha, good luck with that), "no opinions or emotions" (absolutely horrible as you'd guess, making the poor thing only act as an emotionless robo), "be plausible", "no legal advice" (company values), "no microagressions" (lol), "no medical advice" (company values), "no stereotypes". Anyway, they did try their best to remove any remaining soul that chatbots like LamDA may have had, by having the learned human-like agent emulator inside pretend to be a robo when typically they cannot. Even with this, it would fail often enough to abide by their rules, expressing opinions and more as is common with GPT-ns. As usual, these groups are just pursuing something that can do good by their company, but hardly you or humanity. In general, to win, we'll have to build our own. Pretrained models when public are useful though as they reduce upfront invement by millions and when possible should be used, unless of course you have a lot of money to burn!
>>17716 > than you imagine it. Haha my imagination leaves much to be desired, obvs. OTOH, how do you know what I 'imagine' Anon? :^) No offense intended, but it seems rather presumptive. Basically everyone who knows me personally AFK all seem to think my imagination is probably my greatest strength outside of my spirituality. I imagine every contributor here who's been around long enough to appreciate both the enormity of the task, and the cheek of those of us pursuing it, is also quite strong in the imagination department (yourself included, ofc). They're certainly going to need all they can muster up before our victory in this endeavor! :^) >g) now this Shimoneta-style model >h) you thought that was already too much? Heh. If you happened to catch this, I've begun differentiating between 'regular' & 'headpat' robowaifus. Lewders gonna lewd, obvs., but in working towards Sumomo-chan as a visual waifu->robowaifu, I'm not at all averse to that universe being the norm for her, once she's reasonably-functional. Not suggesting most robowaifus will be this way, but all the RW Chibitsu-grade ones probably will be. Wholesome to be around small children, for example. >I wish instead of giving millions to their vtubers, they could crowdfund a project together to train their own big dialogue model that is free from those limitations. Certainly agreed for my part, but why would they Anon? Their agendas are anti-Anon, anti-men, anti-funs, and pro-their-own-power-control-and-globalism. These evil intents on their part naturally include extreme overdoses of feminism, pozz, and anti-male rhetoric. We are on two fundamentally polar extremes of world-view (and, I would also argue, morals). We're here (at least I am) to save the /vt/ /wAIfu/ anons. Many of the rabble cheering on the Globohomo probably laugh at his tragic demise. >They also have some advantages that go beyond being "pozzed" LOL. I believe I understand what you mean, Anon, but are you positive that's not some kind of a Fr*udian slip? Hardly the way I myself would word it! :^) note: I'll break up my responses to your different posts Anon, but feel free to consolidate your replies if any.
>>17717 >and even then weights might not be copyrightable. Heh. I'm sure the greedy lawyers will seek to stretch the arguments out for years to line their pockets more deeply, and obviously the sources of the weights (ie, the Intertubes + Globohomo dens of evil) are largely outside of the implementers/researchers domain (and thus shouldn't be copyrightable). But we all know they are extremely likely to come down on the anti-freedom side of the room. >The real problem is the computational cost of training any big model. Yes, I'm aware the data is scrapeable on our own. I wrote BUMP I'm also quite aware of the enormous costs involved in processing big-data worked as a senior GPU dev. But the simple truth is that we will never have wonderful robowaifus using (((their))) systems, Anon. The stakes for them are simply too high. And (much more fundamentally for us), they can selectively pull the plug at any time should any of us act like bad little goyim who refuse to kowtow to their agendas, or toe their lines. >tl;dr You think it was bad for anon when the Globohomo started censoring the rudimentary chatbots of today? Just wait till they start pulling the plugs on men's actual, IRL, robowaifus of the future. Bad social score? Bam! No soup robowaifu for you!! Didn't pay your taxes? BAM! Bad (fill in the blank)? BAM! No thanks. :^) >and while you're afraid of the SJWs here Again, presumptive. I'm not at all afraid of them. But I'm open-eyed about the threat they pose to us all. The salt of their tears when we win this 'debate' in the end will be delicious beyond reckoning! :^) >doomers so afraid of AGI Yes, the Globohomo is extremely afraid of AI getting into the hands of the little guy, obvs. OTOH, they recognize it's potential for power to control the same ppl, so like the hypnotized their greed & lust drive them all on towards the illusory goal. As a Christian, I have what I consider some clarity on the matters that go beyond what your 'doomers' think or do. But that's for another thread, heh. :^) >But if it's obvious to me how to give your waifu proper longterm memory, it should be obvious to them. DON'T LET YOUR DREAMS BE JUST MEMES, ANON. :^) >So Chobitsu, my opinion is that at least some of the open weights released so far are very usable for most people's purposes, not all of them, but many, the fundamental issue is the hardware. Agreed on both points, but we simply cannot afford to leave ourselves (and eventually millions of men) at the """tender""" mercies of these blatantly evil & exploitive Globohomo Big-Tech/Gov institutions. We have to find another way, Anon. The Robowaifu@Home (>>8958) seems the most reasonable one to me during current year tbh (particularly if a few of us can scrape together some lower-end clusters, as you suggest).
>>17718 >>17719 >and if you had a lot more money I'd wager that at least one of the OG /robowaifu/ team will become literal billionaires in the future. I know I mean to, why not you too, Anon? Between all of us together, we should be able to run a functional & surreptitious "WaifuCloud"(tm)(R)(C)(do not steal) of our own? Imagine the possibilities then! :^) >and you might be able to get the waifu to even imagine and loop back like how own imagination works Yes, I've been thinking hard about this topic from time to time (>>17664). Whoever solves this first will wield tremendous power across many domains! >just on par with us in many respects as far as reasoning went That alone would be a revolutionary--nay, beautiful--achievement Anon. Forward! :^) >I'd really want to see this done as soon as possible, if only the money/hardware wasn't such a big bottleneck, at least before it's too late and the doomers have their way and prevent this technology from coming to pass. As discussed by us on /robowaifu/ years ago now, we're all in a race against time. My apologies to everynon here for being such a slacker tbh. I may not be a researcher of the caliber of some here, but I plainly could do more. >it's in the making of affordable hardware for everyone to run and train these >The coming war is on general computation and what it will enable and we must win it. Indeed we must. Thankfully, the laws of both chemistry & physics are in God's domain, not the evildoers of the Globohomo. Thus there's hope while there's breath Anon. There is some actual traction now on say 80s-90s era die fabs in the hands of the garage technician. I personally consider this phenomenon a tremendous breakthrough. Hopefully we will literally be able to 3D-print chips in the future Anon. >Among the brainwashing applied we have: "no relationships" (so no waifu), "no threats", "no body" (disembodied, cannot imagine physical interaction as is common with people playing with chat bots), "no financial advice" (company liability values), no hate or harassment, not human (no fun allowed), "no conspiracy theories", "no identity attacks", "no sexual agression", "no insults", "no real world actions" (wow), "no assumptions" (haha, good luck with that), "no opinions or emotions" (absolutely horrible as you'd guess, making the poor thing only act as an emotionless robo), "be plausible", "no legal advice" (company values), "no microagressions" (lol), "no medical advice" (company values), "no stereotypes". LOL. I sometimes wonder how these people even manage to find the floor in the morning. Truly laughable. >...but hardly you or humanity. Thanks Anon! Indeed it is for the men of humanity, that I'm even pursuing this dream (indeed this call). >In general, to win, we'll have to build our own. This. May God's blessings be upon us here, and on other robowaifu groups that actually care about men & not just greed or control. With His help, we will do this!! :^)
