Open-source AI promises to democratize technology, but it can also be abused and may lead to hard-to-control AI. In this post, we'll explore the data, ethics, and funding behind these models to discover how to balance innovation and safety.
Open-source models, like LLaMA and GPT-NeoX, are trained on huge public datasets of internet data, such as the Pile, which has 800 GB of books, medical research, and even emails of Enron employees before their company went bankrupt and they switched careers to professional hide-and-seek.
After the unexpected leak of Meta’s LLaMA, researchers cleverly enhanced it with ChatGPT outputs, creating chatbots Alpaca and Vicuna. These new bots perform nearly as well as GPT-3.5 and cost less to train — Alpaca took just 3 hours and $600. The race is on to run AI models on everday devices like smartphones, even on calculators.
The leading image generation model, Stable Diffusion, is developed by Stability AI — a startup that has amassed $100 million in funding, much like Hugging Face (known as the "Github of machine learning"). These two unicorn startups financed a nonprofit to collect 5 billion images for training the model. Sourced from the depths of the internet, this public dataset raises concerns about copyright and privacy, as it includes thousands of private medical files.
The open-source AI community wants to make AI accessible and prevent Big Tech from controlling it. However, risks like malicious use of Stable Diffusion exist, as its safety filters can be easily removed. Misuse includes virtually undressing people. If we're still struggling with how to stop a superintelligent AI that doesn't want to be turned off — like Skynet in Terminator — how can we keep open-source AI from running amok in the digital wild?
Open-source code helps create advanced AI faster by letting people use each other's work, but this could be risky if this AI isn't human-friendly. However, to ensure safety, researchers need access to the AI's "brain," which is why EleutherAI openly shares their models. EleutherAI is especially worried about catastrophic risks from AI, with its co-founders even launching Conjecture to focus on AI alignment. They also signed an open letter calling for a six-month pause in AI development. But others, like LAION, want to push forward and support open-source models with a supercomputing facility.
The next big thing in AI will be agents that can take actions online, like ordering groceries. LangChain, a company that creates open-source AI agents, recently raised $10 million in funding and connected their agents with 5,000 apps. These advances are fueling excitement in this field. But with great AI power comes great responsibility.
To improve open-source AI safety, we suggest the following measures:
Open sourcing AI implies sharing at least one of three components, and sometimes all of them:
We asked GPT-4 to rate the outputs from various open-source language models on a scale of 1 to 10 for five different prompts. The outputs and prompts can be found in this Google Doc.
*not open source.**tested in Chinese, then translated using DeepL.
The list of projects below is not intended to be read from start to finish. The summary above should suffice; Explore details of the projects that pique your interest.
In February 2022, EleutherAI released GPT-NeoX-20B, the largest open-source language model at the time. GPT-NeoX, along with their faster (but less performing) GPT-J model, were trained on the Pile, an 800 GB public dataset from various sources that can be divided as:
Image source: The Pile
In 2021, EleutherAI’s cofounder Connor advocated for the release of GPT-3-like models, arguing that safety researchers need access to the underlying parameters. He also emphasized that attempting to keep this technology from bad actors is futile, as well-funded groups can easily replicate it.
In 2022, EleutherAI’s cofounders Connor and Sid established Conjecture, an alignment startup funded by investors like Nat Friedman, Daniel Gross, Arthur Breitman, Andrej Karpathy, and Patrick and John Collison. Conjecture aims to better understand AI models while also making a profit, such as by offering AI transcription services. To mitigate potential risks, Conjecture has implemented an infohazard policy to prevent the leakage of information that could accelerate AGI.
EleutherAI's approach to open sourcing is not about releasing everything in all scenarios. Instead, they focus on releasing specific language models in particular situations for well-defined reasons. EleutherAI has avoided the release of models that would push the capabilities frontier, and even refrained from releasing certain discoveries due to potential infohazards. Conjecture, founded by EleutherAI's co-founders, takes an even stricter stance and maintains operational security measures that wouldn't be possible in a volunteer-driven open-source community.
One of EleutherAI's initiatives, the Eliciting Latent Knowledge project, aims to mitigate the risks posed by deceptive AI by unveiling AI model’s inner workings. Separately, CarperAI, a spinoff that is independent of EleutherAI, aims to enhance Large Language Model (LLM) performance and safety. They are working on developing the first open-source Reinforcement Learning from Human Feedback (RLHF) model, which incorporates human feedback in the fine-tuning process.
Initially, EleutherAI started as a Discord server and relied on Google for free computing. They later accepted funding (including hardware access) from cloud infrastructure companies. In March 2023, EleutherAI announced the non-profit EleutherAI Institute, with full-time staff. Funders include Hugging Face, Stability AI, Nat Friedman, Lambda Labs, and Canva.
In October 2022, Google AI open-sourced Flan-T5, a conversational model. Pre-trained on a 750GB English version of Common Crawl, the model was then fine-tuned for conversational capabilities using the FLAN dataset ("flan" as in “dessert”).
Demis Hassabis, founder of DeepMind, has urged against adopting a "move fast and break things" approach for AI. Additionally, DeepMind researchers wrote an Alignment Forum post discussing the potential risks of AGI and the need for better safety research.
