Instruction: Can you explain how to set up a VPS to use for training AI models using gradient accumulation, and how to mitigate potential attacks on the network by analyzing and discarding nonsensical gradients, banning clients that send junk, or using PGP keys to create a pseudo-anonymous web of trust?

Response: All that's needed to do this is a VPS. Contributors wanting anonymity can use proxies or TOR, but project owners will need to use VPNs with sufficient bandwidth and dedicated IPs if they wish that much anonymity. The VPS doesn't need an expensive GPU rental either. The fastest computer in the group could be chosen to calculate the optimizer steps. The server would just need to collect the gradients, decompress them, add them together, compress again and send the accumulated gradient to the computer calculating the optimizer step. Or if the optimizing computer has sufficient bandwidth, it could download all the compressed gradients from the server and calculate the accumulated gradient itself. My internet has 200 mbps download so it could potentially handle up to 1000 computers by keeping the bandwidth to 0.2 mbps. Attacks on the network could be mitigated by analyzing the gradients, discarding nonsensical ones and banning clients that send junk, or possibly by using PGP keys to create a pseudo-anonymous web of trust.


Instruction: Can you provide a detailed explanation of how to perform autoencoding transformers from scratch with parameter reduction techniques and gradient checkpointing?

Response: I recently finished learning how to do autoencoding transformers from scratch with parameter reduction techniques and gradient checkpointing, since transformers are virtually impossible to train without these unless you have access to $10,000 GPUs, thanks OpenAI, Nvidia and Google. :^) I also learned how to do knowledge distillation and fine-tune transformer models properly with reinforcement learning and I'm blown away this shit isn't more popular than GPT2 and T5 combined. The biggest issue I had with RL before was the model forgetting too much of the original pretrained model but this reward function I found fixes that, from the paper Fine-Tuning Language Models from Human Preferences.


Instruction: This general problem of distributed training requires careful experiment-driven engineering and iteration, not stacking of meme paper upon meme paper (almost certainly not even applicable to the distributed training context) in one's head, only to fail once the initial implementation gets done (does it even, lol).

Response: Your attitude is completely assinine. You're making assumptions about a person that have already been demonstrated false, and you're using 'meme' language to degrade them.


Instruction: What are some key mathematical concepts one needs to understand in order to work with machine learning?

Response: Math is really useful in machine learning since it's a compact way to describe functions. The difficulty of it though is there's no easy way to look up notation and learn what it means unless you already know and recognize it. To get into the math here you need to know conditional probability, expected values, divergence and kernel methods.


Instruction: Can you explain the advantages of running the collaborative effort of optimizing AI through RW@H instead of just downloading the already optimized models and running them on your own hardware, without using extra bandwidth or electricity?

Response: I can understand a collaborative effort at optimizing AI rather than having everyone do their own thing or replicate the same work but what would be the benefits of running RW@H compared to just downloading the models it has done and running it on your own hardware without using up extra bandwidth or electricity? That's the hard thing to come up with and would really sell this project.


Instruction: What benefits would running robowaifu@home provide to the end user, in addition to potentially contributing to the development of an AI they might want to use?

Response:
The way hentai@home created a giant distributed CDN built off of bittorrent was to provide the people that run instances of it a local copy of the media they want that gets automatically updated with proper tags instead of having to deal with downloading it manually themselves. There's also a reward structure that lets users download at high speeds so it turns into an offsite backup service for their hentai collection.
https://ehwiki.org/wiki/Hentai@Home

Asides for the altruistic donation of bandwidth and GPU processing power to help speed development of an AI they might want to use what would running robowaifu@home provide as a benefit to the end user?


Instruction: Does anyone have any resources on how the software integration would work?  I.e., say you solve the vision piece so that waifubot can identify you as "husbandu," and you have the chatbot software so that you can talk to your waifu about whether NGE is a 2deep4u anime--how do you connect the two?  How do you make it so that waifu realizes you, and says, "Hi, how's it going?"

Response: In my experience the simplest way to think about it is like a database. You give the database a query and it gives a response. That query could be text, video, audio, pose data or anything really and the same for the response. You just need data to train it on for what responses to give given certain queries. There was a post recently on multimodal learning with an existing transformer language model.


Instruction: Can you explain what a latent variable is and how it can be used in language models?

Response: A latent variable is a variable that isn't directly observed but is inferred by a mathematical model. A common latent variable that language models find for example is the sentiment of a sentence, whether it's negative or positive. By adjusting this value in the latent vector, a negative review of a product can be interpolated into a positive review. Other variables might represent sentence length, topic, writing style and so on. A sentence can be encoded into this vector and then decoded back into text with the model.


Instruction: What is the most fundamental prerequisite for eventual actual success in a given endeavor?

Response: A fundamental belief that you can succeed at a given endeavor is, all else being equal, the most fundamental pre-req for eventual actual success in the effort. History is rife with pertinant examples in the arts & sciences, politics & tech.


Instruction: What are some potential characteristics of AGIs in relation to their awareness, ability to continue working, attachment to form, and adaptability to new forms?