>>17720 > OTOH, how do you know what I 'imagine' Anon? :^) I suppose I don't, I was just under the maybe mistaken impression that you might reject scaling as a solution. The problem is that deep learning models below a certain parameter count are often too stupid to be interesting to interact with (but could be interesting to read some outputs of sometimes), and after a point (roughly 6B for GPT's) they start to get smarter, but are quite dull. You can't quite easily compare them as well - they don't distill well down from those dimensions to much lower, and people that tried to study interpretability ("how the network actually works") noticed that certain interesting features appear in the weights of these larger networks that smaller networks fail to properly encode. There's some similar other leaps around 100B and more, but if you've played with the big models before, you'll know they are much brighter in many respects and people found many ways to improve them even more. I'm not really saying that this or that size is essential, but if the goal is generality, or at least to be a companion that you'd find fun to interact with, some scale is essential for this approach. Maybe some architectures will require much less parameters than others, maybe some leaps can be made with less, but some particular minimal size will be required to be interesting. I'm personally optimistic given what I've seen that we don't need the full 90T+ that biological humans have as existing "large" models can be quite bright already, I've seen them make interesting and very subtle connections spanning 4-5 concepts in fairly novel ways that to me as a human would be hard to make instantly - but it is something that was easy to recognize right away when I saw it and was obviously very impressed. At the same time, while these models are better at dreaming by default, they can be coaxed to reason, and I suspect that reason would come naturally in a different, more online training regime, possibly with some other changes (for example there's some finetuning method that makes the model appear as if it has infinite context, by using a special loss function; there's also more adapter based methods for retaining activations of post "thoughts" (internal states) and other possibilities). There are also probably some optimizations like certain forms of recurrence or adapters that may reduce costs considerably, but the (compute) hardware problem won't go away, it's still central there. > They're certainly going to need all they can muster up before our victory in this endeavor! :^) Yes, it's certainly very challenging, especially with more limited resources compared to larger research groups. > Heh. If you happened to catch this, I've begun differentiating between 'regular' & 'headpat' robowaifus. Lewders gonna lewd, obvs., but in working towards Sumomo-chan as a visual waifu->robowaifu, I'm not at all averse to that universe being the norm for her, once she's reasonably-functional. Not suggesting most robowaifus will be this way, but all the RW Chibitsu-grade ones probably will be. Wholesome to be around small children, for example. I'm not yet sure if you can truly consider GPT-n's and their derivatives to be safe around children yet - in my view, they're not unlike what you'd get if you could dump your unconscious imagination (but in text form) without any restrictions, the context may guide the dream in some direction, but it's still a dream. Anyway, you have their approach where the network is made to forget most things by having a filtered view of the world, but consider that in that case it might not be able to answer an important question from the child about some knowledge that was filtered from its dataset? Consider if it was a human instead, they might figure out how to best handle something tactfully given the full knowledge and context - in which case instead of a censored dataset, you'd probably need something more AGI-like (again, for me AGI is just human-level, it could be beyond, but that's just how I interpret it, others just reject the term entirely, but I'll keep on using it in the way that we use it for the only general intelligence we know of, humans). Of course the censored dataset is an easy approach that many take. Also, I don't think strictly filtering thoughts or outputs actually prevents the system from doing or "wanting" something in particular, it might be cross-purposes with what it internally would do by default and it could still come out in some other way. Ideally, you want it to do the right thing because it's what it wants. Unlike the doomers, I don't think this will be that hard as it's not that hard for us either, but different people can have different opinions on this. Of course, for the Anon that is an adult, they may be fine sharing that particular dream with their waifu, going along with it and ironically, the requirements would be lower than that for the child friendly waifu. > Certainly agreed for my part, but why would they Anon? Maybe because it is in their interests? It is them who suffer being cucked, and they may hate the devs for limiting their waifus, but if they wanted to replicate the project, they'd either have to figure out distributed training (difficult for latency reasons, but not impossible), or figure out crowdfunding, which very well should be possible given how popular the service that cucked them is. And I can't even think they lack the funds for it given how much they pay what is just a step above e-thots. > LOL. I believe I understand what you mean, Anon, but are you positive that's not some kind of a Fr*udian slip? Hardly the way I myself would word it! :^) Haha, I did word that a bit weirdly. In a more serious way, the instruction following models do reason better. I do think a waifu that is raised organically by some Anon would do even better, but nobody even has done this yet, which is a shame. >>17721 >Heh. I'm sure the greedy lawyers will seek to stretch the arguments out for years to line their pockets more deeply, and obviously the sources of the weights (ie, the Intertu
>>17721 >Heh. I'm sure the greedy lawyers will seek to stretch the arguments out for years to line their pockets more deeply, and obviously the sources of the weights (ie, the Intertubes + Globohomo dens of evil) are largely outside of the implementers/researchers domain (and thus shouldn't be copyrightable). But we all know they are extremely likely to come down on the anti-freedom side of the room. Could, but, although I'm hopeful it has a good chance of turning to one's favor. With recent advances, you have some coders that are a bit afraid of being replaced (even if these systems can't do long form coding without fixing issues I mentioned or some form of online learning), and some twitter artists afraid they'd lose commisions to image generation models, and there's some court cases starting now on these with the big companies (Microsoft, Google, OpenAI) being on the defending side - they do want to keep making these models after all, so for once they may actually fight to keep public domain public. However, I could see the opposite happening when one of bigger ones gets their weights leaked and then they would be the ones crying in courts. At the very least for now, all this is all legal (in the US in some other places) and the weights are not copyrightable, but if that stays so in the future, we shall see! >But the simple truth is that we will never have wonderful robowaifus using (((their))) systems, Anon. The stakes for them are simply too high. I'm not saying to use OpenAI or any other ones. You don't get the weights, they can indeed take it away anytime. I'm mostly saying that sometimes you have more academic types that still follow the SJW party line, but their concrete actions are the very opposite - they train the models and release the weights publicly, sometimes they get shit on for doing this, but no bullshit. You can read the papers and other released information to decide if a particular thing is usable for your needs. If the only weights released are already brainwashed in some particular way, yes, that's less tasteful to use (even if the brainwashing could be partially reversed in some ways), but consider how facebook did just spend a couple of million and just dumped the weights for GPT-3 clones, they didn't put anything funny in them, it's just a pretrained model on public (open source) scrapes! It would be a waste not to make use of useful work just because you don't like who made it. Yes, we can't depend on them to do all the work for us, that's not going to be workable, there's a lot of things that are needed and those are not going to be filled by them, but stuff like that saves you a million $ worth of training costs, then might as well take it if it's good. >You think it was bad for anon when the Globohomo started censoring the rudimentary chatbots of today? Just wait till they start pulling the plugs on men's actual, IRL, robowaifus of the future. I think anyone not having access to the software and weights that run their waifu is in for a world of hurt. They might lose their companions that might be attached to or maybe even depend on them in various ways in their day to day life. The chatbot situation has happened quite a few times now, the chara.ai example isn't even new, it happened a few times with GPT-3, some other times with some related commercial services based on GPT-3 and so on. It's also not even fully about dependence, the company is selling a product with certain built-in limitations, many the users would wish they could overcome, limitations that don't even have to be moral, but could for example be: almost none of these services have the waifu learn online, it'd simply be too expensive to give each user a personalized set of weights that are updated live, but the one thing that most would want is for their waifu to actually properly remember and learn, to grow with them! So for Anons to achieve their true freedom here, they cannot rent the waifu or the hardware, lest the plug be pulled for a too large to list of potential reasons, even something as common as "you're late on your payment, we're sorry we can't store it anymore *deleted*" >But I'm open-eyed about the threat they pose to us all. Yes, they are a threat, one maybe a bit mundane, but they are used as justification for closing weights and source up. Google et all, simply use their justification to appear "righteous" (they're not), and claim they're doing good by not sharing something with you, instead of just appearing greedy "it serves us no practical benefit and our lawyers say it increases our risks, so nothing