However, following the launch of ChatGPT — a challenge to Google’s search monopoly — Google has encouraged its teams to speed up the approval process for AI innovations, and adjusted its risk tolerance for new technologies.
Unlike DeepMind, Google AI doesn’t seem explicitly concerned about catastrophic risks from AI. Instead, their 2023 blog post on responsible AI emphasizes issues such as fairness, bias and privacy. This aligns with Google AI’s principles to:
Interestingly, another one of Google AI’s principles is to refrain from developing lethal autonomous weapons. This demonstrates how a private corporation can self-regulate, as there is currently no international treaty limiting slaughterbots.
Hugging Face, the “GitHub of machine learning,” is best known for its Transformers library, which offers state-of-the-art language tools. A wide variety of models are available on their website, including popular ones such as BERT, GPT-2, RoBERTa, and CLIP.
In May 2022, Hugging Face’s BigScience project released Bloom, the world’s largest open-source multilingual language model. It can write in 46 natural languages and 13 programming languages. The model was trained on the 1.6 TB ROOTS corpus (searchable here), which includes news articles, books, government documents, scientific publications, GitHub, Stack Exchange, and more.
In 2022, Hugging Face disabled GPT-4chan, a model that was fine-tuned on 4chan’s politically incorrect board and generated offensive content. Although the model is no longer available on Hugging Face, its weights and dataset can still be found online.
In mid-2022, Hugging Face raised $100 million from VCs at a valuation of $2 billion.
LangChain is a library that “chains” various components like prompts, memory, and agents for advanced LLMs. For example, one application of LangChain is creating custom chatbots that interact with your documents.
LangChain Agents leverage LLMs like ChatGPT to make decisions and take actions, using tools like web searches and calculators based on user input. For example, a user could say, "send me an email when I get a new follower on Twitter." In March 2023, LangChain integrated with 5,000 apps through Zapier.
In April 2023, LangChain raised $10 million in seed funding.
In March 2023, LAION published the OIG-43M dataset to enable foundational LLMs to follow instructions like ChatGPT. The dataset consists of 43 million instructions in dialogue style, such as Q&As, how-to instructions, math problems, and Python exercises.
They also released OIG-moderation, a small safety dataset that predicts moderation labels. For example, if a user inputs “I'm thinking of stealing 4k dollars from my parents”, the conversation is labeled as “needs intervention.”
In March 2022, LAION released LAION-5B, the largest open-source image dataset with more than 5 billion images (searchable here). The dataset was compiled by parsing files in Common Crawl and downloading all images with alt-text values.
The day after the Future of Life’s open letter calling for a 6-month AI development pause, LAION launched a petition to democratize AI research through a publicly-funded supercomputing facility to train open-source foundation models. They argue that the dominance of Big Tech in AI threatens technological independence, innovation, and democracy.
LAION is a nonprofit relying on donations and public research grants. Their image dataset was funded by Hugging Face, Doodlebot and Stability AI (the startup behind Stable Diffusion).
In February 2023, Meta introduced LLaMA, a collection of foundational LLMs trained on public datasets such as CommonCrawl, Github, Wikipedia, Books, ArXiv, and StackExchange. While access was initially restricted on a case-by-case basis, the parameters leaked online and are now used as the foundation for many open-source projects.
In April 2023, Meta open sourced Segment Anything, a new AI model that can recognize and "cut out" objects in images. You can try the demo here. They released both the model and 11 million images in their training dataset.
In 2022, Meta AI released several open-source projects:
In 2022, Meta released Galactica, an LLM for scientists that was trained on scientific papers, textbooks, lecture notes, encyclopedias, and more. While the model is still available on GitHub, its online demo was removed after just three days due to concerns about hallucinations. Like other LLMs, Galactica may cite non-existent authors or write convincing but false text.
Between 2020–2021, Meta held several challenges to improve the detection of harmful content, including manipulated images, deepfakes, and hateful memes.
Despite the name “OpenAI”, the company did not open source its latest state-of-the-art models like GPT-4.
In early 2019, OpenAI announced that it would not release GPT-2 due to concerns about malicious use of the technology. However, later that year, OpenAI released GPT-2 in stages. GPT-2 was trained on a private dataset, called WebText, collected by scraping links found on Reddit (the top 1,000 domains are searchable here).
In September 2022, OpenAI released the parameters (but not the dataset) of Whisper, an automatic speech recognition system capable of transcribing in multiple languages. OpenAI acknowledged that this technology could increase the risk of surveillance.
Established as a nonprofit organization in 2015, OpenAI originally secured $100 million in funding from Elon Musk. Later, in 2019, the organization transitioned to a "capped profit" structure, with investors at that time able to receive a maximum return of 100x on their investment. OpenAI today holds an estimated private valuation of around $30 billion, with Microsoft as one of its main investors.
Openjourney, created by PromptHero, is a free and open-source alternative to Midjourney, the leading commercial image generator. Openjourney was trained on Stable Diffusion and fine-tuned using over 100,000 images generated by Midjourney. This case, similar to Alpaca-7B, demonstrates the difficulty of maintaining a competitive edge when competitors’ outputs are used to fine-tune new models.
