AI_dev Europe 2024 - From OpenAI to Opensource AI
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The document discusses the evolution and impact of payments technology and AI models, emphasizing the balance between commercial ownership and collaborative openness. It highlights the transition of major companies like OpenAI and Meta towards more restrictive licensing while exploring the implications for research and responsible AI usage. The document concludes with key takeaways on fostering innovation through openness and collaboration in AI development.
![Market-Leading Player: OpenAI
Deviation from original vision of research transparency & openness
Non/For-profit (US)
Component Score
Level
description
Model 4 Totally open
Dataset 1
Published
research
only
Code 1
Published
research
only
0 Closed
→
GPT-1 & 2
ChatGPT
research paper only
No training of other commercial LLMs
You may not: […] Use Output to
develop models that compete with
OpenAI.
GPT-3.x & 4.x/o](https://image.slidesharecdn.com/aideveurope2024-llmopenness-240630135618-b90e89d2/85/AI_dev-Europe-2024-From-OpenAI-to-Opensource-AI-8-320.jpg)
![Market-Leading Player: Google
Transition from open research to a pragmatic approach
Enterprise (US)
Component Score
Level
description
Model 4 Totally open
Dataset 2
Restricted
access
Code 4 Totally open
1
Published
research only
1
Published
research only
0 Closed
→
3
Open with
limitations
1
Published
research only
4
Toolchain
available
→
You may not use nor allow others to use Gemma or
Model Derivatives to: [illegals activities, unlicensed
practices of profession, abuse, security bypass and
promotion of hatred, abuse, violence, monitoring people
without consent, misinformation/defamation, automate
decisions concerning human rights and well-being, etc.]
Responsible AI contradicts Open Source Definition](https://image.slidesharecdn.com/aideveurope2024-llmopenness-240630135618-b90e89d2/85/AI_dev-Europe-2024-From-OpenAI-to-Opensource-AI-10-320.jpg)
![Collaborative fine-tuned LLMs
Enterprise (US) Enterprise (US) Enterprise (US) Consortium (UAE/US) Research (US)
Dolly BLOOMChat Zephyr LLM360 OLMo-Instruct
Model 4 Based on GPT-J 3
Based on
BLOOM
4
Based on
Mistral
4 Open source 4 Open source
Pre-training
Dataset
3 Based on GPT-J 3
Based on
BLOOM
0
Based on
Mistral
4
RedPajama,
Falcon,
StarCoder
3
Dolma
(ImpACT MR)
Fine-tuning
Dataset
4
Access and
reuse without
restriction
4
Dolly and
LAION
2
Research use
only (OpenAI)
2
Research use
only (OpenAI)
3
Tulu 2
(IMPACT LR)
Reward
model
0
No public
information
available
0
No public
information
available
3
Paper and code
examples
0
No public
information
available
4
UltraFeedback
(MIT)
Code 4 Open source 3 OpenRAIL 3
Example code
available
4 Open source 4 Open source
AI2 ImpACT Licenses - Restrictions
[…] a. military weapons purposes […]
b. purposes of military surveillance […]
c. purposes of generating or disseminating information or content […] without
expressly and intelligibly disclaiming that the text is machine generated;
d. purposes of ‘real time’ remote biometric processing […]
e. fully automated decision-making without a human in the loop […] as spreading
misinformation […]
f. purposes of the predictive administration of justice, law enforcement, immigration,
or asylum processes, such as predicting an individual will commit fraud/crime
Responsible AI contradicts Open Source Definition](https://image.slidesharecdn.com/aideveurope2024-llmopenness-240630135618-b90e89d2/85/AI_dev-Europe-2024-From-OpenAI-to-Opensource-AI-21-320.jpg)
AI_dev Europe 2024 - From OpenAI to Opensource AI
- 1. Payments to grow your world Navigating between Commercial Ownership and Collaborative Openness Raphaël Semeteys Head of DevRel Open Source Expert Senior Architect at Worldline 19 June 2024 Paris, France From OpenAI to Open Source AI
- 2. We design payments technology that powers the growth of millions of businesses around the world. 7000+ engineers in over 40 countries Managing 43+ billion transactions per year €250M spent in R&D every year Handling 150+ payment methods
- 3. The early days of LLMs From rule-based and simpler statistical models to LLMs 2010’s 2020’s 2017-2018 Word embeddings Word2Vec, GloVe “Attention is All You Need" Transformers GenAI, ChatGPT Responsibility concerns Tomorrow? Small Language Models Mobile, Agents & LAMs
- 4. GenAI is having its Linux Moment • Just like open source and Internet, bust much faster! • Dynamics between collaborative openness and commercial ownership • Need of clarity on licenses Labs & Universities Individuals Enterprises Commodities
- 5. Defining Openness of a Model Pre-training Dataset Fine-tuning Dataset Reward Model Model Data Processing Code
- 6. Defining Openness of a Model Score Level Description Model (weights) Pre- training Dataset Fine- tuning Dataset Reward model Data Processing Code 0 Closed No access to any public information, data or asset 1 Published research only Research papers(s) published but with no more information, data or asset 2 Restricted access Access to asset is possible only with special agreement (commercial, research…) 3 Open with limitations Access and reuse of asset is possible but with certain limitations on usage 4 Totally open Access and reuse of asset is possible without restriction on usage (ex. open source license)
