Clairo AI provides a service to customers that is model agnostic. This means that Clairo’s AI platform is not built on a single model, but rather serves as an access point to many models. We firmly commit to staying up to date with the latest models to ensure that we have a future proof solution, emphasising responsible and ethical AI usage when developing private LLMs. Language models have been around for many years, but their creation and adoption have been accelerated since the 2017 when Google Brain published the paper Attention Is All You Need. This paper sparked many artificial intelligence companies to start funding research in the development of artificial general intelligence (AGI), most notably Google and OpenAI. In 2020, OpenAI released GPT-3 which was the first Large Language Model to make a significant contribution to businesses. Fast-forward to the end of 2021, when ChatGPT was released, it was obvious that LLMs are here to stay.
So, what different types of large language models are out there, and what characterises them?
There are two main distinctions in how people use LLMs - private LLMs and public LLMs - both of which Clairo AI offers its users. A private large language model is one that is deployed and managed within a secure environment, allowing full control of where data flows and is stored. A public large language model is one that is accessed through an API via another company’s server, which forces a business to forfeit the control of its data. It’s important that a business chooses their model type based on their data strategy.
Understanding Large Language Models (LLMs)
LLMs are a groundbreaking advancement in artificial intelligence, designed to process and understand human language with remarkable accuracy due to their reasoning capabilities. These models are trained on vast amounts of text data, enabling them to learn intricate patterns and relationships within language. As a result, LLMs have revolutionised various fields, including natural language processing, content generation, and decision-making processes. However, the extensive use of LLMs also brings forth significant concerns about data privacy and security. Since these models often rely on sensitive data to function effectively, ensuring the protection of such data is paramount. The balance between leveraging the power of LLMs and maintaining robust data protection measures is a critical consideration for any organisation. Additionally, private LLMs face internal security concerns which complicate their customisation and modification.
Open-source models and closed-source AI models: what's the difference?
The AI space is divided into two areas, open-source and closed-source models. Although now matched in their reasoning capabilities, open-source and closed-source models represent two different approaches to the development and distribution of software, including Large Language Models.
Open-source models are characterised by their transparency and collaborative nature. The source code and weights of these models is made publicly available, allowing anyone to view, use, modify, and distribute it. This openness fosters a community-driven approach to development, where improvements and innovations can come from any user. Open-source LLMs can be fine-tuned for specific tasks, and their transparency allows for better understanding of the model’s behaviour and decision-making process. However, they may require significant computational resources and technical expertise to use effectively.
On the other hand, closed-source models are proprietary software where the source code and models are not publicly available. These models are typically developed by a single organisation which maintains exclusive control over the models. Closed source LLMs, like GPT-4, are often provided as a service, where users can interact with the model through an API or a user interface but cannot access or modify the underlying code. The role of the LLM model in ethical AI governance is crucial, as public LLMs need to consider societal norms and provide real-time audits of their outputs. Private firms have the responsibility to monitor their LLMs to prevent biases and ensure socially responsible AI deployment. This approach allows for more control over the model’s use and can provide a more user-friendly experience, but it lacks the transparency and customisability of open-source AI models.
The Need for Private LLMs
In today’s data-driven world, the need for private LLMs has become increasingly evident. As organisations across various industries handle sensitive data, maintaining confidentiality and data privacy is of utmost importance. Public LLMs, while accessible and convenient, may not offer the stringent security measures required to protect sensitive information. This is where private LLMs come into play. By providing a secure and local solution, private LLMs enable organisations to harness the power of advanced language models while ensuring the privacy and protection of their data. This is particularly crucial for industries such as healthcare, finance, and government, where the handling of sensitive data is a daily necessity. Private LLMs offer a tailored approach to data security, allowing organisations to maintain control over their information and comply with stringent privacy regulations.
Key Differences Between Public and Private LLMs
Public and private LLMs serve different purposes and come with distinct characteristics. Public LLMs are widely accessible and are often used for a variety of applications, including natural language processing, machine translation, and sentiment analysis. These models are typically hosted on external servers, which means that security and control can be compromised. In contrast, private LLMs are designed for exclusive use by specific entities, prioritising data protection and control. The key differences between public and private LLMs include accessibility, ownership, data privacy, usage, scalability, cost, and integration. Public LLMs offer ease of use and lower costs but at the expense of data security. Private LLMs, on the other hand, provide enhanced protection and control, making them ideal for organisations with stringent confidentiality requirements. Understanding these differences is essential for determining the appropriate use cases and selecting the right LLM to meet specific needs.
Data Privacy and Security
Data privacy and security are paramount when it comes to building and deploying a private Large Language Model (LLM). A private LLM is designed to protect sensitive data and maintain confidentiality, ensuring that sensitive information is not exposed to unauthorised parties. To achieve this, it is essential to implement robust security measures, such as encryption, access controls, and regular security audits. Additionally, organisations must ensure that their data collection and storage practices comply with relevant privacy regulations, such as GDPR and CCPA. By prioritising security, organisations can leverage the power of private LLMs while safeguarding their sensitive data and maintaining compliance with stringent privacy regulations.
Private LLM Architecture and Implementation
Designing and deploying a private LLM involves a meticulous process that requires careful planning and technical expertise. The architecture of a private LLM must be secure and capable of handling sensitive data while ensuring robust data privacy measures. Organisations can develop private LLMs using open-source models, such as GPT-2, as a foundation, or by fine-tuning pre-trained models to meet specific requirements. The implementation process includes several critical steps: data collection, model training, evaluation, deployment, and ongoing maintenance. Each step must prioritise data sovereignty and security to protect sensitive information. Technical expertise is essential to navigate the complexities of model training and deployment, ensuring that the private LLM operates efficiently and securely. By leveraging open-source models and adhering to best practices in data privacy, organisations can successfully implement private LLMs that meet their unique needs.
