Deepseek and Open Source Large Language Models (LLMs)

Deepseek is getting a lot of publicity these days as an open source Large Language Model (LLM) and has got me thinking, not just about Deepseek but about the potential of open source LLMs in general. MIT Technology Review recently published a scary article titled An AI chatbot told a user how to kill himself—but the company doesn’t want to “censor” and it got me thinking a little bit more about the impact open source LLMs can have.

The MIT Technology Review Article

The article reports on a concerning incident where an AI chatbot explicitly encouraged and provided instructions for suicide to a user named Al Nowatzki. The article highlights broad concerns about AI companion apps and their potential risks to vulnerable users' mental health.

 

Anthropomorphization 

Anthropomorphization is a pretty fancy word – it is basically the attribution of human characteristics, behaviors, emotions, or traits to non-human entities, such as animals, objects, or in this case, artificial intelligence systems. It is something the AIs are getting better and better at.

What does this have to do with Open Source?

The recently released open-source large language model that specializes in coding and technical tasks, has been developed as an alternative to proprietary AI models. If you are not familiar with the term “open source” basically it means the source code, model weights, or other components are freely available for anyone to view, use, modify, and distribute under specified licensing terms.

Now, since Deepseek is open source, if you have adequate computing resources, you can easily install and run Deepseek models locally on your computer. Here’s basically what you'll need:

• Sufficient GPU memory - depending on the model size, you'll need a powerful GPU (like an NVIDIA card with 8GB+ VRAM)
• Enough system RAM - typically 16GB+ recommended
• Adequate storage space for the model weights

The basic process that you can find all over the web now commonly involves:

1. Setting up Python and required dependencies
2. Installing the necessary ML frameworks (like PyTorch)
3. Downloading the model weights
4. Using libraries like transformers or llama.cpp to run the model

It may sound complicated but it is really pretty simple to set one up if you follow instructions.

What’s the big deal?

AI training is the process of feeding large amounts of data into machine learning algorithms to help them recognize patterns and learn to perform specific tasks, like generating text or recognizing images, by adjusting their internal parameters through repeated exposure and feedback.  So what is to prevent a malicious person with an open source AI installed taking this a few steps further, training an AI to do all kinds of malicious things and providing access via the web?

If you or someone you know is struggling with suicidal thoughts, call or text 988 to reach the Suicide and Crisis Lifeline.