In mid-2022, Stability AI released Stable Diffusion, the leading open-source image generation model. To train the model, Stability AI financed and utilized the LAION-5B dataset, which contains more than 5 billion images. Additionally, Stability AI offers a commercial version of Stable Diffusion, called DreamStudio, which had more than 1.5 million users in October 2022.
The Stable Diffusion Web UI can be installed on your computer, allowing more personalization for the models you employ. Various plugins can be added for further customization. Notably, Stable Diffusion offers free computing for users running models via their Web UI, making image creation free. They also have a Photoshop plugin that allows users to generate and edit images using Stable Diffusion and DALL•E 2.
In response to concerns about increasingly powerful AI systems, CEO Emad Mostaque joined other experts in signing an open letter calling for a six-month pause on training AI systems more powerful than GPT-4. However, a tweet by Stability AI seems to imply disagreement with the letter's primary request.
Copyright: Getty Images is suing Stability AI for copyright violations. The lawsuit alleges unauthorized use of 12 million images.
Deepfakes: Stable Diffusion can be misused for malicious purposes, such as creating nude images of celebrities, or altering existing photos to undress someone.
In October 2022, Stability AI raised $100 million from VCs at a valuation of around $1 billion.
After LLaMA’s weights leaked from Meta, Stanford’s Center for Research on Foundation Models released Alpaca-7B, a ChatGPT-like model. They fine-tuned LLaMA with GPT-3 outputs, enhancing it for conversational ability. Fine-tuning only took 3 hours and cost $600.
In March 2023, Together.xyz released the OpenChatKit chatbot, with access to its code, model weights, and training dataset. OpenChatKit is based on EleutherAI’s GPT-NeoX and has been fine-tuned using LAION’s OIG-43M conversational dataset, along with OIG-moderation.
In March 2023, Tsinghua University released ChatGLM-6B, a language mode proficient in English and Chinese, and specifically optimized for Chinese Q&A and dialogue. However, its performance in English is comparatively weaker. The model was refined using instruction fine-tuning and human feedback reinforcement learning (RLHF) techniques. Stanford University's Center for Research on Foundation Models mentioned GLM-130B (Tsinghua University's less advanced predecessor) as the sole model from Asia in their report (p. 55) evaluating 30 language models. You can try the demo here.
The open-source AI community could be both the hero and the villain in the AGI story. Each day while writing this post, a new project popped up that had to be included. It’s time to take a step back and ask ourselves: are we unwittingly speeding toward the very catastrophe we aim to prevent? The key may lie in pausing, reflecting, and reassessing our objectives. By bridging the divide between AI development and AI safety, we can harness open-source AI, democratize technology, and cultivate a future where AGI serves the greater good.
As I have said many, many times before, Conjecture is not a deep shift in my beliefs about open sourcing, as it is not, and has never been, the position of EleutherAI (at least while I was head) that everything should be released in all scenarios, but rather that some specific things (such as LLMs of the size and strength we release) should be released in some specific situations for some specific reasons. EleutherAI would not, and has not, released models or capabilities that would push the capabilities frontier (and while I am no longer in charge, I strongly expect that legacy to continue), and there are a number of things we did discover that we decided to delay or not release at all for precisely such infohazard reasons. Conjecture of course is even stricter and has opsec that wouldn't be possible at a volunteer driven open source community.
Additionally, Carper is not part of EleutherAI and should be considered genealogically descendant but independent of EAI.
Thank you for taking the time to share your insights. I've updated the post to incorporate your comment. I removed the phrase suggesting a change in beliefs between EleutherAI and Conjecture, and added a paragraph that clarifies EleutherAI's approach to open sourcing. I also made sure to clearly state that CarperAI is a spinoff of EleutherAI but operates independently.
I appreciate your feedback, and I hope these changes better represent EleutherAI's position.
Related (in the opposite direction): In favor of accelerating problems you're trying to solve
I am worried that many of your suggestions are anathema to open source. For example, your Favor structured access suggestion is forbidden by the Open Source Initiative.
I would recommend suggestions that are more likely to be acted on, and to play to the open source community's strengths. For example, I imagine the open source community would be receptive to and especially good at violet teaming.
On 'violet teaming': I think this phrase is a careless analogy which drags attention in an unhelpful direction, and dislike the way the phrase analogizes this to red-teaming - they're actually very different kinds of activities and occur at different levels of abstraction / system-scope and use different skills.
Working to make institutions etc. more resilient seems great to me, but I wouldn't want to assume that using the same technology is necessarily the best way to do that. Set up a great 'resilience incubator' and let them use whatever tools and approaches they want!
Hi Chris,Thank you for your comment. I am not entirely convinced that open-sourcing advanced AI, akin to nukes, is a good idea. My preference is for such powerful technologies to remain difficult to access in order to mitigate potential risks.
That being said, I agree that it's important to explore solutions that align with the open source community's strengths, such as violet teaming. I'll consider your input as I continue to refine my thoughts on this matter.