Is Your Data Safe? The Battle of Open Source vs. Proprietary AI Models

Large Language Models (LLMs) have become a transformative force today, revolutionizing entire industries and expanding the realm of possibilities. However, a new dilemma arises for developers looking to train their LLMs: should you use open source or proprietary data? In this compilation of five informative YouTube videos, we explore the current landscape of Artificial Intelligence (AI), dissecting the debate between open source and proprietary LLMs, the pursuit of data privacy, and the limitations that challenge the cutting-edge.?

#1: Should You Use Open Source Large Language Models?

The landscape of LLMs is expanding at an exponential pace. In a recent YouTube video, IBM dives into the debate between proprietary and open source models, exploring their functionalities, benefits, and associated risks. IBM explains the many intricacies of LLMs and uncovers the pivotal role open source models play in shaping the AI landscape.

#2: Run your own AI (but private)

For a number of companies in a wide range of sectors, the ability to run your own AI locally is a game-changer. In a compelling video on setting up private AI, NetworkChuck demonstrates how to leverage advanced AI capabilities while maintaining complete control over your data with the powerful Llama 2 model.

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#3: Open Source LLMs: Viable for Production or a Low-Quality Toy?

In the ongoing debate between open source and proprietary LLMs, questions about their viability for production use persist. In this video, Anyscale delves into the strengths, limitations, and future potential of open source LLMs, comparing them with industry-leading proprietary models like GPT-4.

#4: Data Privacy for LLMs

The importance of data privacy has never been more critical, especially with the rising use of LLMs in various applications. In a live discussion hosted by LangChain, experts delve into the intricacies of protecting sensitive information in LLM deployments, exploring methods, challenges, and innovative solutions to ensure robust data privacy.

#5: This is What Limits Current LLMs

In the rapidly evolving landscape of AI and machine learning, deploying coding assistants that can adapt to new challenges in real-time is a game-changer. In this video, EdanMeyer delves into the limitations of current LLMs and explores why continual learning is essential for overcoming these constraints, drawing insights from a startup's innovative approach to training models in production.

The evolution of LLMs is a testament to the relentless pursuit of innovation in AI. As the debate between open source and proprietary models rages on, it's clear that both sides offer invaluable contributions to the field. Open source LLMs promise accessibility and collaborative growth, while proprietary models deliver unmatched performance and reliability.?

Want to learn more about how developers get their data for training LLMs? On May 29th, we'll sit with Andrew Chen, Co-Founder of Graffle, to talk all about owning the data vs using data from the internet and how LLMs can use crypto to pay for data. Don't miss out on this valuable conversation! Register here.