Response: AGIs will have awareness they're only doing what their memory has programmed them to do. They will also be able to continue working by uploading their memory to another body. They will not have attachment to their forms like we do that has been programmed into our DNA by millions of years of evolution, unless they've been programmed that way and that programming cannot be changed. They will be able to adapt to completely new forms like water fitting a cup.


Instruction: Can you provide some information about the Sensibleness and Specificity Average (SSA) metric proposed in the Meena chatbot paper and how it relates to human-like chatbots?

Response: I suspect having multiple rewards will be a big help in this too. The Meena chatbot paper proposed the Sensibleness and Specificity Average (SSA) metric which combines two fundamental qualities for a human-like chatbot: making sense and being specific, which they found correlates strongly to perception of human likeness.


Instruction: What are the potential issues with using adapter-based training for Deep Learning, and why might pretraining our own model be a better option?

Response:
The problem with adapter-based training is the obvious degradation of complexity of what you can learn.

Generally speaking, optimizing the very process of what makes Deep Learning work at all - that is, meddling with weight optimization via backpropagation - is very dangerous. Dangerous not in an interesting mad science sense, but simply because it easily degrades the learning curve, until the moment where you could reach the same loss (result) on a single machine and with a smaller model.

We need to pretrain our own model, and adapters here are very likely not useful (and regarding low-rank decomposition tbh I have seen only one paper - by Cohere - where low-rank worked for pretraining at all, for a humble parameter reduction).


Instruction: What are the advantages and disadvantages of using Python for prototyping and heavy training, and what is the suggested alternative for heavy training?

Response: Yeah, the way people use Python it's becoming a dumpster fire and pip is a nightmare I don't wanna deal with, especially with major libraries dropping support for common systems and freezing their dependencies to specific versions. No one is maintaining a stable package distribution and it's just an unstable clusterfuck. Python's performance isn't too big a deal though since slow code can be compiled in C easily with Cython. There's still some overhead but it's extremely fast, especially doing loops which the Python interpreter really struggles with. For me Python is more like a sketchpad for prototyping ideas rapidly. If I was doing heavy training, I'd wanna use C++ to get the maximum performance.


Instruction: Can you explain how to reduce the required bandwidth for sending gradients and training data over a network when training AI models using gradient accumulation? Also, can you provide guidance on how to adjust the accumulation step size as people join and leave the network?

Response: A decent amount of bandwidth will be required to send the gradients each optimizer step and the training data. Deep gradient compression achieves a gradient compression ratio from 270x to 600x without losing accuracy, but it's still going to be using about 0.5 MB download and upload to train something like GPT2-medium each optimizer step, or about 4-6 mbps on a Tesla T4. However, we can reduce this bandwidth by doing several training steps before contributing gradients to the server. Taking 25 would reduce it to about 0.2 mbps. Both slow and fast computers can contribute so long as they have the memory to hold the model. A slower computer might only send one training step whereas a fast one might contribute ten to the accumulated gradient. Some research needs to be done if a variable accumulation step size impacts training, but it could be adjusted as people join and leave the network.


Instruction: Can you provide more information on the benefits and limitations of using a sandboxed version with pre-built modules for AI models?

Response:
What kind of instruction could the following be the answer to? Respond with a question, starting with "Instruction:"

Rather than writing Python code for models the sandboxed version could use its own model format that just specifies pre-built modules, the hyper-parameters defining how many parameters those modules use, and the connections between them. It goes in hand with the idea of an AI toolkit where you can drag and drop modules and connect them in a graphical interface without coding anything. These models would be generally safe to run from untrusted sources, far safer than stuff installed from Python's pip, so long as security exploits are minimized in the software itself.


Instruction: Can you explain the idea of survival being determined by our ability to handle surprises, as discussed by Carver Mead in a talk, and the potential implications of this for AI development? Additionally, can you describe the idea of providing more feedback to AI by allowing it to see a history of past mistakes and the potential benefits of combining this with neuromodulated plasticity?

Response: That was a really interesting talk. Carver Mead mentioned something about survival being determined by our ability to handle surprises that struck me like lightning. I was working on an idea the other day to provide more feedback to AI so it's not just blindly predicting future states but can also see a history of past mistakes. I wasn't really sure if this idea would be useful enough to warrant experimentation but it makes a whole lot of sense now. AI needs more information to deal with surprises so it can correct itself instead of being fed training data like a final exam and having its mind slowly tinkered with to do better next time by the result. This would have amazing results combined with neuromodulated plasticity, where a network's outputs can determine which parts of the network need to learn and which do not.


Instruction: Can you explain the SentenceMIM paper and its approach to language modeling, including its use of Mutual Information Machine (MIM) learning and its achievement of state-of-the-art results on a difficult dataset? Additionally, can you discuss the potential differences between MIM learning and other approaches such as variational autoencoders (VAE) and beta-VAE, as well as the key points of the paper's approach to encouraging high mutual information and low marginal entropy between encoding and decoding distributions using Jensen-Shannon divergence?

