We are getting to a point where AI will be able to construct sound arguments in prose. They will make logical sense. Dismissing them only because of their origin is fallacious thinking.
Conclusion:
Dismissing arguments solely because they are AI-generated constitutes a class of genetic fallacy, which should be called 'Argumentum ad machina'.
Premises:
1. The validity of a logical argument is determined by the truth of its premises and the soundness of its inferences, not by the identity of the entity presenting it.
2. Dismissing an argument based on its source rather than its content constitutes a genetic fallacy.
3. The phrase 'that's AI-generated' functions as a dismissal based on source rather than content.
Assumptions:
1. AI-generated arguments can have true premises and sound inferences
2. The genetic fallacy is a legitimate logical error to avoid
3. Source-based dismissals are categorically inappropriate in logical evaluation
4. AI should be treated as equivalent to any other source when evaluating arguments
One way to slightly mitigate the difficulties of nuance in language when translating to formal arguments is to attwmpt to always steelman the argument. Afford it all the guarded language and nuance you can, and then formalize in premises and conclusion.
This would also make interaction much more civil as well, given so much proclivity to do the opposite (straw man).
It's not a perfect approach, but it helps. LLMs are quite decent at steelmanning as well, because they can easiky pivot language to caveat and decorate with nuamce.
Intake the following block of text and then formulate it as a steelmanned deductive argument. Use the format of premises and conclusion. After the argument, list possible fallacies in the argument. DO NOT fact check - simply analyze the logic. do not search.
After the fallacies list, show the following:
1. Evaluate Argument Strength: Assess the strength of each premise and the overall argument.
2. Provide Counterarguments: Suggest possible counterarguments to the premises and conclusion.
3. Highlight Assumptions: Identify any underlying assumptions that need examination.
4. Suggest Improvements: Recommend ways to strengthen the argument's logical structure.
5. Test with Scenarios: Apply the argument to various scenarios to see how it holds up.
6. Analyze Relevance: Check the relevance and connection between each premise and the conclusion.
Not just chip exports. It also limits model weights.
From the article:
In addition to the semiconductor controls, the new rules also limit the export of closed AI model weights, which are the numerical parameters that software uses to process data and make predictions or decisions.
Companies would be prohibited from hosting powerful closed model weights in Tier 3 countries, like China and Russia, and would have to abide by security standards to host those weights in Tier 2 countries. That means the controls on model weights don’t apply to companies that obtain universal VEU status, one of the people said.
Open weight models — which allow the public to access underlying code — aren’t affected by the rules, nor are closed models that are less powerful than an already-available open model. But if an AI company wants to fine-tune a general-purpose open weight model for a specific purpose, and that process uses a significant amount of computing power, they would need to apply for a US government license to do so in a Tier 2 country.
Premise 1: AI (specifically, statistical modeling based on hidden layer neural networks) has been increasingly integrated into various technological products and services.
Premise 2: Major companies like Microsoft, Apple, and Intel are intensifying their efforts in AI development and integration, with Microsoft announcing products like CoPilot+ and Recall.
Premise 3: CoPilot+, which is AI-driven, is built on users’ actual usage data, potentially offering more realistic and user-friendly assistance than previous AI iterations like Microsoft’s Clippy.
Premise 4: Recall, another AI feature by Microsoft, aims to enhance user productivity by automatically capturing and storing screenshots and textual content, but it stores this data unencrypted locally, posing significant privacy risks.
Premise 5: The continuous expansion of AI features in technology products often correlates with increased privacy risks and potential legal issues, as evidenced by the concerns surrounding Recall’s handling of personal data.
Conclusion: The rapid integration of AI into technology products, while intended to enhance functionality and user interaction, simultaneously amplifies privacy and security concerns, necessitating a cautious and regulated approach to AI deployment in consumer technologies.
Possible Fallacies in the Argument
Hasty Generalization: Concluding that all AI integrations pose privacy risks based on specific examples like Microsoft’s Recall might not account for other AI integrations that prioritize security and privacy.
Slippery Slope: The argument implies that increasing AI functionalities will inevitably lead to greater privacy and legal issues, which may not necessarily hold true if appropriate measures are taken.
Appeal to Fear: Highlighting the severe privacy risks and potential legal issues may play on the fears of surveillance and loss of privacy, overshadowing potential benefits of AI.
Biased Sample: The argument focuses mainly on Microsoft’s implementations of AI, which may not represent the broader industry approach to AI integration and its implications.
Those are good suggestions. I will use some of them!
It is also interesting to go back and forth with the model, asking it to mitigate fallacies listed, and then re-check for fallacies, then mitigate again, etc, etc.
I have found that a workflow using pytube into OpenAPI Whisper into the above prompt is a decent way of breaking down a YouTube video into formulated arguments.
I have found the below to be a good starting point for formulating text into classical formulated arguments.
Intake the following block of text and then formulate it as a steelmanned deductive argument. Use the format of premises and conclusion. After the argument, list possible fallacies in the argument. DO NOT fact check - simply analyze the logic. do not search.
Premise 1:
The consumption of energy and natural resources required for AI infrastructure, particularly if it continues to grow, is massive and potentially unsustainable.
Premise 2:
The economic investment of $7 trillion into AI by Sam Altman is significantly higher than the funds allocated to essential global needs like education and hunger, indicating a misallocation of financial resources.
Premise 3:
The financial risk of investing $7 trillion into AI is enormous, with potential repercussions including a global financial depression that could surpass previous economic crises.
Premise 4:
The development of AI at the scale proposed threatens to infringe upon intellectual property rights, harming artists, musicians, writers, and other creators.
Premise 5:
Negative externalities, such as misinformation, cybercrimes, and the exacerbation of global resource conflicts, are not being adequately addressed by AI developers like OpenAI.
Premise 6:
The rush to invest in AI before understanding the specific technological needs and proving real use cases is premature and risks significant financial and societal setbacks.
Conclusion:
Investing $7 trillion into AI as proposed by Sam Altman is a reckless expansion that overlooks significant environmental, economic, and societal risks, and should be reconsidered until the technology is proven to be safe, effective, and beneficial on a net basis.
Slippery Slope: The argument may overstate the direct line from investment in AI to catastrophic outcomes like global financial depression or war over resources.
Appeal to Fear: Highlighting extreme potential risks (e.g., worldwide depression, war over resources) without acknowledging the possible mitigations or the improbability of worst-case scenarios could play on irrational fears.
False Dichotomy: The argument presents the situation as an either/or scenario—investing $7 trillion in AI versus addressing global needs like hunger and education—without considering that investment in technology can also lead to economic growth and solutions for these issues.
Straw Man: The argument might misrepresent Sam Altman's or AI proponents' positions, implying they disregard any potential negative outcomes or alternative uses for the funds, which may not be accurate.
Overgeneralization: Using specific instances of negative outcomes related to AI to argue against a massive investment in AI could ignore the diversity of AI applications and their potential benefits.
Most GPUs, sitting in racks in datacenters, aren't "processing graphics" anyhow.