- 7. Market-Leading Player: OpenAI Deviation from original vision of research transparency & openness Non/For-profit (US) Component Score Level description Model 4 Totally open Dataset 1 Published research only Code 1 Published research only 0 Closed → GPT-1 & 2 GPT-3.x & 4.x/o ChatGPT research paper only
- 8. Market-Leading Player: OpenAI Deviation from original vision of research transparency & openness Non/For-profit (US) Component Score Level description Model 4 Totally open Dataset 1 Published research only Code 1 Published research only 0 Closed → GPT-1 & 2 ChatGPT research paper only No training of other commercial LLMs You may not: […] Use Output to develop models that compete with OpenAI. GPT-3.x & 4.x/o
- 9. Market-Leading Player: Google Transition from open research to a pragmatic approach Enterprise (US) Component Score Level description Model 4 Totally open Dataset 2 Restricted access Code 4 Totally open 1 Published research only 1 Published research only 0 Closed → 3 Open with limitations 1 Published research only 4 Toolchain available ↔
- 10. Market-Leading Player: Google Transition from open research to a pragmatic approach Enterprise (US) Component Score Level description Model 4 Totally open Dataset 2 Restricted access Code 4 Totally open 1 Published research only 1 Published research only 0 Closed → 3 Open with limitations 1 Published research only 4 Toolchain available → You may not use nor allow others to use Gemma or Model Derivatives to: [illegals activities, unlicensed practices of profession, abuse, security bypass and promotion of hatred, abuse, violence, monitoring people without consent, misinformation/defamation, automate decisions concerning human rights and well-being, etc.] Responsible AI contradicts Open Source Definition
- 11. Other Big Players Catching up and making their mark in the GenAI Gold Rush Partner for Infrastructure (inference and training) Create their own (open) models
- 12. Market-Leading Player: Meta Journey to openness Enterprise (US) Component Score Level description Model 4 Totally open Dataset 3 Open with limitations Code 4 Totally open RoBERTa 3 Open with limitations 1 Published research only 1 Published research only →
- 13. Market-Leading Player: Meta Journey to openness Enterprise (US) Component Score Level description Model 4 Totally open Dataset 3 Open with limitations Code 4 Totally open RoBERTa 3 Open with limitations 1 Published research only 1 Published research only → Restriction on usage: license for platforms with 700+ M users Additional Commercial Terms. If, on the Llama 2 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
- 14. Market-Leading Player: Meta Journey to openness Enterprise (US) Component Score Level description Model 4 Totally open Dataset 3 Open with limitations Code 4 Totally open RoBERTa 3 Open with limitations 1 Published research only 1 Published research only → LLaMA 3 now more restrictive on redistribution and reuse Redistribution and Use. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Meta Llama 3” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama 3” at the beginning of any such AI model name.
- 15. Llama 2 offspring’s: Alpaca and Vicuna Fine-tuned models from Llama 2 by universities Research (US) Component Score Level description Model 3 Open with limitations Pre-training Dataset 1 Published research only Fine-tuning Dataset 2 Research use only Code 4 Under Apache 2 license Restrictions from both Llama 2 and OpenAI (ShareGPT)
- 16. Collaborative foundational LLMs Non-profit (US) Research (UAE) Research (EU) Research (US) Enterprise (FR) EleutherAI GPT-J Falcon BLOOM OpenLLaMa Mistral Model 4 Access and reuse without restriction 3 Open with limitations 3 Open RAIL license 4 Access and reuse without restriction 4 Access and reuse without restriction Dataset 3 Open with limitations 4 Access and reuse without restriction 3 Open with limitations 4 Access and reuse without restriction 0 No public information or access Code 4 Completely open 1 General instructions 4 Completely open 1 Just examples 4 Completely open Dataset fuzziness: please refer to the specific license depending on the subset you use Notion of responsible usage
- 17. Collaborative foundational LLMs Modified open source licenses Non-profit (US) Research (UAE) Research (EU) Research (US) Enterprise (FR) EleutherAI GPT-J Falcon BLOOM OpenLLaMa Mistral Model 4 Access and reuse without restriction 3 Open with limitations 3 Open RAIL license 4 Access and reuse without restriction 4 Access and reuse without restriction Dataset 3 Open with limitations 4 Access and reuse without restriction 3 Open with limitations 4 Access and reuse without restriction 0 No public information or access Code 4 Completely open 1 General instructions 4 Completely open 1 Just examples 4 Completely open This license is, in part, based on the Apache License Version 2.0, with a series of modifications. The contribution of the Apache License 2.0 to the framing of this document is acknowledged. Please read this license carefully, as it is different to other ‘open access’ licenses you may have encountered previously. Use of Falcon180B for hosted services may require a separate license.