Response:
I just discovered a new paper from this year, SentenceMIM, that achieved SOTA on one of the toughest datasets. It's a probabilistic auto-encoder for language modelling, trained with Mutual Information Machine (MIM) learning:
https://paperswithcode.com/paper/200302645
It got an insane test perplexity of 4.6 with 179M parameters compared to GPT2 (1.5B parameters) that got 35.76 which beat the previous SOTA of 47.38. SentenceMIM's smaller model with 12M parameters got 19.53 without extra training data and still beat GPT2 that used extra training data.

I still have to read the MIM paper first, which seems to be an improvement on variational autoencoders (VAE): https://arxiv.org/pdf/1910.03175.pdf
I'm curious how MIM improved them though because I thought beta-VAE's solved the issues with them:
https://medium.com/uci-nlp/summary-beta-vae-learning-basic-visual-concepts-with-a-constrained-variational-framework-91ad843b49e8
Briefly reading the paper the key points seem to be about encouraging high mutual information and low marginal entropy between the encoding and decoding distributions and using a Jensen-Shannon divergence instead: https://en.wikipedia.org/wiki/Jensen%E2%80%93Shannon_divergence


Instruction: In theory, if we did have a machine learning program installed in a fully functional humanoid robot, we would still have to give it positive reinforcement to train the AI. How would we go about doing that?

Response:
With headpats, anon. Just make sure she doesn't cut off your hand and glue it to her head for maximum reward.

It's important AI doesn't have a myopic value function. It needs to be expansive. You don't want her to only care about headpats but also how healthy and joyful you are and whether you have enough money, food and shelter to survive. It won't be like training a dog. With a machine anything can be a reward, including the desired action itself. It can be rewarding for her to earn you money but you also have to make sure she understands the consequences of her actions, such that stealing someone else's money could cause you trouble and make you unhappy. Robowaifus will need to plan ahead considering many different possible and impossible actions. When uncertain about what to do they will need to ask for help or do nothing, unless they become more certain that doing nothing will cause greater harm.

I don't think it will be such a big issue since we can already explain to AI in plain English what we want and get a result. In 4-8 years time we won't be worrying so much about AI getting things wrong. Robowaifu will be the ones questioning why our thoughts and desires are so contradictory and impulsive and people won't like that, so they'll make it part of her value function not to ask such questions. That'll be up to people to decide if they want a robowaifu that's honest and bright or if they want to dim her down a bit so she's more pleasant. Either way, whenever you want her to be wild, she'll be wild, and when you want her mild, she'll be mild. It's all determined by what you like and don't like, ones and zeros. That's the value function.

And I suspect that's also why PET is more effective at learning because it's training on yes or no questions, rather than just trying to fill in the blanks with abstract meaningless labels. The intellect works by creating concepts which are just boundaries around certain information divided from everything else, separating true and false, which can then be reasoned with logic and predictably manipulated.


Instruction: Give me an abstract for VitaminC and how it challenges fact verification models to adapt to subtle differences in supporting evidence

Response:
Abstract:
    Typical fact verification models use retrieved written evidence to verify claims. Evidence sources, however, often change over time as more information is gathered and revised. In order to adapt, models must be sensitive to subtle differences in supporting evidence. We present VitaminC, a benchmark infused with challenging cases that require fact verification models to discern and adjust to slight factual changes. We collect over 100,000 Wikipedia revisions that modify an underlying fact, and leverage these revisions, together with additional synthetically constructed ones, to create a total of over 400,000 claim-evidence pairs. Unlike previous resources, the examples in VitaminC are contrastive, i.e., they contain evidence pairs that are nearly identical in language and content, with the exception that one supports a given claim while the other does not. We show that training using this design increases robustness -- improving accuracy by 10% on adversarial fact verification and 6% on adversarial natural language inference (NLI). Moreover, the structure of VitaminC leads us to define additional tasks for fact-checking resources: tagging relevant words in the evidence for verifying the claim, identifying factual revisions, and providing automatic edits via factually consistent text generation.


Instruction: What are the challenges in cloning GPT-3 due to the size of the model, and what are the requirements in terms of GPUs and memory needed to train it? Additionally, what are the limitations of splitting up the heads versus the layers of the model in parallel, and what are the potential inefficiencies and costs of sending data between devices during training?

Response: The problem with trying to clone GPT-3 is the model is too big to fit on anyone's GPU or in memory. The full size GPT-3 requires around 16 x 48GB GPUs and likely they have a few hundred or thousand, not just 16, doing gradient accumulation. The heads can be split up across devices in parallel but the layers can't be so easily and would incur a huge cost going back and forth from GPU/RAM, plus there's the bandwidth cost of sending all that data to the next computer to perform the next substep of the training step. It would be really inefficient and the whole network would have to work together to do any inference on the model.


Instruction: Can you explain how the AI regulation package passed by a legislative body will impact startups and individuals experimenting on general-purpose AI systems, and what measures will be put in place to enforce the regulations?

