Using Artificial Intelligence Tools – Privately
The Rise of AI in Our Daily Lives: Weighing Convenience Against Privacy
Imagine having a personal assistant that can help you write emails, create content, or find information specific to your profession. Microsoft’s CoPilot promises this convenience by integrating powerful Artificial Intelligence (AI) features into Windows operating systems. However, this convenience comes with potential risks to our privacy and autonomy.
As AI tools become increasingly prevalent in our daily lives, understanding Large Language Models (LLMs) is crucial for navigating these advancements. LLMs can be trained within specific domains and run both locally and remotely, providing benefits such as enhanced privacy, reduced censorship risks, and offline usage capabilities.
The Importance of Data Protection
For many users, keeping information secure while using AI tools is critical. Local solutions like Ollama store resources locally on your device, ensuring maximum security without sacrificing functionality. This approach allows you to maintain control over your data and protect it from unauthorised access.
The Drawbacks of Cloud-Based Services
Cloud-based service providers like Microsoft’s CoPilot offer powerful AI features that enhance productivity and creativity. However, they also come with potential challenges, including lack of transparency around data collection practices and filtering levels set without user control. This raises concerns about user autonomy and privacy.
Understanding LLMs in Depth
LLMs are a type of artificial intelligence model capable of understanding natural language processing tasks such as translation and summarisation. When running locally on devices without internet connection requirements, they offer users greater control over their data privacy compared to cloud-based alternatives.
The Benefits of Domain-Specific Local LLMs
Local LLMs trained within specific domains, such as healthcare or finance, gain access to industry-specific terminology and context, significantly improving their accuracy compared to generic AI tools like Microsoft CoPilot. This leads to more relevant outputs tailored towards each user’s individual needs.
All AI tools make mistakes and have the potential to lead you to make incorrect conclusions. Technically, this is affectionately called AI “hallucinations”. Note the disclaimer on the bottom of your ChatGPT or CoPilot screen. You are reminded to always double check the results. If your health, business decisions or purchasing decisions are made on a system which has just hallucinated, then the outcome could be disastrous.
With domain-specific local LLMs, you are much less risk of such hallucinations, as the models have a bias towards the amount of knowledge (source data) required to support questions within the domain that were trained on.
The Verdict: Convenience vs. Privacy
As AI continues to shape our digital landscapes, it’s essential to understand the nuances between cloud-based models like Copilot and local LLMs trained in specific domains. While convenience is appealing, it’s crucial to weigh this against the potential risks to our privacy and autonomy. By embracing responsible adoption practices and opting for local LLM solutions like Ollama, we can harness the power of AI without compromising security or control.
The Optimal Solution: Linux and Local LLMs
Given the benefits of local LLMs, it’s worth considering an alternative operating system like Linux, which is optimised for running these tools. By switching to a Linux-based system and utilising local LLM solutions, you can ensure maximum security, privacy, and control over your data while still enjoying the convenience of AI-powered tools.