The future of Artificial Intelligence (AI) is not just about developing more sophisticated models, but also about ensuring that they are transparent, accountable, and trustworthy. Unfortunately, the current trend of large corporations and governments developing AI systems behind closed doors is not only hindering progress but also creating systemic risks that threaten our society.
Why Should AI Be Open-Source?
The answer lies in the fundamental principles of open-source software development. When software is open-source, it is free from the shackles of corporate control, allowing developers to modify and improve the code to suit their needs. This leads to a proliferation of innovative applications, as well as increased collaboration and knowledge-sharing among developers. Open-source AI projects encompass various categories such as large language models, machine translation tools, and chatbots. These resources are often built upon existing tools and technologies shared by large companies as open-source software (OSS). The openness of these resources allows developers to learn, use, share, and improve them, leading to a virtuous cycle of innovation.
Open-Source AI: The Way Forward
Open-source AI entails more than just access to the source code. It requires distinct definitions, protocols, and development processes that cater to the unique complexities of AI systems. AI’s dependence on data makes it challenging to ensure transparency and accountability. Merely looking at the source code does not necessarily explain or shed light on why AI systems generate the outputs they do. Even AI developers concede that they cannot readily explain the outputs of AI systems they are developing.
The Current State of Closed-Source AI
Closed-source AI models are maintained by corporations and their code is not made publicly available for use or audit. Examples of closed-source large language models (LLMs) are PaLM from Google, the family of GPT models from OpenAI, and Claude from Anthropic. While third parties can take advantage of some closed-source models through an Application Programming Interface (API), the code itself cannot be manipulated. This lack of transparency and openness can lead to:
- Lack of Trustworthiness: AI systems that are not transparent or accountable can lead to mistrust among users.
- Biased Decisions: Closed-source AI models can perpetuate discriminatory lending practices, amplify societal prejudices, and target minority communities.
- Security Risks: Vulnerabilities in closed-source AI models can be exploited by bad actors, compromising the integrity of AI systems.
- Limited Innovation: Closed-source AI models can stifle innovation, as developers are restricted from exploring new ideas or improving existing models.
Why OpenAI is Not the Answer
OpenAI, a non-profit AI research organization, has made significant contributions to the field of AI. However, OpenAI’s approach to AI development is not entirely open-source. OpenAI’s models are trained on large amounts of data, which can be biased, incomplete, or inaccurate. Additionally, OpenAI’s models are not entirely transparent, making it difficult to determine their quality and reliability.
Moreover, OpenAI’s business model is based on data collection and analysis, which raises concerns about privacy and data security. OpenAI’s data collection practices are not transparent, and the organization has been criticized for its lack of accountability.
The Benefits of Open-Source AI
To mitigate these risks, it is essential to promote open-source AI development. The benefits of open-source AI are numerous and far-reaching. For one, it can promote a culture of transparency and accountability in AI development, ensuring that AI systems are designed and deployed with the best interests of society in mind. Open-source AI can also facilitate knowledge-sharing and collaboration among developers, accelerating the pace of innovation and improving the quality of AI-powered applications.
Furthermore, open-source AI can help to democratize access to AI technology, enabling smaller organizations and individuals to develop and deploy AI-powered applications without the need for significant financial resources. This can help to level the playing field and promote greater diversity and inclusion in the development and deployment of AI technology.
Recent Developments
Recent developments in the AI landscape have seen several companies releasing open-source models, such as Meta’s Llama 2, Google’s Gemini, and Stability AI’s models. While there is some disagreement about whether these models can truly be described as open source due to licensing restrictions, their availability has fostered innovation and competition beyond the largest tech companies.
Smaller, more specific models are outperforming larger ones, enabling small organizations and even individuals to innovate using techniques like LoRA (Low-Rank Adaptation of Large Language Models) and QLoRA for faster iterations. These models can now be run on desktops, mobile devices, and browsers, respecting user privacy while allowing them to take advantage of AI.
Moreover, open-source AI has already outperformed closed-source models in several areas, such as:
- Scalability: Open-source AI can leverage a vast, distributed workforce, allowing it to cover more ground in less time.
- Innovation: The open-source community has shown a remarkable ability to innovate and adapt, often surpassing the capabilities of closed-source models.
- Transparency: Open-source AI provides unparalleled transparency, allowing users to audit and verify the code and data used in AI development.
- Collaboration: Open-source AI fosters collaboration and community engagement, ensuring that the benefits of AI are shared equitably.
In conclusion, Artificial Intelligence needs to be open-source to ensure transparency, accountability, and trustworthiness. Open-source AI models can be inspected, modified, and enhanced by anyone, allowing for a community-driven development process that promotes innovation and collaboration. Additionally, open-source AI models can be audited and verified by third parties, ensuring their quality and reliability.
It is time to unleash the potential of open-source AI and create a more transparent, accountable, and trustworthy AI ecosystem. By doing so, we can ensure that AI is developed with the best interests of society in mind, rather than being controlled by a select few.