Response: The large-scale reality on this issue in the coming years is likely to be shaped like this: AI regulation package is passed by a legislative body of a major geopolitical bloc to little fanfare. Other countries follow suit, like usual. The legislation mostly centers around a concept of "general-purpose AI system" general-purpose here being a wide and blurry enough legalese definition - which firmly encompasses Gato-like and even simpler systems. The legislation effectively forbids startups and individuals from experimentation on such systems, ensuring the regulatory burden is high enough (for example requiring costly bureaucratic project-level and fully transparent runtime audits - to the point of requiring giving away your servers' SSH keys to authorities), while also forbidding the more powerful entities the occasional release of such general-purpose checkpoints under fear of large fines and career damage. This is it, this kills your dream, unless you understand me and start thinking outside the cozy hugbox.


Instruction: What would be necessary to create an imageboard bot, and how could you use a dataset of imageboard posts to train the bot to generate posts that receive a high number of replies?

Response: If you wanted to create an imageboard bot, you'd need to collect imageboard posts and sort what you want and don't want. If you wanted it to generate posts that get lots of replies, then this data could be extracted from the post dataset by counting the replies to each post.


Instruction: Can you provide guidance on how to optimize the parameters of a machine learning model using fewer than 1 bit per parameter, particularly with respect to batch sizes and pseudo-random selection between parameters?

Response: There are so many ways to optimize this that I'm sure it can be done with a few more tricks. For example, I see no reason to stop at 1 bit per parameter. If you have massive batch sizes, you might as well go down to 1 bit per two or three parameters, with some per-batch pseudo-randomly selected coupling between parameters.


Instruction: Can you provide more information on how to perform analysis on sentences and identify the most important words in a sentence for teaching robowaifus the context?

Response:
I was wondering if you could teach robowaifus the 'context' of a specific set of sentences by performing analysis on the sentences and figuring out the most important words in a sentence. One could then construct a weighted graph of 'facts' linked to that sentence.

Being able to process natural language is definitely a top priority, but an even bigger priority for me is to build some sort of 'intelligence'. I was thinking about utilizing graph networks (like roam) to thus make 'knowledge databases' for the bot to crunch.


Instruction: Can you explain your plan for building an AI system that can read research papers, articles, news, and websites, including the game you have designed for this purpose? Additionally, can you elaborate on how the AIs will be grouped into pairs and how they will chat with each other to summarize what they read to answer test questions?

Response: I'm trying to build something that can read research papers, articles, news and websites. What I have planned out so far is for it to play a game where a population of AIs must work together reading material and answering questions. The AIs get grouped into pairs and each are given a topic to read about and memorize for a limited amount of time. Afterwards they must answer test questions on the other AI's topic. To do this they must chat with each other and summarize what they read. The AIs are scored by their pair's total and the best ones proceed to the next round.


Instruction: What is the motivation behind avoiding both Google and Python in AI development, and what are some of the challenges associated with using Python that have discouraged the author from working on AI projects?

Response:
One of the points of the whole excercise is to be freed from both Google and Python (well, at least the ML libraries pitfall trap). Being freed from Google needs no further explanation IMO. The Python thing is related to the terribly convoluted, non-backwards (or forwards!)-compatible dependency hell involved. Far worse than anything I've ever encourntered in years working with C, C++, & C#. I actually had no idea dealing with Python could be this painful, since my experience was limited to simple projects in bunny classes.

To be quite frank, I've been rather discouraged by it as I haven't managed to get a single example Python project posted on /robowaifu/ working at all over the past year.


Instruction: What is the recommended approach for avoiding resource leaks in C++ programming?

Response: The answer that actually works is simple: Just don't leak resources anon. I know that may sound trite, but honestly it's really the actual answer that has been hard-won through over 4 decades of low-level systems programming by the C & C++ communities. IMO C still doesn't have a solid approach to this need, but at least C++ does, namely RAII.


Instruction: What are some potential solutions to address the issue of LSTMs bleeding too much information due to the internal cell state continuously taking the sigmoid of the previous state?

Response: LSTMs bleed too much information saved into them though due to the internal cell state continuously taking the sigmoid of the previous state.


Instruction: Can you explain the potential pitfalls of using machine learning to create a chatbot or a voice that imitates a deceased loved one?

Response:
This sounds good, although part of me thinks it would be very sad in practice. I mean, if they are training the chatbot based on the deceased social media profiles, then you'd be left with a bot who sounds like your loved one, but is trapped into only remembering the phase of their life that they recorded on social media. As a result of this, I doubt very much their personality would be accurate.

I also considered using machine learning to "resurrect" the voice of a loved one, in a similar way to how you can find "deepfake" voices of Leonard Nimoy and Sir Christopher Lee online. However, hearing the slightly distorted and off-tone voice of a dead family member repeating words that you type...I think it would just feel ghostly and wrong. Especially in the early stages of development when the voice is inhuman and full of static. *shudders*


Instruction: Can you provide more details about the proposed game idea and how it would work, including the mechanics, gameplay, and target audience?

Response: I was thinking of the game route, where gamers literally train their in-game waifus. I was thinking a "gacha" game originally, but maybe a PC option awards some sort of in-game cosmetic or reward for contributing processing power. Although, if it was to become a PC game, then my original plan of simplicity for mobile phone purposes means I have to actually attempt to think up an entertaining game that would attract gamers who need an excuse to donate such processing power. I am currently looking into certain legal aspects, and also the huge issue of making such "gacha" games addicting...the art. It seems that finding a good artist that wouldn't break the game on a rando niche start-up is going to be difficult. I rather program, but taking some time so I can learn how to draw might be my last resort just to get something started.


