Open source LLMs: A pathway to safer and more sustainable AI

Large language models (LLMs) have become increasingly powerful in recent years, capable of generating human-quality text, translating languages, and writing different kinds of creative content. However, the development of LLMs has also raised concerns about safety and sustainability.

Open-source LLMs are a promising approach to addressing safety and sustainability concerns in AI. Open-source LLMs are LLMs that are freely available to anyone to use, modify, and distribute. This makes them more transparent and accountable than proprietary LLMs, and it also fosters collaboration among researchers and developers.

Using open-source Large Language Models (LLMs) for AI safety and sustainability comes with a host of advantages.

• Transparency: Since their code is out in the open for everyone to see, it’s easier for anyone to spot and rectify any issues related to safety or sustainability. It’s like having a window into the workings of the model, which isn’t usually possible with proprietary LLMs.
• Accountability: Open-source LLMs are created and fine-tuned by a community of dedicated researchers and developers. This community-driven approach reduces the risk of any single entity having undue influence over the model’s development. It’s a bit like having a team of watchful guardians ensuring everything stays on track.
• Collaboration: Open-source LLMs encourage collaboration among researchers and developers. This collective effort helps speed up the progress towards safer and more sustainable AI. It’s like harnessing the power of a hive mind to drive innovation.

A number of open-source LLMs have been developed in recent years, including GPT-3, Bard, and LaMDA. These LLMs have been used to develop a variety of AI applications, including chatbots, translation systems, and creative writing tools.

In addition to using open-source LLMs, there are a number of other things that we can do to improve the safety and sustainability of AI.

• Develop AI safety guidelines: We need to develop clear and comprehensive guidelines for the development and use of AI. These guidelines should address issues such as bias, fairness, and safety.
• Invest in AI safety research: We need to invest in research into AI safety. This research should focus on developing methods to identify and mitigate the risks associated with AI.
• Educate the public about AI: We need to educate the public about AI so that they can understand its potential benefits and risks. This will help to build public trust in AI and support the development of safe and sustainable AI systems.

By using open-source LLMs and taking other steps to improve the safety and sustainability of AI, we can ensure that AI is used for good and that it benefits all of humanity.

References:

1. "GitHub - eugeneyan/open-llms: A list of open LLMs available for commercial use"
2. "5 Best Open Source LLMs (October 2023) - Unite.AI"
3. "17 Best Open-Source LLMs Data Scientists Must Know in 2023 - ProjectPro"
4. "The List of 11 Most Popular Open Source LLMs of 2023"
5. "AI safety - Wikipedia"
6. "Our approach to AI safety - OpenAI"
7. "Achieving a sustainable future for AI | MIT Technology Review"
8. "Sustainable AI: AI for sustainability and the sustainability of AI - AI and Ethics"
9. "5 Actions to develop more sustainable AI - IT World Canada"
10. "How We Can Use AI To Help Achieve Sustainability Goals - Forbes"
11. "The alliance between Artificial Intelligence and sustainable development"