Open vs Closed Models

Open Large Language Models (LLMs)

Open LLMs are accessible to the public or specific research communities, allowing for broad usage, experimentation, and study. These models often come with detailed documentation, source code, and sometimes even the trained parameters or datasets used in their development. Open LLMs promote transparency, innovation, and collaborative research, as they enable developers, researchers, and companies to understand the model's workings, adapt it to new applications, and contribute to its improvement. Examples include models released by academic institutions or open-source initiatives.

Closed Large Language Models (LLMs)

Closed LLMs, on the other hand, are proprietary systems developed and owned by organizations that restrict access to their underlying code, training data, and operational mechanisms. These models are often commercialized or used exclusively within the confines of the owning entity, with access provided as a service or through an API under specific terms of use. Closed LLMs prioritize protecting intellectual property, competitive advantage, and commercial interests. The inner workings and data of these models are not transparent to the public, which can limit external innovation and scrutiny.

The primary difference between open and closed LLMs lies in their accessibility and transparency. Open LLMs foster an environment of shared knowledge and collective advancement, making it easier for the broader community to innovate, audit for biases or errors, and understand AI's impact. They contribute to the democratization of AI technology, allowing more stakeholders to partake in its development and application.

Conversely, closed LLMs control and limit access to protect proprietary interests, which can accelerate the development of highly specialized applications and services within a competitive market. However, this approach can also stifle external innovation and raises concerns about transparency, ethical use, and the potential for biases hidden within the model's black box.

The fear surrounding the use of closed models

• Lack of Transparency: Closed models do not provide insight into their internal workings, training data, or algorithms. This opacity makes it difficult for external parties to understand how decisions are made, potentially hiding biases, errors, or unethical reasoning paths.

• Bias and Fairness: Without access to the model and its training data, it's challenging to audit these systems for bias or fairness issues. Biased training data can lead to skewed outputs, perpetuating stereotypes or unfair treatment across different demographic groups.

• Accountability: When something goes wrong, such as the model generating harmful or incorrect outputs, the lack of transparency in closed models complicates pinpointing the source of the issue. This can make it difficult to hold the creators or operators of these models accountable.

• Innovation Stifling: Closed models limit the ability of the broader community to learn from, improve upon, or even challenge the technology. This can stifle innovation and prevent the emergence of diverse approaches to problem-solving within the field of AI.

• Dependency and Control: Relying on closed models can lead to dependency on specific vendors or creators for updates, improvements, or even the continued availability of the service. This dependency can give disproportionate control and influence to a few entities, raising concerns about monopoly power and its implications for competition and choice.

• Ethical Use and Misuse: Closed models, by their nature, make it hard to assess whether they're being used ethically and responsibly. There's a fear that without sufficient oversight, these models could be deployed in ways that infringe on privacy, manipulate information, or harm individuals or groups.

• Security Risks: While not exclusive to closed models, the opacity of such systems can also obscure vulnerabilities or flaws that could be exploited maliciously. Open scrutiny is often a critical component of identifying and addressing security issues.

The crux of the concern lies in the balance between protecting intellectual property and the broader implications of deploying powerful AI technologies without adequate oversight, transparency, or opportunities for external evaluation. These fears underscore the need for ethical considerations, regulatory frameworks, and mechanisms for accountability in the development and deployment of AI systems.