Data and AI Governance

The landscape of artificial intelligence is witnessing rapid evolution, with Google’s latest offering, Gemini Ultra, taking center stage. This third installment in the Gemini series marks a significant leap from its predecessors, Gemini Pro and Nano, setting a new benchmark in the AI domain.

Unveiling Gemini Ultra: Google’s Flagship AI

Gemini Ultra emerges as Google’s flagship AI, positioned to compete with heavyweights like GPT-4 and Claude-2. Unlike its siblings, Gemini Ultra is designed to offer enhanced capabilities, indicating a strategic shift in Google’s approach to AI development. Its launch not only demonstrates Google’s commitment to advancing AI technology but also highlights the competitive landscape, where innovation is key to leadership.

According to Google’s research, Gemini Ultra surpasses GPT-4 in most benchmarking conditions, albeit by a narrow margin. These benchmarks likely encompass a range of tasks, including language understanding, problem-solving, and creativity assessments, underscoring Gemini Ultra’s refined capabilities. However, the omission of GPT-4 Turbo from these comparisons raises questions about the competitive dynamics and the evolving capabilities of AI models.

The Context Window advantage

One key benefit for Gemini Ultra is the massive context window of up to 1 million tokens (currently for select testers only). This is compared to GPT-4’s 128k token context window.

For those unfamiliar with these terms, a context window is the amount of text or data that can be included as the prompt for the LLM. A token itself could be part of a word, image or video. In the case of words, a token is part of a word, punctuation or spaces.

According to Google, Gemini 1.5 Pro at 1 million tokens can fit over 700,000 words. This is almost enough to fit the entire Harry Potter book series (roughly 1,100,000 words) in a prompt. The linked article above even mentioned they tested at 10 million tokens, easily enough to fit the whole series.

Why do we even need such large context windows?

Although most users will likely only use a fraction of the large context window, there will be plenty of applications that use the entire window. Here are a few possibilities:

• Analyzing or summarizing large documents like legal documents in Mergers & Acquisitions. With a large context window the entire document or set of documents could be fed into the LLM for processing, potentially saving days of work.
• Highly personalized responses from LLM’s. Imagine Google providing highly customized responses by incorporating your emails, text messages or phone calls into its prompts. Google already has access to much of this data, and adding it to your prompts could provide responses specifically tailored to your needs.
• Including videos in prompts. 1 million tokens is roughly 1 hour of video. In the future its likely that AI will be used to monitor security videos, moderate content on social media platforms and analyze shopper behaviors and reactions in store.

Looking Ahead: Exploring LLM Utilization Strategies

In our next blog post, we will delve deeper into the diverse ways organizations and individuals can harness the power of LLMs. Beyond leveraging large context windows, there are several strategies for utilizing LLMs, including:

• Prompt Engineering: Crafting precise prompts to elicit the desired output from an LLM, optimizing for accuracy and relevance.
• Fine-Tuning: Adjusting the model on a specific dataset to tailor its responses more closely to the needs of a particular application or domain.
• Retrieval-Augmented Generation (RAG): Combining the generative capabilities of LLMs with external knowledge sources to enhance the quality and factual accuracy of responses.

These methodologies open up a spectrum of possibilities for enhancing the interaction with LLMs, each with its own set of advantages and considerations. By exploring these strategies, organizations can optimize their use of AI technologies to achieve specific goals, from improving customer interactions to accelerating research and development.