- 18. Mistral AI’s French sauce Navigating both open and close waters Just like with Open Source, rise of Community VS Enterprise Mix of AI Models • Mixture-of-Experts (SMoE): Mixtral 8x7B, 8x22B • Foundational and fine-tuned models Mix of Business Models & Licenses • “Open Source” models, mistral-finetune SDK • Commercial: optimized Small, Large & Embed Models • Sustainable openness: new non-production license for codestral
- 19. Mistral AI’s French sauce Navigation both open and close waters Just like with Open Source, revisiting Open in Cloud era Mix of AI Models • Mixture-of-Experts (SMoE): Mixtral 8x7B, 8x22B • Foundational and fine-tuned models Mix of Business Models & Licenses • “Open Source” models, mistral-finetune SDK • Commercial: optimized Small, Large & Embed Models • Sustainable openness: new non-production license for codestral MNPL - 3.2. Usage Limitation - You shall only use the Mistral Models and Derivatives (whether or not created by Mistral AI) for testing, research, Personal, or evaluation purposes in Non- Production Environments; - Subject to the foregoing, You shall not supply the Mistral Models or Derivatives in the course of a commercial activity, whether in return for payment or free of charge, in any medium or form, including but not limited to through a hosted or managed service (e.g. SaaS, cloud instances, etc.), or behind a software layer.
- 20. Collaborative fine-tuned LLMs Impact of foundational models or pre-training datasets Enterprise (US) Enterprise (US) Enterprise (US) Consortium (UAE/US) Research (US) Dolly BLOOMChat Zephyr LLM360 OLMo-Instruct Model 4 Based on GPT-J 3 Based on BLOOM 4 Based on Mistral 4 Open source 4 Open source Pre-training Dataset 3 Based on GPT-J 3 Based on BLOOM 0 Based on Mistral 4 RedPajama, Falcon, StarCoder 3 Dolma (ImpACT MR) Fine-tuning Dataset 4 Access and reuse without restriction 4 Dolly and LAION 2 Research use only (OpenAI) 2 Research use only (OpenAI) 3 Tülu 2 (IMPACT LR) Reward model 0 No public information available 0 No public information available 3 Paper and code examples 0 No public information available 4 UltraFeedback (MIT) Code 4 Open source 3 OpenRAIL 3 Example code available 4 Open source 4 Open source
- 21. Collaborative fine-tuned LLMs Enterprise (US) Enterprise (US) Enterprise (US) Consortium (UAE/US) Research (US) Dolly BLOOMChat Zephyr LLM360 OLMo-Instruct Model 4 Based on GPT-J 3 Based on BLOOM 4 Based on Mistral 4 Open source 4 Open source Pre-training Dataset 3 Based on GPT-J 3 Based on BLOOM 0 Based on Mistral 4 RedPajama, Falcon, StarCoder 3 Dolma (ImpACT MR) Fine-tuning Dataset 4 Access and reuse without restriction 4 Dolly and LAION 2 Research use only (OpenAI) 2 Research use only (OpenAI) 3 Tulu 2 (IMPACT LR) Reward model 0 No public information available 0 No public information available 3 Paper and code examples 0 No public information available 4 UltraFeedback (MIT) Code 4 Open source 3 OpenRAIL 3 Example code available 4 Open source 4 Open source AI2 ImpACT Licenses - Restrictions […] a. military weapons purposes […] b. purposes of military surveillance […] c. purposes of generating or disseminating information or content […] without expressly and intelligibly disclaiming that the text is machine generated; d. purposes of ‘real time’ remote biometric processing […] e. fully automated decision-making without a human in the loop […] as spreading misinformation […] f. purposes of the predictive administration of justice, law enforcement, immigration, or asylum processes, such as predicting an individual will commit fraud/crime Responsible AI contradicts Open Source Definition
- 22. Other aspects of GenAI’s Linux Moment Democratize and Decentralize (re)use and innovation Notebooks Communities New Business Models Collaborative Tools & Ecosystems AI Chips Quantization Decentralization Hardware Optimization Do One Thing Well Interoperable Standards Beyond Python Opensource Tools & Frameworks
- 23. Key takeaways • Closed APIs → Open Weights → Free AI (as in freedom) • Datasets and upstream transitivity • Competitive clauses • Responsible AI restrictions • Open Research → Competitive Market → Coopetitive Ecosystem • Openness fosters reuse and collaboration • Collaboration brings commoditization and innovation Just like Open Source!
- 24. Thank you Raphaël Semeteys - Worldline @RaphaelSemeteys raphiki.github.io