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"If it's not fully closed ML, it's open" - is it?

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Summary

This article discusses the current definitions of open-source software and how machine learning technologies are straining those definitions. It provides a brief history of open-source software and explains how the term "open source" is losing its freedom connotations and is becoming almost synonymous with "source available" in developers' minds. It also discusses the license limitations and dataset obfuscation of the Llama 2 model, and how this is a bigger strike against the openness of Meta. The article also talks about how the open-source community is bending their own values in order to encourage the broader movement in their own direction, and how the Open Source Initiative has released a call for proposals to define the landscape of open-source AI technologies.

Q&As

What are the three categories of artifacts included in open-source machine learning?
The three categories of artifacts included in open-source machine learning are access to the model itself, components that enable further risk analysis, and components that enable model replication.

What is the Open Source Initiative's (OSI) plan to define the landscape of open-source AI technologies?
The Open Source Initiative's (OSI) plan to define the landscape of open-source AI technologies is to release a call for proposals.

What are the two clauses in the Llama 2 license that preclude the model itself from being open-source?
The two clauses in the Llama 2 license that preclude the model itself from being open-source are the 700 million user clause and the downstream large language model (LLM) clause.

How do the values of open-source software differ from those of the free software movement?
The values of open-source software differ from those of the free software movement in that open-source software is designed to be publicly accessible, while free software is only a subset of open-source software and uses very permissive licenses such as GPL and Apache.

What is the "open-source as vibes" mentality in the open versus closed debate?
The "open-source as vibes" mentality in the open versus closed debate is that open-source community members are bending their own values in order to encourage the broader movement in their own direction.

AI Comments

👍 This article provides a comprehensive overview of the debate surrounding open-source software and machine learning. It highlights the importance of developing a new taxonomy of open-source and machine learning and the need to address open-source questions independently.

👎 This article does not provide a clear resolution to the debate surrounding open-source software and machine learning and does little to bridge the gap between open-source and closed-source models.

AI Discussion

Me: It's about how definitions from open-source software are being bent by new machine learning technologies. It talks about the tensions between personalities and the growing pressure to pick a side in the open versus closed debate from the reality of needing a new taxonomy for open-source and machine learning.

Friend: That's really interesting. It seems like open-source software is under a lot of pressure right now, and it's important to find a balance between open and closed so that the open-source community can still thrive.

Me: Yeah, absolutely. It's also interesting how the debate is being shaped by the power dynamics of the tech industry and the need to protect intellectual property. The article also talks about the need to define what constitutes an open-source model, and how that might change in the future as the technology evolves and different components of the system become open or closed.

Action items

Research the history of open-source software and the Free Software Foundation to gain a better understanding of the open-source movement.
Read the Open Source Initiative's call for proposals to define the landscape of open-source AI technologies.
Analyze the different components of an AI system and how they can be used for replication and risk analysis.

Technical terms

Open-source software (OSS): Software that is made available to the public with a license that allows users to view, modify, and distribute the code as they see fit.
Machine learning (ML): A type of artificial intelligence that uses algorithms to learn from data and make predictions.
Open ChatGPT: A social network for discussing open source machine learning.
Llama 2: A machine learning model released by Meta.
GPL: The GNU General Public License, a type of open-source software license.
OpenAI: A research lab focused on artificial intelligence.
SSPL: The Server-Side Public License, a type of open-source software license.
LLM: Large language model, a type of machine learning model.
API: Application Programming Interface, a set of protocols and tools for building software applications.
Open-source AI taxonomy: A classification system for open-source artificial intelligence technologies.