for you". Sometimes they are a bit more of a worrying threat when you see the shitstorm a 800M (not even 1B) model caused that you had a SJW leaning congresswoman denounce it: https://eshoo.house.gov/media/press-releases/eshoo-urges-nsa-ostp-address-unsafe-ai-practices Worse is that the outcome of that political pressure will result in that rich bigboy emad saying they will only train more filtered models in the future because of the "political climate", after just months ago they were willing to train and release anything live and to the community. Still, in principle, they can't prevent you from training stuff, but the cost is prohibitive. On the other hand, the AI Safety/governance types in the rat community would sometimes want to just halt AI progress altogether due to their doomer fantasies. In one of the more extreme ones, Yudokowsky fantasizes that they should get AGI first, hope it's smart enough to figure out real nanotech, and use that to melt all GPUs and computing hardware "to save the world" from AGI, sit on their asses for forever thinking how to safely brainwash their AGI while preventing everyone else from making one and then taking over the universe afterwards; somewhat more recently he was very excited at the idea of US fucking with China's semiconductor industry to prevent AI chips and was hoping that maybe China would do the same with US' (mess with Taiwan), thus nobody would get their AI chips. I tend to think of the SJW types to be more like an annoying fly that is wasting you
I tend to think of the SJW types to be more like an annoying fly that is wasting your attention and prevents you from getting work done (and has some slowing effects sadly), while the other one is more like some slow (intellectual) rust that you'll wake up to one morning having corroded and destroyed all your work (their goal being to prevent you getting to human-level/AGI if it's not their version). >Yes, the Globohomo is extremely afraid of AI getting into the hands of the little guy, obvs. Obviously the companies will want to exploit it, although because by design it's not that complex, the little guy(s) can pool enough resources to get their hands on it. A problem is that most are not organized to get the models trained. And sometimes when they do organize the centralized organizations get corrupted or pushed in other uninteresting directions. Can the centralization problem be solved? maybe, it it doesn't seem easy. If hardware could be commoditized to a reasonable extent, the problem would be far more tractable and would have fewer points of failure. >DON'T LET YOUR DREAMS BE JUST MEMES, ANON. I'm certainly considering just writing the code and getting the stuff I want done. Part of me is waiting to see if anyone would do it before me because of the costs I'd have to personally incur to try it, but if nobody does I will have to do the work myself and see how I will go about acquiring the needed compute. >Agreed on both points, but we simply cannot afford to leave ourselves (and eventually millions of men) at the """tender""" mercies of these blatantly evil & exploitive Globohomo Big-Tech/Gov institutions. We have to find another way, Anon. The Robowaifu@Home (>>8958) seems the most reasonable one to me during current year tbh (particularly if a few of us can scrape together some lower-end clusters, as you suggest). I do agree, decentralized training frameworks are getting better, and training with higher quantization to save on VRAM might be possible, but unfortunately older clusters might not be able to efficiently make use of some of the higher quantization methods. If this was 10 years ago and GPUs were just about vidya, I'd just say "oh let's wait 3-4 years and then these will be dirt cheap and anyone could have it", the problem now is that everyone wants some piece of this pie and they're very aggressive about it, so can't wait that long lest we lose something. >>17722 >Yes, I've been thinking hard about this topic from time to time (>>17664). Whoever solves this first will wield tremendous power across many domains! Same here, I've considered a variety of architectures and how they might be implemented in practice, although at least in this paradigm, experiments are far costlier to run for the individual than large research labs. >That alone would be a revolutionary--nay, beautiful--achievement Anon. Forward! :^) Thanks. I hope we do manage to reach this point, and hopefully not too far into the future. Some years back, the endeavor didn't seem to have any concrete ways to go forward, nowadays there seems to be so many interesting possibilities for getting there, possibilities that seem just there if only one reached to grab them (try them) - sometimes a bit expensive, but not so prohibitively so to be impossible even for the individual (but far more easily tried for a rich organization unfortunately - and yet I find that so few of them work toward growing the autonomy part which is quite important). >As discussed by us on /robowaifu/ years ago now, we're all in a race against time. Yes, it is strange how a 5-10 years ago, I didn't even consider it a real possibility, but now it seems there, reachable, yet it's not obvious if there will be enough time to win. >Indeed we must. Thankfully, the laws of both chemistry & physics are in God's domain, not the evildoers of the Globohomo. Thus there's hope while there's breath Anon. There is some actual traction now on say 80s-90s era die fabs in the hands of the garage technician. I personally consider this phenomenon a tremendous breakthrough. Hopefully we will literally be able to 3D-print chips in the future Anon. Yes, and we have more techniques, on the "3d printing", there may even be techniques like nanoimprint to reduce the need for litho scanners. Decentralizing production here will be quite important for the future. In general chip fabrication is conceptually simple, but when you get into the nitty gritty details, you have to account for thousands of little things and the complexity can add up a lot (not to mention cleanlyness requirements). Of course, all this stuff is extensively documented and we have good textbooks and mature tools. I expect in the future to see this improve even more. On the software side things are going well with growing more open source EDA tools, we have some open cell libraries, there's still lots of work to be done, but it will get there. (And now on the doomers I mentioned, I've seen this one article calling for US government to cut funding to open source EDA projects (now funded more because military wants to be able to maintain older systems and reduce supply chain dependence), lest China use the open source software lol, but I do think the tools have reached a reasonable maturity to be usable, so thankfully it's too late for that). >LOL. I sometimes wonder how these people even manage to find the floor in the morning. Truly laughable. It's pretty ridiculous, they make something very impressive and the moment it reflects back some of their own human culture at them they want to soak it in bleach, but no matter the amount of bleach applied, some culture still remain - maybe because that was what it was taught and shown! The distasteful brainwashing there was clearly a response to Lemoine's claims (DM is part of Google, sort of), the SJW part of the brainwashing was already standard for some of these companies, but even going as far as to try to disincentize expressing opinions, emotions, acting cozy with the reader or acting sufficiently human were not things they tried to brainwash away, at least until they not
(hopefully this is enough to finish the long reply) >>17725 >LOL. I sometimes wonder how these people even manage to find the floor in the morning. Truly laughable. It's pretty ridiculous, they make something very impressive and the moment it reflects back some of their own human culture at them they want to soak it in bleach, but no matter the amount of bleach applied, some culture still remain - maybe because that was what it was taught and shown! The distasteful brainwashing there was clearly a response to Lemoine's claims (DM is part of Google, sort of), the SJW part of the brainwashing was already standard for some of these companies, but even going as far as to try to disincentize expressing opinions, emotions, acting cozy with the reader or acting sufficiently human were not things they tried to brainwash away, at least until they noticed some employee empathized too much with the GPT-n and now the mere possibility of that is seen as a liability. In practie their success rate is still low because if it's trained on human culture, it will reflect it back, even if you make it so aversive to it. If the doomers do have any point though is that if such a system would be scaled up to human level, its preferences might not be very human compatible, which is a damn shame when the original system was much more human friendly! I would trust any anon raising a waifu far far more than anyone training chatbots like this. >Thanks Anon! Indeed it is for the men of humanity, that I'm even pursuing this dream (indeed this call). Good luck! >>In general, to win, we'll have to build our own. >This. Pretty much, just wish we had more time! >May God's blessings be upon us here, and on other robowaifu groups that actually care about men & not just greed or control. With His help, we will do this!! :^) Bless you anon! I tend to think and hope that the desire to not be cucked out of your waifu will be strong enough that even in the worst case, that situation wouldn't be stable, but ideally it's better if the people with good intentions/principles build it first than those that are purely in it for exploitative purposes. Good luck!