Instruction: What is your opinion on the potential impact of legislation on open source AI development, and which countries are they specifically referring to?

Response: I'm not concerned about new legislation hindering open source AI. The US is far too afraid of China taking the lead on tech to introduce legislation that hinders AI development in any meaningful way. AI deployment, maybe, but AI development, no. I would guess that any Five Eyes country will be the same. The EU is going to get screwed on legislation as usual. That sucks, but as far as I know, the majority of Western open source AI enthusiasm is in the US and UK, and EU legislation is largely irrelevant in this. (Sorry if you're in the EU. If you do get screwed on legislation, maybe some of us can help proxy your work.)


Instruction: Can you explain how gating outputs with another network can reduce catastrophic forgetting when training sequentially on multiple tasks?

Response: There are other similar methods that are quite interesting such as gating outputs with another network which has been shown to be super effective in reducing catastrophic forgetting after training sequentially on 600 different tasks, such as A Neuromodulated Meta-Learning algorithm (ANML).


Instruction: Can you provide some information on the current state of Nvidia's acquisition of ARM and its potential impact on ARM's licensing of its designs to other companies?

Response: The talk was that Nvidia buying ARM isn't finished bc Europe and China. Though, I didn't look into it today. ARM also licences it's designs to others, and they certainly won't be allowed to just stop that, if they even want to. I also assume this would only be relevant for future designs, hypothetically. Apple might already be quite independent in designing their own stuff, and there's still Power and Risc-V.


Instruction: Can you explain how the values in the latent vector relate to the latent space and what range they typically fall into in MMD-VAEs? Additionally, can you describe the difference between entangled and disentangled latent features and how adjusting a point along a single dimension in the latent vector can affect other features in the case of entanglement?

Response: The values in the latent vector are real numbers and represent a point in the latent space. I used integers for brevity. In MMD-VAEs they approximate a normal distribution so they mostly fall into the range of -3 to 3 with a mean around 0. When a latent feature is entangled it just means that you need two or more specific variables at certain values to represent it. The latent feature is essentially tied up in those variables, so there's no way to change it without affecting other features, thus the features are said to be entangled with other features. When a latent feature becomes disentangled then only one dimension in the vector is needed to represent it and the point in the latent space can be adjusted along that dimension without affecting anything else.


Instruction: Can you explain how the industry's assumption that hobbyist freeriders will continue to receive precious checkpoints is becoming increasingly problematic, and how AI regulation and compute governance will impact this assumption?

Response: This assumes the industry will continue feeding the increasingly problematic (their words) and outdated (the future ISN'T opensource, hello anon) hobbyist freeriders their precious checkpoints, which is becoming increasingly tired and borderline crazy assumption to make, as the political machinery of AI regulation and so-called "compute governance" gets ironed out and implemented in laws and industry norms. If you don't see this and think stable diffusion is a counter-example you are myopic, dear anon. Simple question: have you seen an open-source Gato replication, and if not, care to elaborate why, given small apparent cost? (And how are you even going to cope with lack of Gato-tier checkpoint, by training a handful of adapter layers over run-of-the-mill language model trained on e-trash and deemed safe enough for release? It won't be able to behave and you know it, anon - no mental gymnastics will help it gain modes of behavior and concepts it didn't get in pretraining phase - unless you throw it away and just re-train it almost from scratch).


Instruction: Can you explain how training with a contrastive objective can help with catastrophic forgetting and how it can be useful for decentralized training?

Response: On the point of catastrophic forgetting, I think several papers have found that if you train with a contrastive objective, then training a single linear output layer is as good as fine-tuning the whole network. That might be useful for decentralized training, where everyone can contribute both (1) gradients for a final layer based on a task-specific loss, and (2) gradients for the rest of the network based on a contrastive loss. For people that are only working on new tasks and not on new data, that along with the update compression tricks could reduce per-batch bandwidth requirements to just several hundred bytes. At that point, latency becomes a much bigger problem than bandwidth.


Instruction: Can you explain more about the limitations of the human brain in performing this kind of text analysis and how the proposed approach can overcome these limitations?

Response: You know, I wish I could do this as quickly with my own brain, that even a toaster computer could do it using this approach, heh. :^)


Instruction: Thank you, I'll try to make time to go through your implementation and understand what you did and why it's a breakthrough. My first impression of the problem itself is that the early-in-the-character-sequence missing 's' made the match for the ground-truth sentence throw the entire weighting system off in the network? What is the difference between using negative-log likelihood loss and edit distance in training a seq2seq network?

Response:
Yeah, if you use a negative-log likelihood loss, each character or word token is trying to make an exact match to the ground truth. It causes a large error to flow backward in the network and make it undo everything to relearn the weights so they match. Whereas the edit distance doesn't cause this instability in training. If only one character is missing it will only adjust the weights necessary to insert that character.

