The Closing Chapters of Open AI Research: An Era of Secrecy and Industrialization

The release of OpenAI’s technical report on GPT-4 has sparked a wave of introspection within the AI research community, signaling a poignant shift from the open research ethos to a new phase marked by industrial secrecy.

The Veil Over AI Innovations

The GPT-4 technical report, rather than shedding light on the intricacies of the model’s architecture and training methodologies, has drawn a curtain over the details that AI researchers around the world eagerly anticipate. This decision reflects a broader industry trend towards protecting the intellectual property of large-scale models.

OpenAI’s Selective Silence

In the report published on arXiv, OpenAI deliberately withholds information on the architecture, including model size, hardware specifics, training compute, dataset construction, and training methods of GPT-4. Citing competitive reasons and safety implications, OpenAI chooses guarded discretion over open discourse, a move that has reverberated through the research community.

Google’s PaLM-2 and the Trend of Concealment

Google followed a similar path with its PaLM-2 report, opting to keep the details of model size and architecture under wraps. This pattern of behavior from two of the most influential entities in AI research marks a stark departure from the once collaborative and transparent nature of the field.

Anthropic’s Silence on Claude

Anthropic, an OpenAI spinoff, has taken this shift a step further by not releasing a technical report for its Claude models at all. This lack of transparency has raised eyebrows and questions about the future direction of AI research sharing.

The High Stakes of AI Research

As the economic and safety stakes of AI continue to climb, once traditionally open companies are navigating the thin line between advancement and accountability. This new “culture of opacity” around cutting-edge research might be driven by the need to safeguard commercial interests and manage safety concerns, leaving the AI community to grapple with the implications of such a change.

In this blog, we ponder the consequences of this shift towards industrialization and secrecy in AI research. As we tread into an era where the most significant breakthroughs may be shrouded in mystery, one must wonder how this will shape not just the AI we develop, but the very nature of collaborative innovation that has long been the bedrock of scientific progress.

Stay with us as we continue to analyze the evolving dynamics of AI research and its impact on the collective knowledge of humanity.

Scott Felten