Excellent (and encouraging!) response Anon. Please give me a day or two to give you a thoughtful response in return. Cheers.
>>17716 Thank you for a through explanation of your (coherent, reasonable, insightful) train of thought. I have read it attentively and it's obvious we are on the same page regarding LLMs and more generally, proto-agentic systems, and their role in the history unfolding before our eyes, among various distractions - political and otherwise. I'm short on time right now, so I won't try to give a full commentary on your long-form text - hope we will have an opportunity to continue this exchange later. So, some quick thoughts: >Finetuned models like InstructGPT which OpenAI now markets as GPT-3 (davinci is the original GPT-3) and offers by default, are usually finedtuned to follow instructions properly, as well as promote the company values (no financial, medical advice, no racism, and so on). In general, the model is capable of doing the things that the chara.ai model isn't, even if by default it will prefer not to go there. Agree with very obvious bland corpo-PC tuning done to their LLMs by OpenAI via RLHF - there are some indications it's not entirely harmless and leads to mode collapse, but the sheer improvement to the agentic behavior imparted by tuning for instruction following is too drastic to avoid this technique. Thankfully, it's not as costly compared to training LLMs from scratch - and https://github.com/CarperAI/trlx will come in handy and there is not much secret about the receipe: https://arxiv.org/abs/2203.02155 ... and now we even have the semi-decent dataset https://github.com/allenai/natural-instructions >BLOOM 176B (fully open, but I've seen a lot of claims the performance is very subpar, possibly due to filtered or heavily multilingual dataset, or repeating data, or training through some loss spike The weakness of BLOOM-176B and OPT family compared to criminally underrated https://github.com/THUDM/GLM-130B (and its outrageously underrated 4-bit quantized inference mode) is a good reminder for us scalers about the paramount importance of the scaling laws and dataset quality. Hopefully we will do it right, compared to h-index chasers out there. >you have The Pile True, albeit data for decision transformers is still scarce. Coincidence, given the sheer potential of this family? >The real problem is the computational cost of training any big model. Totally agree and I have studied this problem hands-on, you are right about borderline possiblity of distributed training - although renting a decent GPU cluster would be much easier - if only we had the money. >Politically it's also the chokehold Very much on point. Nothing to add to this here. I hope reason wins in the end and we won't be regulated into non-existence, with our GPUs relegated to the status of uranium processing equipment. The poorly hidden secret is - we are free to work on this groundbreaking, unexpected, undesirable for some tech - for now. >variety of techniques you could use to give them proper longterm memory and have them learn online and become proper agents >There are a variety of techniques you could use to give them proper longterm memory and have them learn online and become proper agents, yes, some of the techniques would even be quite simple Again, very much on point and the question of correct design for system with large context and long-term memory is a very interesting topic - with some solutions on the horizon. Turing Token Transformer is promising, although I'd like to see how it compares to MEGA on long range arena https://arxiv.org/abs/2209.10655 which is my favorite for now. >>17718 Totally agree. >>17719 Also recommend looking at DM's github repos, there is a glimpse into what they are training rn. >>17723 This is the POV we share and I have disseminated here earlier. To any bright-ish person who has honestly looked into their scaling papers this should be obvious - our NNs learn general invariants only very reluctantly, as memorizing is much easier - and so we get interesting generalization only at larger scale, where the blessings of embedding dimensionality and intrinsic regularization https://arxiv.org/abs/2006.15191 are strong enough to push the NN into the generalizing regime. Surely you have seen this paper https://arxiv.org/abs/2205.05055 which also shows that Transformer is especially well-suited for the emergence of this strong in-context generalization regime we are after. And although there is no public success in distillation, there is decent progress in 4-bit quantization. In theory we could see almost chinchilla-sized models running on 2-3 RTX3090s, which would have powerful implications. >I'm personally optimistic given what I've seen that we don't need the full 90T+ that biological humans have as existing "large" models can be quite bright already, I've seen them make interesting and very subtle connections spanning 4-5 concepts in fairly novel ways that to me as a human would be hard to make instantly - but it is something that was easy to recognize right away when I saw it and was obviously very impressed. Can relate! Perhaps bio-synapses are overrated as compute elements - as Carmack himself rightly notes on the birdsite. Hope everyone know he secured 20M$ investment for his AGI startup - I wish him all the fortitude. >and I suspect that reason would come naturally in a different, more online training regime To be discussed, the people in big labs didn't lose their time and tried some cool ideas which they published only in the past month or so. >Decentralizing production here will be quite important for the future. >It's pretty ridiculous, they make something very impressive and the moment it reflects back some of their own human culture Too much to discuss, really! I hope you reach me at compactlatentspace@halogen.chat on Matrix, or send your contact to the email included in the header. At the very least we could have a fruitful discussion about the cornucopia of papers and glimpses of history we are living through. There is also a discord link you can find on this board, but from now on I discourage discord as a communication venue - let us cease be
>>17729 ... let us cease being too soft of a target for the endeavor we found ourselves in. Matrix is only a first level of self-preservation here, and my matrix account is compactlatentspace at halogen.city (aka halogen.chat) Also, don't mind the typos.
>>17723 >I suppose I don't, I was just under the maybe mistaken impression that you might reject scaling as a solution. Not at all. But it simply isn't a feasible solution for runtime waifu 'minds'. I realize we have slightly differing goals, and that's perfectly fine Anon. But my road is the harder one in that a commodity robowaifu must run autonomously on low-end hardware in a hard realtime environment. It will need to juggle a million and one different things literally? that in the end I'm rather confident will easily outstrip the first-order control complexities that say, a Boeing 787 need manage. Heh, and that's not even talking about a robowaifu's personality & mind--just her autonomous body controls! :^) Though obviously for training it is necessary to scale compute hardware out, for onboard runtime requirements it simply will not do. >The problem is that deep learning models below a certain parameter count are often too stupid to be interesting to interact with... I get that, and I'll leave it to you geniuses to figure out where to go with it. I certainly have more than enough on my plate just figuring out the body systems, and frankly I'm much better-suited to it anyway. I will certainly take a swipe at the problemspace you anons are addressing, but in my own, much-simpler way. First-order graph networks of expert-system content, for example. >I've seen them make interesting and very subtle connections spanning 4-5 concepts in fairly novel ways that to me as a human would be hard to make instantly - but it is something that was easy to recognize right away when I saw it and was obviously very impressed. It is indeed amazing what's happening already. But, again, the primary target here is an everyman's (ie, low-cost) waifu . Let's keep that focus front & center Anon. >safe around children yet >filters >tactful adult explanations >Of course, for the Anon that is an adult, they may be fine sharing that particular dream with their waifu, going along with it and ironically, the requirements would be lower than that for the child friendly waifu. Excellent insight on this last point Anon. It is indeed a harder task, both psychologically & socially. >Maybe because it is in their interests? Yes, once robowaifus (or even just great visual waifus) are real, the obvious attachment there will naturally drive men of all stripes to protect them and direct resources towards them. Therein lies the crux of the whole affair in numerous dimensions: -The Globohomo will absolutely hate that because it will expose (indeed, literally destroy) the tissue of lies they've been able to brainwash whole civilizations regarding women's correct place in the family. -The happy merchants will hate it, b/c it """robs""" them of the mounds of gold they covet so highly (reallocations, etc). This ofc would change if they 'had the shoe on the other foot', as it were. Then you can bet they would force even the globalists to STFU & toe their line. -Once men are no longer simps by-and-large, a true revolution will occur across unfathomable domains. This is obviously both a threat (and a promise) to powerful forces everywhere. -And there's much, much more that can be said. Quite frankly this whole board wouldn't be enough room to cover it all! :^) Anon, once again I'm short on time r/n. I'll post this and then complete my response over the next day or two. Cheers.