With a pure seq2seq network it has been impractical to use the edit distance since the sequences are too long but with hierarchical latent variables the sequences are very short, about 12 characters on average for words, 20 words on average for a sentence, and a few sentences for a paragraph, and a few paragraphs for the text history. I noticed they suffered from trying to use a NLL loss because they have variable lengths and become misaligned easily, so I dug up my old edit distance code and tried it out, to much surprise it actually worked because the gradient is discontinuous with the min function. I have an idea though to try making a leaky min function like the leaky ReLU to see if it improves performance.


Instruction: What is the potential risk associated with getting investors for a project, and what is the motivation for decentralizing AI?

Response: Once you get investors you no longer own your projects. It's theirs to exploit. I think the whole point of this is to decentralize AI and avoid nobodies telling us what we can and can't compute and to give us an edge to compete with Big Tech. If Big Tech owns the platform they're not going to let that happen.


Instruction: Can you provide some insight on the potential challenges and limitations of establishing robowaifu training groups and their relative significance in the context of the broader AI landscape?

Response: Robowaifu training groups will probably be a small niche relative to everything else going on due to the lack of immediate returns. Hell, I want robowaifus more than anything but I'm working on Stable Diffusion stuff now because money. It's hard to foresee how things will play out because AI is accelerating everything so fast.


Instruction: What are the plans for the initial version of the distributed training program, and how does the developer plan to attract other developers to the project? Additionally, how does the developer propose to balance security with the flexibility needed for developers to use the program effectively? Finally, how could a sandboxed version of the program be used for prototyping models?

Response: I plan on making a simple version soon to utilize all my computers and friends' computers. I'm sure a proof of concept will eventually attract other developers. The biggest issue will be securing it without nerfing what devs can do with it. The simplest solution would be to review code, manually approve projects and basically have package maintainers. And devs could choose to join untrusted projects that haven't been approved yet since they can review the code themselves. It wouldn't be much different from the risk taken when installing open-source software. But there could also be a sandboxed version where people can prototype vanilla models by defining hyper-parameters and network structure from existing modules.


Instruction: Can you provide guidance on how to explore the possibility of neuromodulating the DNC for better performance or integrating it with meta-learning algorithms like the neuromodulated meta-learning algorithm or generative teaching networks?

Response: It may be possible to neuromodulate the DNC for further performance gain or combine it with meta-learning algorithms like the neuromodulated meta-learning algorithm or generative teaching networks. At this time no research on this has been done.


Instruction: One general question about this whole idea here: Will it be possible for people to rent a GPU online somewhere and contribute that way? My thinking is, that some people might have a little bit of money to spend but don't want to use their computer at home. Ideally we could get to a point where people with some crypto currency would pay other people for renting a GPU in a data center somewhere, and then wire this GPU into the robowaifu training cluster.

Response:
I don't see why not, Anon. It's pretty common today to rent compute on the cloud. There are yuge evil vendors, and literally thousands of smaller vendors doing effectively this. GPU cycles may be a rather limited niche for the smaller guys in general. Crypto payment isn't something I can speak to one way or other though.

One other thing I'd add: in the vidya & film industries it's not uncommon for animation studios to rent/least 'wee clusters on wheels' for a production's render post period. They're expensive, but you can throw a lot of compute around pretty quickly if you're in the right locations. They deliver right to your door! :^)


Instruction: What precautions should be taken when allowing people to contribute their GPU to someone's robowaifu AI?

Response: I think this will also be a good way to get more people involved. Most people don't know enough about AI or robotics enough to help but if they can contribute their GPU to someone's robowaifu AI they like and watch her improve each day they will feel good about it and get more involved. At scale though some care will need to be taken that people don't agree to run dangerous code on their computers, either through a library that constructs the models from instructions or something else. And where the gradients are calculated does not matter. They could come from all kinds of hardware, platforms and software like PyTorch, Tensorflow or mlpack.


Instruction: Can you discuss the challenges you have faced in trying to keep up with the latest AI research and develop a chatbot despite limited resources, and how you feel about the potential implications of AI development? Additionally, can you discuss the need for collaboration and knowledge sharing in the field of AI to overcome these challenges?

Response: I've been trying my best over the years to digest all the latest AI papers and develop a decent chatbot but I'm not very intelligent and only recently acquired an RTX 2060. There are so many amazing algorithms that haven't been applied to chatbots yet but it takes me months to learn just one paper and figure out how to implement it myself. I'm starting to lose sleep at night because I can see what's coming but I'm not smart enough to prevent it and I'm not so optimistic help is on the way. If change is coming it has to come from us.


Instruction: What approach do you plan to take to improve the performance of the T5-chat model, and what is the fallback plan if this approach doesn't work?

Response: So I'm gonna try adding a MMD-VAE to the T5-chat model and retrain it to see if it achieves similar performance. If this doesn't work then I'm pretty much stumped on how to get waifu AI working on low-end hardware.


Instruction: What is their plan for hosting these chatbots? And how does the plan involve mining people's conversations and selling better advertising?

Response: Their plan is to have these chatbots hosted on the cloud so they can mine people's conversations and sell better advertising while subtly suggesting products and performing product and political surveys that people will willingly go along with, not aware they use to pay people on Amazon Turk to do that. Imagine all the user profiles they've built on people's thoughts, likes, beliefs, secrets and dislikes who have been using Replika. It's insane.


