EA - Data Taxation: A Proposal for Slowing Down AGI Progress by Per Ivar Friborg

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Data Taxation: A Proposal for Slowing Down AGI Progress, published by Per Ivar Friborg on April 11, 2023 on The Effective Altruism Forum.Co-authored by Sammet, Joshua P. S. and Wale, William.ForewordThis report was written for the Policies for slowing down progress towards artificial general intelligence (AGI) case of the AI governance hackathon organized by Apart Research.We are keen on receiving your thoughts and feedback on this proposal. We are considering publishing this on a more public platform such as arXiv, and therefore would love for you to point out potential issues and shortcomings with our proposal that we should further address in our article. If you think there are parts that need to be flushed out more to be understandable for the reader or any other things we should include to round it up, we are more than happy to hear your comments.IntroductionIn this paper, we propose a tax that affects the training of any model of sufficient size, and a concrete formula for implementing it. We explain why we think our framework is robust, future-proof and applicable in practice. We also give some concrete examples of how the formula would apply to current models, and demonstrate that it would heavily disincentivize work on ML models that could develop AGI-like capabilities, but not other useful narrow AI work that does not pose existential risks.Currently, the most promising path towards AGI involves increasingly big networks with billions of parameters trained with huge amounts of text data. The most famous example being GPT-3, whose 175 billion parameters were trained on over 45 TB of text data. The size of this data is what sets apart LLMs from both more narrow AI models developed before and classical high-performance computing. Most likely, any development of general or even humanoid AI will require large swathes of data, as the human body gathers 11 million bits per second (around 120 GB per day) to train its approx. 100 billion neurons. Therefore, tackling the data usage of these models could be a promising approach to slowing down the progress of the development of new and more capable general AIs, without harming the development of models that pose no AGI risk.The proposal aims to slow down the progress towards AGI and mitigate the associated existential risks. The funds collected through data taxation can be used to support broader societal goals, such as redistribution of wealth and investments in AI safety research. We also discuss how the proposal plays well with other current criticisms of the relationship between AI and copyright, and persons and their personal data, and how it could consequently levy those social currents for more widespread support.The Data Taxation MechanismA challenge in devising an effective data tax formula lies in differentially disincentivizing the development of models that could lead to AGI without hindering the progress of other useful and narrow ML technologies. A simplistic approach, such as imposing a flat fee per byte of training data used could inadvertently discourage beneficial research that poses no AGI risk.For instance, a study aimed at identifying the most common words in the English language across the entire internet would become prohibitively expensive under the naïve flat fee proposal, despite posing zero danger in terms of AGI development. Similarly, ML applications in medical imaging or genomics, which often rely on vast datasets, would also be adversely affected, even though they do not contribute to AGI risk.To overcome the challenge of differentially disincentivizing AGI development without impeding progress in other beneficial narrow AI applications, we propose a data tax formula that incorporates not only the amount of training data used but also the number of parameters being updated du...