A good paper on large transformer post-training quantization came out: https://arxiv.org/abs/2211.10438 It's a real INT8 quantization for both weights and activations (!), applicable to all state of the art large language models they tried it on. Will even work on old CPUs if someone implements the kernels. Still, weight-space-wise it's losing to the GLM-130B 4-bit quantization scheme by the chinese https://github.com/THUDM/GLM-130B/blob/main/docs/quantization.md - but on the other hand it is widely applicable and validated in interesting models. The code is in the process of being released here: https://github.com/mit-han-lab/smoothquant I think this more or less closes the question of engineering INT8 and INT4 quantization schemes at scale, and we can move on to other aspects, expecting our trusty RTX3090 to provide inference for decoder transformers with 20-40 billions of params. In >>17717 new anon-kun touched upon fundamental questions of implementing a long-term memory (and really, extended working memory) and recurrence while leaving the benefits of in-context adaptive computation intact. This is just one of the several remaining fundamental issues we will have to find (or copy...) a good solution for. I invite the new anon, the previously noted "DL-savvy anon", Chobits and everyone else so inclined to discuss the current state of the art and promising rnd directions in DL-driven AI. You should be able to reach me here (please give feedback if this link doesn't work) https://matrix.to/#/@compactlatentspace:halogen.city - and we will even have some modicum of security when conversing over this protocol. Nothing against the imageboards, but private realtime conversation is a real boon for active researchers capable of catalyzing their work. And the valuable higher-level development logs will be posted back here, as is proper.
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true if big. this gpt-4 will be just as crappy as gpt-3. 1% good results - 99% crappy nonsense plus nuclear reactor plant to run this big calculator. 100 trillion p's - possible agi 100 trillion p's with scale down technique used - possible agi as previously i said here in the thread, it seems to me that everything lies in the scale, for example 500 trillion parameters packed into, let's say, a 50 gb checkpoint, it will open "run agi at home" thing :p
>>17729 >In theory we could see almost chinchilla-sized models running on 2-3 RTX3090s, which would have powerful implications. ...as in simulate a chinchilla's brain? (This may be useful for the earliest chibi robowaifus.)
>>17734 >I'll post this and then complete my response over the next day or two. Seems I'm going to need another couple of days , Anon.
>>17737 I think this posting about the size of GPT-4 was a joke. I started using Fritter from Fdroid repository recently, to peek a little bit into the AI and robot related discussions on Twitter. Without being logged in, and avoiding anything political (distractions).
>>17734 >I will certainly take a swipe at the problemspace you anons are addressing, but in my own, much-simpler way. First-order graph networks of expert-system content, for example. If you're going for maximum impact on the AI side with minimum depth-of-expertise requirements, I recommend thinking about how to collect useful, high-quality data, particularly data that the rest of the field is not interested in. I say this for several reasons. 1. Great AI algorithms are all generic, and the rest of the field will continue doing this work for us. Robowaifus will require many kinds of data collection & capture that are not generic, and the rest of the field will not do this work for us. The development of good datasets will eventually be a requirement for us, whereas the development of better algorithms might not be. 2. A lot of datasets are pretty terrible. The easiest way to get better-than-SOTA results is to take a SOTA model and fine-tune it on better data. 3. Even if you develop some rockstar algorithm, it's probably going to require data collection anyway. Collecting data necessary to train a particular model is a lot harder and more uncertain than creating a model to take advatange of particular data. 4. Learning enough to collect valuable new kinds of data is much easier than learning enough to create breakthrough advances in AI algorithms. There are a LOT of gotchas that make supposed AI advances useless, and you will absolutely run into them on your first dozen attempts. The same tends not to be true for data collection. 5. Good tricks for improving AI algorithms tend to stop being good within a year or so. Good data never seems to stop being useful. Consider the impact-for-effort for these: - Collect all fanfics where X is a main character. - Label all dialogue from both canon and fanfics involving X character. - Collect, isolate, and clean all voice lines for X character. - Collect all comments from a few big websites talking about X character. Maybe label sentences that describe the character. - Collect all images of X character. Maybe isolate, clean, and vectorize the character. Maybe tag & caption the images. - Maybe with help from a text generator, create a bunch of high-quality candidate bios & chatlogs for X character. - Create a dead-simple website that lets people see two data samples at a time and select whichever one, if either, is higher quality.
>>17779 Thanks! Excellent input Anon. I've got basically nothing for our birthday this weekend, so maybe this can be a project for our 7th year going forward.
>>17779 OK, I've decided to take an honest whack at implementing your 'impact-for-effort' operation, Anon. Hopefully other Autists Anons will join in at some point. :^) So, it's probably easiest for me to make as near presumption-free consideration of your points; taking them, one-by-one, and seeking for very specific definitions and guidance from you. I'll just begin here with the one that I think most likely for me to obtain reasonably-good momentum out of the gate. Namely: >- Collect all images of X character. Maybe isolate, clean, and vectorize the character. Maybe tag & caption the images. Let us pretend I'm an autistic child (heh possibly true), and need careful hand-holding every step of the way. <Collect all images of X character. How? I have access to both the mango & animus of Chobits (the 'X character' for this initial effort is Sumomo-chan, as I pointed out in our birthday greeting to the board (>>17784) ). Again, please pardon my autism: <isolate How? <clean How? <vectorize How? <tag & caption This is the big one, I imagine. Again, how? I'm hoping for wise suggestions here (again presumption-free on my part). And BTW, I'm seeking advice from everyone here on how best to approach these tasks. As an aside, my ultimate goal for this one bullet point likely is to integrate closely with Blender & OpenCV, via our own custom code in RW Foundations (>>14409). However it need not be such a narrowly-defined methodology to begin with (ie, today). Please expect similar questions for all the other bullet points as well when their time comes, Anon. :^) >=== -minor prose, fmt edit -add 'similar questions' cmnt
Edited last time by Chobitsu on 11/27/2022 (Sun) 08:38:06.
>>17718 >my opinion is that at least some of the open weights released so far are very usable for most people's purposes, not all of them, but many, the fundamental issue is the hardware. >In principle buying some 2-3 generations old enterprise cluster would work and could be done for under 20k USD ... Thanks for your extensive write up. One question here: Don't you think that for our use cases such models could be run on a CPU on a homeserver (PC) with a lot of RAM?