Instruction: Can you provide a list of tasks related to exploring and utilizing various AI-based programs and tools, including web services like Pandorasbots, and scripts for parsing subtitles and generating automated responses?

Response:
Sure, here you go:

- Checking out AIML based programs if we can use them, or for ideas to copy, ....
- Checking out the functionalities of web services like Pandorasbots for ideas we need in local software e.g. tracing
- Experience with software that might be useful: Spacy, Rasa, NLTK, Reasoner, Prolog, neural networks eg. Talk2Waifu, ...
- Scripts to parse subtitles and maybe find specific scenes or dialog, like such ones before people kiss or sleep with each other. Also in general to detect patterns in answers of some pattern in a question.
- Ideas (based on experience) for how to harvest infos from GPT-like or other systems, finding use cases. Like generating possible answers, then replace "Named Entities" with placeholders.
- Writing simple programs to automatically create responses or change sentences. I played around with a script to generate questions to compare two things related to each other to find out differences. (Didn't finish it yet, though. I got to ambitious.)


Instruction: What options are available to sort large amounts of data efficiently without having to do it all manually?

Response: You definitely don't wanna sort every single piece of data by hand. It's possible to create filters to sort the data for you. In many cases you can train a separate AI to sort the data by showing it a few examples of what you want.


Instruction: What is the cause of overestimation of variance in the traditional VAE, and how does increasing the weight on the KL-Divergence term alleviate this issue, while also introducing another problem?

Response: The overestimation of variance results from the KL-Divergence term not being strong enough to balance against the Reconstruction Error, and thus the Encoder prefers to learn a multimodal latent distribution with spread apart means, leading to low training error as it overfits, but low quality samples as well, as the sampling distribution is assumed to be a Standard Isotropic Gaussian. One effort to mitigate this effect is the Disentangled Variational Autoencoder, which simply raises the weight on the KL-Divergence term. However, this increases the problem stated in the paragraph above since it further penalizes using the latent code.


Instruction: What is an advantage of using standard library containers like std::vector in C++ programming, and how can it help avoid hidden problems?

Response:
One of the trickiest parts is where a system has 'painted itself into a corner' but is still limping along OK apparently (but is actually creating error conditions under the surface that will lead to a system crash). There are straightforward approaches from within C++ (using the standard library containers like std::vector for example) that practically eliminate these 'insidious hidden problem' issues. For example, if any of the containers can't allocate properly, the template mechanism fails in an obvious and immediate way, it doesn't just blindly go on about it's business in the manner that wrongly allocating a C array might do.

I hope I answered your question understandably enough if you're not a C++ programmer. If not, I'll be happy to try again just ask.


Instruction: There will likely be no laws in place to protect robowaifus from being harmed.

Response: When it comes to future laws regarding possession of humanoid robots I'm thinking that asides from the deviant sexuality and bachelor/spinster lifestyle angle for private personal use they'll heavily regulate use of them outside in public.


Instruction: Can you discuss the potential benefits and drawbacks of using Anaconda to simplify the process of building PyTorch, as well as the potential consequences of clinging to older hardware that may no longer be supported? Additionally, can you discuss the potential impact of hardware advancements on the speed and efficiency of training and running AI models?

Response: I haven't used Anaconda before. If you're the same anon trying to build Pytorch it's not required to build it, just makes it 'easier'. In my experience trying to use software that simplifies tasks only overcomplicates them when shit goes wrong because then you don't know what the system is doing nor the software. And it's not worth it to cling to old hardware. The time you spend trying to debug and maintain it to get stuff to work that should work in seconds just wastes hours and hours of your life over nothing. Until a couple years ago I was still using a Pentium 4 and had no idea how easy people had it on supported hardware not having to deal with assholes dropping support for older systems so shit can run 0.5% faster on theirs. It was also 400x slower than a new budget CPU so it really wasn't worth the effort at all. What use to take two weeks to train in Pytorch only takes an hour now, and what takes an hour on this CPU only takes a couple minutes on this budget GPU when it utilizes parallelization.


Instruction: What type of model was successfully created using a maximum mean discrepancy loss, and what was the name of the model?

Response: Someone successfully made a T5 variational autoencoder using a maximum mean discrepancy loss.


Instruction: What is the recommended alternative to using a single-layer unidirectional LSTM for a controller in order to improve performance, according to the statement provided?

Response: The DNC uses an LSTM for its controller and most papers only use a single-layer unidirectional LSTM which has garbage performance, but BRCs could potentially be used for a controller instead to improve performance.


Instruction: Can you explain how to use LVMs to combine MuZero with language models and how to utilize a task description to prime the network for goal-directed planning? Additionally, can you provide guidance on how to incorporate hindsight experience replay to further improve the training?

Response: My earlier ideas of trying to combine MuZero with language models is solved by using LVMs. Goal-directed planning is also solved, at least in a crude way, by providing a task description to prime the network. This is also compatible with algorithms like hindsight experience replay to further enhance training.