Model Training and Evaluation
Model training and evaluation are pivotal stages in the development of private LLMs. Training a model involves fine-tuning an existing language model or creating a new one from scratch using sensitive data. The quality and relevance of the training data are crucial for the model’s success. Evaluation, on the other hand, involves testing the model against predefined metrics or conducting user acceptance testing to ensure its effectiveness. To enhance data privacy and security during these stages, organisations can implement privacy-preserving techniques such as federated learning and homomorphic encryption. These techniques allow for secure model training and evaluation without compromising sensitive data. By prioritising data privacy and employing advanced security measures, organisations can develop robust private LLMs that deliver accurate and reliable results while safeguarding sensitive information.
Challenges and Considerations
Security and Governance
Building and deploying a private LLM comes with its own set of challenges and considerations. One of the primary concerns is security and governance. Organisations must ensure that their private LLM is secure and compliant with relevant regulations, such as privacy laws and industry standards. This requires implementing robust security measures, such as encryption, access controls, and regular security audits. Additionally, organisations must establish clear governance policies and procedures for managing and maintaining their private LLM. By addressing these security and governance challenges, organisations can ensure that their private LLM operates securely and in compliance with all relevant regulations.
Ethical Usage and User Education
Another critical consideration is ethical usage and user education. Organisations must ensure that their private LLM is used in a responsible and ethical manner, and that users are educated on the potential risks and benefits of using the model. This includes providing clear guidelines on data usage, model limitations, and potential biases. Additionally, organisations must establish procedures for addressing ethical concerns and ensuring that their private LLM is used in a way that respects individual privacy rights. By promoting ethical usage and educating users, organisations can foster a responsible and informed approach to leveraging private LLMs.
Future Trends and Considerations
Emerging Technologies
The field of natural language processing is rapidly evolving, with emerging technologies such as federated learning, homomorphic encryption, and open-source models. These technologies have the potential to revolutionise the way we build and deploy private LLMs, enabling more secure, efficient, and effective models. However, they also raise new challenges and considerations, such as ensuring data protection and security, addressing potential biases, and establishing clear governance policies. As the field continues to evolve, it is essential to stay up-to-date with the latest developments and consider the potential implications for private LLMs. By embracing these emerging technologies and addressing the associated challenges, organisations can stay at the forefront of innovation while maintaining robust data privacy and security measures.
What are the pros and cons of Private and Public Large Language Models?
When deciding whether to use a private LLM or a public LLM, a company must consider several key factors: data privacy needs, computational costs, technical capabilities, and the desire to stay abreast of industry advancements.
Firstly, the priority of data protection is paramount. Private LLMs ensure that private data remains exactly that: private. If a company deals with sensitive data or has a stringent data strategy, a private LLM would be more suitable, as it offers full control over data flow and storage. Needing to adhere to privacy regulations and standards, private LLMs are hosted on secure and trusted infrastructure that protects company and user data from the risk of data breaches or unauthorised access.
However, this additional stringency comes with the cost of compute, which can be significant, and requires a tech team with the necessary skills to manage and maintain the model. Training and inference of large language models requires vast data processing and computational demands, leading to additional operational costs for a business. On the other hand, a public LLM, while more cost-effective and requiring less technical expertise, risks compromising data control as it operates on another company's server. However, As AI workloads scale and GPU utilisation increases, a notable tipping point emerges where Private LLMs become significantly more cost-effective than Public LLMs, offering benefits like increased data velocity, while Public LLMs decrease in their processing efficiency. This widening price-performance gap makes Private LLMs a more scalable and predictable option for organisations in production environments.
Lastly, the ability to keep up with the industry is a crucial factor. Public LLMs, often provided as a service by AI companies, are typically updated to keep pace with the latest advancements in the field. In contrast, private LLMs require the company's own resources to stay current, which can be challenging given the rapid pace of development in the AI space.
The decision between a private or a public large language model for your business is a hefty one and should be based on a balanced assessment.
Clairo AI guarantees data privacy at a fraction of the cost
Clairo AI offers a unique solution that leverages the benefits of private LLMs, providing businesses with a flexible, secure, and efficient Generative AI platform. By prioritising security and sovereignty, Clairo AI ensures that client data remains private and under the client's control at all times. To achieve this, Clairo AI provides two deployment options. Firstly, businesses can choose to deploy Clairo's AI platform on their net-zero servers located in Iceland, benefiting from the country's renewable energy resources and Clairo's commitment to sustainability. This option ensures that data is processed and stored in a secure, environmentally responsible manner, while also benefiting from the expertise of Clairo's AI specialists who can help businesses fully understand the risks and costs associated with a managed service.
Alternatively, for businesses with strict data residency requirements, or those who prefer to maintain full control over their data environment, Clairo AI offers the option to deploy the application in the company's own environment. This approach allows businesses to leverage the power of Clairo's Generative AI platform while ensuring that data never leaves their premises, providing an additional layer of security and control. In both scenarios, Clairo's commitment to privacy, data sovereignty, and sustainability remains unchanged, offering businesses a flexible and secure solution that meets their specific needs.
Furthermore, Clairo AI's open-source approach and fixed pricing model ensure economic efficiency, making it an attractive option for businesses looking to leverage the power of Generative AI without incurring unpredictable costs. By offering customised applications across various sectors and emphasising a net-zero carbon footprint, Clairo AI sets a new standard in the digital transformation landscape, providing a unique blend of privacy, efficiency, emphasising a net-zero carbon footprint, and productivity.