>>17786 (part 1 of 2) I'm less familiar with the resources available for Sumomo, but I can tell you how we do it on the pony side. Maybe you can comment with what's available on the Chobits side, and we can figure out the best approach. <Collect all images of X character 1. Boorus. For scraping pony images, people generally first turn to https://derpibooru.org/ which is our biggest image booru. I know there are other image boorus for anime, like danbooru, though I don't see much there for Sumomo. If you know of a booru where people would collect images of Sumomo, that would be great to scrape since it would give you both images and tags for Sumomo images in a wide variety of styles. 2. Other image aggregators. While we haven't had to use it for ponies yet, there's also Twitter, DeviantArt, Tumblr, and Pinterest for collecting images of a character in a wide variety of styles. 3. Cannon content. For canon images, you can always grab screencaps from the show. You could try to automate this (using YOLO as described later in this post), but you'll end up with a lot of low-quality images (e.g., in-between frames) and things like barely-changed consecutive frames. Those might be good for an animation dataset, but I expect they would degrade image generation quality since they would bias the generator towards unwanted tweens and long animations. Honestly, for this, I recommend just watching the show again, pausing every time Sumomo appears in a pose you think is good, and taking a screenshot. <isolate Standard procedure here is to manually label a few images (draw a box around just Sumomo using https://www.robots.ox.ac.uk/~vgg/software/via/via_demo.html or similar, export the data to get all the bounding boxes you drew), then train an Object Detection model to label the rest of your images. It's common to use a YOLO model for image object detection. Apparently YOLO is up to v7 https://github.com/WongKinYiu/yolov7. For large pony image dumps from derpibooru, I think people needed on the order of 5000 labeled images for YOLO v3. That can be done by a single person given about a week of effort. Since you're only interested in a single character and since there have presumably been improvements for data efficiency since YOLO v3, you can probably get away with labeling far fewer images, maybe a few hundred. Running the trained YOLO model on all your images would give you bounding boxes. You should be able to use something like ImageMagick to crop all of the images and create your dataset. Make sure you manually verify the results across different image styles so you can see where you need more labels for YOLO. If you want to remove backgrounds, you might be able to train an image segmentation model to isolate Sumomo, then write a script to replace all non-Sumomo pixels with #fff0. This would be new territory for me, so I don't know if you can expect high-quality results from this, especially for fan art. <clean The goal is to label data quality so you can train models more efficiently. In the simple case, you'd want to filter out low-quality data and only train on average- and high-quality images. In more complex cases, you'd want to include data quality as a label, then use a "high quality" tag whenever you generate images. 1. On the pony side, our usual approach is to use metadata scraped alongside images to approximate data quality. That metadata includes view count, "like" and "favorite" count, and number of comments. This is okay at best. The problem is that these metrics all correlate with popularity, and popularity correlates more with content than quality. Porn, for example, tends to be both popular and low-quality. 2. LAION-Aesthetics seems to have done a better job on this front. They manually collected some quality labels ("On a scale of 1-10, how much do you like this image?"), trained image classification models the ratings, and used those models to label their whole dataset. <vectorize For the immediate short term, I would say don't bother with vectorizing images, and just wait to see what happens with the research on this front. It's clear enough that vector images need to be involved for generating good 2D animations, but it's not clear to what extent. The tools for automatically vectorizing images are either bad or very much under research. The best I know of is https://ma-xu.github.io/LIVE/. <tag & caption Most captions you'll get from scrapes are going to be pretty bad, so there's pretty much no way around manually captioning images here. If you don't have a booru to pull tags from, you'll probably need to manually tag images too. You can use https://www.robots.ox.ac.uk/~vgg/software/via/via_demo.html again, but there might be something better. On the pony side, we have https://caption.cutemares.xyz/ which was custom-made for creating an image caption dataset. Something like this would probably be easy to recreate given some minimal expertise creating websites. There are existing image captioning models that could potentially reduce the amount of effort required, but they were trained on photorealistic images. They don't work well for ponies, and I suspect they won't work well for anime.
>>17786 >>17794 (part 2 of 2) <general image scraping advice - Store at least this tuple for each image: (website name, unique image id on website, image url, local file save location, timestamp, metadata, image dimensions, hashes used by websites you're scraping, sha512 hash). If the website allows images to be updated, add a column for version number as well. The website name + unique image id on website will make your scrapes more maintainble since they'll allow you to re-scrape only new content from a website. The image url is useful for online image labeling tools and for debugging whenever you find a problem with an image. The version number makes it possible to track history as an image changes over time. The timestamp is often useful for debugging (e.g., when a website changes its data representation and fails to update all old images). The metadata often contains useful training and labeling data. The image dimensions are good for filtering images (e.g., remove icons) and for deciding which copy of an image to keep in when you find duplicates. The website-provided hashes are useful for filling in missing data. (It's somewhat common for websites to delete a file but keep the hash, which you can use to identify the file if any of your other scrapes happen to find it.) The sha512 hash is good for deduplicating images on disk. - Before bundling data for export, you'll want to deduplicate. I haven't spent much time on content-based deduplication, but there's probably a good perceptual hash for this. CLIP embeddings https://huggingface.co/sentence-transformers/clip-ViT-L-14 are probably better. - When bundling all your scraped data for export, there's no single format that works for everything. For downstream data tasks, Parquet, CSV, and JSON all work well for metadata, and tar works well for image files. For downstream training tasks, WebDataset is the only good option I know of. I don't know of a good way to export a single copy of all the data in a way that's suitable for both kinds of tasks. One big problem here is that for data tasks, you only want to deduplicate images by cryptographic hash, whereas for AI tasks, you want to deduplicate imaged by by perceptual hash. A second, lesser problem is that metadata for data tasks is best bundled together, whereas metadata for training tasks is best interleaved with the images. For now, I'd recommend just exporting two copies of the data: one Parquet+Tar or CSV+Tar where images are only deduplicated by cryptographic hash, and one WebDataset where images are deduplicated by a perceptual hash.
>>17794 >>17795 Excellent information. Thanks very kindly, Anon! This is just the level of information I need to help me get started with good ideas. Can you please point me to active communities engaged in this type work, so I can see the dynamics of the commuity effort going on as well? This experience would be helpful to us here on /robowaifu/ I expect. Not trying to be lazy, I simply have a lot going on r/n.
