Source URL: https://www.theregister.com/2024/12/02/open_ai_research/
Source: The Register
Title: Claims of ‘open’ AIs are often open lies, research argues
Feedly Summary: ‘When policy is being shaped, definitions matter’
Rhetoric around “open" AI concentrates power in the AI sector rather than making it more open to competition and scrutiny, according to a research paper published in Nature.…
AI Summary and Description: Yes
Summary: The research paper critiques the rhetoric surrounding “open” AI, arguing that it may actually concentrate power within the AI sector rather than promote openness and competition. It compares the concepts of open AI with open source software, highlighting significant gaps in transparency and accessibility in the AI landscape dominated by major tech companies.
Detailed Description:
The paper, authored by David Widder, focuses on the misleading narrative around “open” AI, which is often presented as beneficial for innovation and democracy. Key points of the research include:
– **Misleading Definitions**: The term “openness” in AI often lacks precision, focusing on selective aspects of the AI lifecycle and neglecting the concentration of power within major tech entities.
– **Comparison to Open Source**: The paper draws parallels between open AI and free/open source software, suggesting that while open source has historically contributed to democratization in software development, open AI fails to mirror these benefits due to industry consolidation.
– **Power Dynamics**: Major technology companies are depicted as utilizing the narrative of open AI to maintain market dominance while deflecting scrutiny regarding monopolistic practices.
– **Analysis Frameworks**: The research examines several dimensions such as models, data, labor, frameworks, and computational power to define what “openness” means in the AI context.
– **Examples of Open AI**: The paper contrasts entities like Meta, which is criticized for its lack of genuine openness (e.g., LLaMA-3), against EleutherAI’s Pythia, recognized for its true open nature providing full documentation and access.
– **Barriers to True Openness**: The study highlights the significant barriers created by data, development, and computational resources that limit market entry for smaller players, reinforcing a concentration of power among tech giants.
– **Policy Implications**: The author argues that relying solely on “open” AI will not advance diversity or accountability in the industry. Instead, complementary measures such as antitrust regulations and data privacy protections are vital.
– **Concluding Thoughts**: The researcher warns that investing hopes in “open” AI may lead to unfulfilled expectations and potentially exacerbate issues related to corporate concentration and market fairness.
Overall, this text provides a critical examination of the concept of “open” AI, emphasizing the need for a nuanced understanding among policymakers and the public regarding its implications for the development and deployment of AI technologies. For security and compliance professionals, this points to the necessity of integrating broader regulatory measures to address the risks associated with corporate dominance in the AI domain.