Google DeepMind's Gemini 1.5 Pro and the Power of Long Context Memory

This week, we delve into the exciting world of large language models (LLMs) with Google DeepMind’s groundbreaking creation, Gemini 1.5 Pro. Buckle up, because Gemini boasts a feature that sets it apart: a super-powered memory called a “long context window.”

The History of Long Context Learning in AI

The concept of long context learning in AI has been an active area of research for several years. Here’s a glimpse into the historical landscape:

• Attention Mechanisms: The foundation for long context models lies in attention mechanisms, a technique introduced in the seminal paper “Attention is All You Need” (2017) by Vaswani et al. (https://arxiv.org/abs/1706.03762). Attention allows models to focus on specific parts of an input sequence while processing, enabling them to capture long-range dependencies within data.
• Transformer-XL (2019): Building on attention mechanisms, researchers introduced Transformer-XL, a model designed for improved handling of long sequences. This work by Dai et al. (https://arxiv.org/abs/1901.02860) demonstrated the feasibility of training models on massive datasets while maintaining context over longer sequences.
• Longformer (2020): Building on Transformer-XL, Longformer by Belgard et al. (https://arxiv.org/abs/2004.05150) tackled the issue of computational inefficiency associated with processing long sequences. It introduced techniques like “local attention” to focus on relevant parts of the sequence, improving both efficiency and context retention.

The Next Step in Long Context Learning

Google DeepMind’s Gemini 1.5 Pro stands on the shoulders of these advancements. It leverages a transformer-based architecture with a specifically designed long context window, allowing it to remember and process vast amounts of information. This enables functionalities like:

• Summarising lectures from lengthy videos
• Analysing workout sessions and tracking reps and sets
• Creating book inventories and generating summaries
• Answering complex questions based on extensive documents

Addressing Challenges and the Road Ahead

The video highlights a significant challenge - quadratic complexity. As the amount of information stored increases, processing time explodes. While this complexity is inherent to transformer networks, researchers are actively exploring solutions. The release of Gemini for testing by Google DeepMind suggests they might be working on methods to address this limitation.

Exploring the Open-Source Alternative, Meet Gemma

For those eager to experiment with this technology, the video introduces Gemma, a smaller, open-source version of Gemini with a shorter context window. While not as powerful, it’s accessible to a wider audience and can potentially run on smartphones.

Read more on Gemma here.

The Future is Now

Gemini 1.5 Pro represents a significant leap forward in AI capabilities. While challenges remain, the potential for transformative applications across various sectors is undeniable. As AI continues to evolve, the possibilities seem endless.

Stay tuned for future updates as we explore the ever-evolving landscape of AI!