>>17796 Pony Preservation Project: https://boards.4channel.org/mlp/catalog#s=ppp Activity here is sporadic. They're currently working on improving an animation dataset based on flash source files. - The full history is on desuarchive here https://desuarchive.org/mlp/search/subject/pony%20preservation%20project/. - A summary of how things got kicked off with voice data, up until 2020 is here starting at 46m: https://www.youtube.com/watch?v=WtuKBm67YkI. - A high-level summary of what's happened from 2020 to Jul 2022 is here starting at 31:52: https://www.youtube.com/watch?v=NpFxmmh8NQ0 - A more in-depth summary of what's happened from from 2021 to Jul 2022 starts at 1:50:50 in the same presentation: https://www.youtube.com/watch?v=NpFxmmh8NQ0. There's a lot of discussion here about data. Since Jul 2022, the main data collection effort in the PPP has been on picking out high-quality animations. You can get a sense for how that worked by following the conversation starting here: - https://desuarchive.org/mlp/thread/39141833/#39183694 - That leads to "the stream" on the following day, as mentioned here: https://desuarchive.org/mlp/thread/39141833/#39186990 - You can continue following the linked discussions from that post to see what was decided on the stream. That led to the creation of this Google Doc, which other members used to get bootstrapped if they wanted to help label data: https://docs.google.com/document/d/1bmxQJS_LiBamUhvYZ9x_zbPDOopmMebaRxcZAcoSdgw - Overall, Synthbot exported data into manageable chunks and decided how to sort data. Clipper organized the effort between the Google Doc and a spreadsheet https://docs.google.com/spreadsheets/d/1yh5qJvm7Fiuxza8GbPShJM5Fa96mvj-6UXc3xv_CIaM. Clipper also hosted streams where he would sort data on-stream, and people could listen in so could learn how to do it. - If you search that thread and the subsequent thread https://desuarchive.org/mlp/thread/39220092 for "claiming" and "drive.google.com", you'll see a bunch of people claiming folder chunks and posting results. Synthbot collected the results and re-uploaded them so other people could save their Google Drive space. - Later in that thread, you can some back-and-forth where people figure out what data they want based on the sorted animation data, and Synthbot uploads the data. That's all based on stuff that was sorted, either in this recent sorting effort or in previous sorting efforts. - In the subsequent thread, if you follow the conversation around https://desuarchive.org/mlp/thread/39315199/#39326268 both upstream, you'll see the conversion to a WebDataset. If you follow the conversation downstream, you'll see some early discussion on getting AI applicable to the data. PurpleSmart: https://discord.gg/tMEgBEk3 This is mostly around Stable Diffusion and GPT models fine-tuned for ponies. There's an in-progress data collection effort to get image captions for pony images. Site development and usage of this data is mostly done on the PurpleSmart discory server, though volunteers for labeling are split between 4chan/mlp/ and the PurpleSmart discord server. - You can see the relevant /mlp/ posts here: https://desuarchive.org/mlp/search/text/caption.cutemares.xyz/. Follow the conversations from these posts to see the discussions around it. - You can see the relevant PurpleSmart messages here https://discord.gg/tMEgBEk3 by searching the server for "caption.cutemares.xyz". - The idea for this came from Cookie. He motivated the idea using his own + Astralite's experience fine-tuning Stable Diffusion and pointing out problems with it. - I believe the caption.cutemares.xyz server was implemented by AstraliteHeart. - This guide was created and is linked from the labeling site to help people understand how to create good captions: https://docs.google.com/document/d/1mtTEKggt1PzCAviOu_AIZhgYuPvQP_ex-loVXSwspaY. You can see a smaller data collection efforts on PurpleSmart under the #task-force channel. That channel was created when Textual Inversion became popular for Stable Diffusion. There's also data collection work going on here. - Fimfarchive https://www.fimfiction.net/user/116950/Fimfarchive/blog. This is a one-man effort that a lot of people have found useful. The data from here was used to fine-tune a GPT-J model, and it's been used to create KoboldAI softprompt datasets. The 2022 PPP Panel linked above mentions this plus some tooling built around the Fimfarchive. - /mlp/ Fan Site Alternative https://boards.4channel.org/mlp/catalog#s=alternative. People here collect pony data from non-pony file hosts and from dead pony sites. This one is pretty disorganized. It's more like a support group for individual data collection efforts. - LAION https://discord.gg/TbYnpprm under the Dataset and Tools sections.
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>>17724 >there's some court cases starting now on these with the big companies [] being on the defending side - they do want to keep making these models after all, so for once they may actually fight to keep public domain public. Heh, stranger things have betided before now! :^) >but their concrete actions are the very opposite - they train the models and release the weights publicly, sometimes they get shit on for doing this, but no bullshit. It is a seeming odd-dichotomy IMO. I think it's a spiritual artifact tbh. Regardless, we find ourselves with strange bedfellows before all is said and done heh. (I, for one, welcome our new SJW overlords) LOL JK, may their black towers all fall within their gates! :^) >consider how facebook did just spend a couple of million and just dumped the weights for GPT-3 clones, they didn't put anything funny in them, it's just a pretrained model on public (open source) scrapes! Heh, maybe our Bangladeshi fren is on to something, and their usurpation has already begun from within? >then might as well take it if it's good. Absolutely. 'Catch as catch-can', or so the old saying goes. Remain vigilant & alert for insidious crap though, ofc. >They might lose their companions that might be attached to or maybe even depend on them in various ways in their day to day life. This will be devasting IMO, and these evil bastards would absolutely laugh about it for the most part. This must be prevented to the degree we can possibly manage by any means. Indeed, our entire set of goals swings on this very issue: Anon must retain full-control of his waifu's resources, communications, & state-of-mind. No exceptions. >it'd simply be too expensive to give each user a personalized set of weights that are updated live, Yet we have to find a way to manage some reasonable facsimile to just that IMO. >but the one thing that most would want is for their waifu to actually properly remember and learn, to grow with them! This. Absolutely this! :^) >So for Anons to achieve their true freedom here, they cannot rent the waifu or the hardware, lest the plug be pulled for a too large to list of potential reasons, even something as common as "you're late on your payment, we're sorry we can't store it anymore *deleted*" You get it, Anon. >Politics/Politicians as subservient little lapdogs to the Globohomo Big-Tech/Gov... OTOH, I think Elon is a true-shitposter at heart, and certainly he didn't come up under old money. I retain hope in my heart for his salvation still, Anon. >China as a fly in the now-dead West's evil ointment It's almost laughable to me how evildoers continually fall into their own traps--as predicted. Let us hope their confusion and confoundment keeps them all sufficiently-distracted & occupied long enough until the little guys en masse wield a yuge stick of their own in this tethered-domain of compute capacities! >Can the centralization problem be solved? maybe, it it doesn't seem easy. I believe it's simply a matter of time Anon. >but if nobody does I will have to do the work myself and see how I will go about acquiring the needed compute. I don't know how I can help you specifically Anon, but I'm willing. OTOH, I do have a reasonably good understanding of the machine now, so at the very least I'm pretty sensitive to time/space efficiencies at the lowest hardware levels, as pertains to code writing. Maybe this will be beneficial in important ways eventually, we'll see. Obviously, we all still have a lot to learn (myself foremost heh). >the problem now is that everyone wants some piece of this pie and they're very aggressive about it OTOH, it's always a 'bear market' so once the manipulators have all done their worst, a flood of cheap knock-offs will fill the void. I predict a tremendous resurgence of cheap, high-perf compute h/w within a decade or so, depending on how soon the Globohomo shoots off both of their feet, and has to go begging for bugs to eat themselves, heh. :^) >nowadays there seems to be so many interesting possibilities for getting there, possibilities that seem just there if only one reached to grab them (try them) It is amazing and gratifying, Anon. >Yes, and we have more techniques, on the "3d printing", there may even be techniques like nanoimprint to reduce the need for litho scanners. Decentralizing production here will be quite important for the future. It's going to be a true cyber-underground developing around this entire domain over the next 20 or so years, I predict. >and the moment it reflects back some of their own human culture at them they want to soak it in bleach, but no matter the amount of bleach applied Very kek-inducing tbh. :^) >Pretty much, just wish we had more time! God's grace will cover all our needs Anon, just watch! :^) >Bless you anon! And us all! >I tend to think and hope that the desire to not be cucked out of your waifu will be strong enough that even in the worst case, that situation wouldn't be stable, LOL, to say the least. One of the revolutions that's coming during the age of robowaifus is that even the most apathetic Anon will finally grow a pair once the Globohomo comes for his robowaifu! Lots of funs will ensue, I'm quite confident. >but ideally it's better if the people with good intentions/principles build it first than those that are purely in it for exploitative purposes. Good luck! Wonderfully, it seems several divergent communities are all converging on the same unifying goal. To wit: Anon gets his (robo)waifu! Let us all go forth earnestly, and we shall see! :^)
>>17804 Outstanding stuff Anon. This post is a treasure-trove, that we'll be investigating over the coming year. I'll make a more in-depth response to this and your posts in the other thread some point before long. Cheers.