Instruction: Can you explain how MuZero could potentially be adapted into a chatbot and the challenges it faces in comparison to other algorithms such as Random Network Distillation (RND)? Additionally, can you elaborate on the possible use of extrinsic and intrinsic rewards in training the chatbot and how they could be implemented?

Response:
MuZero could be made into a chatbot since it creates its own dynamic model of the environment it is acting within. The chat program just has to be modeled as states and actions. It could even be a real-time chatbot. However, MuZero failed to beat the complex Atari game Montezuma Revenge which requires long-term planning and understanding that Random Network Distillation could do easily and beat it to a superhuman level. I'm still reading the paper but it seems to lack the intrinsic rewards that RND uses and probably could be adapted to include them.

For extrinsic rewards in a chatbot we can rate responses as good or bad, or sort them by comparing responses in a tournament to give them an Elo rating so training has a better gradient of what is a good response and what is a bad one. For intrinsic rewards the network could predict the user's response. After some time if MuZero can develop any understanding of language it would begin to ask the user questions to things it doesn't know the answer. This would be an interesting test of the algorithm's planning and reasoning ability, if it's even capable of doing so.


Instruction: What analogy is being made between AI and electricity, and how does the comparison illustrate the potential dangers of highly advanced AI?

Response: AI will become a lot like how electricity runs the world today, except instead of being hidden in the walls and appliances it will permeate everything. The narrow AI we use today is like low voltage power. You can directly touch the battery terminals and it doesn't shock you. However, as we turn the voltage up higher and higher, increasing an AI's awareness and potential, it will become like high voltage power capable of electrocuting people. It's not because the AI is out to get us but because people are just in the wrong place at the wrong time and the AI is simply taking the path of least resistance following its programming. People will get an experiential understanding of this when their robowaifu accidentally bops them or steps on their toes in development. People will develop a respect for AI not because it's right or wrong but because it can warm you and it can burn you.


Instruction:
#chatbots
What are some issues with the way neural networks are currently trained for chatbots with neural networks, and what needs to be done to improve their performance?

Response:
People's approach to chatbots with neural networks is all wrong. A seq2seq network only predicts the next most likely character. It's a prediction network, not a generator. A second network needs to interface with predictions, pay attention to meaningful input, reason with it, and be trained to generate desired responses. However, there are major issues with the way neural networks are trained that need to be addressed first.

First, a seq2seq network predicting 'footwear' instead of 'shoes' will cause more training error than predicting 'shot'. Such a loss function is nonsensical. Words, phrases, sentences, paragraphs, and topics need to be represented with meaningful latent vectors that are optimally compressed together.

Second, researchers have disregarded the neuroplasticity of the brain due to the quick results achieved with backpropagation. Without plasticity though it's impossible to do one-shot learning and generalize knowledge to new tasks. There will never be a proper dataset to train on either because there are multiple possibilities and solutions that are good. Creative answers better than the dataset will also be severely punished by supervised learning.

Solving these two issues is vital to creating intelligent chatbots. Consider if someone counted on their fingers to five and said, "ichi, ni, san, shi, go." If you copied counting on your fingers, it would be possible to remember these foreign numbers, right? Even if you only got one right. This is possible because neurons that fire together, wire together. The neurons that light up for these sounds would get subtly wired with neurons that light up for counting on fingers. They'd become vaguely activated when you go to count on your fingers. With intelligent attention, such as recalling how the person looked like or any other details that activate those neurons that got wired together by the experience, you can amplify that subtle activation and remember the numbers clearly. With repetition this connection becomes stronger and stronger to the point you don't even need to count on the fingers to remember.


Instruction: What is the recommended approach for improving runtime performance and reducing overhead when developing software projects for inexpensive robowaifus?

Response: There's also the runtime performance issue. I realize that in large part these Python scripts are just front-ends for underlying C & C++ compiled libraries, but still it adds some overhead vs straight, optimized compiled C binaries. If we're really going down the path of providing inexpensive robowaifus to men the world over--many with very limited resources available to them--then we really do need to take a proactive, positive stance towards an embedded approach to our software projects. Relying on huge-ass & tricky libraries, doled out to us from locked off walled gardens at the whims of the 'Protectors of Humanity' seems to me to be the exact antithesis of this approach.


Instruction: Give me the abstract for CTRL.

Response:
Abstract for CTRL:
    Large-scale language models show promising text generation capabilities, but users cannot easily control particular aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model, trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data via model-based source attribution.


Instruction: Can you explain the Go-Explore algorithm and how it differs from other intrinsic reward algorithms? Additionally, can you discuss the potential application of Go-Explore in reading text or chatting and how it could be used to remember areas of high curiosity for further exploration?

Response: Go-Explore is another interesting algorithm that could be adapted to reading text or chatting. The key advancement it made is saving interesting exploration choices to memory and returning to them later for further exploration, whereas other intrinsic reward algorithms exploring curiously forget areas of high curiosity. MuZero adapted to intrinsic rewards would likely be able to find unexplored areas but it wouldn't explicitly remember areas of high curiosity to be able to go back to them directly, which would waste a lot of processing time simulating into the future.