The Imperative of Private Large Language Models
We are dedicated to the development of generative AI assistants that are based on Private and Secure Large Language Models
Personalized User Experiences
Private LLMs can be fine-tuned to cater to the specific needs and preferences of individual users or organizations. This customization enhances the relevance and effectiveness of AI assistants, providing more accurate responses and personalized interactions. By leveraging private LLMs, organizations can offer bespoke AI solutions that align closely with their operational objectives and user expectations.
rivacy Concerns
Public LLMs, while highly effective, often require vast amounts of user data to function optimally. This data collection can inadvertently lead to privacy breaches, where sensitive personal information is exposed to unauthorized entities. Private LLMs, on the other hand, can be tailored to operate within strict privacy guidelines, ensuring that user data remains secure and confidential.
Data Security
The centralized nature of public LLMs makes them attractive targets for cyberattacks. A breach in a public LLM’s security infrastructure can have far-reaching consequences, potentially compromising the data of millions of users. Private LLMs mitigate this risk by decentralizing data storage and processing, making it more challenging for malicious actors to access sensitive information.
Ethical Use of Data
The ethical implications of AI and LLMs cannot be overlooked. Public LLMs, governed by broader organizational policies, may not always align with individual user ethics and values. Private LLMs allow for greater control over data usage policies, ensuring that the deployment and operation of AI assistants adhere to ethical standards that respect user autonomy and consent.