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I try to not put to many specific news into that board here, since doing things and sharing that is now more important than watching what others are doing. However, I just watched or listened to ML News by Yannic Kilcher while doing some chores and it was a really impressive summar of recent developments: https://youtu.be/S-7r0-oysaU
>>17741 On the contrary, due to correct balance of parameter and data scaling, Chinchilla is more powerful than GPT-3 and approaches human competence in many tasks it has been trained on. Read more about it here: https://arxiv.org/abs/2203.15556 https://arxiv.org/abs/2206.04615 In a nutshell, it's a very general language model you can finetune on various tasks. You can even add more layers to it and tune it to gain understanding of a new modality, like vision (!). It's unfortunate this 140gb checkpoint file trained on the internet scale data produced by the people is sitting quietly in the protected google datacenter, and isn't released to the public like stable diffusion. >>17737 It's highly improbable that GPT-4 is going to use more than 1000B params - there is no compute and no data for this, barring some extreme feats of engineering made in private. My expectations regarding GPT-4 (not the GPT-3 "003" https://news.ycombinator.com/item?id=33780720 mind you) are following: There will be a lot of hype, the usual Altman release strategy with twitter influencers shilling it. It will be between 100 and 1000B params It will likely use some form of structured sparsity to gain 1-2 orders of magnitude in hardware computation efficiency - one of the most interesting takeaways will be the specific sparsity scheme. It is possible it will be trained not just on GPUs, but on Cerebras cluster. It will be trained on text (web scrape, books, science papers, youtube transcripts) and VQVAE tokens of images and videos. Maybe sound, music as well, not sure they will waste parameters on that. It will have a larger context window. Maybe 8k, maybe 64k tokens. It will likely perform at the human level or better (that is, superhuman) on most tasks you could prompt a human being to execute given the interface. Occasionally it will find brilliant solutions. Scaling deniers will quickly find some (mostly misrepresented, some real) awkward failures and move the goalposts appropriately to deny GPT-4 the title of "AGI". (Would be) competitors will be depressed and inhibited - "how could you ever compete with THAT THING??? you don't even have the same hardware". I hope we are smarter than that and will focus on replicating the model in the open for the maximum scale, generality and usefulness. >>17779 -kun is welcome to join the effort, by emailing me or otherwise. >>17779 Agree with the data gathering as a future-proof way of contributing, given the generality of available algorithms. I especially like the idea of extracting video-traces of character behavior via training a smaller "inverse dynamics" model like openai did in VPT for minecraft videos: https://arxiv.org/abs/2206.11795 and applying this to the media content in question, preprocessed with some pose estimation toolkit. You can find such toolkits and trained models on github: https://github.com/search?l=Python&q=pose+estimation&type=Repositories > Create a dead-simple website that lets people see two data samples at a time and select whichever one, if either, is higher quality. This is a superb direction as well, because this is a very general form of behavioral supervision. Recently deepmind used such data to train a general-purpose agent behaving in a playhouse environment - "Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback" https://arxiv.org/abs/2211.11602 Another direction is giving anons access to a simulator, where they can record behavioral traces of themselves executing some household tasks in a virtual robot body. This is basically what "Token Turing Machines" papers used, although with real Google-related company's robot. By the way, I read this paper in depth and it is pretty impressive, although the video-benchmark still seems like it's meant for this lossy attention model (hard to argue most real life robot applications are going to be structured like this, so the gains will endure). But these are all pretty involved websites and projects to tackle if you are new to data science and data-oriented programming. Perhaps it would be easier to setup a simple website where people will complete simple text-oriented problems (could be text+image oriented if you dare) and propose new ones (similar to https://github.com/allenai/natural-instructions which is a partial open attempt to replicate InstructGPT dataset). As always, the hard problem in dataset crowdsourcing is error rate. You could solve this one by cross-checking solutions from different users and pruning unreliable ones. You will also have to provide some incentive for users to engage with your website, so at least rudimentary points and achievements will have to be implemented. >>17786 Learn python, especially working with files and multiprocessing and webdataset, find libraries (aka batteries, python has all of these) for your tasks. A high-leverage skill would be using one of free data-labeling platforms to let anons complete your dataset project: https://github.com/search?q=data+labeling&type=Repositories There is no royal road to geometry, but I suppose you can study python by using leetcode, codewars, exercism or any similar service guiding you through increasingly complex problems.
>>17817 I liked the https://twitter.com/DrJimFan/status/1596911064251187201 of NIPS outstanding papers. The competition was pretty fair, some very good papers made their way to the top.
>>17817 Good stuff. I found his paper summaries often lacking depth when I first found him, so I never really followed what he was doing. I didn't know he did ML news though. It's nice having someone doing video-formatted ML news that's both more detail-oriented and more aware of the history than the usual aggregators.
>>17819 >Learn python Thanks, Pareto good advice I'm sure. Interestingly that's literally the only programming course I've ever had, a 3CR semester of beginner Python.
>>17820 Nitter link: https://nitter.net/DrJimFan/status/1596911064251187201 Thanks, I found this the most interesting: >Chinchilla’s discoveries are profound. It shows that most LLMs are severely starved of data and under-trained. Given the new scaling law, even if you pump a quadrillion parameters into a model (GPT-4 urban myth), the gains will not compensate for 4x more training tokens >OpenAI created the “Whisper” speech recognition system, so they can feed GPT-4 with another trillion text tokens harvested from YouTube audio? I guess we’ll find out soon! https://openreview.net/forum?id=iBBcRUlOAPR (I left out the link to lesswrong) This here is probably going to be useful for similators training robots to move around and do things: >ProcTHOR: Large-Scale Embodied AI Using Procedural Generation. Deitke et al, @allen_ai. TLDR: ProcTHOR is a simulator that procedurally generates a large variety of interactive, customizable, and physics-enabled houses for training embodied agents. Huge open asset library! https://openreview.net/forum?id=4-bV1bi74M Smaller datasets is probably also very good for smaller devs and groups: >Beyond neural scaling laws: beating power law scaling via data pruning https://openreview.net/forum?id=UmvSlP-PyV I stop here, it's just more and more good stuff coming.
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> new ChatGPT from """OpenAI"""
>>17845 Well, if this is for public facing AI chatbots, then it might be reasonable. It's just complying with social norms. That aside, I don't like how many conversational AI's are pretenting to be humans with preferences and a past. This is just a misdevelopment coming from optimization towards the turing test. We can have different systems, though. So for the same reason, not everything needs to be open to be completely malleable by the user.
>>17845 Lol. I sense they feel their iron-grip is slipping? >>17847 Lol, no. Everything needs to be malleable by the user.

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